System

Abstract

A system is provided.SOLUTION: The system includes a means for acquiring the genetic information of the child, a means for analyzing the acquired genetic information and evaluating the characteristics of the child, a means for searching for a suitable educational institution based on the evaluated characteristics, and a means for transmitting the search result to the terminal of the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In today's educational environment, selecting the most appropriate educational institution for each child is crucial to maximizing their individual characteristics and talents. However, traditional methods have made it difficult for parents to accurately assess their children's characteristics and select the most appropriate educational institution based on that assessment. Specifically, due to a lack of scientific methods for assessing children's characteristics and talents, the process of selecting the most appropriate educational environment is complicated and places a heavy burden on parents. [Means for solving the problem]

[0005] The present invention provides a system that scientifically evaluates a child's characteristics and talents based on their genetic information and recommends the most appropriate educational institution based on the results. Specifically, the system includes a means for acquiring the child's genetic information and a means for analyzing the acquired genetic information and evaluating the child's characteristics. Furthermore, the system includes a means for searching for suitable educational institutions based on the evaluated characteristics and a means for transmitting the search results to the user's terminal, allowing parents to easily find the most suitable educational environment for their children. This system can effectively recommend the most appropriate educational institution to maximize a child's talents.

[0006] "Genetic information" refers to DNA sequence data used to determine a child's biological characteristics and traits.

[0007] "Analysis means" refers to algorithms or computational processes used to scientifically evaluate specific traits or talents from input genetic information.

[0008] "Assessment tools" refer to the processes and methods for evaluating a child's characteristics and talents based on data obtained by analytical tools, and generating a specific profile.

[0009] "Educational institution" means a nursery school, kindergarten, elementary school, junior high school, high school, or any facility that provides an equivalent educational program.

[0010] "Search Method" refers to the database search algorithm or logic used to generate a list of suitable educational institutions based on the child's assessed characteristics and profile.

[0011] "Transmission means" refers to the communication protocols and functions for transmitting the list of educational institutions selected by the search means and detailed information to the user's terminal.

[0012] "User terminal" refers to a device used by parents or educators to access the system via a communication means, such as a computer, smartphone, or tablet. [Brief explanation of the drawings]

[0013] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0014] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0017] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0018] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0019] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0021] [First embodiment]

[0022] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0023] 1, a 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.

[0024] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0026] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0027] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0029] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0031] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0033] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0034] The present invention relates to a system that evaluates a child's characteristics and talents based on their genetic information and recommends the most suitable educational institution based on the evaluation results. An embodiment of this system will be described below.

[0035] Overall structure

[0036] The system consists of a user device, a server, and a database. The user device is used by parents and educators, the server provides data analysis and recommendation functions, and the database stores information about educational institutions.

[0037] Collection and transmission of genetic information

[0038] Users use a genetic analysis kit to obtain their child's genetic information, which is then sent to a server via a dedicated application or web portal.

[0039] Receiving and analyzing data

[0040] The server receives the genetic information transmitted from the user terminal.

[0041] Data reception: The server checks the format of the received genetic information and performs error checks.

[0042] Data analysis: Specialized analysis algorithms extract specific traits such as learning ability, athletic ability, and creativity from genetic information.

[0043] Evaluation and Profile Generation

[0044] The server evaluates the child's characteristics based on the analysis results.

[0045] Characteristic evaluation: Generate scores and comments for each characteristic based on the extracted characteristic data.

[0046] Profile generation: Create specific profiles such as "knowledge-seeking" for those with high learning ability, or "sports" for those with high athletic ability.

[0047] Find an educational institution

[0048] The server searches the database and selects the most suitable educational institution based on the evaluated characteristics.

[0049] Database search: Select facilities that fit your characteristics based on information such as the educational institution's program content, features, and location.

[0050] Matching: Match the child's profile with the characteristics of the educational institution to extract the most suitable facility.

[0051] Generating and sending recommendation results

[0052] The server generates a list of suitable educational institutions and compiles details for each facility.

[0053] List Creation: Create a list of recommended facilities and add details about each facility.

[0054] Data transmission: The generated list and detailed information are sent to the user's terminal.

[0055] User Interface

[0056] The terminal displays the received recommendation results to the user.

[0057] List View: Displays a list of facilities in an easy-to-read format, with links to access more information about each facility.

[0058] Specific examples

[0059] For example, suppose a user sends their child's genetic information to a server. The server analyzes the information and evaluates the child's musical ability. Next, the server searches a database for nurseries and schools specializing in music education and creates a list of the most suitable facilities. Finally, the server sends the results to the user's device, where the user can view the list. The user can then select a facility based on the list and make an inquiry or application.

[0060] In this way, the present invention provides an educational environment that maximizes a child's talents by combining scientific evaluation based on genetic information with recommendations for appropriate educational institutions.

[0061] The processing flow will be explained below.

[0062] Step 1:

[0063] Users collect their children's genetic information and send it to a server via a dedicated application or web portal.

[0064] The user analyzes the saliva sample using a genetic analysis kit and obtains the generated genetic data.

[0065] The user enters the genetic data into the application and uploads it to the server.

[0066] Step 2:

[0067] The server receives the genetic information sent from the user.

[0068] The server checks the format of the received genetic data and verifies the integrity of the data.

[0069] The server performs error checking and notifies the user if necessary.

[0070] Step 3:

[0071] The server analyzes the genetic information.

[0072] The server uses specialized analytical algorithms to extract specific traits from the genetic data, such as learning ability, athletic ability, and creativity.

[0073] The server organizes the extracted characteristic data and converts it into a format that can be used in the next step.

[0074] Step 4:

[0075] The server evaluates the child's characteristics and generates a profile.

[0076] The server calculates a score for each characteristic based on the analysis results and generates a comment.

[0077] Based on the evaluation results, the server creates specific profiles such as "knowledge-seeking type" or "sports type."

[0078] Step 5:

[0079] The server searches the educational institution database.

[0080] The server then searches for facilities that offer specific programs or activities based on the evaluated characteristics.

[0081] The server runs a matching algorithm to match genetic characteristics with characteristics of educational institutions.

[0082] Step 6:

[0083] The server generates a recommendation result.

[0084] The server creates a list of suitable educational institutions and adds details about each facility.

[0085] The server converts the recommendation results into a format for transmission to the user.

[0086] Step 7:

[0087] The server transmits the recommendation results to the user's terminal.

[0088] The server sends the list of educational institutions and their details to the user's terminal via a communication protocol.

[0089] Step 8:

[0090] The terminal displays the received recommendation results.

[0091] The device displays the list in an easy-to-read format and provides links to access more information about each facility.

[0092] The user can browse the displayed list to find out more about institutions that interest them.

[0093] Example 1

[0094] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0095] In the current educational environment, there is a lack of a system for scientifically evaluating a child's talents and characteristics and selecting the most suitable educational institution based on that evaluation, which makes it difficult to find an appropriate educational institution that meets individual educational needs.In addition, parents and educators are unable to efficiently search for educational institutions that meet their specific requirements, making it difficult to select an educational institution that will maximize their child's talents.

[0096] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0097] In this invention, the server includes means for acquiring biological information, means for analyzing the acquired biological information and evaluating characteristics, means for searching for suitable educational institutions based on the evaluated characteristics, and means for transmitting the search results to the user's terminal, thereby making it possible to scientifically evaluate a child's characteristics based on the biological information and to suggest the most suitable educational institution based on the evaluation results.

[0098] "Biological information" refers to data obtained from living organisms, such as genetic information.

[0099] "Analysis" refers to the process of analyzing acquired data and extracting characteristics and trends.

[0100] "Characteristics" refers to a child's unique qualities and abilities, such as learning ability, physical ability, and creativity.

[0101] "Educational institutions" refer to facilities that provide educational services, such as schools and nurseries.

[0102] An "algorithm" refers to a procedure or computational method for solving a specific problem.

[0103] "User devices" refers to digital devices such as computers and smartphones used by parents and educators.

[0104] The present invention relates to a system that evaluates a child's characteristics and talents based on their biological information and recommends the most suitable educational institution based on the evaluation results. An embodiment of this system will be described below.

[0105] Overall structure

[0106] The system consists of a user device, a server, and a database. The user device is used by parents and educators, the server provides data analysis and recommendation functions, and the database stores information about educational institutions.

[0107] Collection and transmission of biological information

[0108] Users use a genetic analysis kit to obtain biological information about their children, which is then sent to a server via a dedicated application or web portal.

[0109] Receiving and analyzing data

[0110] The server receives the biological information transmitted from the user terminal.

[0111] Data reception: The server checks the format of the received biological information and performs error checking, for example, checking that the received data is in XML or JSON format and detecting incomplete data or formatting errors.

[0112] Data analysis: The server uses the genetic analysis software "GeneAnalyzer" to extract characteristics such as learning ability, physical ability, and creativity from biological information.

[0113] Evaluation and Profile Generation

[0114] The server evaluates the child's characteristics based on the analysis results and generates a profile.

[0115] Characteristic evaluation: The server generates a score and comment for each characteristic based on the extracted characteristic data. For example, if the learning ability is high, the server evaluates it as "Learning ability: High."

[0116] Profile generation: The server creates specific profiles such as "knowledge seeker," "sportsman," or "artistic," and adds feedback comments and scores.

[0117] Find an educational institution

[0118] The server searches the database for the most suitable educational institution.

[0119] Database search: Find facilities that fit your characteristics based on information such as the program content, features, location, etc. For example, if searching for facilities specializing in music education, filter to find institutions that are suitable for a specific musical ability.

[0120] Matching: The server matches the child's profile with the characteristics of the educational institutions to identify the most suitable facilities. For example, it uses a matching algorithm to compare the profile and the details of the educational institutions, and then creates a ranked list of the most suitable educational institutions.

[0121] Generating and sending recommendation results

[0122] The server generates a list of suitable educational institutions, compiles the details and sends them to the user terminal.

[0123] List Creation: Create a list of recommended facilities and add details about each facility, such as facility name, location, features, and contact information.

[0124] Data transmission: The server encrypts the generated list and transmits it to the user terminal through a secure channel.

[0125] User Interface

[0126] The terminal displays the received recommendation results to the user.

[0127] List view: Displays a list of facilities in an easy-to-read format and provides links to access detailed information. For example, a link to the details page and an inquiry button for "Music Education College A" can be displayed on the device.

