system

Abstract

The system according to the embodiment aims to identify a child's talents and interests and, based on the identified talents and interests, suggest appropriate learning plans and hobbies to parents. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, and an update unit. The collection unit collects a child's behavior or learning history. The analysis unit analyzes the data collected by the collection unit to identify the child's talents or interests. The suggestion unit makes suggestions to parents about study plans or hobbies based on the talents or interests identified by the analysis unit. The update unit updates the content suggested by the suggestion unit as the child grows.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately identify children's talents and interests at an early stage and suggest appropriate learning plans and hobbies based on those talents and interests, and there is room for improvement.

[0005] The system according to the embodiment aims to identify a child's talents and interests and, based on the identified talents and interests, suggest appropriate learning plans and hobbies to parents. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, and an update unit. The collection unit collects a child's behavior or learning history. The analysis unit analyzes the data collected by the collection unit and identifies the child's talents or interests. The suggestion unit suggests a learning plan or hobby to the parent based on the talents or interests identified by the analysis unit. The update unit updates the content suggested by the suggestion unit as the child grows. [Effects of the Invention]

[0007] The system according to the embodiment can identify a child's talents and interests and, based on that, suggest appropriate study plans and hobbies to parents. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention uses AI to analyze a child's behavior and learning history to discover their talents and interests. This system collects a child's daily behavior and learning history, analyzes it using AI to identify their talents and interests, and then suggests study plans and hobbies to parents. For example, the system collects data such as school grades, participation in extracurricular activities, and study time at home. Next, the AI ​​analyzes the collected data to identify the child's talents and interests. For example, the AI ​​detects trends such as high math grades or active participation in sports. The AI ​​then suggests study plans and hobbies to parents based on the identified talents and interests. For example, if a child excels in math, it suggests math workbooks and online courses, and if a child likes sports, it suggests sports clubs and related events. Furthermore, the AI ​​updates its suggestions as the child grows. For example, if a child develops a new interest or their grades improve, the AI ​​detects these changes and makes appropriate suggestions. This allows the system to provide flexible support tailored to the child's development. This makes it easier for parents to understand their child's talents and interests and provide appropriate study plans and hobbies. As a result, it helps children to develop their potential and grow.

[0029] A talent discovery system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, and an update unit. The collection unit collects a child's behavior or learning history. For example, the collection unit can collect data such as grades at school, participation in extracurricular activities, and study time at home. The collection unit can also estimate the child's emotions and adjust the timing of data collection based on the estimated child's emotions. For example, the collection unit can collect data when the child is relaxed and refrain from collecting data when the child is stressed. The analysis unit analyzes the data collected by the collection unit to identify the child's talents and interests. For example, the analysis unit can identify the child's academic talent or sports talent based on the collected data. The analysis unit can also estimate the child's emotions and adjust the method of presentation of the analysis based on the estimated child's emotions. For example, the analysis unit can provide detailed analysis results when the child is relaxed and brief analysis results when the child is stressed. The suggestion unit suggests study plans and hobbies to the parent based on the talents and interests identified by the analysis unit. For example, the suggestion unit can suggest math workbooks and online courses to a child who is good at math, and suggest sports clubs and related events to a child who likes sports. The suggestion unit can also estimate a child's emotions and adjust the way the suggestions are expressed based on the estimated child's emotions. For example, detailed suggestions can be made when the child is relaxed, and concise suggestions can be made when the child is stressed. The update unit updates the content suggested by the suggestion unit in accordance with the child's growth. For example, the update unit can update the content of the suggestions in accordance with the child's growth, thereby always providing optimal support. The update unit can also estimate a child's emotions and adjust the timing of updates based on the estimated child's emotions. For example, the content of the suggestions can be updated when the child is relaxed, and the update can be refrained from when the child is stressed. In this way, the talent discovery system according to the embodiment can discover a child's talents and interests, and help expand a child's potential by suggesting learning plans and hobbies that are tailored to the child's growth.

[0030] The collection unit can collect data on school grades, participation in extracurricular activities, and study time at home. The collection unit, for example, collects school grades. For example, the collection unit can collect test scores and report card grades. The collection unit can also collect participation in extracurricular activities. For example, the collection unit can collect the type, frequency, and results of activities participated in. The collection unit can also collect study time at home. For example, the collection unit can collect the time spent studying, the content of study, and the quality of study. This enables more accurate analysis by collecting detailed information on a child's daily behavior and study history. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input school grade data into a generation AI and have the generation AI analyze the grade data.

[0031] The analysis unit can analyze the collected data and identify the child's talents and interests. The analysis unit can, for example, identify the child's talents and interests based on the collected data. For example, the analysis unit can identify the child's talents and interests, such as whether the child has excellent grades in math or actively participates in sports. The analysis unit can also identify the child's academic talents and sports talents based on the collected data. For example, the analysis unit can identify the child's academic talents based on test scores and grades on report cards. The analysis unit can also identify the child's sports talents based on the types, frequency, and results of extracurricular activities the child participates in. In this way, the child's talents and interests can be accurately identified by analyzing the collected data. Some or all of the above-described processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input the collected data into a generation AI and cause the generation AI to identify the talents and interests.

[0032] The suggestion unit can suggest study plans and hobbies to parents based on the identified talents and interests. For example, the suggestion unit can suggest math workbooks and online courses to a child who is good at math, and suggest sports clubs and related events to a child who likes sports. The suggestion unit can also suggest study plans and hobbies to parents based on the identified talents and interests. For example, the suggestion unit can provide parents with details of study plans, types of hobbies, and a suggestion format. This can support a child's development by suggesting appropriate study plans and hobbies based on the identified talents and interests. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data on the identified talents and interests into a generation AI and cause the generation AI to suggest study plans and hobbies.

[0033] The update unit can update the suggestion content in accordance with the child's growth. The update unit, for example, updates the suggestion content in accordance with the child's growth. For example, the update unit can update the suggestion content when the child begins to develop a new interest or when their grades improve. The update unit can also update the suggestion content in accordance with the child's growth to always provide optimal support. For example, the update unit can update the suggestion content according to the child's age or learning progress. This makes it possible to always provide optimal support by updating the suggestion content in accordance with the child's growth. Some or all of the above-mentioned processing in the update unit may be performed using AI, for example, or may be performed without using AI. For example, the update unit can input the child's growth data into the generation AI and cause the generation AI to update the suggestion content.

[0034] The suggestion unit can suggest math workbooks and online courses to children who are good at math, and sports clubs and related events to children who like sports. For example, the suggestion unit can suggest math workbooks and online courses to children who are good at math. For example, the suggestion unit can suggest math workbooks and online courses to children who have excellent math grades. The suggestion unit can also suggest sports clubs and related events to children who like sports. For example, the suggestion unit can suggest sports clubs and related events to children who actively participate in sports. This makes it possible to effectively support the child's development by making specific suggestions based on the child's talents and interests. Some or all of the above-mentioned processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data on the identified talents and interests into the generation AI and cause the generation AI to suggest study plans and hobbies.

