system

Abstract

We provide the system. [Solution] A means for accumulating data about children received from multiple information terminals, A means of analyzing accumulated data to evaluate children's potential talents and aptitudes, A means of proposing learning activities or hobby activities for children based on the evaluation results, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern times, it is very important to discover children's potential abilities and characteristics at an early stage and provide appropriate education. However, objectively evaluating children's abilities and selecting appropriate learning activities and hobby activities can be a heavy burden for guardians. In addition, in conventional evaluation methods, data analysis may be subjectively biased and lack reliability. There is a need to solve such problems, discover new talents of children, and provide an environment in which guardians can determine educational policies with confidence.

Means for Solving the Problems

[0005] This invention provides a system that analyzes data on children acquired from multiple information terminals to evaluate their potential abilities and aptitudes. Specifically, it stores the received data in the cloud and analyzes it using a generating AI to achieve an objective evaluation. Furthermore, it supports parents in making appropriate choices by suggesting learning activities and hobby activities suitable for children based on the evaluation. In addition, it accepts feedback on the evaluation results and uses it to improve the accuracy of the analysis, thereby continuously enhancing the reliability of the evaluation. Moreover, it provides a system that can be used with peace of mind by incorporating means for anonymizing data to protect privacy.

[0006] An "information terminal" is an electronic device used for inputting data from a user, and includes devices such as smartphones, tablets, and personal computers.

[0007] "Data concerning children" refers to a collection of information related to the individual characteristics and activities of each child, such as their age, gender, interests, past achievements, and works.

[0008] "Means of storage" refers to the process or function of securely storing received data and making it accessible as needed.

[0009] "Means of analysis" refers to the methods and techniques of data processing and analysis used to evaluate children's potential talents and aptitudes using accumulated data.

[0010] "Potential talents and aptitudes" refer to abilities and characteristics that a child may not yet be fully recognized but that could potentially be expressed in the future.

[0011] "Proposed methods" refer to methods and systems for indicating learning and recreational activities suitable for children, based on the analysis results.

[0012] "Feedback" refers to the opinions and evaluations that users provide regarding a proposed solution.

[0013] "Improving analytical accuracy" refers to the process of improvement undertaken to enhance the accuracy and reliability of data analysis results.

[0014] "Anonymization" refers to measures taken to protect privacy by processing data while concealing children's personal information. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This 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 Embodiment 2 when the 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 the emotion engine is combined.

Mode for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the language used in the following description will be explained.

[0018] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of 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.

[0019] 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.

[0020] 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.

[0021] 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).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] This invention provides a system for analyzing children's potential abilities and characteristics and proposing appropriate learning and recreational activities. To implement this invention, it is desirable to configure it as follows.

[0037] Users input data about their children using a dedicated information terminal or web application. This data includes the child's age, gender, interests, past achievements, and works (images, text, music files, etc.). The terminal is responsible for initial formatting of the data entered by the user and then sending it to the server.

[0038] The server securely stores the received data in the cloud. This stored data is then analyzed using generative AI. The analysis process involves data cleansing, feature extraction, and comparison with past success stories to evaluate the children's potential talents and aptitudes. For example, image data of children's drawings could be analyzed to extract features such as color sense and originality of composition, thereby evaluating their potential artistic talent.

[0039] After the evaluation is complete, the server generates suggestions for optimal learning and recreational activities based on the child's characteristics. These suggestions are customized specifically for each child; for example, a child who is interested in music and has a good sense of rhythm might be recommended drum or dance lessons.

[0040] Subsequently, the server sends the generated suggestions to the information terminal, providing the user with the results. The user reviews the suggestions on the terminal and provides feedback as needed. This feedback is sent to the server and used to improve the generating AI model. This provides an environment in which the accuracy of the analysis results continuously improves.

[0041] Furthermore, this system prioritizes privacy protection; data received from users is anonymized, ensuring safe and secure use. This allows parents to confidently utilize the data they provide while supporting their children's development.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user uses a dedicated terminal to input data about the child, including their age, gender, interests, past achievements, and works (such as images and texts).

[0045] Step 2:

[0046] The terminal receives the input data and performs initial formatting. This formatting includes resizing images and standardizing the format of text data.

[0047] Step 3:

[0048] The terminal sends the formatted data to the server using a secure communication protocol.

[0049] Step 4:

[0050] The server stores the received data in cloud storage. During this process, the data is always encrypted, ensuring privacy.

[0051] Step 5:

[0052] The server inputs the accumulated data into the generating AI, which then cleanses the data. This removes noise and processes missing data.

[0053] Step 6:

[0054] The server uses generative AI to extract features from the data. For example, in the case of image data, it quantifies features such as color and composition.

[0055] Step 7:

[0056] The server evaluates the child's potential and aptitude by comparing the extracted features with a database of past success stories to find similar patterns.

[0057] Step 8:

[0058] Based on the evaluation results, the server generates content suggesting learning and hobby activities suitable for the children.

[0059] Step 9:

[0060] The server sends the generated suggestions to the information terminal, allowing the user to easily review them.

[0061] Step 10:

[0062] Users review the provided suggestions and provide feedback via their devices. This feedback includes the usefulness of the suggestions and the children's reactions.

[0063] Step 11:

[0064] The server incorporates the received feedback into its analysis and uses it to improve the accuracy of future generative AI models.

[0065] (Example 1)

[0066] 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."

[0067] Traditionally, there has been no system that accurately analyzes children's potential and characteristics and proposes optimal learning and recreational activities based on the results. Furthermore, there has been a lack of means to meet diverse needs, such as secure management of collected data, data conversion for analysis, and improvement of analysis accuracy through user feedback. As a result, optimizing education by leveraging the individual characteristics of each child has been difficult.

[0068] 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.

[0069] In this invention, the server includes means for storing information about children received from users via a data communication device, means for analyzing the stored information using a generative model to evaluate the children's potential abilities and characteristics, and means for recommending individually suitable educational activities or hobby activities based on the analysis results. This makes it possible to provide individualized suggestions tailored to the characteristics of each child.

[0070] "User" refers to the entity that operates the system and inputs and verifies information about the child.

[0071] "Data communication device" refers to equipment or applications used to collect information and communicate with a server.

[0072] "Information about children" refers to various data about children, including their age, gender, interests, past achievements, and works.

[0073] "Means of storage" refers to the process of securely saving acquired information to a storage device or cloud storage.

[0074] A "generative model" refers to AI technology used to analyze received information and evaluate the characteristics of children.

[0075] "Means of analysis" refers to the process of processing received information and evaluating the child's potential abilities and characteristics.

[0076] "Recommendation methods" refer to the process of selecting and proposing educational and recreational activities suitable for children based on analysis.

[0077] "Means of securely managing and converting information into an analyzable format" refers to the process of ensuring the security of the input information and then formatting it into a format suitable for analysis.

[0078] This invention is a system that collects information about children and proposes optimal activities based on their individual characteristics. Specific embodiments are shown below.

[0079] Users input information about their children using a dedicated data communication device or web application. This information includes the child's age, gender, interests, past achievements, and works (e.g., images, text, audio files, etc.). The data communication device is equipped with functions for initial formatting and standardization of the information.

[0080] Information transmitted from the device is received by the server and securely stored in cloud storage. The server analyzes the received information using a generative AI model. This generative AI model has the function of extracting features and cleaning the information, and evaluating the child's potential abilities and characteristics. Furthermore, based on the analyzed information, it generates suggestions for educational and recreational activities.

[0081] For example, if a child's drawing is input as image data, the server can analyze the data and evaluate the child's artistic talent based on characteristics such as color sense and originality of composition. Based on this evaluation, it can then suggest participation in art classes or art events.

[0082] Users can review the suggestions on their devices and submit feedback, which helps the server further improve the accuracy of its analysis. This feedback is used to improve the generated AI model, increasing the accuracy and reliability of the system.

[0083] By the way, in order to ensure the security of information, the server anonymizes the information it receives, providing an environment in which users can provide data with peace of mind.

[0084] (Example of a prompt message)

[0085] "He is a 10-year-old boy who loves to draw. We have image data of his past artwork. Please evaluate his artistic abilities and suggest appropriate activities."

[0086] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0087] Step 1:

[0088] Users input information about their children through a dedicated data communication device or web application. This information includes the child's age, gender, interests, past achievements, and works (images, text, etc.). The input information is initially formatted on the terminal to ensure a consistent format. The output is formatted data, ready for transmission to the server.

[0089] Step 2:

[0090] The terminal sends the formatted information to the server using a secure protocol. The output is the received data sequence, and processing begins on the server side.

[0091] Step 3:

[0092] The server securely stores the received information in cloud storage. The input here is data sent from the terminal, and the output is data stored in cloud storage. This data is then organized in a database for analysis.

[0093] Step 4:

[0094] The server launches a generative AI model to analyze the accumulated information. The input is organized data retrieved from cloud storage. In this phase, the data is cleansed, and unwanted noise is removed. The output is passed to the generative AI model as cleansed data with features extracted.

[0095] Step 5:

[0096] The server uses a generative AI model to extract features and evaluate the children's potential abilities and characteristics. The input is cleansed data, and the output consists of the results of the children's ability assessment and feature data based on their specific interests.

[0097] Step 6:

[0098] The server suggests individualized learning and recreational activities based on the children's evaluation results. The input is the children's evaluation results, and the output is a suggested activity. For example, it might recommend dance lessons to children with a strong sense of rhythm.

[0099] Step 7:

[0100] The server generates suggestions and sends them to the terminal for the user to view. The user can then review the suggestions on the terminal. The output is a displayed activity suggestion, which the user can review or modify.

[0101] Step 8:

[0102] The user inputs feedback on a proposal and sends it to the server via their device. The input is the user's feedback content, and the output is the feedback data received by the server.

[0103] Step 9:

[0104] The server uses user feedback to improve the generated AI model, thereby enhancing the overall analysis accuracy of the system. This is a process of analyzing the input feedback data and adjusting the model. The output, as an improved model, is used for subsequent analyses.

[0105] Step 10:

[0106] The server performs a process to anonymize the received information, protecting data privacy. The input is all information about the user and the child, and the output is anonymized data with protected personal information. This process allows users to use the system with peace of mind.

[0107] (Application Example 1)

[0108] 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."

[0109] There is a need to effectively draw out the potential abilities and aptitudes of minors and propose optimal educational activities tailored to their individual characteristics. However, achieving this requires a system that can analyze large amounts of information and make accurate suggestions. Furthermore, an environment where information about minors can be provided with peace of mind is also crucial.

[0110] 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.

[0111] In this invention, the server includes means for storing information about minors received from multiple information processing devices, means for analyzing the stored information to evaluate the minors' potential abilities and aptitudes, means for proposing learning activities or hobby activities for minors based on the evaluation results, and means for providing proposals based on the evaluation results in a virtual space and recommending educational activities. This enables the provision of individually optimized educational proposals tailored to the characteristics of minors and the appropriate handling of personal information.

[0112] An "information processing device" is a device used to input data about minors, including children, and transmit it to a server.

[0113] "Minor" refers to children and students who are in a developmental stage, and their potential abilities and aptitudes are the subjects of evaluation.

[0114] "Information" includes data such as age, gender, interests, past achievements, and works related to minors.

[0115] "Storage" refers to the process of securely saving received information and using it for subsequent analysis.

[0116] "Analysis" is the process of identifying the potential and aptitudes of minors based on accumulated information.

[0117] "Evaluation" is the process of determining the characteristics and potential of minors based on data obtained through analysis.

[0118] A "virtual space" is a digital environment constructed using information and communication technology, and is primarily used as a venue for educational activities.

[0119] "Educational activities" are activities that support the learning of minors and provide them with skills and knowledge that are tailored to their abilities and interests.

[0120] "Personal information" refers to information about minors or those around them that can identify a specific individual.

[0121] To realize this invention, it is basically necessary to build a system in which an information processing device and a server work together. The information processing device is a device for inputting information about minors, and a smartphone, tablet, or personal computer is used. The user uses these devices to input data such as the minor's age, gender, interests, past achievements, and works. This information is initially formatted and sent to the server.

[0122] The server operates as a backend system using Python and Django. It securely stores received information in the cloud and analyzes the stored data using generative AI. This analysis process involves data cleansing, feature extraction, and comparison with past success stories to evaluate the potential talents and aptitudes of minors. The generative AI model utilizes AI technologies such as the OpenAI® API to generate responses. Based on the evaluation results, this AI technology generates suggestions for optimal learning and hobby activities.

[0123] Evaluation results and suggestions are provided through a user interface set up as a virtual space. Recommended educational activities are easily accessible to users and can be used to select educational and hobby activities. Users can review the suggestions and provide feedback as needed. This feedback is used to improve the accuracy of the analysis. In addition, the received information is anonymized to ensure the appropriate handling of personal information.

[0124] For example, if a 10-year-old minor is entered, the system can analyze drawings they have made in the past, determine that they have "excellent color sense," and then provide specific educational suggestions such as "online digital art courses."

[0125] Example of a prompt:

[0126] "A 10-year-old boy enjoys drawing colorful pictures. Based on examples of his past work, please suggest learning activities that would suit his characteristics."

