system

Abstract

We provide the system. [Solution] A means for determining the optimal learning format based on acquired user cognitive characteristics information, A means for adjusting the method of presenting learning content based on the learning format determined above, A means for analyzing the usage status of the learning content in real time and dynamically adjusting the presentation method, A system that includes this.

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] Conventional language learning methods provided learning content in a uniform way without considering the cognitive characteristics of individual learners, resulting in problems such as learners being unable to adapt to the content or a decline in learning efficiency. As a result, many learners may have encountered setbacks during the learning process. Therefore, there is a need to develop a method that can accommodate the diverse cognitive characteristics of learners and provide an individualized learning experience.

Means for Solving the Problems

[0005] This invention provides a system that determines the optimal learning format based on acquired cognitive characteristic information of the user and adjusts the method of presenting the learning content based on the determined learning format. Furthermore, by including means for analyzing the usage status of the learning content in real time and dynamically adjusting the presentation method, the invention provides a learning environment optimized for each individual learner and improves learning efficiency.

[0006] "Acquired user cognitive characteristics information" refers to data indicating which sensory modalities (visual, auditory, linguistic, kinesthetic, etc.) a user has a dominant skill in.

[0007] "Learning format" refers to the combination of content and modality (text, audio, video, etc.) of the learning material presented.

[0008] "Learning content" refers to materials related to the specific knowledge and skills that learners aim to acquire.

[0009] "Presentation method" refers to the technical techniques used to present learning content to learners.

[0010] "Real-time analysis" refers to the process of immediately processing collected data and providing rapid feedback of the results.

[0011] "Dynamic adjustment" refers to a procedure that aims to optimize by flexibly changing settings and methods in response to changing circumstances. [Brief explanation of the drawing]

[0012] [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]It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0014] First, the language used in the following description will be explained.

[0015] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface that includes a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0019] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 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.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception 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 reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] The 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.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] This invention relates to a system for providing an optimal learning environment tailored to the individual cognitive characteristics of a user. This system consists of three main elements: a server, a terminal, and a user.

[0034] First, the user accesses the system using a terminal. Upon initial access, the terminal administers questionnaires and tests to identify the user's cognitive characteristics. The information collected is used to determine the user's visual, auditory, and other superiorities.

[0035] The device then sends user information to the server. The server analyzes this data and generates a cognitive characteristics profile for each user. This profile indicates, for example, whether the user has a dominant auditory or visual sensibility, and is used to provide content in the future.

[0036] The server optimizes the modality of the learning content based on the user's cognitive profile. In this optimization process, the server selects the most effective combination from multiple modalities, such as text, audio, and video. For example, a visually dominant user would be presented with text that includes diagrams and charts.

[0037] Next, the server sends optimized learning content to the device. The user receives this content through the device and proceeds with their learning. While the user is using the content, the device continuously collects user interaction data. This data includes the time spent learning and the progress made on learning items.

[0038] This data is sent back from the terminal to the server. The server analyzes the received data in real time to determine the user's level of understanding and concentration. Based on this analysis, the server adjusts the modality again if necessary and updates the way the learning content is presented.

[0039] In this way, the system can flexibly adjust the learning environment to suit the individual needs of the user. For example, if a user is struggling with a particular learning item, the system can increase the number of related videos that supplement the visual information for that item. As a result, learners can absorb and acquire information more effectively.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user accesses the learning system using a device. Upon initial access, the device conducts questionnaires and tests to identify the user's cognitive characteristics and collects information.

[0043] Step 2:

[0044] The device sends the collected user cognitive characteristics information to the server. The server analyzes this information to generate a user cognitive characteristics profile and stores it in a database.

[0045] Step 3:

[0046] The server determines the optimal modality of learning content based on the user's profile information. This includes selecting and combining text, audio, and video.

[0047] Step 4:

[0048] The server generates learning content based on the selected modality and sends the optimized content to the device. The user receives this content through the device and begins learning.

[0049] Step 5:

[0050] While the user is learning, the device collects interaction data about the user's learning behavior. This data includes learning time, browsing frequency, and operation history.

[0051] Step 6:

[0052] The device sends the collected interaction data to the server. The server analyzes this data in real time to evaluate the user's learning progress and concentration level.

[0053] Step 7:

[0054] Based on the analysis results, the server adjusts the modality ratio and content presentation method as needed. The optimized content is sent back to the device, and the user continues learning with the new settings.

[0055] (Example 1)

[0056] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0057] In recent years, there has been a growing demand for educational methods tailored to individual cognitive characteristics. However, existing learning systems generally use standardized materials and therefore fail to adequately address the unique characteristics of each learner. This often leads to decreased learning efficiency, and it is particularly difficult to provide an optimal learning environment that leverages the strengths of learners with specialized advantages in areas such as vision or hearing. To address this challenge, a system is needed that adapts to the cognitive characteristics of each user and presents the most suitable learning content.

[0058] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0059] In this invention, the server includes means for determining the optimal learning format based on acquired user cognitive characteristics information, means for adjusting the method of presenting learning content, means for analyzing the usage status of learning content in real time and dynamically adjusting the presentation method, means for generating a cognitive characteristics profile using a generative AI model based on the user's cognitive characteristics, and means for optimizing multiple information presentation formats based on the profile. This makes it possible to provide a flexible and effective individualized learning environment tailored to the user's characteristics.

[0060] "User cognitive characteristics information" refers to information that indicates the individual cognitive strengths of the user, such as visual, auditory, linguistic, and motor skills.

[0061] "Learning format" refers to a method of presenting learning materials optimized for users to learn efficiently, and includes different media such as text, audio, and video.

[0062] A "generative AI model" is an artificial intelligence model that analyzes data and generates output based on specific input information. In this context, it is used to generate a profile of the user's cognitive characteristics.

[0063] A "cognitive characteristics profile" is a profile created to understand each user's individual cognitive abilities and learning characteristics, and to provide the most suitable learning format based on that understanding.

[0064] "Information presentation format" refers to the method by which learning content is displayed and presented to users, and specifically includes combinations of text, audio, and video.

[0065] This invention is a system for providing an optimal learning environment tailored to the cognitive characteristics of the user. This system consists of three main elements: a server, a terminal, and the user.

[0066] First, the user accesses the system using a device. Upon initial access, the device presents the user with questionnaires and tests designed to understand their cognitive characteristics. These tests may include questions such as whether the user prefers visual or audio learning materials. The user's response data is saved to the device in real time.

[0067] The device then sends the collected user data to the server. The server uses this data to generate an AI model and create a profile of the user's cognitive characteristics. This profile shows the user's strengths based on their visual, auditory, and other characteristics.

[0068] The server optimizes the modality of the learning content based on the generated cognitive characteristics profile. This optimization selects the optimal combination of information presentation formats, such as text, audio, and video, for the user. For example, for a visually dominant user, the server generates learning materials that include many diagrams and charts.

[0069] Optimized learning content is sent from the server to the device. The user receives this content through the device and begins learning. During this time, the device collects user interaction data and manages learning time and progress.

[0070] The collected data is sent back to the server, where it is analyzed in real time. Based on the analysis results, the modality is readjusted as needed, and the learning content is updated in the format best suited to the user.

[0071] In this way, the system builds a learning environment adapted to each user, providing an efficient learning experience. An example of a prompt message is: "To analyze your learning style, please answer the following questions. Do you prefer visual or audio materials?"

[0072] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0073] Step 1:

[0074] When a user accesses the system using a device, the device displays questionnaires and tests designed to assess the user's cognitive characteristics. The input is the user's responses, which the device collects and uses to generate data to determine the user's visual and auditory dominance. Specifically, each time the user completes a test, that data is stored in a database within the device.

[0075] Step 2:

[0076] The terminal sends collected user cognitive characteristic information to the server. The input is the response data received from the terminal. The server uses a generative AI model to analyze this data and generate a user cognitive characteristic profile. The output of this profile shows the dominance of each characteristic of the user. Specifically, profiles such as visual dominance and auditory dominance are generated.

[0077] Step 3:

[0078] The server optimizes the modality of the learning content based on the generated cognitive characteristics profile. The input is the user's cognitive characteristics profile, and through the optimization process, it selects the most appropriate combination from different information presentation formats such as text, audio, and video. Specifically, for users with a visually dominant profile, adjustments are made, such as preparing learning materials that include many diagrams and charts.

[0079] Step 4:

[0080] The server sends optimized learning content to the terminal. The input is the data of the optimized learning content, and the output is the content displayed on the user's terminal. Specifically, the terminal displays the received content in its user interface and prepares it to begin learning.

[0081] Step 5:

[0082] While a user is using the content, the device collects user interaction data. Inputs include the user's actions and learning progress, while outputs include interaction data such as learning time and progress. Specifically, user learning time and click data are recorded in a log, which is updated periodically.

[0083] Step 6:

[0084] The server re-analyzes the interaction data sent from the terminal and readjusts the learning modality as needed. The input is the interaction data received from the terminal, and updated learning content information is generated as output based on the analysis results. Specifically, if the user is taking a long time on a particular learning item, the server will make adjustments such as adding supplementary information for that item.

[0085] (Application Example 1)

[0086] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0087] In today's learning environment, learners often do not receive learning materials suited to their individual cognitive characteristics, leading to decreased learning efficiency. Furthermore, while the learning content provided needs to be adaptively adjusted according to the learner's level of understanding and concentration, current systems cannot achieve this in real time.

[0088] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0089] In this invention, the server includes means for determining the optimal learning format based on acquired user cognitive characteristics information, means for adjusting the method of presenting learning content based on the determined learning format, means for analyzing the usage status of the learning content in real time, dynamically adjusting the presentation method and evaluating the user's level of understanding, and means for adaptively suggesting the next learning material based on the evaluation of the level of understanding. This makes it possible to provide an optimal learning environment that is tailored to the individual cognitive characteristics and learning progress of each user.

[0090] "Cognitive characteristics information" refers to information that indicates the individual cognitive characteristics of a learner, such as their visual, auditory, linguistic, and motor skills.

[0091] "Learning format" refers to the method of presenting learning content, including modalities such as text, audio, and video.

[0092] "Presentation method" refers to the means by which learning content is shown or heard by learners.

[0093] "Usage status" refers to data on how learners are using the provided learning materials, including the time spent studying and their progress.

[0094] "Comprehension level" is an indicator that shows how well learners understand the material presented to them.

[0095] "Adaptive" means that the system changes or adjusts in response to the learner's behavior and circumstances.

[0096] "Learning materials" refer to the textbooks and information necessary for learning.

[0097] The system realizing this invention consists of three elements: a server, a terminal, and a user. The server determines the optimal learning format based on cognitive characteristics information and helps in presenting learning content. The terminal functions as an interface with the user, allowing the user to access the system and identify their cognitive characteristics. The user plays a role in progressing through the presented content.

[0098] The program is developed using programming languages ​​such as Python and JavaScript (registered trademark), and uses web frameworks such as Django to implement questionnaires and tests that identify the user's cognitive characteristics. This allows the server to generate a profile based on the user's visual and auditory dominance. Based on this profile, the server optimally combines learning content from text, audio, and video, and sends it to the device. Real-time analysis uses data analysis libraries such as Pandas and SciPy to analyze user interaction data. For example, it analyzes how often learners play specific content and adds video to supplement visual information as needed.

[0099] For example, if the user is an elementary school student, a science learning module is provided. In this case, visually-oriented learners will primarily use experimental animations, while auditory-oriented learners will use materials that emphasize narration.

[0100] An example of a prompt message when using a generative AI model would be: "When the user is visually dominant, suggest the most suitable educational videos for them. Emphasize visual information and include concise and easy-to-understand explanations." This allows the system to generate and deliver content tailored to the user's characteristics.

[0101] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0102] Step 1:

[0103] The terminal administers a questionnaire and a cognitive characteristics test to the user upon their first access. It takes user response data as input and extracts data regarding visual and auditory dominance based on this data. This cognitive characteristics information is then sent to the server as output. The questionnaire interface is implemented using Django.

[0104] Step 2:

[0105] The server generates a user profile based on the received cognitive characteristics information. It receives cognitive characteristics information from the terminal as input and uses the Pandas data analysis library to create profiles for visual, auditory, and other cognitive functions. As output, it stores the generated user profile and uses it to suggest the next learning content.

[0106] Step 3:

[0107] The server determines the optimal learning format and selects learning content based on the user profile. Using the user profile as input, it utilizes a generated AI model and prompts to determine the content format (text, audio, video, etc.). As output, it sends the modality-optimized learning content to the terminal.

[0108] Step 4:

[0109] The device provides the user with optimized learning content received from the server. It receives learning content data sent from the server as input and displays or plays it in a format usable by the user. It also prepares to monitor user interaction.

[0110] Step 5:

[0111] The device collects user interaction data in real time during the learning process. It takes user actions and indicators of comprehension (such as playback frequency and gaze time) as input, processes the data, and sends it to the server.