[0035] The collection unit can analyze the child's past behavioral history and select the optimal data collection method. The collection unit, for example, analyzes the child's past behavioral history and selects the optimal data collection method. For example, the collection unit can prioritize collecting similar activity data based on activities the child has previously liked. The collection unit can also adjust the data collection method based on activities the child has previously avoided to avoid stress. The collection unit can also analyze the child's past behavioral patterns and select the most efficient data collection method. For example, the collection unit selects the optimal data collection method based on the child's past behavioral patterns. In this way, the optimal data collection method can be selected by analyzing the past behavioral history. 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 behavioral data into the generation AI and cause the generation AI to select the optimal data collection method.

[0036] The collection unit can filter data based on the child's current learning situation and areas of interest when collecting data. For example, the collection unit can filter data based on the child's current learning situation and areas of interest when collecting data. For example, the collection unit can prioritize collecting data on areas in which the child is currently interested. The collection unit can also collect only necessary data and eliminate unnecessary data depending on the child's learning situation. The collection unit can also filter and collect related data based on the child's areas of interest. For example, the collection unit filters and collects related data based on the child's areas of interest. In this way, by filtering data based on the child's current learning situation and areas of interest, unnecessary data can be eliminated and only necessary data can be collected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the child's current learning situation and areas of interest to the generation AI and have the generation AI perform the filtering.

[0037] The collection unit can select the optimal collection means depending on the child's input method when collecting data. For example, the collection unit selects the optimal collection means depending on the child's input method when collecting data. 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 prioritize collecting text data. Furthermore, if the child prefers image input, the collection unit can prioritize collecting image data. For example, the collection unit selects the optimal collection means depending on the child's input method. In this way, data can be collected efficiently 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 to a generation AI and cause the generation AI to select the optimal collection means.

[0038] The collection unit can prioritize collecting highly relevant data by taking into account the child's geographical location information when collecting data. For example, the collection unit can prioritize collecting highly relevant data by taking into account the child's geographical location information when collecting data. For example, the collection unit can prioritize collecting school activity data when the child is at school, and prioritize collecting home learning data when the child is at home. The collection unit can also prioritize collecting data related to exercise and play when the child is in a park. In this way, highly relevant data can be prioritized by taking into account the geographical location information. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the child's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0039] The collection unit may analyze the child's social media activities and collect related data when collecting data. For example, the collection unit may analyze the child's social media activities and collect related data when collecting data. For example, the collection unit may collect related data based on activities shared by the child on social media. The collection unit may also collect related data by referring to the activities of the child's friends on social media. The collection unit may also analyze the content of the child's posts on social media and collect related data. For example, the collection unit may collect related data based on the content of the child's posts on social media. In this way, related data can be efficiently collected by analyzing social media activities. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit may input the child's social media data into the generation AI and cause the generation AI to collect related data.

[0040] The collection unit can customize the collection method by reflecting the child's past feedback when collecting data. For example, the collection unit customizes the collection method by reflecting the child's past feedback when collecting data. For example, the collection unit can collect data using a similar method based on a collection method that the child has previously preferred. The collection unit can also adjust the collection method based on a collection method that the child has previously avoided. The collection unit can also analyze the child's past feedback and customize the optimal collection method. For example, the collection unit customizes the optimal collection method based on the child's past feedback. In this way, the optimal collection method can be customized by reflecting the past feedback. Some or all of the above-described 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 past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit can perform a detailed analysis on important data and a concise analysis on less important data. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the data. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. 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 importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an algorithm that evaluates learning outcomes to learning data and an algorithm that analyzes behavioral patterns to behavioral data. The analysis unit can also apply an algorithm that analyzes interest trends to interest data. For example, the analysis unit applies different analysis algorithms depending on the data category. This improves the accuracy of the analysis by applying an appropriate analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply the analysis algorithm.

[0043] The analysis unit can improve the accuracy of the analysis by referring to the child's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the child's past analysis results during analysis. For example, the analysis unit can correct the current analysis result based on the child's past analysis results. The analysis unit can also optimize the analysis algorithm by referring to the child's past analysis results. The analysis unit can also improve the accuracy of the analysis by using the child's past analysis results. For example, the analysis unit corrects the current analysis result based on the child's past analysis results. In this way, the accuracy of the analysis is improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the child's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0044] The analysis unit can determine the analysis priority based on the time of data collection during analysis. The analysis unit, for example, determines the analysis priority based on the time of data collection during analysis. For example, the analysis unit can prioritize analysis of the most recent data. The analysis unit can also prioritize analysis of data from important periods. The analysis unit can also dynamically adjust the analysis priority based on the time of data collection. For example, the analysis unit determines the analysis priority based on the time of data collection. In this way, by determining the analysis priority based on the time of data collection, the most recent data can be analyzed preferentially. 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 time of data collection to the generation AI and have the generation AI determine the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the data. For example, the analysis unit adjusts the order of analysis based on the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. 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 relevance of the data to the generation AI and cause the generation AI to adjust the order of analysis.

[0046] The analysis unit can adjust the use of technical terms in the analysis according to the child's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the child's level of expertise during analysis. For example, if the child's level of expertise is high, the analysis unit can use a lot of technical terms. Furthermore, if the child's level of expertise is low, the analysis unit can explain the analysis results in simple terms. Furthermore, the analysis unit can dynamically adjust the use of technical terms in the analysis according to the child's level of expertise. For example, the analysis unit adjusts the use of technical terms in the analysis according to the child's level of expertise. This allows for the provision of analysis results that are easy to understand by adjusting the use of technical terms in the analysis according to the child's level of expertise. 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 into the generation AI and cause the generation AI to adjust the use of technical terms.

[0047] The suggestion unit can adjust the level of detail of the proposal based on the importance of the talents and interests when making a suggestion. For example, the suggestion unit can adjust the level of detail of the proposal based on the importance of the talents and interests when making a suggestion. For example, the suggestion unit can make detailed suggestions for important talents and interests and brief suggestions for less important talents and interests. The suggestion unit can also dynamically adjust the level of detail of the proposal according to the importance of the talents and interests. For example, the suggestion unit adjusts the level of detail of the proposal based on the importance of the talents and interests. This enables efficient suggestions by adjusting the level of detail of the proposal based on the importance of the talents and interests. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the importance of the talents and interests to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0048] The suggestion unit can apply different suggestion algorithms depending on the categories of talents and interests when making a suggestion. For example, the suggestion unit can apply different suggestion algorithms depending on the categories of talents and interests when making a suggestion. For example, the suggestion unit can apply an algorithm that suggests study plans for learning talents and interests, and an algorithm that suggests sports clubs and events for sports talents and interests. The suggestion unit can also apply an algorithm that suggests art classes and exhibitions for art talents and interests. For example, the suggestion unit applies different suggestion algorithms depending on the categories of talents and interests. This improves the accuracy of suggestions by applying an appropriate suggestion algorithm depending on the categories of talents and interests. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input talent and interest categories into a generation AI and cause the generation AI to apply a suggestion algorithm.

