system

Abstract

The system according to this embodiment aims to improve financial literacy between parents and children and to promote smooth communication. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a plan creation unit, a conversation analysis unit, and a communication support unit. The collection unit collects learning progress and needs for each household. The analysis unit analyzes the data collected by the collection unit. The plan creation unit creates a learning plan based on the analysis results obtained by the analysis unit. The conversation analysis unit supports conversations between parents and children based on the learning plan created by the plan creation unit. The communication support unit facilitates communication between parents and children based on the conversation content analyzed by the conversation analysis unit.

Description

Technical Field

[0004] , , , , , ,

[0003] , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that effective financial education is difficult to be carried out due to the difference in financial literacy between parents and children.

[0005] The system according to the embodiment aims to enhance the financial literacy between parents and children and promote smooth communication.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a plan creation unit, a conversation analysis unit, and a communication support unit. The data collection unit collects learning progress and needs for each household. The analysis unit analyzes the data collected by the data collection unit. The plan creation unit creates a learning plan based on the analysis results obtained by the analysis unit. The conversation analysis unit supports parent-child conversations based on the learning plan created by the plan creation unit. The communication support unit facilitates parent-child communication based on the conversation content analyzed by the conversation analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can improve financial literacy between parents and children and promote smooth communication. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9]This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The educational platform system according to an embodiment of the present invention is a system for parents and children to mutually enhance their financial literacy. This system provides content for parents and children to learn together. For example, for children, games and animations for learning financial concepts and quizzes that utilize learned knowledge are provided, while for parents, guidelines for providing financial education to children, expert advice, and the sharing of case studies among users are provided. Next, a generating AI creates an individualized learning plan tailored to the needs of each family. For example, it suggests topics according to learning progress, allows users to share tips, and conducts Q&A sessions. As a result, the AI ​​attends and supports conversations within the family, enabling both parents and children to acquire financial knowledge smoothly and accurately. Furthermore, effects such as improved financial literacy, promotion of economic independence, and improved parent-child communication are expected. When all family members have financial knowledge, the financial literacy of individuals and society as a whole improves, and learning financial knowledge early supports future economic independence. In addition, talking about finance deepens trust and communication between parents and children. Thus, the educational platform system can enhance the financial literacy of parents and children, promote economic independence, and improve parent-child communication.

[0029] The educational platform system according to this embodiment comprises a data collection unit, an analysis unit, a plan creation unit, a conversation analysis unit, and a communication support unit. The data collection unit collects learning progress and needs for each household. For example, the data collection unit collects learning progress and needs for each household using questionnaires. The data collection unit can also monitor the home learning environment using sensors and collect data. Furthermore, the data collection unit can collect online data to understand the learning progress and needs of each household. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes the data using statistical analysis. Furthermore, the analysis unit can also analyze the data using machine learning algorithms. Furthermore, the analysis unit can also analyze the data using data mining techniques. The plan creation unit creates a learning plan based on the analysis results obtained by the analysis unit. For example, the plan creation unit sets weekly learning goals. Furthermore, the plan creation unit can also set criteria for selecting teaching materials. Furthermore, the plan creation unit can adjust the learning plan according to learning progress. The conversation analysis unit supports conversations between parents and children based on the learning plan created by the plan creation unit. The conversation analysis unit analyzes conversations between parents and children, for example, using speech recognition technology. The conversation analysis unit can also analyze the content of conversations using natural language processing technology. Furthermore, the conversation analysis unit can analyze the tone and emotions of the conversation. The communication support unit facilitates communication between parents and children based on the conversation content analyzed by the conversation analysis unit. The communication support unit provides feedback, for example. It can also offer advice. Furthermore, the communication support unit can provide tools to promote communication between parents and children. As a result, the educational platform system according to this embodiment can enhance the financial literacy of parents and children, promote economic independence, and improve parent-child communication.

[0030] The data collection unit collects learning progress and needs for each household. For example, it uses questionnaires to gather this information. Specifically, it collects detailed information about household learning situations and needs through online and paper-based questionnaires. These questionnaires include various items such as study time, learning materials used, learning progress, and parent-child attitudes towards learning. The data collection unit can also monitor the home learning environment using sensors and collect data. For example, it can install sensors to monitor indoor temperature, humidity, lighting brightness, and noise levels to collect data on the learning environment. This allows for the evaluation of the impact of the learning environment on learning efficiency. Furthermore, the data collection unit can collect online data to understand each household's learning progress and needs. For example, it can collect data from online learning platforms and educational apps to analyze learning progress and the effectiveness of learning materials. This allows the data collection unit to gather comprehensive information from diverse data sources and accurately understand each household's learning situation. The collected data is stored in a central database and made accessible to the analysis and planning units. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses statistical analysis to analyze the data. Specifically, it analyzes the collected data using statistical methods to understand trends in learning progress and needs. For instance, it can analyze the correlation between learning time and learning outcomes and propose the optimal learning time. The analysis unit can also analyze data using machine learning algorithms. For example, it can use supervised learning to build a model that predicts learning outcomes from past data and propose the optimal learning plan for each family. Furthermore, the analysis unit can analyze data using data mining techniques. For example, it can use clustering methods to group families with similar learning needs and provide the optimal learning plan for each group. This allows the analysis unit to analyze the collected data from multiple perspectives and make optimal suggestions tailored to the learning needs of each family. Additionally, the analysis unit can analyze data in real time and respond quickly to fluctuations in learning progress. For example, it can issue early warnings to families whose learning progress is lagging and propose appropriate countermeasures. This allows the analysis unit to continuously monitor the learning situation of each family and provide appropriate support.

[0032] The planning department creates learning plans based on the analysis results obtained by the analysis department. For example, the planning department sets weekly learning objectives. Specifically, it sets specific weekly learning objectives according to each family's learning progress and needs, clarifying the tasks and goals to be achieved. The planning department can also set criteria for selecting learning materials. For example, it selects the most suitable materials according to the learning content and the learner's level to support effective learning. Furthermore, the planning department can adjust the learning plan according to the learning progress. For example, if learning progress is falling behind, it can review the learning plan and suggest additional support or supplementary lessons. In this way, the planning department can provide flexible learning plans tailored to each family's learning situation, maximizing learning effectiveness. In addition, the planning department can regularly evaluate the progress of the learning plan and revise the plan as needed. For example, it can conduct monthly evaluations to check the degree of achievement of learning objectives and adjust the learning plan for the following month. In this way, the planning department can continuously optimize the learning plan and improve the learning effectiveness for each family.