[0112] Step 6:

[0113] The server evaluates the user's level of understanding based on interaction data received from the terminal and dynamically adjusts the learning content and presentation method. It receives interaction data as input, performs data calculations, and measures the level of understanding. As output, it adjusts the presented learning materials as needed and reflects this in subsequent content suggestions.

[0114] Step 7:

[0115] The server adaptively suggests the next learning material and sends the suggested content back to the terminal. Based on the learning content adjusted in the previous step as input, it generates content to instruct the user on the next learning stage. As output, it delivers this updated learning content back to the terminal.

[0116] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0117] This invention relates to a system that dynamically provides an optimal learning environment by combining user cognitive characteristic information and an emotion recognition engine. This system consists of a server, a terminal, and an emotion recognition engine.

[0118] First, when users access the learning system through their device, they undergo questionnaires and tests to identify their basic cognitive characteristics. The device collects this information and sends it to the server. Based on the user's cognitive characteristics profile, the server generates an optimized learning format. This format consists of modalities such as text, audio, and video.

[0119] In parallel, the emotion recognition engine installed in the device recognizes the user's emotional state. This utilizes methods such as facial expression analysis using the camera, voice intonation analysis using the microphone, or biosensor data obtained from the smart device.

[0120] The server comprehensively analyzes the user's cognitive characteristics and emotional state, and adjusts how learning content is presented. For example, if the server detects that the user is stressed, it may change the learning format to lower difficulty levels or display encouraging messages to support motivation. Conversely, if positive emotions are detected, it may stimulate a sense of challenge by presenting more difficult problems.

[0121] While the user is learning, the device continuously collects interaction data, including their emotional state, and sends it to the server. The server analyzes this data in real time and dynamically adjusts the learning content and presentation methods as needed. This process optimizes each user's individual learning experience and promotes efficient acquisition.

[0122] Therefore, the present invention enables detailed responses tailored to the learner's characteristics and emotional state, significantly improving the efficiency and effectiveness of learning.

[0123] The following describes the processing flow.

[0124] Step 1:

[0125] The user logs into the learning system using a device. The device then administers questionnaires and tests to assess the user's cognitive characteristics and collects the results.

[0126] Step 2:

[0127] The device sends the collected cognitive characteristics data to the server. The server analyzes this data, generates a user cognitive characteristics profile, and stores it in a database.

[0128] Step 3:

[0129] The server determines the optimal modality for learning content based on the user's cognitive characteristics profile. This includes selecting and combining elements such as text, audio, and video.

[0130] Step 4:

[0131] The device uses a built-in emotion recognition engine to monitor the user's emotional state in real time. This includes analyzing facial expressions using the camera and analyzing voice tone using the microphone.

[0132] Step 5:

[0133] The device sends the acquired emotional data to the server. The server combines the emotional data with the cognitive characteristics profile for analysis and adjusts the way the learning content is presented.

[0134] Step 6:

[0135] The server sends the adjusted learning content to the device. The user continues learning through the learning content presented on the device.

[0136] Step 7:

[0137] As the user progresses through the learning process, the device continuously collects interaction and emotional data. This data helps in understanding the user's learning progress and emotional state.

[0138] Step 8:

[0139] The device sends the collected data back to the server, which analyzes it and dynamically adjusts the learning method as needed. Through this process, the system continuously provides the user with the optimal learning environment.

[0140] (Example 2)

[0141] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0142] In recent years, there has been a growing need for educational systems that optimize learning content according to the individual characteristics of each learner, thereby improving efficiency and effectiveness. However, conventional systems, while capable of providing learning formats based on learners' cognitive characteristics, have the challenge of not being able to consider the emotional states that change during learning in real time. As a result, there may be a lack of immediate support when learners feel stressed or their motivation declines, potentially affecting learning effectiveness.

[0143] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0144] In this invention, the server includes means for determining the optimal learning format based on acquired user cognitive characteristics information, means for adjusting the method of presenting learning content based on the determined learning format, a recognition mechanism for recognizing the user's emotional state, means for dynamically adjusting the method of presenting learning content based on the recognized emotional state, and means for analyzing the usage status of the learning content in real time and further adjusting the presentation method. This provides an optimal learning environment that comprehensively considers the learner's cognitive characteristics and emotional state, enabling improvements in learning efficiency and effectiveness.

[0145] "User" refers to any person who uses the system to engage in learning activities.

[0146] "Cognitive characteristics information" refers to information about an individual learner's learning style and characteristics, including data on visual, auditory, verbal, or kinesthetic dominance.

[0147] "Learning format" refers to the optimal combination of learning methods and media, determined based on the learner's cognitive characteristics.

[0148] An "emotion recognition mechanism" refers to a hardware or software system used to identify a user's emotional state in real time.

[0149] "Presentation method" refers to the procedures and means for determining the format in which learning content will be presented to learners.

[0150] This invention is an information processing system that provides a personalized learning environment by comprehensively utilizing learners' cognitive characteristics and emotional states. The system consists of a server, a terminal, and an emotion recognition engine.

[0151] First, the user accesses the learning system via a device and takes questionnaires and tests to identify their cognitive characteristics. At this stage, the device collects information about the user's cognitive characteristics based on the test results and questionnaire responses. The information obtained is immediately transmitted to the server.

[0152] The server generates a user profile based on the received cognitive characteristics information. This profile is used to determine what learning format is most effective for the user. Based on this profile information, the server then designs an optimized learning format. This learning format effectively combines text, audio, and video modalities.

[0153] Meanwhile, an emotion recognition engine built into the device monitors the user's emotional state. This uses methods such as facial expression analysis via camera, voice tone analysis via microphone, and biosensor data acquired from wearable devices. This allows the user's emotional state to be recognized in real time.

[0154] The server combines the user's cognitive characteristics and emotional state to determine the optimal way to present learning content. For example, if a user shows signs of stress, the system is designed to lower learning barriers by changing to simpler problems or playing relaxing audio. Conversely, if a positive emotional state is detected, the system enhances learning effectiveness by presenting the user with more challenging content.

[0155] Furthermore, user interactions and emotional changes during learning are continuously monitored, and data is sent from the device to the server in real time. The server then analyzes this data and dynamically adjusts the learning content and presentation methods in real time. Through this process, efficient learning tailored to each individual learner is made possible.

[0156] A concrete example would be a user participating in a foreign language course via their home device. Based on an initial questionnaire and a short vocabulary test, if the server determines that the user prefers visual information, it will recommend a learning format that includes a lot of visual materials. Furthermore, if the emotion recognition engine detects signs of fatigue during learning, it will temporarily slow down the learning speed or suggest a break to support the user's learning experience.

[0157] An example of a prompt might be: "Explain the process by which an AI system dynamically adjusts its learning based on cognitive characteristics information and emotional state. For example, include how it would respond if it detected that the user was experiencing stress." This prompt is designed to ensure the system provides responses and adjustments tailored to specific situations.

[0158] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0159] Step 1:

[0160] Users access the system via a terminal and take questionnaires and tests to collect cognitive characteristics information. The input is the user's answers, and the output is cognitive characteristics information. The terminal collects this information and sends it to the server. Specifically, the system processes the user's answers to questions on the terminal in real time, and the results are sent to the server.

[0161] Step 2:

[0162] The server receives cognitive characteristics information and generates a characteristic profile. The input is cognitive characteristics information sent from the terminal, and the output is the generated cognitive characteristics profile. The server analyzes this data and forms a profile that is suitable for the user's learning style. Specifically, it performs a process of classifying the user into categories such as visual dominance or auditory dominance.

[0163] Step 3:

[0164] The emotion recognition engine installed in the device recognizes the user's emotional state. The input is data from the user's facial expressions and voice, and the output is data from the recognized emotional state. In this process, the device uses the camera and microphone to identify emotions in real time. For example, if the user is smiling, it labels it as "joy," and if they are frowning, it labels it as "anxiety."

[0165] Step 4:

[0166] The server integrates cognitive trait profiles and emotional state data to determine the optimal learning format. The input is the integrated cognitive trait profile and emotional state data, and the output is the optimized learning format. Based on this information, the server selects the learning format from text, audio, and video modalities. Specifically, if a user has a visually dominant profile and is experiencing anxiety, the server will recommend relaxation content using video.

[0167] Step 5:

[0168] As the user progresses through the learning process, the device continuously collects interaction data and sends it to the server. Input consists of user actions and responses, while output is learning history data. The device records the user's mouse clicks, keyboard input, and learning progress. This allows the server to perform real-time data analysis and prepare to adjust the learning content and methods as needed.

[0169] Step 6:

[0170] The server dynamically adjusts how learning content is presented based on all collected data. The input is all data updated in real time, and the output is the adjusted learning presentation method. The server continuously adjusts the presentation method to maximize learning effectiveness. For example, if the server detects that the user is experiencing fatigue, it temporarily lowers the difficulty level of the learning material and suggests break times to optimize the user's learning experience.

[0171] (Application Example 2)

[0172] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0173] In modern information services, there is a demand for providing optimal content based on the user's characteristics and emotional state. Existing technologies only provide uniform information without adequately considering the user's cognitive characteristics or emotional state, thus failing to deliver information that is optimal for each individual user. Therefore, the challenge lies in creating a system that provides efficient and effective information tailored to each individual user.

[0174] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0175] In this invention, the server includes means for determining the optimal information provision format based on acquired user cognitive characteristics information and emotional state; means for adjusting the method of presenting the information content based on the determined information provision format; and means for analyzing the usage status and emotional state of the information content in real time and dynamically adjusting the presentation method. This makes it possible to provide optimal information tailored to each individual user.

[0176] "User" refers to an individual who receives information through this system.

[0177] "Cognitive characteristics information" refers to information related to the user's visual, auditory, linguistic, or motor skill dominance.

[0178] "Emotional state" refers to the psychological state analyzed based on the user's facial expressions, voice, or biosignals.

[0179] "Information presentation format" refers to the method of presenting information determined based on the user's cognitive characteristics and emotional state.

[0180] "Information content" refers to the content provided, which consists of text, audio, and video.

[0181] "Presentation method" refers to the method by which information content is displayed or reproduced for the user.

[0182] "Analysis" refers to the process of analyzing users' usage patterns and emotional states based on acquired data.

[0183] The system for carrying out this invention comprises a server, a terminal, and an emotion recognition engine. Users access the information provision system through an application installed on the terminal. The terminal uses a camera, microphone, and biosensors to collect the user's facial expressions, voice, heart rate, etc., and analyzes their emotional state in real time. Software such as an emotion analysis API (for example, Microsoft® Azure® emotion analysis API) is used for the analysis.

[0184] The server receives cognitive characteristics information and emotional states transmitted by the user and determines the optimal information presentation format. Based on this, it executes an algorithm to adjust how the information is presented. Because the user's usage and emotional state are constantly changing, the server processes this data in real time and dynamically optimizes the information presentation method.

[0185] For example, if a user is feeling stressed, the system can suggest a relaxing music playlist. Conversely, if positive emotions are detected, it can recommend challenging documentary content.

[0186] By utilizing a generative AI model, prompt messages can be used to analyze the user's state and suggest content. For example, using a prompt message such as "Generate keywords for content suitable when the user's facial expression recognition result is [stress]" enables the provision of information optimized for the user's state.

[0187] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0188] Step 1:

[0189] The device uses a camera, microphone, and biosensors to collect data on the user's facial expressions, voice, and heart rate. This input data forms the basis for analyzing the user's emotional state. The device then transmits this data to an emotion recognition engine to perform real-time analysis of the emotional state.

[0190] Step 2:

[0191] The device uses an emotion recognition engine to analyze the user's emotional state. Based on the input data (facial expressions, voice, biosignals), the emotion analysis API determines the emotional state and outputs indicators such as stress, relaxation, and positiveness. These output results will serve as important indicators for future information provision.

[0192] Step 3:

[0193] The terminal communicates with the server based on the user's cognitive characteristics information and sends the user's profile information to the server. The server receives the transmitted profile information and uses it as reference for providing information.

[0194] Step 4:

[0195] The server uses a generative AI model to determine the optimal information delivery format based on the received emotional state and cognitive characteristics information. Specifically, if the emotional state is relaxed, it generates keywords for learning-related content as prompts; if it is stressed, it generates keywords for relaxing content and outputs the appropriate information delivery format.

[0196] Step 5:

[0197] The server dynamically adjusts how information is presented based on the determined information delivery format. It combines content (text, audio, video) according to the user's emotional state and sends optimized information to the device. For example, if the user is feeling stressed, a relaxing music playlist is delivered to the device.

[0198] Step 6:

[0199] After a user actually uses the content, the device monitors its usage and sends feedback back to the server. This feedback data includes the actual date and time of use, the end time, and changes in usage behavior related to emotional state.

[0200] Step 7:

[0201] The server analyzes feedback data and uses it to optimize the entire information delivery process. The results of the analysis are reflected in future information deliveries, enabling the provision of more optimal information to users. The feedback analysis results are stored in a database and used for future improvements.

[0202] 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.

[0203] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0204] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0205] [Second Embodiment]

[0206] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0207] 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.