[0049] The suggestion unit can improve the accuracy of the suggestion by referring to the child's past suggestion results when making a suggestion. For example, the suggestion unit can correct the current suggestion based on the child's past suggestion results. The suggestion unit can also optimize the suggestion algorithm by referring to the child's past suggestion results. The suggestion unit can also improve the accuracy of the suggestion by using the child's past suggestion results. For example, the suggestion unit corrects the current suggestion based on the child's past suggestion results. In this way, the accuracy of the suggestion is improved by referring to the past suggestion results. 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 past suggestion results into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0050] The suggestion unit can determine the priority of the suggestions based on when the talents and interests were discovered when making suggestions. The suggestion unit, for example, determines the priority of the suggestions based on when the talents and interests were discovered when making suggestions. For example, the suggestion unit can prioritize suggestions for recently discovered talents and interests. The suggestion unit can also make continuous suggestions for talents and interests that have continued for a long period of time. The suggestion unit can also dynamically adjust the priority of the suggestions based on when the talents and interests were discovered. For example, the suggestion unit determines the priority of the suggestions based on when the talents and interests were discovered. In this way, by determining the priority of the suggestions based on when the talents and interests were discovered, the most recent talents and interests can be prioritized for suggestions. 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 time when the talents and interests were discovered into the generation AI and cause the generation AI to determine the priority of the suggestions.

[0051] The suggestion unit can adjust the order of suggestions based on the relevance of talents and interests when making suggestions. The suggestion unit, for example, adjusts the order of suggestions based on the relevance of talents and interests when making suggestions. For example, the suggestion unit can prioritize suggestions for highly relevant talents and interests. The suggestion unit can also postpone suggestions for less relevant talents and interests. The suggestion unit can also dynamically adjust the order of suggestions based on the relevance of talents and interests. For example, the suggestion unit adjusts the order of suggestions based on the relevance of talents and interests. This enables efficient suggestions by adjusting the order of suggestions based on the relevance of talents and interests. Some or all of the above-described 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 the relevance of talents and interests to a generation AI and cause the generation AI to adjust the order of suggestions.

[0052] The suggestion unit can adjust the use of technical terminology in the suggestion according to the parent's level of expertise when making a suggestion. For example, the suggestion unit can adjust the use of technical terminology in the suggestion according to the parent's level of expertise when making a suggestion. For example, if the parent's level of expertise is high, the suggestion unit can use a lot of technical terminology. Furthermore, if the parent's level of expertise is low, the suggestion unit can explain the suggestion content in simple terms. Furthermore, the suggestion unit can dynamically adjust the use of technical terminology in the suggestion according to the parent's level of expertise. For example, the suggestion unit adjusts the use of technical terminology in the suggestion according to the parent's level of expertise. In this way, by adjusting the use of technical terminology in the suggestion according to the parent's level of expertise, it is possible to provide an easy-to-understand suggestion. Some or all of the above-described 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 the parent's level of expertise to the generation AI and cause the generation AI to adjust the use of technical terminology.

[0053] The update unit can optimize the update algorithm by referring to past update data during an update. The update unit, for example, optimizes the update algorithm by referring to past update data during an update. For example, the update unit can correct the current update algorithm based on past update data. The update unit can also optimize the update algorithm by referring to past update data. The update unit can also improve the accuracy of the update by using past update data. For example, the update unit corrects the current update algorithm based on past update data. In this way, the update algorithm is optimized by referring to the past update data, and the accuracy of the update is improved. Some or all of the above-mentioned processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input past update data to a generation AI and cause the generation AI to optimize the update algorithm.

[0054] The update unit can adjust the update data to reflect the child's growth history during an update. The update unit, for example, adjusts the update data to reflect the child's growth history during an update. For example, the update unit can correct the current update data based on the child's growth history. The update unit can also optimize the update data by referring to the child's growth history. The update unit can also improve the accuracy of the update data by using the child's growth history. For example, the update unit corrects the current update data based on the child's growth history. In this way, more appropriate update data can be provided by reflecting the child's growth history. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input the child's growth history into the generation AI and cause the generation AI to adjust the update data.

[0055] The update unit can weight the update data based on the time of data collection during an update. The update unit, for example, weights the update data based on the time of data collection during an update. For example, the update unit can perform an update by prioritizing the most recent data. The update unit can also perform an update by prioritizing data from an important period. The update unit can also dynamically adjust the weighting of the update data based on the time of data collection. For example, the update unit weights the update data based on the time of data collection. In this way, by weighting the update data based on the time of data collection, it is possible to perform an update that prioritizes the most recent data. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input the time of data collection to the generation AI and cause the generation AI to perform weighting of the update data.

[0056] The update unit can integrate information from different data sources to enrich the updated data during an update. For example, the update unit can integrate information from different data sources to enrich the updated data during an update. For example, the update unit can integrate school grade data and home learning data to perform an update. The update unit can also integrate extracurricular activity data and social media data to perform an update. The update unit can also integrate information from different data sources to improve the accuracy of the updated data. For example, the update unit integrates school grade data and home learning data to perform an update. In this way, the accuracy of the updated data is improved by integrating information from different data sources. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input information from different data sources to the generation AI and cause the generation AI to integrate the information.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] When analyzing a child's behavioral data, the analysis unit can identify talents and interests by taking into account the frequency and duration of the behavior. For example, if a child frequently participates in a particular extracurricular activity, it can be determined that the child has a high interest in that activity. Also, if the behavior lasts for a long time, it can be determined that the child has the ability to concentrate and persevere in that activity. Furthermore, the analysis unit can analyze fluctuation patterns in the behavioral data to detect changes in the child's interests. For example, if the frequency of participation in a particular activity suddenly increases, it can be determined that a new interest has emerged. This allows the analysis unit to more accurately identify a child's talents and interests through detailed analysis of the behavioral data.

[0059] When making suggestions to parents, the suggestion unit can use infographics to make the suggestions visually easy to understand. For example, the progress of the learning plan can be displayed in graphs or charts so that parents can understand at a glance. The suggestions can also be provided in video format so that parents can easily understand visually. Furthermore, the suggestion unit can notify the parents of the suggestions on their smartphones or tablets so that the parents can check the suggestions anytime, anywhere. In this way, the suggestion unit can make suggestions visually easy to understand for parents, thereby increasing the effectiveness of the suggestions.

