System

Abstract

To provide a system capable of providing a proper learning plan or carrier advice based on the interest or ability of a child.SOLUTION: The system includes a collection unit, an analysis unit, a proposal unit, a carrier analysis unit, a monitoring unit, a network construction unit, and a successful case sharing unit. The collection unit collects the interest and ability of the child. The analysis unit performs aptitude diagnosis based on the collected data. The proposing section proposes a learning plan based on the aptitude diagnosis result. The carrier analyzer provides carrier advice based on the learning plan. The monitoring unit monitors a learning status and progress based on the provided advice. The network construction unit constructs a professional network based on the monitored data. The successful case sharing unit shares the successful case based on a network.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 technology has the drawback of making it difficult to provide appropriate learning plans and career advice based on a child's interests and abilities.

[0005] The system according to the embodiment aims to provide appropriate learning plans and career advice based on children's interests and abilities. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, a career analysis unit, a monitoring unit, a network construction unit, and a success story sharing unit. The collection unit collects information on a child's interests and abilities. The analysis unit performs an aptitude diagnosis based on the data collected by the collection unit. The proposal unit proposes a study plan based on the aptitude diagnosis results obtained by the analysis unit. The career analysis unit provides career advice based on the study plan proposed by the proposal unit. The monitoring unit monitors the learning situation and progress based on the advice provided by the career analysis unit. The network construction unit constructs a professional network based on the data monitored by the monitoring unit. The success story sharing unit shares success stories based on the network constructed by the network construction unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide appropriate learning plans and career advice based on the child's interests and abilities. [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 AI-assisted system according to an embodiment of the present invention is a system that helps children achieve their future dreams by conducting aptitude tests, proposing study plans, giving career advice, maintaining motivation, building a professional network, and sharing success stories. The AI-assisted system collects information about children's interests and abilities, conducts aptitude tests, proposes study plans, provides career advice, monitors learning status and progress, builds a professional network, and shares success stories. For example, the AI-assisted system collects information about children's interests and abilities. For example, it collects past learning data and survey results. Next, the AI-assisted system performs an aptitude test based on the collected data. For example, the AI ​​analyzes psychological tests and survey results to suggest suitable dreams. Next, the AI-assisted system proposes a study plan based on the aptitude test results. For example, the AI ​​generates specific study content and schedules. Next, the AI-assisted system provides career advice based on the study plan. For example, the AI ​​provides advice on career selection and further education. Next, the AI-assisted system monitors learning status and progress. For example, the AI ​​analyzes regular tests and study logs to provide appropriate feedback and encouragement. Next, the AI-assisted system builds a professional network. For example, AI can build a network with experts and professionals and provide opportunities for interaction. Next, the AI ​​assist system can share success stories. For example, AI can collect and share details of successful projects and the factors behind their success. This allows the AI ​​assist system to help children become motivated to work toward their future dreams. In this way, the AI ​​assist system can provide aptitude tests, propose study plans, give career advice, maintain motivation, build professional networks, and share success stories to help children achieve their future dreams. For example, it can help children become motivated to work toward their future dreams. The AI ​​assist system can also monitor children's learning situation and progress and provide appropriate feedback and encouragement to improve learning outcomes.

[0029] The AI ​​assist system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, a career analysis unit, a monitoring unit, a network construction unit, and a success story sharing unit. The collection unit collects information about a child's interests and abilities. The collection unit collects, for example, past learning data and survey results. The collection unit can also collect data about the child's hobbies and daily life. For example, the collection unit collects information about the child's favorite activities and hobbies. The collection unit can also collect data about the child's daily routines and habits. The collection unit can also collect information about topics and themes that interest the child. The analysis unit performs an aptitude diagnosis based on the data collected by the collection unit. For example, the analysis unit analyzes psychological tests and survey results and suggests dreams that are suitable for the child. The analysis unit can also perform an aptitude diagnosis based on the collected data. For example, the analysis unit analyzes the child's interests and abilities based on the collected data and suggests dreams that are suitable for the child. The proposal unit proposes a study plan based on the aptitude diagnosis results obtained by the analysis unit. The proposal unit generates, for example, specific study content and a schedule. The suggestion unit can also adjust the way the study plan is presented. For example, the suggestion unit estimates the child's emotions and adjusts the way the study plan is presented based on the child's estimated emotions. The career analysis unit provides career advice based on the study plan proposed by the suggestion unit. The career analysis unit provides, for example, advice on career selection or further education. The career analysis unit can also adjust the way the career advice is presented. For example, the career analysis unit estimates the child's emotions and adjusts the way the career advice is presented based on the child's estimated emotions. The monitoring unit monitors the learning situation and progress based on the advice provided by the career analysis unit. For example, the monitoring unit analyzes periodic tests and study logs and provides appropriate feedback and encouragement. The monitoring unit can also adjust the monitoring method. For example, the monitoring unit estimates the child's emotions and adjusts the monitoring method based on the child's estimated emotions.The network construction unit constructs a professional network based on the data monitored by the monitoring unit. The network construction unit, for example, constructs a network with experts and professionals and provides opportunities for interaction. The network construction unit can also adjust the network construction method. For example, the network construction unit estimates a child's emotions and adjusts the network construction method based on the estimated child's emotions. The success story sharing unit shares success stories based on the network constructed by the network construction unit. For example, the success story sharing unit collects and shares details of successful projects and factors behind their success. The success story sharing unit can also adjust the method of sharing the success stories. For example, the success story sharing unit estimates a child's emotions and adjusts the method of sharing the success stories based on the estimated child's emotions. As a result, the AI ​​assist system according to the embodiment can perform aptitude tests, propose study plans, provide career advice, maintain motivation, construct a professional network, and share success stories to help children achieve their future dreams.

[0030] The collection unit can collect data on the child's hobbies and daily life in addition to past learning data and questionnaire results. For example, the collection unit collects information on the child's favorite activities and hobbies. The collection unit can also collect data on the child's daily routines and habits. The collection unit can also collect information on topics and themes that the child is interested in. By collecting data on the child's hobbies and daily life, more detailed aptitude diagnosis becomes possible. 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 information on the child's hobbies into the generation AI and have the generation AI analyze the hobbies.

[0031] When collecting data, the collection unit can filter the data based on the child's learning environment and home environment. For example, the collection unit filters the data based on the child's learning environment (quiet place, noisy place, etc.). The collection unit can also filter the data based on the child's home environment (whether the child has siblings, parental support, etc.). The collection unit can also filter the data based on the child's learning style (visual, auditory, tactile, etc.). In this way, by filtering the data based on the child's learning environment and home environment, more appropriate data can be collected. Some or all of the above-described 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 about the child's learning environment to a generation AI and have the generation AI perform data filtering.

[0032] When collecting data, 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 prioritize collecting voice data. Furthermore, if the child prefers text input, the collection unit can also prioritize collecting text data. Furthermore, if the child prefers image input, the collection unit can also prioritize collecting image data. This improves the efficiency of data 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, for example, AI, or may be performed without using AI. For example, the collection unit can input the child's input data into a generation AI and have the generation AI collect the data.

[0033] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the child's geographical location information. For example, if the child is in a specific location, the collection unit can prioritize collecting data related to that location. Furthermore, if the child is traveling, the collection unit can prioritize collecting data related to the travel destination. Furthermore, if the child is at school, the collection unit can prioritize collecting data related to the school. In this way, by collecting data by taking the child's geographical location information into account, more relevant data can be collected. 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 the child's location information data into the generation AI and cause the generation AI to collect highly relevant data.

[0034] When collecting data, the collection unit can analyze the child's social media activities and collect relevant data. For example, the collection unit collects relevant data based on information shared by the child on social media. The collection unit can also analyze the child's social media activity patterns and collect relevant data. The collection unit can also collect relevant data by referring to the activities of the child's friends on social media. In this way, more relevant data can be collected by analyzing the child's 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 the child's social media data into the generation AI and cause the generation AI to collect relevant data.

[0035] When collecting data, the collection unit can customize the collection method by reflecting the child's past feedback. The collection unit, for example, adjusts the collection method based on feedback provided by the child in the past. The collection unit can also prioritize the use of collection methods that the child has previously preferred. The collection unit can also eliminate collection methods that the child has previously avoided. In this way, a more appropriate collection method can be selected by reflecting the child's past feedback. Some or all of the above-described 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 the child's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0036] The analysis unit can analyze fluctuations in the child's interests and abilities in real time based on the collected data. For example, the analysis unit can analyze the child's learning data in real time to detect fluctuations in interests. The analysis unit can also analyze the child's daily life data in real time to detect fluctuations in abilities. The analysis unit can also analyze the child's social media activities in real time to detect fluctuations in interests and abilities. This enables more accurate aptitude diagnosis by analyzing fluctuations in the child's interests and abilities in real time. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the child's learning data into the generation AI and cause the generation AI to analyze fluctuations in interests and abilities.

[0037] The analysis unit can adjust the analysis algorithm according to the child's learning style and pace during aptitude diagnosis. For example, if the child has a visual learning style, the analysis unit can emphasize visual data in the analysis. Also, if the child has an auditory learning style, the analysis unit can emphasize auditory data in the analysis. The analysis unit can also adjust the frequency and timing of analysis according to the child's learning pace. This allows for more appropriate aptitude diagnosis by adjusting the analysis algorithm according to the child's learning style and pace. 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 the child's learning style data into the generation AI and have the generation AI adjust the analysis algorithm.

