system

Abstract

The system according to this embodiment aims to analyze the user's learning data and provide a consistent learning plan. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit collects the user's learning data. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes a learning plan based on the analysis results obtained by the analysis unit. The provision unit provides the learning plan proposed by the proposal unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the user's learning data has not been fully utilized effectively to provide a consistent learning plan, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the user's learning data and provide a consistent learning plan.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a provision unit. The data collection unit collects the user's learning data. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes a learning plan based on the analysis results obtained by the analysis unit. The provision unit provides the learning plan proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the user's learning data and provide a consistent learning plan. [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. shows an example of the configuration of the 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 learning platform according to an embodiment of the present invention is a comprehensive platform for studying, learning, and reskilling that can be used throughout a person's life, from early childhood to employment and into old age. This learning platform is designed to be used by users from early childhood, with the learning content and methods evolving as the user grows. For example, in early childhood, it provides basic learning and education through play via a "children's platform," etc. At this stage, a generative AI collects the user's learning data and analyzes their individual learning progress and interests. Next, as the user grows, the learning content and methods evolve. For example, from elementary school to high school, more advanced learning content and test preparation are provided. At this stage, the generative AI also analyzes the user's learning data and proposes an optimal learning plan. Furthermore, university students and working adults are provided with content for acquiring specialized knowledge and skills, and for reskilling. The generative AI proposes optimal learning resources based on the user's career path and interests. Support is also provided to help users continue acquiring new knowledge and skills throughout their lives, including into old age. For example, learning content related to hobbies and health is provided, and the generative AI proposes optimal content based on the user's interests and health status. This platform centrally manages users' learning data and utilizes AI to provide personalized learning plans, supporting users' lifelong learning. Furthermore, by leveraging account infrastructure data, it can improve user loyalty and LTV (Lifetime Value). This system allows users to continue learning throughout their lives, maximizing learning effectiveness by providing learning plans tailored to individual needs. For example, learning can be conducted through play in early childhood, exam preparation for elementary and high school students, acquisition of specialized knowledge for university students and working adults, and learning related to hobbies and health in retirement. This allows users to constantly acquire the latest knowledge and skills, enabling them to contribute to society. In this way, the learning platform supports users' lifelong learning and provides learning plans tailored to individual needs.

[0029] The learning platform according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit collects user learning data. The collection unit can, for example, collect learning data from the user from infancy to old age. For example, the collection unit collects data when the user learns through a "children's platform" during infancy. The collection unit can also collect data on the evolution of learning content and methods as the user grows. For example, the collection unit collects learning data from elementary school to high school. Furthermore, the collection unit can collect data on the acquisition of specialized knowledge and skills for university students and working adults. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze the data to identify the user's learning progress and interests. For example, the analysis unit analyzes the user's learning history and test results to evaluate learning progress. The analysis unit can also analyze learning data to identify the user's interests. For example, the analysis unit analyzes what kind of learning content the user is interested in. The proposal unit proposes a learning plan based on the analysis results obtained by the analysis unit. The proposal unit can, for example, propose an optimal learning plan tailored to the user's life stage. For example, the proposal unit can propose a learning plan through play for early childhood. It can also propose a learning plan for exam preparation for elementary, middle, and high school students. Furthermore, the proposal unit can propose a learning plan for acquiring specialized knowledge for university students and working adults. The delivery unit provides the learning plan proposed by the proposal unit. For example, the delivery unit can provide the proposed learning plan to the user online. For example, the delivery unit can provide the learning plan in a format that is easily accessible to the user. The delivery unit can also provide learning content related to hobbies and health based on the user's interests and health status. For example, the delivery unit can provide learning content related to health based on the user's health status. In this way, the learning platform according to the embodiment can support the user's lifelong learning and provide a learning plan tailored to individual needs.

[0030] The data collection unit collects user learning data. For example, the data collection unit can collect learning data from a user's early childhood to old age. Specifically, in early childhood, it meticulously records what educational apps and games the user uses and what content they are interested in through a "children's platform." This includes data such as how much time the user spends on specific activities and what skills they acquire. Furthermore, the data collection unit can also collect data on how learning content and methods evolve as the user grows. For example, from elementary school to high school, it collects school lesson content, homework progress, test results, and activity history on online learning platforms. This allows for a detailed understanding of the user's learning patterns and areas of strength and weakness. In addition, the data collection unit can collect data on the acquisition of specialized knowledge and skills from university students and working adults. For example, in the case of university students, it collects data on courses taken, grades, research activities, and internship experiences, and in the case of working adults, it collects data on vocational training, qualification acquisition, and skill development at work. This allows the data collection unit to centrally manage diverse learning data according to the user's life stage and track individual learning histories in detail. Furthermore, the data collection unit stores this data on a cloud server, making it accessible to the analysis and proposal 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 can analyze data to identify a user's learning progress and interests. Specifically, it analyzes a user's learning history and test results to evaluate learning progress. For example, it can use AI to analyze a user's learning data and grasp their progress in specific subjects or skills in real time. This makes it clear where a user is lagging behind and where they excel. The analysis unit can also analyze learning data to identify a user's interests. For example, it can analyze what kind of learning content a user is interested in and provide content related to those areas. This includes analyzing patterns of content that users frequently access and content they view for extended periods. Furthermore, the analysis unit also analyzes data related to a user's learning style and learning environment. For example, it analyzes learning data in detail to identify when a user can learn most effectively and in what kind of environment learning progresses. This allows the analysis unit to provide foundational data for proposing learning plans tailored to the individual needs of each user. In addition, the analysis unit can utilize historical data and statistical information to analyze long-term learning effectiveness and trends. This allows the analysis unit to comprehensively evaluate the user's learning performance and provide information to formulate the optimal learning strategy.

