system

Abstract

We provide the system. [Solution] Using expert-selected information obtained from historical databases, Through the generated numerical model, we analyze complex data obtained from individuals. Identify optimal growth areas based on similarity analysis results. A system that includes the means.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern educational environments, many parents and educational institutions have difficulty discovering the hidden talents of children and providing a growth environment suitable for each child's aptitude. Such a situation can cause children's talents not to be properly nurtured, and many talents may be overlooked. Therefore, there is a need for an effective system for efficiently analyzing individual children's performance data and identifying their talents and aptitudes.

Means for Solving the Problems

[0005] This invention provides a system that identifies individual talents and aptitudes by analyzing complex data obtained from individuals using a numerical model generated based on expert-selected information obtained from historical databases. This system can identify optimal areas of growth based on similarity analysis results, and further generates personalized educational plans to present these identified areas of growth. This makes it possible to discover a child's hidden talents and provide educational support tailored to their aptitudes.

[0006] A "historical database" is a collection of data that includes information collected and organized by prominent figures and experts of the past.

[0007] "Information selected by experts" refers to data and materials that experts with experience and knowledge have deemed to be valuable.

[0008] "Generated numerical models" refer to computational methods created using algorithms and statistical techniques developed for data analysis.

[0009] "Complex data" refers to a dataset that contains a wide variety of information in different formats and structures.

[0010] "Analysis" is the process of evaluation and calculation performed to identify patterns and relationships contained in data.

[0011] "Similarity analysis results" refer to results that numerically or visually show the shared characteristics or similarities between the items being compared.

[0012] "Growth areas" refer to academic fields or areas of activity where an individual's talents and aptitudes are considered to be particularly effective.

[0013] An "individualized education plan" refers to a curriculum for education and instruction designed to suit the talents and needs of a particular individual. [Brief explanation of the drawing]

[0014] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It 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.

MODE FOR CARRYING OUT THE INVENTION

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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.

[0018] 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.

[0019] 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, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] The system of this invention analyzes complex data to identify individual talents and suggest areas for growth. This system mainly consists of three components: a server, a terminal, and a user.

[0036] The server retrieves information selected by experts from historical databases. This information includes data in various forms, such as the works, research, and records of famous geniuses of the past. The server collects this data and prepares it in a format that can be analyzed.

[0037] Users input their child's performance data via their device. This performance data includes a wide range of things, such as drawings, essays, and records of science experiments. Users can also send image data using the device's camera or scanner. The input data is sent to the server in text, image, and other formats.

[0038] The server analyzes the input data using a generated numerical model. This model is built using deep learning algorithms and evaluates the similarity between the input data and the data in the historical database. The similarity is quantified through the analysis of each feature.

[0039] Based on the analysis results, the server identifies the growth areas best suited to the user. This identification is performed using a similarity score, and the user is notified of the identified growth areas and the reasons for their identification. The server also generates and provides a personalized education plan to the user. This plan includes suggestions for education and training to further develop skills in the identified growth areas.

[0040] As a concrete example, suppose a user takes a picture of a drawing their child has made with their device and inputs it. The server analyzes this image data and evaluates its similarity to works by famous past artists. If the server determines that the child has particularly high talent in the arts, it will propose a personalized art-related educational plan to the user. This plan may include introductions to specialized art schools or information on local art competitions that the child can participate in.

[0041] As described above, the system of the present invention helps to identify a child's potential talents and to create an environment that promotes their growth.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The server retrieves historical data selected by experts from the database and prepares it in a format that can be analyzed. This includes standardizing text and image data formats, removing errors and noise, and adding metadata.

[0045] Step 2:

[0046] The user inputs the child's performance data into the device. Using the device's camera or scanner, digital images of paintings and artwork are captured, and essays and research records are input as text data.

[0047] Step 3:

[0048] The terminal sends data entered by the user to the server. At this time, it checks whether the data format is properly formatted and adjusts the format if necessary.

[0049] Step 4:

[0050] The server preprocesses the received data. For image data, important features are extracted using image recognition technology, and for text data, linguistic information is extracted using text analysis tools.

[0051] Step 5:

[0052] The server uses a generated AI model to calculate a similarity score between the processed user data and the data in the historical database. A deep learning algorithm evaluates and quantifies the similarity within the feature space.

[0053] Step 6:

[0054] The server aggregates the analysis results and identifies which growth areas the individual has the highest aptitude for. Multiple fields, such as mathematics, science, art, and literature, are considered, and the field with the highest score is selected.

[0055] Step 7:

[0056] The server generates a personalized education plan based on identified growth areas. This plan includes, if necessary, expert referrals and suggestions for relevant workshops.

[0057] Step 8:

[0058] The server sends the generated report to the terminal. The terminal then presents the user with identified growth areas and corresponding curriculum suggestions, supporting them in selecting educational policies.

[0059] (Example 1)

[0060] 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."

[0061] In recent years, there has been a growing demand for technologies that can identify individual talents early on and appropriately support their development. However, methods for identifying individual talents from diverse data and building effective educational and training strategies based on them remain insufficient. Traditional systems have problems in that it is difficult to analyze large amounts of data and compare them with historical success stories, making it difficult to provide individualized educational strategies.

[0062] 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.

[0063] In this invention, the server includes means for analyzing multidimensional data of individuals generated using expert-selected data obtained from an information set, means for generating personalized educational strategies based on similarity evaluations, and means for providing the generated educational strategies. This makes it possible to provide users with highly personalized areas of growth and educational strategies to support their development.

[0064] An "information set" is a dataset that compiles historical databases or data selected by experts.

[0065] "Generative artificial intelligence technology" refers to a technology that uses deep learning algorithms to analyze input data and evaluate its similarity.

[0066] "Individual" refers to a user targeted by the system or an individual who is of interest to that user.

[0067] "Multidimensional data" refers to data that contains diverse information in different formats, such as text and images.

[0068] A "feature space" is the space in which features extracted to evaluate the similarity of data are arranged.

[0069] "Similarity assessment" is the process of quantifying and evaluating the degree of similarity between input data and historical data.

[0070] A "personalized education strategy" is a plan that proposes education and training tailored to individual needs, based on identified areas of growth.

[0071] "User input devices" refer to terminal devices such as cameras and scanners that users use to input data.

[0072] A "generated educational strategy" refers to specific suggestions and plans created to support the user's growth.

[0073] This invention relates to a system that identifies an individual's talents and supports their development. The system consists of three components: a server, a terminal, and a user.

[0074] The server accesses a database via the network to collect historical data from the information set. This database contains works, research, and records of historical figures selected by experts. The server organizes this data in an analyzable format.

[0075] Users input their personal performance data using their devices. This includes a variety of data formats, such as children's drawings and essays, and records of science experiments. Users can input image data using the camera or scanner function built into their devices. The input data is sent to the server in the form of text and images.

[0076] Upon receiving input data, the server performs analysis using generative artificial intelligence technology. This technology is based on deep learning algorithms, extracting features from each data point and evaluating the similarity between them. The similarity is calculated numerically and used to identify growth areas.

[0077] Based on the analysis results, the server identifies the user's optimal areas for growth. Based on this, the server generates and provides a personalized educational strategy. This strategy includes specific educational and training suggestions to develop skills in the identified areas of growth.

[0078] As a concrete example, consider a scenario where a user takes a picture of their child's drawing with their device and uploads it to the system. The server analyzes this image data and evaluates its similarity to the works of accomplished artists. If the server determines that the child has excellent drawing skills, it proposes a personalized art-related educational plan. This plan may include introductions to art schools and information on local art events.

[0079] An example of a prompt message would be: "Take a picture of your child's drawing and evaluate their talent. In particular, we will analyze similarities to historical artists and suggest areas for optimal growth."

[0080] Thus, the present invention is a system aimed at identifying an individual's potential talents and creating an environment for developing them.

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] The server retrieves data from the information set. Specifically, the server accesses a database containing historical data selected by experts via the network. From this database, works and research of prominent figures are retrieved. The retrieved data is formatted on the server into an appropriate format for subsequent analysis. The input is the database, and the output is data ready for analysis.

[0084] Step 2:

[0085] The user inputs performance data using a terminal. The user takes pictures of information such as children's drawings and essays using the terminal's camera function or scanner and inputs that data into the system. The input data is sent to the server in the form of text, images, etc. In this step, the data that the user inputs is on the terminal, and the output is the data sent to the server.

[0086] Step 3:

[0087] The server applies generative artificial intelligence techniques to analyze the input data. Deep learning algorithms are used to extract features from the input data and evaluate their similarity to historical data. This similarity is quantified and used as the primary output of the analysis. Here, user-provided data is the input, and the feature extraction results and similarity score are the outputs.

[0088] Step 4:

[0089] The server identifies the optimal growth areas for the user based on the analysis results. It analyzes similarity scores and determines which growth areas are appropriate based on those scores. Scores indicating potential talent in specific areas are considered. The input is the similarity score, and the output is the identified growth areas.

[0090] Step 5:

[0091] The server generates and provides a personalized educational strategy to the user based on the identified growth area. This educational strategy includes specific suggestions and plans to promote the user's growth. For example, if the arts field is identified, the server will provide the user with information on specialized art schools and local events. The input is the identified growth area, and the output is the generated educational strategy.

[0092] (Application Example 1)

[0093] 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."

[0094] Effectively identifying an individual's talents and understanding their optimal areas for growth is crucial in educational settings. However, conventional methods have limitations in providing individualized educational plans to identify and promote talent development. In particular, there are challenges regarding immediacy and individualization. This invention aims to enable real-time individual talent analysis based on historical data, facilitating immediate feedback and the provision of personalized educational plans in physical stores.

[0095] 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.

[0096] This invention includes a server that analyzes detailed information obtained from an individual through a numerical model generated using expertise acquired from historical sources, and determines the optimal growth area based on the similarity evaluation results; a server that provides the analysis results to the user in real time through a terminal in a physical store; and a server that generates a personalized education plan and notifies the user's terminal of it. This enables immediate assessment of an individual's talents and the presentation of an optimal growth strategy.

[0097] A "historical source" is a collection of information that records important knowledge and data from the past, including data carefully selected by experts.

[0098] A "numerical model" is a computational model constructed to analyze data and derive specific conclusions, primarily using mathematical methods.

[0099] "Detailed information obtained from an individual" refers to specific data provided by that individual, and includes information in the form of records of works of art or performances, or other forms of information.

[0100] The "similarity evaluation result" is a numerical evaluation of how similar the analyzed data is to the comparison target, and the result is shown.

[0101] A "growth area" is a field or subfield that is considered most suitable for an individual to develop their talents.

[0102] A "physical store terminal" is a device located in a physical store that is a computer or device equipped with an interface for inputting and retrieving information.

[0103] "Real-time provision" refers to the act of presenting or reflecting information immediately without delay, and is a process that requires immediacy.

[0104] An "individualized education plan" is a special educational program designed based on the specific needs and abilities of an individual.

[0105] As an embodiment of this invention, a system is constructed that primarily involves servers, terminals, and users.

[0106] The server uses a database obtained from historical sources to analyze detailed information acquired from individuals using a generated numerical model. The server executes machine learning algorithms using a deep learning framework based on Python (e.g., TENSORFLOW® or PyTorch). In this process, it evaluates the similarity between the acquired data and historical data and identifies areas of growth.

[0107] The device functions as a smartphone or tablet application and is developed using Flutter® or React Native. This device acquires detailed information provided by the user and sends it to a server using its camera and scanning functions. The device also provides the user with real-time analysis results received from the server.

[0108] The user operates this device and receives a personalized educational plan. This plan includes suggestions for education and training based on identified areas of growth.

[0109] As a concrete example, suppose an elementary school teacher takes photos of students' artwork with their smartphone and sends the data through an app. The server analyzes the image data and evaluates its similarity to that of famous past artists. As a result, areas of artistic growth are identified, and information on specialized art workshops and local competitions is notified to the user's device. This process is represented by a prompt message such as: "Analyze this student's recent work and identify the most suitable educational areas. Please also consider similarities to famous past artists."

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] Users use their devices to input records of students' artwork and activities. Specifically, they take photos of students' drawings with their smartphone cameras and send the data to the server via the app. The input data is in image format, and the device prepares it as digital data.