[0060] The update unit can reflect feedback from parents when updating the suggestions as the child grows. For example, if the parent submits opinions or requests regarding the suggestions, the update unit can adjust the suggestions based on that feedback. Also, if the parent reports the results of implementing the suggestions, the update unit can improve the suggestions based on those results. Furthermore, the update unit can collect feedback from parents and evaluate the effectiveness of the suggestions. For example, the update unit can evaluate changes in the child's grades and behavior after the parent implements the suggestions, and update the suggestions based on those results. In this way, the update unit can provide more effective suggestions by reflecting feedback from parents.

[0061] The collection unit can use a wearable device to collect the child's behavioral data. For example, a smart watch or fitness tracker worn by the child can be used to collect daily activity data in real time. The wearable device can also collect biological data such as the child's heart rate and sleep patterns to monitor their health. Furthermore, the collection unit can analyze the data obtained from the wearable device to understand the child's behavioral patterns and health condition. By using the wearable device, the collection unit can collect more detailed behavioral data and improve the accuracy of the analysis.

[0062] The analysis unit can use social network analysis when analyzing the child's behavioral data. For example, the analysis unit can analyze the type of friendships the child has and evaluate the influence of the friendships on the child's behavior and interests. The analysis unit can also analyze the activities of groups and communities in which the child participates and evaluate the influence of the groups. Furthermore, the analysis unit can use social network analysis to understand what information the child receives and evaluate the influence of that information on the child's behavior and interests. As a result, the analysis unit can use social network analysis to more accurately evaluate the influence on the child's behavior and interests.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The collection unit collects the child's behavior or learning history. For example, the collection unit can collect data such as grades at school, participation in extracurricular activities, and time spent studying at home. The collection unit can also estimate the child's emotions and adjust the timing of data collection based on the estimated child's emotions. For example, the collection unit can collect data when the child is relaxed and refrain from collecting data when the child is stressed. Step 2: The analysis unit analyzes the data collected by the collection unit and identifies the child's talents and interests. For example, the analysis unit can identify the child's academic talents or sports talents based on the collected data. The analysis unit can also estimate the child's emotions and adjust the way the analysis is presented based on the estimated child's emotions. For example, the analysis unit can provide detailed analysis results when the child is relaxed and provide concise analysis results when the child is stressed. Step 3: The suggestion unit makes suggestions to the parent about study plans and hobbies based on the talents and interests identified by the analysis unit. For example, the suggestion unit can suggest math workbooks and online courses for a child who is good at math, and suggest sports clubs and related events for a child who likes sports. The suggestion unit can also estimate the child's emotions and adjust the way the suggestions are presented based on the estimated child's emotions. For example, the suggestion unit can make detailed suggestions when the child is relaxed and make concise suggestions when the child is stressed. Step 4: The update unit updates the content suggested by the suggestion unit in accordance with the child's growth. For example, the update unit updates the content suggested in accordance with the child's growth, so that optimal support can always be provided. The update unit can also estimate the child's emotions and adjust the timing of updates based on the estimated child's emotions. For example, the update unit can update the content suggested when the child is relaxed and refrain from updating when the child is feeling stressed.

[0065] (Example 2) A system according to an embodiment of the present invention uses AI to analyze a child's behavior and learning history to discover their talents and interests. This system collects a child's daily behavior and learning history, analyzes it using AI to identify their talents and interests, and then suggests study plans and hobbies to parents. For example, the system collects data such as school grades, participation in extracurricular activities, and study time at home. Next, the AI ​​analyzes the collected data to identify the child's talents and interests. For example, the AI ​​detects trends such as high math grades or active participation in sports. The AI ​​then suggests study plans and hobbies to parents based on the identified talents and interests. For example, if a child excels in math, it suggests math workbooks and online courses, and if a child likes sports, it suggests sports clubs and related events. Furthermore, the AI ​​updates its suggestions as the child grows. For example, if a child develops a new interest or their grades improve, the AI ​​detects these changes and makes appropriate suggestions. This allows the system to provide flexible support tailored to the child's development. This makes it easier for parents to understand their child's talents and interests and provide appropriate study plans and hobbies. As a result, it helps children to develop their potential and grow.

[0066] A talent discovery system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, and an update unit. The collection unit collects a child's behavior or learning history. For example, the collection unit can collect data such as grades at school, participation in extracurricular activities, and study time at home. The collection unit can also estimate the child's emotions and adjust the timing of data collection based on the estimated child's emotions. For example, the collection unit can collect data when the child is relaxed and refrain from collecting data when the child is stressed. The analysis unit analyzes the data collected by the collection unit to identify the child's talents and interests. For example, the analysis unit can identify the child's academic talent or sports talent based on the collected data. The analysis unit can also estimate the child's emotions and adjust the method of presentation of the analysis based on the estimated child's emotions. For example, the analysis unit can provide detailed analysis results when the child is relaxed and brief analysis results when the child is stressed. The suggestion unit suggests study plans and hobbies to the parent based on the talents and interests identified by the analysis unit. For example, the suggestion unit can suggest math workbooks and online courses to a child who is good at math, and suggest sports clubs and related events to a child who likes sports. The suggestion unit can also estimate a child's emotions and adjust the way the suggestions are expressed based on the estimated child's emotions. For example, detailed suggestions can be made when the child is relaxed, and concise suggestions can be made when the child is stressed. The update unit updates the content suggested by the suggestion unit in accordance with the child's growth. For example, the update unit can update the content of the suggestions in accordance with the child's growth, thereby always providing optimal support. The update unit can also estimate a child's emotions and adjust the timing of updates based on the estimated child's emotions. For example, the content of the suggestions can be updated when the child is relaxed, and the update can be refrained from when the child is stressed. In this way, the talent discovery system according to the embodiment can discover a child's talents and interests, and help expand a child's potential by suggesting learning plans and hobbies that are tailored to the child's growth.

[0067] The collection unit can collect data on school grades, participation in extracurricular activities, and study time at home. The collection unit, for example, collects school grades. For example, the collection unit can collect test scores and report card grades. The collection unit can also collect participation in extracurricular activities. For example, the collection unit can collect the type, frequency, and results of activities participated in. The collection unit can also collect study time at home. For example, the collection unit can collect the time spent studying, the content of study, and the quality of study. This enables more accurate analysis by collecting detailed information on a child's daily behavior and study history. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input school grade data into a generation AI and have the generation AI analyze the grade data.

[0068] The analysis unit can analyze the collected data and identify the child's talents and interests. The analysis unit can, for example, identify the child's talents and interests based on the collected data. For example, the analysis unit can identify the child's talents and interests, such as whether the child has excellent grades in math or actively participates in sports. The analysis unit can also identify the child's academic talents and sports talents based on the collected data. For example, the analysis unit can identify the child's academic talents based on test scores and grades on report cards. The analysis unit can also identify the child's sports talents based on the types, frequency, and results of extracurricular activities the child participates in. In this way, the child's talents and interests can be accurately identified by analyzing the collected data. Some or all of the above-described processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input the collected data into a generation AI and cause the generation AI to identify the talents and interests.