[0032] The Proposal Department proposes learning plans based on the analysis results obtained by the Analysis Department. For example, the Proposal Department can propose optimal learning plans tailored to the user's life stage. Specifically, for early childhood, it proposes learning plans through play. For example, it might propose interactive games for learning colors and shapes, or puzzles for understanding basic number concepts. The Proposal Department can also propose study plans for exam preparation for elementary, middle, and high school students. For example, it might propose online courses focused on specific subjects or plans that include mock exams for exam preparation. Furthermore, the Proposal Department can propose learning plans for acquiring specialized knowledge for university students and working adults. For example, it might propose online courses specializing in specific fields or internship programs to gain practical experience. The Proposal Department uses AI to optimize its suggestions, taking into account the user's learning progress and interests, in order to provide learning plans that are best suited to individual needs. This allows the Proposal Department to provide concrete action plans that enable users to learn effectively. In addition, the Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. This allows the Proposal Department to always provide optimal learning plans based on the latest information, maximizing the user's learning effectiveness.

[0033] The service provider delivers the learning plans proposed by the proposal provider. For example, the service provider can deliver the proposed learning plans to users online. Specifically, they can provide learning plans in a user-friendly format, such as through a website or mobile app, allowing users to access the learning plans anytime, anywhere. The service provider can also provide learning content related to hobbies and health based on the user's interests and health status. For example, they can provide health-related learning content based on the user's health status, including online courses on health management and fitness, nutrition information, and mental health resources. Furthermore, the service provider can monitor the user's learning progress in real time and adjust the learning plan as needed. For example, if a user is falling behind on a particular task, the service provider can provide additional resources and support. The service provider also collects user feedback and data to evaluate the effectiveness of the learning plan. This allows the service provider to provide flexible learning support tailored to user needs and maximize learning effectiveness. Finally, the service provider also provides a platform to facilitate communication and collaboration among users. For example, they can provide an environment where users learn from and support each other through online forums and group chat functions. This allows the service provider to enrich the user's learning experience and increase their motivation to learn.

[0034] The data collection unit can collect user learning data from infancy to old age. For example, the data collection unit can collect data on how users learn through a "children's platform" during their infancy. The data collection unit can also collect data on how learning content and methods evolve as users grow. For example, the data collection unit can collect learning data from elementary school to high school. Furthermore, the data collection unit can collect data on the acquisition of specialized knowledge and skills for university students and working adults. By collecting learning data according to the user's life stage, it is possible to provide learning plans tailored to individual needs. 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 user learning data into a generating AI, and the generating AI can perform data collection.

[0035] The analysis unit can analyze the collected learning data to identify the user's learning progress and interests. For example, the analysis unit can analyze the user's learning history and test results to evaluate learning progress. The analysis unit can also analyze the learning data to identify the user's interests. For example, the analysis unit can analyze what kind of learning content the user is interested in. By identifying the user's learning progress and interests, a more appropriate learning plan can be proposed. 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 collected learning data into a generating AI, and the generating AI can perform the data analysis.

[0036] The suggestion unit can propose an optimal learning plan tailored to the user's life stage based on the analysis results. For example, the suggestion unit can propose a learning plan through play for early childhood. It can also propose a study plan for exam preparation for elementary, middle, and high school students. Furthermore, it can propose a learning plan for acquiring specialized knowledge for university students and working adults. This maximizes learning effectiveness by proposing an optimal learning plan tailored to the user's life stage. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the analysis results into a generating AI, which can then propose an optimal learning plan.

[0037] The service provider can provide the proposed learning plan to the user. For example, the service provider can provide the proposed learning plan to the user online. For example, the service provider can provide the learning plan in a format that is easily accessible to the user. The service provider can also provide learning content related to hobbies and health based on the user's interests and health status. For example, the service provider can provide learning content related to health based on the user's health status. This supports the user's learning by providing the user with the proposed learning plan. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the proposed learning plan into a generating AI, and the generating AI can provide the learning plan.

[0038] The service provider can provide learning content related to hobbies and health based on the user's interests and health status. For example, the service provider can provide learning content related to health based on the user's health status. The service provider can also provide learning content related to hobbies based on the user's interests. For example, the service provider can provide learning content related to hobbies that the user is interested in. This improves user satisfaction by providing learning content based on the user's interests and health status. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the user's interests and health status into a generating AI, which can then provide optimal learning content.

[0039] The data collection unit can analyze the user's past learning history and select the optimal data collection method. For example, the data collection unit may prioritize collecting data based on learning methods the user has preferred to use in the past. It can also collect data based on learning methods in which the user has achieved high results in the past. Furthermore, the data collection unit can exclude learning methods the user has avoided in the past when collecting data. This allows the optimal data collection method to be selected by analyzing the user's 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 the user's past learning history into a generating AI, which can then select the optimal data collection method.

[0040] The data collection unit can filter the collected training data based on the user's current lifestyle and areas of interest. For example, the data collection unit can prioritize collecting data related to projects the user is currently working on. The data collection unit can also collect appropriate training data based on the user's current health status. Furthermore, the data collection unit can collect relevant training data based on the user's current interests. This allows for the collection of highly relevant data by filtering the data based on the user's current lifestyle 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 data about the user's current lifestyle and areas of interest into a generating AI, which can then filter the data.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting training data. For example, if the user is in a specific region, the data collection unit will prioritize the collection of training data related to that region. Furthermore, if the user is traveling, the data collection unit can prioritize the collection of training data related to the travel destination. Additionally, if the user is at home, the data collection unit can prioritize the collection of data suitable for home learning. This allows for the priority collection of highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then prioritize the collection of highly relevant data.