[0113] Step 2:

[0114] The server receives digital data sent from the terminal and performs analysis using a generative AI model. Deep learning algorithms using TensorFlow or PyTorch are applied to the data analysis to evaluate its similarity to data in historical databases. The input here is student artwork data, and the output is the similarity evaluation result based on that data.

[0115] Step 3:

[0116] Based on the analysis results, the server identifies the optimal areas of growth for the user. This includes a process of selecting specific educational fields using similarity scores. The output is the identified areas of growth for a personalized educational plan.

[0117] Step 4:

[0118] The server notifies the terminal of identified growth areas and generates a personalized educational plan. The terminal provides the user with analysis results in real time, and the educational plan may include information on art schools and competitions. The output is information that the user can use to review the educational plan.

[0119] Step 5:

[0120] Users utilize the provided educational plans through their devices to help promote student growth. This allows users to plan specific educational activities as the next steps. The input is the content of the educational plan, and the output is a specific action plan for the students.

[0121] 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.

[0122] The system of this invention recognizes the user's emotions by combining them with an emotion engine, and based on that, identifies talents and generates personalized educational plans. The system mainly consists of a server, terminals, and users, and each of these parts works together to achieve this.

[0123] First, the server retrieves information selected by experts from a historical database and generates a numerical model using a deep learning algorithm. This model is then used to analyze talents and aptitudes based on data submitted by users.

[0124] The user inputs the child's performance data using a device, and also records the emotional state in real time using sensors equipped with an emotion engine. Specifically, it uses voice analysis to infer emotions from voice using a microphone, and facial expression recognition to determine emotions from facial expressions captured through a camera.

[0125] The device sends acquired performance data and emotional data to the server. Based on the emotional data analyzed by the emotion engine, the server takes the user's emotional state into consideration and performs a highly accurate talent analysis.

[0126] The server integrates the acquired data with emotional states, calculates a similarity score using a deep learning algorithm, and identifies specific areas of growth. Furthermore, by incorporating emotional data, it can more effectively adjust personalized educational plans.

[0127] As a concrete example, suppose a user inputs a child's composition performance from a device, and simultaneously, an emotion engine records the child's emotions during the performance. The server analyzes this data, and if it highlights musical talent, it generates a personalized music education plan. This plan may include recommendations for music schools or suggestions for lessons using specific instruments.

[0128] Thus, by incorporating emotion recognition, the system of the present invention enables more sophisticated educational support based on the characteristics of children.

[0129] The following describes the processing flow.

[0130] Step 1:

[0131] The server retrieves information selected by experts from historical databases and generates numerical models using deep learning algorithms. This builds the foundation necessary for analyzing talent and aptitude.

[0132] Step 2:

[0133] The user uses a device to input the child's performance data. Performance data includes various forms of information, such as artistic works like drawings and music, written works like essays, and the results of scientific experiments.

[0134] Step 3:

[0135] The device receives data input from the user and activates the emotion engine. The emotion engine analyzes the voice using the microphone and recognizes facial expressions through the camera to record the user's emotional state in real time.

[0136] Step 4:

[0137] The device sends performance data and sentiment data to the server. During this process, the data format is standardized, and necessary metadata is added.

[0138] Step 5:

[0139] The server preprocesses the received data, and in particular, it quantifies emotional tendencies based on analysis, especially for emotional data. This makes the user's emotional state available as data.

[0140] Step 6:

[0141] The server uses pre-processed data to calculate similarity scores using a deep learning algorithm. Sentimental data is also incorporated into the analysis and considered as a variable that influences talent identification.

[0142] Step 7:

[0143] The server concretizes identified growth areas based on similarity scores and sentiment analysis results, thereby identifying which areas of talent particularly stand out.

[0144] Step 8:

[0145] Based on the results generated by the server, an individualized education plan is created that takes emotional states into account. This plan includes suggestions for learning projects and activities based on interests and comfort levels.

[0146] Step 9:

[0147] The server sends the generated report to the terminal. The terminal displays the identified growth areas and the contents of the personalized education plan to the user, providing information for reviewing and selecting educational policies.

[0148] (Example 2)

[0149] 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".

[0150] Traditional educational support systems, while analyzing performance data to provide individualized learning plans, struggled to accurately analyze talents and aptitudes while considering an individual's emotional state. Ignoring the influence of emotions on learning and talent development resulted in insufficient identification of optimal areas of growth and adjustment of learning plans to suit individual characteristics.

[0151] 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.

[0152] In this invention, the server includes means for analyzing complex data obtained from individuals through a numerical model generated using information selected by experts, means for calculating similarity scores in a feature space using a deep learning algorithm, and means for generating personalized educational plans based on acquired sentiment data. This enables precise analysis that takes emotions into account and the provision of optimal educational support.

[0153] "Information selected by experts" refers to data and knowledge that experts with past experience and knowledge have deemed appropriate.

[0154] A "numerical model" is a mathematical representation of real-world phenomena, serving as the foundation for making predictions and analyses based on data.

[0155] A "deep learning algorithm" refers to a technology that uses multi-layered neural networks to analyze data and automatically learn complex patterns and features.

[0156] A "similarity score in the feature space" is an index that quantifies the relationships and commonalities between specific data points, and is used to compare and evaluate data.

[0157] An "emotion engine" refers to a system that evaluates and quantifies a person's emotional state using methods such as voice analysis and facial recognition.

[0158] An "individualized education plan" refers to an educational instruction program that is tailored to the individual characteristics and progress of each learner.

[0159] "Methods for analyzing talent" refer to methods and techniques for evaluating an individual's characteristics and abilities based on acquired data, and identifying their potential in that field.

[0160] This invention relates to a system that enables highly accurate talent analysis, including emotion recognition, and the generation of personalized educational plans. The system mainly consists of three elements: a server, a terminal, and a user.

[0161] The server retrieves information selected by experts from historical databases and uses deep learning algorithms to generate numerical models based on this information. These deep learning algorithms analyze complex data and calculate similarity scores in the feature space. This numerical model is used to analyze user talents and aptitudes using data transmitted from the terminal. Furthermore, the server uses emotional data obtained by the emotion engine to perform detailed analyses that consider the user's emotional state, and utilizes this information to generate personalized educational plans.

[0162] The terminal functions as the user interface, acquiring real-time emotional data from an emotion engine in addition to performance data entered by the user. This emotional data acquisition utilizes hardware such as microphones for voice analysis and cameras for facial recognition. The terminal then aggregates this data and transmits it to the server.

[0163] Users provide information to the server by inputting their child's performance data through their device. An emotion engine is also used to record the child's emotional state in real time. This allows users to receive personalized educational plans based on their child's characteristics.

[0164] For example, if a user inputs a child's composition performance into a device, and the emotion engine simultaneously records the child's joyful facial expressions during the performance, the server analyzes this data and obtains results that highlight the child's talent in the field of music. Based on this, a personalized music education plan is generated. This plan may include recommendations for music schools or suggestions for lessons using specific instruments.

[0165] Examples of prompt statements include:

[0166] "Based on emotional data obtained simultaneously with children's songwriting performance data, please generate musical talent indicators and individualized educational plans."

[0167] These are some examples.

[0168] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0169] Step 1:

[0170] The user uses a device to input the child's performance data. This includes records of musical performance and information on learning progress. This data is initially processed within the device and converted into a format that can be sent to the server. The converted data is then output and ready to be sent to the server.

[0171] Step 2:

[0172] The device acquires emotional data in real time using an emotion engine. Specifically, it quantifies the emotional state by analyzing voice tone with a microphone and recognizing facial expressions with a camera. The acquired emotional data is output and similarly sent to the server.

[0173] Step 3:

[0174] The device sends user-entered performance data and acquired sentiment data to the server via a secure connection. Once all data preparation on the device is complete, the performance data and sentiment data are aggregated on the server.

[0175] Step 4:

[0176] The server applies a deep learning algorithm to the received performance and sentiment data. The input here consists of various data sent from the terminal. Based on this data, the server uses a numerical model to analyze talent and aptitude and calculates a similarity score. This score is obtained as the output.

[0177] Step 5:

[0178] The server then takes emotional data into account to generate a personalized education plan. This involves further data analysis to assess how the user's emotional state influences their talent traits. The generated education plan becomes the final output and is provided to the user.

[0179] (Application Example 2)

[0180] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0181] Conventional systems have struggled to provide real-time decision-making support that takes into account an individual's emotional state, and have faced challenges in making meaningful suggestions that utilize emotional recognition information, particularly in situations such as shopping. Therefore, there is a need for a new method that analyzes an individual's emotional information in real time and dynamically optimizes product and service recommendations based on the results.

[0182] 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.

[0183] In this invention, the server includes means for standardizing and analyzing complex information obtained from individuals through numerical representations generated using expert-selected information obtained from historical information resources; means for analyzing emotional information obtained using an emotion recognition device and making suggestions to support decision-making in real time; and means for presenting identified information using a display device and making dynamically optimized recommendations based on emotional information. This enables real-time optimized suggestions that take into account an individual's emotional state.

[0184] "Historical information resources" refer to databases and knowledge bases that contain accumulated past data and insights, including important information selected by experts.

[0185] A "numerical representation" is a mathematical or numerical model or data structure generated to analyze complex information obtained from an individual.

[0186] "Standardized analysis results" refer to information obtained as a result of an analysis that has been standardized to make different datasets comparable.

[0187] An "emotion recognition device" is a device or system that uses voice analysis or facial recognition technology to detect an individual's emotional state in real time.

[0188] "Real-time decision support" is a system that helps with decision-making by instantly analyzing an individual's emotional data and other relevant information, and providing the most appropriate suggestions and recommendations on the spot.

[0189] A "display device" is a device used to visually display information and is used to directly provide users with specific information or recommendations.

[0190] "Dynamically optimized recommendations" is a method that enhances the effectiveness of recommendations to users by adjusting content according to an individual's current state and emotions, and suggesting more appropriate products and services.

[0191] This embodiment of the invention is a system that enhances the in-store shopping experience using smart glasses. When a user wears smart glasses, an emotion recognition device acquires emotional information in real time using voice analysis and facial recognition technology. This makes it possible to understand the user's current emotional state.

[0192] The server performs standardized analysis based on data obtained from historical information resources and complex information acquired from individuals. This process includes generating numerical representations and analyzing identified growth areas in real time using deep learning algorithms. The server further integrates this information with emotional states obtained from sentiment recognition to generate dynamically optimized product recommendations.

[0193] The smart glasses, acting as a display device, show the information generated in this way. This enables the recommendation of optimal products and services to the user, personalizing the shopping experience to suit individual circumstances.

[0194] For example, if a user shows delight or interest in a particular product, a template recommending related items will be displayed on the screen. A specific example of the prompt message would look like this:

[0195] "This customer is currently very happy. Please suggest products and services that will enhance their shopping experience."

[0196] "Customers showed surprise when trying new products. Please recommend products they might purchase next."

[0197] This system allows users to have a more intuitive and satisfying shopping experience.

[0198] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0199] Step 1:

[0200] The user puts on smart glasses and begins walking around the store. The smart glasses' microphone and camera capture the user's voice and facial expressions in real time. This input data includes both audio and video information.

[0201] Step 2:

[0202] The device transmits the acquired audio and video information to the emotion recognition device. The emotion recognition device performs audio analysis and facial expression recognition to infer the user's emotional state. The output of this process is the user's identified emotional state.

[0203] Step 3:

[0204] The terminal sends user emotional state data to the server. The server generates a numerical representation based on the emotional state data, past purchase history, and store inventory information, and analyzes it using a deep learning algorithm. The output of this process is a standardized analysis result.

[0205] Step 4:

[0206] The server generates dynamically optimized product recommendations based on standardized analysis results. These recommendations are adjusted according to the user's current emotional state. This output is a list of recommendations for a specific product.

[0207] Step 5:

[0208] The server sends the generated recommendation list to the smart glasses. The recommendations are visualized and displayed on the smart glasses' display. Users can then obtain information about the suggested products and services and make purchases on the spot.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] [Second Embodiment]

[0213] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0214] 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.