[0069] The suggestion unit can suggest study plans and hobbies to parents based on the identified talents and interests. For example, the suggestion unit can suggest math workbooks and online courses to a child who is good at math, and suggest sports clubs and related events to a child who likes sports. The suggestion unit can also suggest study plans and hobbies to parents based on the identified talents and interests. For example, the suggestion unit can provide parents with details of study plans, types of hobbies, and a suggestion format. This can support a child's development by suggesting appropriate study plans and hobbies based on the identified talents and interests. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data on the identified talents and interests into a generation AI and cause the generation AI to suggest study plans and hobbies.

[0070] The update unit can update the suggestion content in accordance with the child's growth. The update unit, for example, updates the suggestion content in accordance with the child's growth. For example, the update unit can update the suggestion content when the child begins to develop a new interest or when their grades improve. The update unit can also update the suggestion content in accordance with the child's growth to always provide optimal support. For example, the update unit can update the suggestion content according to the child's age or learning progress. This makes it possible to always provide optimal support by updating the suggestion content in accordance with the child's growth. Some or all of the above-mentioned processing in the update unit may be performed using AI, for example, or may be performed without using AI. For example, the update unit can input the child's growth data into the generation AI and cause the generation AI to update the suggestion content.

[0071] The suggestion unit can suggest math workbooks and online courses to children who are good at math, and sports clubs and related events to children who like sports. For example, the suggestion unit can suggest math workbooks and online courses to children who are good at math. For example, the suggestion unit can suggest math workbooks and online courses to children who have excellent math grades. The suggestion unit can also suggest sports clubs and related events to children who like sports. For example, the suggestion unit can suggest sports clubs and related events to children who actively participate in sports. This makes it possible to effectively support the child's development by making specific suggestions based on the child's talents and interests. Some or all of the above-mentioned processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data on the identified talents and interests into the generation AI and cause the generation AI to suggest study plans and hobbies.

[0072] 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 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 can collect data on study time and extracurricular activities when the child is relaxed and refrain from collecting data when the child is stressed. The collection unit can also collect behavioral data in real time when the child is excited and analyze it later. This allows for more accurate data collection by adjusting the timing of data collection 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 such examples. Some or all of the above-described processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input the child's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0073] The collection unit can analyze the child's past behavioral history and select the optimal data collection method. The collection unit, for example, analyzes the child's past behavioral history and selects the optimal data collection method. For example, the collection unit can prioritize collecting similar activity data based on activities the child has previously liked. The collection unit can also adjust the data collection method based on activities the child has previously avoided to avoid stress. The collection unit can also analyze the child's past behavioral patterns and select the most efficient data collection method. For example, the collection unit selects the optimal data collection method based on the child's past behavioral patterns. In this way, the optimal data collection method can be selected by analyzing the past behavioral history. 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 behavioral data into the generation AI and cause the generation AI to select the optimal data collection method.

[0074] The collection unit can filter data based on the child's current learning situation and areas of interest when collecting data. For example, the collection unit can filter data based on the child's current learning situation and areas of interest when collecting data. For example, the collection unit can prioritize collecting data on areas in which the child is currently interested. The collection unit can also collect only necessary data and eliminate unnecessary data depending on the child's learning situation. The collection unit can also filter and collect related data based on the child's areas of interest. For example, the collection unit filters and collects related data based on the child's areas of interest. In this way, by filtering data based on the child's current learning situation and areas of interest, unnecessary data can be eliminated and only necessary data can be collected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the child's current learning situation and areas of interest to the generation AI and have the generation AI perform the filtering.

[0075] The collection unit can select the optimal collection means depending on the child's input method when collecting data. For example, the collection unit selects the optimal collection means depending on the child's input method when collecting data. 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 prioritize collecting text data. Furthermore, if the child prefers image input, the collection unit can prioritize collecting image data. For example, the collection unit selects the optimal collection means depending on the child's input method. In this way, data can be collected efficiently 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 to a generation AI and cause the generation AI to select the optimal collection means.

[0076] 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. The collection unit, for example, estimates the child's emotions and determines the priority of data to be collected based on the estimated child's emotions. For example, the collection unit can prioritize collecting learning data when the child is relaxed, and prioritize collecting data related to hobbies and relaxation when the child is stressed. The collection unit can also prioritize collecting behavioral data when the child is excited. This allows important data to be collected preferentially by determining the priority of data based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using an AI, for example, or without an AI. For example, the collection unit can input the child's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0077] The collection unit can prioritize collecting highly relevant data by taking into account the child's geographical location information when collecting data. For example, the collection unit can prioritize collecting highly relevant data by taking into account the child's geographical location information when collecting data. For example, the collection unit can prioritize collecting school activity data when the child is at school, and prioritize collecting home learning data when the child is at home. The collection unit can also prioritize collecting data related to exercise and play when the child is in a park. In this way, highly relevant data can be prioritized by taking into account the geographical location information. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the child's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0078] The collection unit may analyze the child's social media activities and collect related data when collecting data. For example, the collection unit may analyze the child's social media activities and collect related data when collecting data. For example, the collection unit may collect related data based on activities shared by the child on social media. The collection unit may also collect related data by referring to the activities of the child's friends on social media. The collection unit may also analyze the content of the child's posts on social media and collect related data. For example, the collection unit may collect related data based on the content of the child's posts on social media. In this way, related data can be efficiently collected by analyzing social media activities. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit may input the child's social media data into the generation AI and cause the generation AI to collect related data.

[0079] The collection unit can customize the collection method by reflecting the child's past feedback when collecting data. For example, the collection unit customizes the collection method by reflecting the child's past feedback when collecting data. For example, the collection unit can collect data using a similar method based on a collection method that the child has previously preferred. The collection unit can also adjust the collection method based on a collection method that the child has previously avoided. The collection unit can also analyze the child's past feedback and customize the optimal collection method. For example, the collection unit customizes the optimal collection method based on the child's past feedback. In this way, the optimal collection method can be customized by reflecting the past feedback. Some or all of the above-described 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 past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0080] The analysis unit can estimate the child's emotions and adjust the way the analysis is presented based on the estimated child's emotions. The analysis unit, for example, estimates the child's emotions and adjusts the way the analysis is presented based on the estimated child's emotions. For example, the analysis unit can provide detailed analysis results when the child is relaxed and concise analysis results when the child is stressed. The analysis unit can also provide visually appealing analysis results when the child is excited. This allows for more appropriate analysis results to be provided by adjusting the way the analysis 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the child's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit can perform a detailed analysis on important data and a concise analysis on less important data. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the data. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. 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 importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an algorithm that evaluates learning outcomes to learning data and an algorithm that analyzes behavioral patterns to behavioral data. The analysis unit can also apply an algorithm that analyzes interest trends to interest data. For example, the analysis unit applies different analysis algorithms depending on the data category. This improves the accuracy of the analysis by applying an appropriate analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply the analysis algorithm.