[0033] The conversation analysis unit supports parent-child conversations based on the learning plan created by the plan creation unit. The conversation analysis unit analyzes parent-child conversations using, for example, speech recognition technology. Specifically, it records parent-child conversations and converts them into text using speech recognition technology. This allows for detailed analysis of the conversation content. The conversation analysis unit can also analyze conversation content using natural language processing technology. For example, it analyzes the content of the conversation to understand the frequency of learning-related topics and questions. Furthermore, the conversation analysis unit can also analyze the tone and emotion of the conversation. For example, it analyzes the tone and emotion of the voice to evaluate the quality of parent-child communication. This allows the conversation analysis unit to analyze parent-child communication in detail and evaluate the effectiveness of the learning plan. In addition, based on the analysis results, the conversation analysis unit can make suggestions for improving parent-child communication. For example, if there is little parent-child conversation, it provides advice to encourage conversation. This allows the conversation analysis unit to support parent-child communication and enhance learning effectiveness.

[0034] The Communication Support Department facilitates parent-child communication based on the conversation content analyzed by the Conversation Analysis Department. For example, the Communication Support Department provides feedback. Specifically, it provides appropriate feedback based on the content of parent-child conversations to improve the quality of communication. The Communication Support Department can also offer advice. For example, it provides specific advice to improve parent-child communication and enhance the parent-child relationship. Furthermore, the Communication Support Department can provide tools to promote parent-child communication. For example, it suggests learning games and activities that parents and children can engage in together, making learning enjoyable. This allows the Communication Support Department to facilitate parent-child communication and enhance learning effectiveness. Additionally, the Communication Support Department can continuously evaluate the quality of parent-child communication and improve support as needed. For example, it regularly collects feedback, evaluates the quality of communication, and identifies areas for improvement. This allows the Communication Support Department to continuously support parent-child communication and maximize learning effectiveness.

[0035] The data collection unit can analyze past learning history and select the optimal data collection method. For example, the data collection unit can identify the time of day when parents and children can learn most effectively based on past learning history and collect data during that time. The data collection unit can also select the learning format (video, text, etc.) preferred by parents and children based on past learning history and collect data in that format. Furthermore, the data collection unit can analyze past learning history to identify the environment in which parents and children can concentrate best and collect data in that environment. This enables efficient data collection by selecting the optimal data collection method based on past learning history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past learning history data into a generating AI and have the generating AI select the optimal data collection method.

[0036] The data collection unit can filter data based on the family's living situation and areas of interest when collecting learning progress data. For example, the data collection unit can collect data at the appropriate time, taking into account the family's living situation (e.g., work schedule, children's school schedule). The data collection unit can also prioritize the collection of relevant data based on the family's areas of interest (e.g., investment, savings). Furthermore, the data collection unit can efficiently collect data by filtering out unnecessary data based on the family's living situation and areas of interest. This enables efficient data collection by filtering data based on the family's living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input family living situation data into a generating AI and have the generating AI perform the filtering.

[0037] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location of the home when collecting learning progress data. For example, the data collection unit can collect local financial education event information based on the geographical location of the home. It can also collect information on local financial institutions by considering the geographical location of the home. Furthermore, the data collection unit can collect data related to the local economic situation based on the geographical location of the home. This enables effective data collection by collecting highly relevant data based on the geographical location of the home. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location data of the home into a generating AI and have the generating AI perform the collection of highly relevant data.

[0038] The data collection unit can analyze household social media activity and collect relevant data when collecting learning progress. For example, the data collection unit can analyze household social media activity and collect posts and topics of interest related to finance. The data collection unit can also collect community information on financial education based on household social media activity. Furthermore, the data collection unit can analyze household social media activity and collect financial trends and topics. This enables effective data collection by collecting relevant data based on household social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input household social media data into a generating AI and have the generating AI perform the collection of relevant data.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit will perform a detailed analysis on data with high importance. Conversely, the analysis unit can perform a simplified analysis on data with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a risk analysis algorithm to investment-related data. It can also apply a future prediction algorithm to savings-related data. Furthermore, it can apply an expenditure pattern analysis algorithm to expenditure-related data. This enables effective data analysis by applying different analysis algorithms depending on the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI select the analysis algorithm to apply.

[0041] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent data. It can also postpone the analysis of older data. Furthermore, the analysis unit can adjust the analysis schedule based on the submission date. This enables efficient data analysis by determining the priority of analysis based on the data submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data submission date into a generating AI and have the generating AI determine the analysis priority.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit may prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can determine the order of analysis based on the relevance of the data. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0043] The plan creation unit can create an optimal plan by analyzing the family's past learning history. For example, the plan creation unit can create a plan that allows parents and children to learn most effectively based on past learning history. The plan creation unit can also analyze past learning history and create a plan that incorporates the learning format preferred by parents and children. Furthermore, the plan creation unit can use past learning history as a reference to create a plan that is tailored to the time of day when parents and children can concentrate best. This enables effective learning by creating an optimal plan based on past learning history. Some or all of the above processes in the plan creation unit may be performed using AI, for example, or not. For example, the plan creation unit can input past learning history data into a generating AI and have the generating AI create an optimal plan.

[0044] The plan creation unit can customize plans based on the family's current living situation. For example, the plan creation unit creates an appropriate learning plan by considering the family's living situation (work workload, children's school schedules, etc.). The plan creation unit can also create plans that allow for adjustment of learning progress based on the family's living situation. Furthermore, the plan creation unit can create a manageable learning plan by considering the family's living situation. This makes it possible to learn at a manageable pace by customizing the plan based on the current living situation. Some or all of the above processes in the plan creation unit may be performed using AI, for example, or not using AI. For example, the plan creation unit can input family living situation data into a generating AI and have the generating AI perform the plan customization.

[0045] The plan creation unit can create an optimal plan by considering the geographical location of the household. For example, the plan creation unit can incorporate local financial education event information into the learning plan based on the household's geographical location. It can also incorporate information on local financial institutions into the learning plan, taking the household's geographical location into consideration. Furthermore, the plan creation unit can incorporate content related to the local economic situation into the learning plan, based on the household's geographical location. This enables effective learning by creating an optimal plan based on geographical location information. Some or all of the above processes in the plan creation unit may be performed using AI, for example, or without AI. For example, the plan creation unit can input the household's geographical location data into a generating AI and have the generating AI create an optimal plan.

[0046] The planning unit can analyze a family's social media activity and propose a plan when creating one. For example, the planning unit can analyze a family's social media activity and incorporate posts related to finance and topics of interest into the learning plan. It can also incorporate community information on financial education into the learning plan based on the family's social media activity. Furthermore, the planning unit can analyze a family's social media activity and incorporate financial trends and topics into the learning plan. This enables effective learning by proposing the optimal plan based on social media activity. Some or all of the above processes in the planning unit may be performed using AI, for example, or not. For example, the planning unit can input family social media data into a generating AI and have the generating AI generate plan suggestions.