[0215] 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).

[0216] 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.

[0217] 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.

[0218] 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).

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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".

[0225] The system of this invention analyzes complex data to identify individual talents and suggest areas for growth. This system mainly consists of three components: a server, a terminal, and a user.

[0226] The server retrieves information selected by experts from historical databases. This information includes data in various forms, such as the works, research, and records of famous geniuses of the past. The server collects this data and prepares it in a format that can be analyzed.

[0227] Users input their child's performance data via their device. This performance data includes a wide range of things, such as drawings, essays, and records of science experiments. Users can also send image data using the device's camera or scanner. The input data is sent to the server in text, image, and other formats.

[0228] The server analyzes the input data using a generated numerical model. This model is built using deep learning algorithms and evaluates the similarity between the input data and the data in the historical database. The similarity is quantified through the analysis of each feature.

[0229] Based on the analysis results, the server identifies the growth areas best suited to the user. This identification is performed using a similarity score, and the user is notified of the identified growth areas and the reasons for their identification. The server also generates and provides a personalized education plan to the user. This plan includes suggestions for education and training to further develop skills in the identified growth areas.

[0230] As a concrete example, suppose a user takes a picture of a drawing their child has made with their device and inputs it. The server analyzes this image data and evaluates its similarity to works by famous past artists. If the server determines that the child has particularly high talent in the arts, it will propose a personalized art-related educational plan to the user. This plan may include introductions to specialized art schools or information on local art competitions that the child can participate in.

[0231] As described above, the system of the present invention helps to identify a child's potential talents and to create an environment that promotes their growth.

[0232] The following describes the processing flow.

[0233] Step 1:

[0234] The server retrieves historical data selected by experts from the database and prepares it in a format that can be analyzed. This includes standardizing text and image data formats, removing errors and noise, and adding metadata.

[0235] Step 2:

[0236] The user inputs the child's performance data into the device. Using the device's camera or scanner, digital images of paintings and artwork are captured, and essays and research records are input as text data.

[0237] Step 3:

[0238] The terminal sends data entered by the user to the server. At this time, it checks whether the data format is properly formatted and adjusts the format if necessary.

[0239] Step 4:

[0240] The server preprocesses the received data. For image data, important features are extracted using image recognition technology, and for text data, linguistic information is extracted using text analysis tools.

[0241] Step 5:

[0242] The server uses a generated AI model to calculate a similarity score between the processed user data and the data in the historical database. A deep learning algorithm evaluates and quantifies the similarity within the feature space.

[0243] Step 6:

[0244] The server aggregates the analysis results and identifies which growth areas the individual has the highest aptitude for. Multiple fields, such as mathematics, science, art, and literature, are considered, and the field with the highest score is selected.

[0245] Step 7:

[0246] The server generates a personalized education plan based on identified growth areas. This plan includes, if necessary, expert referrals and suggestions for relevant workshops.

[0247] Step 8:

[0248] The server sends the generated report to the terminal. The terminal then presents the user with identified growth areas and corresponding curriculum suggestions, supporting them in selecting educational policies.

[0249] (Example 1)

[0250] 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."

[0251] In recent years, there has been a growing demand for technologies that can identify individual talents early on and appropriately support their development. However, methods for identifying individual talents from diverse data and building effective educational and training strategies based on them remain insufficient. Traditional systems have problems in that it is difficult to analyze large amounts of data and compare them with historical success stories, making it difficult to provide individualized educational strategies.

[0252] 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.

[0253] In this invention, the server includes means for analyzing multidimensional data of individuals generated using expert-selected data obtained from an information set, means for generating personalized educational strategies based on similarity evaluations, and means for providing the generated educational strategies. This makes it possible to provide users with highly personalized areas of growth and educational strategies to support their development.

[0254] An "information set" is a dataset that compiles historical databases or data selected by experts.

[0255] "Generative artificial intelligence technology" refers to a technology that uses deep learning algorithms to analyze input data and evaluate its similarity.

[0256] "Individual" refers to a user targeted by the system or an individual who is of interest to that user.

[0257] "Multidimensional data" refers to data that contains diverse information in different formats, such as text and images.

[0258] A "feature space" is the space in which features extracted to evaluate the similarity of data are arranged.

[0259] "Similarity assessment" is the process of quantifying and evaluating the degree of similarity between input data and historical data.

[0260] A "personalized education strategy" is a plan that proposes education and training tailored to individual needs, based on identified areas of growth.

[0261] "User input devices" refer to terminal devices such as cameras and scanners that users use to input data.

[0262] A "generated educational strategy" refers to specific suggestions and plans created to support the user's growth.

[0263] This invention relates to a system that identifies an individual's talents and supports their development. The system consists of three components: a server, a terminal, and a user.

[0264] The server accesses a database via the network to collect historical data from the information set. This database contains works, research, and records of historical figures selected by experts. The server organizes this data in an analyzable format.

[0265] Users input their personal performance data using their devices. This includes a variety of data formats, such as children's drawings and essays, and records of science experiments. Users can input image data using the camera or scanner function built into their devices. The input data is sent to the server in the form of text and images.

[0266] Upon receiving input data, the server performs analysis using generative artificial intelligence technology. This technology is based on deep learning algorithms, extracting features from each data point and evaluating the similarity between them. The similarity is calculated numerically and used to identify growth areas.

[0267] Based on the analysis results, the server identifies the user's optimal areas for growth. Based on this, the server generates and provides a personalized educational strategy. This strategy includes specific educational and training suggestions to develop skills in the identified areas of growth.

[0268] As a concrete example, consider a scenario where a user takes a picture of their child's drawing with their device and uploads it to the system. The server analyzes this image data and evaluates its similarity to the works of accomplished artists. If the server determines that the child has excellent drawing skills, it proposes a personalized art-related educational plan. This plan may include introductions to art schools and information on local art events.

[0269] An example of a prompt message would be: "Take a picture of your child's drawing and evaluate their talent. In particular, we will analyze similarities to historical artists and suggest areas for optimal growth."

[0270] Thus, the present invention is a system aimed at identifying an individual's potential talents and creating an environment for developing them.

[0271] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0272] Step 1:

[0273] The server retrieves data from the information set. Specifically, the server accesses a database containing historical data selected by experts via the network. From this database, works and research of prominent figures are retrieved. The retrieved data is formatted on the server into an appropriate format for subsequent analysis. The input is the database, and the output is data ready for analysis.

[0274] Step 2:

[0275] The user inputs performance data using a terminal. The user takes pictures of information such as children's drawings and essays using the terminal's camera function or scanner and inputs that data into the system. The input data is sent to the server in the form of text, images, etc. In this step, the data that the user inputs is on the terminal, and the output is the data sent to the server.

[0276] Step 3:

[0277] The server applies generative artificial intelligence techniques to analyze the input data. Deep learning algorithms are used to extract features from the input data and evaluate their similarity to historical data. This similarity is quantified and used as the primary output of the analysis. Here, user-provided data is the input, and the feature extraction results and similarity score are the outputs.

[0278] Step 4:

[0279] The server identifies the optimal growth area for the user based on the analysis results. It analyzes the similarity scores and determines which growth area is appropriate based on them. The scores indicating potential talents in a specific area are considered. The input is the similarity score, and the output is the identified growth area.

[0280] Step 5:

[0281] The server generates and provides an individualized education strategy based on the identified growth area to the user. This education strategy includes specific proposals and plans to promote the user's growth. For example, if the art field is identified, information on specialized art schools and local events is provided to the user. The input is the identified growth area, and the output is the generated education strategy.

[0282] (Application Example 1)

[0283] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0284] Effectively discovering an individual's talents and grasping the optimal growth area is important in the field of education. However, conventional methods have limitations in providing an education plan for talent identification and promoting its growth individually. In particular, there are problems with immediacy and individualization. The present invention aims to perform real-time analysis of an individual's talents based on historical data and realize immediate feedback in a physical store and the provision of an individualized education plan.

[0285] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0286] In this invention, the server includes means for analyzing detailed information obtained from an individual through a generated numerical model by using specialized knowledge acquired from historical information sources, and for determining an optimal growth area based on the similarity evaluation result; means for providing the analysis result to the user in real time through a terminal of a physical store; and means for generating an individualized education plan and notifying the user's terminal of it. Thereby, it becomes possible to immediately evaluate an individual's talent and present an optimal growth strategy.

[0287] The "historical information source" is an accumulation of information that records important knowledge and data from the past and includes data selected by experts.

[0288] The "numerical model" is a computational model constructed to analyze data and derive specific conclusions, mainly using mathematical methods.

[0289] The "detailed information obtained from an individual" is specific data provided by that individual and is information composed of records of artworks, performances, and other forms.

[0290] The "similarity evaluation result" numerically evaluates how similar the analyzed data is to the comparison target and shows the result.

[0291] The "growth area" is a field or sub-field considered to be most suitable for an individual to develop their talents.

[0292] The "terminal of a physical store" is a device placed in a physical store and is a computer or device equipped with an interface for inputting and acquiring information.

[0293] "Real-time provision" refers to the act of presenting or reflecting information immediately without delay and is a process that requires immediacy.

[0294] The "individualized education plan" is a special education program designed based on the needs and abilities of a specific individual.

[0295] As an embodiment of this invention, a system is constructed that primarily involves servers, terminals, and users.

[0296] The server uses a database obtained from historical sources to analyze detailed information acquired from individuals using a generated numerical model. The server executes machine learning algorithms using a deep learning framework (e.g., TensorFlow or PyTorch) based on Python. During this process, it evaluates the similarity between the acquired data and historical data to identify areas of growth.

[0297] The device functions as a smartphone or tablet application and is developed using Flutter or React Native. This device retrieves detailed information provided by the user and sends it to a server using its camera and scanning functions. The device also provides the user with real-time analysis results received from the server.

[0298] The user operates this device and receives a personalized educational plan. This plan includes suggestions for education and training based on identified areas of growth.

[0299] As a concrete example, suppose an elementary school teacher takes photos of students' artwork with their smartphone and sends the data through an app. The server analyzes the image data and evaluates its similarity to that of famous past artists. As a result, areas of artistic growth are identified, and information on specialized art workshops and local competitions is notified to the user's device. This process is represented by a prompt message such as: "Analyze this student's recent work and identify the most suitable educational areas. Please also consider similarities to famous past artists."

[0300] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0301] Step 1:

[0302] The user uses the terminal to input records of students' works and activities. Specifically, the user takes pictures of students' paintings with the camera of a smartphone and sends the data to the server through an app. The input data is in image format, and the terminal prepares it as digital data.

[0303] Step 2:

[0304] The server receives the digital data sent from the terminal and performs analysis using a generative AI model. For data analysis, deep learning algorithms using TensorFlow or PyTorch are applied to evaluate the similarity with the data in the past historical database. Here, the input is the work data of students, and the output is the evaluation result of similarity based on that data.

[0305] Step 3:

[0306] Based on the analysis results, the server determines the optimal growth areas for the user. This includes a process of selecting specific educational fields using the calculation results of similarity scores. The output is the identified growth areas for an individualized education plan.

[0307] Step 4:

[0308] The server notifies the terminal of the identified growth areas and generates an individualized education plan. The terminal provides the analysis results to the user in real time, and the education plan may include information about art schools and contests. The output is information that allows the user to view the education plan.

[0309] Step 5:

[0310] The user utilizes the provided education plan through the terminal to help promote the growth of students. As a result, the user can plan specific educational activities as the next step. The input is the content of the education plan, and the output is a specific action plan for students.

[0311] 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.

[0312] The system of this invention recognizes the user's emotions by combining them with an emotion engine, and based on that, identifies talents and generates personalized educational plans. The system mainly consists of a server, terminals, and users, and each of these parts works together to achieve this.

[0313] First, the server retrieves information selected by experts from a historical database and generates a numerical model using a deep learning algorithm. This model is then used to analyze talents and aptitudes based on data submitted by users.

[0314] The user inputs the child's performance data using a device, and also records the emotional state in real time using sensors equipped with an emotion engine. Specifically, it uses voice analysis to infer emotions from voice using a microphone, and facial expression recognition to determine emotions from facial expressions captured through a camera.