[0083] The analysis unit can improve the accuracy of the analysis by referring to the child's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the child's past analysis results during analysis. For example, the analysis unit can correct the current analysis result based on the child's past analysis results. The analysis unit can also optimize the analysis algorithm by referring to the child's past analysis results. The analysis unit can also improve the accuracy of the analysis by using the child's past analysis results. For example, the analysis unit corrects the current analysis result based on the child's past analysis results. In this way, the accuracy of the analysis is improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the child's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0084] The analysis unit can estimate the child's emotions and adjust the length of the analysis based on the estimated child's emotions. The analysis unit, for example, estimates the child's emotions and adjusts the length of the analysis based on the estimated child's emotions. For example, the analysis unit can perform a detailed analysis when the child is relaxed and a brief analysis when the child is stressed. The analysis unit can also perform a visually appealing analysis when the child is excited. This allows for adjusting the length of the analysis according to the child's emotions, thereby providing more appropriate analysis results. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the child's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0085] The analysis unit can determine the analysis priority based on the time of data collection during analysis. The analysis unit, for example, determines the analysis priority based on the time of data collection during analysis. For example, the analysis unit can prioritize analysis of the most recent data. The analysis unit can also prioritize analysis of data from important periods. The analysis unit can also dynamically adjust the analysis priority based on the time of data collection. For example, the analysis unit determines the analysis priority based on the time of data collection. In this way, by determining the analysis priority based on the time of data collection, the most recent data can be analyzed preferentially. 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 time of data collection to the generation AI and have the generation AI determine the analysis priority.

[0086] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the data. For example, the analysis unit adjusts the order of analysis based on the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. 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 relevance of the data to the generation AI and cause the generation AI to adjust the order of analysis.

[0087] The analysis unit can adjust the use of technical terms in the analysis according to the child's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the child's level of expertise during analysis. For example, if the child's level of expertise is high, the analysis unit can use a lot of technical terms. Furthermore, if the child's level of expertise is low, the analysis unit can explain the analysis results in simple terms. Furthermore, the analysis unit can dynamically adjust the use of technical terms in the analysis according to the child's level of expertise. For example, the analysis unit adjusts the use of technical terms in the analysis according to the child's level of expertise. This allows for the provision of analysis results that are easy to understand by adjusting the use of technical terms in the analysis according to the child's level of expertise. 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 into the generation AI and cause the generation AI to adjust the use of technical terms.

[0088] The suggestion unit can estimate the child's emotions and adjust the way the suggestions are expressed based on the estimated child's emotions. The suggestion unit, for example, estimates the child's emotions and adjusts the way the suggestions are expressed based on the estimated child's emotions. For example, the suggestion unit can provide detailed suggestions when the child is relaxed and brief suggestions when the child is stressed. The suggestion unit can also provide visually appealing suggestions when the child is excited. This allows for more appropriate suggestions to be provided by adjusting the way the suggestions are expressed 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 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 an AI, for example, or without an AI. For example, the suggestion unit can input the child's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0089] The suggestion unit can adjust the level of detail of the proposal based on the importance of the talents and interests when making a suggestion. For example, the suggestion unit can adjust the level of detail of the proposal based on the importance of the talents and interests when making a suggestion. For example, the suggestion unit can make detailed suggestions for important talents and interests and brief suggestions for less important talents and interests. The suggestion unit can also dynamically adjust the level of detail of the proposal according to the importance of the talents and interests. For example, the suggestion unit adjusts the level of detail of the proposal based on the importance of the talents and interests. This enables efficient suggestions by adjusting the level of detail of the proposal based on the importance of the talents and interests. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the importance of the talents and interests to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0090] The suggestion unit can apply different suggestion algorithms depending on the categories of talents and interests when making a suggestion. For example, the suggestion unit can apply different suggestion algorithms depending on the categories of talents and interests when making a suggestion. For example, the suggestion unit can apply an algorithm that suggests study plans for learning talents and interests, and an algorithm that suggests sports clubs and events for sports talents and interests. The suggestion unit can also apply an algorithm that suggests art classes and exhibitions for art talents and interests. For example, the suggestion unit applies different suggestion algorithms depending on the categories of talents and interests. This improves the accuracy of suggestions by applying an appropriate suggestion algorithm depending on the categories of talents and interests. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input talent and interest categories into a generation AI and cause the generation AI to apply a suggestion algorithm.

[0091] The suggestion unit can improve the accuracy of the suggestion by referring to the child's past suggestion results when making a suggestion. For example, the suggestion unit can correct the current suggestion based on the child's past suggestion results. The suggestion unit can also optimize the suggestion algorithm by referring to the child's past suggestion results. The suggestion unit can also improve the accuracy of the suggestion by using the child's past suggestion results. For example, the suggestion unit corrects the current suggestion based on the child's past suggestion results. In this way, the accuracy of the suggestion is improved by referring to the past suggestion results. 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 past suggestion results into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0092] The suggestion unit can estimate the child's emotion and adjust the length of the suggestion based on the estimated emotion. The suggestion unit, for example, estimates the child's emotion and adjusts the length of the suggestion based on the estimated emotion. For example, the suggestion unit can provide detailed suggestions when the child is relaxed and brief suggestions when the child is stressed. The suggestion unit can also provide visually appealing suggestions when the child is excited. This allows for more appropriate suggestions to be provided by adjusting the length of the suggestion according to the child's emotion. 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 such examples. Some or all of the above-described processing in the suggestion unit can be performed using an AI, for example, or without an AI. For example, the suggestion unit can input the child's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0093] The suggestion unit can determine the priority of the suggestions based on when the talents and interests were discovered when making suggestions. The suggestion unit, for example, determines the priority of the suggestions based on when the talents and interests were discovered when making suggestions. For example, the suggestion unit can prioritize suggestions for recently discovered talents and interests. The suggestion unit can also make continuous suggestions for talents and interests that have continued for a long period of time. The suggestion unit can also dynamically adjust the priority of the suggestions based on when the talents and interests were discovered. For example, the suggestion unit determines the priority of the suggestions based on when the talents and interests were discovered. In this way, by determining the priority of the suggestions based on when the talents and interests were discovered, the most recent talents and interests can be prioritized for suggestions. 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 time when the talents and interests were discovered into the generation AI and cause the generation AI to determine the priority of the suggestions.

[0094] The suggestion unit can adjust the order of suggestions based on the relevance of talents and interests when making suggestions. The suggestion unit, for example, adjusts the order of suggestions based on the relevance of talents and interests when making suggestions. For example, the suggestion unit can prioritize suggestions for highly relevant talents and interests. The suggestion unit can also postpone suggestions for less relevant talents and interests. The suggestion unit can also dynamically adjust the order of suggestions based on the relevance of talents and interests. For example, the suggestion unit adjusts the order of suggestions based on the relevance of talents and interests. This enables efficient suggestions by adjusting the order of suggestions based on the relevance of talents and interests. Some or all of the above-described 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 the relevance of talents and interests to a generation AI and cause the generation AI to adjust the order of suggestions.