[0127] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0128] Step 1:

[0129] The user uses an information processing device to input data such as the age, gender, interests, past achievements, and works of minors. The input data is initially formatted on the information processing device, and processing is performed to remove incomplete information and duplicate data. This processing ensures that highly accurate data is sent to the server.

[0130] Step 2:

[0131] The server securely stores the received data in the cloud. The data is organized based on the database structure and anonymized according to security policies. The output of this step is formatted and anonymized data for use in subsequent analysis.

[0132] Step 3:

[0133] The server performs analysis on the formatted data using a generative AI model. The AI ​​model quantifies characteristics and aptitudes based on the input data and evaluates them. This evaluation includes feature extraction from the input data and comparison with past success stories based on those features. The output is an evaluation result regarding the potential abilities and aptitudes of minors.

[0134] Step 4:

[0135] The server generates suggestions for optimal learning and recreational activities based on the generated evaluation results. Using a generation AI model, it constructs an educational plan that matches the evaluation results in a form that can be executed within a virtual space. This suggestion is output in the form of specific learning courses and activity candidates.

[0136] Step 5:

[0137] The evaluation results and suggestions are sent to the information processing device and displayed on a screen where the user can review them. The user can select recommended activities or provide feedback. This feedback is sent back to the server to be used later to improve the accuracy of the analysis model. This process ensures continuous system improvement.

[0138] 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.

[0139] This invention combines an emotion engine with a system that evaluates children's potential and proposes optimal learning and recreational activities, thereby enabling more personalized suggestions that take the user's emotions into account. Specific embodiments for carrying out this invention are shown below.

[0140] Users input data about their children using a dedicated information terminal. This data includes the child's age, gender, interests, past achievements, and works (e.g., images, text). As the user operates the terminal, the emotion engine recognizes the user's emotions in real time through the camera and microphone built into the terminal. The emotion engine detects various emotions such as joy, surprise, and confusion from the user's facial expressions and voice, and sends this data to the server.

[0141] The server stores and securely manages received emotional and child data in the cloud. Emotional data is stored anonymized to ensure privacy. Generating AI analyzes this data to assess the child's potential talents and aptitudes. Furthermore, it considers the user's emotional data to generate suggestions for the most suitable learning or hobby activities for the child. For example, it customizes emotional suggestions by focusing on the results of analyzing works in which the user expressed "surprise" or "joy."

[0142] The server then sends optimized suggestions to the user's information terminal. The user can then view these suggestions on the terminal. When the user provides feedback on the suggestions, the server analyzes the feedback along with emotional data and uses it to improve the accuracy of future suggestions. In this way, the system, equipped with an emotion engine, incorporates the user's emotional aspects into the evaluation, enabling it to support maximizing the learning effectiveness of children.

[0143] The following describes the processing flow.

[0144] Step 1:

[0145] Users input data about their children (age, gender, interests, past achievements, works, etc.) using an information terminal. During this process, the camera and microphone built into the terminal analyze the user's facial expressions and voice using an emotion engine, acquiring emotional data in real time.

[0146] Step 2:

[0147] The terminal transmits data entered by the user and acquired emotional data to the server using a secure communication protocol.

[0148] Step 3:

[0149] The server stores the received data in cloud storage. During this process, emotional data is anonymized and processed in a way that protects user privacy.

[0150] Step 4:

[0151] The server uses generative AI to analyze child data and sentiment data. Sentiment data is used to consider which parts made a good impression on the user when making suggestions.

[0152] Step 5:

[0153] Based on the analysis results, the server generates suggestions for optimal learning or recreational activities to maximize the child's potential. During this process, the suggestions are prioritized according to the user's expressed emotions.

[0154] Step 6:

[0155] The server sends the generated suggestions to the terminal, where the user can view the detailed suggestions.

[0156] Step 7:

[0157] Based on the suggestions, users select specific educational activities or activities for the children and send their feedback to the server via their device.

[0158] Step 8:

[0159] The server re-analyzes user feedback along with sentiment data and uses the system's generating AI to improve the accuracy of its suggestions. This results in more personalized suggestions in the future.

[0160] (Example 2)

[0161] 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".

[0162] Conventional systems for suggesting learning and hobby activities for minors have the problem of not being able to take into account the user's emotions when evaluating the minor's potential abilities and aptitudes, and therefore not being able to provide sufficiently individualized suggestions. In addition, there were significant challenges in utilizing feedback to improve the accuracy of suggestions and protecting privacy, and in particular, the customization of suggestions based on emotions was insufficient.

[0163] 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.

[0164] In this invention, the server includes means for storing information about minors received from multiple data processing devices, means for analyzing the stored information to evaluate the minors' potential abilities and aptitudes, and means for detecting the user's emotions in real time when information is input via an emotion recognition device and including that information in the analysis. This makes it possible to propose individualized learning activities and hobby activities to minors, enabling more personalized and highly accurate suggestions.

[0165] A "data processing device" is a device that has the function of collecting, processing, storing, and transferring information.

[0166] A "minor" refers to a person who has not reached the legal age of majority.

[0167] "Information" includes data that forms the basis of a proposal, such as the age, gender, interests, past achievements, and works of minors.

[0168] An "emotion recognition device" is a device that uses cameras and microphones to analyze a user's facial expressions and voice in real time and detect their emotional state.

[0169] A "generative AI model" is an algorithm that analyzes collected data, evaluates the potential abilities and aptitudes of minors, and generates optimal suggestions.

[0170] "Privacy protection" means protecting personal information using data anonymization and security technologies so that collected information cannot be identified by third parties.

[0171] "Feedback" refers to user reactions and opinions on proposed content, which will be used to improve the system and enhance the accuracy of future proposals.

[0172] "Suggestions" are presented as options for learning and recreational activities best suited to minors, and are generated based on the user's input data and emotional data.

[0173] Regarding embodiments for carrying out the invention, the present invention is a system that evaluates the potential abilities of minors and proposes optimal learning and recreational activities. This system consists of a combination of hardware and software for performing complex data processing, including emotion recognition.

[0174] Users input data about minors using a dedicated information terminal. This terminal is equipped with a digital camera and microphone, and recognizes the user's facial expressions and voice in real time. Emotion recognition software performs facial expression analysis and voice analysis to detect emotions such as joy and surprise.

[0175] Data collected from the device is sent to the server. Upon receiving the data, the server securely stores it in a cloud environment. From a privacy perspective, the data is anonymized and its security is enhanced by encryption technology.

[0176] On the server, a generative AI model analyzes the data. This model evaluates the potential abilities and aptitudes of minors based on a large amount of historical data. It also utilizes emotional data to generate customized suggestions that take into account the user's mental tendencies.

[0177] Once a proposal is generated, the server sends the information to the user's information terminal. The user can review the proposal and provide feedback as needed. This feedback is also used to improve the accuracy of the system's analysis.

[0178] As a concrete example, let's say a user inputs information such as "a 10-year-old girl, interested in music, with past achievements including a piano recital." Through emotion recognition, if the user smiles while viewing musical works, the system can use that data to suggest something like "participating in a summer workshop at a music school."

[0179] An example of a prompt message is as follows: "A 10-year-old girl is interested in music. Her past achievement is a piano recital. Please suggest learning activities that match this child's interests. Also, the user showed the emotion 'smile' while viewing musical works."

[0180] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0181] Step 1:

[0182] Users input data about minors using an information terminal. This input data includes the minor's age, gender, interests, and past achievements. Users also upload their work (e.g., images, text) to the terminal. This allows the system to collect the necessary basic information.

[0183] In terms of operation, the user accesses the input screen on their device and enters the appropriate data for each item. Images and text can be added using the file upload function. The entered data is temporarily stored in the device's memory.

[0184] Step 2:

[0185] The device activates an emotion recognition device simultaneously with input, collecting the user's emotions in real time. It uses a camera and microphone to capture the user's facial expressions and voice data. An emotion engine analyzes this data to identify emotions such as joy and surprise.

[0186] In this step, the input data consists of an image of the user's face and a recording of their voice, while the output is analyzed emotion data. The emotion engine generates these outputs by applying facial recognition and voice analysis algorithms.

[0187] Step 3:

[0188] The device transmits the collected minor information and emotional data to the server. The data is protected by end-to-end encryption. The server stores the received data in a cloud-based database.

[0189] The input data consists of information and emotional data about minors, and the output is a securely stored database entry. This prepares the data necessary for subsequent analysis.

[0190] Step 4:

[0191] The generative AI model on the server analyzes accumulated data to evaluate the aptitudes and talents of minors. The generative AI model compares this data with large historical datasets to identify optimal learning and recreational activities.

[0192] In this step, the input data consists of minor information and sentiment data from a database, and the output is optimized suggestions. Machine learning algorithms are used for the analysis process, and results are obtained based on evaluation metrics.

[0193] Step 5:

[0194] The server sends the generated proposal to the information terminal. The user can review the proposal on the terminal and examine its contents. The proposal includes solutions and details of the next steps.

[0195] The input is the analysis result from a generative AI model, and the output is suggested information displayed on the device. Based on these results, the user can move the minor's activities to the next step.

[0196] Step 6:

[0197] When a user provides feedback on a suggestion, the device sends that feedback data to the server. The server analyzes the feedback and uses it to improve the system's accuracy.

[0198] The input for this step is user feedback, and the output is an updated analysis result that takes that feedback into account. This process contributes to improving the accuracy and applicability of the proposal.

[0199] (Application Example 2)

[0200] 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".

[0201] In modern education and recreational activities, it is crucial to accurately assess the potential and aptitudes of minors and propose the most suitable activities based on that assessment. However, many systems are not individualized and do not take into account the characteristics of minors or the emotions of users, resulting in generalized suggestions. Furthermore, there are challenges regarding the privacy protection of emotional data.

[0202] 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.

[0203] In this invention, the server includes means for storing data on minors received from multiple terminal devices, means for analyzing the stored data to evaluate the minors' potential abilities and aptitudes, means for presenting learning activities or hobby activities to minors based on the analyzed evaluation results, means for analyzing the user's emotional state using emotion recognition technology to present more personalized activities to minors, and means for anonymizing and securely storing the user's emotional data. This makes it possible to present more personalized activities to minors, improve the accuracy and security of suggestions while ensuring the privacy protection of emotional data.

[0204] A "terminal device" is an electronic device used to input or receive data and to store or display information about minors.

[0205] "Data concerning minors" refers to data that includes information such as the age, interests, past achievements, and works of minors.

[0206] "Potential" refers to the potential and characteristics of minors that contribute to their future growth.

[0207] "Learning activities or recreational activities" refer to educational or recreational activities that are in line with the interests and abilities of minors.

[0208] "Emotion recognition technology" is a technology that analyzes a user's emotional state from their facial expressions and voice.

[0209] "Anonymization" is a technique that protects privacy by processing data in a way that makes it impossible to identify specific individuals.

[0210] "Securely maintaining" means taking measures to ensure that data is stored in a way that protects it from unauthorized access and leakage.

[0211] The system implementing this invention mainly consists of a server, terminal devices, and users. The server receives data about minors from multiple terminal devices and stores it securely. This data includes age, interests, past achievements, and works. The data is anonymized and privacy is protected.

[0212] This system uses cameras and microphones mounted on terminal devices to collect emotional data from users' facial expressions and voices, employing emotion recognition technology. The collected emotional data is sent to a server for analysis. This analysis utilizes the previously stored data on minors, and a generative AI model evaluates the minor's potential and aptitude.

[0213] Based on the evaluation, the server generates content optimized for minors, presenting learning and hobby activities. Feedback based on user sentiment data is used to improve the accuracy of the suggestions. The presented content is displayed on the terminal device for the user to review and accept.

[0214] For example, if a minor creates a work and uploads it to the application, and the system determines that the parents or teachers are experiencing feelings of "surprise" and "joy," then additional educational content or hobby-related material in that field will be suggested.

[0215] Examples of prompts for a generative AI model include:

[0216] "Analyzing a child's recent drawing and considering the emotions expressed by the parent, what new learning activities would you propose?"

[0217] This is how it will be done.

[0218] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0219] Step 1:

[0220] The device uses a camera and microphone to capture the user's facial expressions and voice in real time. This allows the user's emotional state to be collected as input. In addition, relevant data such as the age and interests of minors are also input into the device at the same time.

[0221] Step 2:

[0222] The emotional state data collected by the device is analyzed using emotion recognition technology. Specifically, it is converted into emotional data using image processing algorithms and speech analysis algorithms. The analysis results are output as emotional data.

[0223] Step 3:

[0224] The server receives emotional data and basic data of minors from the device. Within the server, this data is anonymized and stored securely. This ensures that data is accumulated in a privacy-protected manner.

[0225] Step 4:

[0226] The server uses anonymized emotional data and data on minors to evaluate the potential of minors using a generative AI model. The AI ​​model analyzes various data patterns to generate evaluation results. These results serve as input for the next step.