[0047] The conversation analysis unit can select the optimal analysis method by referring to past conversation history during conversation analysis. For example, the conversation analysis unit can select the analysis method that the parent and child can understand most effectively based on past conversation history. The conversation analysis unit can also refer to past conversation history and select the analysis format preferred by the parent and child. Furthermore, the conversation analysis unit can analyze past conversation history and select the analysis method that the parent and child can concentrate on most effectively. This enables effective conversation analysis by selecting the optimal analysis method based on past conversation history. Some or all of the above processing in the conversation analysis unit may be performed using AI, for example, or without AI. For example, the conversation analysis unit can input past conversation history data into a generating AI and have the generating AI select the optimal analysis method.

[0048] The conversation analysis unit can apply different analysis algorithms depending on the content of the conversation during the analysis. For example, the conversation analysis unit can apply a risk analysis algorithm to conversations about investments. It can also apply a future prediction algorithm to conversations about savings. Furthermore, it can apply an expenditure pattern analysis algorithm to conversations about spending. This enables effective conversation analysis by applying different analysis algorithms depending on the content of the conversation. Some or all of the above processing in the conversation analysis unit may be performed using AI, for example, or without AI. For example, the conversation analysis unit can input conversation content data into a generating AI and have the generating AI select the analysis algorithm to apply.

[0049] The conversation analysis unit can determine the priority of analysis based on the submission date of the conversations during conversation analysis. For example, the conversation analysis unit may prioritize the analysis of the most recent conversations. It can also postpone the analysis of older conversations. Furthermore, the conversation analysis unit can adjust the analysis schedule based on the submission date. This enables efficient conversation analysis by determining the priority of analysis based on the submission date of the conversations. Some or all of the above processing in the conversation analysis unit may be performed using AI, for example, or without AI. For example, the conversation analysis unit can input conversation submission date data into a generating AI and have the generating AI perform the determination of analysis priority.

[0050] The conversation analysis unit can adjust the order of analysis based on the relevance of the conversations during the analysis process. For example, the conversation analysis unit can prioritize the analysis of highly relevant conversations. It can also postpone the analysis of less relevant conversations. Furthermore, the conversation analysis unit can determine the order of analysis based on the relevance of the conversations. This allows for efficient conversation analysis by adjusting the order of analysis based on the relevance of the conversations. Some or all of the above-described processes in the conversation analysis unit may be performed using AI, for example, or without AI. For example, the conversation analysis unit can input conversation relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0051] The communication support unit can select the optimal support method by referring to past communication history during communication support. For example, the communication support unit can select the support method that allows parent and child to communicate most effectively based on past communication history. The communication support unit can also refer to past communication history and select a support format preferred by parent and child. Furthermore, the communication support unit can analyze past communication history and select a support method that allows parent and child to concentrate most effectively. This enables effective communication support by selecting the optimal support method based on past communication history. Some or all of the above processing in the communication support unit may be performed using AI, for example, or without AI. For example, the communication support unit can input past communication history data into a generating AI and have the generating AI select the optimal support method.

[0052] The communication support unit can apply different support methods depending on the content of the communication. For example, the communication support unit can apply a risk analysis support method to communications related to investment. It can also apply a future forecasting support method to communications related to savings. Furthermore, it can apply an expenditure pattern analysis support method to communications related to expenditure. By applying different support methods depending on the content of the communication, effective communication support becomes possible. Some or all of the above processing in the communication support unit may be performed using AI, for example, or without AI. For example, the communication support unit can input communication content data into a generating AI and have the generating AI select the support method to apply.

[0053] The Communication Support Department can select the optimal support method when providing communication support, taking into account the geographical location of the household. For example, the Communication Support Department can incorporate information on local financial education events into the support based on the household's geographical location. It can also incorporate information on local financial institutions into the support, taking into account the household's geographical location. Furthermore, the Communication Support Department can incorporate content related to the local economic situation into the support based on the household's geographical location. This enables effective communication support by selecting the optimal support method based on geographical location. Some or all of the above processing in the Communication Support Department may be performed using AI, for example, or without AI. For example, the Communication Support Department can input the household's geographical location data into a generating AI and have the generating AI select the optimal support method.

[0054] The Communication Support Department can analyze a family's social media activity and propose support methods during communication support. For example, the Communication Support Department can analyze a family's social media activity and incorporate financial posts and topics of interest into the support. Furthermore, based on the family's social media activity, the Communication Support Department can also incorporate community information on financial education into the support. In addition, the Communication Support Department can analyze a family's social media activity and incorporate financial trends and topics into the support. This enables effective communication support by proposing the most suitable support methods based on social media activity. Some or all of the above processing in the Communication Support Department may be performed using AI, for example, or not. For example, the Communication Support Department can input family social media data into a generating AI and have the generating AI propose support methods.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The educational platform system can also include a reward system. This reward system awards points or badges to parents and children when they achieve learning goals. For example, points might be awarded when a child understands a specific financial concept. Badges could also be awarded to parents for providing effective education to their children. Furthermore, the reward system could offer special rewards when parents and children learn together. This can increase motivation and encourage continuous learning for both parents and children.

[0057] The educational platform system can further incorporate gamification elements. These gamification elements provide a mechanism for parents and children to enjoy learning together. For example, learning content could be presented in a quest format, allowing parents and children to cooperate to complete quests. Furthermore, level-ups and item acquisition could be implemented based on learning progress. A ranking system allowing parents and children to compete against each other could also be introduced to increase motivation for learning. This allows parents and children to improve their financial literacy while having fun.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The data collection unit collects learning progress and needs for each household. For example, it can collect learning progress and needs for each household using questionnaires. It can also monitor the home learning environment using sensors and collect data. Furthermore, it can collect online data to understand the learning progress and needs of each household. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it can analyze the data using statistical analysis. It can also analyze the data using machine learning algorithms. Furthermore, it can analyze the data using data mining techniques. Step 3: The planning unit creates a learning plan based on the analysis results obtained by the analysis unit. For example, it sets weekly learning objectives. It can also set criteria for selecting learning materials. Furthermore, it can adjust the learning plan according to the learning progress. Step 4: The conversation analysis unit supports parent-child conversations based on the learning plan created by the plan creation unit. For example, it analyzes parent-child conversations using speech recognition technology. It can also analyze the content of conversations using natural language processing technology. Furthermore, it can analyze the tone and emotions of the conversations. Step 5: The communication support unit facilitates parent-child communication based on the conversation content analyzed by the conversation analysis unit. For example, it provides feedback. It can also offer advice. Furthermore, it can provide tools to promote parent-child communication.