[0095] The suggestion unit can adjust the use of technical terminology in the suggestion according to the parent's level of expertise when making a suggestion. For example, the suggestion unit can adjust the use of technical terminology in the suggestion according to the parent's level of expertise when making a suggestion. For example, if the parent's level of expertise is high, the suggestion unit can use a lot of technical terminology. Furthermore, if the parent's level of expertise is low, the suggestion unit can explain the suggestion content in simple terms. Furthermore, the suggestion unit can dynamically adjust the use of technical terminology in the suggestion according to the parent's level of expertise. For example, the suggestion unit adjusts the use of technical terminology in the suggestion according to the parent's level of expertise. In this way, by adjusting the use of technical terminology in the suggestion according to the parent's level of expertise, it is possible to provide an easy-to-understand suggestion. Some or all of the above-described 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 the parent's level of expertise to the generation AI and cause the generation AI to adjust the use of technical terminology.

[0096] The update unit can estimate the child's emotions and adjust the timing of updates based on the estimated child's emotions. The update unit, for example, estimates the child's emotions and adjusts the timing of updates based on the estimated child's emotions. For example, the update unit can update the suggestions when the child is relaxed and refrain from updating when the child is stressed. The update unit can also update the suggestions in real time when the child is excited. This allows updates to be performed at more appropriate times by adjusting the timing of updates according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the update unit may be performed using an AI, for example, or without an AI. For example, the update unit can input the child's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0097] The update unit can optimize the update algorithm by referring to past update data during an update. The update unit, for example, optimizes the update algorithm by referring to past update data during an update. For example, the update unit can correct the current update algorithm based on past update data. The update unit can also optimize the update algorithm by referring to past update data. The update unit can also improve the accuracy of the update by using past update data. For example, the update unit corrects the current update algorithm based on past update data. In this way, the update algorithm is optimized by referring to the past update data, and the accuracy of the update is improved. Some or all of the above-mentioned processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input past update data to a generation AI and cause the generation AI to optimize the update algorithm.

[0098] The update unit can adjust the update data to reflect the child's growth history during an update. The update unit, for example, adjusts the update data to reflect the child's growth history during an update. For example, the update unit can correct the current update data based on the child's growth history. The update unit can also optimize the update data by referring to the child's growth history. The update unit can also improve the accuracy of the update data by using the child's growth history. For example, the update unit corrects the current update data based on the child's growth history. In this way, more appropriate update data can be provided by reflecting the child's growth history. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input the child's growth history into the generation AI and cause the generation AI to adjust the update data.

[0099] The update unit can estimate the child's emotion and adjust the update frequency based on the estimated emotion. The update unit, for example, estimates the child's emotion and adjusts the update frequency based on the estimated emotion. For example, the update unit can perform updates frequently when the child is relaxed and reduce the update frequency when the child is stressed. The update unit can also perform updates in real time when the child is excited. This allows updates to be performed at a more appropriate frequency by adjusting the update frequency according to the child's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the update unit may be performed using an AI, for example, or without an AI. For example, the update unit can input the child's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0100] The update unit can weight the update data based on the time of data collection during an update. The update unit, for example, weights the update data based on the time of data collection during an update. For example, the update unit can perform an update by prioritizing the most recent data. The update unit can also perform an update by prioritizing data from an important period. The update unit can also dynamically adjust the weighting of the update data based on the time of data collection. For example, the update unit weights the update data based on the time of data collection. In this way, by weighting the update data based on the time of data collection, it is possible to perform an update that prioritizes the most recent data. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input the time of data collection to the generation AI and cause the generation AI to perform weighting of the update data.

[0101] The update unit can integrate information from different data sources to enrich the updated data during an update. For example, the update unit can integrate information from different data sources to enrich the updated data during an update. For example, the update unit can integrate school grade data and home learning data to perform an update. The update unit can also integrate extracurricular activity data and social media data to perform an update. The update unit can also integrate information from different data sources to improve the accuracy of the updated data. For example, the update unit integrates school grade data and home learning data to perform an update. In this way, the accuracy of the updated data is improved by integrating information from different data sources. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input information from different data sources to the generation AI and cause the generation AI to integrate the information. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and update unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects a child's behavior and learning history using the camera 42 and microphone 38B of the smart device 14 and transmits the collected data to the data processing device 12 via the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data to identify the child's talents and interests. The suggestion unit, realized, for example, by the specific processing unit 290 of the data processing device 12, suggests a learning plan or a hobby to the parent based on the identified talents and interests. The update unit, realized, for example, by the specific processing unit 290 of the data processing device 12, updates the suggestions as the child grows. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and update unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects a child's behavior and learning history using the camera 42 and microphone 238 of the smart glasses 214 and transmits the collected data to the data processing device 12 via the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data to identify the child's talents and interests. The suggestion unit, realized, for example, by the specific processing unit 290 of the data processing device 12, suggests a learning plan or hobby to the parent based on the identified talents and interests. The update unit, realized, for example, by the specific processing unit 290 of the data processing device 12, updates the suggestions as the child grows. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and update unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects the child's behavior and learning history using the camera 42 and microphone 238 of the headset-type terminal 314 and transmits the collected data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to identify the child's talents and interests. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests study plans and hobbies to parents based on the identified talents and interests. The update unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and updates the suggestions as the child grows. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, and update unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects the child's behavior and learning history using the camera 42 and microphone 238 of the robot 414 and transmits the collected data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to identify the child's talents and interests. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests learning plans and hobbies to parents based on the identified talents and interests. The update unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and updates the suggestions as the child grows.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] When analyzing a child's behavioral data, the analysis unit can identify talents and interests by taking into account the frequency and duration of the behavior. For example, if a child frequently participates in a particular extracurricular activity, it can be determined that the child has a high interest in that activity. Also, if the behavior lasts for a long time, it can be determined that the child has the ability to concentrate and persevere in that activity. Furthermore, the analysis unit can analyze fluctuation patterns in the behavioral data to detect changes in the child's interests. For example, if the frequency of participation in a particular activity suddenly increases, it can be determined that a new interest has emerged. This allows the analysis unit to more accurately identify a child's talents and interests through detailed analysis of the behavioral data.

[0104] When making suggestions to parents, the suggestion unit can use infographics to make the suggestions visually easy to understand. For example, the progress of the learning plan can be displayed in graphs or charts so that parents can understand at a glance. The suggestions can also be provided in video format so that parents can easily understand visually. Furthermore, the suggestion unit can notify the parents of the suggestions on their smartphones or tablets so that the parents can check the suggestions anytime, anywhere. In this way, the suggestion unit can make suggestions visually easy to understand for parents, thereby increasing the effectiveness of the suggestions.