[0042] The data collection unit can analyze the user's social media activity and collect relevant data when collecting training data. For example, the data collection unit can collect relevant training data based on articles shared by the user on social media. It can also collect training data based on the content of posts from accounts the user follows. Furthermore, the data collection unit can collect training data based on topics in online communities the user participates in. This allows for the collection of relevant data by analyzing the user's social media activity. 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 user's social media activity data into a generating AI, which can then collect relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the training data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. Furthermore, it can perform an analysis with an appropriate level of detail on data with moderate importance. By adjusting the level of detail of the analysis based on the importance of the training data, efficient analysis becomes possible. 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 importance of the training data into a generating AI, and the generating AI can adjust the level of detail of the analysis based on the importance.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the training data during analysis. For example, the analysis unit can apply a numerical analysis algorithm to mathematical data. It can also apply a natural language processing algorithm to language learning data. Furthermore, it can apply an experimental data analysis algorithm to scientific data. By applying an appropriate analysis algorithm according to the category of the training data, the accuracy of the analysis is improved. 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 category of the training data into a generating AI, and the generating AI can apply different analysis algorithms according to the category.

[0045] The analysis unit can determine the priority of analysis based on the timing of training data collection during the analysis process. For example, the analysis unit may prioritize the analysis of recently collected data. It can also postpone the analysis of previously collected data. Furthermore, the analysis unit may prioritize the analysis of data collected during a specific period. This enables efficient analysis by determining the priority of analysis based on the timing of training data collection. 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 timing of training data collection into a generating AI, which can then determine the priority of analysis based on the collection timing.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the training data during analysis. For example, the analysis unit may prioritize the analysis of data with high relevance. It can also postpone the analysis of data with low relevance. Furthermore, it can analyze data with moderate relevance to a reasonable extent. By adjusting the order of analysis based on the relevance of the training data, efficient analysis becomes possible. 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 relevance of the training data into a generating AI, which can then adjust the order of analysis based on the relevance.

[0047] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the learning plan. For example, it can provide detailed suggestions for highly important learning plans, concise suggestions for less important learning plans, and suggestions with an appropriate level of detail for moderately important learning plans. By adjusting the level of detail based on the importance of the learning plan, efficient suggestions become possible. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the importance of the learning plan into a generating AI, which can then adjust the level of detail in its suggestions based on the importance.

[0048] The proposal unit can apply different proposal algorithms depending on the category of the learning plan during the proposal process. For example, the proposal unit can apply a numerical analysis algorithm to a mathematics learning plan. It can also apply a natural language processing algorithm to a language learning plan. Furthermore, it can apply an experimental data analysis algorithm to a science learning plan. By applying the appropriate proposal algorithm according to the category of the learning plan, the accuracy of the proposal is improved. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the category of the learning plan into a generating AI, which can then apply different proposal algorithms depending on the category.

[0049] The proposal department can prioritize proposals based on the submission deadlines for learning plans. For example, the proposal department may prioritize proposals for learning plans with approaching deadlines. It can also postpone proposals for learning plans with later submission deadlines. Furthermore, it can appropriately propose learning plans with medium-term submission deadlines. This allows for efficient proposals by prioritizing proposals based on the submission deadlines for learning plans. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the submission deadlines for learning plans into a generating AI, which can then determine the priority of proposals based on the submission deadlines.

[0050] The suggestion unit can adjust the order of suggestions based on the relevance of the learning plans. For example, the suggestion unit may prioritize suggesting learning plans with high relevance. It can also postpone suggesting learning plans with low relevance. Furthermore, it can appropriately suggest learning plans with moderate relevance. This allows for efficient suggestions by adjusting the order of suggestions based on the relevance of the learning plans. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the relevance of the learning plans into a generating AI, which can then adjust the order of suggestions based on the relevance.

[0051] The delivery unit can analyze the user's past learning behavior at the time of delivery to select the optimal delivery method. For example, the delivery unit can prioritize providing learning methods that the user has preferred in the past. It can also provide learning methods based on those in which the user has achieved high results in the past. Furthermore, the delivery unit can exclude learning methods that the user has avoided in the past. In this way, the optimal delivery method can be selected by analyzing the user's past learning behavior. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's past learning behavior data into a generating AI, which can then select the optimal delivery method.

[0052] The service provider can customize the means of delivery based on the user's current living situation at the time of delivery. For example, the service provider can provide a learning plan related to a project the user is currently working on. The service provider can also provide an appropriate learning plan based on the user's current health condition. Furthermore, the service provider can provide a relevant learning plan based on the user's current interests. This allows for the provision of a more appropriate learning plan by customizing the means of delivery based on the user's current living situation. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's current living situation data into a generating AI, which can then customize the means of delivery.

[0053] The service provider can select the optimal delivery method by considering the user's geographical location information at the time of delivery. For example, if the user is in a specific region, the service provider can prioritize providing learning plans related to that region. Furthermore, if the user is traveling, the service provider can prioritize providing learning plans related to the travel destination. Additionally, if the user is at home, the service provider can prioritize providing plans suitable for home learning. This allows the service provider to select the optimal delivery method by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI, which can then select the optimal delivery method.

[0054] The service provider can analyze the user's social media activity and propose a delivery method at the time of delivery. For example, the service provider can provide a relevant learning plan based on articles the user has shared on social media. It can also provide a learning plan based on the content of posts from accounts the user follows. Furthermore, it can provide a learning plan based on topics in online communities the user participates in. In this way, by analyzing the user's social media activity, the service provider can propose the most suitable delivery method. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's social media activity data into a generating AI, which can then propose the most suitable delivery method.

[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] Learning platforms can also include customization features tailored to users' learning styles. For example, users who prefer visual learning can be provided with content that heavily utilizes videos and infographics. Users who prefer auditory learning can be offered content that includes podcasts and audio explanations. Furthermore, users who prefer hands-on learning can be provided with content that includes interactive simulations and experiments. This allows for the provision of an optimal learning experience that matches each user's learning style.

[0057] Learning platforms can also provide dashboards that visualize learning progress based on the user's learning history. For example, they can display the goals the user has achieved and the courses they have completed using graphs and charts. They can also suggest the next tasks the user should tackle and recommend learning resources. Furthermore, they can provide real-time updates on the user's progress towards their set learning goals and offer feedback to help maintain motivation. This allows users to grasp their learning progress at a glance and get clear guidance on how to move forward to the next step.