[0060] (Example of form 2) The educational platform system according to an embodiment of the present invention is a system for parents and children to mutually enhance their financial literacy. This system provides content for parents and children to learn together. For example, for children, games and animations for learning financial concepts and quizzes that utilize learned knowledge are provided, while for parents, guidelines for providing financial education to children, expert advice, and the sharing of case studies among users are provided. Next, a generating AI creates an individualized learning plan tailored to the needs of each family. For example, it suggests topics according to learning progress, allows users to share tips, and conducts Q&A sessions. As a result, the AI ​​attends and supports conversations within the family, enabling both parents and children to acquire financial knowledge smoothly and accurately. Furthermore, effects such as improved financial literacy, promotion of economic independence, and improved parent-child communication are expected. When all family members have financial knowledge, the financial literacy of individuals and society as a whole improves, and learning financial knowledge early supports future economic independence. In addition, talking about finance deepens trust and communication between parents and children. Thus, the educational platform system can enhance the financial literacy of parents and children, promote economic independence, and improve parent-child communication.

[0061] The educational platform system according to this embodiment comprises a data collection unit, an analysis unit, a plan creation unit, a conversation analysis unit, and a communication support unit. The data collection unit collects learning progress and needs for each household. For example, the data collection unit collects learning progress and needs for each household using questionnaires. The data collection unit can also monitor the home learning environment using sensors and collect data. Furthermore, the data collection unit can collect online data to understand the learning progress and needs of each household. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes the data using statistical analysis. Furthermore, the analysis unit can also analyze the data using machine learning algorithms. Furthermore, the analysis unit can also analyze the data using data mining techniques. The plan creation unit creates a learning plan based on the analysis results obtained by the analysis unit. For example, the plan creation unit sets weekly learning goals. Furthermore, the plan creation unit can also set criteria for selecting teaching materials. Furthermore, the plan creation unit can adjust the learning plan according to learning progress. The conversation analysis unit supports conversations between parents and children based on the learning plan created by the plan creation unit. The conversation analysis unit analyzes conversations between parents and children, for example, using speech recognition technology. The conversation analysis unit can also analyze the content of conversations using natural language processing technology. Furthermore, the conversation analysis unit can analyze the tone and emotions of the conversation. The communication support unit facilitates communication between parents and children based on the conversation content analyzed by the conversation analysis unit. The communication support unit provides feedback, for example. It can also offer advice. Furthermore, the communication support unit can provide tools to promote communication between parents and children. As a result, the educational platform system according to this embodiment can enhance the financial literacy of parents and children, promote economic independence, and improve parent-child communication.

[0062] The data collection unit collects learning progress and needs for each household. For example, it uses questionnaires to gather this information. Specifically, it collects detailed information about household learning situations and needs through online and paper-based questionnaires. These questionnaires include various items such as study time, learning materials used, learning progress, and parent-child attitudes towards learning. The data collection unit can also monitor the home learning environment using sensors and collect data. For example, it can install sensors to monitor indoor temperature, humidity, lighting brightness, and noise levels to collect data on the learning environment. This allows for the evaluation of the impact of the learning environment on learning efficiency. Furthermore, the data collection unit can collect online data to understand each household's learning progress and needs. For example, it can collect data from online learning platforms and educational apps to analyze learning progress and the effectiveness of learning materials. This allows the data collection unit to gather comprehensive information from diverse data sources and accurately understand each household's learning situation. The collected data is stored in a central database and made accessible to the analysis and planning units. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0063] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses statistical analysis to analyze the data. Specifically, it analyzes the collected data using statistical methods to understand trends in learning progress and needs. For instance, it can analyze the correlation between learning time and learning outcomes and propose the optimal learning time. The analysis unit can also analyze data using machine learning algorithms. For example, it can use supervised learning to build a model that predicts learning outcomes from past data and propose the optimal learning plan for each family. Furthermore, the analysis unit can analyze data using data mining techniques. For example, it can use clustering methods to group families with similar learning needs and provide the optimal learning plan for each group. This allows the analysis unit to analyze the collected data from multiple perspectives and make optimal suggestions tailored to the learning needs of each family. Additionally, the analysis unit can analyze data in real time and respond quickly to fluctuations in learning progress. For example, it can issue early warnings to families whose learning progress is lagging and propose appropriate countermeasures. This allows the analysis unit to continuously monitor the learning situation of each family and provide appropriate support.

[0064] The planning department creates learning plans based on the analysis results obtained by the analysis department. For example, the planning department sets weekly learning objectives. Specifically, it sets specific weekly learning objectives according to each family's learning progress and needs, clarifying the tasks and goals to be achieved. The planning department can also set criteria for selecting learning materials. For example, it selects the most suitable materials according to the learning content and the learner's level to support effective learning. Furthermore, the planning department can adjust the learning plan according to the learning progress. For example, if learning progress is falling behind, it can review the learning plan and suggest additional support or supplementary lessons. In this way, the planning department can provide flexible learning plans tailored to each family's learning situation, maximizing learning effectiveness. In addition, the planning department can regularly evaluate the progress of the learning plan and revise the plan as needed. For example, it can conduct monthly evaluations to check the degree of achievement of learning objectives and adjust the learning plan for the following month. In this way, the planning department can continuously optimize the learning plan and improve the learning effectiveness for each family.

[0065] The conversation analysis unit supports parent-child conversations based on the learning plan created by the plan creation unit. The conversation analysis unit analyzes parent-child conversations using, for example, speech recognition technology. Specifically, it records parent-child conversations and converts them into text using speech recognition technology. This allows for detailed analysis of the conversation content. The conversation analysis unit can also analyze conversation content using natural language processing technology. For example, it analyzes the content of the conversation to understand the frequency of learning-related topics and questions. Furthermore, the conversation analysis unit can also analyze the tone and emotion of the conversation. For example, it analyzes the tone and emotion of the voice to evaluate the quality of parent-child communication. This allows the conversation analysis unit to analyze parent-child communication in detail and evaluate the effectiveness of the learning plan. In addition, based on the analysis results, the conversation analysis unit can make suggestions for improving parent-child communication. For example, if there is little parent-child conversation, it provides advice to encourage conversation. This allows the conversation analysis unit to support parent-child communication and enhance learning effectiveness.

[0066] The Communication Support Department facilitates parent-child communication based on the conversation content analyzed by the Conversation Analysis Department. For example, the Communication Support Department provides feedback. Specifically, it provides appropriate feedback based on the content of parent-child conversations to improve the quality of communication. The Communication Support Department can also offer advice. For example, it provides specific advice to improve parent-child communication and enhance the parent-child relationship. Furthermore, the Communication Support Department can provide tools to promote parent-child communication. For example, it suggests learning games and activities that parents and children can engage in together, making learning enjoyable. This allows the Communication Support Department to facilitate parent-child communication and enhance learning effectiveness. Additionally, the Communication Support Department can continuously evaluate the quality of parent-child communication and improve support as needed. For example, it regularly collects feedback, evaluates the quality of communication, and identifies areas for improvement. This allows the Communication Support Department to continuously support parent-child communication and maximize learning effectiveness.