[0128] Specific examples

[0129] For example, suppose a user sends their child's genetic information to a server. The server analyzes the information and evaluates the child's musical ability. The server then searches a database for nurseries and schools specializing in music education and creates a list of the most suitable facilities. Finally, the server sends the list of facilities and detailed information to the user's device, where the user can view the list. The user can then select a facility based on the list and make an inquiry or application.

[0130] An example of a prompt for the generative AI model for this system might be:

[0131] "Please explain a system that recommends educational institutions based on a child's genetic information."

[0132] "Please tell me the detailed process of genetic information analysis and the educational institution recommendation system."

[0133] In this way, the present invention provides an educational environment that maximizes children's talents by combining scientific evaluation based on biological information with recommendations for appropriate educational institutions.

[0134] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0135] Step 1: Collecting and sending biological information

[0136] The user uses a genetic analysis kit to obtain biological information about the child. Specifically, the user follows the instructions included in the genetic analysis kit and inserts a cell sample taken from the child's cheek into the kit. Next, the genetic information is scanned using a dedicated application, and after the scan is complete, the application generates genetic information data. This generated data (input) is sent to a server via a dedicated portal (output).

[0137] Step 2: Receiving data and checking for errors

[0138] The server receives biological information sent from the user terminal (input). Specifically, the server verifies that the received data is in the correct format (e.g., XML or JSON format) and performs error checking. If there is an error in the format, the server generates an error message and sends it to the user terminal (output). Only data in the correct format proceeds to the next analysis step.

[0139] Step 3: Data analysis

[0140] The server analyzes the received biological information using an analytical algorithm (input). Specifically, the server uses a software module called "GeneAnalyzer" to analyze the genetic marker information and generate trait scores for learning ability, physical ability, creativity, etc. (output). This process includes data processing to extract various traits from the genetic data.

[0141] Step 4: Characterization and profile generation

[0142] The server evaluates the child's characteristics based on the analysis results and generates a profile (input). Specifically, the server generates scores and comments for each characteristic based on the generated characteristic data. For example, it evaluates the child's learning ability as "high" and "medium" and adds the feedback comments and score to the profile (output).

[0143] Step 5: Find your institution

[0144] The server searches a database for the most suitable educational institutions based on the characteristic evaluation results (input). Specifically, it searches a database called "EducationDB" and filters educational institutions that match the characteristics. For example, when searching for facilities suitable for music education, a specific musical ability score is used as a filter condition. This generates a list of highly suitable educational institutions (output).

[0145] Step 6: Matching and List Generation

[0146] The server compares the generated characteristic profile with the characteristics of educational institutions and extracts highly compatible facilities (input). Specifically, it uses a matching algorithm to compare the profile with the detailed information of educational institutions and creates a list of highly compatible facilities (output). This list includes detailed information such as the facility name, location, features, and contact information.

[0147] Step 7: Send data

[0148] The server encrypts the generated list of educational institutions and sends it to the user terminal through a secure channel (input). Specifically, the server encrypts the generated list and sends it to the user terminal through a secure channel such as SSL / TLS (output).

[0149] Step 8: Displaying the Recommendations

[0150] The terminal displays the received list of educational institutions to the user (input). Specifically, the terminal displays the list of facilities in an easy-to-read format and provides links to access detailed information about each facility. For example, it displays a link to the details page for "Music Education College A" and an inquiry button (output).

[0151] In this way, the entire system processes and analyzes the child's biological information, allowing the most suitable educational institution to be scientifically and efficiently recommended.

[0152] (Application example 1)

[0153] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0154] In the past, parents and educators struggled to understand their children's characteristics and talents and find the right educational facility based on those. Furthermore, there was no system in place that could scientifically evaluate a child's characteristics using genetic information and recommend the most suitable educational facility. Furthermore, there was no established method for easily finding brick-and-mortar stores that offered educational programs and activities suited to a child's characteristics.

[0155] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0156] In this invention, the server includes a means for acquiring a child's genetic information, a means for analyzing the acquired genetic information and evaluating the child's characteristics, a means for searching for suitable educational facilities or activities based on the evaluated characteristics, and a means for transmitting the search results to the user's mobile information terminal. This allows parents and educators to easily find the best educational environment for their children based on scientific evaluation. Furthermore, by using a generative AI model to identify educational facilities suited to the characteristics and generate prompt sentences, more accurate recommendations can be achieved.

[0157] "Child's genetic information" is information obtained from the child's DNA, including gene sequences and specific genetic markers.

[0158] "Trait assessment tools" are algorithms or processes that analyze genetic information to determine a child's learning ability, athletic ability, creativity, or other traits.

[0159] A "means for searching suitable educational facilities or activities" is an algorithm or process for identifying and recommending facilities from a database that offer optimal educational programs or activities based on the evaluated characteristics.

[0160] The "means for sending to the user's mobile information terminal" refers to a communication protocol or interface for sending search results to a mobile information terminal such as a smartphone or tablet held by a parent or educator.

[0161] A "generative AI model" is an algorithm or system that uses artificial intelligence to learn patterns from large amounts of data and perform specific tasks.

[0162] A "prompt sentence" is text generated as an instruction or question to be input into a generative AI model, and serves as a guideline for obtaining the required information.

[0163] The present invention relates to a system that evaluates a child's characteristics and talents based on their genetic information and recommends optimal educational facilities and activities based on the evaluation results. A detailed description of an embodiment of this system is provided below.

[0164] Overall structure

[0165] The system consists of a user's mobile information device, a server, and a database. The user's mobile information device is used by parents or educators, and the server provides data analysis and recommendation functions. The database stores information about educational facilities and activities.

[0166] Collection and transmission of genetic information

[0167] Users obtain their child's genetic information using a dedicated genetic analysis kit. The obtained genetic information is then sent to a server via a smartphone application or web portal using a secure protocol (e.g., HTTPS).

[0168] Receiving and analyzing data

[0169] The server receives the genetic information sent from the user's device. The received data is first checked for formatting and errors. Then, a specialized analysis algorithm (e.g., implemented in Python) is used to analyze the genetic information and extract characteristics such as learning ability, athletic ability, and creativity.

[0170] Evaluation and Profile Generation

[0171] The server evaluates the child's characteristics based on the analysis results. This evaluation includes a score and comments for each characteristic. For example, if a child has a high learning ability, a profile called "Knowledge Seeker" will be generated. Other profiles include "Sports Type" and "Artistic Type."

[0172] Find an educational facility or activity

[0173] The server searches the database to select the educational facility or activity that best suits the assessed characteristics, using an algorithm that selects facilities with specific programs or activities, and uses a generative AI model to generate prompts and analyzes the prompts to identify the facilities that best suit the characteristics.

[0174] Generating and sending recommendation results

[0175] The server generates a list of suitable educational facilities or activities and compiles detailed information about each facility or activity. The generated list and detailed information are sent to the user's mobile information device, which displays the list of facilities and activities in an easy-to-read format and provides links to access detailed information about each facility.

[0176] Specific examples

[0177] For example, suppose a user sends their child's genetic information to a server. The server analyzes the information and evaluates the child's musical ability. Next, the server searches a database for educational facilities and music schools specializing in music education and creates a list of the most suitable facilities. Finally, the server sends the results to the user's mobile information terminal, where the user can view the list. The user can then select a facility based on the list and make an inquiry or application.

[0178] As an example of a prompt sentence using a generative AI model, by generating and analyzing the prompt "Please suggest the best music school for a child with high musical talent," it is possible to identify and recommend specific educational facilities.

[0179] In this way, the present invention can provide an educational environment that maximizes children's talents by combining scientific evaluation based on genetic information with more accurate recommendations.

[0180] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0181] Step 1: Collecting and transmitting genetic information

[0182] Users use a genetic analysis kit to obtain their child's genetic information, which is then sent to a server via a dedicated smartphone application or web portal. The input is the obtained genetic information, and the output is confirmation that transmission to the server has been completed.

[0183] Step 2: Receiving genetic information and checking for errors

[0184] The server receives the genetic information sent from the user terminal. After receiving it, it checks the format of the genetic information and performs an error check. The input is the genetic information received from the user terminal, and the output is the format check and error check status.

[0185] Step 3: Analysis of genetic information

[0186] The server analyzes the genetic information and extracts traits such as learning ability, athletic ability, and creativity. This analysis uses specialized analysis algorithms (e.g., implemented in Python). The input is error-checked genetic information, and the output is the extracted trait data.

[0187] Step 4: Characterization and profile generation

[0188] The server evaluates the child's characteristics based on the extracted characteristic data and generates a profile. For example, if the child has high learning ability, a profile called "knowledge-seeking" is generated. The input is the characteristic data, and the output is the generated profile.

[0189] Step 5: Find an educational facility or activity

[0190] The server searches the database and selects suitable educational facilities or activities based on the assessed characteristics, using algorithms that select facilities with specific programs or activities. The input is the generated profile, and the output is a list of suitable educational facilities or activities.

[0191] Step 6: Generate and analyze prompts using a generative AI model

[0192] The server uses a generative AI model to generate prompts and analyzes them to identify facilities that fit the profile. For example, it generates a prompt such as, "Please suggest the best music school for a child with high musical talent." The input is the profile information, and the output is the analyzed specific educational facility.

[0193] Step 7: Generate and send recommendations

[0194] The server generates a list of suitable educational facilities or activities and compiles detailed information about each facility or activity. The generated list and detailed information are sent to the user's mobile information device. The input is the data of the identified educational facilities or activities, and the output is the recommendation results sent to the user's device.

[0195] Step 8: Display in the user interface

[0196] The user's mobile device displays a list of facilities and activities in an easy-to-read format and provides links to access detailed information about each facility. The input is the recommendation results received from the server, and the output is the information displayed on the device screen.

[0197] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0198] The present invention combines a system that evaluates a child's characteristics and talents based on their genetic information and recommends the most suitable educational institution based on the results with an emotion engine that recognizes the user's emotions. The following describes an embodiment of this system.

[0199] Overall structure

[0200] The system consists of a user device, a server, a database, and an emotion engine. The user device is used by parents and educators, and the server provides data analysis and recommendation functions. The database stores information about educational institutions, and the emotion engine recognizes users' emotions and influences the display of recommendation results.