[0058] The learning platform can also suggest optimal break times to maximize learning effectiveness based on the user's learning data. For example, it can notify users to take appropriate breaks after long study sessions. It can also suggest short breaks when the user begins to lose focus. Furthermore, it can provide relaxation content to help users refresh themselves. This allows users to learn more efficiently and reduce fatigue.

[0059] Learning platforms can also suggest personalized learning schedules to maximize learning effectiveness based on users' learning data. For example, they can analyze users' past learning patterns and suggest optimal learning times. They can also create learning schedules that fit the user's lifestyle. Furthermore, they can provide step-by-step plans to help users achieve their goals. This allows users to learn efficiently and gain a clear path to achieving their objectives.

[0060] Learning platforms can also provide group learning features to maximize learning effectiveness based on users' learning data. For example, they can match users with similar interests and goals and allow them to learn together. Furthermore, learning can be deepened through group discussions and feedback. In addition, motivation can be increased through competition and cooperation within the group. As a result, users can learn more effectively by learning alongside other learners.

[0061] Learning platforms can also provide reflection features to maximize learning effectiveness based on users' learning data. For example, they can provide tools for users to review what they have learned and conduct self-assessments. They can also provide feedback for users to record their learning outcomes and move on to the next step. Furthermore, they can provide journal functions for users to record insights and reflections gained during the learning process. This allows users to reflect on their learning and gain clear guidance for moving on to the next step.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data collection unit collects user learning data. For example, the data collection unit can collect learning data from a user from infancy to old age. The data collection unit collects data on when a user learns through a "children's platform" in infancy, learning data from elementary school to high school, and data on the acquisition of specialized knowledge and skills for university students and working adults. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data to identify the user's learning progress and interests, and evaluates learning progress by analyzing learning history and test results. It also analyzes what kind of learning content the user is interested in. Step 3: The proposal department proposes a learning plan based on the analysis results obtained by the analysis department. The proposal department proposes an optimal learning plan tailored to the user's life stage, such as a learning plan through play for preschoolers, a learning plan for exam preparation for elementary and high school students, and a learning plan for acquiring specialized knowledge for university students and working adults. Step 4: The provisioning department provides the learning plan proposed by the suggestion department. The provisioning department provides the proposed learning plan to the user online, in a format that is easy for the user to access. In addition, it provides learning content related to hobbies and health based on the user's interests and health status.

[0064] (Example of form 2) The learning platform according to an embodiment of the present invention is a comprehensive platform for studying, learning, and reskilling that can be used throughout a person's life, from early childhood to employment and into old age. This learning platform is designed to be used by users from early childhood, with the learning content and methods evolving as the user grows. For example, in early childhood, it provides basic learning and education through play via a "children's platform," etc. At this stage, a generative AI collects the user's learning data and analyzes their individual learning progress and interests. Next, as the user grows, the learning content and methods evolve. For example, from elementary school to high school, more advanced learning content and test preparation are provided. At this stage, the generative AI also analyzes the user's learning data and proposes an optimal learning plan. Furthermore, university students and working adults are provided with content for acquiring specialized knowledge and skills, and for reskilling. The generative AI proposes optimal learning resources based on the user's career path and interests. Support is also provided to help users continue acquiring new knowledge and skills throughout their lives, including into old age. For example, learning content related to hobbies and health is provided, and the generative AI proposes optimal content based on the user's interests and health status. This platform centrally manages users' learning data and utilizes AI to provide personalized learning plans, supporting users' lifelong learning. Furthermore, by leveraging account infrastructure data, it can improve user loyalty and LTV (Lifetime Value). This system allows users to continue learning throughout their lives, maximizing learning effectiveness by providing learning plans tailored to individual needs. For example, learning can be conducted through play in early childhood, exam preparation for elementary and high school students, acquisition of specialized knowledge for university students and working adults, and learning related to hobbies and health in retirement. This allows users to constantly acquire the latest knowledge and skills, enabling them to contribute to society. In this way, the learning platform supports users' lifelong learning and provides learning plans tailored to individual needs.

[0065] The learning platform according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit collects user learning data. The collection unit can, for example, collect learning data from the user from infancy to old age. For example, the collection unit collects data when the user learns through a "children's platform" during infancy. The collection unit can also collect data on the evolution of learning content and methods as the user grows. For example, the collection unit collects learning data from elementary school to high school. Furthermore, the collection unit can collect data on the acquisition of specialized knowledge and skills for university students and working adults. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze the data to identify the user's learning progress and interests. For example, the analysis unit analyzes the user's learning history and test results to evaluate learning progress. The analysis unit can also analyze learning data to identify the user's interests. For example, the analysis unit analyzes what kind of learning content the user is interested in. The proposal unit proposes a learning plan based on the analysis results obtained by the analysis unit. The proposal unit can, for example, propose an optimal learning plan tailored to the user's life stage. For example, the proposal unit can propose a learning plan through play for early childhood. It can also propose a learning plan for exam preparation for elementary, middle, and high school students. Furthermore, the proposal unit can propose a learning plan for acquiring specialized knowledge for university students and working adults. The delivery unit provides the learning plan proposed by the proposal unit. For example, the delivery unit can provide the proposed learning plan to the user online. For example, the delivery unit can provide the learning plan in a format that is easily accessible to the user. The delivery unit can also provide learning content related to hobbies and health based on the user's interests and health status. For example, the delivery unit can provide learning content related to health based on the user's health status. In this way, the learning platform according to the embodiment can support the user's lifelong learning and provide a learning plan tailored to individual needs.