[0227] Step 5:

[0228] The server generates optimal learning or hobby activities for minors based on evaluation results and user sentiment data. Specifically, a generation AI model uses the evaluation results to suggest customized content. The suggested content is then output to the terminal.

[0229] Step 6:

[0230] Users review the suggestions on their devices and provide feedback. This feedback is then sent back to the server to improve the accuracy of future suggestions.

[0231] Step 7:

[0232] The server analyzes the received feedback and sentiment data and uses it to improve future suggestion processes. The analyzed results are used as data to improve the accuracy of the system.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] [Second Embodiment]

[0237] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0238] 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.

[0239] 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).

[0240] 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.

[0241] 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.

[0242] 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).

[0243] 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.

[0244] 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.

[0245] 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.

[0246] 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.

[0247] 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.

[0248] 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".

[0249] This invention provides a system for analyzing children's potential abilities and characteristics and proposing appropriate learning and recreational activities. To implement this invention, it is desirable to configure it as follows.

[0250] Users input data about their children using a dedicated information terminal or web application. This data includes the child's age, gender, interests, past achievements, and works (images, text, music files, etc.). The terminal is responsible for initial formatting of the data entered by the user and then sending it to the server.

[0251] The server securely stores the received data in the cloud. This stored data is then analyzed using generative AI. The analysis process involves data cleansing, feature extraction, and comparison with past success stories to evaluate the children's potential talents and aptitudes. For example, image data of children's drawings could be analyzed to extract features such as color sense and originality of composition, thereby evaluating their potential artistic talent.

[0252] After the evaluation is complete, the server generates suggestions for optimal learning and recreational activities based on the child's characteristics. These suggestions are customized specifically for each child; for example, a child who is interested in music and has a good sense of rhythm might be recommended drum or dance lessons.

[0253] Subsequently, the server sends the generated suggestions to the information terminal, providing the user with the results. The user reviews the suggestions on the terminal and provides feedback as needed. This feedback is sent to the server and used to improve the generating AI model. This provides an environment in which the accuracy of the analysis results continuously improves.

[0254] Furthermore, this system prioritizes privacy protection; data received from users is anonymized, ensuring safe and secure use. This allows parents to confidently utilize the data they provide while supporting their children's development.

[0255] The following describes the processing flow.

[0256] Step 1:

[0257] The user uses a dedicated terminal to input data about the child, including their age, gender, interests, past achievements, and works (such as images and texts).

[0258] Step 2:

[0259] The terminal receives the input data and performs initial formatting. This formatting includes resizing images and standardizing the format of text data.

[0260] Step 3:

[0261] The terminal sends the formatted data to the server using a secure communication protocol.

[0262] Step 4:

[0263] The server stores the received data in cloud storage. During this process, the data is always encrypted, ensuring privacy.

[0264] Step 5:

[0265] The server inputs the accumulated data into the generating AI, which then cleanses the data. This removes noise and processes missing data.

[0266] Step 6:

[0267] The server uses generative AI to extract features from the data. For example, in the case of image data, it quantifies features such as color and composition.

[0268] Step 7:

[0269] The server evaluates the child's potential and aptitude by comparing the extracted features with a database of past success stories to find similar patterns.

[0270] Step 8:

[0271] Based on the evaluation results, the server generates content suggesting learning and hobby activities suitable for the children.

[0272] Step 9:

[0273] The server sends the generated suggestions to the information terminal, allowing the user to easily review them.

[0274] Step 10:

[0275] Users review the provided suggestions and provide feedback via their devices. This feedback includes the usefulness of the suggestions and the children's reactions.

[0276] Step 11:

[0277] The server incorporates the received feedback into its analysis and uses it to improve the accuracy of future generative AI models.

[0278] (Example 1)

[0279] 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."

[0280] Traditionally, there has been no system that accurately analyzes children's potential and characteristics and proposes optimal learning and recreational activities based on the results. Furthermore, there has been a lack of means to meet diverse needs, such as secure management of collected data, data conversion for analysis, and improvement of analysis accuracy through user feedback. As a result, optimizing education by leveraging the individual characteristics of each child has been difficult.

[0281] 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.

[0282] In this invention, the server includes means for storing information about children received from users via a data communication device, means for analyzing the stored information using a generative model to evaluate the children's potential abilities and characteristics, and means for recommending individually suitable educational activities or hobby activities based on the analysis results. This makes it possible to provide individualized suggestions tailored to the characteristics of each child.

[0283] "User" refers to the entity that operates the system and inputs and confirms information about children.

[0284] "Data communication device" refers to devices or applications for collecting information and communicating with the server.

[0285] "Information about children" refers to various data such as children's age, gender, interests, past achievements, and data related to their works.

[0286] "Means of accumulation" refers to the process of safely storing the acquired information in a storage device or cloud storage.

[0287] "Generation model" refers to AI technology used to analyze the received information and evaluate children's characteristics.

[0288] "Means of performing analysis" refers to the operation of processing the received information and evaluating children's potential abilities and characteristics.

[0289] "Means of recommendation" refers to the process of selecting and proposing educational activities and hobby activities suitable for children based on the analysis.

[0290] "Means of securely managing and converting to an analyzable format" refers to the process of formatting the input information into a format suitable for analysis after ensuring security.

[0291] The present invention is a system that collects information about children and proposes optimal activities based on individual characteristics. Specific embodiments are shown below.

[0292] The user inputs information about children using a dedicated data communication device or web application. This information includes children's age, gender, interests, past achievements, and works (e.g., images, texts, audio files, etc.). The data communication device is equipped with a function for initial formatting and format unification of the information.

[0293] Information transmitted from the device is received by the server and securely stored in cloud storage. The server analyzes the received information using a generative AI model. This generative AI model has the function of extracting features and cleaning the information, and evaluating the child's potential abilities and characteristics. Furthermore, based on the analyzed information, it generates suggestions for educational and recreational activities.

[0294] For example, if a child's drawing is input as image data, the server can analyze the data and evaluate the child's artistic talent based on characteristics such as color sense and originality of composition. Based on this evaluation, it can then suggest participation in art classes or art events.

[0295] Users can review the suggestions on their devices and submit feedback, which helps the server further improve the accuracy of its analysis. This feedback is used to improve the generated AI model, increasing the accuracy and reliability of the system.

[0296] By the way, in order to ensure the security of information, the server anonymizes the information it receives, providing an environment in which users can provide data with peace of mind.

[0297] (Example of a prompt message)

[0298] "He is a 10-year-old boy who loves to draw. We have image data of his past artwork. Please evaluate his artistic abilities and suggest appropriate activities."

[0299] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0300] Step 1:

[0301] The user inputs information about a child through a dedicated data communication device or a web application. The information input includes the child's age, gender, interests, past achievements, works (such as images and articles). The input information is initially formatted on the terminal and the format is unified. The output is the formatted data, which is in a state ready to be transmitted to the server.

[0302] Step 2:

[0303] The terminal transmits the formatted information to the server using a secure protocol. The output is the received series of data, and the processing on the server side is started.

[0304] Step 3:

[0305] The server securely stores the received information in cloud storage. The input here is the data transmitted from the terminal, and the output is the data stored in cloud storage. This data is organized in a database for analysis.

[0306] Step 4:

[0307] The server starts a generative AI model to analyze the stored information. The input is the organized data retrieved from cloud storage. Data cleaning is performed in this phase, and unnecessary noise is removed. The output is passed to the generative AI model in the form of data with features extracted, as the cleaned data.

[0308] Step 5:

[0309] The server performs feature extraction using the generative AI model and evaluates the child's potential abilities and characteristics. The input is the cleaned data, and the output is the result of the child's ability evaluation and feature data based on specific interests.

[0310] Step 6:

[0311] The server suggests individualized learning and recreational activities based on the children's evaluation results. The input is the children's evaluation results, and the output is a suggested activity. For example, it might recommend dance lessons to children with a strong sense of rhythm.

[0312] Step 7:

[0313] The server generates suggestions and sends them to the terminal for the user to view. The user can then review the suggestions on the terminal. The output is a displayed activity suggestion, which the user can review or modify.

[0314] Step 8:

[0315] The user inputs feedback on a proposal and sends it to the server via their device. The input is the user's feedback content, and the output is the feedback data received by the server.

[0316] Step 9:

[0317] The server uses user feedback to improve the generated AI model, thereby enhancing the overall analysis accuracy of the system. This is a process of analyzing the input feedback data and adjusting the model. The output, as an improved model, is used for subsequent analyses.

[0318] Step 10:

[0319] The server performs a process to anonymize the received information, protecting data privacy. The input is all information about the user and the child, and the output is anonymized data with protected personal information. This process allows users to use the system with peace of mind.

[0320] (Application Example 1)

[0321] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0322] There is a need to effectively draw out the potential abilities and aptitudes of minors and propose optimal educational activities tailored to their individual characteristics. However, achieving this requires a system that can analyze large amounts of information and make accurate suggestions. Furthermore, an environment where information about minors can be provided with peace of mind is also crucial.

[0323] 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.

[0324] In this invention, the server includes means for storing information about minors received from multiple information processing devices, means for analyzing the stored information to evaluate the minors' potential abilities and aptitudes, means for proposing learning activities or hobby activities for minors based on the evaluation results, and means for providing proposals based on the evaluation results in a virtual space and recommending educational activities. This enables the provision of individually optimized educational proposals tailored to the characteristics of minors and the appropriate handling of personal information.

[0325] An "information processing device" is a device used to input data about minors, including children, and transmit it to a server.

[0326] "Minor" refers to children and students who are in a developmental stage, and their potential abilities and aptitudes are the subjects of evaluation.

[0327] "Information" includes data such as age, gender, interests, past achievements, and works related to minors.

[0328] "Storage" refers to the process of securely saving received information and using it for subsequent analysis.

[0329] "Analysis" is the process of identifying the potential and aptitudes of minors based on accumulated information.

[0330] "Evaluation" is the process of determining the characteristics and potential of minors based on data obtained through analysis.

[0331] A "virtual space" is a digital environment constructed using information and communication technology, and is primarily used as a venue for educational activities.

[0332] "Educational activities" are activities that support the learning of minors and provide them with skills and knowledge that are tailored to their abilities and interests.

[0333] "Personal information" refers to information about minors or those around them that can identify a specific individual.

[0334] To realize this invention, it is basically necessary to build a system in which an information processing device and a server work together. The information processing device is a device for inputting information about minors, and a smartphone, tablet, or personal computer is used. The user uses these devices to input data such as the minor's age, gender, interests, past achievements, and works. This information is initially formatted and sent to the server.

[0335] The server operates as a backend system using Python and Django. It securely stores received information in the cloud and analyzes the stored data using generative AI. This analysis process involves data cleansing, feature extraction, and comparison with past success stories to evaluate the potential talents and aptitudes of minors. The generative AI model utilizes AI technologies such as the OpenAI API to generate responses. Based on the evaluation results, this AI technology generates suggestions for optimal learning and hobby activities.

[0336] Evaluation results and suggestions are provided through a user interface set up as a virtual space. Recommended educational activities are easily accessible to users and can be used to select educational and hobby activities. Users can review the suggestions and provide feedback as needed. This feedback is used to improve the accuracy of the analysis. In addition, the received information is anonymized to ensure the appropriate handling of personal information.

[0337] For example, if a 10-year-old minor is entered, the system can analyze drawings they have made in the past, determine that they have "excellent color sense," and then provide specific educational suggestions such as "online digital art courses."

[0338] Example of a prompt:

[0339] "A 10-year-old boy enjoys drawing colorful pictures. Based on examples of his past work, please suggest learning activities that would suit his characteristics."

[0340] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0341] Step 1:

[0342] The user uses an information processing device to input data such as the age, gender, interests, past achievements, and works of minors. The input data is initially formatted on the information processing device, and processing is performed to remove incomplete information and duplicate data. This processing ensures that highly accurate data is sent to the server.

[0343] Step 2:

[0344] The server securely stores the received data in the cloud. The data is organized based on the database structure and anonymized according to security policies. The output of this step is formatted and anonymized data for use in subsequent analysis.

[0345] Step 3:

[0346] The server performs analysis on the formatted data using a generative AI model. The AI ​​model quantifies characteristics and aptitudes based on the input data and evaluates them. This evaluation includes feature extraction from the input data and comparison with past success stories based on those features. The output is an evaluation result regarding the potential abilities and aptitudes of minors.

[0347] Step 4:

[0348] The server generates suggestions for optimal learning and recreational activities based on the generated evaluation results. Using a generation AI model, it constructs an educational plan that matches the evaluation results in a form that can be executed within a virtual space. This suggestion is output in the form of specific learning courses and activity candidates.

[0349] Step 5:

[0350] The evaluation results and suggestions are sent to the information processing device and displayed on a screen where the user can review them. The user can select recommended activities or provide feedback. This feedback is sent back to the server to be used later to improve the accuracy of the analysis model. This process ensures continuous system improvement.

[0351] 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.

[0352] This invention combines an emotion engine with a system that evaluates children's potential and proposes optimal learning and recreational activities, thereby enabling more personalized suggestions that take the user's emotions into account. Specific embodiments for carrying out this invention are shown below.