[0315] The device sends acquired performance data and emotional data to the server. Based on the emotional data analyzed by the emotion engine, the server takes the user's emotional state into consideration and performs a highly accurate talent analysis.

[0316] The server integrates the acquired data with emotional states, calculates a similarity score using a deep learning algorithm, and identifies specific areas of growth. Furthermore, by incorporating emotional data, it can more effectively adjust personalized educational plans.

[0317] As a concrete example, suppose a user inputs a child's composition performance from a device, and simultaneously, an emotion engine records the child's emotions during the performance. The server analyzes this data, and if it highlights musical talent, it generates a personalized music education plan. This plan may include recommendations for music schools or suggestions for lessons using specific instruments.

[0318] Thus, by incorporating emotion recognition, the system of the present invention enables more sophisticated educational support based on the characteristics of children.

[0319] The following describes the processing flow.

[0320] Step 1:

[0321] The server retrieves information selected by experts from historical databases and generates numerical models using deep learning algorithms. This builds the foundation necessary for analyzing talent and aptitude.

[0322] Step 2:

[0323] The user uses a device to input the child's performance data. Performance data includes various forms of information, such as artistic works like drawings and music, written works like essays, and the results of scientific experiments.

[0324] Step 3:

[0325] The device receives data input from the user and activates the emotion engine. The emotion engine analyzes the voice using the microphone and recognizes facial expressions through the camera to record the user's emotional state in real time.

[0326] Step 4:

[0327] The device sends performance data and sentiment data to the server. During this process, the data format is standardized, and necessary metadata is added.

[0328] Step 5:

[0329] The server preprocesses the received data, and in particular, it quantifies emotional tendencies based on analysis, especially for emotional data. This makes the user's emotional state available as data.

[0330] Step 6:

[0331] The server uses pre-processed data to calculate similarity scores using a deep learning algorithm. Sentimental data is also incorporated into the analysis and considered as a variable that influences talent identification.

[0332] Step 7:

[0333] The server concretizes identified growth areas based on similarity scores and sentiment analysis results, thereby identifying which areas of talent particularly stand out.

[0334] Step 8:

[0335] Based on the results generated by the server, an individualized education plan is created that takes emotional states into account. This plan includes suggestions for learning projects and activities based on interests and comfort levels.

[0336] Step 9:

[0337] The server sends the generated report to the terminal. The terminal displays the identified growth areas and the contents of the personalized education plan to the user, providing information for reviewing and selecting educational policies.

[0338] (Example 2)

[0339] 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".

[0340] Traditional educational support systems, while analyzing performance data to provide individualized learning plans, struggled to accurately analyze talents and aptitudes while considering an individual's emotional state. Ignoring the influence of emotions on learning and talent development resulted in insufficient identification of optimal areas of growth and adjustment of learning plans to suit individual characteristics.

[0341] 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.

[0342] In this invention, the server includes means for analyzing complex data obtained from individuals through a numerical model generated using information selected by experts, means for calculating similarity scores in a feature space using a deep learning algorithm, and means for generating personalized educational plans based on acquired sentiment data. This enables precise analysis that takes emotions into account and the provision of optimal educational support.

[0343] "Information selected by experts" refers to data and knowledge that experts with past experience and knowledge have deemed appropriate.

[0344] A "numerical model" is a mathematical representation of real-world phenomena, serving as the foundation for making predictions and analyses based on data.

[0345] A "deep learning algorithm" refers to a technology that uses multi-layered neural networks to analyze data and automatically learn complex patterns and features.

[0346] A "similarity score in the feature space" is an index that quantifies the relationships and commonalities between specific data points, and is used to compare and evaluate data.

[0347] An "emotion engine" refers to a system that evaluates and quantifies a person's emotional state using methods such as voice analysis and facial recognition.

[0348] An "individualized education plan" refers to an educational instruction program that is tailored to the individual characteristics and progress of each learner.

[0349] "Methods for analyzing talent" refer to methods and techniques for evaluating an individual's characteristics and abilities based on acquired data, and identifying their potential in that field.

[0350] This invention relates to a system that enables highly accurate talent analysis, including emotion recognition, and the generation of personalized educational plans. The system mainly consists of three elements: a server, a terminal, and a user.

[0351] The server retrieves information selected by experts from historical databases and uses deep learning algorithms to generate numerical models based on this information. These deep learning algorithms analyze complex data and calculate similarity scores in the feature space. This numerical model is used to analyze user talents and aptitudes using data transmitted from the terminal. Furthermore, the server uses emotional data obtained by the emotion engine to perform detailed analyses that consider the user's emotional state, and utilizes this information to generate personalized educational plans.

[0352] The terminal functions as the user interface, acquiring real-time emotional data from an emotion engine in addition to performance data entered by the user. This emotional data acquisition utilizes hardware such as microphones for voice analysis and cameras for facial recognition. The terminal then aggregates this data and transmits it to the server.

[0353] Users provide information to the server by inputting their child's performance data through their device. An emotion engine is also used to record the child's emotional state in real time. This allows users to receive personalized educational plans based on their child's characteristics.

[0354] For example, if a user inputs a child's composition performance into a device, and the emotion engine simultaneously records the child's joyful facial expressions during the performance, the server analyzes this data and obtains results that highlight the child's talent in the field of music. Based on this, a personalized music education plan is generated. This plan may include recommendations for music schools or suggestions for lessons using specific instruments.

[0355] Examples of prompt statements include:

[0356] "Based on emotional data obtained simultaneously with children's songwriting performance data, please generate musical talent indicators and individualized educational plans."

[0357] These are some examples.

[0358] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0359] Step 1:

[0360] The user uses a device to input the child's performance data. This includes records of musical performance and information on learning progress. This data is initially processed within the device and converted into a format that can be sent to the server. The converted data is then output and ready to be sent to the server.

[0361] Step 2:

[0362] The device acquires emotional data in real time using an emotion engine. Specifically, it quantifies the emotional state by analyzing voice tone with a microphone and recognizing facial expressions with a camera. The acquired emotional data is output and similarly sent to the server.

[0363] Step 3:

[0364] The device sends user-entered performance data and acquired sentiment data to the server via a secure connection. Once all data preparation on the device is complete, the performance data and sentiment data are aggregated on the server.

[0365] Step 4:

[0366] The server applies a deep learning algorithm to the received performance and sentiment data. The input here consists of various data sent from the terminal. Based on this data, the server uses a numerical model to analyze talent and aptitude and calculates a similarity score. This score is obtained as the output.

[0367] Step 5:

[0368] The server then takes emotional data into account to generate a personalized education plan. This involves further data analysis to assess how the user's emotional state influences their talent traits. The generated education plan becomes the final output and is provided to the user.

[0369] (Application Example 2)

[0370] 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 will be referred to as the "terminal."

[0371] Conventional systems have struggled to provide real-time decision-making support that takes into account an individual's emotional state, and have faced challenges in making meaningful suggestions that utilize emotional recognition information, particularly in situations such as shopping. Therefore, there is a need for a new method that analyzes an individual's emotional information in real time and dynamically optimizes product and service recommendations based on the results.

[0372] 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.

[0373] In this invention, the server includes means for standardizing and analyzing complex information obtained from individuals through numerical representations generated using expert-selected information obtained from historical information resources; means for analyzing emotional information obtained using an emotion recognition device and making suggestions to support decision-making in real time; and means for presenting identified information using a display device and making dynamically optimized recommendations based on emotional information. This enables real-time optimized suggestions that take into account an individual's emotional state.

[0374] "Historical information resources" refer to databases and knowledge bases that contain accumulated past data and insights, including important information selected by experts.

[0375] A "numerical representation" is a mathematical or numerical model or data structure generated to analyze complex information obtained from an individual.

[0376] "Standardized analysis results" refer to information obtained as a result of an analysis that has been standardized to make different datasets comparable.

[0377] An "emotion recognition device" is a device or system that uses voice analysis or facial recognition technology to detect an individual's emotional state in real time.

[0378] "Real-time decision support" is a system that helps with decision-making by instantly analyzing an individual's emotional data and other relevant information, and providing the most appropriate suggestions and recommendations on the spot.

[0379] A "display device" is a device used to visually display information and is used to directly provide users with specific information or recommendations.

[0380] "Dynamically optimized recommendations" is a method that enhances the effectiveness of recommendations to users by adjusting content according to an individual's current state and emotions, and suggesting more appropriate products and services.

[0381] This embodiment of the invention is a system that enhances the in-store shopping experience using smart glasses. When a user wears smart glasses, an emotion recognition device acquires emotional information in real time using voice analysis and facial recognition technology. This makes it possible to understand the user's current emotional state.

[0382] The server performs standardized analysis based on data obtained from historical information resources and complex information acquired from individuals. This process includes generating numerical representations and analyzing identified growth areas in real time using deep learning algorithms. The server further integrates this information with emotional states obtained from sentiment recognition to generate dynamically optimized product recommendations.

[0383] The smart glasses, acting as a display device, show the information generated in this way. This enables the recommendation of optimal products and services to the user, personalizing the shopping experience to suit individual circumstances.

[0384] For example, if a user shows delight or interest in a particular product, a template recommending related items will be displayed on the screen. A specific example of the prompt message would look like this:

[0385] "This customer is currently very happy. Please suggest products and services that will enhance their shopping experience."

[0386] "Customers showed surprise when trying new products. Please recommend products they might purchase next."

[0387] This system allows users to have a more intuitive and satisfying shopping experience.

[0388] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0389] Step 1:

[0390] The user puts on smart glasses and begins walking around the store. The smart glasses' microphone and camera capture the user's voice and facial expressions in real time. This input data includes both audio and video information.

[0391] Step 2:

[0392] The device transmits the acquired audio and video information to the emotion recognition device. The emotion recognition device performs audio analysis and facial expression recognition to infer the user's emotional state. The output of this process is the user's identified emotional state.

[0393] Step 3:

[0394] The terminal sends user emotional state data to the server. The server generates a numerical representation based on the emotional state data, past purchase history, and store inventory information, and analyzes it using a deep learning algorithm. The output of this process is a standardized analysis result.

[0395] Step 4:

[0396] The server generates dynamically optimized product recommendations based on standardized analysis results. These recommendations are adjusted according to the user's current emotional state. This output is a list of recommendations for a specific product.

[0397] Step 5:

[0398] The server sends the generated recommendation list to the smart glasses. The recommendations are visualized and displayed on the smart glasses' display. Users can then obtain information about the suggested products and services and make purchases on the spot.

[0399] 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.

[0400] 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.

[0401] 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.

[0402] [Third Embodiment]

[0403] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0404] 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.

[0405] 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).

[0406] 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.

[0407] 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.

[0408] 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).

[0409] 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.

[0410] 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.

[0411] 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.

[0412] 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.

[0413] 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.

[0414] 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".

[0415] The system of this invention analyzes complex data to identify individual talents and suggest areas for growth. This system mainly consists of three components: a server, a terminal, and a user.

[0416] The server retrieves information selected by experts from historical databases. This information includes data in various forms, such as the works, research, and records of famous geniuses of the past. The server collects this data and prepares it in a format that can be analyzed.

[0417] Users input their child's performance data via their device. This performance data includes a wide range of things, such as drawings, essays, and records of science experiments. Users can also send image data using the device's camera or scanner. The input data is sent to the server in text, image, and other formats.

[0418] The server analyzes the input data using a generated numerical model. This model is built using deep learning algorithms and evaluates the similarity between the input data and the data in the historical database. The similarity is quantified through the analysis of each feature.

[0419] Based on the analysis results, the server identifies the growth areas best suited to the user. This identification is performed using a similarity score, and the user is notified of the identified growth areas and the reasons for their identification. The server also generates and provides a personalized education plan to the user. This plan includes suggestions for education and training to further develop skills in the identified growth areas.

[0420] As a concrete example, suppose a user takes a picture of a drawing their child has made with their device and inputs it. The server analyzes this image data and evaluates its similarity to works by famous past artists. If the server determines that the child has particularly high talent in the arts, it will propose a personalized art-related educational plan to the user. This plan may include introductions to specialized art schools or information on local art competitions that the child can participate in.