[0038] The analysis unit can improve the accuracy of the analysis by referring to the child's past diagnostic results when conducting an aptitude diagnosis. For example, the analysis unit corrects the current diagnostic result based on the child's past aptitude diagnosis results. The analysis unit can also analyze the child's past diagnostic results and improve the analysis algorithm. The analysis unit can also improve the accuracy of the aptitude diagnosis by referring to the child's past diagnostic results. In this way, the accuracy of the aptitude diagnosis is improved by referring to the child's past diagnostic 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 the child's past diagnostic result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0039] The analysis unit can determine the priority of aptitude tests based on the time of submission of the child during aptitude tests. For example, if a child submits early, the analysis unit can prioritize aptitude tests. The analysis unit can also quickly perform aptitude tests when the child's submission deadline approaches. The analysis unit can also adjust the order of aptitude tests depending on the time of submission of the child. This enables rapid aptitude tests by determining the priority of tests based on the time of submission of the child. 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 data on the time of submission of the child into the generation AI and have the generation AI execute the priority of tests.

[0040] The analysis unit can adjust the order of diagnoses based on the child's relevance during aptitude diagnosis. For example, the analysis unit prioritizes diagnosis of items related to the child's interests and abilities. The analysis unit can also prioritize diagnosis of items related to the child's learning style. The analysis unit can also prioritize diagnosis of items related to the child's past diagnosis results. By adjusting the order of diagnoses based on the child's relevance, more relevant diagnoses are possible. 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 relevance data into the generation AI and have the generation AI execute the order of diagnoses.

[0041] During aptitude diagnosis, the analysis unit can adjust the use of diagnostic terminology according to the child's level of expertise. For example, if the child is a beginner, the analysis unit can use simple terminology. If the child is an intermediate learner, the analysis unit can also use appropriate terminology. If the child is an advanced learner, the analysis unit can also use detailed terminology. By adjusting the use of diagnostic terminology according to the child's level of expertise, a more understandable diagnostic result can be provided. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without 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 use the terminology.

[0042] When proposing a study plan, the suggestion unit can adjust the level of detail of the plan based on the child's learning progress and level of understanding. For example, if the child's learning progress is fast, the suggestion unit can provide a detailed study plan. Also, if the child's level of understanding is high, the suggestion unit can provide a more difficult study plan. Also, the suggestion unit can adjust the level of detail of the study plan according to the child's learning progress and level of understanding. In this way, by adjusting the level of detail of the plan based on the child's learning progress and level of understanding, a more appropriate study plan can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the child's learning progress data into the generation AI and have the generation AI execute the level of detail of the plan.

[0043] When proposing a study plan, the suggestion unit can apply different suggestion algorithms depending on the child's learning style and pace. For example, if the child has a visual learning style, the suggestion unit can provide a visual study plan. Also, if the child has an auditory learning style, the suggestion unit can provide an auditory study plan. The suggestion unit can also adjust the suggestion algorithm depending on the child's learning pace. This allows for a more appropriate study plan to be provided by applying different suggestion algorithms depending on the child's learning style and pace. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the child's learning style data into the generation AI and cause the generation AI to apply the suggestion algorithm.

[0044] When proposing a study plan, the suggestion unit can improve the accuracy of the suggestion by referring to the child's past study results. The suggestion unit, for example, corrects the current study plan based on the child's past study results. The suggestion unit can also analyze the child's past study results and improve the proposal algorithm. The suggestion unit can also improve the accuracy of the study plan by referring to the child's past study results. In this way, the accuracy of the study plan is improved by referring to the child's past study results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the child's past study result data into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0045] When proposing a study plan, the suggestion unit can determine the priority of the plans based on the time of submission of the child. For example, if the child submits early, the suggestion unit can provide a study plan with priority. The suggestion unit can also quickly provide a study plan when the child's submission deadline is approaching. The suggestion unit can also adjust the order of study plans depending on the time of submission of the child. This makes it possible to quickly provide a study plan by determining the priority of plans based on the time of submission of the child. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input data on the time of submission of the child into the generation AI and have the generation AI execute the plan priority.

[0046] When proposing a study plan, the suggestion unit can adjust the order of the plan based on the child's relevance. For example, the suggestion unit can prioritize suggesting items related to the child's interests and abilities. The suggestion unit can also prioritize suggesting items related to the child's learning style. The suggestion unit can also prioritize suggesting items related to the child's past learning results. By adjusting the order of the plan based on the child's relevance, a more relevant study plan can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the child's relevance data into a generation AI and have the generation AI execute the order of the plan.

[0047] When proposing a learning plan, the suggestion unit can adjust the use of technical terminology in the plan according to the child's level of expertise. For example, if the child is a beginner, the suggestion unit can use simple technical terminology. If the child is an intermediate learner, the suggestion unit can also use appropriate technical terminology. If the child is an advanced learner, the suggestion unit can also use detailed technical terminology. By adjusting the use of technical terminology in the plan according to the child's level of expertise, a more understandable learning plan can be provided. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the child's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0048] The career analysis unit can analyze fluctuations in the child's interests and abilities in real time when providing career advice. The career analysis unit, for example, analyzes the child's learning data in real time to detect fluctuations in interests. The career analysis unit can also analyze the child's daily life data in real time to detect fluctuations in abilities. The career analysis unit can also analyze the child's social media activity in real time to detect fluctuations in interests and abilities. This enables more accurate career advice by analyzing fluctuations in the child's interests and abilities in real time. Some or all of the above-mentioned processing in the career analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the career analysis unit can input the child's learning data into the generation AI and cause the generation AI to analyze fluctuations in interests and abilities.

[0049] When providing career advice, the career analysis unit can adjust the analysis algorithm according to the child's learning style and pace. For example, if the child has a visual learning style, the career analysis unit can analyze the data by emphasizing visual data. Also, if the child has an auditory learning style, the career analysis unit can analyze the data by emphasizing auditory data. The career analysis unit can also adjust the frequency and timing of analysis according to the child's learning pace. This allows for more appropriate career advice by adjusting the analysis algorithm according to the child's learning style and pace. Some or all of the above-described processing in the career analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the career analysis unit can input the child's learning style data into the generation AI and have the generation AI adjust the analysis algorithm.

[0050] When providing career advice, the career analysis unit can improve the accuracy of the analysis by referring to the child's past advice results. The career analysis unit, for example, corrects current advice based on the child's past career advice results. The career analysis unit can also analyze the child's past advice results and improve the analysis algorithm. The career analysis unit can also improve the accuracy of career advice by referring to the child's past advice results. In this way, the accuracy of career advice is improved by referring to the child's past advice results. Some or all of the above-mentioned processing in the career analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the career analysis unit can input the child's past advice result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0051] When providing career advice, the career analysis unit can determine the priority of advice based on the child's submission time. For example, if a child submits early, the career analysis unit can provide career advice preferentially. The career analysis unit can also provide career advice quickly when a child's submission deadline is approaching. The career analysis unit can also adjust the order of career advice depending on the child's submission time. This enables quick career advice by determining the priority of advice based on the child's submission time. Some or all of the above-mentioned processing in the career analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the career analysis unit can input data on the child's submission time into the generation AI and have the generation AI execute the advice priority.

[0052] When providing career advice, the career analysis unit can adjust the order of advice based on the child's relevance. For example, the career analysis unit prioritizes advice on items related to the child's interests and abilities. The career analysis unit can also prioritize advice on items related to the child's learning style. The career analysis unit can also prioritize advice on items related to the child's past advice results. By adjusting the order of advice based on the child's relevance, more relevant career advice can be provided. Some or all of the above-mentioned processing in the career analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the career analysis unit can input the child's relevance data into the generation AI and have the generation AI execute the order of advice.

[0053] When providing career advice, the career analysis unit can adjust the use of technical terminology in the advice depending on the child's level of expertise. For example, if the child is a beginner, the career analysis unit can use simple technical terminology. If the child is an intermediate expert, the career analysis unit can also use appropriate technical terminology. If the child is an advanced expert, the career analysis unit can also use detailed technical terminology. By adjusting the use of technical terminology in the advice depending on the child's level of expertise, more understandable career advice can be provided. Some or all of the above-described processing in the career analysis unit can be performed using AI, for example, or without AI. For example, the career analysis unit can input the child's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0054] The monitoring unit can analyze the child's learning progress and level of understanding in real time during monitoring. For example, the monitoring unit analyzes the child's learning data in real time and monitors the progress. The monitoring unit can also analyze and monitor the child's level of understanding in real time. The monitoring unit can also monitor in real time according to the child's learning style. This enables more accurate monitoring by analyzing the child's learning progress and level of understanding in real time. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, for example, or may be performed without using AI. For example, the monitoring unit can input the child's learning data into a generation AI and have the generation AI analyze the progress and level of understanding.

[0055] During monitoring, the monitoring unit can adjust the monitoring algorithm according to the child's learning style and pace. For example, if the child has a visual learning style, the monitoring unit can monitor by focusing on visual data. Also, if the child has an auditory learning style, the monitoring unit can monitor by focusing on auditory data. The monitoring unit can also adjust the frequency and timing of monitoring according to the child's learning pace. This allows for more appropriate monitoring by adjusting the monitoring algorithm according to the child's learning style and pace. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the child's learning style data into the generation AI and cause the generation AI to adjust the monitoring algorithm.

[0056] During monitoring, the monitoring unit can improve the accuracy of monitoring by referring to the child's past monitoring results. The monitoring unit, for example, corrects the current monitoring based on the child's past monitoring results. The monitoring unit can also analyze the child's past monitoring results and improve the monitoring algorithm. The monitoring unit can also improve the accuracy of monitoring by referring to the child's past monitoring results. In this way, the accuracy of monitoring is improved by referring to the child's past monitoring results. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the child's past monitoring result data into the generation AI and cause the generation AI to improve the accuracy of monitoring.