[0353] Users input data about their children using a dedicated information terminal. This data includes the child's age, gender, interests, past achievements, and works (e.g., images, text). As the user operates the terminal, the emotion engine recognizes the user's emotions in real time through the camera and microphone built into the terminal. The emotion engine detects various emotions such as joy, surprise, and confusion from the user's facial expressions and voice, and sends this data to the server.

[0354] The server stores and securely manages received emotional and child data in the cloud. Emotional data is stored anonymized to ensure privacy. Generating AI analyzes this data to assess the child's potential talents and aptitudes. Furthermore, it considers the user's emotional data to generate suggestions for the most suitable learning or hobby activities for the child. For example, it customizes emotional suggestions by focusing on the results of analyzing works in which the user expressed "surprise" or "joy."

[0355] The server then sends optimized suggestions to the user's information terminal. The user can then view these suggestions on the terminal. When the user provides feedback on the suggestions, the server analyzes the feedback along with emotional data and uses it to improve the accuracy of future suggestions. In this way, the system, equipped with an emotion engine, incorporates the user's emotional aspects into the evaluation, enabling it to support maximizing the learning effectiveness of children.

[0356] The following describes the processing flow.

[0357] Step 1:

[0358] Users input data about their children (age, gender, interests, past achievements, works, etc.) using an information terminal. During this process, the camera and microphone built into the terminal analyze the user's facial expressions and voice using an emotion engine, acquiring emotional data in real time.

[0359] Step 2:

[0360] The terminal transmits data entered by the user and acquired emotional data to the server using a secure communication protocol.

[0361] Step 3:

[0362] The server stores the received data in cloud storage. During this process, emotional data is anonymized and processed in a way that protects user privacy.

[0363] Step 4:

[0364] The server uses generative AI to analyze child data and sentiment data. Sentiment data is used to consider which parts made a good impression on the user when making suggestions.

[0365] Step 5:

[0366] Based on the analysis results, the server generates suggestions for optimal learning or recreational activities to maximize the child's potential. During this process, the suggestions are prioritized according to the user's expressed emotions.

[0367] Step 6:

[0368] The server sends the generated suggestions to the terminal, where the user can view the detailed suggestions.

[0369] Step 7:

[0370] Based on the suggestions, users select specific educational activities or activities for the children and send their feedback to the server via their device.

[0371] Step 8:

[0372] The server re-analyzes user feedback along with sentiment data and uses the system's generating AI to improve the accuracy of its suggestions. This results in more personalized suggestions in the future.

[0373] (Example 2)

[0374] 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".

[0375] Conventional systems for suggesting learning and hobby activities for minors have the problem of not being able to take into account the user's emotions when evaluating the minor's potential abilities and aptitudes, and therefore not being able to provide sufficiently individualized suggestions. In addition, there were significant challenges in utilizing feedback to improve the accuracy of suggestions and protecting privacy, and in particular, the customization of suggestions based on emotions was insufficient.

[0376] 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.

[0377] In this invention, the server includes means for storing information about minors received from multiple data processing devices, means for analyzing the stored information to evaluate the minors' potential abilities and aptitudes, and means for detecting the user's emotions in real time when information is input via an emotion recognition device and including that information in the analysis. This makes it possible to propose individualized learning activities and hobby activities to minors, enabling more personalized and highly accurate suggestions.

[0378] A "data processing device" is a device that has the function of collecting, processing, storing, and transferring information.

[0379] A "minor" refers to a person who has not reached the legal age of majority.

[0380] "Information" includes data that forms the basis of a proposal, such as the age, gender, interests, past achievements, and works of minors.

[0381] An "emotion recognition device" is a device that uses cameras and microphones to analyze a user's facial expressions and voice in real time and detect their emotional state.

[0382] A "generative AI model" is an algorithm that analyzes collected data, evaluates the potential abilities and aptitudes of minors, and generates optimal suggestions.

[0383] "Privacy protection" means protecting personal information using data anonymization and security technologies so that collected information cannot be identified by third parties.

[0384] "Feedback" refers to user reactions and opinions on proposed content, which will be used to improve the system and enhance the accuracy of future proposals.

[0385] "Suggestions" are presented as options for learning and recreational activities best suited to minors, and are generated based on the user's input data and emotional data.

[0386] Regarding embodiments for carrying out the invention, the present invention is a system that evaluates the potential abilities of minors and proposes optimal learning and recreational activities. This system consists of a combination of hardware and software for performing complex data processing, including emotion recognition.

[0387] Users input data about minors using a dedicated information terminal. This terminal is equipped with a digital camera and microphone, and recognizes the user's facial expressions and voice in real time. Emotion recognition software performs facial expression analysis and voice analysis to detect emotions such as joy and surprise.

[0388] Data collected from the device is sent to the server. Upon receiving the data, the server securely stores it in a cloud environment. From a privacy perspective, the data is anonymized and its security is enhanced by encryption technology.

[0389] On the server, a generative AI model analyzes the data. This model evaluates the potential abilities and aptitudes of minors based on a large amount of historical data. It also utilizes emotional data to generate customized suggestions that take into account the user's mental tendencies.

[0390] Once a proposal is generated, the server sends the information to the user's information terminal. The user can review the proposal and provide feedback as needed. This feedback is also used to improve the accuracy of the system's analysis.

[0391] As a concrete example, let's say a user inputs information such as "a 10-year-old girl, interested in music, with past achievements including a piano recital." Through emotion recognition, if the user smiles while viewing musical works, the system can use that data to suggest something like "participating in a summer workshop at a music school."

[0392] An example of a prompt message is as follows: "A 10-year-old girl is interested in music. Her past achievement is a piano recital. Please suggest learning activities that match this child's interests. Also, the user showed the emotion 'smile' while viewing musical works."

[0393] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0394] Step 1:

[0395] Users input data about minors using an information terminal. This input data includes the minor's age, gender, interests, and past achievements. Users also upload their work (e.g., images, text) to the terminal. This allows the system to collect the necessary basic information.

[0396] In terms of operation, the user accesses the input screen on their device and enters the appropriate data for each item. Images and text can be added using the file upload function. The entered data is temporarily stored in the device's memory.

[0397] Step 2:

[0398] The device activates an emotion recognition device simultaneously with input, collecting the user's emotions in real time. It uses a camera and microphone to capture the user's facial expressions and voice data. An emotion engine analyzes this data to identify emotions such as joy and surprise.

[0399] In this step, the input data consists of an image of the user's face and a recording of their voice, while the output is analyzed emotion data. The emotion engine generates these outputs by applying facial recognition and voice analysis algorithms.

[0400] Step 3:

[0401] The device transmits the collected minor information and emotional data to the server. The data is protected by end-to-end encryption. The server stores the received data in a cloud-based database.

[0402] The input data consists of information and emotional data about minors, and the output is a securely stored database entry. This prepares the data necessary for subsequent analysis.

[0403] Step 4:

[0404] The generative AI model on the server analyzes accumulated data to evaluate the aptitudes and talents of minors. The generative AI model compares this data with large historical datasets to identify optimal learning and recreational activities.

[0405] In this step, the input data consists of minor information and sentiment data from a database, and the output is optimized suggestions. Machine learning algorithms are used for the analysis process, and results are obtained based on evaluation metrics.

[0406] Step 5:

[0407] The server sends the generated proposal to the information terminal. The user can review the proposal on the terminal and examine its contents. The proposal includes solutions and details of the next steps.

[0408] The input is the analysis result from a generative AI model, and the output is suggested information displayed on the device. Based on these results, the user can move the minor's activities to the next step.

[0409] Step 6:

[0410] When a user provides feedback on a suggestion, the device sends that feedback data to the server. The server analyzes the feedback and uses it to improve the system's accuracy.

[0411] The input for this step is user feedback, and the output is an updated analysis result that takes that feedback into account. This process contributes to improving the accuracy and applicability of the proposal.

[0412] (Application Example 2)

[0413] 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."

[0414] In modern education and recreational activities, it is crucial to accurately assess the potential and aptitudes of minors and propose the most suitable activities based on that assessment. However, many systems are not individualized and do not take into account the characteristics of minors or the emotions of users, resulting in generalized suggestions. Furthermore, there are challenges regarding the privacy protection of emotional data.

[0415] 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.

[0416] In this invention, the server includes means for storing data on minors received from multiple terminal devices, means for analyzing the stored data to evaluate the minors' potential abilities and aptitudes, means for presenting learning activities or hobby activities to minors based on the analyzed evaluation results, means for analyzing the user's emotional state using emotion recognition technology to present more personalized activities to minors, and means for anonymizing and securely storing the user's emotional data. This makes it possible to present more personalized activities to minors, improve the accuracy and security of suggestions while ensuring the privacy protection of emotional data.

[0417] A "terminal device" is an electronic device used to input or receive data and to store or display information about minors.

[0418] "Data concerning minors" refers to data that includes information such as the age, interests, past achievements, and works of minors.

[0419] "Potential" refers to the potential and characteristics of minors that contribute to their future growth.

[0420] "Learning activities or recreational activities" refer to educational or recreational activities that are in line with the interests and abilities of minors.

[0421] "Emotion recognition technology" is a technology that analyzes a user's emotional state from their facial expressions and voice.

[0422] "Anonymization" is a technique that protects privacy by processing data in a way that makes it impossible to identify specific individuals.

[0423] "Securely maintaining" means taking measures to ensure that data is stored in a way that protects it from unauthorized access and leakage.

[0424] The system implementing this invention mainly consists of a server, terminal devices, and users. The server receives data about minors from multiple terminal devices and stores it securely. This data includes age, interests, past achievements, and works. The data is anonymized and privacy is protected.

[0425] This system uses cameras and microphones mounted on terminal devices to collect emotional data from users' facial expressions and voices, employing emotion recognition technology. The collected emotional data is sent to a server for analysis. This analysis utilizes the previously stored data on minors, and a generative AI model evaluates the minor's potential and aptitude.

[0426] Based on the evaluation, the server generates content optimized for minors, presenting learning and hobby activities. Feedback based on user sentiment data is used to improve the accuracy of the suggestions. The presented content is displayed on the terminal device for the user to review and accept.

[0427] For example, if a minor creates a work and uploads it to the application, and the system determines that the parents or teachers are experiencing feelings of "surprise" and "joy," then additional educational content or hobby-related material in that field will be suggested.

[0428] Examples of prompts for a generative AI model include:

[0429] "Analyzing a child's recent drawing and considering the emotions expressed by the parent, what new learning activities would you propose?"

[0430] This is how it will be done.

[0431] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0432] Step 1:

[0433] The device uses a camera and microphone to capture the user's facial expressions and voice in real time. This allows the user's emotional state to be collected as input. In addition, relevant data such as the age and interests of minors are also input into the device at the same time.

[0434] Step 2:

[0435] The emotional state data collected by the device is analyzed using emotion recognition technology. Specifically, it is converted into emotional data using image processing algorithms and speech analysis algorithms. The analysis results are output as emotional data.

[0436] Step 3:

[0437] The server receives emotional data and basic data of minors from the device. Within the server, this data is anonymized and stored securely. This ensures that data is accumulated in a privacy-protected manner.

[0438] Step 4:

[0439] The server uses anonymized emotional data and data on minors to evaluate the potential of minors using a generative AI model. The AI ​​model analyzes various data patterns to generate evaluation results. These results serve as input for the next step.

[0440] Step 5:

[0441] The server generates optimal learning or hobby activities for minors based on evaluation results and user sentiment data. Specifically, a generation AI model uses the evaluation results to suggest customized content. The suggested content is then output to the terminal.

[0442] Step 6:

[0443] Users review the suggestions on their devices and provide feedback. This feedback is then sent back to the server to improve the accuracy of future suggestions.

[0444] Step 7:

[0445] The server analyzes the received feedback and sentiment data and uses it to improve future suggestion processes. The analyzed results are used as data to improve the accuracy of the system.

[0446] 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.

[0447] 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.

[0448] 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.

[0449] [Third Embodiment]

[0450] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0451] 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.

[0452] 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).

[0453] 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.

[0454] 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.

[0455] 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).

[0456] 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.

[0457] 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.

[0458] 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.

[0459] 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.

[0460] 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.

[0461] 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".

[0462] This invention provides a system for analyzing children's potential abilities and characteristics and proposing appropriate learning and recreational activities. To implement this invention, it is desirable to configure it as follows.

[0463] Users input data about their children using a dedicated information terminal or web application. This data includes the child's age, gender, interests, past achievements, and works (images, text, music files, etc.). The terminal is responsible for initial formatting of the data entered by the user and then sending it to the server.

[0464] The server securely stores the received data in the cloud. This stored data is then analyzed using generative AI. The analysis process involves data cleansing, feature extraction, and comparison with past success stories to evaluate the children's potential talents and aptitudes. For example, image data of children's drawings could be analyzed to extract features such as color sense and originality of composition, thereby evaluating their potential artistic talent.

[0465] After the evaluation is complete, the server generates suggestions for optimal learning and recreational activities based on the child's characteristics. These suggestions are customized specifically for each child; for example, a child who is interested in music and has a good sense of rhythm might be recommended drum or dance lessons.