[0421] As described above, the system of the present invention helps to identify a child's potential talents and to create an environment that promotes their growth.

[0422] The following describes the processing flow.

[0423] Step 1:

[0424] The server retrieves historical data selected by experts from the database and prepares it in a format that can be analyzed. This includes standardizing text and image data formats, removing errors and noise, and adding metadata.

[0425] Step 2:

[0426] The user inputs the child's performance data into the device. Using the device's camera or scanner, digital images of paintings and artwork are captured, and essays and research records are input as text data.

[0427] Step 3:

[0428] The terminal sends data entered by the user to the server. At this time, it checks whether the data format is properly formatted and adjusts the format if necessary.

[0429] Step 4:

[0430] The server preprocesses the received data. For image data, important features are extracted using image recognition technology, and for text data, linguistic information is extracted using text analysis tools.

[0431] Step 5:

[0432] The server uses a generated AI model to calculate a similarity score between the processed user data and the data in the historical database. A deep learning algorithm evaluates and quantifies the similarity within the feature space.

[0433] Step 6:

[0434] The server aggregates the analysis results and identifies which growth areas the individual has the highest aptitude for. Multiple fields, such as mathematics, science, art, and literature, are considered, and the field with the highest score is selected.

[0435] Step 7:

[0436] The server generates a personalized education plan based on identified growth areas. This plan includes, if necessary, expert referrals and suggestions for relevant workshops.

[0437] Step 8:

[0438] The server sends the generated report to the terminal. The terminal then presents the user with identified growth areas and corresponding curriculum suggestions, supporting them in selecting educational policies.

[0439] (Example 1)

[0440] 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."

[0441] In recent years, there has been a growing demand for technologies that can identify individual talents early on and appropriately support their development. However, methods for identifying individual talents from diverse data and building effective educational and training strategies based on them remain insufficient. Traditional systems have problems in that it is difficult to analyze large amounts of data and compare them with historical success stories, making it difficult to provide individualized educational strategies.

[0442] 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.

[0443] In this invention, the server includes means for analyzing multidimensional data of individuals generated using expert-selected data obtained from an information set, means for generating personalized educational strategies based on similarity evaluations, and means for providing the generated educational strategies. This makes it possible to provide users with highly personalized areas of growth and educational strategies to support their development.

[0444] An "information set" is a dataset that compiles historical databases or data selected by experts.

[0445] "Generative artificial intelligence technology" refers to a technology that uses deep learning algorithms to analyze input data and evaluate its similarity.

[0446] "Individual" refers to a user targeted by the system or an individual who is of interest to that user.

[0447] "Multidimensional data" refers to data that contains diverse information in different formats, such as text and images.

[0448] A "feature space" is the space in which features extracted to evaluate the similarity of data are arranged.

[0449] "Similarity assessment" is the process of quantifying and evaluating the degree of similarity between input data and historical data.

[0450] A "personalized education strategy" is a plan that proposes education and training tailored to individual needs, based on identified areas of growth.

[0451] "User input devices" refer to terminal devices such as cameras and scanners that users use to input data.

[0452] A "generated educational strategy" refers to specific suggestions and plans created to support the user's growth.

[0453] This invention relates to a system that identifies an individual's talents and supports their development. The system consists of three components: a server, a terminal, and a user.

[0454] The server accesses a database via the network to collect historical data from the information set. This database contains works, research, and records of historical figures selected by experts. The server organizes this data in an analyzable format.

[0455] Users input their personal performance data using their devices. This includes a variety of data formats, such as children's drawings and essays, and records of science experiments. Users can input image data using the camera or scanner function built into their devices. The input data is sent to the server in the form of text and images.

[0456] Upon receiving input data, the server performs analysis using generative artificial intelligence technology. This technology is based on deep learning algorithms, extracting features from each data point and evaluating the similarity between them. The similarity is calculated numerically and used to identify growth areas.

[0457] Based on the analysis results, the server identifies the user's optimal areas for growth. Based on this, the server generates and provides a personalized educational strategy. This strategy includes specific educational and training suggestions to develop skills in the identified areas of growth.

[0458] As a concrete example, consider a scenario where a user takes a picture of their child's drawing with their device and uploads it to the system. The server analyzes this image data and evaluates its similarity to the works of accomplished artists. If the server determines that the child has excellent drawing skills, it proposes a personalized art-related educational plan. This plan may include introductions to art schools and information on local art events.

[0459] An example of a prompt message would be: "Take a picture of your child's drawing and evaluate their talent. In particular, we will analyze similarities to historical artists and suggest areas for optimal growth."

[0460] Thus, the present invention is a system aimed at identifying an individual's potential talents and creating an environment for developing them.

[0461] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0462] Step 1:

[0463] The server retrieves data from the information set. Specifically, the server accesses a database containing historical data selected by experts via the network. From this database, works and research of prominent figures are retrieved. The retrieved data is formatted on the server into an appropriate format for subsequent analysis. The input is the database, and the output is data ready for analysis.

[0464] Step 2:

[0465] The user inputs performance data using a terminal. The user takes pictures of information such as children's drawings and essays using the terminal's camera function or scanner and inputs that data into the system. The input data is sent to the server in the form of text, images, etc. In this step, the data that the user inputs is on the terminal, and the output is the data sent to the server.

[0466] Step 3:

[0467] The server applies generative artificial intelligence techniques to analyze the input data. Deep learning algorithms are used to extract features from the input data and evaluate their similarity to historical data. This similarity is quantified and used as the primary output of the analysis. Here, user-provided data is the input, and the feature extraction results and similarity score are the outputs.

[0468] Step 4:

[0469] The server identifies the optimal growth areas for the user based on the analysis results. It analyzes similarity scores and determines which growth areas are appropriate based on those scores. Scores indicating potential talent in specific areas are considered. The input is the similarity score, and the output is the identified growth areas.

[0470] Step 5:

[0471] The server generates and provides a personalized educational strategy to the user based on the identified growth area. This educational strategy includes specific suggestions and plans to promote the user's growth. For example, if the arts field is identified, the server will provide the user with information on specialized art schools and local events. The input is the identified growth area, and the output is the generated educational strategy.

[0472] (Application Example 1)

[0473] 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."

[0474] Effectively identifying an individual's talents and understanding their optimal areas for growth is crucial in educational settings. However, conventional methods have limitations in providing individualized educational plans to identify and promote talent development. In particular, there are challenges regarding immediacy and individualization. This invention aims to enable real-time individual talent analysis based on historical data, facilitating immediate feedback and the provision of personalized educational plans in physical stores.

[0475] 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.

[0476] This invention includes a server that analyzes detailed information obtained from an individual through a numerical model generated using expertise acquired from historical sources, and determines the optimal growth area based on the similarity evaluation results; a server that provides the analysis results to the user in real time through a terminal in a physical store; and a server that generates a personalized education plan and notifies the user's terminal of it. This enables immediate assessment of an individual's talents and the presentation of an optimal growth strategy.

[0477] A "historical source" is a collection of information that records important knowledge and data from the past, including data carefully selected by experts.

[0478] A "numerical model" is a computational model constructed to analyze data and derive specific conclusions, primarily using mathematical methods.

[0479] "Detailed information obtained from an individual" refers to specific data provided by that individual, and includes information in the form of records of works of art or performances, or other forms of information.

[0480] The "similarity evaluation result" is a numerical evaluation of how similar the analyzed data is to the comparison target, and the result is shown.

[0481] A "growth area" is a field or subfield that is considered most suitable for an individual to develop their talents.

[0482] A "physical store terminal" is a device located in a physical store that is a computer or device equipped with an interface for inputting and retrieving information.

[0483] "Real-time provision" refers to the act of presenting or reflecting information immediately without delay, and is a process that requires immediacy.

[0484] An "individualized education plan" is a special educational program designed based on the specific needs and abilities of an individual.

[0485] As an embodiment of this invention, a system is constructed that primarily involves servers, terminals, and users.

[0486] The server uses a database obtained from historical sources to analyze detailed information acquired from individuals using a generated numerical model. The server executes machine learning algorithms using a deep learning framework (e.g., TensorFlow or PyTorch) based on Python. During this process, it evaluates the similarity between the acquired data and historical data to identify areas of growth.

[0487] The device functions as a smartphone or tablet application and is developed using Flutter or React Native. This device retrieves detailed information provided by the user and sends it to a server using its camera and scanning functions. The device also provides the user with real-time analysis results received from the server.

[0488] The user operates this device and receives a personalized educational plan. This plan includes suggestions for education and training based on identified areas of growth.

[0489] As a concrete example, suppose an elementary school teacher takes photos of students' artwork with their smartphone and sends the data through an app. The server analyzes the image data and evaluates its similarity to that of famous past artists. As a result, areas of artistic growth are identified, and information on specialized art workshops and local competitions is notified to the user's device. This process is represented by a prompt message such as: "Analyze this student's recent work and identify the most suitable educational areas. Please also consider similarities to famous past artists."

[0490] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0491] Step 1:

[0492] Users use their devices to input records of students' artwork and activities. Specifically, they take photos of students' drawings with their smartphone cameras and send the data to the server via the app. The input data is in image format, and the device prepares it as digital data.

[0493] Step 2:

[0494] The server receives digital data sent from the terminal and performs analysis using a generative AI model. Deep learning algorithms using TensorFlow or PyTorch are applied to the data analysis to evaluate its similarity to data in historical databases. The input here is student artwork data, and the output is the similarity evaluation result based on that data.

[0495] Step 3:

[0496] Based on the analysis results, the server identifies the optimal areas of growth for the user. This includes a process of selecting specific educational fields using similarity scores. The output is the identified areas of growth for a personalized educational plan.

[0497] Step 4:

[0498] The server notifies the terminal of identified growth areas and generates a personalized educational plan. The terminal provides the user with analysis results in real time, and the educational plan may include information on art schools and competitions. The output is information that the user can use to review the educational plan.

[0499] Step 5:

[0500] Users utilize the provided educational plans through their devices to help promote student growth. This allows users to plan specific educational activities as the next steps. The input is the content of the educational plan, and the output is a specific action plan for the students.

[0501] 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.

[0502] The system of this invention recognizes the user's emotions by combining them with an emotion engine, and based on that, identifies talents and generates personalized educational plans. The system mainly consists of a server, terminals, and users, and each of these parts works together to achieve this.

[0503] First, the server retrieves information selected by experts from a historical database and generates a numerical model using a deep learning algorithm. This model is then used to analyze talents and aptitudes based on data submitted by users.

[0504] The user inputs the child's performance data using a device, and also records the emotional state in real time using sensors equipped with an emotion engine. Specifically, it uses voice analysis to infer emotions from voice using a microphone, and facial expression recognition to determine emotions from facial expressions captured through a camera.

[0505] The device sends acquired performance data and emotional data to the server. Based on the emotional data analyzed by the emotion engine, the server takes the user's emotional state into consideration and performs a highly accurate talent analysis.

[0506] The server integrates the acquired data with emotional states, calculates a similarity score using a deep learning algorithm, and identifies specific areas of growth. Furthermore, by incorporating emotional data, it can more effectively adjust personalized educational plans.

[0507] As a concrete example, suppose a user inputs a child's composition performance from a device, and simultaneously, an emotion engine records the child's emotions during the performance. The server analyzes this data, and if it highlights musical talent, it generates a personalized music education plan. This plan may include recommendations for music schools or suggestions for lessons using specific instruments.

[0508] Thus, by incorporating emotion recognition, the system of the present invention enables more sophisticated educational support based on the characteristics of children.

[0509] The following describes the processing flow.

[0510] Step 1:

[0511] The server retrieves information selected by experts from historical databases and generates numerical models using deep learning algorithms. This builds the foundation necessary for analyzing talent and aptitude.

[0512] Step 2:

[0513] The user uses a device to input the child's performance data. Performance data includes various forms of information, such as artistic works like drawings and music, written works like essays, and the results of scientific experiments.

[0514] Step 3:

[0515] The device receives data input from the user and activates the emotion engine. The emotion engine analyzes the voice using the microphone and recognizes facial expressions through the camera to record the user's emotional state in real time.