[0057] During monitoring, the monitoring unit can determine the monitoring priority based on the time of submission of the child. For example, if the child submits early, the monitoring unit can prioritize monitoring. The monitoring unit can also quickly monitor if the child's submission deadline approaches. The monitoring unit can also adjust the monitoring order depending on the time of submission of the child. This enables rapid monitoring by determining the monitoring priority based on the time of submission of the child. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data on the time of submission of the child to the generation AI and have the generation AI execute the monitoring priority.

[0058] During monitoring, the monitoring unit can adjust the monitoring order based on the child's relevance. For example, the monitoring unit prioritizes monitoring items related to the child's interests and abilities. The monitoring unit can also prioritize monitoring items related to the child's learning style. The monitoring unit can also prioritize monitoring items related to the child's past monitoring results. This allows for more relevant monitoring by adjusting the monitoring order based on the child's relevance. Some or all of the above-described processing in the monitoring unit may be performed using, or without, AI, for example. For example, the monitoring unit can input the child's relevance data into a generation AI and have the generation AI execute the monitoring order.

[0059] During monitoring, the monitoring unit can adjust the use of technical terminology in the monitoring according to the child's level of expertise. For example, if the child is a beginner, the monitoring unit can use simple technical terminology. If the child is an intermediate learner, the monitoring unit can also use moderate technical terminology. If the child is an advanced learner, the monitoring unit can also use detailed technical terminology. This allows for easier-to-understand monitoring by adjusting the use of technical terminology in the monitoring according to the child's level of expertise. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or without AI. For example, the monitoring unit can input the child's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0060] The network construction unit can analyze fluctuations in the child's interests and abilities in real time when constructing the network. For example, the network construction unit can analyze the child's learning data in real time to detect fluctuations in interests. The network construction unit can also analyze the child's daily life data in real time to detect fluctuations in abilities. The network construction unit can also analyze the child's social media activity in real time to detect fluctuations in interests and abilities. This enables more accurate network construction by analyzing fluctuations in the child's interests and abilities in real time. Some or all of the above-mentioned processing in the network construction unit may be performed using AI, for example, or may be performed without using AI. For example, the network construction unit can input the child's learning data into the generation AI and cause the generation AI to analyze fluctuations in interests and abilities.

[0061] When constructing a network, the network construction unit can adjust the construction algorithm according to the child's learning style and pace. For example, if the child has a visual learning style, the network construction unit can construct a network by emphasizing visual data. Also, if the child has an auditory learning style, the network construction unit can construct a network by emphasizing auditory data. The network construction unit can also adjust the frequency and timing of network construction according to the child's learning pace. This enables more appropriate network construction by adjusting the construction algorithm according to the child's learning style and pace. Some or all of the above-described processing in the network construction unit may be performed using, for example, AI, or may be performed without using AI. For example, the network construction unit can input the child's learning style data into the generation AI and cause the generation AI to adjust the construction algorithm.

[0062] When constructing a network, the network construction unit can improve the accuracy of the construction by referring to the child's past network construction results. The network construction unit, for example, corrects the current network construction based on the child's past network construction results. The network construction unit can also analyze the child's past network construction results and improve the construction algorithm. The network construction unit can also improve the accuracy of the network construction by referring to the child's past network construction results. In this way, the accuracy of the network construction is improved by referring to the child's past network construction results. Some or all of the above-mentioned processing in the network construction unit may be performed, for example, using AI or may be performed without using AI. For example, the network construction unit can input the child's past network construction result data into the generation AI and cause the generation AI to improve the construction accuracy.

[0063] When constructing a network, the network construction unit can determine the construction priority based on the time of child submission. For example, if a child submits early, the network construction unit prioritizes network construction. The network construction unit can also quickly construct a network when the child's submission deadline approaches. The network construction unit can also adjust the order of network construction according to the time of child submission. This enables rapid network construction by determining the construction priority based on the time of child submission. Some or all of the above-mentioned processing in the network construction unit may be performed using AI, for example, or may be performed without using AI. For example, the network construction unit can input data on the time of child submission into the generation AI and have the generation AI execute the construction priority.

[0064] When constructing a network, the network construction unit can adjust the construction order based on the child's relevance. For example, the network construction unit prioritizes building a network based on items related to the child's interests and abilities. The network construction unit can also prioritize building a network based on items related to the child's learning style. The network construction unit can also prioritize building a network based on items related to the child's past network construction results. This makes it possible to construct a more relevant network by adjusting the construction order based on the child's relevance. Some or all of the above-described processing in the network construction unit may be performed using AI, for example, or may be performed without using AI. For example, the network construction unit can input the child's relevance data into a generation AI and have the generation AI execute the construction order.

[0065] When constructing a network, the network construction unit can adjust the use of technical terminology in the construction according to the child's level of expertise. For example, if the child is a beginner, the network construction unit can use simple technical terminology. If the child is an intermediate learner, the network construction unit can also use appropriate technical terminology. If the child is an advanced learner, the network construction unit can also use detailed technical terminology. By adjusting the use of technical terminology in the construction according to the child's level of expertise, it is possible to construct a network that is easier to understand. Some or all of the above-mentioned processing in the network construction unit may be performed using AI, for example, or may be performed without using AI. For example, the network construction unit can input the child's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0066] The success story sharing unit can analyze fluctuations in the child's interests and abilities in real time when sharing the success story. For example, the success story sharing unit can analyze the child's learning data in real time to detect fluctuations in interests. The success story sharing unit can also analyze the child's daily life data in real time to detect fluctuations in abilities. The success story sharing unit can also analyze the child's social media activity in real time to detect fluctuations in interests and abilities. This enables more accurate sharing of success stories by analyzing fluctuations in the child's interests and abilities in real time. Some or all of the above-mentioned processing in the success story sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the success story sharing unit can input the child's learning data into a generation AI and cause the generation AI to analyze fluctuations in interests and abilities.

[0067] The success story sharing unit can adjust the sharing algorithm according to the child's learning style and pace when sharing success stories. For example, if the child has a visual learning style, the success story sharing unit can share success stories by emphasizing visual data. Also, if the child has an auditory learning style, the success story sharing unit can share success stories by emphasizing auditory data. The success story sharing unit can also adjust the frequency and timing of success story sharing according to the child's learning pace. This allows for more appropriate success story sharing by adjusting the sharing algorithm according to the child's learning style and pace. Some or all of the above-described processing in the success story sharing unit may be performed using, or without, AI, for example. For example, the success story sharing unit can input the child's learning style data into the generation AI and cause the generation AI to adjust the sharing algorithm.

[0068] When sharing a success story, the success story sharing unit can improve the accuracy of the sharing by referring to the child's past success story sharing results. The success story sharing unit, for example, corrects the current sharing based on the child's past success story sharing results. The success story sharing unit can also analyze the child's past success story sharing results and improve the sharing algorithm. The success story sharing unit can also improve the accuracy of the success story sharing by referring to the child's past success story sharing results. In this way, the accuracy of the success story sharing is improved by referring to the child's past success story sharing results. Some or all of the above-mentioned processing in the success story sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the success story sharing unit can input the child's past success story sharing result data into the generation AI and cause the generation AI to improve the accuracy of sharing.

[0069] When sharing success stories, the success story sharing unit can determine the priority of sharing based on the time of submission by the child. For example, if a child submits early, the success story sharing unit prioritizes sharing of the success story. The success story sharing unit can also quickly share success stories when the child's submission deadline is approaching. The success story sharing unit can also adjust the order of success story sharing depending on the time of submission by the child. This enables quick sharing of success stories by determining the priority of sharing based on the time of submission by the child. Some or all of the above-mentioned processing in the success story sharing unit may be performed using AI, for example, or may be performed without using AI. For example, the success story sharing unit can input data on the time of submission by the child to the generation AI and have the generation AI execute the sharing priority.

[0070] When sharing success stories, the success story sharing unit can adjust the sharing order based on the child's relevance. For example, the success story sharing unit prioritizes sharing items related to the child's interests and abilities as success stories. The success story sharing unit can also prioritize sharing items related to the child's learning style as success stories. The success story sharing unit can also prioritize sharing items related to the child's past success story sharing results as success stories. By adjusting the sharing order based on the child's relevance, more relevant success stories can be shared. Some or all of the above-described processing in the success story sharing unit may be performed using AI, for example, or without AI. For example, the success story sharing unit can input the child's relevance data into a generation AI and have the generation AI execute the sharing order.

[0071] When sharing a success story, the success story sharing unit can adjust the use of shared terminology depending on the child's level of expertise. For example, if the child is a beginner, the success story sharing unit can use simple terminology. If the child is an intermediate learner, the success story sharing unit can also use moderate terminology. If the child is an advanced learner, the success story sharing unit can also use detailed terminology. This allows for the sharing of success stories that are easier to understand by adjusting the use of shared terminology depending on the child's level of expertise. Some or all of the above-described processing in the success story sharing unit can be performed using AI, for example, or without AI. For example, the success story sharing unit can input the child's level of expertise data into the generation AI and have the generation AI execute the use of terminology.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The AI ​​assist system may further include an environmental adaptation unit. The environmental adaptation unit may adjust the system's operation based on the child's learning environment. For example, if the child is learning in a quiet environment, the environmental adaptation unit may refrain from providing audio feedback and prioritize text-based feedback. If the child is on the move, the environmental adaptation unit may provide an interface optimized for mobile devices. Furthermore, the environmental adaptation unit may detect changes in the child's learning environment in real time and make appropriate adjustments. For example, if the child moves to a noisy place, the environmental adaptation unit may activate a noise-canceling function.

[0074] The AI ​​assist system may further include a data security unit. The data security unit may provide functions for safely protecting children's personal information and learning data. For example, data encryption and access control may be used to protect data from unauthorized access. The data security unit may also provide a dashboard that transparently shows how children's data is being used. Furthermore, the data security unit may perform regular security checks to detect and correct system vulnerabilities. This ensures that children's data is always protected.