[0067] The data collection unit can estimate the emotions of the parent and child and adjust the timing of data collection based on the estimated emotions. For example, if the parent and child are stressed, the data collection unit can delay the collection timing to collect data when they are relaxed. Conversely, if the parent and child are relaxed, the data collection unit can advance the collection timing to collect data more efficiently. Furthermore, if the parent and child are busy, the data collection unit can adjust the collection timing to collect data during their free time. This allows for more effective data collection by adjusting the collection timing according to the emotions of the parent and child. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input parent and child facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0068] The data collection unit can analyze past learning history and select the optimal data collection method. For example, the data collection unit can identify the time of day when parents and children can learn most effectively based on past learning history and collect data during that time. The data collection unit can also select the learning format (video, text, etc.) preferred by parents and children based on past learning history and collect data in that format. Furthermore, the data collection unit can analyze past learning history to identify the environment in which parents and children can concentrate best and collect data in that environment. This enables efficient data collection by selecting the optimal data collection method based on past learning history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past learning history data into a generating AI and have the generating AI select the optimal data collection method.

[0069] The data collection unit can filter data based on the family's living situation and areas of interest when collecting learning progress data. For example, the data collection unit can collect data at the appropriate time, taking into account the family's living situation (e.g., work schedule, children's school schedule). The data collection unit can also prioritize the collection of relevant data based on the family's areas of interest (e.g., investment, savings). Furthermore, the data collection unit can efficiently collect data by filtering out unnecessary data based on the family's living situation and areas of interest. This enables efficient data collection by filtering data based on the family's living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input family living situation data into a generating AI and have the generating AI perform the filtering.

[0070] The data collection unit can estimate the emotions of the parent and child and determine the priority of the data to be collected based on the estimated emotions. For example, if the parent and child are feeling stressed, the data collection unit will prioritize collecting relaxing content. If the parent and child are relaxed, the data collection unit can also prioritize collecting challenging content. Furthermore, if the parent and child are excited, the data collection unit can also prioritize collecting engaging content. This enables effective data collection by prioritizing data according to the emotions of the parent and child. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input parent and child emotion data into a generative AI and have the generative AI determine the priority of the data.

[0071] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location of the home when collecting learning progress data. For example, the data collection unit can collect local financial education event information based on the geographical location of the home. It can also collect information on local financial institutions by considering the geographical location of the home. Furthermore, the data collection unit can collect data related to the local economic situation based on the geographical location of the home. This enables effective data collection by collecting highly relevant data based on the geographical location of the home. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location data of the home into a generating AI and have the generating AI perform the collection of highly relevant data.

[0072] The data collection unit can analyze household social media activity and collect relevant data when collecting learning progress. For example, the data collection unit can analyze household social media activity and collect posts and topics of interest related to finance. The data collection unit can also collect community information on financial education based on household social media activity. Furthermore, the data collection unit can analyze household social media activity and collect financial trends and topics. This enables effective data collection by collecting relevant data based on household social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input household social media data into a generating AI and have the generating AI perform the collection of relevant data.

[0073] The analysis unit can estimate the emotions of the parent and child and adjust the data analysis method based on the estimated emotions. For example, if the parent and child are stressed, the analysis unit can analyze the data using a simple analysis method. If the parent and child are relaxed, the analysis unit can also analyze the data using a detailed analysis method. Furthermore, if the parent and child are excited, the analysis unit can analyze the data using a visually easy-to-understand analysis method. This allows for effective data analysis by adjusting the data analysis method according to the emotions of the parent and child. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is 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 processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input parent and child emotion data into a generative AI and have the generative AI adjust the data analysis method.

[0074] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit will perform a detailed analysis on data with high importance. Conversely, the analysis unit can perform a simplified analysis on data with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0075] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a risk analysis algorithm to investment-related data. It can also apply a future prediction algorithm to savings-related data. Furthermore, it can apply an expenditure pattern analysis algorithm to expenditure-related data. This enables effective data analysis by applying different analysis algorithms depending on the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI select the analysis algorithm to apply.

[0076] The analysis unit can estimate the emotions of the parent and child and adjust the display method of the analysis results based on the estimated emotions. For example, if the parent and child are feeling stressed, the analysis unit provides a simple and highly visible display method. If the parent and child are relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the parent and child are excited, the analysis unit can provide a visually stimulating display method. This allows for effective data display by adjusting the display method of the analysis results according to the emotions of the parent and child. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input parent and child emotion data into the generative AI and have the generative AI adjust the display method of the analysis results.

[0077] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent data. It can also postpone the analysis of older data. Furthermore, the analysis unit can adjust the analysis schedule based on the submission date. This enables efficient data analysis by determining the priority of analysis based on the data submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data submission date into a generating AI and have the generating AI determine the analysis priority.

[0078] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit may prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can determine the order of analysis based on the relevance of the data. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0079] The planning unit can estimate the emotions of the parent and child and adjust the content of the learning plan based on the estimated emotions. For example, if the parent and child are feeling stressed, the planning unit can create a simple learning plan. If the parent and child are relaxed, the planning unit can also create a detailed learning plan. Furthermore, if the parent and child are excited, the planning unit can create a learning plan with engaging content. In this way, by adjusting the content of the learning plan according to the emotions of the parent and child, an effective learning plan can be created. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is 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 processing in the planning unit may be performed using AI, for example, or without AI. For example, the planning unit can input parent and child emotion data into the generative AI and have the generative AI adjust the content of the learning plan.

[0080] The plan creation unit can create an optimal plan by analyzing the family's past learning history. For example, the plan creation unit can create a plan that allows parents and children to learn most effectively based on past learning history. The plan creation unit can also analyze past learning history and create a plan that incorporates the learning format preferred by parents and children. Furthermore, the plan creation unit can use past learning history as a reference to create a plan that is tailored to the time of day when parents and children can concentrate best. This enables effective learning by creating an optimal plan based on past learning history. Some or all of the above processes in the plan creation unit may be performed using AI, for example, or not. For example, the plan creation unit can input past learning history data into a generating AI and have the generating AI create an optimal plan.

[0081] The plan creation unit can customize plans based on the family's current living situation. For example, the plan creation unit creates an appropriate learning plan by considering the family's living situation (work workload, children's school schedules, etc.). The plan creation unit can also create plans that allow for adjustment of learning progress based on the family's living situation. Furthermore, the plan creation unit can create a manageable learning plan by considering the family's living situation. This makes it possible to learn at a manageable pace by customizing the plan based on the current living situation. Some or all of the above processes in the plan creation unit may be performed using AI, for example, or not using AI. For example, the plan creation unit can input family living situation data into a generating AI and have the generating AI perform the plan customization.