[0208] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0209] 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.

[0210] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0211] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0212] 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.

[0213] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0214] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0215] The 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.

[0216] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0217] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0218] This invention relates to a system for providing an optimal learning environment tailored to the individual cognitive characteristics of a user. This system consists of three main elements: a server, a terminal, and a user.

[0219] First, the user accesses the system using a terminal. Upon initial access, the terminal administers questionnaires and tests to identify the user's cognitive characteristics. The information collected is used to determine the user's visual, auditory, and other superiorities.

[0220] The device then sends user information to the server. The server analyzes this data and generates a cognitive characteristics profile for each user. This profile indicates, for example, whether the user has a dominant auditory or visual sensibility, and is used to provide content in the future.

[0221] The server optimizes the modality of the learning content based on the user's cognitive profile. In this optimization process, the server selects the most effective combination from multiple modalities, such as text, audio, and video. For example, a visually dominant user would be presented with text that includes diagrams and charts.

[0222] Next, the server sends optimized learning content to the device. The user receives this content through the device and proceeds with their learning. While the user is using the content, the device continuously collects user interaction data. This data includes the time spent learning and the progress made on learning items.

[0223] This data is sent back from the terminal to the server. The server analyzes the received data in real time to determine the user's level of understanding and concentration. Based on this analysis, the server adjusts the modality again if necessary and updates the way the learning content is presented.

[0224] In this way, the system can flexibly adjust the learning environment to suit the individual needs of the user. For example, if a user is struggling with a particular learning item, the system can increase the number of related videos that supplement the visual information for that item. As a result, learners can absorb and acquire information more effectively.

[0225] The following describes the processing flow.

[0226] Step 1:

[0227] The user accesses the learning system using a device. Upon initial access, the device conducts questionnaires and tests to identify the user's cognitive characteristics and collects information.

[0228] Step 2:

[0229] The device sends the collected user cognitive characteristics information to the server. The server analyzes this information to generate a user cognitive characteristics profile and stores it in a database.

[0230] Step 3:

[0231] The server determines the optimal modality of learning content based on the user's profile information. This includes selecting and combining text, audio, and video.

[0232] Step 4:

[0233] The server generates learning content based on the selected modality and sends the optimized content to the device. The user receives this content through the device and begins learning.

[0234] Step 5:

[0235] While the user is learning, the device collects interaction data about the user's learning behavior. This data includes learning time, browsing frequency, and operation history.

[0236] Step 6:

[0237] The device sends the collected interaction data to the server. The server analyzes this data in real time to evaluate the user's learning progress and concentration level.

[0238] Step 7:

[0239] Based on the analysis results, the server adjusts the modality ratio and content presentation method as needed. The optimized content is sent back to the device, and the user continues learning with the new settings.

[0240] (Example 1)

[0241] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0242] In recent years, there has been a growing demand for educational methods tailored to individual cognitive characteristics. However, existing learning systems generally use standardized materials and therefore fail to adequately address the unique characteristics of each learner. This often leads to decreased learning efficiency, and it is particularly difficult to provide an optimal learning environment that leverages the strengths of learners with specialized advantages in areas such as vision or hearing. To address this challenge, a system is needed that adapts to the cognitive characteristics of each user and presents the most suitable learning content.

[0243] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0244] In this invention, the server includes means for determining the optimal learning format based on acquired user cognitive characteristics information, means for adjusting the method of presenting learning content, means for analyzing the usage status of learning content in real time and dynamically adjusting the presentation method, means for generating a cognitive characteristics profile using a generative AI model based on the user's cognitive characteristics, and means for optimizing multiple information presentation formats based on the profile. This makes it possible to provide a flexible and effective individualized learning environment tailored to the user's characteristics.

[0245] "User cognitive characteristics information" refers to information that indicates the individual cognitive strengths of the user, such as visual, auditory, linguistic, and motor skills.

[0246] "Learning format" refers to a method of presenting learning materials optimized for users to learn efficiently, and includes different media such as text, audio, and video.

[0247] A "generative AI model" is an artificial intelligence model that analyzes data and generates output based on specific input information. In this context, it is used to generate a profile of the user's cognitive characteristics.

[0248] A "cognitive characteristics profile" is a profile created to understand each user's individual cognitive abilities and learning characteristics, and to provide the most suitable learning format based on that understanding.

[0249] "Information presentation format" refers to the method by which learning content is displayed and presented to users, and specifically includes combinations of text, audio, and video.

[0250] This invention is a system for providing an optimal learning environment tailored to the cognitive characteristics of the user. This system consists of three main elements: a server, a terminal, and the user.

[0251] First, the user accesses the system using a device. Upon initial access, the device presents the user with questionnaires and tests designed to understand their cognitive characteristics. These tests may include questions such as whether the user prefers visual or audio learning materials. The user's response data is saved to the device in real time.

[0252] The device then sends the collected user data to the server. The server uses this data to generate an AI model and create a profile of the user's cognitive characteristics. This profile shows the user's strengths based on their visual, auditory, and other characteristics.

[0253] The server optimizes the modality of the learning content based on the generated cognitive characteristics profile. This optimization selects the optimal combination of information presentation formats, such as text, audio, and video, for the user. For example, for a visually dominant user, the server generates learning materials that include many diagrams and charts.

[0254] Optimized learning content is sent from the server to the device. The user receives this content through the device and begins learning. During this time, the device collects user interaction data and manages learning time and progress.

[0255] The collected data is sent back to the server, where it is analyzed in real time. Based on the analysis results, the modality is readjusted as needed, and the learning content is updated in the format best suited to the user.

[0256] In this way, the system builds a learning environment adapted to each user, providing an efficient learning experience. An example of a prompt message is: "To analyze your learning style, please answer the following questions. Do you prefer visual or audio materials?"

[0257] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0258] Step 1:

[0259] When a user accesses the system using a device, the device displays questionnaires and tests designed to assess the user's cognitive characteristics. The input is the user's responses, which the device collects and uses to generate data to determine the user's visual and auditory dominance. Specifically, each time the user completes a test, that data is stored in a database within the device.

[0260] Step 2:

[0261] The terminal sends collected user cognitive characteristic information to the server. The input is the response data received from the terminal. The server uses a generative AI model to analyze this data and generate a user cognitive characteristic profile. The output of this profile shows the dominance of each characteristic of the user. Specifically, profiles such as visual dominance and auditory dominance are generated.

[0262] Step 3:

[0263] The server optimizes the modality of the learning content based on the generated cognitive characteristics profile. The input is the user's cognitive characteristics profile, and through the optimization process, it selects the most appropriate combination from different information presentation formats such as text, audio, and video. Specifically, for users with a visually dominant profile, adjustments are made, such as preparing learning materials that include many diagrams and charts.

[0264] Step 4:

[0265] The server sends optimized learning content to the terminal. The input is the data of the optimized learning content, and the output is the content displayed on the user's terminal. Specifically, the terminal displays the received content in its user interface and prepares it to begin learning.

[0266] Step 5:

[0267] While a user is using the content, the device collects user interaction data. Inputs include the user's actions and learning progress, while outputs include interaction data such as learning time and progress. Specifically, user learning time and click data are recorded in a log, which is updated periodically.

[0268] Step 6:

[0269] The server re-analyzes the interaction data sent from the terminal and readjusts the learning modality as needed. The input is the interaction data received from the terminal, and updated learning content information is generated as output based on the analysis results. Specifically, if the user is taking a long time on a particular learning item, the server will make adjustments such as adding supplementary information for that item.

[0270] (Application Example 1)

[0271] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0272] In today's learning environment, learners often do not receive learning materials suited to their individual cognitive characteristics, leading to decreased learning efficiency. Furthermore, while the learning content provided needs to be adaptively adjusted according to the learner's level of understanding and concentration, current systems cannot achieve this in real time.

[0273] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0274] In this invention, the server includes means for determining the optimal learning format based on acquired user cognitive characteristics information, means for adjusting the method of presenting learning content based on the determined learning format, means for analyzing the usage status of the learning content in real time, dynamically adjusting the presentation method and evaluating the user's level of understanding, and means for adaptively suggesting the next learning material based on the evaluation of the level of understanding. This makes it possible to provide an optimal learning environment that is tailored to the individual cognitive characteristics and learning progress of each user.

[0275] "Cognitive characteristics information" refers to information that indicates the individual cognitive characteristics of a learner, such as their visual, auditory, linguistic, and motor skills.

[0276] "Learning format" refers to the method of presenting learning content, including modalities such as text, audio, and video.

[0277] "Presentation method" refers to the means by which learning content is shown or heard by learners.

[0278] "Usage status" refers to data on how learners are using the provided learning materials, including the time spent studying and their progress.

[0279] "Comprehension level" is an indicator that shows how well learners understand the material presented to them.

[0280] "Adaptive" means that the system changes or adjusts in response to the learner's behavior and circumstances.

[0281] "Learning materials" refer to the textbooks and information necessary for learning.

[0282] The system realizing this invention consists of three elements: a server, a terminal, and a user. The server determines the optimal learning format based on cognitive characteristics information and helps in presenting learning content. The terminal functions as an interface with the user, allowing the user to access the system and identify their cognitive characteristics. The user plays a role in progressing through the presented content.

[0283] The program is developed using programming languages such as Python and JavaScript, and implements questionnaires and tests to identify users' cognitive characteristics using web frameworks such as Django. As a result, the server generates a profile based on the user's visual and auditory superiority. The server optimally combines learning content from text, audio, and video according to this profile and sends it to the terminal. Data analysis libraries such as Pandas and SciPy are used for real-time analysis to analyze users' interaction data. For example, analyze how often a learner plays a specific content and add videos to complement visual information if necessary.

[0284] As a specific example, when the user is an elementary school student, a science learning module is provided. In this case, for visually dominant learners, mainly use animations of experiments, and for aurally dominant learners, use teaching materials that emphasize narration.

[0285] As an example of a prompt sentence when using a generative AI model, instructions such as "When the user is visually dominant, propose the most suitable educational videos for them. Emphasize visual information and include concise and easy-to-understand explanations." are used. As a result, the system generates and provides content tailored to the user's characteristics.

[0286] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0287] Step 1:

[0288] The terminal conducts a questionnaire and a test to identify cognitive characteristics when the user first accesses. It obtains response data from the user as input, extracts data related to visual and auditory superiority based on this, and sends this cognitive characteristic information to the server as output. The questionnaire interface is implemented using Django.

[0289] Step 2:

[0290] The server generates a user profile based on the received cognitive characteristics information. It receives cognitive characteristics information from the terminal as input and uses the Pandas data analysis library to create profiles for visual, auditory, and other cognitive functions. As output, it stores the generated user profile and uses it to suggest the next learning content.

[0291] Step 3:

[0292] The server determines the optimal learning format and selects learning content based on the user profile. Using the user profile as input, it utilizes a generated AI model and prompts to determine the content format (text, audio, video, etc.). As output, it sends the modality-optimized learning content to the terminal.

[0293] Step 4:

[0294] The device provides the user with optimized learning content received from the server. It receives learning content data sent from the server as input and displays or plays it in a format usable by the user. It also prepares to monitor user interaction.

[0295] Step 5:

[0296] The device collects user interaction data in real time during the learning process. It takes user actions and indicators of comprehension (such as playback frequency and gaze time) as input, processes the data, and sends it to the server.

[0297] Step 6:

[0298] The server evaluates the user's level of understanding based on interaction data received from the terminal and dynamically adjusts the learning content and presentation method. It receives interaction data as input, performs data calculations, and measures the level of understanding. As output, it adjusts the presented learning materials as needed and reflects this in subsequent content suggestions.

[0299] Step 7:

[0300] The server adaptively suggests the next learning material and sends the suggested content back to the terminal. Based on the learning content adjusted in the previous step as input, it generates content to instruct the user on the next learning stage. As output, it delivers this updated learning content back to the terminal.

[0301] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0302] This invention relates to a system that dynamically provides an optimal learning environment by combining user cognitive characteristic information and an emotion recognition engine. This system consists of a server, a terminal, and an emotion recognition engine.

[0303] First, when users access the learning system through their device, they undergo questionnaires and tests to identify their basic cognitive characteristics. The device collects this information and sends it to the server. Based on the user's cognitive characteristics profile, the server generates an optimized learning format. This format consists of modalities such as text, audio, and video.

[0304] In parallel, the emotion recognition engine installed in the device recognizes the user's emotional state. This utilizes methods such as facial expression analysis using the camera, voice intonation analysis using the microphone, or biosensor data obtained from the smart device.

[0305] The server comprehensively analyzes the user's cognitive characteristics and emotional state, and adjusts the presentation method of learning content. For example, when the user is recognized as feeling stressed, the learning format is changed to content with a lower difficulty level, or a motivating message is displayed to support motivation. Conversely, when a positive emotion is detected, it is also possible to stimulate the spirit of challenge by presenting problems with a higher difficulty level.