[0201] Collection and transmission of genetic information

[0202] Users use a genetic analysis kit to obtain their child's genetic information, which is then sent to a server via a dedicated application or web portal.

[0203] Receiving and analyzing data

[0204] The server receives the genetic information transmitted from the user terminal.

[0205] Data reception: The server checks the format of the received genetic data and verifies the data integrity.

[0206] Data analysis: Specialized analysis algorithms extract specific traits such as learning ability, athletic ability, and creativity from genetic information.

[0207] Evaluation and Profile Generation

[0208] The server evaluates the child's characteristics based on the analysis results.

[0209] Characteristic evaluation: Generate scores and comments for each characteristic based on the extracted characteristic data.

[0210] Profile generation: Create specific profiles such as "knowledge-seeking" for those with high learning ability, or "sports" for those with high athletic ability.

[0211] Find an educational institution

[0212] The server searches the database and selects the most suitable educational institution based on the evaluated characteristics.

[0213] Database search: Select facilities that fit your characteristics based on information such as the educational institution's program content, features, and location.

[0214] Matching: Match the child's profile with the characteristics of the educational institution to extract the most suitable facility.

[0215] Generating and sending recommendation results

[0216] The server generates a list of suitable educational institutions and compiles details for each facility.

[0217] List Creation: Create a list of recommended facilities and add details about each facility.

[0218] Data transmission: The generated list and detailed information are sent to the user's terminal.

[0219] User Interface and Emotion Engine

[0220] The terminal displays the received recommendation results to the user.

[0221] List View: Displays a list of facilities in an easy-to-read format, with links to access more information about each facility.

[0222] Emotion recognition: The device uses its built-in camera and microphone to transmit the user's voice and facial expression data to the emotion engine.

[0223] Emotion engine processing

[0224] The server uses an emotion engine to recognize the user's emotional state and adjust the display of recommendation results.

[0225] Emotion Analysis: The emotion engine analyzes the user's voice and facial expression data to identify the user's emotional state.

[0226] Display adjustment: Change the priority of recommendation results and adjust the display method depending on emotional state.

[0227] Feedback: Analyze users' emotional feedback in real time and reflect it in the next recommendation results.

[0228] Specific examples

[0229] For example, suppose a user sends their child's genetic information to a server. The server analyzes the information and evaluates the child's musical ability. Next, the server searches a database for nurseries and schools specializing in music education and creates a list of the most suitable facilities. Finally, the server sends the results to the user's device, where the user can view the list. Furthermore, the emotion engine adjusts the recommendation results based on user feedback, providing information in a form that best suits the user's emotional state. The user can then select a facility from the list and make an inquiry or application.

[0230] In this way, the present invention combines scientific evaluation based on genetic information with user emotion recognition to more effectively provide an educational environment that maximizes children's talents.

[0231] The processing flow will be explained below.

[0232] Step 1:

[0233] Users collect their children's genetic information and send it to a server via a dedicated application or web portal.

[0234] The user collects a saliva sample and analyzes it using a genetic analysis kit.

[0235] The user inputs the genetic data obtained as a result of the analysis into a dedicated application and transmits it to be uploaded to the server.

[0236] Step 2:

[0237] The server receives the genetic information sent from the user.

[0238] The server checks the format of the received genetic data and verifies the consistency and completeness of the data.

[0239] The server detects incomplete data or errors and notifies the user to resend if necessary.

[0240] Step 3:

[0241] The server analyzes the genetic information.

[0242] The server runs specialized genetic analysis algorithms to extract traits such as learning ability, athletic ability, and creativity.

[0243] The server compiles the analysis results and generates a score and comment for each characteristic.

[0244] Step 4:

[0245] The server evaluates the child's characteristics and generates a profile.

[0246] The server creates a comment along with a score for each characteristic based on the extracted characteristic data.

[0247] Based on the evaluation results, the server creates specific profiles such as "knowledge-seeking type" or "sports type."

[0248] Step 5:

[0249] The server searches the educational institution database.

[0250] The server conditionally searches the educational institution database based on the evaluated characteristics.

[0251] The server picks facilities that offer specific programs or activities and runs a matching algorithm.

[0252] Step 6:

[0253] The server generates a recommendation result.

[0254] The server creates a list of suitable educational institutions and adds details about each facility (location, programs, features, etc.).

[0255] The server transmits the generated list and detailed information about each facility to the user terminal.

[0256] Step 7:

[0257] The terminal displays the received recommendation results to the user.

[0258] The device displays the recommendation list in an easy-to-read format and provides links to access more information about each establishment.

[0259] Step 8:

[0260] The emotion engine recognizes the user's emotional state.

[0261] The device uses a built-in camera and microphone to collect the user's voice and facial expression data and transmits it to the emotion engine.

[0262] The emotion engine analyzes the collected data to determine the user's emotional state.

[0263] Step 9:

[0264] The server uses feedback from the emotion engine to adjust the recommendation results.

[0265] The server changes the priority of recommendations based on the user's emotional state.

[0266] The server adjusts the display method and content of the suggestions to match the user's emotions and reflects this in the next recommendation.

[0267] Step 10:

[0268] Users can further research educational institutions that interest them based on the recommendations displayed.

[0269] Users can view detailed information about each facility and make inquiries or apply for tours.

[0270] The user selects the most suitable facility and determines the educational environment for their child.

[0271] In this way, the present invention provides a system that effectively recommends educational institutions that are best suited to a child's characteristics by combining scientific evaluation based on genetic information with user emotion recognition.

[0272] Example 2

[0273] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0274] Conventional systems for selecting educational institutions for children only evaluate characteristics based on genetic information and search for educational institutions. However, it is difficult to recognize how users feel about the displayed information and provide recommendation results that reflect that. This can result in the selection of an educational institution that is optimal for the user. In particular, there is a need for a more personalized selection of educational institutions that takes user emotions into account.

[0275] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0276] In this invention, the server includes means for acquiring the child's genetic information, means for analyzing the acquired genetic information and evaluating the child's characteristics, means for searching for suitable educational institutions based on the evaluated characteristics, means for transmitting the search results to the user's terminal, and means for recognizing the user's emotions and adjusting the display of the recommendation results, thereby enabling a more personalized selection of educational institutions that takes the user's emotional state into consideration.

[0277] "Child's genetic information" means information that indicates the child's genetic characteristics, including biological data such as DNA.

[0278] "Means of acquisition" refers to tools and methods for collecting genetic information and storing it electronically as data, such as genetic analysis kits and dedicated applications.

[0279] "Means for analyzing and evaluating characteristics" refers to algorithms and analytical functions for scientifically and digitally evaluating a child's characteristics and abilities based on the acquired genetic information.

[0280] "Learning ability" refers to a child's ability to understand and absorb knowledge.

[0281] "Motoring ability" refers to a child's physical abilities, such as strength, muscle power, and sense of balance, when performing sports.

[0282] "Creativity" refers to a child's ability to come up with new ideas and methods, that is, to generate original thoughts.

[0283] "Educational institutions" refers to facilities such as schools, nurseries, cram schools, and extracurricular classes where children receive an education.

[0284] "Search means" refers to the functions and algorithms used to find and select educational institutions that suit a child's characteristics based on the information in the database.

[0285] "Means for sending" refers to the network communication functions and protocols for sending analysis results and search results to the user's terminal.

[0286] "Means for recognizing emotions" refers to devices or algorithms that analyze the user's voice and facial expression data to identify their emotional state at that time.

[0287] "Means for adjusting the display" refers to a function for changing the display method and priority of recommendation results depending on the user's emotional state.

[0288] The present invention combines a system for evaluating a child's characteristics and talents based on their genetic information, a system for recommending the most suitable educational institution, and an engine for recognizing user emotions. Specific embodiments of the present invention will be described in detail below.

[0289] Overall structure

[0290] This system consists of a user terminal, a server, a database, and an emotion engine.

[0291] The user terminals are used by parents and educators to collect genetic information, send it to a server, and display recommendation results.

[0292] The server provides data analysis and recommendation functions, analyzing genetic information, searching educational institutions, recognizing emotions, and adjusting the display of recommendation results.

[0293] The database holds information about educational institutions and is used when making recommendations.

[0294] The emotion engine recognizes the user's emotions and influences the display of recommendation results.

[0295] Collection and transmission of genetic information

[0296] Users use a genetic analysis kit to obtain their child's genetic information, which is then sent to a server via a dedicated application or web portal. The genetic analysis kit also includes tools for collecting samples such as saliva and hair.

[0297] Receiving and analyzing data

[0298] The server receives the genetic information sent from the user's device. It checks the format of the received data and verifies its integrity. If any inconsistencies or errors are detected, it notifies the user. The server then uses specialized analysis algorithms to extract characteristics such as learning ability, athletic ability, and creativity from the genetic information. The analysis uses machine learning models using Python and data analysis tools (e.g., TENSORFLOW (registered trademark)).

[0299] Evaluation and Profile Generation

[0300] The server evaluates the child's characteristics based on the analysis results. Based on the extracted data, it calculates scores for learning ability, athletic ability, creativity, etc., and also generates comments for each characteristic. For example, "highly skilled at learning" or "well-developed athletic ability." Finally, a specific profile is generated based on these scores and comments. The profile is categorized into categories such as "knowledge-seeking type" and "sports type."

[0301] Find an educational institution

[0302] The server searches a database based on the generated profile. The database contains information about each educational institution, including their program content, features, and location. The server compares the profile with the information about the educational institution, selects facilities that fit the characteristics, and extracts the most suitable facilities based on the degree of match between the educational institution and the child's profile.

[0303] Generating and sending recommendation results

[0304] The server generates a list of suitable educational institutions, creates a list of recommended facilities, adds detailed information about each facility, and sends that information to the user's device. The file format used is JSON or XML.

[0305] User interface and emotion engine display

[0306] The device displays the received recommendation results to the user, presenting a list of facilities in an easy-to-read format and providing links to access detailed information about each facility. It also uses the built-in camera and microphone to transmit the user's voice and facial expression data to the emotion engine.

[0307] Emotion engine processing

[0308] The server uses an emotion engine to recognize the user's emotional state and adjust the display of recommendation results. The emotion engine analyzes the user's voice and facial expression data to identify their emotional state at that time. For the analysis, it uses voice recognition tools (e.g., Google® Cloud Speech-to-Text) and facial expression analysis tools (e.g., Microsoft® Azure® Face API). Depending on the user's emotional state, it changes the priority of recommendation results and adjusts the display method. It also analyzes the user's emotional feedback in real time and reflects it in the next recommendation results.