[0516] Step 4:

[0517] The device sends performance data and sentiment data to the server. During this process, the data format is standardized, and necessary metadata is added.

[0518] Step 5:

[0519] The server preprocesses the received data, and in particular, it quantifies emotional tendencies based on analysis, especially for emotional data. This makes the user's emotional state available as data.

[0520] Step 6:

[0521] The server uses pre-processed data to calculate similarity scores using a deep learning algorithm. Sentimental data is also incorporated into the analysis and considered as a variable that influences talent identification.

[0522] Step 7:

[0523] The server concretizes identified growth areas based on similarity scores and sentiment analysis results, thereby identifying which areas of talent particularly stand out.

[0524] Step 8:

[0525] Based on the results generated by the server, an individualized education plan is created that takes emotional states into account. This plan includes suggestions for learning projects and activities based on interests and comfort levels.

[0526] Step 9:

[0527] The server sends the generated report to the terminal. The terminal displays the identified growth areas and the contents of the personalized education plan to the user, providing information for reviewing and selecting educational policies.

[0528] (Example 2)

[0529] 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."

[0530] Traditional educational support systems, while analyzing performance data to provide individualized learning plans, struggled to accurately analyze talents and aptitudes while considering an individual's emotional state. Ignoring the influence of emotions on learning and talent development resulted in insufficient identification of optimal areas of growth and adjustment of learning plans to suit individual characteristics.

[0531] 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.

[0532] In this invention, the server includes means for analyzing complex data obtained from individuals through a numerical model generated using information selected by experts, means for calculating similarity scores in a feature space using a deep learning algorithm, and means for generating personalized educational plans based on acquired sentiment data. This enables precise analysis that takes emotions into account and the provision of optimal educational support.

[0533] "Information selected by experts" refers to data and knowledge that experts with past experience and knowledge have deemed appropriate.

[0534] A "numerical model" is a mathematical representation of real-world phenomena, serving as the foundation for making predictions and analyses based on data.

[0535] A "deep learning algorithm" refers to a technology that uses multi-layered neural networks to analyze data and automatically learn complex patterns and features.

[0536] A "similarity score in the feature space" is an index that quantifies the relationships and commonalities between specific data points, and is used to compare and evaluate data.

[0537] An "emotion engine" refers to a system that evaluates and quantifies a person's emotional state using methods such as voice analysis and facial recognition.

[0538] An "individualized education plan" refers to an educational instruction program that is tailored to the individual characteristics and progress of each learner.

[0539] "Methods for analyzing talent" refer to methods and techniques for evaluating an individual's characteristics and abilities based on acquired data, and identifying their potential in that field.

[0540] This invention relates to a system that enables highly accurate talent analysis, including emotion recognition, and the generation of personalized educational plans. The system mainly consists of three elements: a server, a terminal, and a user.

[0541] The server retrieves information selected by experts from historical databases and uses deep learning algorithms to generate numerical models based on this information. These deep learning algorithms analyze complex data and calculate similarity scores in the feature space. This numerical model is used to analyze user talents and aptitudes using data transmitted from the terminal. Furthermore, the server uses emotional data obtained by the emotion engine to perform detailed analyses that consider the user's emotional state, and utilizes this information to generate personalized educational plans.

[0542] The terminal functions as the user interface, acquiring real-time emotional data from an emotion engine in addition to performance data entered by the user. This emotional data acquisition utilizes hardware such as microphones for voice analysis and cameras for facial recognition. The terminal then aggregates this data and transmits it to the server.

[0543] Users provide information to the server by inputting their child's performance data through their device. An emotion engine is also used to record the child's emotional state in real time. This allows users to receive personalized educational plans based on their child's characteristics.

[0544] For example, if a user inputs a child's composition performance into a device, and the emotion engine simultaneously records the child's joyful facial expressions during the performance, the server analyzes this data and obtains results that highlight the child's talent in the field of music. Based on this, a personalized music education plan is generated. This plan may include recommendations for music schools or suggestions for lessons using specific instruments.

[0545] Examples of prompt statements include:

[0546] "Based on emotional data obtained simultaneously with children's songwriting performance data, please generate musical talent indicators and individualized educational plans."

[0547] These are some examples.

[0548] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0549] Step 1:

[0550] The user uses a device to input the child's performance data. This includes records of musical performance and information on learning progress. This data is initially processed within the device and converted into a format that can be sent to the server. The converted data is then output and ready to be sent to the server.

[0551] Step 2:

[0552] The device acquires emotional data in real time using an emotion engine. Specifically, it quantifies the emotional state by analyzing voice tone with a microphone and recognizing facial expressions with a camera. The acquired emotional data is output and similarly sent to the server.

[0553] Step 3:

[0554] The device sends user-entered performance data and acquired sentiment data to the server via a secure connection. Once all data preparation on the device is complete, the performance data and sentiment data are aggregated on the server.

[0555] Step 4:

[0556] The server applies a deep learning algorithm to the received performance and sentiment data. The input here consists of various data sent from the terminal. Based on this data, the server uses a numerical model to analyze talent and aptitude and calculates a similarity score. This score is obtained as the output.

[0557] Step 5:

[0558] The server then takes emotional data into account to generate a personalized education plan. This involves further data analysis to assess how the user's emotional state influences their talent traits. The generated education plan becomes the final output and is provided to the user.

[0559] (Application Example 2)

[0560] 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."

[0561] Conventional systems have struggled to provide real-time decision-making support that takes into account an individual's emotional state, and have faced challenges in making meaningful suggestions that utilize emotional recognition information, particularly in situations such as shopping. Therefore, there is a need for a new method that analyzes an individual's emotional information in real time and dynamically optimizes product and service recommendations based on the results.

[0562] 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.

[0563] In this invention, the server includes means for standardizing and analyzing complex information obtained from individuals through numerical representations generated using expert-selected information obtained from historical information resources; means for analyzing emotional information obtained using an emotion recognition device and making suggestions to support decision-making in real time; and means for presenting identified information using a display device and making dynamically optimized recommendations based on emotional information. This enables real-time optimized suggestions that take into account an individual's emotional state.

[0564] "Historical information resources" refer to databases and knowledge bases that contain accumulated past data and insights, including important information selected by experts.

[0565] A "numerical representation" is a mathematical or numerical model or data structure generated to analyze complex information obtained from an individual.

[0566] "Standardized analysis results" refer to information obtained as a result of an analysis that has been standardized to make different datasets comparable.

[0567] An "emotion recognition device" is a device or system that uses voice analysis or facial recognition technology to detect an individual's emotional state in real time.

[0568] "Real-time decision support" is a system that helps with decision-making by instantly analyzing an individual's emotional data and other relevant information, and providing the most appropriate suggestions and recommendations on the spot.

[0569] A "display device" is a device used to visually display information and is used to directly provide users with specific information or recommendations.

[0570] "Dynamically optimized recommendations" is a method that enhances the effectiveness of recommendations to users by adjusting content according to an individual's current state and emotions, and suggesting more appropriate products and services.

[0571] This embodiment of the invention is a system that enhances the in-store shopping experience using smart glasses. When a user wears smart glasses, an emotion recognition device acquires emotional information in real time using voice analysis and facial recognition technology. This makes it possible to understand the user's current emotional state.

[0572] The server performs standardized analysis based on data obtained from historical information resources and complex information acquired from individuals. This process includes generating numerical representations and analyzing identified growth areas in real time using deep learning algorithms. The server further integrates this information with emotional states obtained from sentiment recognition to generate dynamically optimized product recommendations.

[0573] The smart glasses, acting as a display device, show the information generated in this way. This enables the recommendation of optimal products and services to the user, personalizing the shopping experience to suit individual circumstances.

[0574] For example, if a user shows delight or interest in a particular product, a template recommending related items will be displayed on the screen. A specific example of the prompt message would look like this:

[0575] "This customer is currently very happy. Please suggest products and services that will enhance their shopping experience."

[0576] "Customers showed surprise when trying new products. Please recommend products they might purchase next."

[0577] This system allows users to have a more intuitive and satisfying shopping experience.

[0578] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0579] Step 1:

[0580] The user puts on smart glasses and begins walking around the store. The smart glasses' microphone and camera capture the user's voice and facial expressions in real time. This input data includes both audio and video information.

[0581] Step 2:

[0582] The device transmits the acquired audio and video information to the emotion recognition device. The emotion recognition device performs audio analysis and facial expression recognition to infer the user's emotional state. The output of this process is the user's identified emotional state.

[0583] Step 3:

[0584] The terminal sends user emotional state data to the server. The server generates a numerical representation based on the emotional state data, past purchase history, and store inventory information, and analyzes it using a deep learning algorithm. The output of this process is a standardized analysis result.

[0585] Step 4:

[0586] The server generates dynamically optimized product recommendations based on standardized analysis results. These recommendations are adjusted according to the user's current emotional state. This output is a list of recommendations for a specific product.

[0587] Step 5:

[0588] The server sends the generated recommendation list to the smart glasses. The recommendations are visualized and displayed on the smart glasses' display. Users can then obtain information about the suggested products and services and make purchases on the spot.

[0589] 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.

[0590] 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.

[0591] 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.

[0592] [Fourth Embodiment]

[0593] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0594] 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.

[0595] 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).

[0596] 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.

[0597] 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.

[0598] 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).

[0599] 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.

[0600] 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.

[0601] 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.

[0602] 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.

[0603] 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.

[0604] 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.

[0605] 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".

[0606] The system of this invention analyzes complex data to identify individual talents and suggest areas for growth. This system mainly consists of three components: a server, a terminal, and a user.

[0607] The server retrieves information selected by experts from historical databases. This information includes data in various forms, such as the works, research, and records of famous geniuses of the past. The server collects this data and prepares it in a format that can be analyzed.

[0608] Users input their child's performance data via their device. This performance data includes a wide range of things, such as drawings, essays, and records of science experiments. Users can also send image data using the device's camera or scanner. The input data is sent to the server in text, image, and other formats.

[0609] The server analyzes the input data using a generated numerical model. This model is built using deep learning algorithms and evaluates the similarity between the input data and the data in the historical database. The similarity is quantified through the analysis of each feature.

[0610] Based on the analysis results, the server identifies the growth areas best suited to the user. This identification is performed using a similarity score, and the user is notified of the identified growth areas and the reasons for their identification. The server also generates and provides a personalized education plan to the user. This plan includes suggestions for education and training to further develop skills in the identified growth areas.

[0611] As a concrete example, suppose a user takes a picture of a drawing their child has made with their device and inputs it. The server analyzes this image data and evaluates its similarity to works by famous past artists. If the server determines that the child has particularly high talent in the arts, it will propose a personalized art-related educational plan to the user. This plan may include introductions to specialized art schools or information on local art competitions that the child can participate in.

[0612] As described above, the system of the present invention helps to identify a child's potential talents and to create an environment that promotes their growth.

[0613] The following describes the processing flow.

[0614] Step 1:

[0615] The server retrieves historical data selected by experts from the database and prepares it in a format that can be analyzed. This includes standardizing text and image data formats, removing errors and noise, and adding metadata.

[0616] Step 2:

[0617] The user inputs the child's performance data into the device. Using the device's camera or scanner, digital images of paintings and artwork are captured, and essays and research records are input as text data.

[0618] Step 3:

[0619] The terminal sends data entered by the user to the server. At this time, it checks whether the data format is properly formatted and adjusts the format if necessary.

[0620] Step 4:

[0621] The server preprocesses the received data. For image data, important features are extracted using image recognition technology, and for text data, linguistic information is extracted using text analysis tools.

[0622] Step 5:

[0623] The server uses a generated AI model to calculate a similarity score between the processed user data and the data in the historical database. A deep learning algorithm evaluates and quantifies the similarity within the feature space.

[0624] Step 6:

[0625] The server aggregates the analysis results and identifies which growth areas the individual has the highest aptitude for. Multiple fields, such as mathematics, science, art, and literature, are considered, and the field with the highest score is selected.

[0626] Step 7:

[0627] The server generates a personalized education plan based on identified growth areas. This plan includes, if necessary, expert referrals and suggestions for relevant workshops.