[0466] Subsequently, the server sends the generated suggestions to the information terminal, providing the user with the results. The user reviews the suggestions on the terminal and provides feedback as needed. This feedback is sent to the server and used to improve the generating AI model. This provides an environment in which the accuracy of the analysis results continuously improves.

[0467] Furthermore, this system prioritizes privacy protection; data received from users is anonymized, ensuring safe and secure use. This allows parents to confidently utilize the data they provide while supporting their children's development.

[0468] The following describes the processing flow.

[0469] Step 1:

[0470] The user uses a dedicated terminal to input data about the child, including their age, gender, interests, past achievements, and works (such as images and texts).

[0471] Step 2:

[0472] The terminal receives the input data and performs initial formatting. This formatting includes resizing images and standardizing the format of text data.

[0473] Step 3:

[0474] The terminal sends the formatted data to the server using a secure communication protocol.

[0475] Step 4:

[0476] The server stores the received data in cloud storage. During this process, the data is always encrypted, ensuring privacy.

[0477] Step 5:

[0478] The server inputs the accumulated data into the generating AI, which then cleanses the data. This removes noise and processes missing data.

[0479] Step 6:

[0480] The server uses generative AI to extract features from the data. For example, in the case of image data, it quantifies features such as color and composition.

[0481] Step 7:

[0482] The server evaluates the child's potential and aptitude by comparing the extracted features with a database of past success stories to find similar patterns.

[0483] Step 8:

[0484] Based on the evaluation results, the server generates content suggesting learning and hobby activities suitable for the children.

[0485] Step 9:

[0486] The server sends the generated suggestions to the information terminal, allowing the user to easily review them.

[0487] Step 10:

[0488] Users review the provided suggestions and provide feedback via their devices. This feedback includes the usefulness of the suggestions and the children's reactions.

[0489] Step 11:

[0490] The server incorporates the received feedback into its analysis and uses it to improve the accuracy of future generative AI models.

[0491] (Example 1)

[0492] 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."

[0493] Traditionally, there has been no system that accurately analyzes children's potential and characteristics and proposes optimal learning and recreational activities based on the results. Furthermore, there has been a lack of means to meet diverse needs, such as secure management of collected data, data conversion for analysis, and improvement of analysis accuracy through user feedback. As a result, optimizing education by leveraging the individual characteristics of each child has been difficult.

[0494] 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.

[0495] In this invention, the server includes means for storing information about children received from users via a data communication device, means for analyzing the stored information using a generative model to evaluate the children's potential abilities and characteristics, and means for recommending individually suitable educational activities or hobby activities based on the analysis results. This makes it possible to provide individualized suggestions tailored to the characteristics of each child.

[0496] "User" refers to the entity that operates the system and inputs and verifies information about the child.

[0497] "Data communication device" refers to equipment or applications used to collect information and communicate with a server.

[0498] "Information about children" refers to various data about children, including their age, gender, interests, past achievements, and works.

[0499] "Means of storage" refers to the process of securely saving acquired information to a storage device or cloud storage.

[0500] A "generative model" refers to AI technology used to analyze received information and evaluate the characteristics of children.

[0501] "Means of analysis" refers to the process of processing received information and evaluating the child's potential abilities and characteristics.

[0502] "Recommendation methods" refer to the process of selecting and proposing educational and recreational activities suitable for children based on analysis.

[0503] "Means of securely managing and converting information into an analyzable format" refers to the process of ensuring the security of the input information and then formatting it into a format suitable for analysis.

[0504] This invention is a system that collects information about children and proposes optimal activities based on their individual characteristics. Specific embodiments are shown below.

[0505] Users input information about their children using a dedicated data communication device or web application. This information includes the child's age, gender, interests, past achievements, and works (e.g., images, text, audio files, etc.). The data communication device is equipped with functions for initial formatting and standardization of the information.

[0506] Information transmitted from the device is received by the server and securely stored in cloud storage. The server analyzes the received information using a generative AI model. This generative AI model has the function of extracting features and cleaning the information, and evaluating the child's potential abilities and characteristics. Furthermore, based on the analyzed information, it generates suggestions for educational and recreational activities.

[0507] For example, if a child's drawing is input as image data, the server can analyze the data and evaluate the child's artistic talent based on characteristics such as color sense and originality of composition. Based on this evaluation, it can then suggest participation in art classes or art events.

[0508] Users can review the suggestions on their devices and submit feedback, which helps the server further improve the accuracy of its analysis. This feedback is used to improve the generated AI model, increasing the accuracy and reliability of the system.

[0509] By the way, in order to ensure the security of information, the server anonymizes the information it receives, providing an environment in which users can provide data with peace of mind.

[0510] (Example of a prompt message)

[0511] "He is a 10-year-old boy who loves to draw. We have image data of his past artwork. Please evaluate his artistic abilities and suggest appropriate activities."

[0512] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0513] Step 1:

[0514] Users input information about their children through a dedicated data communication device or web application. This information includes the child's age, gender, interests, past achievements, and works (images, text, etc.). The input information is initially formatted on the terminal to ensure a consistent format. The output is formatted data, ready for transmission to the server.

[0515] Step 2:

[0516] The terminal sends the formatted information to the server using a secure protocol. The output is the received data sequence, and processing begins on the server side.

[0517] Step 3:

[0518] The server securely stores the received information in cloud storage. The input here is data sent from the terminal, and the output is data stored in cloud storage. This data is then organized in a database for analysis.

[0519] Step 4:

[0520] The server launches a generative AI model to analyze the accumulated information. The input is organized data retrieved from cloud storage. In this phase, the data is cleansed, and unwanted noise is removed. The output is passed to the generative AI model as cleansed data with features extracted.

[0521] Step 5:

[0522] The server uses a generative AI model to extract features and evaluate the children's potential abilities and characteristics. The input is cleansed data, and the output consists of the results of the children's ability assessment and feature data based on their specific interests.

[0523] Step 6:

[0524] The server suggests individualized learning and recreational activities based on the children's evaluation results. The input is the children's evaluation results, and the output is a suggested activity. For example, it might recommend dance lessons to children with a strong sense of rhythm.

[0525] Step 7:

[0526] The server generates suggestions and sends them to the terminal for the user to view. The user can then review the suggestions on the terminal. The output is a displayed activity suggestion, which the user can review or modify.

[0527] Step 8:

[0528] The user inputs feedback on a proposal and sends it to the server via their device. The input is the user's feedback content, and the output is the feedback data received by the server.

[0529] Step 9:

[0530] The server uses user feedback to improve the generated AI model, thereby enhancing the overall analysis accuracy of the system. This is a process of analyzing the input feedback data and adjusting the model. The output, as an improved model, is used for subsequent analyses.

[0531] Step 10:

[0532] The server performs a process to anonymize the received information, protecting data privacy. The input is all information about the user and the child, and the output is anonymized data with protected personal information. This process allows users to use the system with peace of mind.

[0533] (Application Example 1)

[0534] 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."

[0535] There is a need to effectively draw out the potential abilities and aptitudes of minors and propose optimal educational activities tailored to their individual characteristics. However, achieving this requires a system that can analyze large amounts of information and make accurate suggestions. Furthermore, an environment where information about minors can be provided with peace of mind is also crucial.

[0536] 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.

[0537] In this invention, the server includes means for storing information about minors received from multiple information processing devices, means for analyzing the stored information to evaluate the minors' potential abilities and aptitudes, means for proposing learning activities or hobby activities for minors based on the evaluation results, and means for providing proposals based on the evaluation results in a virtual space and recommending educational activities. This enables the provision of individually optimized educational proposals tailored to the characteristics of minors and the appropriate handling of personal information.

[0538] An "information processing device" is a device used to input data about minors, including children, and transmit it to a server.

[0539] "Minor" refers to children and students who are in a developmental stage, and their potential abilities and aptitudes are the subjects of evaluation.

[0540] "Information" includes data such as age, gender, interests, past achievements, and works related to minors.

[0541] "Storage" refers to the process of securely saving received information and using it for subsequent analysis.

[0542] "Analysis" is the process of identifying the potential and aptitudes of minors based on accumulated information.

[0543] "Evaluation" is the process of determining the characteristics and potential of minors based on data obtained through analysis.

[0544] A "virtual space" is a digital environment constructed using information and communication technology, and is primarily used as a venue for educational activities.

[0545] "Educational activities" are activities that support the learning of minors and provide them with skills and knowledge that are tailored to their abilities and interests.

[0546] "Personal information" refers to information about minors or those around them that can identify a specific individual.

[0547] To realize this invention, it is basically necessary to build a system in which an information processing device and a server work together. The information processing device is a device for inputting information about minors, and a smartphone, tablet, or personal computer is used. The user uses these devices to input data such as the minor's age, gender, interests, past achievements, and works. This information is initially formatted and sent to the server.

[0548] The server operates as a backend system using Python and Django. It securely stores received information in the cloud and analyzes the stored data using generative AI. This analysis process involves data cleansing, feature extraction, and comparison with past success stories to evaluate the potential talents and aptitudes of minors. The generative AI model utilizes AI technologies such as the OpenAI API to generate responses. Based on the evaluation results, this AI technology generates suggestions for optimal learning and hobby activities.

[0549] Evaluation results and suggestions are provided through a user interface set up as a virtual space. Recommended educational activities are easily accessible to users and can be used to select educational and hobby activities. Users can review the suggestions and provide feedback as needed. This feedback is used to improve the accuracy of the analysis. In addition, the received information is anonymized to ensure the appropriate handling of personal information.

[0550] For example, if a 10-year-old minor is entered, the system can analyze drawings they have made in the past, determine that they have "excellent color sense," and then provide specific educational suggestions such as "online digital art courses."

[0551] Example of a prompt:

[0552] "A 10-year-old boy enjoys drawing colorful pictures. Based on examples of his past work, please suggest learning activities that would suit his characteristics."

[0553] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0554] Step 1:

[0555] The user uses an information processing device to input data such as the age, gender, interests, past achievements, and works of minors. The input data is initially formatted on the information processing device, and processing is performed to remove incomplete information and duplicate data. This processing ensures that highly accurate data is sent to the server.

[0556] Step 2:

[0557] The server securely stores the received data in the cloud. The data is organized based on the database structure and anonymized according to security policies. The output of this step is formatted and anonymized data for use in subsequent analysis.

[0558] Step 3:

[0559] The server performs analysis on the formatted data using a generative AI model. The AI ​​model quantifies characteristics and aptitudes based on the input data and evaluates them. This evaluation includes feature extraction from the input data and comparison with past success stories based on those features. The output is an evaluation result regarding the potential abilities and aptitudes of minors.

[0560] Step 4:

[0561] The server generates suggestions for optimal learning and recreational activities based on the generated evaluation results. Using a generation AI model, it constructs an educational plan that matches the evaluation results in a form that can be executed within a virtual space. This suggestion is output in the form of specific learning courses and activity candidates.

[0562] Step 5:

[0563] The evaluation results and suggestions are sent to the information processing device and displayed on a screen where the user can review them. The user can select recommended activities or provide feedback. This feedback is sent back to the server to be used later to improve the accuracy of the analysis model. This process ensures continuous system improvement.

[0564] 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.

[0565] This invention combines an emotion engine with a system that evaluates children's potential and proposes optimal learning and recreational activities, thereby enabling more personalized suggestions that take the user's emotions into account. Specific embodiments for carrying out this invention are shown below.

[0566] Users input data about their children using a dedicated information terminal. This data includes the child's age, gender, interests, past achievements, and works (e.g., images, text). As the user operates the terminal, the emotion engine recognizes the user's emotions in real time through the camera and microphone built into the terminal. The emotion engine detects various emotions such as joy, surprise, and confusion from the user's facial expressions and voice, and sends this data to the server.

[0567] The server stores and securely manages received emotional and child data in the cloud. Emotional data is stored anonymized to ensure privacy. Generating AI analyzes this data to assess the child's potential talents and aptitudes. Furthermore, it considers the user's emotional data to generate suggestions for the most suitable learning or hobby activities for the child. For example, it customizes emotional suggestions by focusing on the results of analyzing works in which the user expressed "surprise" or "joy."

[0568] The server then sends optimized suggestions to the user's information terminal. The user can then view these suggestions on the terminal. When the user provides feedback on the suggestions, the server analyzes the feedback along with emotional data and uses it to improve the accuracy of future suggestions. In this way, the system, equipped with an emotion engine, incorporates the user's emotional aspects into the evaluation, enabling it to support maximizing the learning effectiveness of children.

[0569] The following describes the processing flow.

[0570] Step 1:

[0571] Users input data about their children (age, gender, interests, past achievements, works, etc.) using an information terminal. During this process, the camera and microphone built into the terminal analyze the user's facial expressions and voice using an emotion engine, acquiring emotional data in real time.

[0572] Step 2:

[0573] The terminal transmits data entered by the user and acquired emotional data to the server using a secure communication protocol.

[0574] Step 3:

[0575] The server stores the received data in cloud storage. During this process, emotional data is anonymized and processed in a way that protects user privacy.