[0105] The update unit can reflect feedback from parents when updating the suggestions as the child grows. For example, if the parent submits opinions or requests regarding the suggestions, the update unit can adjust the suggestions based on that feedback. Also, if the parent reports the results of implementing the suggestions, the update unit can improve the suggestions based on those results. Furthermore, the update unit can collect feedback from parents and evaluate the effectiveness of the suggestions. For example, the update unit can evaluate changes in the child's grades and behavior after the parent implements the suggestions, and update the suggestions based on those results. In this way, the update unit can provide more effective suggestions by reflecting feedback from parents.

[0106] The collection unit can use a wearable device to collect the child's behavioral data. For example, a smart watch or fitness tracker worn by the child can be used to collect daily activity data in real time. The wearable device can also collect biological data such as the child's heart rate and sleep patterns to monitor their health. Furthermore, the collection unit can analyze the data obtained from the wearable device to understand the child's behavioral patterns and health condition. By using the wearable device, the collection unit can collect more detailed behavioral data and improve the accuracy of the analysis.

[0107] The analysis unit can use social network analysis when analyzing the child's behavioral data. For example, the analysis unit can analyze the type of friendships the child has and evaluate the influence of the friendships on the child's behavior and interests. The analysis unit can also analyze the activities of groups and communities in which the child participates and evaluate the influence of the groups. Furthermore, the analysis unit can use social network analysis to understand what information the child receives and evaluate the influence of that information on the child's behavior and interests. As a result, the analysis unit can use social network analysis to more accurately evaluate the influence on the child's behavior and interests.

[0108] The suggestion unit can estimate the child's emotions and adjust the timing of suggestions based on the estimated child's emotions. For example, when the child is relaxed, the suggestion unit can suggest a new study plan, and when the child is stressed, the suggestion unit can suggest relaxation or hobby-related activities. Also, when the child is excited, the suggestion unit can suggest activities that make use of the child's excitement. Furthermore, the suggestion unit can adjust the content of the suggestions according to the child's emotions and make suggestions in a way that is easy for the child to accept. In this way, the suggestion unit can make more effective suggestions by adjusting the timing and content of the suggestions based on the child's emotions.

[0109] The collection unit can estimate the child's emotions and select the type of data to collect based on the estimated child's emotions. For example, learning data can be collected when the child is relaxed, and data related to relaxation and hobbies can be collected when the child is stressed. Also, behavioral data can be collected when the child is excited and analyzed later. Furthermore, the collection unit can adjust the data collection method according to the child's emotions and collect data so that the child does not feel stressed. In this way, the collection unit can collect more accurate data by selecting the type of data to collect based on the child's emotions.

[0110] The analysis unit can estimate the child's emotions and determine the priority of analysis based on the estimated child's emotions. For example, when the child is relaxed, the analysis of learning data can be prioritized, and when the child is stressed, the analysis of data related to relaxation and hobbies can be prioritized. Also, when the child is excited, the analysis of behavioral data can be prioritized, and a more detailed analysis can be performed later. Furthermore, the analysis unit can adjust the analysis method according to the child's emotions and provide the analysis results in a format that is easy for the child to understand. In this way, the analysis unit can perform more effective analysis by determining the priority of analysis based on the child's emotions.

[0111] The suggestion unit can estimate the child's emotions and adjust the format of the suggestion based on the estimated child's emotions. For example, detailed suggestions can be made when the child is relaxed, and concise suggestions can be made when the child is stressed. Also, visually appealing suggestions can be made when the child is excited, making the suggestion in a format that is easy for the child to accept. Furthermore, the suggestion unit can adjust the content of the suggestion according to the child's emotions and make the suggestion in a format that is easy for the child to be interested in. In this way, the suggestion unit can make more effective suggestions by adjusting the format of the suggestion based on the child's emotions.

[0112] The update unit can estimate the child's emotions and adjust the content of updates based on the estimated child's emotions. For example, when the child is relaxed, it can suggest a new study plan, and when the child is stressed, it can suggest relaxation or hobbies. Also, when the child is excited, it can suggest activities that make use of the child's excitement. Furthermore, the update unit can adjust the frequency of updates according to the child's emotions and provide updates in a way that is easy for the child to accept. In this way, the update unit can provide more effective updates by adjusting the content of updates based on the child's emotions.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The collection unit collects the child's behavior or learning history. For example, the collection unit can collect data such as grades at school, participation in extracurricular activities, and time spent studying at home. The collection unit can also estimate the child's emotions and adjust the timing of data collection based on the estimated child's emotions. For example, the collection unit can collect data when the child is relaxed and refrain from collecting data when the child is stressed. Step 2: The analysis unit analyzes the data collected by the collection unit and identifies the child's talents and interests. For example, the analysis unit can identify the child's academic talents or sports talents based on the collected data. The analysis unit can also estimate the child's emotions and adjust the way the analysis is presented based on the estimated child's emotions. For example, the analysis unit can provide detailed analysis results when the child is relaxed and provide concise analysis results when the child is stressed. Step 3: The suggestion unit makes suggestions to the parent about study plans and hobbies based on the talents and interests identified by the analysis unit. For example, the suggestion unit can suggest math workbooks and online courses for a child who is good at math, and suggest sports clubs and related events for a child who likes sports. The suggestion unit can also estimate the child's emotions and adjust the way the suggestions are presented based on the estimated child's emotions. For example, the suggestion unit can make detailed suggestions when the child is relaxed and make concise suggestions when the child is stressed. Step 4: The update unit updates the content suggested by the suggestion unit in accordance with the child's growth. For example, the update unit updates the content suggested in accordance with the child's growth, so that optimal support can always be provided. The update unit can also estimate the child's emotions and adjust the timing of updates based on the estimated child's emotions. For example, the update unit can update the content suggested when the child is relaxed and refrain from updating when the child is feeling stressed.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0119] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0120] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] 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.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0136] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0166] 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.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0185] 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.

[0186] [Explanation of symbols]

[0187] 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 system comprising: a collection unit that collects a child's behavior or learning history; an analysis unit that analyzes the data collected by the collection unit and identifies the child's talents or interests; a suggestion unit that makes suggestions to parents about study plans or hobbies based on the talents or interests identified by the analysis unit; and an update unit that updates the content suggested by the suggestion unit as the child grows.

2. The system according to claim 1 , wherein the collection unit collects data on grades at school, participation in extracurricular activities, and time spent studying at home.

3. The analysis unit Analyze the collected data to identify your child's talents and interests 2. The system of claim 1.

4. The proposal unit Providing parents with study plans and hobby suggestions based on identified talents and interests 2. The system of claim 1.

5. The update unit Update suggestions as your child grows 2. The system of claim 1.

6. The proposal unit For kids who are good at math, suggest math workbooks and online courses, and for kids who like sports, suggest sports clubs and related events.

2. The system of claim 1.

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

2. The system of claim 1.

8. The collecting unit Analyze the child's past behavioral history and select the most appropriate data collection method 2. The system of claim 1.

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