[0309] Specific examples

[0310] For example, when a user sends their child's genetic information to a server, the server analyzes the information and evaluates the child's musical ability. Next, the server searches a database for nurseries and schools specializing in music education and creates a list of the most suitable facilities. Finally, the server sends the results to the user's device, where the user can view the list. Furthermore, the emotion engine adjusts the recommendation results based on user feedback, providing information in a form that best suits the user's emotional state. The user can then select a facility from the list and make an inquiry or application.

[0311] Prompt Sentence Examples

[0312] "Please outline a system that uses a child's genetic information to assess their musical ability and recommend educational institutions that specialize in music education. Also, explain how the results are adjusted using the user's emotional feedback."

[0313] In this way, the present invention combines scientific evaluation based on genetic information with user emotion recognition to more effectively provide an educational environment that maximizes children's talents.

[0314] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0315] Step 1:

[0316] Users collect their child's genetic information using a genetic analysis kit, which includes tools for collecting samples such as saliva and hair. They then log in to a dedicated application or web portal and upload the collected genetic information.

[0317] Input: Child's genetic information (sample data)

[0318] Output: Sending genetic information to the server

[0319] Step 2:

[0320] The server receives the genetic information sent from the user's device, checks the format of the received data, and verifies the data's integrity. If any discrepancies or errors are detected, the server notifies the user.

[0321] Input: Genetic data submitted by the user

[0322] Output: Data integrity check result, notification to user (if there are any errors)

[0323] Step 3:

[0324] The server analyzes the genetic information using specialized analysis algorithms, using Python and TensorFlow to extract characteristics such as learning ability, motor skills, and creativity.

[0325] Input: integrity-checked genetic data

[0326] Output: Trait analysis results (scores for learning ability, motor ability, and creativity)

[0327] Step 4:

[0328] The server evaluates the child's characteristics based on the analysis results, generates a score and comments, and creates a specific profile, such as "knowledge-seeking" if the child has good learning ability, or "sports-oriented" if the child has good athletic ability.

[0329] Input: Analysis result of characteristics

[0330] Output: Child characterization and profile

[0331] Step 5:

[0332] The server then searches a database based on the generated profile. The database contains information about each educational institution, including their program content, features, and location. The server matches the child's profile with the educational institution information to identify the most suitable facilities.

[0333] Input: Child profile, educational institution database

[0334] Output: A list of matching institutions

[0335] Step 6:

[0336] The server generates a list of suitable educational institutions, compiles details about each institution, and sends this information in JSON or XML format to the user's device.

[0337] Input: List of highly relevant institutions

[0338] Output: A list of institutions in JSON or XML format

[0339] Step 7:

[0340] The device displays the received recommendation results to the user, displaying a list of facilities in an easy-to-read format and providing links to each facility.

[0341] Input: List of educational institutions sent from the server

[0342] Output: A list of educational institutions displayed on the user's device

[0343] Step 8:

[0344] The device's built-in camera and microphone capture the user's voice and facial expression data and send it to the emotion engine.

[0345] Input: User's voice and facial expression data

[0346] Output: Sending data to the emotion engine

[0347] Step 9:

[0348] The server's emotion engine analyzes the user's voice and facial expression data to identify their emotional state, and adjusts the priority of recommendation results and changes the way they are displayed based on this information.

[0349] Input: Voice and facial expression data to the emotion engine

[0350] Output: Display of adjusted recommendation results

[0351] Step 10:

[0352] Users can check the recommendation results, select the most suitable educational institution, and make inquiries or applications.

[0353] Input: Adjusted recommendation results

[0354] Output: Inquiries and applications to educational institutions

[0355] (Application example 2)

[0356] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0357] The present invention relates to a system that analyzes a child's genetic information, evaluates their characteristics, and suggests the most suitable educational institution. However, current systems have the problem that they are unable to take into account the user's emotional state, which limits the acceptability of the recommendation results.

[0358] In addition, there was a problem that the recommendation results ended up being a simple information provision, and the optimal display method according to the user's emotions was not provided.

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

[0360] In this invention, the server includes means for acquiring the child's genetic information, means for analyzing the acquired genetic information and evaluating the child's characteristics, means for searching for suitable educational institutions based on the evaluated characteristics, means for transmitting the search results to the user's terminal, and means for recognizing the user's emotional state and adjusting the display of recommendation results, thereby making it possible to provide appropriate recommendation results according to the user's emotional state.

[0361] "Child's genetic information" refers to data about the child's genes obtained using a genetic analysis kit or the like.

[0362] "Traits" refer to factors such as a child's learning ability, athletic ability, and creativity that are extracted from the results of analyzing genetic information.

[0363] "Evaluation" is the process of analyzing genetic information and generating scores and comments for each trait.

[0364] "Educational institutions" is a general term for facilities that provide education and training to children, such as kindergartens, schools, cram schools, and sports clubs.

[0365] "Search" is the process of selecting suitable institutions from the database based on the assessed characteristics.

[0366] "User devices" are devices such as smartphones, tablets, and computers used by parents and educators.

[0367] The "emotional state" is a psychological state that is identified by analyzing the user's voice and facial expression data.

[0368] A "recommendation result" is a list of suitable educational institutions that are searched based on the evaluated characteristics.

[0369] "Display" refers to the process by which the recommendation results are visually presented on the user's device.

[0370] "Adjustment" is the process of changing the priority of recommendation results and changing the display method depending on the user's emotional state.

[0371] The present invention combines a system that evaluates a child's characteristics and talents based on their genetic information and recommends the most suitable educational institution based on the results with an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the present invention will be described below.

[0372] Overall structure

[0373] This system consists of a user device, a server, a database, and an emotion engine. The user device is used by parents and educators, and the server provides data analysis and recommendation functions. The database stores information about educational institutions, and the emotion engine recognizes the user's emotions and influences the display of recommendation results.

[0374] Collection and transmission of genetic information

[0375] Users use a genetic analysis kit to obtain their child's genetic information, which is then sent to a server via a dedicated application or web portal.

[0376] Receiving and analyzing data

[0377] The server receives the genetic information sent from the user's device, checks the format of the received genetic data, verifies the integrity of the data, and then uses specialized analysis algorithms to extract characteristics such as learning ability, athletic ability, and creativity from the genetic information.

[0378] Evaluation and Profile Generation

[0379] The server evaluates the child's characteristics based on the analysis results, generating scores and comments for each characteristic based on the extracted data, and creating specific profiles such as a "knowledge-seeking type" for children with high learning ability, or an "sports type" for children with high athletic ability.

[0380] Find an educational institution

[0381] The server searches the database and selects the most suitable educational institution based on the evaluated characteristics. It picks out facilities that suit the characteristics based on information such as the program content, features, and location of the educational institution, and matches the child's profile with the characteristics of the educational institution to select the most suitable facility.

[0382] Generating and sending recommendation results

[0383] The server generates a list of suitable educational institutions, compiles the details of each institution, and sends the list and details to the user's device.

[0384] User Interface and Emotion Engine

[0385] The user device displays the received recommendation results to the user. The display method is to display a list of facilities in an easy-to-read format and provide links to access detailed information about each facility. The user device also uses its built-in camera and microphone to send the user's voice and facial expression data to the emotion engine.

[0386] Emotion engine processing

[0387] The server uses an emotion engine to recognize the user's emotional state and adjust the display of recommendation results. The emotion engine analyzes the user's voice and facial expression data to identify the user's emotional state, and changes the priority of recommendation results and adjusts the display method according to the user's emotional state. The server analyzes the user's emotional feedback in real time and reflects it in the next recommendation results.

[0388] For example, when a user sends their child's genetic information to a server, the server analyzes the information and evaluates the child's musical ability. It then searches a database for nurseries and schools specializing in music education and creates a list of the most suitable facilities. Finally, the results are sent to the user's device, where the user can view the list. Furthermore, the emotion engine adjusts the recommendation results based on user feedback, providing information in a form that best suits the user's emotional state. The user can then select a facility based on the list and make an inquiry or application.

[0389] Example prompt sentence:

[0390] "Please provide us with an algorithm that recommends the most suitable stores and products based on genetic information and shopping history. Also, please provide us with the logic for recognizing specific emotions and adjusting the priority of recommendations in real time."

[0391] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0392] Step 1:

[0393] Users use a genetic analysis kit to obtain their child's genetic information, which is then sent to a server via a dedicated application or web portal. At this stage, the input data is the child's genetic information, and the output is the data being sent to the server.

[0394] Step 2:

[0395] The server receives the genetic information sent from the user terminal. The format of the received genetic information is confirmed and the data integrity is verified. The input data here is the genetic information, and the output data is the genetic information whose integrity has been confirmed.

[0396] Step 3:

[0397] The server then uses a specialized analysis algorithm to analyze the genetic information whose integrity has been confirmed and extract characteristics such as learning ability, athletic ability, and creativity. This algorithm uses a machine learning model. The input data is the genetic information whose integrity has been confirmed, and the output data is the results of each analyzed characteristic.

[0398] Step 4:

[0399] The server evaluates the child's characteristics based on the analysis results, generates a characteristic score and comments, and then creates a profile and assigns labels such as "knowledge-seeking" or "sports-oriented." The input data here is the analyzed characteristic information, and the output data is the evaluation results and profile information.

[0400] Step 5:

[0401] The server searches the database and selects the most suitable educational institution based on the evaluated characteristics. The database contains information such as the institution's program content, features, and location, and by comparing this information, it extracts the most suitable facilities. The input data is the evaluation results and database information, and the output data is a list of suitable educational institutions.

[0402] Step 6:

[0403] The server generates a list of suitable educational institutions and compiles detailed information about each institution, which is then sent to the user's device. The list includes details about the institution's location, characteristics, admission procedures, etc. The input data is the list of suitable educational institutions and their details, and the output data is the list of recommendation results sent to the user's device.

[0404] Step 7:

[0405] The user terminal displays the recommendation results sent from the server to the user. The recommendation results are displayed as a list of facilities in an easy-to-read format, and links to detailed information about each facility are provided. The input data is the recommendation results sent from the server, and the output data is the displayed recommendation list.