[0306] While the user is progressing in learning, the terminal continuously collects interaction data and also transmits the emotional state to the server. The server analyzes these data in real time and dynamically adjusts the learning content and presentation method as appropriate. Through such a process, the individual learning experience of the user can be optimized, and efficient acquisition can be promoted.

[0307] Therefore, according to the present invention, it is possible to make a detailed response according to the characteristics and emotional state of the learner, and the efficiency and effect of learning can be greatly improved.

[0308] The processing flow will be described below.

[0309] Step 1:

[0310] The user logs in to the learning system using the terminal. The terminal conducts a questionnaire or test to confirm the user's cognitive characteristics and collects the results.

[0311] Step 2:

[0312] The terminal transmits the collected cognitive characteristic data to the server. The server analyzes this data, generates a cognitive characteristic profile of the user, and saves it in the database.

[0313] Step 3:

[0314] The server determines the optimal modality of the learning content based on the user's cognitive characteristic profile. This includes the selection and combination of text, audio, video, etc.

[0315] Step 4:

[0316] The device uses a built-in emotion recognition engine to monitor the user's emotional state in real time. This includes analyzing facial expressions using the camera and analyzing voice tone using the microphone.

[0317] Step 5:

[0318] The device sends the acquired emotional data to the server. The server combines the emotional data with the cognitive characteristics profile for analysis and adjusts the way the learning content is presented.

[0319] Step 6:

[0320] The server sends the adjusted learning content to the device. The user continues learning through the learning content presented on the device.

[0321] Step 7:

[0322] As the user progresses through the learning process, the device continuously collects interaction and emotional data. This data helps in understanding the user's learning progress and emotional state.

[0323] Step 8:

[0324] The device sends the collected data back to the server, which analyzes it and dynamically adjusts the learning method as needed. Through this process, the system continuously provides the user with the optimal learning environment.

[0325] (Example 2)

[0326] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0327] In recent years, there has been a growing need for educational systems that optimize learning content according to the individual characteristics of each learner, thereby improving efficiency and effectiveness. However, conventional systems, while capable of providing learning formats based on learners' cognitive characteristics, have the challenge of not being able to consider the emotional states that change during learning in real time. As a result, there may be a lack of immediate support when learners feel stressed or their motivation declines, potentially affecting learning effectiveness.

[0328] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0329] In this invention, the server includes means for determining the optimal learning format based on acquired user cognitive characteristics information, means for adjusting the method of presenting learning content based on the determined learning format, a recognition mechanism for recognizing the user's emotional state, means for dynamically adjusting the method of presenting learning content based on the recognized emotional state, and means for analyzing the usage status of the learning content in real time and further adjusting the presentation method. This provides an optimal learning environment that comprehensively considers the learner's cognitive characteristics and emotional state, enabling improvements in learning efficiency and effectiveness.

[0330] "User" refers to any person who uses the system to engage in learning activities.

[0331] "Cognitive characteristics information" refers to information about an individual learner's learning style and characteristics, including data on visual, auditory, verbal, or kinesthetic dominance.

[0332] "Learning format" refers to the optimal combination of learning methods and media, determined based on the learner's cognitive characteristics.

[0333] An "emotion recognition mechanism" refers to a hardware or software system used to identify a user's emotional state in real time.

[0334] "Presentation method" refers to the procedures and means for determining the format in which learning content will be presented to learners.

[0335] This invention is an information processing system that provides a personalized learning environment by comprehensively utilizing learners' cognitive characteristics and emotional states. The system consists of a server, a terminal, and an emotion recognition engine.

[0336] First, the user accesses the learning system via a device and takes questionnaires and tests to identify their cognitive characteristics. At this stage, the device collects information about the user's cognitive characteristics based on the test results and questionnaire responses. The information obtained is immediately transmitted to the server.

[0337] The server generates a user profile based on the received cognitive characteristics information. This profile is used to determine what learning format is most effective for the user. Based on this profile information, the server then designs an optimized learning format. This learning format effectively combines text, audio, and video modalities.

[0338] Meanwhile, an emotion recognition engine built into the device monitors the user's emotional state. This uses methods such as facial expression analysis via camera, voice tone analysis via microphone, and biosensor data acquired from wearable devices. This allows the user's emotional state to be recognized in real time.

[0339] The server combines the user's cognitive characteristics and emotional state to determine the optimal way to present learning content. For example, if a user shows signs of stress, the system is designed to lower learning barriers by changing to simpler problems or playing relaxing audio. Conversely, if a positive emotional state is detected, the system enhances learning effectiveness by presenting the user with more challenging content.

[0340] Furthermore, user interactions and emotional changes during learning are continuously monitored, and data is sent from the device to the server in real time. The server then analyzes this data and dynamically adjusts the learning content and presentation methods in real time. Through this process, efficient learning tailored to each individual learner is made possible.

[0341] A concrete example would be a user participating in a foreign language course via their home device. Based on an initial questionnaire and a short vocabulary test, if the server determines that the user prefers visual information, it will recommend a learning format that includes a lot of visual materials. Furthermore, if the emotion recognition engine detects signs of fatigue during learning, it will temporarily slow down the learning speed or suggest a break to support the user's learning experience.

[0342] An example of a prompt might be: "Explain the process by which an AI system dynamically adjusts its learning based on cognitive characteristics information and emotional state. For example, include how it would respond if it detected that the user was experiencing stress." This prompt is designed to ensure the system provides responses and adjustments tailored to specific situations.

[0343] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0344] Step 1:

[0345] Users access the system via a terminal and take questionnaires and tests to collect cognitive characteristics information. The input is the user's answers, and the output is cognitive characteristics information. The terminal collects this information and sends it to the server. Specifically, the system processes the user's answers to questions on the terminal in real time, and the results are sent to the server.

[0346] Step 2:

[0347] The server receives cognitive characteristics information and generates a characteristic profile. The input is cognitive characteristics information sent from the terminal, and the output is the generated cognitive characteristics profile. The server analyzes this data and forms a profile that is suitable for the user's learning style. Specifically, it performs a process of classifying the user into categories such as visual dominance or auditory dominance.

[0348] Step 3:

[0349] The emotion recognition engine installed in the device recognizes the user's emotional state. The input is data from the user's facial expressions and voice, and the output is data from the recognized emotional state. In this process, the device uses the camera and microphone to identify emotions in real time. For example, if the user is smiling, it labels it as "joy," and if they are frowning, it labels it as "anxiety."

[0350] Step 4:

[0351] The server integrates cognitive trait profiles and emotional state data to determine the optimal learning format. The input is the integrated cognitive trait profile and emotional state data, and the output is the optimized learning format. Based on this information, the server selects the learning format from text, audio, and video modalities. Specifically, if a user has a visually dominant profile and is experiencing anxiety, the server will recommend relaxation content using video.

[0352] Step 5:

[0353] As the user progresses through the learning process, the device continuously collects interaction data and sends it to the server. Input consists of user actions and responses, while output is learning history data. The device records the user's mouse clicks, keyboard input, and learning progress. This allows the server to perform real-time data analysis and prepare to adjust the learning content and methods as needed.

[0354] Step 6:

[0355] The server dynamically adjusts how learning content is presented based on all collected data. The input is all data updated in real time, and the output is the adjusted learning presentation method. The server continuously adjusts the presentation method to maximize learning effectiveness. For example, if the server detects that the user is experiencing fatigue, it temporarily lowers the difficulty level of the learning material and suggests break times to optimize the user's learning experience.

[0356] (Application Example 2)

[0357] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0358] In modern information services, there is a demand for providing optimal content based on the user's characteristics and emotional state. Existing technologies only provide uniform information without adequately considering the user's cognitive characteristics or emotional state, thus failing to deliver information that is optimal for each individual user. Therefore, the challenge lies in creating a system that provides efficient and effective information tailored to each individual user.

[0359] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0360] In this invention, the server includes means for determining the optimal information provision format based on acquired user cognitive characteristics information and emotional state; means for adjusting the method of presenting the information content based on the determined information provision format; and means for analyzing the usage status and emotional state of the information content in real time and dynamically adjusting the presentation method. This makes it possible to provide optimal information tailored to each individual user.

[0361] "User" refers to an individual who receives information through this system.

[0362] "Cognitive characteristics information" refers to information related to the user's visual, auditory, linguistic, or motor skill dominance.

[0363] "Emotional state" refers to the psychological state analyzed based on the user's facial expressions, voice, or biosignals.

[0364] "Information presentation format" refers to the method of presenting information determined based on the user's cognitive characteristics and emotional state.

[0365] "Information content" refers to the content provided, which consists of text, audio, and video.

[0366] "Presentation method" refers to the method by which information content is displayed or reproduced for the user.

[0367] "Analysis" refers to the process of analyzing users' usage patterns and emotional states based on acquired data.

[0368] The system for carrying out this invention comprises a server, a terminal, and an emotion recognition engine. Users access the information provision system through an application installed on the terminal. The terminal uses a camera, microphone, and biosensors to collect the user's facial expressions, voice, heart rate, etc., and analyzes their emotional state in real time. Software such as an emotion analysis API (for example, Microsoft Azure's emotion analysis API) is used for the analysis.

[0369] The server receives cognitive characteristics information and emotional states transmitted by the user and determines the optimal information presentation format. Based on this, it executes an algorithm to adjust how the information is presented. Because the user's usage and emotional state are constantly changing, the server processes this data in real time and dynamically optimizes the information presentation method.

[0370] For example, if a user is feeling stressed, the system can suggest a relaxing music playlist. Conversely, if positive emotions are detected, it can recommend challenging documentary content.

[0371] By utilizing a generative AI model, prompt messages can be used to analyze the user's state and suggest content. For example, using a prompt message such as "Generate keywords for content suitable when the user's facial expression recognition result is [stress]" enables the provision of information optimized for the user's state.

[0372] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0373] Step 1:

[0374] The device uses a camera, microphone, and biosensors to collect data on the user's facial expressions, voice, and heart rate. This input data forms the basis for analyzing the user's emotional state. The device then transmits this data to an emotion recognition engine to perform real-time analysis of the emotional state.

[0375] Step 2:

[0376] The device uses an emotion recognition engine to analyze the user's emotional state. Based on the input data (facial expressions, voice, biosignals), the emotion analysis API determines the emotional state and outputs indicators such as stress, relaxation, and positiveness. These output results will serve as important indicators for future information provision.

[0377] Step 3:

[0378] The terminal communicates with the server based on the user's cognitive characteristics information and sends the user's profile information to the server. The server receives the transmitted profile information and uses it as reference for providing information.

[0379] Step 4:

[0380] The server uses a generative AI model to determine the optimal information delivery format based on the received emotional state and cognitive characteristics information. Specifically, if the emotional state is relaxed, it generates keywords for learning-related content as prompts; if it is stressed, it generates keywords for relaxing content and outputs the appropriate information delivery format.

[0381] Step 5:

[0382] The server dynamically adjusts how information is presented based on the determined information delivery format. It combines content (text, audio, video) according to the user's emotional state and sends optimized information to the device. For example, if the user is feeling stressed, a relaxing music playlist is delivered to the device.

[0383] Step 6:

[0384] After a user actually uses the content, the device monitors its usage and sends feedback back to the server. This feedback data includes the actual date and time of use, the end time, and changes in usage behavior related to emotional state.

[0385] Step 7:

[0386] The server analyzes feedback data and uses it to optimize the entire information delivery process. The results of the analysis are reflected in future information deliveries, enabling the provision of more optimal information to users. The feedback analysis results are stored in a database and used for future improvements.

[0387] 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.

[0388] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0389] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0390] [Third Embodiment]

[0391] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0392] 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.

[0393] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0394] 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.

[0395] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0396] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0397] 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.

[0398] 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.

[0399] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0400] The 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.

[0401] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0402] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0403] This invention relates to a system for providing an optimal learning environment tailored to the individual cognitive characteristics of a user. This system consists of three main elements: a server, a terminal, and a user.

[0404] First, the user accesses the system using a terminal. Upon initial access, the terminal administers questionnaires and tests to identify the user's cognitive characteristics. The information collected is used to determine the user's visual, auditory, and other superiorities.

[0405] The device then sends user information to the server. The server analyzes this data and generates a cognitive characteristics profile for each user. This profile indicates, for example, whether the user has a dominant auditory or visual sensibility, and is used to provide content in the future.

[0406] The server optimizes the modality of the learning content based on the user's cognitive profile. In this optimization process, the server selects the most effective combination from multiple modalities, such as text, audio, and video. For example, a visually dominant user would be presented with text that includes diagrams and charts.

[0407] Next, the server sends optimized learning content to the device. The user receives this content through the device and proceeds with their learning. While the user is using the content, the device continuously collects user interaction data. This data includes the time spent learning and the progress made on learning items.

[0408] This data is sent back from the terminal to the server. The server analyzes the received data in real time to determine the user's level of understanding and concentration. Based on this analysis, the server adjusts the modality again if necessary and updates the way the learning content is presented.

[0409] In this way, the system can flexibly adjust the learning environment to suit the individual needs of the user. For example, if a user is struggling with a particular learning item, the system can increase the number of related videos that supplement the visual information for that item. As a result, learners can absorb and acquire information more effectively.