[0628] Step 8:

[0629] The server sends the generated report to the terminal. The terminal then presents the user with identified growth areas and corresponding curriculum suggestions, supporting them in selecting educational policies.

[0630] (Example 1)

[0631] 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".

[0632] In recent years, there has been a growing demand for technologies that can identify individual talents early on and appropriately support their development. However, methods for identifying individual talents from diverse data and building effective educational and training strategies based on them remain insufficient. Traditional systems have problems in that it is difficult to analyze large amounts of data and compare them with historical success stories, making it difficult to provide individualized educational strategies.

[0633] 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.

[0634] In this invention, the server includes means for analyzing multidimensional data of individuals generated using expert-selected data obtained from an information set, means for generating personalized educational strategies based on similarity evaluations, and means for providing the generated educational strategies. This makes it possible to provide users with highly personalized areas of growth and educational strategies to support their development.

[0635] An "information set" is a dataset that compiles historical databases or data selected by experts.

[0636] "Generative artificial intelligence technology" refers to a technology that uses deep learning algorithms to analyze input data and evaluate its similarity.

[0637] "Individual" refers to a user targeted by the system or an individual who is of interest to that user.

[0638] "Multidimensional data" refers to data that contains diverse information in different formats, such as text and images.

[0639] A "feature space" is the space in which features extracted to evaluate the similarity of data are arranged.

[0640] "Similarity assessment" is the process of quantifying and evaluating the degree of similarity between input data and historical data.

[0641] A "personalized education strategy" is a plan that proposes education and training tailored to individual needs, based on identified areas of growth.

[0642] "User input devices" refer to terminal devices such as cameras and scanners that users use to input data.

[0643] A "generated educational strategy" refers to specific suggestions and plans created to support the user's growth.

[0644] This invention relates to a system that identifies an individual's talents and supports their development. The system consists of three components: a server, a terminal, and a user.

[0645] The server accesses a database via the network to collect historical data from the information set. This database contains works, research, and records of historical figures selected by experts. The server organizes this data in an analyzable format.

[0646] Users input their personal performance data using their devices. This includes a variety of data formats, such as children's drawings and essays, and records of science experiments. Users can input image data using the camera or scanner function built into their devices. The input data is sent to the server in the form of text and images.

[0647] Upon receiving input data, the server performs analysis using generative artificial intelligence technology. This technology is based on deep learning algorithms, extracting features from each data point and evaluating the similarity between them. The similarity is calculated numerically and used to identify growth areas.

[0648] Based on the analysis results, the server identifies the user's optimal areas for growth. Based on this, the server generates and provides a personalized educational strategy. This strategy includes specific educational and training suggestions to develop skills in the identified areas of growth.

[0649] As a concrete example, consider a scenario where a user takes a picture of their child's drawing with their device and uploads it to the system. The server analyzes this image data and evaluates its similarity to the works of accomplished artists. If the server determines that the child has excellent drawing skills, it proposes a personalized art-related educational plan. This plan may include introductions to art schools and information on local art events.

[0650] An example of a prompt message would be: "Take a picture of your child's drawing and evaluate their talent. In particular, we will analyze similarities to historical artists and suggest areas for optimal growth."

[0651] Thus, the present invention is a system aimed at identifying an individual's potential talents and creating an environment for developing them.

[0652] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0653] Step 1:

[0654] The server retrieves data from the information set. Specifically, the server accesses a database containing historical data selected by experts via the network. From this database, works and research of prominent figures are retrieved. The retrieved data is formatted on the server into an appropriate format for subsequent analysis. The input is the database, and the output is data ready for analysis.

[0655] Step 2:

[0656] The user inputs performance data using a terminal. The user takes pictures of information such as children's drawings and essays using the terminal's camera function or scanner and inputs that data into the system. The input data is sent to the server in the form of text, images, etc. In this step, the data that the user inputs is on the terminal, and the output is the data sent to the server.

[0657] Step 3:

[0658] The server applies generative artificial intelligence techniques to analyze the input data. Deep learning algorithms are used to extract features from the input data and evaluate their similarity to historical data. This similarity is quantified and used as the primary output of the analysis. Here, user-provided data is the input, and the feature extraction results and similarity score are the outputs.

[0659] Step 4:

[0660] The server identifies the optimal growth areas for the user based on the analysis results. It analyzes similarity scores and determines which growth areas are appropriate based on those scores. Scores indicating potential talent in specific areas are considered. The input is the similarity score, and the output is the identified growth areas.

[0661] Step 5:

[0662] The server generates and provides a personalized educational strategy to the user based on the identified growth area. This educational strategy includes specific suggestions and plans to promote the user's growth. For example, if the arts field is identified, the server will provide the user with information on specialized art schools and local events. The input is the identified growth area, and the output is the generated educational strategy.

[0663] (Application Example 1)

[0664] 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".

[0665] Effectively identifying an individual's talents and understanding their optimal areas for growth is crucial in educational settings. However, conventional methods have limitations in providing individualized educational plans to identify and promote talent development. In particular, there are challenges regarding immediacy and individualization. This invention aims to enable real-time individual talent analysis based on historical data, facilitating immediate feedback and the provision of personalized educational plans in physical stores.

[0666] 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.

[0667] This invention includes a server that analyzes detailed information obtained from an individual through a numerical model generated using expertise acquired from historical sources, and determines the optimal growth area based on the similarity evaluation results; a server that provides the analysis results to the user in real time through a terminal in a physical store; and a server that generates a personalized education plan and notifies the user's terminal of it. This enables immediate assessment of an individual's talents and the presentation of an optimal growth strategy.

[0668] A "historical source" is a collection of information that records important knowledge and data from the past, including data carefully selected by experts.

[0669] A "numerical model" is a computational model constructed to analyze data and derive specific conclusions, primarily using mathematical methods.

[0670] "Detailed information obtained from an individual" refers to specific data provided by that individual, and includes information in the form of records of works of art or performances, or other forms of information.

[0671] The "similarity evaluation result" is a numerical evaluation of how similar the analyzed data is to the comparison target, and the result is shown.

[0672] A "growth area" is a field or subfield that is considered most suitable for an individual to develop their talents.

[0673] A "physical store terminal" is a device located in a physical store that is a computer or device equipped with an interface for inputting and retrieving information.

[0674] "Real-time provision" refers to the act of presenting or reflecting information immediately without delay, and is a process that requires immediacy.

[0675] An "individualized education plan" is a special educational program designed based on the specific needs and abilities of an individual.

[0676] As an embodiment of this invention, a system is constructed that primarily involves servers, terminals, and users.

[0677] The server uses a database obtained from historical sources to analyze detailed information acquired from individuals using a generated numerical model. The server executes machine learning algorithms using a deep learning framework (e.g., TensorFlow or PyTorch) based on Python. During this process, it evaluates the similarity between the acquired data and historical data to identify areas of growth.

[0678] The device functions as a smartphone or tablet application and is developed using Flutter or React Native. This device retrieves detailed information provided by the user and sends it to a server using its camera and scanning functions. The device also provides the user with real-time analysis results received from the server.

[0679] The user operates this device and receives a personalized educational plan. This plan includes suggestions for education and training based on identified areas of growth.

[0680] As a concrete example, suppose an elementary school teacher takes photos of students' artwork with their smartphone and sends the data through an app. The server analyzes the image data and evaluates its similarity to that of famous past artists. As a result, areas of artistic growth are identified, and information on specialized art workshops and local competitions is notified to the user's device. This process is represented by a prompt message such as: "Analyze this student's recent work and identify the most suitable educational areas. Please also consider similarities to famous past artists."

[0681] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0682] Step 1:

[0683] Users use their devices to input records of students' artwork and activities. Specifically, they take photos of students' drawings with their smartphone cameras and send the data to the server via the app. The input data is in image format, and the device prepares it as digital data.

[0684] Step 2:

[0685] The server receives digital data sent from the terminal and performs analysis using a generative AI model. Deep learning algorithms using TensorFlow or PyTorch are applied to the data analysis to evaluate its similarity to data in historical databases. The input here is student artwork data, and the output is the similarity evaluation result based on that data.

[0686] Step 3:

[0687] Based on the analysis results, the server identifies the optimal areas of growth for the user. This includes a process of selecting specific educational fields using similarity scores. The output is the identified areas of growth for a personalized educational plan.

[0688] Step 4:

[0689] The server notifies the terminal of identified growth areas and generates a personalized educational plan. The terminal provides the user with analysis results in real time, and the educational plan may include information on art schools and competitions. The output is information that the user can use to review the educational plan.

[0690] Step 5:

[0691] Users utilize the provided educational plans through their devices to help promote student growth. This allows users to plan specific educational activities as the next steps. The input is the content of the educational plan, and the output is a specific action plan for the students.

[0692] 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.

[0693] The system of this invention recognizes the user's emotions by combining them with an emotion engine, and based on that, identifies talents and generates personalized educational plans. The system mainly consists of a server, terminals, and users, and each of these parts works together to achieve this.

[0694] First, the server retrieves information selected by experts from a historical database and generates a numerical model using a deep learning algorithm. This model is then used to analyze talents and aptitudes based on data submitted by users.

[0695] The user inputs the child's performance data using a device, and also records the emotional state in real time using sensors equipped with an emotion engine. Specifically, it uses voice analysis to infer emotions from voice using a microphone, and facial expression recognition to determine emotions from facial expressions captured through a camera.

[0696] The device sends acquired performance data and emotional data to the server. Based on the emotional data analyzed by the emotion engine, the server takes the user's emotional state into consideration and performs a highly accurate talent analysis.

[0697] The server integrates the acquired data with emotional states, calculates a similarity score using a deep learning algorithm, and identifies specific areas of growth. Furthermore, by incorporating emotional data, it can more effectively adjust personalized educational plans.

[0698] As a concrete example, suppose a user inputs a child's composition performance from a device, and simultaneously, an emotion engine records the child's emotions during the performance. The server analyzes this data, and if it highlights musical talent, it generates a personalized music education plan. This plan may include recommendations for music schools or suggestions for lessons using specific instruments.

[0699] Thus, by incorporating emotion recognition, the system of the present invention enables more sophisticated educational support based on the characteristics of children.

[0700] The following describes the processing flow.

[0701] Step 1:

[0702] The server retrieves information selected by experts from historical databases and generates numerical models using deep learning algorithms. This builds the foundation necessary for analyzing talent and aptitude.

[0703] Step 2:

[0704] The user uses a device to input the child's performance data. Performance data includes various forms of information, such as artistic works like drawings and music, written works like essays, and the results of scientific experiments.

[0705] Step 3:

[0706] The device receives data input from the user and activates the emotion engine. The emotion engine analyzes the voice using the microphone and recognizes facial expressions through the camera to record the user's emotional state in real time.

[0707] Step 4:

[0708] The device sends performance data and sentiment data to the server. During this process, the data format is standardized, and necessary metadata is added.

[0709] Step 5:

[0710] The server preprocesses the received data, and in particular, it quantifies emotional tendencies based on analysis, especially for emotional data. This makes the user's emotional state available as data.

[0711] Step 6:

[0712] The server uses pre-processed data to calculate similarity scores using a deep learning algorithm. Sentimental data is also incorporated into the analysis and considered as a variable that influences talent identification.

[0713] Step 7:

[0714] The server concretizes identified growth areas based on similarity scores and sentiment analysis results, thereby identifying which areas of talent particularly stand out.

[0715] Step 8:

[0716] Based on the results generated by the server, an individualized education plan is created that takes emotional states into account. This plan includes suggestions for learning projects and activities based on interests and comfort levels.

[0717] Step 9:

[0718] The server sends the generated report to the terminal. The terminal displays the identified growth areas and the contents of the personalized education plan to the user, providing information for reviewing and selecting educational policies.

[0719] (Example 2)

[0720] 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".

[0721] Traditional educational support systems, while analyzing performance data to provide individualized learning plans, struggled to accurately analyze talents and aptitudes while considering an individual's emotional state. Ignoring the influence of emotions on learning and talent development resulted in insufficient identification of optimal areas of growth and adjustment of learning plans to suit individual characteristics.

[0722] 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.