[0576] Step 4:

[0577] The server uses generative AI to analyze child data and sentiment data. Sentiment data is used to consider which parts made a good impression on the user when making suggestions.

[0578] Step 5:

[0579] Based on the analysis results, the server generates suggestions for optimal learning or recreational activities to maximize the child's potential. During this process, the suggestions are prioritized according to the user's expressed emotions.

[0580] Step 6:

[0581] The server sends the generated suggestions to the terminal, where the user can view the detailed suggestions.

[0582] Step 7:

[0583] Based on the suggestions, users select specific educational activities or activities for the children and send their feedback to the server via their device.

[0584] Step 8:

[0585] The server re-analyzes user feedback along with sentiment data and uses the system's generating AI to improve the accuracy of its suggestions. This results in more personalized suggestions in the future.

[0586] (Example 2)

[0587] 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."

[0588] Conventional systems for suggesting learning and hobby activities for minors have the problem of not being able to take into account the user's emotions when evaluating the minor's potential abilities and aptitudes, and therefore not being able to provide sufficiently individualized suggestions. In addition, there were significant challenges in utilizing feedback to improve the accuracy of suggestions and protecting privacy, and in particular, the customization of suggestions based on emotions was insufficient.

[0589] 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.

[0590] In this invention, the server includes means for storing information about minors received from multiple data processing devices, means for analyzing the stored information to evaluate the minors' potential abilities and aptitudes, and means for detecting the user's emotions in real time when information is input via an emotion recognition device and including that information in the analysis. This makes it possible to propose individualized learning activities and hobby activities to minors, enabling more personalized and highly accurate suggestions.

[0591] A "data processing device" is a device that has the function of collecting, processing, storing, and transferring information.

[0592] A "minor" refers to a person who has not reached the legal age of majority.

[0593] "Information" includes data that forms the basis of a proposal, such as the age, gender, interests, past achievements, and works of minors.

[0594] An "emotion recognition device" is a device that uses cameras and microphones to analyze a user's facial expressions and voice in real time and detect their emotional state.

[0595] A "generative AI model" is an algorithm that analyzes collected data, evaluates the potential abilities and aptitudes of minors, and generates optimal suggestions.

[0596] "Privacy protection" means protecting personal information using data anonymization and security technologies so that collected information cannot be identified by third parties.

[0597] "Feedback" refers to user reactions and opinions on proposed content, which will be used to improve the system and enhance the accuracy of future proposals.

[0598] "Suggestions" are presented as options for learning and recreational activities best suited to minors, and are generated based on the user's input data and emotional data.

[0599] Regarding embodiments for carrying out the invention, the present invention is a system that evaluates the potential abilities of minors and proposes optimal learning and recreational activities. This system consists of a combination of hardware and software for performing complex data processing, including emotion recognition.

[0600] Users input data about minors using a dedicated information terminal. This terminal is equipped with a digital camera and microphone, and recognizes the user's facial expressions and voice in real time. Emotion recognition software performs facial expression analysis and voice analysis to detect emotions such as joy and surprise.

[0601] Data collected from the device is sent to the server. Upon receiving the data, the server securely stores it in a cloud environment. From a privacy perspective, the data is anonymized and its security is enhanced by encryption technology.

[0602] On the server, a generative AI model analyzes the data. This model evaluates the potential abilities and aptitudes of minors based on a large amount of historical data. It also utilizes emotional data to generate customized suggestions that take into account the user's mental tendencies.

[0603] Once a proposal is generated, the server sends the information to the user's information terminal. The user can review the proposal and provide feedback as needed. This feedback is also used to improve the accuracy of the system's analysis.

[0604] As a concrete example, let's say a user inputs information such as "a 10-year-old girl, interested in music, with past achievements including a piano recital." Through emotion recognition, if the user smiles while viewing musical works, the system can use that data to suggest something like "participating in a summer workshop at a music school."

[0605] An example of a prompt message is as follows: "A 10-year-old girl is interested in music. Her past achievement is a piano recital. Please suggest learning activities that match this child's interests. Also, the user showed the emotion 'smile' while viewing musical works."

[0606] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0607] Step 1:

[0608] Users input data about minors using an information terminal. This input data includes the minor's age, gender, interests, and past achievements. Users also upload their work (e.g., images, text) to the terminal. This allows the system to collect the necessary basic information.

[0609] In terms of operation, the user accesses the input screen on their device and enters the appropriate data for each item. Images and text can be added using the file upload function. The entered data is temporarily stored in the device's memory.

[0610] Step 2:

[0611] The device activates an emotion recognition device simultaneously with input, collecting the user's emotions in real time. It uses a camera and microphone to capture the user's facial expressions and voice data. An emotion engine analyzes this data to identify emotions such as joy and surprise.

[0612] In this step, the input data consists of an image of the user's face and a recording of their voice, while the output is analyzed emotion data. The emotion engine generates these outputs by applying facial recognition and voice analysis algorithms.

[0613] Step 3:

[0614] The device transmits the collected minor information and emotional data to the server. The data is protected by end-to-end encryption. The server stores the received data in a cloud-based database.

[0615] The input data consists of information and emotional data about minors, and the output is a securely stored database entry. This prepares the data necessary for subsequent analysis.

[0616] Step 4:

[0617] The generative AI model on the server analyzes accumulated data to evaluate the aptitudes and talents of minors. The generative AI model compares this data with large historical datasets to identify optimal learning and recreational activities.

[0618] In this step, the input data consists of minor information and sentiment data from a database, and the output is optimized suggestions. Machine learning algorithms are used for the analysis process, and results are obtained based on evaluation metrics.

[0619] Step 5:

[0620] The server sends the generated proposal to the information terminal. The user can review the proposal on the terminal and examine its contents. The proposal includes solutions and details of the next steps.

[0621] The input is the analysis result from a generative AI model, and the output is suggested information displayed on the device. Based on these results, the user can move the minor's activities to the next step.

[0622] Step 6:

[0623] When a user provides feedback on a suggestion, the device sends that feedback data to the server. The server analyzes the feedback and uses it to improve the system's accuracy.

[0624] The input for this step is user feedback, and the output is an updated analysis result that takes that feedback into account. This process contributes to improving the accuracy and applicability of the proposal.

[0625] (Application Example 2)

[0626] 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."

[0627] In modern education and recreational activities, it is crucial to accurately assess the potential and aptitudes of minors and propose the most suitable activities based on that assessment. However, many systems are not individualized and do not take into account the characteristics of minors or the emotions of users, resulting in generalized suggestions. Furthermore, there are challenges regarding the privacy protection of emotional data.

[0628] 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.

[0629] In this invention, the server includes means for storing data on minors received from multiple terminal devices, means for analyzing the stored data to evaluate the minors' potential abilities and aptitudes, means for presenting learning activities or hobby activities to minors based on the analyzed evaluation results, means for analyzing the user's emotional state using emotion recognition technology to present more personalized activities to minors, and means for anonymizing and securely storing the user's emotional data. This makes it possible to present more personalized activities to minors, improve the accuracy and security of suggestions while ensuring the privacy protection of emotional data.

[0630] A "terminal device" is an electronic device used to input or receive data and to store or display information about minors.

[0631] "Data concerning minors" refers to data that includes information such as the age, interests, past achievements, and works of minors.

[0632] "Potential" refers to the potential and characteristics of minors that contribute to their future growth.

[0633] "Learning activities or recreational activities" refer to educational or recreational activities that are in line with the interests and abilities of minors.

[0634] "Emotion recognition technology" is a technology that analyzes a user's emotional state from their facial expressions and voice.

[0635] "Anonymization" is a technique that protects privacy by processing data in a way that makes it impossible to identify specific individuals.

[0636] "Securely maintaining" means taking measures to ensure that data is stored in a way that protects it from unauthorized access and leakage.

[0637] The system implementing this invention mainly consists of a server, terminal devices, and users. The server receives data about minors from multiple terminal devices and stores it securely. This data includes age, interests, past achievements, and works. The data is anonymized and privacy is protected.

[0638] This system uses cameras and microphones mounted on terminal devices to collect emotional data from users' facial expressions and voices, employing emotion recognition technology. The collected emotional data is sent to a server for analysis. This analysis utilizes the previously stored data on minors, and a generative AI model evaluates the minor's potential and aptitude.

[0639] Based on the evaluation, the server generates content optimized for minors, presenting learning and hobby activities. Feedback based on user sentiment data is used to improve the accuracy of the suggestions. The presented content is displayed on the terminal device for the user to review and accept.

[0640] For example, if a minor creates a work and uploads it to the application, and the system determines that the parents or teachers are experiencing feelings of "surprise" and "joy," then additional educational content or hobby-related material in that field will be suggested.

[0641] Examples of prompts for a generative AI model include:

[0642] "Analyzing a child's recent drawing and considering the emotions expressed by the parent, what new learning activities would you propose?"

[0643] This is how it will be done.

[0644] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0645] Step 1:

[0646] The device uses a camera and microphone to capture the user's facial expressions and voice in real time. This allows the user's emotional state to be collected as input. In addition, relevant data such as the age and interests of minors are also input into the device at the same time.

[0647] Step 2:

[0648] The emotional state data collected by the device is analyzed using emotion recognition technology. Specifically, it is converted into emotional data using image processing algorithms and speech analysis algorithms. The analysis results are output as emotional data.

[0649] Step 3:

[0650] The server receives emotional data and basic data of minors from the device. Within the server, this data is anonymized and stored securely. This ensures that data is accumulated in a privacy-protected manner.

[0651] Step 4:

[0652] The server uses anonymized emotional data and data on minors to evaluate the potential of minors using a generative AI model. The AI ​​model analyzes various data patterns to generate evaluation results. These results serve as input for the next step.

[0653] Step 5:

[0654] The server generates optimal learning or hobby activities for minors based on evaluation results and user sentiment data. Specifically, a generation AI model uses the evaluation results to suggest customized content. The suggested content is then output to the terminal.

[0655] Step 6:

[0656] Users review the suggestions on their devices and provide feedback. This feedback is then sent back to the server to improve the accuracy of future suggestions.

[0657] Step 7:

[0658] The server analyzes the received feedback and sentiment data and uses it to improve future suggestion processes. The analyzed results are used as data to improve the accuracy of the system.

[0659] 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.

[0660] 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.

[0661] 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.

[0662] [Fourth Embodiment]

[0663] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0664] 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.

[0665] 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).

[0666] 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.

[0667] 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.

[0668] 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).

[0669] 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.

[0670] 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.

[0671] 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.

[0672] 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.

[0673] 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.

[0674] 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.

[0675] 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".

[0676] This invention provides a system for analyzing children's potential abilities and characteristics and proposing appropriate learning and recreational activities. To implement this invention, it is desirable to configure it as follows.

[0677] Users input data about their children using a dedicated information terminal or web application. This data includes the child's age, gender, interests, past achievements, and works (images, text, music files, etc.). The terminal is responsible for initial formatting of the data entered by the user and then sending it to the server.

[0678] The server securely stores the received data in the cloud. This stored data is then analyzed using generative AI. The analysis process involves data cleansing, feature extraction, and comparison with past success stories to evaluate the children's potential talents and aptitudes. For example, image data of children's drawings could be analyzed to extract features such as color sense and originality of composition, thereby evaluating their potential artistic talent.

[0679] After the evaluation is complete, the server generates suggestions for optimal learning and recreational activities based on the child's characteristics. These suggestions are customized specifically for each child; for example, a child who is interested in music and has a good sense of rhythm might be recommended drum or dance lessons.

[0680] Subsequently, the server sends the generated suggestions to the information terminal, providing the user with the results. The user reviews the suggestions on the terminal and provides feedback as needed. This feedback is sent to the server and used to improve the generating AI model. This provides an environment in which the accuracy of the analysis results continuously improves.

[0681] Furthermore, this system prioritizes privacy protection; data received from users is anonymized, ensuring safe and secure use. This allows parents to confidently utilize the data they provide while supporting their children's development.

[0682] The following describes the processing flow.

[0683] Step 1:

[0684] The user uses a dedicated terminal to input data about the child, including their age, gender, interests, past achievements, and works (such as images and texts).

[0685] Step 2:

[0686] The terminal receives the input data and performs initial formatting. This formatting includes resizing images and standardizing the format of text data.

[0687] Step 3:

[0688] The terminal sends the formatted data to the server using a secure communication protocol.

[0689] Step 4:

[0690] The server stores the received data in cloud storage. During this process, the data is always encrypted, ensuring privacy.

[0691] Step 5:

[0692] The server inputs the accumulated data into the generating AI, which then cleanses the data. This removes noise and processes missing data.

[0693] Step 6:

[0694] The server uses generative AI to extract features from the data. For example, in the case of image data, it quantifies features such as color and composition.

[0695] Step 7:

[0696] The server evaluates the child's potential and aptitude by comparing the extracted features with a database of past success stories to find similar patterns.

[0697] Step 8:

[0698] Based on the evaluation results, the server generates content suggesting learning and hobby activities suitable for the children.

[0699] Step 9:

[0700] The server sends the generated suggestions to the information terminal, allowing the user to easily review them.