[0075] The AI ​​assistance system can further include a learning resource management unit. The learning resource management unit can centrally manage learning resources available to a child and provide them at the optimal time. For example, if a child shows interest in a particular topic, it can automatically suggest related teaching materials or videos. The learning resource management unit can also prioritize the next learning resource to be provided based on the child's learning progress. Furthermore, the learning resource management unit can adjust the format and difficulty of the resources according to the child's learning style and pace. This ensures that a child can always use the most appropriate learning resources.

[0076] The processing flow of the first embodiment will be briefly explained below.

[0077] Step 1: The collection unit collects the child's interests and abilities. For example, the collection unit collects past learning data and survey results. The collection unit can also collect data on the child's hobbies and daily life. For example, the collection unit collects information on the child's favorite activities and hobbies. The collection unit can also collect data on the child's daily routines and habits. Furthermore, the collection unit can collect information on topics and themes that the child is interested in. Step 2: The analysis unit performs an aptitude diagnosis based on the data collected by the collection unit. The analysis unit, for example, analyzes the results of a psychological test or questionnaire and suggests suitable dreams. The analysis unit can also perform an aptitude diagnosis based on the collected data. For example, the analysis unit analyzes a child's interests and abilities based on the collected data and suggests suitable dreams. Step 3: The suggestion unit proposes a study plan based on the aptitude test results obtained by the analysis unit. The suggestion unit generates, for example, specific study content and schedules. The suggestion unit can also adjust the way the study plan is presented. For example, the suggestion unit estimates the child's emotions and adjusts the way the study plan is presented based on the estimated child's emotions. Step 4: The career analysis unit provides career advice based on the study plan proposed by the proposal unit. The career analysis unit provides, for example, advice on career selection or further education. The career analysis unit can also adjust the way the career advice is presented. For example, the career analysis unit estimates the child's emotions and adjusts the way the career advice is presented based on the estimated child's emotions. Step 5: The monitoring unit monitors the learning situation and progress based on the advice provided by the career analysis unit. For example, the monitoring unit analyzes regular tests and learning logs and provides appropriate feedback and encouragement. The monitoring unit can also adjust the monitoring method. For example, the monitoring unit estimates the child's emotions and adjusts the monitoring method based on the estimated child's emotions. Step 6: The network construction unit constructs a professional network based on the data monitored by the monitoring unit. For example, the network construction unit constructs a network with experts and professionals and provides opportunities for interaction. The network construction unit can also adjust the network construction method. For example, the network construction unit estimates the child's emotions and adjusts the network construction method based on the estimated child's emotions. Step 7: The success story sharing unit shares success stories based on the network built by the network building unit. The success story sharing unit, for example, collects and shares details of successful projects and factors behind their success. The success story sharing unit can also adjust how the success stories are shared. For example, the success story sharing unit estimates the child's emotions and adjusts how the success stories are shared based on the estimated child's emotions.

[0078] (Example 2) An AI-assisted system according to an embodiment of the present invention is a system that helps children achieve their future dreams by conducting aptitude tests, proposing study plans, giving career advice, maintaining motivation, building a professional network, and sharing success stories. The AI-assisted system collects information about children's interests and abilities, conducts aptitude tests, proposes study plans, provides career advice, monitors learning status and progress, builds a professional network, and shares success stories. For example, the AI-assisted system collects information about children's interests and abilities. For example, it collects past learning data and survey results. Next, the AI-assisted system performs an aptitude test based on the collected data. For example, the AI ​​analyzes psychological tests and survey results to suggest suitable dreams. Next, the AI-assisted system proposes a study plan based on the aptitude test results. For example, the AI ​​generates specific study content and schedules. Next, the AI-assisted system provides career advice based on the study plan. For example, the AI ​​provides advice on career selection and further education. Next, the AI-assisted system monitors learning status and progress. For example, the AI ​​analyzes regular tests and study logs to provide appropriate feedback and encouragement. Next, the AI-assisted system builds a professional network. For example, AI can build a network with experts and professionals and provide opportunities for interaction. Next, the AI ​​assist system can share success stories. For example, AI can collect and share details of successful projects and the factors behind their success. This allows the AI ​​assist system to help children become motivated to work toward their future dreams. In this way, the AI ​​assist system can provide aptitude tests, propose study plans, give career advice, maintain motivation, build professional networks, and share success stories to help children achieve their future dreams. For example, it can help children become motivated to work toward their future dreams. The AI ​​assist system can also monitor children's learning situation and progress and provide appropriate feedback and encouragement to improve learning outcomes.

[0079] The AI ​​assist system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, a career analysis unit, a monitoring unit, a network construction unit, and a success story sharing unit. The collection unit collects information about a child's interests and abilities. The collection unit collects, for example, past learning data and survey results. The collection unit can also collect data about the child's hobbies and daily life. For example, the collection unit collects information about the child's favorite activities and hobbies. The collection unit can also collect data about the child's daily routines and habits. The collection unit can also collect information about topics and themes that interest the child. The analysis unit performs an aptitude diagnosis based on the data collected by the collection unit. For example, the analysis unit analyzes psychological tests and survey results and suggests dreams that are suitable for the child. The analysis unit can also perform an aptitude diagnosis based on the collected data. For example, the analysis unit analyzes the child's interests and abilities based on the collected data and suggests dreams that are suitable for the child. The proposal unit proposes a study plan based on the aptitude diagnosis results obtained by the analysis unit. The proposal unit generates, for example, specific study content and a schedule. The suggestion unit can also adjust the way the study plan is presented. For example, the suggestion unit estimates the child's emotions and adjusts the way the study plan is presented based on the child's estimated emotions. The career analysis unit provides career advice based on the study plan proposed by the suggestion unit. The career analysis unit provides, for example, advice on career selection or further education. The career analysis unit can also adjust the way the career advice is presented. For example, the career analysis unit estimates the child's emotions and adjusts the way the career advice is presented based on the child's estimated emotions. The monitoring unit monitors the learning situation and progress based on the advice provided by the career analysis unit. For example, the monitoring unit analyzes periodic tests and study logs and provides appropriate feedback and encouragement. The monitoring unit can also adjust the monitoring method. For example, the monitoring unit estimates the child's emotions and adjusts the monitoring method based on the child's estimated emotions.The network construction unit constructs a professional network based on the data monitored by the monitoring unit. The network construction unit, for example, constructs a network with experts and professionals and provides opportunities for interaction. The network construction unit can also adjust the network construction method. For example, the network construction unit estimates a child's emotions and adjusts the network construction method based on the estimated child's emotions. The success story sharing unit shares success stories based on the network constructed by the network construction unit. For example, the success story sharing unit collects and shares details of successful projects and factors behind their success. The success story sharing unit can also adjust the method of sharing the success stories. For example, the success story sharing unit estimates a child's emotions and adjusts the method of sharing the success stories based on the estimated child's emotions. As a result, the AI ​​assist system according to the embodiment can perform aptitude tests, propose study plans, provide career advice, maintain motivation, construct a professional network, and share success stories to help children achieve their future dreams.

[0080] The collection unit can estimate the child's emotions and adjust the timing of data collection based on the estimated child's emotions. For example, the collection unit collects learning data and questionnaire results when the child is relaxed. The collection unit can also collect data on the child's hobbies and daily life when the child is concentrating. The collection unit can also temporarily suspend data collection when the child is feeling stressed and resume it later. This allows data collection to be done at a more appropriate time by adjusting the timing of data 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input the child's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0081] The collection unit can collect data on the child's hobbies and daily life in addition to past learning data and questionnaire results. For example, the collection unit collects information on the child's favorite activities and hobbies. The collection unit can also collect data on the child's daily routines and habits. The collection unit can also collect information on topics and themes that the child is interested in. By collecting data on the child's hobbies and daily life, more detailed aptitude diagnosis becomes possible. 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 information on the child's hobbies into the generation AI and have the generation AI analyze the hobbies.

[0082] When collecting data, the collection unit can filter the data based on the child's learning environment and home environment. For example, the collection unit filters the data based on the child's learning environment (quiet place, noisy place, etc.). The collection unit can also filter the data based on the child's home environment (whether the child has siblings, parental support, etc.). The collection unit can also filter the data based on the child's learning style (visual, auditory, tactile, etc.). In this way, by filtering the data based on the child's learning environment and home environment, more appropriate data can be collected. Some or all of the above-described 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 about the child's learning environment to a generation AI and have the generation AI perform data filtering.

[0083] When collecting data, 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 prioritize collecting voice data. Furthermore, if the child prefers text input, the collection unit can also prioritize collecting text data. Furthermore, if the child prefers image input, the collection unit can also prioritize collecting image data. This improves the efficiency of data 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, for example, AI, or may be performed without using AI. For example, the collection unit can input the child's input data into a generation AI and have the generation AI collect the data.

[0084] The collection unit can estimate the child's emotions and determine the priority of data to be collected based on the estimated child's emotions. For example, if the child is excited, the collection unit can prioritize collecting data related to topics the child is interested in. Furthermore, if the child is relaxed, the collection unit can prioritize collecting data related to daily life. Furthermore, if the child is concentrating, the collection unit can prioritize collecting learning data. Thus, by determining the priority of data to be collected according to the child's emotions, more important data 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 facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0085] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the child's geographical location information. For example, if the child is in a specific location, the collection unit can prioritize collecting data related to that location. Furthermore, if the child is traveling, the collection unit can prioritize collecting data related to the travel destination. Furthermore, if the child is at school, the collection unit can prioritize collecting data related to the school. In this way, by collecting data by taking the child's geographical location information into account, more relevant data can be collected. 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 the child's location information data into the generation AI and cause the generation AI to collect highly relevant data.