[0082] The planning unit can estimate the emotions of parents and children and determine the priority of the learning plan based on the estimated emotions. For example, if parents and children are feeling stressed, the planning unit will prioritize incorporating relaxing content into the learning plan. If parents and children are relaxed, the planning unit can also prioritize incorporating more challenging content into the learning plan. Furthermore, if parents and children are excited, the planning unit can prioritize incorporating engaging content into the learning plan. This allows for effective learning by prioritizing the learning plan according to the emotions of parents and children. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the planning unit may be performed using AI, or not. For example, the planning unit can input parent-child emotion data into a generative AI and have the generative AI determine the priority of the learning plan.

[0083] The plan creation unit can create an optimal plan by considering the geographical location of the household. For example, the plan creation unit can incorporate local financial education event information into the learning plan based on the household's geographical location. It can also incorporate information on local financial institutions into the learning plan, taking the household's geographical location into consideration. Furthermore, the plan creation unit can incorporate content related to the local economic situation into the learning plan, based on the household's geographical location. This enables effective learning by creating an optimal plan based on geographical location information. Some or all of the above processes in the plan creation unit may be performed using AI, for example, or without AI. For example, the plan creation unit can input the household's geographical location data into a generating AI and have the generating AI create an optimal plan.

[0084] The planning unit can analyze a family's social media activity and propose a plan when creating one. For example, the planning unit can analyze a family's social media activity and incorporate posts related to finance and topics of interest into the learning plan. It can also incorporate community information on financial education into the learning plan based on the family's social media activity. Furthermore, the planning unit can analyze a family's social media activity and incorporate financial trends and topics into the learning plan. This enables effective learning by proposing the optimal plan based on social media activity. Some or all of the above processes in the planning unit may be performed using AI, for example, or not. For example, the planning unit can input family social media data into a generating AI and have the generating AI generate plan suggestions.

[0085] The conversation analysis unit can estimate the emotions of the parent and child and adjust the conversation analysis method based on the estimated emotions. For example, if the parent and child are feeling stressed, the conversation analysis unit will analyze the conversation using a simple analysis method. Furthermore, if the parent and child are relaxed, the conversation analysis unit can analyze the conversation using a more detailed analysis method. In addition, if the parent and child are excited, the conversation analysis unit can analyze the conversation using a visually easy-to-understand analysis method. This allows for effective conversation analysis by adjusting the conversation analysis method according to the emotions of the parent and child. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the conversation analysis unit may be performed using AI, or not. For example, the conversation analysis unit can input parent-child emotion data into the generative AI and have the generative AI adjust the conversation analysis method.

[0086] The conversation analysis unit can select the optimal analysis method by referring to past conversation history during conversation analysis. For example, the conversation analysis unit can select the analysis method that the parent and child can understand most effectively based on past conversation history. The conversation analysis unit can also refer to past conversation history and select the analysis format preferred by the parent and child. Furthermore, the conversation analysis unit can analyze past conversation history and select the analysis method that the parent and child can concentrate on most effectively. This enables effective conversation analysis by selecting the optimal analysis method based on past conversation history. Some or all of the above processing in the conversation analysis unit may be performed using AI, for example, or without AI. For example, the conversation analysis unit can input past conversation history data into a generating AI and have the generating AI select the optimal analysis method.

[0087] The conversation analysis unit can apply different analysis algorithms depending on the content of the conversation during the analysis. For example, the conversation analysis unit can apply a risk analysis algorithm to conversations about investments. It can also apply a future prediction algorithm to conversations about savings. Furthermore, it can apply an expenditure pattern analysis algorithm to conversations about spending. This enables effective conversation analysis by applying different analysis algorithms depending on the content of the conversation. Some or all of the above processing in the conversation analysis unit may be performed using AI, for example, or without AI. For example, the conversation analysis unit can input conversation content data into a generating AI and have the generating AI select the analysis algorithm to apply.

[0088] The conversation analysis unit can estimate the emotions of the parent and child and adjust the way the conversation is displayed based on the estimated emotions. For example, if the parent and child are feeling stressed, the conversation analysis unit can provide a simple and highly visible display method. If the parent and child are relaxed, the conversation analysis unit can also provide a display method that includes detailed information. Furthermore, if the parent and child are excited, the conversation analysis unit can provide a visually stimulating display method. This allows for effective conversation display by adjusting the way the conversation is displayed according to the emotions of the parent and child. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the conversation analysis unit may be performed using AI, for example, or without AI. For example, the conversation analysis unit can input parent and child emotion data into the generative AI and have the generative AI adjust the way the conversation is displayed.

[0089] The conversation analysis unit can determine the priority of analysis based on the submission date of the conversations during conversation analysis. For example, the conversation analysis unit may prioritize the analysis of the most recent conversations. It can also postpone the analysis of older conversations. Furthermore, the conversation analysis unit can adjust the analysis schedule based on the submission date. This enables efficient conversation analysis by determining the priority of analysis based on the submission date of the conversations. Some or all of the above processing in the conversation analysis unit may be performed using AI, for example, or without AI. For example, the conversation analysis unit can input conversation submission date data into a generating AI and have the generating AI perform the determination of analysis priority.

[0090] The conversation analysis unit can adjust the order of analysis based on the relevance of the conversations during the analysis process. For example, the conversation analysis unit can prioritize the analysis of highly relevant conversations. It can also postpone the analysis of less relevant conversations. Furthermore, the conversation analysis unit can determine the order of analysis based on the relevance of the conversations. This allows for efficient conversation analysis by adjusting the order of analysis based on the relevance of the conversations. Some or all of the above-described processes in the conversation analysis unit may be performed using AI, for example, or without AI. For example, the conversation analysis unit can input conversation relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0091] The communication support unit can estimate the emotions of the parent and child and adjust the communication support method based on the estimated emotions. For example, if the parent and child are feeling stressed, the communication support unit can provide a relaxing support method. It can also provide a more detailed support method if the parent and child are relaxed. Furthermore, if the parent and child are agitated, the communication support unit can provide a visually stimulating support method. This allows for effective communication support by adjusting the communication support method according to the emotions of the parent and child. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the communication support unit may be performed using AI, or not. For example, the communication support unit can input parent-child emotion data into a generative AI and have the generative AI adjust the communication support method.

[0092] The communication support unit can select the optimal support method by referring to past communication history during communication support. For example, the communication support unit can select the support method that allows parent and child to communicate most effectively based on past communication history. The communication support unit can also refer to past communication history and select a support format preferred by parent and child. Furthermore, the communication support unit can analyze past communication history and select a support method that allows parent and child to concentrate most effectively. This enables effective communication support by selecting the optimal support method based on past communication history. Some or all of the above processing in the communication support unit may be performed using AI, for example, or without AI. For example, the communication support unit can input past communication history data into a generating AI and have the generating AI select the optimal support method.