[0410] The following describes the processing flow.

[0411] Step 1:

[0412] The user accesses the learning system using a device. Upon initial access, the device conducts questionnaires and tests to identify the user's cognitive characteristics and collects information.

[0413] Step 2:

[0414] The device sends the collected user cognitive characteristics information to the server. The server analyzes this information to generate a user cognitive characteristics profile and stores it in a database.

[0415] Step 3:

[0416] The server determines the optimal modality of learning content based on the user's profile information. This includes selecting and combining text, audio, and video.

[0417] Step 4:

[0418] The server generates learning content based on the selected modality and sends the optimized content to the device. The user receives this content through the device and begins learning.

[0419] Step 5:

[0420] While the user is learning, the device collects interaction data about the user's learning behavior. This data includes learning time, browsing frequency, and operation history.

[0421] Step 6:

[0422] The device sends the collected interaction data to the server. The server analyzes this data in real time to evaluate the user's learning progress and concentration level.

[0423] Step 7:

[0424] Based on the analysis results, the server adjusts the modality ratio and content presentation method as needed. The optimized content is sent back to the device, and the user continues learning with the new settings.

[0425] (Example 1)

[0426] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0427] In recent years, there has been a growing demand for educational methods tailored to individual cognitive characteristics. However, existing learning systems generally use standardized materials and therefore fail to adequately address the unique characteristics of each learner. This often leads to decreased learning efficiency, and it is particularly difficult to provide an optimal learning environment that leverages the strengths of learners with specialized advantages in areas such as vision or hearing. To address this challenge, a system is needed that adapts to the cognitive characteristics of each user and presents the most suitable learning content.

[0428] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0429] In this invention, the server includes means for determining the optimal learning format based on acquired user cognitive characteristics information, means for adjusting the method of presenting learning content, means for analyzing the usage status of learning content in real time and dynamically adjusting the presentation method, means for generating a cognitive characteristics profile using a generative AI model based on the user's cognitive characteristics, and means for optimizing multiple information presentation formats based on the profile. This makes it possible to provide a flexible and effective individualized learning environment tailored to the user's characteristics.

[0430] "User cognitive characteristics information" refers to information that indicates the individual cognitive strengths of the user, such as visual, auditory, linguistic, and motor skills.

[0431] "Learning format" refers to a method of presenting learning materials optimized for users to learn efficiently, and includes different media such as text, audio, and video.

[0432] A "generative AI model" is an artificial intelligence model that analyzes data and generates output based on specific input information. In this context, it is used to generate a profile of the user's cognitive characteristics.

[0433] A "cognitive characteristics profile" is a profile created to understand each user's individual cognitive abilities and learning characteristics, and to provide the most suitable learning format based on that understanding.

[0434] "Information presentation format" refers to the method by which learning content is displayed and presented to users, and specifically includes combinations of text, audio, and video.

[0435] This invention is a system for providing an optimal learning environment tailored to the cognitive characteristics of the user. This system consists of three main elements: a server, a terminal, and the user.

[0436] First, the user accesses the system using a device. Upon initial access, the device presents the user with questionnaires and tests designed to understand their cognitive characteristics. These tests may include questions such as whether the user prefers visual or audio learning materials. The user's response data is saved to the device in real time.

[0437] The device then sends the collected user data to the server. The server uses this data to generate an AI model and create a profile of the user's cognitive characteristics. This profile shows the user's strengths based on their visual, auditory, and other characteristics.

[0438] The server optimizes the modality of the learning content based on the generated cognitive characteristics profile. This optimization selects the optimal combination of information presentation formats, such as text, audio, and video, for the user. For example, for a visually dominant user, the server generates learning materials that include many diagrams and charts.

[0439] Optimized learning content is sent from the server to the device. The user receives this content through the device and begins learning. During this time, the device collects user interaction data and manages learning time and progress.

[0440] The collected data is sent back to the server, where it is analyzed in real time. Based on the analysis results, the modality is readjusted as needed, and the learning content is updated in the format best suited to the user.

[0441] In this way, the system builds a learning environment adapted to each user, providing an efficient learning experience. An example of a prompt message is: "To analyze your learning style, please answer the following questions. Do you prefer visual or audio materials?"

[0442] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0443] Step 1:

[0444] When a user accesses the system using a device, the device displays questionnaires and tests designed to assess the user's cognitive characteristics. The input is the user's responses, which the device collects and uses to generate data to determine the user's visual and auditory dominance. Specifically, each time the user completes a test, that data is stored in a database within the device.

[0445] Step 2:

[0446] The terminal sends collected user cognitive characteristic information to the server. The input is the response data received from the terminal. The server uses a generative AI model to analyze this data and generate a user cognitive characteristic profile. The output of this profile shows the dominance of each characteristic of the user. Specifically, profiles such as visual dominance and auditory dominance are generated.

[0447] Step 3:

[0448] The server optimizes the modality of the learning content based on the generated cognitive characteristics profile. The input is the user's cognitive characteristics profile, and through the optimization process, it selects the most appropriate combination from different information presentation formats such as text, audio, and video. Specifically, for users with a visually dominant profile, adjustments are made, such as preparing learning materials that include many diagrams and charts.

[0449] Step 4:

[0450] The server sends optimized learning content to the terminal. The input is the data of the optimized learning content, and the output is the content displayed on the user's terminal. Specifically, the terminal displays the received content in its user interface and prepares it to begin learning.

[0451] Step 5:

[0452] While a user is using the content, the device collects user interaction data. Inputs include the user's actions and learning progress, while outputs include interaction data such as learning time and progress. Specifically, user learning time and click data are recorded in a log, which is updated periodically.

[0453] Step 6:

[0454] The server re-analyzes the interaction data sent from the terminal and readjusts the learning modality as needed. The input is the interaction data received from the terminal, and updated learning content information is generated as output based on the analysis results. Specifically, if the user is taking a long time on a particular learning item, the server will make adjustments such as adding supplementary information for that item.

[0455] (Application Example 1)

[0456] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0457] In today's learning environment, learners often do not receive learning materials suited to their individual cognitive characteristics, leading to decreased learning efficiency. Furthermore, while the learning content provided needs to be adaptively adjusted according to the learner's level of understanding and concentration, current systems cannot achieve this in real time.

[0458] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0459] In this invention, the server includes means for determining the optimal learning format based on acquired user cognitive characteristics information, means for adjusting the method of presenting learning content based on the determined learning format, means for analyzing the usage status of the learning content in real time, dynamically adjusting the presentation method and evaluating the user's level of understanding, and means for adaptively suggesting the next learning material based on the evaluation of the level of understanding. This makes it possible to provide an optimal learning environment that is tailored to the individual cognitive characteristics and learning progress of each user.

[0460] "Cognitive characteristics information" refers to information that indicates the individual cognitive characteristics of a learner, such as their visual, auditory, linguistic, and motor skills.

[0461] "Learning format" refers to the method of presenting learning content, including modalities such as text, audio, and video.

[0462] "Presentation method" refers to the means by which learning content is shown or heard by learners.

[0463] "Usage status" refers to data on how learners are using the provided learning materials, including the time spent studying and their progress.

[0464] "Comprehension level" is an indicator that shows how well learners understand the material presented to them.

[0465] "Adaptive" means that the system changes or adjusts in response to the learner's behavior and circumstances.

[0466] "Learning materials" refer to the textbooks and information necessary for learning.

[0467] The system realizing this invention consists of three elements: a server, a terminal, and a user. The server determines the optimal learning format based on cognitive characteristics information and helps in presenting learning content. The terminal functions as an interface with the user, allowing the user to access the system and identify their cognitive characteristics. The user plays a role in progressing through the presented content.

[0468] The program is developed using programming languages ​​such as Python and JavaScript, and uses web frameworks such as Django to implement questionnaires and tests that identify the user's cognitive characteristics. This allows the server to generate a profile based on the user's visual and auditory dominance. Based on this profile, the server optimally combines learning content from text, audio, and video, and sends it to the device. Real-time analysis uses data analysis libraries such as Pandas and SciPy to analyze user interaction data. For example, it analyzes how often learners play specific content and adds video to supplement visual information as needed.

[0469] For example, if the user is an elementary school student, a science learning module is provided. In this case, visually-oriented learners will primarily use experimental animations, while auditory-oriented learners will use materials that emphasize narration.

[0470] An example of a prompt message when using a generative AI model would be: "When the user is visually dominant, suggest the most suitable educational videos for them. Emphasize visual information and include concise and easy-to-understand explanations." This allows the system to generate and deliver content tailored to the user's characteristics.

[0471] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0472] Step 1:

[0473] The terminal administers a questionnaire and a cognitive characteristics test to the user upon their first access. It takes user response data as input and extracts data regarding visual and auditory dominance based on this data. This cognitive characteristics information is then sent to the server as output. The questionnaire interface is implemented using Django.

[0474] Step 2:

[0475] The server generates a user profile based on the received cognitive characteristics information. It receives cognitive characteristics information from the terminal as input and uses the Pandas data analysis library to create profiles for visual, auditory, and other cognitive functions. As output, it stores the generated user profile and uses it to suggest the next learning content.

[0476] Step 3:

[0477] The server determines the optimal learning format and selects learning content based on the user profile. Using the user profile as input, it utilizes a generated AI model and prompts to determine the content format (text, audio, video, etc.). As output, it sends the modality-optimized learning content to the terminal.

[0478] Step 4:

[0479] The device provides the user with optimized learning content received from the server. It receives learning content data sent from the server as input and displays or plays it in a format usable by the user. It also prepares to monitor user interaction.

[0480] Step 5:

[0481] The device collects user interaction data in real time during the learning process. It takes user actions and indicators of comprehension (such as playback frequency and gaze time) as input, processes the data, and sends it to the server.

[0482] Step 6:

[0483] The server evaluates the user's level of understanding based on interaction data received from the terminal and dynamically adjusts the learning content and presentation method. It receives interaction data as input, performs data calculations, and measures the level of understanding. As output, it adjusts the presented learning materials as needed and reflects this in subsequent content suggestions.

[0484] Step 7:

[0485] The server adaptively suggests the next learning material and sends the suggested content back to the terminal. Based on the learning content adjusted in the previous step as input, it generates content to instruct the user on the next learning stage. As output, it delivers this updated learning content back to the terminal.

[0486] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0487] This invention relates to a system that dynamically provides an optimal learning environment by combining user cognitive characteristic information and an emotion recognition engine. This system consists of a server, a terminal, and an emotion recognition engine.

[0488] First, when users access the learning system through their device, they undergo questionnaires and tests to identify their basic cognitive characteristics. The device collects this information and sends it to the server. Based on the user's cognitive characteristics profile, the server generates an optimized learning format. This format consists of modalities such as text, audio, and video.

[0489] In parallel, the emotion recognition engine installed in the device recognizes the user's emotional state. This utilizes methods such as facial expression analysis using the camera, voice intonation analysis using the microphone, or biosensor data obtained from the smart device.

[0490] The server comprehensively analyzes the user's cognitive characteristics and emotional state, and adjusts how learning content is presented. For example, if the server detects that the user is stressed, it may change the learning format to lower difficulty levels or display encouraging messages to support motivation. Conversely, if positive emotions are detected, it may stimulate a sense of challenge by presenting more difficult problems.

[0491] While the user is learning, the device continuously collects interaction data, including their emotional state, and sends it to the server. The server analyzes this data in real time and dynamically adjusts the learning content and presentation methods as needed. This process optimizes each user's individual learning experience and promotes efficient acquisition.

[0492] Therefore, the present invention enables detailed responses tailored to the learner's characteristics and emotional state, significantly improving the efficiency and effectiveness of learning.

[0493] The following describes the processing flow.

[0494] Step 1:

[0495] The user logs into the learning system using a device. The device then administers questionnaires and tests to assess the user's cognitive characteristics and collects the results.

[0496] Step 2:

[0497] The device sends the collected cognitive characteristics data to the server. The server analyzes this data, generates a user cognitive characteristics profile, and stores it in a database.

[0498] Step 3:

[0499] The server determines the optimal modality for learning content based on the user's cognitive characteristics profile. This includes selecting and combining elements such as text, audio, and video.

[0500] Step 4:

[0501] The device uses a built-in emotion recognition engine to monitor the user's emotional state in real time. This includes analyzing facial expressions using the camera and analyzing voice tone using the microphone.

[0502] Step 5:

[0503] The device sends the acquired emotional data to the server. The server combines the emotional data with the cognitive characteristics profile for analysis and adjusts the way the learning content is presented.

[0504] Step 6:

[0505] The server sends the adjusted learning content to the device. The user continues learning through the learning content presented on the device.

[0506] Step 7:

[0507] As the user progresses through the learning process, the device continuously collects interaction and emotional data. This data helps in understanding the user's learning progress and emotional state.

[0508] Step 8:

[0509] The device sends the collected data back to the server, which analyzes it and dynamically adjusts the learning method as needed. Through this process, the system continuously provides the user with the optimal learning environment.