[0086] When collecting data, the collection unit can analyze the child's social media activities and collect relevant data. For example, the collection unit collects relevant data based on information shared by the child on social media. The collection unit can also analyze the child's social media activity patterns and collect relevant data. The collection unit can also collect relevant data by referring to the activities of the child's friends on social media. In this way, more relevant data can be collected by analyzing the child's 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 the child's social media data into the generation AI and cause the generation AI to collect relevant data.

[0087] When collecting data, the collection unit can customize the collection method by reflecting the child's past feedback. The collection unit, for example, adjusts the collection method based on feedback provided by the child in the past. The collection unit can also prioritize the use of collection methods that the child has previously preferred. The collection unit can also eliminate collection methods that the child has previously avoided. In this way, a more appropriate collection method can be selected by reflecting the child's past feedback. Some or all of the above-described 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 the child's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0088] The analysis unit can estimate the child's emotions and adjust the presentation method of the aptitude test based on the estimated child's emotions. For example, if the child is relaxed, the analysis unit can provide an aptitude test with detailed explanations. Furthermore, if the child is nervous, the analysis unit can provide a simple, highly visible aptitude test. Furthermore, if the child is excited, the analysis unit can provide an aptitude test with visually stimulating effects. This allows for adjusting the presentation method of the aptitude test according to the child's emotions, thereby providing a more appropriate diagnostic result. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using AI, or without AI. For example, the analysis unit can input the child's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0089] The analysis unit can analyze fluctuations in the child's interests and abilities in real time based on the collected data. For example, the analysis unit can analyze the child's learning data in real time to detect fluctuations in interests. The analysis unit can also analyze the child's daily life data in real time to detect fluctuations in abilities. The analysis unit can also analyze the child's social media activities in real time to detect fluctuations in interests and abilities. This enables more accurate aptitude diagnosis by analyzing fluctuations in the child's interests and abilities in real time. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the child's learning data into the generation AI and cause the generation AI to analyze fluctuations in interests and abilities.

[0090] The analysis unit can adjust the analysis algorithm according to the child's learning style and pace during aptitude diagnosis. For example, if the child has a visual learning style, the analysis unit can emphasize visual data in the analysis. Also, if the child has an auditory learning style, the analysis unit can emphasize auditory data in the analysis. The analysis unit can also adjust the frequency and timing of analysis according to the child's learning pace. This allows for more appropriate aptitude diagnosis by adjusting the analysis algorithm according to the child's learning style and pace. 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 the child's learning style data into the generation AI and have the generation AI adjust the analysis algorithm.

[0091] The analysis unit can improve the accuracy of the analysis by referring to the child's past diagnostic results when conducting an aptitude diagnosis. For example, the analysis unit corrects the current diagnostic result based on the child's past aptitude diagnosis results. The analysis unit can also analyze the child's past diagnostic results and improve the analysis algorithm. The analysis unit can also improve the accuracy of the aptitude diagnosis by referring to the child's past diagnostic results. In this way, the accuracy of the aptitude diagnosis is improved by referring to the child's past diagnostic 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 the child's past diagnostic result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0092] The analysis unit can estimate the child's emotions and adjust the level of detail of the aptitude test based on the estimated child's emotions. For example, if the child is relaxed, the analysis unit can provide a detailed aptitude test. If the child is nervous, the analysis unit can provide a concise aptitude test. If the child is excited, the analysis unit can provide a visually stimulating aptitude test. This allows for adjusting the level of detail of the aptitude test according to the child's emotions, thereby providing a more appropriate diagnostic result. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the child's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0093] The analysis unit can determine the priority of aptitude tests based on the time of submission of the child during aptitude tests. For example, if a child submits early, the analysis unit can prioritize aptitude tests. The analysis unit can also quickly perform aptitude tests when the child's submission deadline approaches. The analysis unit can also adjust the order of aptitude tests depending on the time of submission of the child. This enables rapid aptitude tests by determining the priority of tests based on the time of submission of the child. 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 data on the time of submission of the child into the generation AI and have the generation AI execute the priority of tests.

[0094] The analysis unit can adjust the order of diagnoses based on the child's relevance during aptitude diagnosis. For example, the analysis unit prioritizes diagnosis of items related to the child's interests and abilities. The analysis unit can also prioritize diagnosis of items related to the child's learning style. The analysis unit can also prioritize diagnosis of items related to the child's past diagnosis results. By adjusting the order of diagnoses based on the child's relevance, more relevant diagnoses are possible. 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 relevance data into the generation AI and have the generation AI execute the order of diagnoses.

[0095] During aptitude diagnosis, the analysis unit can adjust the use of diagnostic terminology according to the child's level of expertise. For example, if the child is a beginner, the analysis unit can use simple terminology. If the child is an intermediate learner, the analysis unit can also use appropriate terminology. If the child is an advanced learner, the analysis unit can also use detailed terminology. By adjusting the use of diagnostic terminology according to the child's level of expertise, a more understandable diagnostic result can be provided. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without 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 use the terminology.

[0096] The suggestion unit can estimate the child's emotions and adjust the presentation of the lesson plan based on the estimated child's emotions. For example, if the child is relaxed, the suggestion unit can provide a lesson plan with detailed explanations. If the child is nervous, the suggestion unit can provide a simple, highly visible lesson plan. If the child is excited, the suggestion unit can provide a lesson plan with visually stimulating effects. This allows for adjusting the presentation of the lesson plan according to the child's emotions, thereby providing a more appropriate lesson plan. 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 suggestion unit can be performed using AI, for example, or without AI. For example, the suggestion unit can input the child's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0097] When proposing a study plan, the suggestion unit can adjust the level of detail of the plan based on the child's learning progress and level of understanding. For example, if the child's learning progress is fast, the suggestion unit can provide a detailed study plan. Also, if the child's level of understanding is high, the suggestion unit can provide a more difficult study plan. Also, the suggestion unit can adjust the level of detail of the study plan according to the child's learning progress and level of understanding. In this way, by adjusting the level of detail of the plan based on the child's learning progress and level of understanding, a more appropriate study plan can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the child's learning progress data into the generation AI and have the generation AI execute the level of detail of the plan.

[0098] When proposing a study plan, the suggestion unit can apply different suggestion algorithms depending on the child's learning style and pace. For example, if the child has a visual learning style, the suggestion unit can provide a visual study plan. Also, if the child has an auditory learning style, the suggestion unit can provide an auditory study plan. The suggestion unit can also adjust the suggestion algorithm depending on the child's learning pace. This allows for a more appropriate study plan to be provided by applying different suggestion algorithms depending on the child's learning style and pace. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the child's learning style data into the generation AI and cause the generation AI to apply the suggestion algorithm.

[0099] When proposing a study plan, the suggestion unit can improve the accuracy of the suggestion by referring to the child's past study results. The suggestion unit, for example, corrects the current study plan based on the child's past study results. The suggestion unit can also analyze the child's past study results and improve the proposal algorithm. The suggestion unit can also improve the accuracy of the study plan by referring to the child's past study results. In this way, the accuracy of the study plan is improved by referring to the child's past study results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the child's past study result data into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0100] The suggestion unit can estimate the child's emotions and adjust the length of the lesson plan based on the estimated child's emotions. For example, if the child is relaxed, the suggestion unit can provide a longer lesson plan. If the child is nervous, the suggestion unit can also provide a shorter lesson plan. If the child is excited, the suggestion unit can also provide a lesson plan with visually stimulating effects. This allows for adjusting the length of the lesson plan according to the child's emotions, thereby providing a more appropriate lesson plan. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using AI, for example, or without AI. For example, the suggestion unit can input the child's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0101] When proposing a study plan, the suggestion unit can determine the priority of the plans based on the time of submission of the child. For example, if the child submits early, the suggestion unit can provide a study plan with priority. The suggestion unit can also quickly provide a study plan when the child's submission deadline is approaching. The suggestion unit can also adjust the order of study plans depending on the time of submission of the child. This makes it possible to quickly provide a study plan by determining the priority of plans based on the time of submission of the child. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input data on the time of submission of the child into the generation AI and have the generation AI execute the plan priority.

[0102] When proposing a study plan, the suggestion unit can adjust the order of the plan based on the child's relevance. For example, the suggestion unit can prioritize suggesting items related to the child's interests and abilities. The suggestion unit can also prioritize suggesting items related to the child's learning style. The suggestion unit can also prioritize suggesting items related to the child's past learning results. By adjusting the order of the plan based on the child's relevance, a more relevant study plan can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the child's relevance data into a generation AI and have the generation AI execute the order of the plan.

[0103] When proposing a learning plan, the suggestion unit can adjust the use of technical terminology in the plan according to the child's level of expertise. For example, if the child is a beginner, the suggestion unit can use simple technical terminology. If the child is an intermediate learner, the suggestion unit can also use appropriate technical terminology. If the child is an advanced learner, the suggestion unit can also use detailed technical terminology. By adjusting the use of technical terminology in the plan according to the child's level of expertise, a more understandable learning plan can be provided. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the child's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0104] The career analysis unit can estimate the child's emotions and adjust the way in which career advice is presented based on the estimated child's emotions. For example, if the child is relaxed, the career analysis unit can provide career advice that includes detailed explanations. Furthermore, if the child is nervous, the career analysis unit can provide simple, highly visible career advice. Furthermore, if the child is excited, the career analysis unit can provide career advice that adds visually stimulating effects. This allows for more appropriate advice to be provided by adjusting the way in which career advice is presented based on the child's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, 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 career analysis unit can be performed using, for example, an AI, or without an AI. For example, the career analysis unit can input the child's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0105] The career analysis unit can analyze fluctuations in the child's interests and abilities in real time when providing career advice. The career analysis unit, for example, analyzes the child's learning data in real time to detect fluctuations in interests. The career analysis unit can also analyze the child's daily life data in real time to detect fluctuations in abilities. The career analysis unit can also analyze the child's social media activity in real time to detect fluctuations in interests and abilities. This enables more accurate career advice by analyzing fluctuations in the child's interests and abilities in real time. Some or all of the above-mentioned processing in the career analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the career analysis unit can input the child's learning data into the generation AI and cause the generation AI to analyze fluctuations in interests and abilities.