[0406] Step 8:

[0407] The user terminal uses a built-in camera and microphone to transmit the user's voice and facial expression data to the emotion engine. The input data is the user's voice and facial expression data, and the output data is data transmitted to the emotion engine.

[0408] Step 9:

[0409] The server uses an emotion engine to analyze the user's emotional state and adjusts the display of recommendation results based on the results. It changes the priority of recommendation results and adjusts the display method according to the emotional state. The input data is the user's voice and facial expression data, and the output data is the adjusted recommendation display.

[0410] Step 10:

[0411] The server analyzes the user's emotional feedback in real time and reflects it in the next recommendation results. By incorporating the feedback data into the emotion engine, the next recommendation will be more adapted to the user's emotional state. It is a dynamic recommendation algorithm whose input data is the user's emotional feedback and whose output data is reflected in the next recommendation.

[0412] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0413] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0414] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0415] [Second embodiment]

[0416] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0417] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0418] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0419] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0420] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0421] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0422] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0423] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0424] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0425] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0426] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0427] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0428] The present invention relates to a system that evaluates a child's characteristics and talents based on their genetic information and recommends the most suitable educational institution based on the evaluation results. An embodiment of this system will be described below.

[0429] Overall structure

[0430] The system consists of a user device, a server, and a database. The user device is used by parents and educators, the server provides data analysis and recommendation functions, and the database stores information about educational institutions.

[0431] Collection and transmission of genetic information

[0432] Users use a genetic analysis kit to obtain their child's genetic information, which is then sent to a server via a dedicated application or web portal.

[0433] Receiving and analyzing data

[0434] The server receives the genetic information transmitted from the user terminal.

[0435] Data reception: The server checks the format of the received genetic information and performs error checks.

[0436] Data analysis: Specialized analysis algorithms extract specific traits such as learning ability, athletic ability, and creativity from genetic information.

[0437] Evaluation and Profile Generation

[0438] The server evaluates the child's characteristics based on the analysis results.

[0439] Characteristic evaluation: Generate scores and comments for each characteristic based on the extracted characteristic data.

[0440] Profile generation: Create specific profiles such as "knowledge-seeking" for those with high learning ability, or "sports" for those with high athletic ability.

[0441] Find an educational institution

[0442] The server searches the database and selects the most suitable educational institution based on the evaluated characteristics.

[0443] Database search: Select facilities that fit your characteristics based on information such as the educational institution's program content, features, and location.

[0444] Matching: Match the child's profile with the characteristics of the educational institution to extract the most suitable facility.

[0445] Generating and sending recommendation results

[0446] The server generates a list of suitable educational institutions and compiles details for each facility.

[0447] List Creation: Create a list of recommended facilities and add details about each facility.

[0448] Data transmission: The generated list and detailed information are sent to the user's terminal.

[0449] User Interface

[0450] The terminal displays the received recommendation results to the user.

[0451] List View: Displays a list of facilities in an easy-to-read format, with links to access more information about each facility.

[0452] Specific examples

[0453] For example, suppose a user sends their child's genetic information to a server. The server analyzes the information and evaluates the child's musical ability. Next, the server searches a database for nurseries and schools specializing in music education and creates a list of the most suitable facilities. Finally, the server sends the results to the user's device, where the user can view the list. The user can then select a facility based on the list and make an inquiry or application.

[0454] In this way, the present invention provides an educational environment that maximizes a child's talents by combining scientific evaluation based on genetic information with recommendations for appropriate educational institutions.

[0455] The processing flow will be explained below.

[0456] Step 1:

[0457] Users collect their children's genetic information and send it to a server via a dedicated application or web portal.

[0458] The user analyzes the saliva sample using a genetic analysis kit and obtains the generated genetic data.

[0459] The user enters the genetic data into the application and uploads it to the server.

[0460] Step 2:

[0461] The server receives the genetic information sent from the user.

[0462] The server checks the format of the received genetic data and verifies the integrity of the data.

[0463] The server performs error checking and notifies the user if necessary.

[0464] Step 3:

[0465] The server analyzes the genetic information.

[0466] The server uses specialized analytical algorithms to extract specific traits from the genetic data, such as learning ability, athletic ability, and creativity.

[0467] The server organizes the extracted characteristic data and converts it into a format that can be used in the next step.

[0468] Step 4:

[0469] The server evaluates the child's characteristics and generates a profile.

[0470] The server calculates a score for each characteristic based on the analysis results and generates a comment.

[0471] Based on the evaluation results, the server creates specific profiles such as "knowledge-seeking type" or "sports type."

[0472] Step 5:

[0473] The server searches the educational institution database.

[0474] The server then searches for facilities that offer specific programs or activities based on the evaluated characteristics.

[0475] The server runs a matching algorithm to match genetic characteristics with characteristics of educational institutions.

[0476] Step 6:

[0477] The server generates a recommendation result.

[0478] The server creates a list of suitable educational institutions and adds details about each facility.

[0479] The server converts the recommendation results into a format for transmission to the user.

[0480] Step 7:

[0481] The server transmits the recommendation results to the user's terminal.

[0482] The server sends the list of educational institutions and their details to the user's terminal via a communication protocol.

[0483] Step 8:

[0484] The terminal displays the received recommendation results.

[0485] The device displays the list in an easy-to-read format and provides links to access more information about each facility.

[0486] The user can browse the displayed list to find out more about institutions that interest them.

[0487] Example 1

[0488] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0489] In the current educational environment, there is a lack of a system for scientifically evaluating a child's talents and characteristics and selecting the most suitable educational institution based on that evaluation, which makes it difficult to find an appropriate educational institution that meets individual educational needs.In addition, parents and educators are unable to efficiently search for educational institutions that meet their specific requirements, making it difficult to select an educational institution that will maximize their child's talents.

[0490] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0491] In this invention, the server includes means for acquiring biological information, means for analyzing the acquired biological information and evaluating characteristics, means for searching for suitable educational institutions based on the evaluated characteristics, and means for transmitting the search results to the user's terminal, thereby making it possible to scientifically evaluate a child's characteristics based on the biological information and to suggest the most suitable educational institution based on the evaluation results.

[0492] "Biological information" refers to data obtained from living organisms, such as genetic information.

[0493] "Analysis" refers to the process of analyzing acquired data and extracting characteristics and trends.

[0494] "Characteristics" refers to a child's unique qualities and abilities, such as learning ability, physical ability, and creativity.

[0495] "Educational institutions" refer to facilities that provide educational services, such as schools and nurseries.

[0496] An "algorithm" refers to a procedure or computational method for solving a specific problem.

[0497] "User devices" refers to digital devices such as computers and smartphones used by parents and educators.

[0498] The present invention relates to a system that evaluates a child's characteristics and talents based on their biological information and recommends the most suitable educational institution based on the evaluation results. An embodiment of this system will be described below.

[0499] Overall structure

[0500] The system consists of a user device, a server, and a database. The user device is used by parents and educators, the server provides data analysis and recommendation functions, and the database stores information about educational institutions.

[0501] Collection and transmission of biological information

[0502] Users use a genetic analysis kit to obtain biological information about their children, which is then sent to a server via a dedicated application or web portal.

[0503] Receiving and analyzing data

[0504] The server receives the biological information transmitted from the user terminal.

[0505] Data reception: The server checks the format of the received biological information and performs error checking, for example, checking that the received data is in XML or JSON format and detecting incomplete data or formatting errors.

[0506] Data analysis: The server uses the genetic analysis software "GeneAnalyzer" to extract characteristics such as learning ability, physical ability, and creativity from biological information.

[0507] Evaluation and Profile Generation

[0508] The server evaluates the child's characteristics based on the analysis results and generates a profile.

[0509] Characteristic evaluation: The server generates a score and comment for each characteristic based on the extracted characteristic data. For example, if the learning ability is high, the server evaluates it as "Learning ability: High."

[0510] Profile generation: The server creates specific profiles such as "knowledge seeker," "sportsman," or "artistic," and adds feedback comments and scores.

[0511] Find an educational institution

[0512] The server searches the database for the most suitable educational institution.

[0513] Database search: Find facilities that fit your characteristics based on information such as the program content, features, location, etc. For example, if searching for facilities specializing in music education, filter to find institutions that are suitable for a specific musical ability.

[0514] Matching: The server matches the child's profile with the characteristics of the educational institutions to identify the most suitable facilities. For example, it uses a matching algorithm to compare the profile and the details of the educational institutions, and then creates a ranked list of the most suitable educational institutions.

[0515] Generating and sending recommendation results

[0516] The server generates a list of suitable educational institutions, compiles the details and sends them to the user terminal.

[0517] List Creation: Create a list of recommended facilities and add details about each facility, such as facility name, location, features, and contact information.

[0518] Data transmission: The server encrypts the generated list and transmits it to the user terminal through a secure channel.

[0519] User Interface

[0520] The terminal displays the received recommendation results to the user.

[0521] List view: Displays a list of facilities in an easy-to-read format and provides links to access detailed information. For example, a link to the details page and an inquiry button for "Music Education College A" can be displayed on the device.

[0522] Specific examples

[0523] For example, suppose a user sends their child's genetic information to a server. The server analyzes the information and evaluates the child's musical ability. The server then searches a database for nurseries and schools specializing in music education and creates a list of the most suitable facilities. Finally, the server sends the list of facilities and detailed information to the user's device, where the user can view the list. The user can then select a facility based on the list and make an inquiry or application.

[0524] An example of a prompt for the generative AI model for this system might be:

[0525] "Please explain a system that recommends educational institutions based on a child's genetic information."

[0526] "Please tell me the detailed process of genetic information analysis and the educational institution recommendation system."

[0527] In this way, the present invention provides an educational environment that maximizes children's talents by combining scientific evaluation based on biological information with recommendations for appropriate educational institutions.

[0528] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0529] Step 1: Collecting and sending biological information

[0530] The user uses a genetic analysis kit to obtain biological information about the child. Specifically, the user follows the instructions included in the genetic analysis kit and inserts a cell sample taken from the child's cheek into the kit. Next, the genetic information is scanned using a dedicated application, and after the scan is complete, the application generates genetic information data. This generated data (input) is sent to a server via a dedicated portal (output).