[0701] Step 10:

[0702] Users review the provided suggestions and provide feedback via their devices. This feedback includes the usefulness of the suggestions and the children's reactions.

[0703] Step 11:

[0704] The server incorporates the received feedback into its analysis and uses it to improve the accuracy of future generative AI models.

[0705] (Example 1)

[0706] 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".

[0707] Traditionally, there has been no system that accurately analyzes children's potential and characteristics and proposes optimal learning and recreational activities based on the results. Furthermore, there has been a lack of means to meet diverse needs, such as secure management of collected data, data conversion for analysis, and improvement of analysis accuracy through user feedback. As a result, optimizing education by leveraging the individual characteristics of each child has been difficult.

[0708] 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.

[0709] In this invention, the server includes means for storing information about children received from users via a data communication device, means for analyzing the stored information using a generative model to evaluate the children's potential abilities and characteristics, and means for recommending individually suitable educational activities or hobby activities based on the analysis results. This makes it possible to provide individualized suggestions tailored to the characteristics of each child.

[0710] "User" refers to the entity that operates the system and inputs and verifies information about the child.

[0711] "Data communication device" refers to equipment or applications used to collect information and communicate with a server.

[0712] "Information about children" refers to various data about children, including their age, gender, interests, past achievements, and works.

[0713] "Means of storage" refers to the process of securely saving acquired information to a storage device or cloud storage.

[0714] A "generative model" refers to AI technology used to analyze received information and evaluate the characteristics of children.

[0715] "Means of analysis" refers to the process of processing received information and evaluating the child's potential abilities and characteristics.

[0716] "Recommendation methods" refer to the process of selecting and proposing educational and recreational activities suitable for children based on analysis.

[0717] "Means of securely managing and converting information into an analyzable format" refers to the process of ensuring the security of the input information and then formatting it into a format suitable for analysis.

[0718] This invention is a system that collects information about children and proposes optimal activities based on their individual characteristics. Specific embodiments are shown below.

[0719] Users input information about their children using a dedicated data communication device or web application. This information includes the child's age, gender, interests, past achievements, and works (e.g., images, text, audio files, etc.). The data communication device is equipped with functions for initial formatting and standardization of the information.

[0720] Information transmitted from the device is received by the server and securely stored in cloud storage. The server analyzes the received information using a generative AI model. This generative AI model has the function of extracting features and cleaning the information, and evaluating the child's potential abilities and characteristics. Furthermore, based on the analyzed information, it generates suggestions for educational and recreational activities.

[0721] For example, if a child's drawing is input as image data, the server can analyze the data and evaluate the child's artistic talent based on characteristics such as color sense and originality of composition. Based on this evaluation, it can then suggest participation in art classes or art events.

[0722] Users can review the suggestions on their devices and submit feedback, which helps the server further improve the accuracy of its analysis. This feedback is used to improve the generated AI model, increasing the accuracy and reliability of the system.

[0723] By the way, in order to ensure the security of information, the server anonymizes the information it receives, providing an environment in which users can provide data with peace of mind.

[0724] (Example of a prompt message)

[0725] "He is a 10-year-old boy who loves to draw. We have image data of his past artwork. Please evaluate his artistic abilities and suggest appropriate activities."

[0726] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0727] Step 1:

[0728] Users input information about their children through a dedicated data communication device or web application. This information includes the child's age, gender, interests, past achievements, and works (images, text, etc.). The input information is initially formatted on the terminal to ensure a consistent format. The output is formatted data, ready for transmission to the server.

[0729] Step 2:

[0730] The terminal sends the formatted information to the server using a secure protocol. The output is the received data sequence, and processing begins on the server side.

[0731] Step 3:

[0732] The server securely stores the received information in cloud storage. The input here is data sent from the terminal, and the output is data stored in cloud storage. This data is then organized in a database for analysis.

[0733] Step 4:

[0734] The server launches a generative AI model to analyze the accumulated information. The input is organized data retrieved from cloud storage. In this phase, the data is cleansed, and unwanted noise is removed. The output is passed to the generative AI model as cleansed data with features extracted.

[0735] Step 5:

[0736] The server uses a generative AI model to extract features and evaluate the children's potential abilities and characteristics. The input is cleansed data, and the output consists of the results of the children's ability assessment and feature data based on their specific interests.

[0737] Step 6:

[0738] The server suggests individualized learning and recreational activities based on the children's evaluation results. The input is the children's evaluation results, and the output is a suggested activity. For example, it might recommend dance lessons to children with a strong sense of rhythm.

[0739] Step 7:

[0740] The server generates suggestions and sends them to the terminal for the user to view. The user can then review the suggestions on the terminal. The output is a displayed activity suggestion, which the user can review or modify.

[0741] Step 8:

[0742] The user inputs feedback on a proposal and sends it to the server via their device. The input is the user's feedback content, and the output is the feedback data received by the server.

[0743] Step 9:

[0744] The server uses user feedback to improve the generated AI model, thereby enhancing the overall analysis accuracy of the system. This is a process of analyzing the input feedback data and adjusting the model. The output, as an improved model, is used for subsequent analyses.

[0745] Step 10:

[0746] The server performs a process to anonymize the received information, protecting data privacy. The input is all information about the user and the child, and the output is anonymized data with protected personal information. This process allows users to use the system with peace of mind.

[0747] (Application Example 1)

[0748] 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".

[0749] There is a need to effectively draw out the potential abilities and aptitudes of minors and propose optimal educational activities tailored to their individual characteristics. However, achieving this requires a system that can analyze large amounts of information and make accurate suggestions. Furthermore, an environment where information about minors can be provided with peace of mind is also crucial.

[0750] 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.

[0751] In this invention, the server includes means for storing information about minors received from multiple information processing devices, means for analyzing the stored information to evaluate the minors' potential abilities and aptitudes, means for proposing learning activities or hobby activities for minors based on the evaluation results, and means for providing proposals based on the evaluation results in a virtual space and recommending educational activities. This enables the provision of individually optimized educational proposals tailored to the characteristics of minors and the appropriate handling of personal information.

[0752] An "information processing device" is a device used to input data about minors, including children, and transmit it to a server.

[0753] "Minor" refers to children and students who are in a developmental stage, and their potential abilities and aptitudes are the subjects of evaluation.

[0754] "Information" includes data such as age, gender, interests, past achievements, and works related to minors.

[0755] "Storage" refers to the process of securely saving received information and using it for subsequent analysis.

[0756] "Analysis" is the process of identifying the potential and aptitudes of minors based on accumulated information.

[0757] "Evaluation" is the process of determining the characteristics and potential of minors based on data obtained through analysis.

[0758] A "virtual space" is a digital environment constructed using information and communication technology, and is primarily used as a venue for educational activities.

[0759] "Educational activities" are activities that support the learning of minors and provide them with skills and knowledge that are tailored to their abilities and interests.

[0760] "Personal information" refers to information about minors or those around them that can identify a specific individual.

[0761] To realize this invention, it is basically necessary to build a system in which an information processing device and a server work together. The information processing device is a device for inputting information about minors, and a smartphone, tablet, or personal computer is used. The user uses these devices to input data such as the minor's age, gender, interests, past achievements, and works. This information is initially formatted and sent to the server.

[0762] The server operates as a backend system using Python and Django. It securely stores received information in the cloud and analyzes the stored data using generative AI. This analysis process involves data cleansing, feature extraction, and comparison with past success stories to evaluate the potential talents and aptitudes of minors. The generative AI model utilizes AI technologies such as the OpenAI API to generate responses. Based on the evaluation results, this AI technology generates suggestions for optimal learning and hobby activities.

[0763] Evaluation results and suggestions are provided through a user interface set up as a virtual space. Recommended educational activities are easily accessible to users and can be used to select educational and hobby activities. Users can review the suggestions and provide feedback as needed. This feedback is used to improve the accuracy of the analysis. In addition, the received information is anonymized to ensure the appropriate handling of personal information.

[0764] For example, if a 10-year-old minor is entered, the system can analyze drawings they have made in the past, determine that they have "excellent color sense," and then provide specific educational suggestions such as "online digital art courses."

[0765] Example of a prompt:

[0766] "A 10-year-old boy enjoys drawing colorful pictures. Based on examples of his past work, please suggest learning activities that would suit his characteristics."

[0767] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0768] Step 1:

[0769] The user uses an information processing device to input data such as the age, gender, interests, past achievements, and works of minors. The input data is initially formatted on the information processing device, and processing is performed to remove incomplete information and duplicate data. This processing ensures that highly accurate data is sent to the server.

[0770] Step 2:

[0771] The server securely stores the received data in the cloud. The data is organized based on the database structure and anonymized according to security policies. The output of this step is formatted and anonymized data for use in subsequent analysis.

[0772] Step 3:

[0773] The server performs analysis on the formatted data using a generative AI model. The AI ​​model quantifies characteristics and aptitudes based on the input data and evaluates them. This evaluation includes feature extraction from the input data and comparison with past success stories based on those features. The output is an evaluation result regarding the potential abilities and aptitudes of minors.

[0774] Step 4:

[0775] The server generates suggestions for optimal learning and recreational activities based on the generated evaluation results. Using a generation AI model, it constructs an educational plan that matches the evaluation results in a form that can be executed within a virtual space. This suggestion is output in the form of specific learning courses and activity candidates.

[0776] Step 5:

[0777] The evaluation results and suggestions are sent to the information processing device and displayed on a screen where the user can review them. The user can select recommended activities or provide feedback. This feedback is sent back to the server to be used later to improve the accuracy of the analysis model. This process ensures continuous system improvement.

[0778] 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.

[0779] This invention combines an emotion engine with a system that evaluates children's potential and proposes optimal learning and recreational activities, thereby enabling more personalized suggestions that take the user's emotions into account. Specific embodiments for carrying out this invention are shown below.

[0780] Users input data about their children using a dedicated information terminal. This data includes the child's age, gender, interests, past achievements, and works (e.g., images, text). As the user operates the terminal, the emotion engine recognizes the user's emotions in real time through the camera and microphone built into the terminal. The emotion engine detects various emotions such as joy, surprise, and confusion from the user's facial expressions and voice, and sends this data to the server.

[0781] The server stores and securely manages received emotional and child data in the cloud. Emotional data is stored anonymized to ensure privacy. Generating AI analyzes this data to assess the child's potential talents and aptitudes. Furthermore, it considers the user's emotional data to generate suggestions for the most suitable learning or hobby activities for the child. For example, it customizes emotional suggestions by focusing on the results of analyzing works in which the user expressed "surprise" or "joy."

[0782] The server then sends optimized suggestions to the user's information terminal. The user can then view these suggestions on the terminal. When the user provides feedback on the suggestions, the server analyzes the feedback along with emotional data and uses it to improve the accuracy of future suggestions. In this way, the system, equipped with an emotion engine, incorporates the user's emotional aspects into the evaluation, enabling it to support maximizing the learning effectiveness of children.

[0783] The following describes the processing flow.

[0784] Step 1:

[0785] Users input data about their children (age, gender, interests, past achievements, works, etc.) using an information terminal. During this process, the camera and microphone built into the terminal analyze the user's facial expressions and voice using an emotion engine, acquiring emotional data in real time.

[0786] Step 2:

[0787] The terminal transmits data entered by the user and acquired emotional data to the server using a secure communication protocol.

[0788] Step 3:

[0789] The server stores the received data in cloud storage. During this process, emotional data is anonymized and processed in a way that protects user privacy.

[0790] Step 4:

[0791] The server uses generative AI to analyze child data and sentiment data. Sentiment data is used to consider which parts made a good impression on the user when making suggestions.

[0792] Step 5:

[0793] Based on the analysis results, the server generates suggestions for optimal learning or recreational activities to maximize the child's potential. During this process, the suggestions are prioritized according to the user's expressed emotions.

[0794] Step 6:

[0795] The server sends the generated suggestions to the terminal, where the user can view the detailed suggestions.

[0796] Step 7:

[0797] Based on the suggestions, users select specific educational activities or activities for the children and send their feedback to the server via their device.

[0798] Step 8:

[0799] The server re-analyzes user feedback along with sentiment data and uses the system's generating AI to improve the accuracy of its suggestions. This results in more personalized suggestions in the future.

[0800] (Example 2)

[0801] 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".

[0802] Conventional systems for suggesting learning and hobby activities for minors have the problem of not being able to take into account the user's emotions when evaluating the minor's potential abilities and aptitudes, and therefore not being able to provide sufficiently individualized suggestions. In addition, there were significant challenges in utilizing feedback to improve the accuracy of suggestions and protecting privacy, and in particular, the customization of suggestions based on emotions was insufficient.

[0803] 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.

[0804] In this invention, the server includes means for storing information about minors received from multiple data processing devices, means for analyzing the stored information to evaluate the minors' potential abilities and aptitudes, and means for detecting the user's emotions in real time when information is input via an emotion recognition device and including that information in the analysis. This makes it possible to propose individualized learning activities and hobby activities to minors, enabling more personalized and highly accurate suggestions.