[0106] When providing career advice, the career analysis unit can adjust the analysis algorithm according to the child's learning style and pace. For example, if the child has a visual learning style, the career analysis unit can analyze the data by emphasizing visual data. Also, if the child has an auditory learning style, the career analysis unit can analyze the data by emphasizing auditory data. The career analysis unit can also adjust the frequency and timing of analysis according to the child's learning pace. This allows for more appropriate career advice by adjusting the analysis algorithm according to the child's learning style and pace. Some or all of the above-described processing in the career analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the career analysis unit can input the child's learning style data into the generation AI and have the generation AI adjust the analysis algorithm.

[0107] When providing career advice, the career analysis unit can improve the accuracy of the analysis by referring to the child's past advice results. The career analysis unit, for example, corrects current advice based on the child's past career advice results. The career analysis unit can also analyze the child's past advice results and improve the analysis algorithm. The career analysis unit can also improve the accuracy of career advice by referring to the child's past advice results. In this way, the accuracy of career advice is improved by referring to the child's past advice results. Some or all of the above-mentioned processing in the career analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the career analysis unit can input the child's past advice result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0108] The career analysis unit can estimate the child's emotions and adjust the level of detail of the career advice based on the estimated child's emotions. For example, if the child is relaxed, the career analysis unit can provide detailed career advice. If the child is nervous, the career analysis unit can also provide concise career advice. If the child is excited, the career analysis unit can also provide visually stimulating career advice. By adjusting the level of detail of the career advice according to the child's emotions, more appropriate advice can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the career analysis unit can be performed using AI, for example, or without AI. For example, the career analysis unit can input the child's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0109] When providing career advice, the career analysis unit can determine the priority of advice based on the child's submission time. For example, if a child submits early, the career analysis unit can provide career advice preferentially. The career analysis unit can also provide career advice quickly when a child's submission deadline is approaching. The career analysis unit can also adjust the order of career advice depending on the child's submission time. This enables quick career advice by determining the priority of advice based on the child's submission time. Some or all of the above-mentioned processing in the career analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the career analysis unit can input data on the child's submission time into the generation AI and have the generation AI execute the advice priority.

[0110] When providing career advice, the career analysis unit can adjust the order of advice based on the child's relevance. For example, the career analysis unit prioritizes advice on items related to the child's interests and abilities. The career analysis unit can also prioritize advice on items related to the child's learning style. The career analysis unit can also prioritize advice on items related to the child's past advice results. By adjusting the order of advice based on the child's relevance, more relevant career advice can be provided. Some or all of the above-mentioned processing in the career analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the career analysis unit can input the child's relevance data into the generation AI and have the generation AI execute the order of advice.

[0111] When providing career advice, the career analysis unit can adjust the use of technical terminology in the advice depending on the child's level of expertise. For example, if the child is a beginner, the career analysis unit can use simple technical terminology. If the child is an intermediate expert, the career analysis unit can also use appropriate technical terminology. If the child is an advanced expert, the career analysis unit can also use detailed technical terminology. By adjusting the use of technical terminology in the advice depending on the child's level of expertise, more understandable career advice can be provided. Some or all of the above-described processing in the career analysis unit can be performed using AI, for example, or without AI. For example, the career analysis unit can input the child's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0112] The monitoring unit can estimate the child's emotions and adjust the monitoring method based on the estimated child's emotions. For example, if the child is relaxed, the monitoring unit can perform detailed monitoring. If the child is nervous, the monitoring unit can also perform simple monitoring. If the child is excited, the monitoring unit can also perform monitoring with visually stimulating effects. This allows for more appropriate monitoring by adjusting the monitoring 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the monitoring unit can be performed using AI, for example, or without AI. For example, the monitoring unit can input the child's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0113] The monitoring unit can analyze the child's learning progress and level of understanding in real time during monitoring. For example, the monitoring unit analyzes the child's learning data in real time and monitors the progress. The monitoring unit can also analyze and monitor the child's level of understanding in real time. The monitoring unit can also monitor in real time according to the child's learning style. This enables more accurate monitoring by analyzing the child's learning progress and level of understanding in real time. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, for example, or may be performed without using AI. For example, the monitoring unit can input the child's learning data into a generation AI and have the generation AI analyze the progress and level of understanding.

[0114] During monitoring, the monitoring unit can adjust the monitoring algorithm according to the child's learning style and pace. For example, if the child has a visual learning style, the monitoring unit can monitor by focusing on visual data. Also, if the child has an auditory learning style, the monitoring unit can monitor by focusing on auditory data. The monitoring unit can also adjust the frequency and timing of monitoring according to the child's learning pace. This allows for more appropriate monitoring by adjusting the monitoring algorithm according to the child's learning style and pace. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the child's learning style data into the generation AI and cause the generation AI to adjust the monitoring algorithm.

[0115] During monitoring, the monitoring unit can improve the accuracy of monitoring by referring to the child's past monitoring results. The monitoring unit, for example, corrects the current monitoring based on the child's past monitoring results. The monitoring unit can also analyze the child's past monitoring results and improve the monitoring algorithm. The monitoring unit can also improve the accuracy of monitoring by referring to the child's past monitoring results. In this way, the accuracy of monitoring is improved by referring to the child's past monitoring results. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the child's past monitoring result data into the generation AI and cause the generation AI to improve the accuracy of monitoring.

[0116] The monitoring unit can estimate the child's emotions and adjust the monitoring frequency based on the estimated child's emotions. For example, the monitoring unit can monitor more frequently when the child is relaxed. The monitoring unit can also reduce the monitoring frequency when the child is nervous. The monitoring unit can also perform monitoring with visually stimulating effects when the child is excited. This allows for more appropriate monitoring by adjusting the monitoring frequency according to the child's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the monitoring unit can be performed using AI, or without AI. For example, the monitoring unit can input the child's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0117] During monitoring, the monitoring unit can determine the monitoring priority based on the time of submission of the child. For example, if the child submits early, the monitoring unit can prioritize monitoring. The monitoring unit can also quickly monitor if the child's submission deadline approaches. The monitoring unit can also adjust the monitoring order depending on the time of submission of the child. This enables rapid monitoring by determining the monitoring priority based on the time of submission of the child. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data on the time of submission of the child to the generation AI and have the generation AI execute the monitoring priority.

[0118] During monitoring, the monitoring unit can adjust the monitoring order based on the child's relevance. For example, the monitoring unit prioritizes monitoring items related to the child's interests and abilities. The monitoring unit can also prioritize monitoring items related to the child's learning style. The monitoring unit can also prioritize monitoring items related to the child's past monitoring results. This allows for more relevant monitoring by adjusting the monitoring order based on the child's relevance. Some or all of the above-described processing in the monitoring unit may be performed using, or without, AI, for example. For example, the monitoring unit can input the child's relevance data into a generation AI and have the generation AI execute the monitoring order.

[0119] During monitoring, the monitoring unit can adjust the use of technical terminology in the monitoring according to the child's level of expertise. For example, if the child is a beginner, the monitoring unit can use simple technical terminology. If the child is an intermediate learner, the monitoring unit can also use moderate technical terminology. If the child is an advanced learner, the monitoring unit can also use detailed technical terminology. This allows for easier-to-understand monitoring by adjusting the use of technical terminology in the monitoring according to the child's level of expertise. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or without AI. For example, the monitoring unit can input the child's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0120] The network construction unit can estimate the child's emotions and adjust the network construction method based on the estimated child's emotions. For example, if the child is relaxed, the network construction unit can provide a network construction method including detailed instructions. Furthermore, if the child is nervous, the network construction unit can provide a simple and highly visible network construction method. Furthermore, if the child is excited, the network construction unit can provide a network construction method with visually stimulating effects. This enables more appropriate network construction by adjusting the network construction method according to the child's emotions. 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 network construction unit can be performed using, for example, AI, or without AI. For example, the network construction unit can input data of the child's facial expression into the generation AI and cause the generation AI to estimate the emotion.

[0121] The network construction unit can analyze fluctuations in the child's interests and abilities in real time when constructing the network. For example, the network construction unit can analyze the child's learning data in real time to detect fluctuations in interests. The network construction unit can also analyze the child's daily life data in real time to detect fluctuations in abilities. The network construction unit can also analyze the child's social media activity in real time to detect fluctuations in interests and abilities. This enables more accurate network construction by analyzing fluctuations in the child's interests and abilities in real time. Some or all of the above-mentioned processing in the network construction unit may be performed using AI, for example, or may be performed without using AI. For example, the network construction unit can input the child's learning data into the generation AI and cause the generation AI to analyze fluctuations in interests and abilities.

[0122] When constructing a network, the network construction unit can adjust the construction algorithm according to the child's learning style and pace. For example, if the child has a visual learning style, the network construction unit can construct a network by emphasizing visual data. Also, if the child has an auditory learning style, the network construction unit can construct a network by emphasizing auditory data. The network construction unit can also adjust the frequency and timing of network construction according to the child's learning pace. This enables more appropriate network construction by adjusting the construction algorithm according to the child's learning style and pace. Some or all of the above-described processing in the network construction unit may be performed using, for example, AI, or may be performed without using AI. For example, the network construction unit can input the child's learning style data into the generation AI and cause the generation AI to adjust the construction algorithm.