[0531] Step 2: Receiving data and checking for errors

[0532] The server receives biological information sent from the user terminal (input). Specifically, the server verifies that the received data is in the correct format (e.g., XML or JSON format) and performs error checking. If there is an error in the format, the server generates an error message and sends it to the user terminal (output). Only data in the correct format proceeds to the next analysis step.

[0533] Step 3: Data analysis

[0534] The server analyzes the received biological information using an analytical algorithm (input). Specifically, the server uses a software module called "GeneAnalyzer" to analyze the genetic marker information and generate trait scores for learning ability, physical ability, creativity, etc. (output). This process includes data processing to extract various traits from the genetic data.

[0535] Step 4: Characterization and profile generation

[0536] The server evaluates the child's characteristics based on the analysis results and generates a profile (input). Specifically, the server generates scores and comments for each characteristic based on the generated characteristic data. For example, it evaluates the child's learning ability as "high" and "medium" and adds the feedback comments and score to the profile (output).

[0537] Step 5: Find your institution

[0538] The server searches a database for the most suitable educational institutions based on the characteristic evaluation results (input). Specifically, it searches a database called "EducationDB" and filters educational institutions that match the characteristics. For example, when searching for facilities suitable for music education, a specific musical ability score is used as a filter condition. This generates a list of highly suitable educational institutions (output).

[0539] Step 6: Matching and List Generation

[0540] The server compares the generated characteristic profile with the characteristics of educational institutions and extracts highly compatible facilities (input). Specifically, it uses a matching algorithm to compare the profile with the detailed information of educational institutions and creates a list of highly compatible facilities (output). This list includes detailed information such as the facility name, location, features, and contact information.

[0541] Step 7: Send data

[0542] The server encrypts the generated list of educational institutions and sends it to the user terminal through a secure channel (input). Specifically, the server encrypts the generated list and sends it to the user terminal through a secure channel such as SSL / TLS (output).

[0543] Step 8: Displaying the Recommendations

[0544] The terminal displays the received list of educational institutions to the user (input). Specifically, the terminal displays the list of facilities in an easy-to-read format and provides links to access detailed information about each facility. For example, it displays a link to the details page for "Music Education College A" and an inquiry button (output).

[0545] In this way, the entire system processes and analyzes the child's biological information, allowing the most suitable educational institution to be scientifically and efficiently recommended.

[0546] (Application example 1)

[0547] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0548] In the past, parents and educators struggled to understand their children's characteristics and talents and find the right educational facility based on those. Furthermore, there was no system in place that could scientifically evaluate a child's characteristics using genetic information and recommend the most suitable educational facility. Furthermore, there was no established method for easily finding brick-and-mortar stores that offered educational programs and activities suited to a child's characteristics.

[0549] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0550] In this invention, the server includes a means for acquiring a child's genetic information, a means for analyzing the acquired genetic information and evaluating the child's characteristics, a means for searching for suitable educational facilities or activities based on the evaluated characteristics, and a means for transmitting the search results to the user's mobile information terminal. This allows parents and educators to easily find the best educational environment for their children based on scientific evaluation. Furthermore, by using a generative AI model to identify educational facilities suited to the characteristics and generate prompt sentences, more accurate recommendations can be achieved.

[0551] "Child's genetic information" is information obtained from the child's DNA, including gene sequences and specific genetic markers.

[0552] "Trait assessment tools" are algorithms or processes that analyze genetic information to determine a child's learning ability, athletic ability, creativity, or other traits.

[0553] A "means for searching suitable educational facilities or activities" is an algorithm or process for identifying and recommending facilities from a database that offer optimal educational programs or activities based on the evaluated characteristics.

[0554] The "means for sending to the user's mobile information terminal" refers to a communication protocol or interface for sending search results to a mobile information terminal such as a smartphone or tablet held by a parent or educator.

[0555] A "generative AI model" is an algorithm or system that uses artificial intelligence to learn patterns from large amounts of data and perform specific tasks.

[0556] A "prompt sentence" is text generated as an instruction or question to be input into a generative AI model, and serves as a guideline for obtaining the required information.

[0557] The present invention relates to a system that evaluates a child's characteristics and talents based on their genetic information and recommends optimal educational facilities and activities based on the evaluation results. A detailed description of an embodiment of this system is provided below.

[0558] Overall structure

[0559] The system consists of a user's mobile information device, a server, and a database. The user's mobile information device is used by parents or educators, and the server provides data analysis and recommendation functions. The database stores information about educational facilities and activities.

[0560] Collection and transmission of genetic information

[0561] Users obtain their child's genetic information using a dedicated genetic analysis kit. The obtained genetic information is then sent to a server via a smartphone application or web portal using a secure protocol (e.g., HTTPS).

[0562] Receiving and analyzing data

[0563] The server receives the genetic information sent from the user's device. The received data is first checked for formatting and errors. Then, a specialized analysis algorithm (e.g., implemented in Python) is used to analyze the genetic information and extract characteristics such as learning ability, athletic ability, and creativity.

[0564] Evaluation and Profile Generation

[0565] The server evaluates the child's characteristics based on the analysis results. This evaluation includes a score and comments for each characteristic. For example, if a child has a high learning ability, a profile called "Knowledge Seeker" will be generated. Other profiles include "Sports Type" and "Artistic Type."

[0566] Find an educational facility or activity

[0567] The server searches the database to select the educational facility or activity that best suits the assessed characteristics, using an algorithm that selects facilities with specific programs or activities, and uses a generative AI model to generate prompts and analyzes the prompts to identify the facilities that best suit the characteristics.

[0568] Generating and sending recommendation results

[0569] The server generates a list of suitable educational facilities or activities and compiles detailed information about each facility or activity. The generated list and detailed information are sent to the user's mobile information device, which displays the list of facilities and activities in an easy-to-read format and provides links to access detailed information about each facility.

[0570] Specific examples

[0571] For example, suppose a user sends their child's genetic information to a server. The server analyzes the information and evaluates the child's musical ability. Next, the server searches a database for educational facilities and music schools specializing in music education and creates a list of the most suitable facilities. Finally, the server sends the results to the user's mobile information terminal, where the user can view the list. The user can then select a facility based on the list and make an inquiry or application.

[0572] As an example of a prompt sentence using a generative AI model, by generating and analyzing the prompt "Please suggest the best music school for a child with high musical talent," it is possible to identify and recommend specific educational facilities.

[0573] In this way, the present invention can provide an educational environment that maximizes children's talents by combining scientific evaluation based on genetic information with more accurate recommendations.

[0574] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0575] Step 1: Collecting and transmitting genetic information

[0576] Users use a genetic analysis kit to obtain their child's genetic information, which is then sent to a server via a dedicated smartphone application or web portal. The input is the obtained genetic information, and the output is confirmation that transmission to the server has been completed.

[0577] Step 2: Receiving genetic information and checking for errors

[0578] The server receives the genetic information sent from the user terminal. After receiving it, it checks the format of the genetic information and performs an error check. The input is the genetic information received from the user terminal, and the output is the format check and error check status.

[0579] Step 3: Analysis of genetic information

[0580] The server analyzes the genetic information and extracts traits such as learning ability, athletic ability, and creativity. This analysis uses specialized analysis algorithms (e.g., implemented in Python). The input is error-checked genetic information, and the output is the extracted trait data.

[0581] Step 4: Characterization and profile generation

[0582] The server evaluates the child's characteristics based on the extracted characteristic data and generates a profile. For example, if the child has high learning ability, a profile called "knowledge-seeking" is generated. The input is the characteristic data, and the output is the generated profile.

[0583] Step 5: Find an educational facility or activity

[0584] The server searches the database and selects suitable educational facilities or activities based on the assessed characteristics, using algorithms that select facilities with specific programs or activities. The input is the generated profile, and the output is a list of suitable educational facilities or activities.

[0585] Step 6: Generate and analyze prompts using a generative AI model

[0586] The server uses a generative AI model to generate prompts and analyzes them to identify facilities that fit the profile. For example, it generates a prompt such as, "Please suggest the best music school for a child with high musical talent." The input is the profile information, and the output is the analyzed specific educational facility.

[0587] Step 7: Generate and send recommendations

[0588] The server generates a list of suitable educational facilities or activities and compiles detailed information about each facility or activity. The generated list and detailed information are sent to the user's mobile information device. The input is the data of the identified educational facilities or activities, and the output is the recommendation results sent to the user's device.

[0589] Step 8: Display in the user interface

[0590] The user's mobile device displays a list of facilities and activities in an easy-to-read format and provides links to access detailed information about each facility. The input is the recommendation results received from the server, and the output is the information displayed on the device screen.

[0591] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0592] The present invention combines a system that evaluates a child's characteristics and talents based on their genetic information and recommends the most suitable educational institution based on the results with an emotion engine that recognizes the user's emotions. The following describes an embodiment of this system.

[0593] Overall structure

[0594] The system consists of a user device, a server, a database, and an emotion engine. The user device is used by parents and educators, and the server provides data analysis and recommendation functions. The database stores information about educational institutions, and the emotion engine recognizes users' emotions and influences the display of recommendation results.

[0595] Collection and transmission of genetic information

[0596] Users use a genetic analysis kit to obtain their child's genetic information, which is then sent to a server via a dedicated application or web portal.

[0597] Receiving and analyzing data

[0598] The server receives the genetic information transmitted from the user terminal.

[0599] Data reception: The server checks the format of the received genetic data and verifies the data integrity.

[0600] Data analysis: Specialized analysis algorithms extract specific traits such as learning ability, athletic ability, and creativity from genetic information.

[0601] Evaluation and Profile Generation

[0602] The server evaluates the child's characteristics based on the analysis results.

[0603] Characteristic evaluation: Generate scores and comments for each characteristic based on the extracted characteristic data.

[0604] Profile generation: Create specific profiles such as "knowledge-seeking" for those with high learning ability, or "sports" for those with high athletic ability.

[0605] Find an educational institution

[0606] The server searches the database and selects the most suitable educational institution based on the evaluated characteristics.

[0607] Database search: Select facilities that fit your characteristics based on information such as the educational institution's program content, features, and location.

[0608] Matching: Match the child's profile with the characteristics of the educational institution to extract the most suitable facility.

[0609] Generating and sending recommendation results

[0610] The server generates a list of suitable educational institutions and compiles details for each facility.

[0611] List Creation: Create a list of recommended facilities and add details about each facility.

[0612] Data transmission: The generated list and detailed information are sent to the user's terminal.