[0723] In this invention, the server includes means for analyzing complex data obtained from individuals through a numerical model generated using information selected by experts, means for calculating similarity scores in a feature space using a deep learning algorithm, and means for generating personalized educational plans based on acquired sentiment data. This enables precise analysis that takes emotions into account and the provision of optimal educational support.

[0724] "Information selected by experts" refers to data and knowledge that experts with past experience and knowledge have deemed appropriate.

[0725] A "numerical model" is a mathematical representation of real-world phenomena, serving as the foundation for making predictions and analyses based on data.

[0726] A "deep learning algorithm" refers to a technology that uses multi-layered neural networks to analyze data and automatically learn complex patterns and features.

[0727] A "similarity score in the feature space" is an index that quantifies the relationships and commonalities between specific data points, and is used to compare and evaluate data.

[0728] An "emotion engine" refers to a system that evaluates and quantifies a person's emotional state using methods such as voice analysis and facial recognition.

[0729] An "individualized education plan" refers to an educational instruction program that is tailored to the individual characteristics and progress of each learner.

[0730] "Methods for analyzing talent" refer to methods and techniques for evaluating an individual's characteristics and abilities based on acquired data, and identifying their potential in that field.

[0731] This invention relates to a system that enables highly accurate talent analysis, including emotion recognition, and the generation of personalized educational plans. The system mainly consists of three elements: a server, a terminal, and a user.

[0732] The server retrieves information selected by experts from historical databases and uses deep learning algorithms to generate numerical models based on this information. These deep learning algorithms analyze complex data and calculate similarity scores in the feature space. This numerical model is used to analyze user talents and aptitudes using data transmitted from the terminal. Furthermore, the server uses emotional data obtained by the emotion engine to perform detailed analyses that consider the user's emotional state, and utilizes this information to generate personalized educational plans.

[0733] The terminal functions as the user interface, acquiring real-time emotional data from an emotion engine in addition to performance data entered by the user. This emotional data acquisition utilizes hardware such as microphones for voice analysis and cameras for facial recognition. The terminal then aggregates this data and transmits it to the server.

[0734] Users provide information to the server by inputting their child's performance data through their device. An emotion engine is also used to record the child's emotional state in real time. This allows users to receive personalized educational plans based on their child's characteristics.

[0735] For example, if a user inputs a child's composition performance into a device, and the emotion engine simultaneously records the child's joyful facial expressions during the performance, the server analyzes this data and obtains results that highlight the child's talent in the field of music. Based on this, a personalized music education plan is generated. This plan may include recommendations for music schools or suggestions for lessons using specific instruments.

[0736] Examples of prompt statements include:

[0737] "Based on emotional data obtained simultaneously with children's songwriting performance data, please generate musical talent indicators and individualized educational plans."

[0738] These are some examples.

[0739] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0740] Step 1:

[0741] The user uses a device to input the child's performance data. This includes records of musical performance and information on learning progress. This data is initially processed within the device and converted into a format that can be sent to the server. The converted data is then output and ready to be sent to the server.

[0742] Step 2:

[0743] The device acquires emotional data in real time using an emotion engine. Specifically, it quantifies the emotional state by analyzing voice tone with a microphone and recognizing facial expressions with a camera. The acquired emotional data is output and similarly sent to the server.

[0744] Step 3:

[0745] The device sends user-entered performance data and acquired sentiment data to the server via a secure connection. Once all data preparation on the device is complete, the performance data and sentiment data are aggregated on the server.

[0746] Step 4:

[0747] The server applies a deep learning algorithm to the received performance and sentiment data. The input here consists of various data sent from the terminal. Based on this data, the server uses a numerical model to analyze talent and aptitude and calculates a similarity score. This score is obtained as the output.

[0748] Step 5:

[0749] The server then takes emotional data into account to generate a personalized education plan. This involves further data analysis to assess how the user's emotional state influences their talent traits. The generated education plan becomes the final output and is provided to the user.

[0750] (Application Example 2)

[0751] 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".

[0752] Conventional systems have struggled to provide real-time decision-making support that takes into account an individual's emotional state, and have faced challenges in making meaningful suggestions that utilize emotional recognition information, particularly in situations such as shopping. Therefore, there is a need for a new method that analyzes an individual's emotional information in real time and dynamically optimizes product and service recommendations based on the results.

[0753] 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.

[0754] In this invention, the server includes means for standardizing and analyzing complex information obtained from individuals through numerical representations generated using expert-selected information obtained from historical information resources; means for analyzing emotional information obtained using an emotion recognition device and making suggestions to support decision-making in real time; and means for presenting identified information using a display device and making dynamically optimized recommendations based on emotional information. This enables real-time optimized suggestions that take into account an individual's emotional state.

[0755] "Historical information resources" refer to databases and knowledge bases that contain accumulated past data and insights, including important information selected by experts.

[0756] A "numerical representation" is a mathematical or numerical model or data structure generated to analyze complex information obtained from an individual.

[0757] "Standardized analysis results" refer to information obtained as a result of an analysis that has been standardized to make different datasets comparable.

[0758] An "emotion recognition device" is a device or system that uses voice analysis or facial recognition technology to detect an individual's emotional state in real time.

[0759] "Real-time decision support" is a system that helps with decision-making by instantly analyzing an individual's emotional data and other relevant information, and providing the most appropriate suggestions and recommendations on the spot.

[0760] A "display device" is a device used to visually display information and is used to directly provide users with specific information or recommendations.

[0761] "Dynamically optimized recommendations" is a method that enhances the effectiveness of recommendations to users by adjusting content according to an individual's current state and emotions, and suggesting more appropriate products and services.

[0762] This embodiment of the invention is a system that enhances the in-store shopping experience using smart glasses. When a user wears smart glasses, an emotion recognition device acquires emotional information in real time using voice analysis and facial recognition technology. This makes it possible to understand the user's current emotional state.

[0763] The server performs standardized analysis based on data obtained from historical information resources and complex information acquired from individuals. This process includes generating numerical representations and analyzing identified growth areas in real time using deep learning algorithms. The server further integrates this information with emotional states obtained from sentiment recognition to generate dynamically optimized product recommendations.

[0764] The smart glasses, acting as a display device, show the information generated in this way. This enables the recommendation of optimal products and services to the user, personalizing the shopping experience to suit individual circumstances.

[0765] For example, if a user shows delight or interest in a particular product, a template recommending related items will be displayed on the screen. A specific example of the prompt message would look like this:

[0766] "This customer is currently very happy. Please suggest products and services that will enhance their shopping experience."

[0767] "Customers showed surprise when trying new products. Please recommend products they might purchase next."

[0768] This system allows users to have a more intuitive and satisfying shopping experience.

[0769] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0770] Step 1:

[0771] The user puts on smart glasses and begins walking around the store. The smart glasses' microphone and camera capture the user's voice and facial expressions in real time. This input data includes both audio and video information.

[0772] Step 2:

[0773] The device transmits the acquired audio and video information to the emotion recognition device. The emotion recognition device performs audio analysis and facial expression recognition to infer the user's emotional state. The output of this process is the user's identified emotional state.

[0774] Step 3:

[0775] The terminal sends user emotional state data to the server. The server generates a numerical representation based on the emotional state data, past purchase history, and store inventory information, and analyzes it using a deep learning algorithm. The output of this process is a standardized analysis result.

[0776] Step 4:

[0777] The server generates dynamically optimized product recommendations based on standardized analysis results. These recommendations are adjusted according to the user's current emotional state. This output is a list of recommendations for a specific product.

[0778] Step 5:

[0779] The server sends the generated recommendation list to the smart glasses. The recommendations are visualized and displayed on the smart glasses' display. Users can then obtain information about the suggested products and services and make purchases on the spot.

[0780] 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.

[0781] 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.

[0782] In the above embodiment, an example was given in which the 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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."

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0801] The following is further disclosed regarding the embodiments described above.

[0802] (Claim 1)

[0803] Using expert-selected information obtained from historical databases,

[0804] Through the generated numerical model, we analyze complex data obtained from individuals.

[0805] Identify optimal growth areas based on similarity analysis results.

[0806] A system that includes the means.

[0807] (Claim 2)

[0808] To present identified growth areas based on the results of similarity analysis,

[0809] Generate individualized education plans

[0810] The system according to claim 1.

[0811] (Claim 3)

[0812] The generated numerical model uses a deep learning algorithm,

[0813] Calculate the similarity score in the feature space.

[0814] The system according to claim 1.

[0815] "Example 1"

[0816] (Claim 1)

[0817] Using expert-selected data obtained from the information set,

[0818] Through generative artificial intelligence technology, we analyze multidimensional data obtained from individuals.

[0819] Identifying the optimal growth region based on similarity evaluation in the feature space.

[0820] means and

[0821] Based on growth areas identified through similarity assessment, we generate personalized educational strategies.

[0822] means and

[0823] Data is received via user input devices.

[0824] means and

[0825] Provide users with generated educational strategies and areas for growth.

[0826] means and

[0827] A system that includes this.

[0828] (Claim 2)

[0829] To generate personalized educational strategies that identify growth areas based on similarity assessments.

[0830] The system according to claim 1.

[0831] (Claim 3)

[0832] The aforementioned generative artificial intelligence technology uses deep learning methods to calculate similarity scores in the feature space.

[0833] The system according to claim 1.

[0834] "Application Example 1"

[0835] (Claim 1)

[0836] Using expertise obtained from historical sources,

[0837] Through the generated numerical model, we analyze detailed information obtained from individuals.

[0838] The optimal growth area is determined based on the similarity evaluation results.

[0839] means and

[0840] The aforementioned analysis results are provided to users in real time via terminals in physical stores.

[0841] means and

[0842] Generate an individualized education plan and notify the user's device.

[0843] means and

[0844] A system that includes this.

[0845] (Claim 2)

[0846] To present identified growth areas based on similarity assessment results and real-time feedback,

[0847] Generate individualized education plans

[0848] The system according to claim 1.

[0849] (Claim 3)

[0850] The generated numerical model uses a machine learning algorithm,

[0851] It calculates a similarity index in the feature space and performs real-time evaluation.

[0852] The system according to claim 1.

[0853] "Example 2 of combining an emotion engine"

[0854] (Claim 1)

[0855] A means of analyzing complex data obtained from individuals through numerical models generated using information selected by experts,

[0856] A means for calculating similarity scores in a feature space using a deep learning algorithm,

[0857] A means of generating personalized education plans based on acquired emotional data,

[0858] A method for analyzing talent by considering emotional states acquired in real time by an emotion engine,

[0859] A means of integrating emotional data and performance data to identify optimal growth areas,

[0860] A system that includes this.

[0861] (Claim 2)

[0862] The system according to claim 1, which generates an individualized educational plan to present growth areas identified based on the results of similarity analysis.

[0863] (Claim 3)

[0864] The system according to claim 1, which integrates real-time emotional data acquisition and talent analysis to provide educational support.

[0865] "Application example 2 when combining with an emotional engine"

[0866] (Claim 1)

[0867] Using information selected by experts and obtained from historical information resources,

[0868] Through the generated numerical representation, we analyze complex information obtained from individuals.

[0869] A means of identifying optimal growth areas based on standardized analysis results,

[0870] We analyze the emotional information obtained using an emotion recognition device.

[0871] A means of making suggestions to support decision-making in real time,

[0872] The information identified using a display device is presented,

[0873] A means of making dynamically optimized recommendations based on sentiment information,

[0874] ...

[0875] A system that includes this.

[0876] (Claim 2)

[0877] To present identified growth areas based on standardized analysis results,

[0878] Generate personalized plans that take emotional information into account.

[0879] The system according to claim 1.

[0880] (Claim 3)

[0881] The numerical representation generated above is then processed using a machine learning algorithm.

[0882] Calculate a standardized score in the feature space.

[0883] The system according to claim 1. [Explanation of Symbols]

[0884] 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. Using expert-selected information obtained from historical databases, Through the generated numerical model, we analyze complex data obtained from individuals. Identify optimal growth areas based on similarity analysis results. A system that includes the means.

2. To present identified growth areas based on the results of similarity analysis, Generate individualized education plans The system according to claim 1.

3. The generated numerical model uses a deep learning algorithm, Calculate the similarity score in the feature space. The system according to claim 1.

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