[0510] (Example 2)

[0511] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0512] In recent years, there has been a growing need for educational systems that optimize learning content according to the individual characteristics of each learner, thereby improving efficiency and effectiveness. However, conventional systems, while capable of providing learning formats based on learners' cognitive characteristics, have the challenge of not being able to consider the emotional states that change during learning in real time. As a result, there may be a lack of immediate support when learners feel stressed or their motivation declines, potentially affecting learning effectiveness.

[0513] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0514] In this invention, the server includes means for determining the optimal learning format based on acquired user cognitive characteristics information, means for adjusting the method of presenting learning content based on the determined learning format, a recognition mechanism for recognizing the user's emotional state, means for dynamically adjusting the method of presenting learning content based on the recognized emotional state, and means for analyzing the usage status of the learning content in real time and further adjusting the presentation method. This provides an optimal learning environment that comprehensively considers the learner's cognitive characteristics and emotional state, enabling improvements in learning efficiency and effectiveness.

[0515] "User" refers to any person who uses the system to engage in learning activities.

[0516] "Cognitive characteristics information" refers to information about an individual learner's learning style and characteristics, including data on visual, auditory, verbal, or kinesthetic dominance.

[0517] "Learning format" refers to the optimal combination of learning methods and media, determined based on the learner's cognitive characteristics.

[0518] An "emotion recognition mechanism" refers to a hardware or software system used to identify a user's emotional state in real time.

[0519] "Presentation method" refers to the procedures and means for determining the format in which learning content will be presented to learners.

[0520] This invention is an information processing system that provides a personalized learning environment by comprehensively utilizing learners' cognitive characteristics and emotional states. The system consists of a server, a terminal, and an emotion recognition engine.

[0521] First, the user accesses the learning system via a device and takes questionnaires and tests to identify their cognitive characteristics. At this stage, the device collects information about the user's cognitive characteristics based on the test results and questionnaire responses. The information obtained is immediately transmitted to the server.

[0522] The server generates a user profile based on the received cognitive characteristics information. This profile is used to determine what learning format is most effective for the user. Based on this profile information, the server then designs an optimized learning format. This learning format effectively combines text, audio, and video modalities.

[0523] Meanwhile, an emotion recognition engine built into the device monitors the user's emotional state. This uses methods such as facial expression analysis via camera, voice tone analysis via microphone, and biosensor data acquired from wearable devices. This allows the user's emotional state to be recognized in real time.

[0524] The server combines the user's cognitive characteristics and emotional state to determine the optimal way to present learning content. For example, if a user shows signs of stress, the system is designed to lower learning barriers by changing to simpler problems or playing relaxing audio. Conversely, if a positive emotional state is detected, the system enhances learning effectiveness by presenting the user with more challenging content.

[0525] Furthermore, user interactions and emotional changes during learning are continuously monitored, and data is sent from the device to the server in real time. The server then analyzes this data and dynamically adjusts the learning content and presentation methods in real time. Through this process, efficient learning tailored to each individual learner is made possible.

[0526] A concrete example would be a user participating in a foreign language course via their home device. Based on an initial questionnaire and a short vocabulary test, if the server determines that the user prefers visual information, it will recommend a learning format that includes a lot of visual materials. Furthermore, if the emotion recognition engine detects signs of fatigue during learning, it will temporarily slow down the learning speed or suggest a break to support the user's learning experience.

[0527] An example of a prompt might be: "Explain the process by which an AI system dynamically adjusts its learning based on cognitive characteristics information and emotional state. For example, include how it would respond if it detected that the user was experiencing stress." This prompt is designed to ensure the system provides responses and adjustments tailored to specific situations.

[0528] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0529] Step 1:

[0530] Users access the system via a terminal and take questionnaires and tests to collect cognitive characteristics information. The input is the user's answers, and the output is cognitive characteristics information. The terminal collects this information and sends it to the server. Specifically, the system processes the user's answers to questions on the terminal in real time, and the results are sent to the server.

[0531] Step 2:

[0532] The server receives cognitive characteristics information and generates a characteristic profile. The input is cognitive characteristics information sent from the terminal, and the output is the generated cognitive characteristics profile. The server analyzes this data and forms a profile that is suitable for the user's learning style. Specifically, it performs a process of classifying the user into categories such as visual dominance or auditory dominance.

[0533] Step 3:

[0534] The emotion recognition engine installed in the device recognizes the user's emotional state. The input is data from the user's facial expressions and voice, and the output is data from the recognized emotional state. In this process, the device uses the camera and microphone to identify emotions in real time. For example, if the user is smiling, it labels it as "joy," and if they are frowning, it labels it as "anxiety."

[0535] Step 4:

[0536] The server integrates cognitive trait profiles and emotional state data to determine the optimal learning format. The input is the integrated cognitive trait profile and emotional state data, and the output is the optimized learning format. Based on this information, the server selects the learning format from text, audio, and video modalities. Specifically, if a user has a visually dominant profile and is experiencing anxiety, the server will recommend relaxation content using video.

[0537] Step 5:

[0538] As the user progresses through the learning process, the device continuously collects interaction data and sends it to the server. Input consists of user actions and responses, while output is learning history data. The device records the user's mouse clicks, keyboard input, and learning progress. This allows the server to perform real-time data analysis and prepare to adjust the learning content and methods as needed.

[0539] Step 6:

[0540] The server dynamically adjusts how learning content is presented based on all collected data. The input is all data updated in real time, and the output is the adjusted learning presentation method. The server continuously adjusts the presentation method to maximize learning effectiveness. For example, if the server detects that the user is experiencing fatigue, it temporarily lowers the difficulty level of the learning material and suggests break times to optimize the user's learning experience.

[0541] (Application Example 2)

[0542] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0543] In modern information services, there is a demand for providing optimal content based on the user's characteristics and emotional state. Existing technologies only provide uniform information without adequately considering the user's cognitive characteristics or emotional state, thus failing to deliver information that is optimal for each individual user. Therefore, the challenge lies in creating a system that provides efficient and effective information tailored to each individual user.

[0544] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0545] In this invention, the server includes means for determining the optimal information provision format based on acquired user cognitive characteristics information and emotional state; means for adjusting the method of presenting the information content based on the determined information provision format; and means for analyzing the usage status and emotional state of the information content in real time and dynamically adjusting the presentation method. This makes it possible to provide optimal information tailored to each individual user.

[0546] "User" refers to an individual who receives information through this system.

[0547] "Cognitive characteristics information" refers to information related to the user's visual, auditory, linguistic, or motor skill dominance.

[0548] "Emotional state" refers to the psychological state analyzed based on the user's facial expressions, voice, or biosignals.

[0549] "Information presentation format" refers to the method of presenting information determined based on the user's cognitive characteristics and emotional state.

[0550] "Information content" refers to the content provided, which consists of text, audio, and video.

[0551] "Presentation method" refers to the method by which information content is displayed or reproduced for the user.

[0552] "Analysis" refers to the process of analyzing users' usage patterns and emotional states based on acquired data.

[0553] The system for carrying out this invention comprises a server, a terminal, and an emotion recognition engine. Users access the information provision system through an application installed on the terminal. The terminal uses a camera, microphone, and biosensors to collect the user's facial expressions, voice, heart rate, etc., and analyzes their emotional state in real time. Software such as an emotion analysis API (for example, Microsoft Azure's emotion analysis API) is used for the analysis.

[0554] The server receives cognitive characteristics information and emotional states transmitted by the user and determines the optimal information presentation format. Based on this, it executes an algorithm to adjust how the information is presented. Because the user's usage and emotional state are constantly changing, the server processes this data in real time and dynamically optimizes the information presentation method.

[0555] For example, if a user is feeling stressed, the system can suggest a relaxing music playlist. Conversely, if positive emotions are detected, it can recommend challenging documentary content.

[0556] By utilizing a generative AI model, prompt messages can be used to analyze the user's state and suggest content. For example, using a prompt message such as "Generate keywords for content suitable when the user's facial expression recognition result is [stress]" enables the provision of information optimized for the user's state.

[0557] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0558] Step 1:

[0559] The device uses a camera, microphone, and biosensors to collect data on the user's facial expressions, voice, and heart rate. This input data forms the basis for analyzing the user's emotional state. The device then transmits this data to an emotion recognition engine to perform real-time analysis of the emotional state.

[0560] Step 2:

[0561] The device uses an emotion recognition engine to analyze the user's emotional state. Based on the input data (facial expressions, voice, biosignals), the emotion analysis API determines the emotional state and outputs indicators such as stress, relaxation, and positiveness. These output results will serve as important indicators for future information provision.

[0562] Step 3:

[0563] The terminal communicates with the server based on the user's cognitive characteristics information and sends the user's profile information to the server. The server receives the transmitted profile information and uses it as reference for providing information.

[0564] Step 4:

[0565] The server uses a generative AI model to determine the optimal information delivery format based on the received emotional state and cognitive characteristics information. Specifically, if the emotional state is relaxed, it generates keywords for learning-related content as prompts; if it is stressed, it generates keywords for relaxing content and outputs the appropriate information delivery format.

[0566] Step 5:

[0567] The server dynamically adjusts how information is presented based on the determined information delivery format. It combines content (text, audio, video) according to the user's emotional state and sends optimized information to the device. For example, if the user is feeling stressed, a relaxing music playlist is delivered to the device.

[0568] Step 6:

[0569] After a user actually uses the content, the device monitors its usage and sends feedback back to the server. This feedback data includes the actual date and time of use, the end time, and changes in usage behavior related to emotional state.

[0570] Step 7:

[0571] The server analyzes feedback data and uses it to optimize the entire information delivery process. The results of the analysis are reflected in future information deliveries, enabling the provision of more optimal information to users. The feedback analysis results are stored in a database and used for future improvements.

[0572] 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.

[0573] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0574] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0575] [Fourth Embodiment]

[0576] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0577] 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.

[0578] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0579] 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.

[0580] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0581] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0582] 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.

[0583] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0584] 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.

[0585] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0586] The 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.

[0587] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0588] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0589] This invention relates to a system for providing an optimal learning environment tailored to the individual cognitive characteristics of a user. This system consists of three main elements: a server, a terminal, and a user.

[0590] First, the user accesses the system using a terminal. Upon initial access, the terminal administers questionnaires and tests to identify the user's cognitive characteristics. The information collected is used to determine the user's visual, auditory, and other superiorities.

[0591] The device then sends user information to the server. The server analyzes this data and generates a cognitive characteristics profile for each user. This profile indicates, for example, whether the user has a dominant auditory or visual sensibility, and is used to provide content in the future.

[0592] The server optimizes the modality of the learning content based on the user's cognitive profile. In this optimization process, the server selects the most effective combination from multiple modalities, such as text, audio, and video. For example, a visually dominant user would be presented with text that includes diagrams and charts.

[0593] Next, the server sends optimized learning content to the device. The user receives this content through the device and proceeds with their learning. While the user is using the content, the device continuously collects user interaction data. This data includes the time spent learning and the progress made on learning items.

[0594] This data is sent back from the terminal to the server. The server analyzes the received data in real time to determine the user's level of understanding and concentration. Based on this analysis, the server adjusts the modality again if necessary and updates the way the learning content is presented.

[0595] In this way, the system can flexibly adjust the learning environment to suit the individual needs of the user. For example, if a user is struggling with a particular learning item, the system can increase the number of related videos that supplement the visual information for that item. As a result, learners can absorb and acquire information more effectively.

[0596] The following describes the processing flow.

[0597] Step 1:

[0598] The user accesses the learning system using a device. Upon initial access, the device conducts questionnaires and tests to identify the user's cognitive characteristics and collects information.

[0599] Step 2:

[0600] The device sends the collected user cognitive characteristics information to the server. The server analyzes this information to generate a user cognitive characteristics profile and stores it in a database.

[0601] Step 3:

[0602] The server determines the optimal modality of learning content based on the user's profile information. This includes selecting and combining text, audio, and video.

[0603] Step 4:

[0604] The server generates learning content based on the selected modality and sends the optimized content to the device. The user receives this content through the device and begins learning.

[0605] Step 5:

[0606] While the user is learning, the device collects interaction data about the user's learning behavior. This data includes learning time, browsing frequency, and operation history.

[0607] Step 6:

[0608] The device sends the collected interaction data to the server. The server analyzes this data in real time to evaluate the user's learning progress and concentration level.

[0609] Step 7:

[0610] Based on the analysis results, the server adjusts the modality ratio and content presentation method as needed. The optimized content is sent back to the device, and the user continues learning with the new settings.

[0611] (Example 1)

[0612] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0613] In recent years, there has been a growing demand for educational methods tailored to individual cognitive characteristics. However, existing learning systems generally use standardized materials and therefore fail to adequately address the unique characteristics of each learner. This often leads to decreased learning efficiency, and it is particularly difficult to provide an optimal learning environment that leverages the strengths of learners with specialized advantages in areas such as vision or hearing. To address this challenge, a system is needed that adapts to the cognitive characteristics of each user and presents the most suitable learning content.