[0613] User Interface and Emotion Engine

[0614] The terminal displays the received recommendation results to the user.

[0615] List View: Displays a list of facilities in an easy-to-read format, with links to access more information about each facility.

[0616] Emotion recognition: The device uses its built-in camera and microphone to transmit the user's voice and facial expression data to the emotion engine.

[0617] Emotion engine processing

[0618] The server uses an emotion engine to recognize the user's emotional state and adjust the display of recommendation results.

[0619] Emotion Analysis: The emotion engine analyzes the user's voice and facial expression data to identify the user's emotional state.

[0620] Display adjustment: Change the priority of recommendation results and adjust the display method depending on emotional state.

[0621] Feedback: Analyze users' emotional feedback in real time and reflect it in the next recommendation results.

[0622] Specific examples

[0623] For example, suppose a user sends their child's genetic information to a server. The server analyzes the information and evaluates the child's musical ability. Next, the server searches a database for nurseries and schools specializing in music education and creates a list of the most suitable facilities. Finally, the server sends the results to the user's device, where the user can view the list. Furthermore, the emotion engine adjusts the recommendation results based on user feedback, providing information in a form that best suits the user's emotional state. The user can then select a facility from the list and make an inquiry or application.

[0624] In this way, the present invention combines scientific evaluation based on genetic information with user emotion recognition to more effectively provide an educational environment that maximizes children's talents.

[0625] The processing flow will be explained below.

[0626] Step 1:

[0627] Users collect their children's genetic information and send it to a server via a dedicated application or web portal.

[0628] The user collects a saliva sample and analyzes it using a genetic analysis kit.

[0629] The user inputs the genetic data obtained as a result of the analysis into a dedicated application and transmits it to be uploaded to the server.

[0630] Step 2:

[0631] The server receives the genetic information sent from the user.

[0632] The server checks the format of the received genetic data and verifies the consistency and completeness of the data.

[0633] The server detects incomplete data or errors and notifies the user to resend if necessary.

[0634] Step 3:

[0635] The server analyzes the genetic information.

[0636] The server runs specialized genetic analysis algorithms to extract traits such as learning ability, athletic ability, and creativity.

[0637] The server compiles the analysis results and generates a score and comment for each characteristic.

[0638] Step 4:

[0639] The server evaluates the child's characteristics and generates a profile.

[0640] The server creates a comment along with a score for each characteristic based on the extracted characteristic data.

[0641] Based on the evaluation results, the server creates specific profiles such as "knowledge-seeking type" or "sports type."

[0642] Step 5:

[0643] The server searches the educational institution database.

[0644] The server conditionally searches the educational institution database based on the evaluated characteristics.

[0645] The server picks facilities that offer specific programs or activities and runs a matching algorithm.

[0646] Step 6:

[0647] The server generates a recommendation result.

[0648] The server creates a list of suitable educational institutions and adds details about each facility (location, programs, features, etc.).

[0649] The server transmits the generated list and detailed information about each facility to the user terminal.

[0650] Step 7:

[0651] The terminal displays the received recommendation results to the user.

[0652] The device displays the recommendation list in an easy-to-read format and provides links to access more information about each establishment.

[0653] Step 8:

[0654] The emotion engine recognizes the user's emotional state.

[0655] The device uses a built-in camera and microphone to collect the user's voice and facial expression data and transmits it to the emotion engine.

[0656] The emotion engine analyzes the collected data to determine the user's emotional state.

[0657] Step 9:

[0658] The server uses feedback from the emotion engine to adjust the recommendation results.

[0659] The server changes the priority of recommendations based on the user's emotional state.

[0660] The server adjusts the display method and content of the suggestions to match the user's emotions and reflects this in the next recommendation.

[0661] Step 10:

[0662] Users can further research educational institutions that interest them based on the recommendations displayed.

[0663] Users can view detailed information about each facility and make inquiries or apply for tours.

[0664] The user selects the most suitable facility and determines the educational environment for their child.

[0665] In this way, the present invention provides a system that effectively recommends educational institutions that are best suited to a child's characteristics by combining scientific evaluation based on genetic information with user emotion recognition.

[0666] Example 2

[0667] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0668] Conventional systems for selecting educational institutions for children only evaluate characteristics based on genetic information and search for educational institutions. However, it is difficult to recognize how users feel about the displayed information and provide recommendation results that reflect that. This can result in the selection of an educational institution that is optimal for the user. In particular, there is a need for a more personalized selection of educational institutions that takes user emotions into account.

[0669] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0670] In this invention, the server includes means for acquiring the child's genetic information, means for analyzing the acquired genetic information and evaluating the child's characteristics, means for searching for suitable educational institutions based on the evaluated characteristics, means for transmitting the search results to the user's terminal, and means for recognizing the user's emotions and adjusting the display of the recommendation results, thereby enabling a more personalized selection of educational institutions that takes the user's emotional state into consideration.

[0671] "Child's genetic information" means information that indicates the child's genetic characteristics, including biological data such as DNA.

[0672] "Means of acquisition" refers to tools and methods for collecting genetic information and storing it electronically as data, such as genetic analysis kits and dedicated applications.

[0673] "Means for analyzing and evaluating characteristics" refers to algorithms and analytical functions for scientifically and digitally evaluating a child's characteristics and abilities based on the acquired genetic information.

[0674] "Learning ability" refers to a child's ability to understand and absorb knowledge.

[0675] "Motoring ability" refers to a child's physical abilities, such as strength, muscle power, and sense of balance, when performing sports.

[0676] "Creativity" refers to a child's ability to come up with new ideas and methods, that is, to generate original thoughts.

[0677] "Educational institutions" refers to facilities such as schools, nurseries, cram schools, and extracurricular classes where children receive an education.

[0678] "Search means" refers to the functions and algorithms used to find and select educational institutions that suit a child's characteristics based on the information in the database.

[0679] "Means for sending" refers to the network communication functions and protocols for sending analysis results and search results to the user's terminal.

[0680] "Means for recognizing emotions" refers to devices or algorithms that analyze the user's voice and facial expression data to identify their emotional state at that time.

[0681] "Means for adjusting the display" refers to a function for changing the display method and priority of recommendation results depending on the user's emotional state.

[0682] The present invention combines a system for evaluating a child's characteristics and talents based on their genetic information, a system for recommending the most suitable educational institution, and an engine for recognizing user emotions. Specific embodiments of the present invention will be described in detail below.

[0683] Overall structure

[0684] This system consists of a user terminal, a server, a database, and an emotion engine.

[0685] The user terminals are used by parents and educators to collect genetic information, send it to a server, and display recommendation results.

[0686] The server provides data analysis and recommendation functions, analyzing genetic information, searching educational institutions, recognizing emotions, and adjusting the display of recommendation results.

[0687] The database holds information about educational institutions and is used when making recommendations.

[0688] The emotion engine recognizes the user's emotions and influences the display of recommendation results.

[0689] Collection and transmission of genetic information

[0690] Users use a genetic analysis kit to obtain their child's genetic information, which is then sent to a server via a dedicated application or web portal. The genetic analysis kit also includes tools for collecting samples such as saliva and hair.

[0691] Receiving and analyzing data

[0692] The server receives the genetic information sent from the user's device. It checks the format of the received data and verifies its integrity. If any inconsistencies or errors are detected, it notifies the user. The server then uses specialized analysis algorithms to extract characteristics such as learning ability, athletic ability, and creativity from the genetic information. The analysis is performed using machine learning models using Python and data analysis tools (e.g., TensorFlow).

[0693] Evaluation and Profile Generation

[0694] The server evaluates the child's characteristics based on the analysis results. Based on the extracted data, it calculates scores for learning ability, athletic ability, creativity, etc., and also generates comments for each characteristic. For example, "highly skilled at learning" or "well-developed athletic ability." Finally, a specific profile is generated based on these scores and comments. The profile is categorized into categories such as "knowledge-seeking type" and "sports type."

[0695] Find an educational institution

[0696] The server searches a database based on the generated profile. The database contains information about each educational institution, including their program content, features, and location. The server compares the profile with the information about the educational institution, selects facilities that fit the characteristics, and extracts the most suitable facilities based on the degree of match between the educational institution and the child's profile.

[0697] Generating and sending recommendation results

[0698] The server generates a list of suitable educational institutions, creates a list of recommended facilities, adds detailed information about each facility, and sends that information to the user's device. The file format used is JSON or XML.

[0699] User interface and emotion engine display

[0700] The device displays the received recommendation results to the user, presenting a list of facilities in an easy-to-read format and providing links to access detailed information about each facility. It also uses the built-in camera and microphone to transmit the user's voice and facial expression data to the emotion engine.

[0701] Emotion engine processing

[0702] The server uses an emotion engine to recognize the user's emotional state and adjust the display of recommendation results. The emotion engine analyzes the user's voice and facial expression data to identify their emotional state at that time. For the analysis, it uses voice recognition tools (e.g., Google Cloud Speech-to-Text) and facial expression analysis tools (e.g., Microsoft Azure Face API). Depending on the user's emotional state, it changes the priority of recommendation results and adjusts the display method. It also analyzes the user's emotional feedback in real time and reflects it in the next recommendation results.

[0703] Specific examples

[0704] For example, when a user sends their child's genetic information to a server, the server analyzes the information and evaluates the child's musical ability. Next, the server searches a database for nurseries and schools specializing in music education and creates a list of the most suitable facilities. Finally, the server sends the results to the user's device, where the user can view the list. Furthermore, the emotion engine adjusts the recommendation results based on user feedback, providing information in a form that best suits the user's emotional state. The user can then select a facility from the list and make an inquiry or application.

[0705] Prompt Sentence Examples

[0706] "Please outline a system that uses a child's genetic information to assess their musical ability and recommend educational institutions that specialize in music education. Also, explain how the results are adjusted using the user's emotional feedback."

[0707] In this way, the present invention combines scientific evaluation based on genetic information with user emotion recognition to more effectively provide an educational environment that maximizes children's talents.

[0708] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0709] Step 1:

[0710] Users collect their child's genetic information using a genetic analysis kit, which includes tools for collecting samples such as saliva and hair. They then log in to a dedicated application or web portal and upload the collected genetic information.