[0066] The data collection unit collects user learning data. For example, the data collection unit can collect learning data from a user's early childhood to old age. Specifically, in early childhood, it meticulously records what educational apps and games the user uses and what content they are interested in through a "children's platform." This includes data such as how much time the user spends on specific activities and what skills they acquire. Furthermore, the data collection unit can also collect data on how learning content and methods evolve as the user grows. For example, from elementary school to high school, it collects school lesson content, homework progress, test results, and activity history on online learning platforms. This allows for a detailed understanding of the user's learning patterns and areas of strength and weakness. In addition, the data collection unit can collect data on the acquisition of specialized knowledge and skills from university students and working adults. For example, in the case of university students, it collects data on courses taken, grades, research activities, and internship experiences, and in the case of working adults, it collects data on vocational training, qualification acquisition, and skill development at work. This allows the data collection unit to centrally manage diverse learning data according to the user's life stage and track individual learning histories in detail. Furthermore, the data collection unit stores this data on a cloud server, making it accessible to the analysis and proposal units. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0067] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can analyze data to identify a user's learning progress and interests. Specifically, it analyzes a user's learning history and test results to evaluate learning progress. For example, it can use AI to analyze a user's learning data and grasp their progress in specific subjects or skills in real time. This makes it clear where a user is lagging behind and where they excel. The analysis unit can also analyze learning data to identify a user's interests. For example, it can analyze what kind of learning content a user is interested in and provide content related to those areas. This includes analyzing patterns of content that users frequently access and content they view for extended periods. Furthermore, the analysis unit also analyzes data related to a user's learning style and learning environment. For example, it analyzes learning data in detail to identify when a user can learn most effectively and in what kind of environment learning progresses. This allows the analysis unit to provide foundational data for proposing learning plans tailored to the individual needs of each user. In addition, the analysis unit can utilize historical data and statistical information to analyze long-term learning effectiveness and trends. This allows the analysis unit to comprehensively evaluate the user's learning performance and provide information to formulate the optimal learning strategy.

[0068] The Proposal Department proposes learning plans based on the analysis results obtained by the Analysis Department. For example, the Proposal Department can propose optimal learning plans tailored to the user's life stage. Specifically, for early childhood, it proposes learning plans through play. For example, it might propose interactive games for learning colors and shapes, or puzzles for understanding basic number concepts. The Proposal Department can also propose study plans for exam preparation for elementary, middle, and high school students. For example, it might propose online courses focused on specific subjects or plans that include mock exams for exam preparation. Furthermore, the Proposal Department can propose learning plans for acquiring specialized knowledge for university students and working adults. For example, it might propose online courses specializing in specific fields or internship programs to gain practical experience. The Proposal Department uses AI to optimize its suggestions, taking into account the user's learning progress and interests, in order to provide learning plans that are best suited to individual needs. This allows the Proposal Department to provide concrete action plans that enable users to learn effectively. In addition, the Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. This allows the Proposal Department to always provide optimal learning plans based on the latest information, maximizing the user's learning effectiveness.

[0069] The service provider delivers the learning plans proposed by the proposal provider. For example, the service provider can deliver the proposed learning plans to users online. Specifically, they can provide learning plans in a user-friendly format, such as through a website or mobile app, allowing users to access the learning plans anytime, anywhere. The service provider can also provide learning content related to hobbies and health based on the user's interests and health status. For example, they can provide health-related learning content based on the user's health status, including online courses on health management and fitness, nutrition information, and mental health resources. Furthermore, the service provider can monitor the user's learning progress in real time and adjust the learning plan as needed. For example, if a user is falling behind on a particular task, the service provider can provide additional resources and support. The service provider also collects user feedback and data to evaluate the effectiveness of the learning plan. This allows the service provider to provide flexible learning support tailored to user needs and maximize learning effectiveness. Finally, the service provider also provides a platform to facilitate communication and collaboration among users. For example, they can provide an environment where users learn from and support each other through online forums and group chat functions. This allows the service provider to enrich the user's learning experience and increase their motivation to learn.

[0070] The data collection unit can collect user learning data from infancy to old age. For example, the data collection unit can collect data on how users learn through a "children's platform" during their infancy. The data collection unit can also collect data on how learning content and methods evolve as users grow. For example, the data collection unit can collect learning data from elementary school to high school. Furthermore, the data collection unit can collect data on the acquisition of specialized knowledge and skills for university students and working adults. By collecting learning data according to the user's life stage, it is possible to provide learning plans tailored to individual needs. 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 user learning data into a generating AI, and the generating AI can perform data collection.

[0071] The analysis unit can analyze the collected learning data to identify the user's learning progress and interests. For example, the analysis unit can analyze the user's learning history and test results to evaluate learning progress. The analysis unit can also analyze the learning data to identify the user's interests. For example, the analysis unit can analyze what kind of learning content the user is interested in. By identifying the user's learning progress and interests, a more appropriate learning plan can be proposed. 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 collected learning data into a generating AI, and the generating AI can perform the data analysis.

[0072] The suggestion unit can propose an optimal learning plan tailored to the user's life stage based on the analysis results. For example, the suggestion unit can propose a learning plan through play for early childhood. It can also propose a study plan for exam preparation for elementary, middle, and high school students. Furthermore, it can propose a learning plan for acquiring specialized knowledge for university students and working adults. This maximizes learning effectiveness by proposing an optimal learning plan tailored to the user's life stage. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the analysis results into a generating AI, which can then propose an optimal learning plan.

[0073] The service provider can provide the proposed learning plan to the user. For example, the service provider can provide the proposed learning plan to the user online. For example, the service provider can provide the learning plan in a format that is easily accessible to the user. The service provider can also provide learning content related to hobbies and health based on the user's interests and health status. For example, the service provider can provide learning content related to health based on the user's health status. This supports the user's learning by providing the user with the proposed learning plan. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the proposed learning plan into a generating AI, and the generating AI can provide the learning plan.