[0123] When constructing a network, the network construction unit can improve the accuracy of the construction by referring to the child's past network construction results. The network construction unit, for example, corrects the current network construction based on the child's past network construction results. The network construction unit can also analyze the child's past network construction results and improve the construction algorithm. The network construction unit can also improve the accuracy of the network construction by referring to the child's past network construction results. In this way, the accuracy of the network construction is improved by referring to the child's past network construction results. Some or all of the above-mentioned processing in the network construction unit may be performed, for example, using AI or may be performed without using AI. For example, the network construction unit can input the child's past network construction result data into the generation AI and cause the generation AI to improve the construction accuracy.

[0124] The network construction unit can estimate the child's emotions and adjust the level of detail of the network construction based on the estimated child's emotions. For example, if the child is relaxed, the network construction unit can provide a detailed network construction. Furthermore, if the child is nervous, the network construction unit can provide a concise network construction. Furthermore, if the child is excited, the network construction unit can provide a visually stimulating network construction. By adjusting the level of detail of the network construction according to the child's emotions, more appropriate network construction becomes possible. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the network construction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the network construction unit can input facial expression data of the child into the generation AI and cause the generation AI to estimate the emotion.

[0125] When constructing a network, the network construction unit can determine the construction priority based on the time of child submission. For example, if a child submits early, the network construction unit prioritizes network construction. The network construction unit can also quickly construct a network when the child's submission deadline approaches. The network construction unit can also adjust the order of network construction according to the time of child submission. This enables rapid network construction by determining the construction priority based on the time of child submission. Some or all of the above-mentioned processing in the network construction unit may be performed using AI, for example, or may be performed without using AI. For example, the network construction unit can input data on the time of child submission into the generation AI and have the generation AI execute the construction priority.

[0126] When constructing a network, the network construction unit can adjust the construction order based on the child's relevance. For example, the network construction unit prioritizes building a network based on items related to the child's interests and abilities. The network construction unit can also prioritize building a network based on items related to the child's learning style. The network construction unit can also prioritize building a network based on items related to the child's past network construction results. This makes it possible to construct a more relevant network by adjusting the construction order based on the child's relevance. Some or all of the above-described processing in the network construction unit may be performed using AI, for example, or may be performed without using AI. For example, the network construction unit can input the child's relevance data into a generation AI and have the generation AI execute the construction order.

[0127] When constructing a network, the network construction unit can adjust the use of technical terminology in the construction according to the child's level of expertise. For example, if the child is a beginner, the network construction unit can use simple technical terminology. If the child is an intermediate learner, the network construction unit can also use appropriate technical terminology. If the child is an advanced learner, the network construction unit can also use detailed technical terminology. By adjusting the use of technical terminology in the construction according to the child's level of expertise, it is possible to construct a network that is easier to understand. Some or all of the above-mentioned processing in the network construction unit may be performed using AI, for example, or may be performed without using AI. For example, the network construction unit can input the child's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0128] The success story sharing unit can estimate the child's emotions and adjust the method of sharing the success story based on the estimated child's emotions. For example, if the child is relaxed, the success story sharing unit can share a success story with detailed explanations. Furthermore, if the child is nervous, the success story sharing unit can share a simple, highly visible success story. Furthermore, if the child is excited, the success story sharing unit can share a success story with visually stimulating effects. This allows for more appropriate success story sharing by adjusting the method of sharing the success story 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 success story sharing unit can be performed using AI, for example, or without AI. For example, the success story sharing unit can input data of the child's facial expression into the generation AI and have the generation AI perform emotion estimation.

[0129] The success story sharing unit can analyze fluctuations in the child's interests and abilities in real time when sharing the success story. For example, the success story sharing unit can analyze the child's learning data in real time to detect fluctuations in interests. The success story sharing unit can also analyze the child's daily life data in real time to detect fluctuations in abilities. The success story sharing unit can also analyze the child's social media activity in real time to detect fluctuations in interests and abilities. This enables more accurate sharing of success stories by analyzing fluctuations in the child's interests and abilities in real time. Some or all of the above-mentioned processing in the success story sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the success story sharing unit can input the child's learning data into a generation AI and cause the generation AI to analyze fluctuations in interests and abilities.

[0130] The success story sharing unit can adjust the sharing algorithm according to the child's learning style and pace when sharing success stories. For example, if the child has a visual learning style, the success story sharing unit can share success stories by emphasizing visual data. Also, if the child has an auditory learning style, the success story sharing unit can share success stories by emphasizing auditory data. The success story sharing unit can also adjust the frequency and timing of success story sharing according to the child's learning pace. This allows for more appropriate success story sharing by adjusting the sharing algorithm according to the child's learning style and pace. Some or all of the above-described processing in the success story sharing unit may be performed using, or without, AI, for example. For example, the success story sharing unit can input the child's learning style data into the generation AI and cause the generation AI to adjust the sharing algorithm.

[0131] When sharing a success story, the success story sharing unit can improve the accuracy of the sharing by referring to the child's past success story sharing results. The success story sharing unit, for example, corrects the current sharing based on the child's past success story sharing results. The success story sharing unit can also analyze the child's past success story sharing results and improve the sharing algorithm. The success story sharing unit can also improve the accuracy of the success story sharing by referring to the child's past success story sharing results. In this way, the accuracy of the success story sharing is improved by referring to the child's past success story sharing results. Some or all of the above-mentioned processing in the success story sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the success story sharing unit can input the child's past success story sharing result data into the generation AI and cause the generation AI to improve the accuracy of sharing.

[0132] The success story sharing unit can estimate the child's emotions and adjust the level of detail of the success story based on the estimated child's emotions. For example, if the child is relaxed, the success story sharing unit can share a detailed success story. If the child is nervous, the success story sharing unit can share a concise success story. If the child is excited, the success story sharing unit can share a visually stimulating success story. By adjusting the level of detail of the success story according to the child's emotions, more appropriate success stories can be shared. The emotion estimation is realized 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 success story sharing unit can be performed using AI, for example, or without AI. For example, the success story sharing unit can input the child's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0133] When sharing success stories, the success story sharing unit can determine the priority of sharing based on the time of submission by the child. For example, if a child submits early, the success story sharing unit prioritizes sharing of the success story. The success story sharing unit can also quickly share success stories when the child's submission deadline is approaching. The success story sharing unit can also adjust the order of success story sharing depending on the time of submission by the child. This enables quick sharing of success stories by determining the priority of sharing based on the time of submission by the child. Some or all of the above-mentioned processing in the success story sharing unit may be performed using AI, for example, or may be performed without using AI. For example, the success story sharing unit can input data on the time of submission by the child to the generation AI and have the generation AI execute the sharing priority.

[0134] When sharing success stories, the success story sharing unit can adjust the sharing order based on the child's relevance. For example, the success story sharing unit prioritizes sharing items related to the child's interests and abilities as success stories. The success story sharing unit can also prioritize sharing items related to the child's learning style as success stories. The success story sharing unit can also prioritize sharing items related to the child's past success story sharing results as success stories. By adjusting the sharing order based on the child's relevance, more relevant success stories can be shared. Some or all of the above-described processing in the success story sharing unit may be performed using AI, for example, or without AI. For example, the success story sharing unit can input the child's relevance data into a generation AI and have the generation AI execute the sharing order.

[0135] When sharing a success story, the success story sharing unit can adjust the use of shared terminology depending on the child's level of expertise. For example, if the child is a beginner, the success story sharing unit can use simple terminology. If the child is an intermediate learner, the success story sharing unit can also use moderate terminology. If the child is an advanced learner, the success story sharing unit can also use detailed terminology. This allows for the sharing of success stories that are easier to understand by adjusting the use of shared terminology depending on the child's level of expertise. Some or all of the above-described processing in the success story sharing unit can be performed using AI, for example, or without AI. For example, the success story sharing unit can input the child's level of expertise data into the generation AI and have the generation AI execute the use of terminology. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, career analysis unit, monitoring unit, network construction unit, and success story sharing 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 can collect information about a child's interests and abilities using the camera 42 and microphone 38B of the smart device 14. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, performs an aptitude diagnosis based on the collected data. The proposal unit, realized, for example, by the control unit 46A of the smart device 14, proposes a study plan based on the results of the aptitude diagnosis. The career analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, provides career advice based on the study plan. The monitoring unit, realized, for example, by the control unit 46A of the smart device 14, monitors the student's learning status and progress. The network construction unit, realized, for example, by the specific processing unit 290 of the data processing device 12, builds a professional network. The success story sharing unit is realized by, for example, the control unit 46A of the smart device 14, and shares success stories. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, career analysis unit, monitoring unit, network construction unit, and success story sharing 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 can collect information about a child's interests and abilities using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, performs an aptitude diagnosis based on the collected data. The proposal unit, realized, for example, by the control unit 46A of the smart glasses 214, proposes a study plan based on the results of the aptitude diagnosis. The career analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, provides career advice based on the study plan. The monitoring unit, realized, for example, by the control unit 46A of the smart glasses 214, monitors the student's learning status and progress. The network construction unit, realized, for example, by the specific processing unit 290 of the data processing device 12, builds a professional network. The success story sharing unit is realized by, for example, the control unit 46A of the smart glasses 214, and shares success stories. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, career analysis unit, monitoring unit, network construction unit, and success story sharing 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 can collect information about a child's interests and abilities using the camera 42 and microphone 238 of the headset-type terminal 314. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, performs an aptitude diagnosis based on the collected data. The proposal unit, realized, for example, by the control unit 46A of the headset-type terminal 314, proposes a study plan based on the results of the aptitude diagnosis. The career analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, provides career advice based on the study plan. The monitoring unit, realized, for example, by the control unit 46A of the headset-type terminal 314, monitors the student's learning status and progress. The network construction unit, realized, for example, by the specific processing unit 290 of the data processing device 12, builds a professional network. The success story sharing unit is realized by, for example, the control unit 46A of the headset type terminal 314, and shares success stories. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, proposal unit, career analysis unit, monitoring unit, network construction unit, and success story sharing unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect information about the child's interests and abilities 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 performs an aptitude diagnosis based on the collected data. The suggestion unit is realized, for example, by the control unit 46A of the robot 414 and proposes a study plan based on the results of the aptitude diagnosis. The career analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides career advice based on the study plan. The monitoring unit is realized, for example, by the control unit 46A of the robot 414 and monitors the learning situation and progress. The network construction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and builds a professional network. The success story sharing unit is realized, for example, by the control unit 46A of the robot 414 and shares success stories.