[0093] The communication support unit can apply different support methods depending on the content of the communication. For example, the communication support unit can apply a risk analysis support method to communications related to investment. It can also apply a future forecasting support method to communications related to savings. Furthermore, it can apply an expenditure pattern analysis support method to communications related to expenditure. By applying different support methods depending on the content of the communication, effective communication support becomes possible. Some or all of the above processing in the communication support unit may be performed using AI, for example, or without AI. For example, the communication support unit can input communication content data into a generating AI and have the generating AI select the support method to apply.

[0094] The communication support unit can estimate the emotions of parents and children and determine communication priorities based on the estimated emotions. For example, if parents and children are feeling stressed, the communication support unit will prioritize support with relaxing content. Conversely, if parents and children are relaxed, the communication support unit can also prioritize support with more challenging content. Furthermore, if parents and children are excited, the communication support unit can prioritize support with content that will capture their interest. This allows for effective communication support by determining communication priorities according to the emotions of parents and children. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the communication support unit may be performed using AI, or not. For example, the communication support unit can input parent-child emotion data into a generative AI and have the generative AI determine communication priorities.

[0095] The Communication Support Department can select the optimal support method when providing communication support, taking into account the geographical location of the household. For example, the Communication Support Department can incorporate information on local financial education events into the support based on the household's geographical location. It can also incorporate information on local financial institutions into the support, taking into account the household's geographical location. Furthermore, the Communication Support Department can incorporate content related to the local economic situation into the support based on the household's geographical location. This enables effective communication support by selecting the optimal support method based on geographical location. Some or all of the above processing in the Communication Support Department may be performed using AI, for example, or without AI. For example, the Communication Support Department can input the household's geographical location data into a generating AI and have the generating AI select the optimal support method.

[0096] The Communication Support Department can analyze a family's social media activity and propose support methods during communication support. For example, the Communication Support Department can analyze a family's social media activity and incorporate financial posts and topics of interest into the support. Furthermore, based on the family's social media activity, the Communication Support Department can also incorporate community information on financial education into the support. In addition, the Communication Support Department can analyze a family's social media activity and incorporate financial trends and topics into the support. This enables effective communication support by proposing the most suitable support methods based on social media activity. Some or all of the above processing in the Communication Support Department may be performed using AI, for example, or not. For example, the Communication Support Department can input family social media data into a generating AI and have the generating AI propose support methods.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The educational platform system can also include a reward system. This reward system awards points or badges to parents and children when they achieve learning goals. For example, points might be awarded when a child understands a specific financial concept. Badges could also be awarded to parents for providing effective education to their children. Furthermore, the reward system could offer special rewards when parents and children learn together. This can increase motivation and encourage continuous learning for both parents and children.

[0099] The educational platform system can further incorporate gamification elements. These gamification elements provide a mechanism for parents and children to enjoy learning together. For example, learning content could be presented in a quest format, allowing parents and children to cooperate to complete quests. Furthermore, level-ups and item acquisition could be implemented based on learning progress. A ranking system allowing parents and children to compete against each other could also be introduced to increase motivation for learning. This allows parents and children to improve their financial literacy while having fun.

[0100] The educational platform system can also be equipped with a virtual assistant. This virtual assistant is a virtual character designed to support parent-child learning. For example, it can explain learning content and answer questions. It can also provide advice and encouraging messages tailored to learning progress. Furthermore, the virtual assistant can estimate the emotions of both parent and child and provide appropriate support. This allows parents and children to learn with peace of mind.

[0101] The educational platform system can also be equipped with a real-time feedback function. This function allows parents and children to receive immediate feedback during learning. For example, when a child answers a quiz, feedback on whether the answer is correct or incorrect is displayed instantly. It can also evaluate the effectiveness of the education provided by parents to their children in real time. Furthermore, it can estimate the emotions of both parents and children and provide appropriate feedback. This allows parents and children to learn more effectively.

[0102] The educational platform system can also feature a customizable learning dashboard. This dashboard provides a visual interface for parents and children to track their learning progress and goals. For example, it can display learning progress using graphs and charts. It can also display a list of learning goals and achievement status set by both parents and children. Furthermore, it can estimate the emotions of both parents and children and adjust the dashboard's display accordingly. This makes it easier for parents and children to understand their learning situation, enabling more effective learning.

[0103] The educational platform system can also include a content recommendation function tailored to the learning styles of parents and children. This function suggests optimal learning content based on the learning history and preferences of both parents and children. For example, if a child prefers game-based learning, game content will be prioritized. Similarly, if a parent prefers text-based learning, text content can be recommended. Furthermore, the system can estimate the emotions of both parents and children and recommend content at the appropriate time. This allows parents and children to learn more effectively.

[0104] The educational platform system can also include a feature for sharing parent-child learning outcomes. This feature allows parents and children to share their learning achievements with other users. For example, they can post their learning progress and achieved goals on social media. They can also view other users' learning outcomes and provide comments and feedback. Furthermore, the system can estimate the emotions of parents and children and share their achievements at the appropriate time. This can increase parent-child motivation for learning and allow them to interact with other users.

[0105] The educational platform system can also be equipped with environmental adjustment functions to optimize the learning environment for parents and children. These environmental adjustment functions provide a system that allows parents and children to concentrate on their learning. For example, they can provide appropriate lighting and music during learning. They can also adjust the temperature and humidity of the learning environment. Furthermore, they can estimate the emotions of parents and children and provide a relaxing environment. This allows parents and children to learn comfortably.

[0106] The educational platform system can also include a function to regularly evaluate the learning progress of parents and children. This evaluation function allows parents and children to regularly check their learning progress and identify areas for improvement. For example, it can evaluate learning progress monthly and generate reports. It can also evaluate the degree of achievement against goals set by parents and children. Furthermore, it can estimate the emotions of parents and children and provide appropriate feedback. This allows parents and children to learn more effectively.

[0107] The educational platform system can also be equipped with a function to analyze parent-child learning data and predict future learning plans. This prediction function is a mechanism for suggesting future learning plans based on parent-child learning data. For example, it can analyze past learning data and suggest topics to learn next. It can also suggest appropriate learning schedules based on the parent-child learning pace. Furthermore, it can estimate the emotions of both parent and child and suggest learning plans at the appropriate time. This allows parents and children to learn more effectively.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The data collection unit collects learning progress and needs for each household. For example, it can collect learning progress and needs for each household using questionnaires. It can also monitor the home learning environment using sensors and collect data. Furthermore, it can collect online data to understand the learning progress and needs of each household. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it can analyze the data using statistical analysis. It can also analyze the data using machine learning algorithms. Furthermore, it can analyze the data using data mining techniques. Step 3: The planning unit creates a learning plan based on the analysis results obtained by the analysis unit. For example, it sets weekly learning objectives. It can also set criteria for selecting learning materials. Furthermore, it can adjust the learning plan according to the learning progress. Step 4: The conversation analysis unit supports parent-child conversations based on the learning plan created by the plan creation unit. For example, it analyzes parent-child conversations using speech recognition technology. It can also analyze the content of conversations using natural language processing technology. Furthermore, it can analyze the tone and emotions of the conversations. Step 5: The communication support unit facilitates parent-child communication based on the conversation content analyzed by the conversation analysis unit. For example, it provides feedback. It can also offer advice. Furthermore, it can provide tools to promote parent-child communication.