[0074] The service provider can provide learning content related to hobbies and health based on the user's interests and health status. For example, the service provider can provide learning content related to health based on the user's health status. The service provider can also provide learning content related to hobbies based on the user's interests. For example, the service provider can provide learning content related to hobbies that the user is interested in. This improves user satisfaction by providing learning content based on the user's interests and health status. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the user's interests and health status into a generating AI, which can then provide optimal learning content.

[0075] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can collect data during times when the user is relaxed. The data collection unit can also collect detailed data when the user is focused. Furthermore, if the user is tired, the data collection unit can collect data after rest. This allows for the collection of more appropriate data by adjusting the timing of data collection according to the user's emotions. 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 data collection unit may be performed using AI, or not. For example, the data collection unit can input user emotion data into a generative AI, which can then adjust the collection timing based on the emotion.

[0076] The data collection unit can analyze the user's past learning history and select the optimal data collection method. For example, the data collection unit may prioritize collecting data based on learning methods the user has preferred to use in the past. It can also collect data based on learning methods in which the user has achieved high results in the past. Furthermore, the data collection unit can exclude learning methods the user has avoided in the past when collecting data. This allows the optimal data collection method to be selected by analyzing the user's 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 the user's past learning history into a generating AI, which can then select the optimal data collection method.

[0077] The data collection unit can filter the collected training data based on the user's current lifestyle and areas of interest. For example, the data collection unit can prioritize collecting data related to projects the user is currently working on. The data collection unit can also collect appropriate training data based on the user's current health status. Furthermore, the data collection unit can collect relevant training data based on the user's current interests. This allows for the collection of highly relevant data by filtering the data based on the user's current lifestyle 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 data about the user's current lifestyle and areas of interest into a generating AI, which can then filter the data.

[0078] The data collection unit can estimate the user's emotions and determine the priority of training data to collect based on the estimated user emotions. For example, if the user is excited, the data collection unit may prioritize collecting high-difficulty training data. Similarly, if the user is relaxed, the data collection unit may prioritize collecting basic training data. Furthermore, if the user is tired, the data collection unit may prioritize collecting lighter training data. This allows for more effective data collection by prioritizing training data according to the user's emotions. 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 data collection unit may be performed using AI, or not. For example, the data collection unit can input user emotion data into a generative AI, which can then determine the priority of training data to collect based on the emotions.

[0079] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting training data. For example, if the user is in a specific region, the data collection unit will prioritize the collection of training data related to that region. Furthermore, if the user is traveling, the data collection unit can prioritize the collection of training data related to the travel destination. Additionally, if the user is at home, the data collection unit can prioritize the collection of data suitable for home learning. This allows for the priority collection of highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then prioritize the collection of highly relevant data.

[0080] The data collection unit can analyze the user's social media activity and collect relevant data when collecting training data. For example, the data collection unit can collect relevant training data based on articles shared by the user on social media. It can also collect training data based on the content of posts from accounts the user follows. Furthermore, the data collection unit can collect training data based on topics in online communities the user participates in. This allows for the collection of relevant data by analyzing the user's social media activity. 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 user's social media activity data into a generating AI, which can then collect relevant data.

[0081] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results that get straight to the point. Furthermore, if the user is excited, the analysis unit can provide visually stimulating analysis results. In this way, by adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. 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 the user's emotion data into the generative AI, and the generative AI can adjust the presentation of the analysis based on the emotions.

[0082] The analysis unit can adjust the level of detail of the analysis based on the importance of the training data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. Furthermore, it can perform an analysis with an appropriate level of detail on data with moderate importance. By adjusting the level of detail of the analysis based on the importance of the training data, efficient analysis becomes possible. 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 importance of the training data into a generating AI, and the generating AI can adjust the level of detail of the analysis based on the importance.

[0083] The analysis unit can apply different analysis algorithms depending on the category of the training data during analysis. For example, the analysis unit can apply a numerical analysis algorithm to mathematical data. It can also apply a natural language processing algorithm to language learning data. Furthermore, it can apply an experimental data analysis algorithm to scientific data. By applying an appropriate analysis algorithm according to the category of the training data, the accuracy of the analysis is improved. 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 category of the training data into a generating AI, and the generating AI can apply different analysis algorithms according to the category.

[0084] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can perform a short, concise analysis. If the user is relaxed, the analysis unit can perform a detailed analysis. Furthermore, if the user is excited, the analysis unit can perform a visually stimulating analysis. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. 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 analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI, which can then adjust the length of the analysis based on the emotions.

[0085] The analysis unit can determine the priority of analysis based on the timing of training data collection during the analysis process. For example, the analysis unit may prioritize the analysis of recently collected data. It can also postpone the analysis of previously collected data. Furthermore, the analysis unit may prioritize the analysis of data collected during a specific period. This enables efficient analysis by determining the priority of analysis based on the timing of training data collection. 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 timing of training data collection into a generating AI, which can then determine the priority of analysis based on the collection timing.

[0086] The analysis unit can adjust the order of analysis based on the relevance of the training data during analysis. For example, the analysis unit may prioritize the analysis of data with high relevance. It can also postpone the analysis of data with low relevance. Furthermore, it can analyze data with moderate relevance to a reasonable extent. By adjusting the order of analysis based on the relevance of the training data, efficient analysis becomes possible. 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 relevance of the training data into a generating AI, which can then adjust the order of analysis based on the relevance.

[0087] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, it can provide concise suggestions. Furthermore, if the user is excited, it can provide visually stimulating suggestions. By adjusting the way suggestions are presented according to the user's emotions, more appropriate suggestions can be made. 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 processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI, which can then adjust the way suggestions are presented based on the emotion.

[0088] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the learning plan. For example, it can provide detailed suggestions for highly important learning plans, concise suggestions for less important learning plans, and suggestions with an appropriate level of detail for moderately important learning plans. By adjusting the level of detail based on the importance of the learning plan, efficient suggestions become possible. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the importance of the learning plan into a generating AI, which can then adjust the level of detail in its suggestions based on the importance.