[0805] A "data processing device" is a device that has the function of collecting, processing, storing, and transferring information.

[0806] A "minor" refers to a person who has not reached the legal age of majority.

[0807] "Information" includes data that forms the basis of a proposal, such as the age, gender, interests, past achievements, and works of minors.

[0808] An "emotion recognition device" is a device that uses cameras and microphones to analyze a user's facial expressions and voice in real time and detect their emotional state.

[0809] A "generative AI model" is an algorithm that analyzes collected data, evaluates the potential abilities and aptitudes of minors, and generates optimal suggestions.

[0810] "Privacy protection" means protecting personal information using data anonymization and security technologies so that collected information cannot be identified by third parties.

[0811] "Feedback" refers to user reactions and opinions on proposed content, which will be used to improve the system and enhance the accuracy of future proposals.

[0812] "Suggestions" are presented as options for learning and recreational activities best suited to minors, and are generated based on the user's input data and emotional data.

[0813] Regarding embodiments for carrying out the invention, the present invention is a system that evaluates the potential abilities of minors and proposes optimal learning and recreational activities. This system consists of a combination of hardware and software for performing complex data processing, including emotion recognition.

[0814] Users input data about minors using a dedicated information terminal. This terminal is equipped with a digital camera and microphone, and recognizes the user's facial expressions and voice in real time. Emotion recognition software performs facial expression analysis and voice analysis to detect emotions such as joy and surprise.

[0815] Data collected from the device is sent to the server. Upon receiving the data, the server securely stores it in a cloud environment. From a privacy perspective, the data is anonymized and its security is enhanced by encryption technology.

[0816] On the server, a generative AI model analyzes the data. This model evaluates the potential abilities and aptitudes of minors based on a large amount of historical data. It also utilizes emotional data to generate customized suggestions that take into account the user's mental tendencies.

[0817] Once a proposal is generated, the server sends the information to the user's information terminal. The user can review the proposal and provide feedback as needed. This feedback is also used to improve the accuracy of the system's analysis.

[0818] As a concrete example, let's say a user inputs information such as "a 10-year-old girl, interested in music, with past achievements including a piano recital." Through emotion recognition, if the user smiles while viewing musical works, the system can use that data to suggest something like "participating in a summer workshop at a music school."

[0819] An example of a prompt message is as follows: "A 10-year-old girl is interested in music. Her past achievement is a piano recital. Please suggest learning activities that match this child's interests. Also, the user showed the emotion 'smile' while viewing musical works."

[0820] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0821] Step 1:

[0822] Users input data about minors using an information terminal. This input data includes the minor's age, gender, interests, and past achievements. Users also upload their work (e.g., images, text) to the terminal. This allows the system to collect the necessary basic information.

[0823] In terms of operation, the user accesses the input screen on their device and enters the appropriate data for each item. Images and text can be added using the file upload function. The entered data is temporarily stored in the device's memory.

[0824] Step 2:

[0825] The device activates an emotion recognition device simultaneously with input, collecting the user's emotions in real time. It uses a camera and microphone to capture the user's facial expressions and voice data. An emotion engine analyzes this data to identify emotions such as joy and surprise.

[0826] In this step, the input data consists of an image of the user's face and a recording of their voice, while the output is analyzed emotion data. The emotion engine generates these outputs by applying facial recognition and voice analysis algorithms.

[0827] Step 3:

[0828] The device transmits the collected minor information and emotional data to the server. The data is protected by end-to-end encryption. The server stores the received data in a cloud-based database.

[0829] The input data consists of information and emotional data about minors, and the output is a securely stored database entry. This prepares the data necessary for subsequent analysis.

[0830] Step 4:

[0831] The generative AI model on the server analyzes accumulated data to evaluate the aptitudes and talents of minors. The generative AI model compares this data with large historical datasets to identify optimal learning and recreational activities.

[0832] In this step, the input data consists of minor information and sentiment data from a database, and the output is optimized suggestions. Machine learning algorithms are used for the analysis process, and results are obtained based on evaluation metrics.

[0833] Step 5:

[0834] The server sends the generated proposal to the information terminal. The user can review the proposal on the terminal and examine its contents. The proposal includes solutions and details of the next steps.

[0835] The input is the analysis result from a generative AI model, and the output is suggested information displayed on the device. Based on these results, the user can move the minor's activities to the next step.

[0836] Step 6:

[0837] When a user provides feedback on a suggestion, the device sends that feedback data to the server. The server analyzes the feedback and uses it to improve the system's accuracy.

[0838] The input for this step is user feedback, and the output is an updated analysis result that takes that feedback into account. This process contributes to improving the accuracy and applicability of the proposal.

[0839] (Application Example 2)

[0840] 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".

[0841] In modern education and recreational activities, it is crucial to accurately assess the potential and aptitudes of minors and propose the most suitable activities based on that assessment. However, many systems are not individualized and do not take into account the characteristics of minors or the emotions of users, resulting in generalized suggestions. Furthermore, there are challenges regarding the privacy protection of emotional data.

[0842] 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.

[0843] In this invention, the server includes means for storing data on minors received from multiple terminal devices, means for analyzing the stored data to evaluate the minors' potential abilities and aptitudes, means for presenting learning activities or hobby activities to minors based on the analyzed evaluation results, means for analyzing the user's emotional state using emotion recognition technology to present more personalized activities to minors, and means for anonymizing and securely storing the user's emotional data. This makes it possible to present more personalized activities to minors, improve the accuracy and security of suggestions while ensuring the privacy protection of emotional data.

[0844] A "terminal device" is an electronic device used to input or receive data and to store or display information about minors.

[0845] "Data concerning minors" refers to data that includes information such as the age, interests, past achievements, and works of minors.

[0846] "Potential" refers to the potential and characteristics of minors that contribute to their future growth.

[0847] "Learning activities or recreational activities" refer to educational or recreational activities that are in line with the interests and abilities of minors.

[0848] "Emotion recognition technology" is a technology that analyzes a user's emotional state from their facial expressions and voice.

[0849] "Anonymization" is a technique that protects privacy by processing data in a way that makes it impossible to identify specific individuals.

[0850] "Securely maintaining" means taking measures to ensure that data is stored in a way that protects it from unauthorized access and leakage.

[0851] The system implementing this invention mainly consists of a server, terminal devices, and users. The server receives data about minors from multiple terminal devices and stores it securely. This data includes age, interests, past achievements, and works. The data is anonymized and privacy is protected.

[0852] This system uses cameras and microphones mounted on terminal devices to collect emotional data from users' facial expressions and voices, employing emotion recognition technology. The collected emotional data is sent to a server for analysis. This analysis utilizes the previously stored data on minors, and a generative AI model evaluates the minor's potential and aptitude.

[0853] Based on the evaluation, the server generates content optimized for minors, presenting learning and hobby activities. Feedback based on user sentiment data is used to improve the accuracy of the suggestions. The presented content is displayed on the terminal device for the user to review and accept.

[0854] For example, if a minor creates a work and uploads it to the application, and the system determines that the parents or teachers are experiencing feelings of "surprise" and "joy," then additional educational content or hobby-related material in that field will be suggested.

[0855] Examples of prompts for a generative AI model include:

[0856] "Analyzing a child's recent drawing and considering the emotions expressed by the parent, what new learning activities would you propose?"

[0857] This is how it will be done.

[0858] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0859] Step 1:

[0860] The device uses a camera and microphone to capture the user's facial expressions and voice in real time. This allows the user's emotional state to be collected as input. In addition, relevant data such as the age and interests of minors are also input into the device at the same time.

[0861] Step 2:

[0862] The emotional state data collected by the device is analyzed using emotion recognition technology. Specifically, it is converted into emotional data using image processing algorithms and speech analysis algorithms. The analysis results are output as emotional data.

[0863] Step 3:

[0864] The server receives emotional data and basic data of minors from the device. Within the server, this data is anonymized and stored securely. This ensures that data is accumulated in a privacy-protected manner.

[0865] Step 4:

[0866] The server uses anonymized emotional data and data on minors to evaluate the potential of minors using a generative AI model. The AI ​​model analyzes various data patterns to generate evaluation results. These results serve as input for the next step.

[0867] Step 5:

[0868] The server generates optimal learning or hobby activities for minors based on evaluation results and user sentiment data. Specifically, a generation AI model uses the evaluation results to suggest customized content. The suggested content is then output to the terminal.

[0869] Step 6:

[0870] Users review the suggestions on their devices and provide feedback. This feedback is then sent back to the server to improve the accuracy of future suggestions.

[0871] Step 7:

[0872] The server analyzes the received feedback and sentiment data and uses it to improve future suggestion processes. The analyzed results are used as data to improve the accuracy of the system.

[0873] 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.

[0874] 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.

[0875] 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.

[0876] 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.

[0877] 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.

[0878] 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.

[0879] 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.

[0880] 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.

[0881] 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."

[0882] 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.

[0883] 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.

[0884] 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.

[0885] 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.

[0886] 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.

[0887] 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.

[0888] 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.

[0889] 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.

[0890] 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.

[0891] 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.

[0892] 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.

[0893] 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.

[0894] The following is further disclosed regarding the embodiments described above.

[0895] (Claim 1)

[0896] A means for accumulating data about children received from multiple information terminals,

[0897] A means of analyzing accumulated data to evaluate children's potential talents and aptitudes,

[0898] A means of proposing learning activities or hobby activities for children based on the evaluation results,

[0899] A system that includes this.

[0900] (Claim 2)

[0901] The system according to claim 1, comprising means for receiving user feedback on evaluation results and proposed content and using it to improve analysis accuracy.

[0902] (Claim 3)

[0903] The system according to claim 1, comprising means for anonymizing received data and implementing privacy protection.

[0904] "Example 1"

[0905] (Claim 1)

[0906] A means for storing information about children received from users via a data communication device,

[0907] A means of evaluating children's potential abilities and characteristics by analyzing accumulated information using generative models,

[0908] A means of recommending individually suitable educational or hobby activities based on the analysis results,

[0909] Means for securely managing the provided information and converting it into an analyzable format,

[0910] A system that includes this.

[0911] (Claim 2)

[0912] The system according to claim 1, further comprising means for receiving user responses to evaluation results and recommendations and using them to improve the accuracy of the generative model.

[0913] (Claim 3)

[0914] The system according to claim 1, comprising means for anonymizing information obtained from users and for protecting that information.

[0915] "Application Example 1"

[0916] (Claim 1)

[0917] A means for storing information about minors received from multiple information processing devices,

[0918] A means of analyzing accumulated information to evaluate the potential abilities and aptitudes of minors,

[0919] A means of proposing learning or hobby activities for minors based on evaluation results,

[0920] A means of providing suggestions based on evaluation results in a virtual space and recommending educational activities,

[0921] A system that includes this.

[0922] (Claim 2)

[0923] The system according to claim 1, comprising means for receiving feedback from information users on evaluation results and proposed content, and using that feedback to improve the accuracy of the analysis.

[0924] (Claim 3)

[0925] The system according to claim 1, comprising means for anonymizing received information and implementing personal information protection.

[0926] "Example 2 of combining an emotion engine"

[0927] (Claim 1)

[0928] A means for storing information about minors received from multiple data processing devices,

[0929] A means of analyzing accumulated information to evaluate the potential abilities and aptitudes of minors,

[0930] A means of proposing learning or hobby activities for minors based on evaluation results,

[0931] A means of detecting the user's emotions in real time when inputting information via an emotion recognition device and including that information in the analysis,

[0932] A means for transmitting the generated proposal to the user's information processing device,

[0933] A system that includes this.

[0934] (Claim 2)

[0935] The system according to claim 1, comprising means for receiving user feedback on evaluation results and proposed content and using it to improve analysis accuracy.

[0936] (Claim 3)

[0937] The system according to claim 1, comprising means for anonymizing received information and implementing personal information protection.

[0938] "Application example 2 when combining with an emotional engine"

[0939] (Claim 1)

[0940] A means for storing data concerning minors received from multiple terminal devices,

[0941] A method for analyzing saved data to evaluate the potential and aptitudes of minors,

[0942] A means of presenting learning activities or hobby activities for minors based on the analyzed evaluation results,

[0943] A means of analyzing a user's emotional state using emotion recognition technology and presenting more personalized activities to minors,

[0944] A means of anonymizing and securely storing user emotional data,

[0945] A system that includes this.

[0946] (Claim 2)

[0947] The system according to claim 1, comprising means for collecting user attribution information regarding evaluation results and presented content, and using it to improve the accuracy of the analysis.

[0948] (Claim 3)

[0949] The system according to claim 1, comprising means for maintaining the anonymity of received data and implementing measures to protect confidential information. [Explanation of Symbols]

[0950] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for accumulating data about children received from multiple information terminals, A means of analyzing accumulated data to evaluate children's potential talents and aptitudes, A means of proposing learning activities or hobby activities for children based on the evaluation results, A system that includes this.

2. The system according to claim 1, further comprising means for receiving user feedback on evaluation results and proposed content, and using that feedback to improve the accuracy of the analysis.

3. The system according to claim 1, comprising means for anonymizing received data and protecting privacy.

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