[0110] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0111] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0112] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0113] Each of the multiple elements described above, including the data collection unit, analysis unit, plan creation unit, conversation analysis unit, and communication support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects learning progress and needs for each household using the sensors and survey functions of the smart device 14. The analysis unit analyzes the collected data using, for example, the specific processing unit 290 of the data processing unit 12. The plan creation unit creates a learning plan using, for example, the specific processing unit 290 of the data processing unit 12. The conversation analysis unit analyzes conversations between parents and children using, for example, the speech recognition technology of the smart device 14. The communication support unit provides feedback and advice based on the conversation content analyzed by, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the data collection unit, analysis unit, plan creation unit, conversation analysis unit, and communication support unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects learning progress and needs for each household using the sensors and survey functions of the smart glasses 214. The analysis unit analyzes the collected data using, for example, the specific processing unit 290 of the data processing unit 12. The plan creation unit creates a learning plan using, for example, the specific processing unit 290 of the data processing unit 12. The conversation analysis unit analyzes conversations between parents and children using, for example, the speech recognition technology of the smart glasses 214. The communication support unit provides feedback and advice based on the conversation content analyzed by, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the data collection unit, analysis unit, plan creation unit, conversation analysis unit, and communication support unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects learning progress and needs for each household using the sensors and survey functions of the headset terminal 314. The analysis unit analyzes the collected data using, for example, the specific processing unit 290 of the data processing unit 12. The plan creation unit creates a learning plan using, for example, the specific processing unit 290 of the data processing unit 12. The conversation analysis unit analyzes conversations between parents and children using, for example, the speech recognition technology of the headset terminal 314. The communication support unit provides feedback and advice based on the conversation content analyzed by, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0154] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the data collection unit, analysis unit, plan creation unit, conversation analysis unit, and communication support unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects learning progress and needs for each household using the robot 414's sensors and survey functions. The analysis unit analyzes the collected data, for example, by the specific processing unit 290 of the data processing unit 12. The plan creation unit creates a learning plan, for example, by the specific processing unit 290 of the data processing unit 12. The conversation analysis unit analyzes conversations between parents and children, for example, using the robot 414's speech recognition technology. The communication support unit provides feedback and advice, for example, based on the conversation content analyzed by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0163] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0165] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0166] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0167] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0171] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0172] 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.

[0173] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0174] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0175] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0176] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0178] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0179] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0180] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0181] (Note 1) A collection department that collects learning progress and needs from each household, An analysis unit analyzes the data collected by the aforementioned collection unit, A plan creation unit creates a learning plan based on the analysis results obtained by the aforementioned analysis unit, A conversation analysis unit that supports parent-child conversations based on the learning plan created by the aforementioned plan creation unit, The system includes a communication support unit that facilitates communication between parent and child based on the conversation content analyzed by the aforementioned conversation analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is The system estimates parent-child emotions and adjusts the timing of learning progress data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Analyze past learning history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is When collecting learning progress data, filtering is performed based on home living conditions and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is The system estimates parent-child emotions and prioritizes the data to be collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting learning progress data, prioritize the collection of highly relevant data by considering the geographical location of the home. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting learning progress data, analyze social media activity at home and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, We estimate parent-child emotions and adjust the data analysis method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, The system estimates parent-child emotions and adjusts the display method of the analysis results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned plan creation unit, The system estimates the emotions of the parent and child and adjusts the content of the learning plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned plan creation unit, When creating a plan, we analyze the family's past learning history to create the optimal plan. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned plan creation unit, When creating a plan, customize it based on your family's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned plan creation unit, It estimates parent-child emotions and prioritizes learning plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned plan creation unit, When creating a plan, we take into account the geographical location of the household to create the optimal plan. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned plan creation unit, When creating a plan, we analyze the family's social media activity and propose a plan based on that. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned conversation analysis unit, We estimate the emotions between parent and child and adjust the conversation analysis method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned conversation analysis unit, During conversation analysis, the system selects the optimal analysis method by referring to past conversation history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned conversation analysis unit, When analyzing a conversation, different analysis algorithms are applied depending on the content of the conversation. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned conversation analysis unit, It estimates the emotions of the parent and child and adjusts how the conversation is displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned conversation analysis unit, During conversation analysis, the analysis priority is determined based on when the conversation was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned conversation analysis unit, During conversation analysis, the order of analysis is adjusted based on the relevance of the conversation. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned Communication Support Department It estimates parent-child emotions and adjusts communication support methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned Communication Support Department When providing communication support, the system will refer to past communication history to select the most appropriate support method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned Communication Support Department When providing communication support, different support methods are applied depending on the content of the communication. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned Communication Support Department It estimates parent-child emotions and determines communication priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned Communication Support Department When providing communication support, the most suitable support method is selected by considering the geographical location of the home. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned Communication Support Department When providing communication support, we analyze the family's social media activity and propose support methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection department that collects learning progress and needs from each household, An analysis unit analyzes the data collected by the aforementioned collection unit, A plan creation unit creates a learning plan based on the analysis results obtained by the aforementioned analysis unit, A conversation analysis unit that supports parent-child conversations based on the learning plan created by the aforementioned plan creation unit, The system includes a communication support unit that facilitates communication between parent and child based on the conversation content analyzed by the aforementioned conversation analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is The system estimates parent-child emotions and adjusts the timing of learning progress data collection based on those estimated emotions. The system according to feature 1.

3. The aforementioned collection unit is Analyze past learning history and select the optimal data collection method. The system according to feature 1.

4. The aforementioned collection unit is When collecting learning progress data, filtering is performed based on home living conditions and areas of interest. The system according to feature 1.

5. The aforementioned collection unit is The system estimates parent-child emotions and prioritizes the data to be collected based on those estimated emotions. The system according to feature 1.

6. The aforementioned collection unit is When collecting learning progress data, prioritize the collection of highly relevant data by considering the geographical location of the home. The system according to feature 1.

7. The aforementioned collection unit is When collecting learning progress data, analyze social media activity at home and collect relevant data. The system according to feature 1.

8. The aforementioned analysis unit, We estimate parent-child emotions and adjust the data analysis method based on the estimated emotions. The system according to feature 1.

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