[0089] The proposal unit can apply different proposal algorithms depending on the category of the learning plan during the proposal process. For example, the proposal unit can apply a numerical analysis algorithm to a mathematics learning plan. It can also apply a natural language processing algorithm to a language learning plan. Furthermore, it can apply an experimental data analysis algorithm to a science learning plan. By applying the appropriate proposal algorithm according to the category of the learning plan, the accuracy of the proposal is improved. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the category of the learning plan into a generating AI, which can then apply different proposal algorithms depending on the category.

[0090] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, it can provide detailed suggestions. Furthermore, if the user is excited, it can provide visually stimulating suggestions. By adjusting the length of suggestions according to the user's emotions, more appropriate suggestions can be made. 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI, which can then adjust the length of the suggestions based on the emotions.

[0091] The proposal department can prioritize proposals based on the submission deadlines for learning plans. For example, the proposal department may prioritize proposals for learning plans with approaching deadlines. It can also postpone proposals for learning plans with later submission deadlines. Furthermore, it can appropriately propose learning plans with medium-term submission deadlines. This allows for efficient proposals by prioritizing proposals based on the submission deadlines for learning plans. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the submission deadlines for learning plans into a generating AI, which can then determine the priority of proposals based on the submission deadlines.

[0092] The suggestion unit can adjust the order of suggestions based on the relevance of the learning plans. For example, the suggestion unit may prioritize suggesting learning plans with high relevance. It can also postpone suggesting learning plans with low relevance. Furthermore, it can appropriately suggest learning plans with moderate relevance. This allows for efficient suggestions by adjusting the order of suggestions based on the relevance of the learning plans. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the relevance of the learning plans into a generating AI, which can then adjust the order of suggestions based on the relevance.

[0093] The service provider can estimate the user's emotions and adjust the method of providing the learning plan based on the estimated emotions. For example, if the user is relaxed, the service provider can provide a detailed learning plan. If the user is in a hurry, the service provider can also provide a concise learning plan. Furthermore, if the user is excited, the service provider can provide a visually stimulating learning plan. This allows for the provision of a more appropriate learning plan by adjusting the method of providing the learning plan according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into the generative AI and adjust the method of providing the learning plan based on the emotions of the generative AI.

[0094] The delivery unit can analyze the user's past learning behavior at the time of delivery to select the optimal delivery method. For example, the delivery unit can prioritize providing learning methods that the user has preferred in the past. It can also provide learning methods based on those in which the user has achieved high results in the past. Furthermore, the delivery unit can exclude learning methods that the user has avoided in the past. In this way, the optimal delivery method can be selected by analyzing the user's past learning behavior. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's past learning behavior data into a generating AI, which can then select the optimal delivery method.

[0095] The service provider can customize the means of delivery based on the user's current living situation at the time of delivery. For example, the service provider can provide a learning plan related to a project the user is currently working on. The service provider can also provide an appropriate learning plan based on the user's current health condition. Furthermore, the service provider can provide a relevant learning plan based on the user's current interests. This allows for the provision of a more appropriate learning plan by customizing the means of delivery based on the user's current living situation. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's current living situation data into a generating AI, which can then customize the means of delivery.

[0096] The service provider can estimate the user's emotions and determine the priority of the learning plans to offer based on those emotions. For example, if the user is excited, the service provider may prioritize offering a more difficult learning plan. Similarly, if the user is relaxed, the service provider may prioritize offering a basic learning plan. Furthermore, if the user is tired, the service provider may prioritize offering a lighter learning plan. This allows for the provision of more effective learning plans by prioritizing them according to the user's emotions. 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-described processes in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI, which can then determine the priority of the learning plans to offer based on those emotions.

[0097] The service provider can select the optimal delivery method by considering the user's geographical location information at the time of delivery. For example, if the user is in a specific region, the service provider can prioritize providing learning plans related to that region. Furthermore, if the user is traveling, the service provider can prioritize providing learning plans related to the travel destination. Additionally, if the user is at home, the service provider can prioritize providing plans suitable for home learning. This allows the service provider to select the optimal delivery method by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI, which can then select the optimal delivery method.

[0098] The service provider can analyze the user's social media activity and propose a delivery method at the time of delivery. For example, the service provider can provide a relevant learning plan based on articles the user has shared on social media. It can also provide a learning plan based on the content of posts from accounts the user follows. Furthermore, it can provide a learning plan based on topics in online communities the user participates in. In this way, by analyzing the user's social media activity, the service provider can propose the most suitable delivery method. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's social media activity data into a generating AI, which can then propose the most suitable delivery method.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] Learning platforms can also include customization features tailored to users' learning styles. For example, users who prefer visual learning can be provided with content that heavily utilizes videos and infographics. Users who prefer auditory learning can be offered content that includes podcasts and audio explanations. Furthermore, users who prefer hands-on learning can be provided with content that includes interactive simulations and experiments. This allows for the provision of an optimal learning experience that matches each user's learning style.

[0101] Learning platforms can also provide dashboards that visualize learning progress based on the user's learning history. For example, they can display the goals the user has achieved and the courses they have completed using graphs and charts. They can also suggest the next tasks the user should tackle and recommend learning resources. Furthermore, they can provide real-time updates on the user's progress towards their set learning goals and offer feedback to help maintain motivation. This allows users to grasp their learning progress at a glance and get clear guidance on how to move forward to the next step.

[0102] The learning platform can estimate the user's emotions and adjust the difficulty level of the learning content based on those emotions. For example, if the user is stressed, it can provide easier content. If the user is relaxed, it can provide more challenging content. Furthermore, if the user is excited, it can provide challenging tasks. This allows for the provision of an optimal learning experience tailored to the user's emotions.

[0103] The learning platform can also suggest optimal break times to maximize learning effectiveness based on the user's learning data. For example, it can notify users to take appropriate breaks after long study sessions. It can also suggest short breaks when the user begins to lose focus. Furthermore, it can provide relaxation content to help users refresh themselves. This allows users to learn more efficiently and reduce fatigue.