[0614] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0615] In this invention, the server includes means for determining the optimal learning format based on acquired user cognitive characteristics information, means for adjusting the method of presenting learning content, means for analyzing the usage status of learning content in real time and dynamically adjusting the presentation method, means for generating a cognitive characteristics profile using a generative AI model based on the user's cognitive characteristics, and means for optimizing multiple information presentation formats based on the profile. This makes it possible to provide a flexible and effective individualized learning environment tailored to the user's characteristics.

[0616] "User cognitive characteristics information" refers to information that indicates the individual cognitive strengths of the user, such as visual, auditory, linguistic, and motor skills.

[0617] "Learning format" refers to a method of presenting learning materials optimized for users to learn efficiently, and includes different media such as text, audio, and video.

[0618] A "generative AI model" is an artificial intelligence model that analyzes data and generates output based on specific input information. In this context, it is used to generate a profile of the user's cognitive characteristics.

[0619] A "cognitive characteristics profile" is a profile created to understand each user's individual cognitive abilities and learning characteristics, and to provide the most suitable learning format based on that understanding.

[0620] "Information presentation format" refers to the method by which learning content is displayed and presented to users, and specifically includes combinations of text, audio, and video.

[0621] This invention is a system for providing an optimal learning environment tailored to the cognitive characteristics of the user. This system consists of three main elements: a server, a terminal, and the user.

[0622] First, the user accesses the system using a device. Upon initial access, the device presents the user with questionnaires and tests designed to understand their cognitive characteristics. These tests may include questions such as whether the user prefers visual or audio learning materials. The user's response data is saved to the device in real time.

[0623] The device then sends the collected user data to the server. The server uses this data to generate an AI model and create a profile of the user's cognitive characteristics. This profile shows the user's strengths based on their visual, auditory, and other characteristics.

[0624] The server optimizes the modality of the learning content based on the generated cognitive characteristics profile. This optimization selects the optimal combination of information presentation formats, such as text, audio, and video, for the user. For example, for a visually dominant user, the server generates learning materials that include many diagrams and charts.

[0625] Optimized learning content is sent from the server to the device. The user receives this content through the device and begins learning. During this time, the device collects user interaction data and manages learning time and progress.

[0626] The collected data is sent back to the server, where it is analyzed in real time. Based on the analysis results, the modality is readjusted as needed, and the learning content is updated in the format best suited to the user.

[0627] In this way, the system builds a learning environment adapted to each user, providing an efficient learning experience. An example of a prompt message is: "To analyze your learning style, please answer the following questions. Do you prefer visual or audio materials?"

[0628] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0629] Step 1:

[0630] When a user accesses the system using a device, the device displays questionnaires and tests designed to assess the user's cognitive characteristics. The input is the user's responses, which the device collects and uses to generate data to determine the user's visual and auditory dominance. Specifically, each time the user completes a test, that data is stored in a database within the device.

[0631] Step 2:

[0632] The terminal sends collected user cognitive characteristic information to the server. The input is the response data received from the terminal. The server uses a generative AI model to analyze this data and generate a user cognitive characteristic profile. The output of this profile shows the dominance of each characteristic of the user. Specifically, profiles such as visual dominance and auditory dominance are generated.

[0633] Step 3:

[0634] The server optimizes the modality of the learning content based on the generated cognitive characteristics profile. The input is the user's cognitive characteristics profile, and through the optimization process, it selects the most appropriate combination from different information presentation formats such as text, audio, and video. Specifically, for users with a visually dominant profile, adjustments are made, such as preparing learning materials that include many diagrams and charts.

[0635] Step 4:

[0636] The server sends optimized learning content to the terminal. The input is the data of the optimized learning content, and the output is the content displayed on the user's terminal. Specifically, the terminal displays the received content in its user interface and prepares it to begin learning.

[0637] Step 5:

[0638] While a user is using the content, the device collects user interaction data. Inputs include the user's actions and learning progress, while outputs include interaction data such as learning time and progress. Specifically, user learning time and click data are recorded in a log, which is updated periodically.

[0639] Step 6:

[0640] The server re-analyzes the interaction data sent from the terminal and readjusts the learning modality as needed. The input is the interaction data received from the terminal, and updated learning content information is generated as output based on the analysis results. Specifically, if the user is taking a long time on a particular learning item, the server will make adjustments such as adding supplementary information for that item.

[0641] (Application Example 1)

[0642] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0643] In today's learning environment, learners often do not receive learning materials suited to their individual cognitive characteristics, leading to decreased learning efficiency. Furthermore, while the learning content provided needs to be adaptively adjusted according to the learner's level of understanding and concentration, current systems cannot achieve this in real time.

[0644] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0645] In this invention, the server includes means for determining the optimal learning format based on acquired user cognitive characteristics information, means for adjusting the method of presenting learning content based on the determined learning format, means for analyzing the usage status of the learning content in real time, dynamically adjusting the presentation method and evaluating the user's level of understanding, and means for adaptively suggesting the next learning material based on the evaluation of the level of understanding. This makes it possible to provide an optimal learning environment that is tailored to the individual cognitive characteristics and learning progress of each user.

[0646] "Cognitive characteristics information" refers to information that indicates the individual cognitive characteristics of a learner, such as their visual, auditory, linguistic, and motor skills.

[0647] "Learning format" refers to the method of presenting learning content, including modalities such as text, audio, and video.

[0648] "Presentation method" refers to the means by which learning content is shown or heard by learners.

[0649] "Usage status" refers to data on how learners are using the provided learning materials, including the time spent studying and their progress.

[0650] "Comprehension level" is an indicator that shows how well learners understand the material presented to them.

[0651] "Adaptive" means that the system changes or adjusts in response to the learner's behavior and circumstances.

[0652] "Learning materials" refer to the textbooks and information necessary for learning.

[0653] The system realizing this invention consists of three elements: a server, a terminal, and a user. The server determines the optimal learning format based on cognitive characteristics information and helps in presenting learning content. The terminal functions as an interface with the user, allowing the user to access the system and identify their cognitive characteristics. The user plays a role in progressing through the presented content.

[0654] The program is developed using programming languages ​​such as Python and JavaScript, and uses web frameworks such as Django to implement questionnaires and tests that identify the user's cognitive characteristics. This allows the server to generate a profile based on the user's visual and auditory dominance. Based on this profile, the server optimally combines learning content from text, audio, and video, and sends it to the device. Real-time analysis uses data analysis libraries such as Pandas and SciPy to analyze user interaction data. For example, it analyzes how often learners play specific content and adds video to supplement visual information as needed.

[0655] For example, if the user is an elementary school student, a science learning module is provided. In this case, visually-oriented learners will primarily use experimental animations, while auditory-oriented learners will use materials that emphasize narration.

[0656] An example of a prompt message when using a generative AI model would be: "When the user is visually dominant, suggest the most suitable educational videos for them. Emphasize visual information and include concise and easy-to-understand explanations." This allows the system to generate and deliver content tailored to the user's characteristics.

[0657] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0658] Step 1:

[0659] The terminal administers a questionnaire and a cognitive characteristics test to the user upon their first access. It takes user response data as input and extracts data regarding visual and auditory dominance based on this data. This cognitive characteristics information is then sent to the server as output. The questionnaire interface is implemented using Django.

[0660] Step 2:

[0661] The server generates a user profile based on the received cognitive characteristics information. It receives cognitive characteristics information from the terminal as input and uses the Pandas data analysis library to create profiles for visual, auditory, and other cognitive functions. As output, it stores the generated user profile and uses it to suggest the next learning content.

[0662] Step 3:

[0663] The server determines the optimal learning format and selects learning content based on the user profile. Using the user profile as input, it utilizes a generated AI model and prompts to determine the content format (text, audio, video, etc.). As output, it sends the modality-optimized learning content to the terminal.

[0664] Step 4:

[0665] The device provides the user with optimized learning content received from the server. It receives learning content data sent from the server as input and displays or plays it in a format usable by the user. It also prepares to monitor user interaction.

[0666] Step 5:

[0667] The device collects user interaction data in real time during the learning process. It takes user actions and indicators of comprehension (such as playback frequency and gaze time) as input, processes the data, and sends it to the server.

[0668] Step 6:

[0669] The server evaluates the user's level of understanding based on interaction data received from the terminal and dynamically adjusts the learning content and presentation method. It receives interaction data as input, performs data calculations, and measures the level of understanding. As output, it adjusts the presented learning materials as needed and reflects this in subsequent content suggestions.

[0670] Step 7:

[0671] The server adaptively suggests the next learning material and sends the suggested content back to the terminal. Based on the learning content adjusted in the previous step as input, it generates content to instruct the user on the next learning stage. As output, it delivers this updated learning content back to the terminal.

[0672] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0673] This invention relates to a system that dynamically provides an optimal learning environment by combining user cognitive characteristic information and an emotion recognition engine. This system consists of a server, a terminal, and an emotion recognition engine.

[0674] First, when users access the learning system through their device, they undergo questionnaires and tests to identify their basic cognitive characteristics. The device collects this information and sends it to the server. Based on the user's cognitive characteristics profile, the server generates an optimized learning format. This format consists of modalities such as text, audio, and video.

[0675] In parallel, the emotion recognition engine installed in the device recognizes the user's emotional state. This utilizes methods such as facial expression analysis using the camera, voice intonation analysis using the microphone, or biosensor data obtained from the smart device.

[0676] The server comprehensively analyzes the user's cognitive characteristics and emotional state, and adjusts how learning content is presented. For example, if the server detects that the user is stressed, it may change the learning format to lower difficulty levels or display encouraging messages to support motivation. Conversely, if positive emotions are detected, it may stimulate a sense of challenge by presenting more difficult problems.

[0677] While the user is learning, the device continuously collects interaction data, including their emotional state, and sends it to the server. The server analyzes this data in real time and dynamically adjusts the learning content and presentation methods as needed. This process optimizes each user's individual learning experience and promotes efficient acquisition.

[0678] Therefore, the present invention enables detailed responses tailored to the learner's characteristics and emotional state, significantly improving the efficiency and effectiveness of learning.

[0679] The following describes the processing flow.

[0680] Step 1:

[0681] The user logs into the learning system using a device. The device then administers questionnaires and tests to assess the user's cognitive characteristics and collects the results.

[0682] Step 2:

[0683] The device sends the collected cognitive characteristics data to the server. The server analyzes this data, generates a user cognitive characteristics profile, and stores it in a database.

[0684] Step 3:

[0685] The server determines the optimal modality for learning content based on the user's cognitive characteristics profile. This includes selecting and combining elements such as text, audio, and video.

[0686] Step 4:

[0687] The device uses a built-in emotion recognition engine to monitor the user's emotional state in real time. This includes analyzing facial expressions using the camera and analyzing voice tone using the microphone.

[0688] Step 5:

[0689] The device sends the acquired emotional data to the server. The server combines the emotional data with the cognitive characteristics profile for analysis and adjusts the way the learning content is presented.

[0690] Step 6:

[0691] The server sends the adjusted learning content to the device. The user continues learning through the learning content presented on the device.

[0692] Step 7:

[0693] As the user progresses through the learning process, the device continuously collects interaction and emotional data. This data helps in understanding the user's learning progress and emotional state.

[0694] Step 8:

[0695] The device sends the collected data back to the server, which analyzes it and dynamically adjusts the learning method as needed. Through this process, the system continuously provides the user with the optimal learning environment.

[0696] (Example 2)

[0697] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0698] In recent years, there has been a growing need for educational systems that optimize learning content according to the individual characteristics of each learner, thereby improving efficiency and effectiveness. However, conventional systems, while capable of providing learning formats based on learners' cognitive characteristics, have the challenge of not being able to consider the emotional states that change during learning in real time. As a result, there may be a lack of immediate support when learners feel stressed or their motivation declines, potentially affecting learning effectiveness.

[0699] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0700] In this invention, the server includes means for determining the optimal learning format based on acquired user cognitive characteristics information, means for adjusting the method of presenting learning content based on the determined learning format, a recognition mechanism for recognizing the user's emotional state, means for dynamically adjusting the method of presenting learning content based on the recognized emotional state, and means for analyzing the usage status of the learning content in real time and further adjusting the presentation method. This provides an optimal learning environment that comprehensively considers the learner's cognitive characteristics and emotional state, enabling improvements in learning efficiency and effectiveness.

[0701] "User" refers to any person who uses the system to engage in learning activities.

[0702] "Cognitive characteristics information" refers to information about an individual learner's learning style and characteristics, including data on visual, auditory, verbal, or kinesthetic dominance.

[0703] "Learning format" refers to the optimal combination of learning methods and media, determined based on the learner's cognitive characteristics.

[0704] An "emotion recognition mechanism" refers to a hardware or software system used to identify a user's emotional state in real time.

[0705] "Presentation method" refers to the procedures and means for determining the format in which learning content will be presented to learners.