[0136] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0137] The AI ​​assistance system may further include an interactive feedback unit. The feedback unit may provide real-time interactive feedback based on the child's learning progress and understanding. For example, if a child is struggling with a particular task, the feedback unit may provide instant hints or additional resources. If the child achieves a goal, the feedback unit may provide praise or badges to increase motivation. Furthermore, the feedback unit may estimate the child's emotions and adjust the content and timing of feedback based on the estimated emotions. For example, if the child is feeling stressed, the feedback unit may send an encouraging message.

[0138] The AI ​​assist system may further include an environmental adaptation unit. The environmental adaptation unit may adjust the system's operation based on the child's learning environment. For example, if the child is learning in a quiet environment, the environmental adaptation unit may refrain from providing audio feedback and prioritize text-based feedback. If the child is on the move, the environmental adaptation unit may provide an interface optimized for mobile devices. Furthermore, the environmental adaptation unit may detect changes in the child's learning environment in real time and make appropriate adjustments. For example, if the child moves to a noisy place, the environmental adaptation unit may activate a noise-canceling function.

[0139] The AI ​​assist system may further include a gamification unit. The gamification unit can enrich a child's learning experience with game elements. For example, a system can be provided that allows a child to earn points or badges according to the degree of completion of a learning plan. The gamification unit can also estimate a child's emotions and adjust the difficulty and rewards of game elements based on the estimated emotions. For example, if a child is losing motivation, the gamification unit can provide an easy task to help the child feel a sense of accomplishment. Furthermore, the gamification unit can provide a function to encourage competition and cooperation among children.

[0140] The AI ​​assist system can further include a personalized reminder unit. The personalized reminder unit can send reminders at appropriate times based on a child's study schedule and progress. For example, a reminder can be sent the day before an important test to ensure the child does not forget to prepare. The personalized reminder unit can also estimate a child's emotions and adjust the content and timing of reminders based on the estimated emotions. For example, if a child is feeling stressed, the reminder content can be softened and an encouraging message can be added.

[0141] The AI ​​assist system can further include a virtual mentor section. The virtual mentor section can provide specialized advice and support to children. For example, it can answer children's questions by utilizing expert knowledge in a specific field. The virtual mentor section can also estimate the child's emotions and adjust the content and tone of its advice based on the estimated emotions. For example, if a child is feeling anxious, the virtual mentor section can provide reassuring advice. Furthermore, the virtual mentor section can suggest what the child should learn next based on their learning progress.

[0142] The AI ​​assist system may further include a data security unit. The data security unit may provide functions for safely protecting children's personal information and learning data. For example, data encryption and access control may be used to protect data from unauthorized access. The data security unit may also provide a dashboard that transparently shows how children's data is being used. Furthermore, the data security unit may perform regular security checks to detect and correct system vulnerabilities. This ensures that children's data is always protected.

[0143] The AI ​​assist system may further include a community support unit. The community support unit may provide a platform where children can interact with other learners and experts. For example, children can post questions and receive answers from other users through online forums or chat rooms. The community support unit may also estimate a child's emotions and adjust the content and timing of interactions based on the estimated emotions. For example, if a child feels lonely, the community support unit may send a message to proactively encourage interaction. Furthermore, the community support unit may host webinars or workshops by experts, providing an opportunity for children to ask questions directly.

[0144] The AI ​​assistance system can further include a learning resource management unit. The learning resource management unit can centrally manage learning resources available to a child and provide them at the optimal time. For example, if a child shows interest in a particular topic, it can automatically suggest related teaching materials or videos. The learning resource management unit can also prioritize the next learning resource to be provided based on the child's learning progress. Furthermore, the learning resource management unit can adjust the format and difficulty of the resources according to the child's learning style and pace. This ensures that a child can always use the most appropriate learning resources.

[0145] The AI ​​assist system can further include a health management unit. The health management unit can monitor a child's health condition and provide appropriate advice. For example, if a child is studying for a long time, it can remind the child to take a break. The health management unit can also estimate the child's emotions and adjust the content and timing of health advice based on the estimated emotions. For example, if a child is tired, it can suggest exercises to help them relax. Furthermore, the health management unit can monitor the child's eating and sleeping patterns and provide advice on living a balanced life.

[0146] The AI ​​assist system can further include a parent-child collaboration unit. The parent-child collaboration unit can share a child's learning progress and emotional state with parents, allowing them to provide appropriate support. For example, if a child is struggling with a particular task, the unit can send a notification to the parent to encourage support at home. The parent-child collaboration unit can also estimate the child's emotions and adjust the content and timing of advice to the parent based on the estimated emotions. For example, if a child is feeling stressed, the unit can suggest ways to help the parent relax. The parent-child collaboration unit can also provide resources and guidelines for parents to support their child's learning.

[0147] The processing flow of the second embodiment will be briefly explained below.

[0148] Step 1: The collection unit collects the child's interests and abilities. For example, the collection unit collects past learning data and survey results. The collection unit can also collect data on the child's hobbies and daily life. For example, the collection unit collects information on the child's favorite activities and hobbies. The collection unit can also collect data on the child's daily routines and habits. Furthermore, the collection unit can collect information on topics and themes that the child is interested in. Step 2: The analysis unit performs an aptitude diagnosis based on the data collected by the collection unit. The analysis unit, for example, analyzes the results of a psychological test or questionnaire and suggests suitable dreams. The analysis unit can also perform an aptitude diagnosis based on the collected data. For example, the analysis unit analyzes a child's interests and abilities based on the collected data and suggests suitable dreams. Step 3: The suggestion unit proposes a study plan based on the aptitude test results obtained by the analysis unit. The suggestion unit generates, for example, specific study content and schedules. The suggestion unit can also adjust the way the study plan is presented. For example, the suggestion unit estimates the child's emotions and adjusts the way the study plan is presented based on the estimated child's emotions. Step 4: The career analysis unit provides career advice based on the study plan proposed by the proposal unit. The career analysis unit provides, for example, advice on career selection or further education. The career analysis unit can also adjust the way the career advice is presented. For example, the career analysis unit estimates the child's emotions and adjusts the way the career advice is presented based on the estimated child's emotions. Step 5: The monitoring unit monitors the learning situation and progress based on the advice provided by the career analysis unit. For example, the monitoring unit analyzes regular tests and learning logs and provides appropriate feedback and encouragement. The monitoring unit can also adjust the monitoring method. For example, the monitoring unit estimates the child's emotions and adjusts the monitoring method based on the estimated child's emotions. Step 6: The network construction unit constructs a professional network based on the data monitored by the monitoring unit. For example, the network construction unit constructs a network with experts and professionals and provides opportunities for interaction. The network construction unit can also adjust the network construction method. For example, the network construction unit estimates the child's emotions and adjusts the network construction method based on the estimated child's emotions. Step 7: The success story sharing unit shares success stories based on the network built by the network building unit. The success story sharing unit, for example, collects and shares details of successful projects and factors behind their success. The success story sharing unit can also adjust how the success stories are shared. For example, the success story sharing unit estimates the child's emotions and adjusts how the success stories are shared based on the estimated child's emotions.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0153] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0154] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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 AI 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.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0169] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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 AI 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.

[0183] 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.

[0184] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0185] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0186] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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).

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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 AI 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.

[0200] 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.

[0201] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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).

[0206] 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.

[0207] 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."

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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, in order to avoid confusion and to 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.

[0219] 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.

[0220] [Explanation of symbols]

[0221] 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 for collecting information on children's interests and abilities; an analysis unit that performs aptitude diagnosis based on the data collected by the collection unit; a suggestion unit that proposes a study plan based on the aptitude test results obtained by the analysis unit; a career analysis unit that provides career advice based on the study plan proposed by the proposal unit; a monitoring unit that monitors the learning situation and progress based on the advice provided by the career analysis unit; a network construction unit that constructs a professional network based on the data monitored by the monitoring unit; a success story sharing unit that shares success stories based on the network constructed by the network construction unit; Equipped with A system characterized by:

2. The collecting unit Estimate the child's emotions and adjust the timing of data collection based on the estimated child's emotions.

2. The system of claim 1.

3. The collecting unit Collect data on children's hobbies and daily life, in addition to past learning data and survey results.

2. The system of claim 1.

4. The collecting unit When collecting data, filtering is performed based on the child's learning and home environment.

2. The system of claim 1.

5. The collecting unit When collecting data, choose the most appropriate collection method based on the child's input method.

2. The system of claim 1.

6. The collecting unit Estimate the child's emotions and prioritize the data to collect based on the estimated emotions 2. The system of claim 1.

7. The collecting unit When collecting data, consider the child's geographic location and prioritize the collection of relevant data.

2. The system of claim 1.

8. The collecting unit At the time of data collection, analyze the social media activity of children and collect relevant data 2. The system of claim 1.

Images