[0104] The learning platform can estimate the user's emotions and customize learning feedback based on those emotions. For example, if the user is feeling down, it can provide encouraging messages. If the user is confident, it can provide messages that encourage further challenges. Furthermore, if the user is feeling anxious, it can provide reassuring feedback. This allows for the provision of appropriate feedback tailored to the user's emotions, helping to maintain their motivation to learn.

[0105] Learning platforms can also suggest personalized learning schedules to maximize learning effectiveness based on users' learning data. For example, they can analyze users' past learning patterns and suggest optimal learning times. They can also create learning schedules that fit the user's lifestyle. Furthermore, they can provide step-by-step plans to help users achieve their goals. This allows users to learn efficiently and gain a clear path to achieving their objectives.

[0106] A learning platform can estimate a user's emotions and provide incentives to boost their motivation based on those emotions. For example, if a user is losing motivation, small, achievable goals can be set. A reward system can also be implemented to increase user motivation. Furthermore, badges or points can be awarded for goals achieved. This allows for the provision of appropriate incentives tailored to the user's emotions, helping to maintain their motivation to learn.

[0107] Learning platforms can also provide group learning features to maximize learning effectiveness based on users' learning data. For example, they can match users with similar interests and goals and allow them to learn together. Furthermore, learning can be deepened through group discussions and feedback. In addition, motivation can be increased through competition and cooperation within the group. As a result, users can learn more effectively by learning alongside other learners.

[0108] The learning platform can estimate the user's emotions and adjust the learning progress based on those emotions. For example, if the user is stressed, the progress can be slowed down. Conversely, if the user is relaxed, the progress can be accelerated. Furthermore, if the user is excited, challenging tasks can be added. This allows for the provision of optimal learning progress tailored to the user's emotions.

[0109] Learning platforms can also provide reflection features to maximize learning effectiveness based on users' learning data. For example, they can provide tools for users to review what they have learned and conduct self-assessments. They can also provide feedback for users to record their learning outcomes and move on to the next step. Furthermore, they can provide journal functions for users to record insights and reflections gained during the learning process. This allows users to reflect on their learning and gain clear guidance for moving on to the next step.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The data collection unit collects user learning data. For example, the data collection unit can collect learning data from a user from infancy to old age. The data collection unit collects data on when a user learns through a "children's platform" in infancy, learning data from elementary school to high school, and data on the acquisition of specialized knowledge and skills for university students and working adults. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data to identify the user's learning progress and interests, and evaluates learning progress by analyzing learning history and test results. It also analyzes what kind of learning content the user is interested in. Step 3: The proposal department proposes a learning plan based on the analysis results obtained by the analysis department. The proposal department proposes an optimal learning plan tailored to the user's life stage, such as a learning plan through play for preschoolers, a learning plan for exam preparation for elementary and high school students, and a learning plan for acquiring specialized knowledge for university students and working adults. Step 4: The provisioning department provides the learning plan proposed by the suggestion department. The provisioning department provides the proposed learning plan to the user online, in a format that is easy for the user to access. In addition, it provides learning content related to hobbies and health based on the user's interests and health status.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects user learning data using the camera 42 and microphone 38B of the smart device 14 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to identify the user's learning progress and interests. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and proposes an optimal learning plan based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the smart device 14, and provides the proposed learning plan to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects user learning data using the camera 42 and microphone 238 of the smart glasses 214 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to identify the user's learning progress and interests. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and proposes an optimal learning plan based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214, and provides the proposed learning plan to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects user learning data using the camera 42 and microphone 238 of the headset terminal 314 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to identify the user's learning progress and interests. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and proposes an optimal learning plan based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314, and provides the proposed learning plan to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects user learning data using the camera 42 and microphone 238 of the robot 414 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to identify the user's learning progress and interests. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and proposes an optimal learning plan based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the robot 414, and provides the proposed learning plan to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) A data collection unit that collects user learning data, An analysis unit analyzes the data collected by the aforementioned collection unit, A proposal unit proposes a learning plan based on the analysis results obtained by the analysis unit, The system comprises a providing unit that provides a learning plan proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect user learning data from infancy to old age. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected learning data is analyzed to identify the user's learning progress and interests. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Based on the analysis results, we propose an optimal learning plan tailored to the user's life stage. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provide the proposed learning plan to the user. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, We provide learning content related to hobbies and health based on the user's interests and health status. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of training data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past learning history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting training data, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and determines the priority of training data to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting training data, the system prioritizes collecting highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting training data, analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the training data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the training data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the training data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the training data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the proposal based on the importance of the learning plan. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, apply a different proposal algorithm depending on the category of the learning plan. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When submitting a proposal, prioritize the proposals based on when the learning plan will be submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relevance of the learning plan. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts the learning plan provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, At the time of delivery, the system analyzes the user's past learning behavior to select the optimal delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the service, the means of delivery will be customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the learning plan to provide based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and propose a delivery method. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0184] 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 data collection unit that collects user learning data, An analysis unit analyzes the data collected by the aforementioned collection unit, A proposal unit proposes a learning plan based on the analysis results obtained by the analysis unit, The system comprises a providing unit that provides a learning plan proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect user learning data from infancy to old age. The system according to feature 1.

3. The aforementioned analysis unit, The collected learning data is analyzed to identify the user's learning progress and interests. The system according to feature 1.

4. The aforementioned proposal section is, Based on the analysis results, we propose an optimal learning plan tailored to the user's life stage. The system according to feature 1.

5. The aforementioned supply unit is, Provide the proposed learning plan to the user. The system according to feature 1.

6. The aforementioned supply unit is, We provide learning content related to hobbies and health based on the user's interests and health status. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of training data collection based on the estimated user emotions. The system according to feature 1.

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

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