[0706] This invention is an information processing system that provides a personalized learning environment by comprehensively utilizing learners' cognitive characteristics and emotional states. The system consists of a server, a terminal, and an emotion recognition engine.

[0707] First, the user accesses the learning system via a device and takes questionnaires and tests to identify their cognitive characteristics. At this stage, the device collects information about the user's cognitive characteristics based on the test results and questionnaire responses. The information obtained is immediately transmitted to the server.

[0708] The server generates a user profile based on the received cognitive characteristics information. This profile is used to determine what learning format is most effective for the user. Based on this profile information, the server then designs an optimized learning format. This learning format effectively combines text, audio, and video modalities.

[0709] Meanwhile, an emotion recognition engine built into the device monitors the user's emotional state. This uses methods such as facial expression analysis via camera, voice tone analysis via microphone, and biosensor data acquired from wearable devices. This allows the user's emotional state to be recognized in real time.

[0710] The server combines the user's cognitive characteristics and emotional state to determine the optimal way to present learning content. For example, if a user shows signs of stress, the system is designed to lower learning barriers by changing to simpler problems or playing relaxing audio. Conversely, if a positive emotional state is detected, the system enhances learning effectiveness by presenting the user with more challenging content.

[0711] Furthermore, user interactions and emotional changes during learning are continuously monitored, and data is sent from the device to the server in real time. The server then analyzes this data and dynamically adjusts the learning content and presentation methods in real time. Through this process, efficient learning tailored to each individual learner is made possible.

[0712] A concrete example would be a user participating in a foreign language course via their home device. Based on an initial questionnaire and a short vocabulary test, if the server determines that the user prefers visual information, it will recommend a learning format that includes a lot of visual materials. Furthermore, if the emotion recognition engine detects signs of fatigue during learning, it will temporarily slow down the learning speed or suggest a break to support the user's learning experience.

[0713] An example of a prompt might be: "Explain the process by which an AI system dynamically adjusts its learning based on cognitive characteristics information and emotional state. For example, include how it would respond if it detected that the user was experiencing stress." This prompt is designed to ensure the system provides responses and adjustments tailored to specific situations.

[0714] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0715] Step 1:

[0716] Users access the system via a terminal and take questionnaires and tests to collect cognitive characteristics information. The input is the user's answers, and the output is cognitive characteristics information. The terminal collects this information and sends it to the server. Specifically, the system processes the user's answers to questions on the terminal in real time, and the results are sent to the server.

[0717] Step 2:

[0718] The server receives cognitive characteristics information and generates a characteristic profile. The input is cognitive characteristics information sent from the terminal, and the output is the generated cognitive characteristics profile. The server analyzes this data and forms a profile that is suitable for the user's learning style. Specifically, it performs a process of classifying the user into categories such as visual dominance or auditory dominance.

[0719] Step 3:

[0720] The emotion recognition engine installed in the device recognizes the user's emotional state. The input is data from the user's facial expressions and voice, and the output is data from the recognized emotional state. In this process, the device uses the camera and microphone to identify emotions in real time. For example, if the user is smiling, it labels it as "joy," and if they are frowning, it labels it as "anxiety."

[0721] Step 4:

[0722] The server integrates cognitive trait profiles and emotional state data to determine the optimal learning format. The input is the integrated cognitive trait profile and emotional state data, and the output is the optimized learning format. Based on this information, the server selects the learning format from text, audio, and video modalities. Specifically, if a user has a visually dominant profile and is experiencing anxiety, the server will recommend relaxation content using video.

[0723] Step 5:

[0724] As the user progresses through the learning process, the device continuously collects interaction data and sends it to the server. Input consists of user actions and responses, while output is learning history data. The device records the user's mouse clicks, keyboard input, and learning progress. This allows the server to perform real-time data analysis and prepare to adjust the learning content and methods as needed.

[0725] Step 6:

[0726] The server dynamically adjusts how learning content is presented based on all collected data. The input is all data updated in real time, and the output is the adjusted learning presentation method. The server continuously adjusts the presentation method to maximize learning effectiveness. For example, if the server detects that the user is experiencing fatigue, it temporarily lowers the difficulty level of the learning material and suggests break times to optimize the user's learning experience.

[0727] (Application Example 2)

[0728] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0729] In modern information services, there is a demand for providing optimal content based on the user's characteristics and emotional state. Existing technologies only provide uniform information without adequately considering the user's cognitive characteristics or emotional state, thus failing to deliver information that is optimal for each individual user. Therefore, the challenge lies in creating a system that provides efficient and effective information tailored to each individual user.

[0730] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0731] In this invention, the server includes means for determining the optimal information provision format based on acquired user cognitive characteristics information and emotional state; means for adjusting the method of presenting the information content based on the determined information provision format; and means for analyzing the usage status and emotional state of the information content in real time and dynamically adjusting the presentation method. This makes it possible to provide optimal information tailored to each individual user.

[0732] "User" refers to an individual who receives information through this system.

[0733] "Cognitive characteristics information" refers to information related to the user's visual, auditory, linguistic, or motor skill dominance.

[0734] "Emotional state" refers to the psychological state analyzed based on the user's facial expressions, voice, or biosignals.

[0735] "Information presentation format" refers to the method of presenting information determined based on the user's cognitive characteristics and emotional state.

[0736] "Information content" refers to the content provided, which consists of text, audio, and video.

[0737] "Presentation method" refers to the method by which information content is displayed or reproduced for the user.

[0738] "Analysis" refers to the process of analyzing users' usage patterns and emotional states based on acquired data.

[0739] The system for carrying out this invention comprises a server, a terminal, and an emotion recognition engine. Users access the information provision system through an application installed on the terminal. The terminal uses a camera, microphone, and biosensors to collect the user's facial expressions, voice, heart rate, etc., and analyzes their emotional state in real time. Software such as an emotion analysis API (for example, Microsoft Azure's emotion analysis API) is used for the analysis.

[0740] The server receives cognitive characteristics information and emotional states transmitted by the user and determines the optimal information presentation format. Based on this, it executes an algorithm to adjust how the information is presented. Because the user's usage and emotional state are constantly changing, the server processes this data in real time and dynamically optimizes the information presentation method.

[0741] For example, if a user is feeling stressed, the system can suggest a relaxing music playlist. Conversely, if positive emotions are detected, it can recommend challenging documentary content.

[0742] By utilizing a generative AI model, prompt messages can be used to analyze the user's state and suggest content. For example, using a prompt message such as "Generate keywords for content suitable when the user's facial expression recognition result is [stress]" enables the provision of information optimized for the user's state.

[0743] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0744] Step 1:

[0745] The device uses a camera, microphone, and biosensors to collect data on the user's facial expressions, voice, and heart rate. This input data forms the basis for analyzing the user's emotional state. The device then transmits this data to an emotion recognition engine to perform real-time analysis of the emotional state.

[0746] Step 2:

[0747] The device uses an emotion recognition engine to analyze the user's emotional state. Based on the input data (facial expressions, voice, biosignals), the emotion analysis API determines the emotional state and outputs indicators such as stress, relaxation, and positiveness. These output results will serve as important indicators for future information provision.

[0748] Step 3:

[0749] The terminal communicates with the server based on the user's cognitive characteristics information and sends the user's profile information to the server. The server receives the transmitted profile information and uses it as reference for providing information.

[0750] Step 4:

[0751] The server uses a generative AI model to determine the optimal information delivery format based on the received emotional state and cognitive characteristics information. Specifically, if the emotional state is relaxed, it generates keywords for learning-related content as prompts; if it is stressed, it generates keywords for relaxing content and outputs the appropriate information delivery format.

[0752] Step 5:

[0753] The server dynamically adjusts how information is presented based on the determined information delivery format. It combines content (text, audio, video) according to the user's emotional state and sends optimized information to the device. For example, if the user is feeling stressed, a relaxing music playlist is delivered to the device.

[0754] Step 6:

[0755] After a user actually uses the content, the device monitors its usage and sends feedback back to the server. This feedback data includes the actual date and time of use, the end time, and changes in usage behavior related to emotional state.

[0756] Step 7:

[0757] The server analyzes feedback data and uses it to optimize the entire information delivery process. The results of the analysis are reflected in future information deliveries, enabling the provision of more optimal information to users. The feedback analysis results are stored in a database and used for future improvements.

[0758] 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.

[0759] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0760] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0761] 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.

[0762] Figure 9 shows an 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.

[0763] 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.

[0764] 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.

[0765] 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, motorcycles, etc., 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, for example, based 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.

[0766] 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."

[0767] 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.

[0768] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0769] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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 the like 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.

[0778] 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 as being incorporated by reference.

[0779] The following is further disclosed regarding the embodiments described above.

[0780] (Claim 1)

[0781] A means for determining the optimal learning format based on acquired user cognitive characteristics information,

[0782] A means for adjusting the method of presenting learning content based on the learning format determined above,

[0783] A means for analyzing the usage status of the learning content in real time and dynamically adjusting the presentation method,

[0784] A system that includes this.

[0785] (Claim 2)

[0786] The system according to claim 1, wherein the cognitive characteristics information includes information relating to the dominance of visual, auditory, linguistic, or motor skills.

[0787] (Claim 3)

[0788] The system according to claim 1, wherein the learning content is selected from text, audio, and video and presented in combination.

[0789] "Example 1"

[0790] (Claim 1)

[0791] A means for determining the optimal learning format based on acquired user cognitive characteristics information,

[0792] A means for adjusting the method of presenting learning content based on the learning format determined above,

[0793] A means for analyzing the usage status of the learning content in real time and dynamically adjusting the presentation method,

[0794] A means for generating a cognitive characteristics profile using a generative AI model based on the cognitive characteristics of the user,

[0795] A means for optimizing multiple information presentation formats based on the aforementioned profile,

[0796] A system that includes this.

[0797] (Claim 2)

[0798] The system according to claim 1, wherein the cognitive characteristics information includes information relating to the dominance of visual, auditory, linguistic, or motor skills.

[0799] (Claim 3)

[0800] The system according to claim 1, wherein the learning content is selected from text, audio, and video and presented in combination.

[0801] "Application Example 1"

[0802] (Claim 1)

[0803] A means for determining the optimal learning format based on acquired user cognitive characteristics information,

[0804] A means for adjusting the method of presenting learning content based on the learning format determined above,

[0805] A means for analyzing the usage status of the aforementioned learning content in real time, dynamically adjusting the presentation method, and evaluating the user's level of understanding,

[0806] A means of adaptively suggesting the next learning material based on the aforementioned assessment of understanding,

[0807] A system that includes this.

[0808] (Claim 2)

[0809] The system according to claim 1, wherein the cognitive characteristics information includes information related to the dominance of visual, auditory, linguistic, or motor skills, and the learning material is adaptively adjusted based on the user's viewing behavior.

[0810] (Claim 3)

[0811] The system according to claim 1, wherein the learning content is selected from text, audio, and video, and presented in combination based on the user's interaction data.

[0812] "Example 2 of combining an emotion engine"

[0813] (Claim 1)

[0814] A means for determining the optimal learning format based on acquired user cognitive characteristics information,

[0815] A means for adjusting the method of presenting learning content based on the learning format determined above,

[0816] A recognition mechanism for recognizing the emotional state of the user,

[0817] A means for dynamically adjusting the method of presenting learning content based on the recognized emotional state,

[0818] A means for analyzing the usage status of the learning content in real time and further adjusting the presentation method,

[0819] A system that includes this.

[0820] (Claim 2)

[0821] The system according to claim 1, wherein the cognitive characteristics information includes information relating to the dominance of visual, auditory, linguistic, or motor skills.

[0822] (Claim 3)

[0823] The system according to claim 1, wherein the learning content is selected from text, audio, and video and presented in combination.

[0824] "Application example 2 of combining emotional engines"

[0825] (Claim 1)

[0826] A means for determining the optimal information provision format based on the acquired user cognitive characteristics and emotional state,

[0827] A means for adjusting the method of presenting information content based on the information provision format determined above,

[0828] Means for analyzing the usage status and emotional state of the aforementioned information content in real time and dynamically adjusting the presentation method,

[0829] A means for using emotion analysis technology to analyze the emotional state of the user,

[0830] A system that includes this.

[0831] (Claim 2)

[0832] The system according to claim 1, wherein the cognitive characteristics information includes information relating to the dominance of visual, auditory, linguistic, or bodily movement, and the emotional state is based on facial expressions, voice, or biosignals.

[0833] (Claim 3)

[0834] The system according to claim 1, wherein the aforementioned information content is selected from text, audio, and video, and presented in combination according to the user's emotional state. [Explanation of Symbols]

[0835] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for determining the optimal learning format based on acquired user cognitive characteristics information, A means for adjusting the method of presenting learning content based on the learning format determined above, A means for analyzing the usage status of the learning content in real time and dynamically adjusting the presentation method, A system that includes this.

2. The system according to claim 1, wherein the cognitive characteristics information includes information relating to the dominance of visual, auditory, linguistic, or motor skills.

3. The system according to claim 1, wherein the learning content is selected from text, audio, and video, and presented in combination.

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