[0711] Input: Child's genetic information (sample data)

[0712] Output: Sending genetic information to the server

[0713] Step 2:

[0714] The server receives the genetic information sent from the user's device, checks the format of the received data, and verifies the data's integrity. If any discrepancies or errors are detected, the server notifies the user.

[0715] Input: Genetic data submitted by the user

[0716] Output: Data integrity check result, notification to user (if there are any errors)

[0717] Step 3:

[0718] The server analyzes the genetic information using specialized analysis algorithms, using Python and TensorFlow to extract characteristics such as learning ability, motor skills, and creativity.

[0719] Input: integrity-checked genetic data

[0720] Output: Trait analysis results (scores for learning ability, motor ability, and creativity)

[0721] Step 4:

[0722] The server evaluates the child's characteristics based on the analysis results, generates a score and comments, and creates a specific profile, such as "knowledge-seeking" if the child has good learning ability, or "sports-oriented" if the child has good athletic ability.

[0723] Input: Analysis result of characteristics

[0724] Output: Child characterization and profile

[0725] Step 5:

[0726] The server then searches a database based on the generated profile. The database contains information about each educational institution, including their program content, features, and location. The server matches the child's profile with the educational institution information to identify the most suitable facilities.

[0727] Input: Child profile, educational institution database

[0728] Output: A list of matching institutions

[0729] Step 6:

[0730] The server generates a list of suitable educational institutions, compiles details about each institution, and sends this information in JSON or XML format to the user's device.

[0731] Input: List of highly relevant institutions

[0732] Output: A list of institutions in JSON or XML format

[0733] Step 7:

[0734] The device displays the received recommendation results to the user, displaying a list of facilities in an easy-to-read format and providing links to each facility.

[0735] Input: List of educational institutions sent from the server

[0736] Output: A list of educational institutions displayed on the user's device

[0737] Step 8:

[0738] The device's built-in camera and microphone capture the user's voice and facial expression data and send it to the emotion engine.

[0739] Input: User's voice and facial expression data

[0740] Output: Sending data to the emotion engine

[0741] Step 9:

[0742] The server's emotion engine analyzes the user's voice and facial expression data to identify their emotional state, and adjusts the priority of recommendation results and changes the way they are displayed based on this information.

[0743] Input: Voice and facial expression data to the emotion engine

[0744] Output: Display of adjusted recommendation results

[0745] Step 10:

[0746] Users can check the recommendation results, select the most suitable educational institution, and make inquiries or applications.

[0747] Input: Adjusted recommendation results

[0748] Output: Inquiries and applications to educational institutions

[0749] (Application example 2)

[0750] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0751] The present invention relates to a system that analyzes a child's genetic information, evaluates their characteristics, and suggests the most suitable educational institution. However, current systems have the problem that they are unable to take into account the user's emotional state, which limits the acceptability of the recommendation results.

[0752] In addition, there was a problem that the recommendation results ended up being a simple information provision, and the optimal display method according to the user's emotions was not provided.

[0753] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0754] In this invention, the server includes means for acquiring the child's genetic information, means for analyzing the acquired genetic information and evaluating the child's characteristics, means for searching for suitable educational institutions based on the evaluated characteristics, means for transmitting the search results to the user's terminal, and means for recognizing the user's emotional state and adjusting the display of recommendation results, thereby making it possible to provide appropriate recommendation results according to the user's emotional state.

[0755] "Child's genetic information" refers to data about the child's genes obtained using a genetic analysis kit or the like.

[0756] "Traits" refer to factors such as a child's learning ability, athletic ability, and creativity that are extracted from the results of analyzing genetic information.

[0757] "Evaluation" is the process of analyzing genetic information and generating scores and comments for each trait.

[0758] "Educational institutions" is a general term for facilities that provide education and training to children, such as kindergartens, schools, cram schools, and sports clubs.

[0759] "Search" is the process of selecting suitable institutions from the database based on the assessed characteristics.

[0760] "User devices" are devices such as smartphones, tablets, and computers used by parents and educators.

[0761] The "emotional state" is a psychological state that is identified by analyzing the user's voice and facial expression data.

[0762] A "recommendation result" is a list of suitable educational institutions that are searched based on the evaluated characteristics.

[0763] "Display" refers to the process by which the recommendation results are visually presented on the user's device.

[0764] "Adjustment" is the process of changing the priority of recommendation results and changing the display method depending on the user's emotional state.

[0765] The present invention combines a system that evaluates a child's characteristics and talents based on their genetic information and recommends the most suitable educational institution based on the results with an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the present invention will be described below.

[0766] Overall structure

[0767] This system consists of a user device, a server, a database, and an emotion engine. The user device is used by parents and educators, and the server provides data analysis and recommendation functions. The database stores information about educational institutions, and the emotion engine recognizes the user's emotions and influences the display of recommendation results.

[0768] Collection and transmission of genetic information

[0769] Users use a genetic analysis kit to obtain their child's genetic information, which is then sent to a server via a dedicated application or web portal.

[0770] Receiving and analyzing data

[0771] The server receives the genetic information sent from the user's device, checks the format of the received genetic data, verifies the integrity of the data, and then uses specialized analysis algorithms to extract characteristics such as learning ability, athletic ability, and creativity from the genetic information.

[0772] Evaluation and Profile Generation

[0773] The server evaluates the child's characteristics based on the analysis results, generating scores and comments for each characteristic based on the extracted data, and creating specific profiles such as a "knowledge-seeking type" for children with high learning ability, or an "sports type" for children with high athletic ability.

[0774] Find an educational institution

[0775] The server searches the database and selects the most suitable educational institution based on the evaluated characteristics. It picks out facilities that suit the characteristics based on information such as the program content, features, and location of the educational institution, and matches the child's profile with the characteristics of the educational institution to select the most suitable facility.

[0776] Generating and sending recommendation results

[0777] The server generates a list of suitable educational institutions, compiles the details of each institution, and sends the list and details to the user's device.

[0778] User Interface and Emotion Engine

[0779] The user device displays the received recommendation results to the user. The display method is to display a list of facilities in an easy-to-read format and provide links to access detailed information about each facility. The user device also uses its built-in camera and microphone to send the user's voice and facial expression data to the emotion engine.

[0780] Emotion engine processing

[0781] The server uses an emotion engine to recognize the user's emotional state and adjust the display of recommendation results. The emotion engine analyzes the user's voice and facial expression data to identify the user's emotional state, and changes the priority of recommendation results and adjusts the display method according to the user's emotional state. The server analyzes the user's emotional feedback in real time and reflects it in the next recommendation results.

[0782] For example, when a user sends their child's genetic information to a server, the server analyzes the information and evaluates the child's musical ability. It then searches a database for nurseries and schools specializing in music education and creates a list of the most suitable facilities. Finally, the results are sent to the user's device, where the user can view the list. Furthermore, the emotion engine adjusts the recommendation results based on user feedback, providing information in a form that best suits the user's emotional state. The user can then select a facility based on the list and make an inquiry or application.

[0783] Example prompt sentence:

[0784] "Please provide us with an algorithm that recommends the most suitable stores and products based on genetic information and shopping history. Also, please provide us with the logic for recognizing specific emotions and adjusting the priority of recommendations in real time."

[0785] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0786] Step 1:

[0787] Users use a genetic analysis kit to obtain their child's genetic information, which is then sent to a server via a dedicated application or web portal. At this stage, the input data is the child's genetic information, and the output is the data being sent to the server.

[0788] Step 2:

[0789] The server receives the genetic information sent from the user terminal. The format of the received genetic information is confirmed and the data integrity is verified. The input data here is the genetic information, and the output data is the genetic information whose integrity has been confirmed.

[0790] Step 3:

[0791] The server then uses a specialized analysis algorithm to analyze the genetic information whose integrity has been confirmed and extract characteristics such as learning ability, athletic ability, and creativity. This algorithm uses a machine learning model. The input data is the genetic information whose integrity has been confirmed, and the output data is the results of each analyzed characteristic.

[0792] Step 4:

[0793] The server evaluates the child's characteristics based on the analysis results, generates a characteristic score and comments, and then creates a profile and assigns labels such as "knowledge-seeking" or "sports-oriented." The input data here is the analyzed characteristic information, and the output data is the evaluation results and profile information.

[0794] Step 5:

[0795] The server searches the database and selects the most suitable educational institution based on the evaluated characteristics. The database contains information such as the institution's program content, features, and location, and by comparing this information, it extracts the most suitable facilities. The input data is the evaluation results and database information, and the output data is a list of suitable educational institutions.

[0796] Step 6:

[0797] The server generates a list of suitable educational institutions and compiles detailed information about each institution, which is then sent to the user's device. The list includes details about the institution's location, characteristics, admission procedures, etc. The input data is the list of suitable educational institutions and their details, and the output data is the list of recommendation results sent to the user's device.

[0798] Step 7:

[0799] The user terminal displays the recommendation results sent from the server to the user. The recommendation results are displayed as a list of facilities in an easy-to-read format, and links to detailed information about each facility are provided. The input data is the recommendation results sent from the server, and the output data is the displayed recommendation list.

[0800] Step 8:

[0801] The user terminal uses a built-in camera and microphone to transmit the user's voice and facial expression data to the emotion engine. The input data is the user's voice and facial expression data, and the output data is data transmitted to the emotion engine.

[0802] Step 9:

[0803] The server uses an emotion engine to analyze the user's emotional state and adjusts the display of recommendation results based on the results. It changes the priority of recommendation results and adjusts the display method according to the emotional state. The input data is the user's voice and facial expression data, and the output data is the adjusted recommendation display.

[0804] Step 10:

[0805] The server analyzes the user's emotional feedback in real time and reflects it in the next recommendation results. By incorporating the feedback data into the emotion engine, the next recommendation will be more adapted to the user's emotional state. It is a dynamic recommendation algorithm whose input data is the user's emotional feedback and whose output data is reflected in the next recommendation.

[0806] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0807] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0808] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0809] [Third embodiment]

[0810] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0811] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0812] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0813] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0814] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0815] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0816] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0817] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0818] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0819] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0820] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0821] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0822] The present invention relates to a system that evaluates a child's characteristics and talents based on their genetic information and recommends the most suitable educational institution based on the evaluation results. An embodiment of this system will be described below.

[0823] Overall structure

[0824] The system consists of a user device, a server, and a database. The user device is used by parents and educators, the server provides data analysis and recommendation functions, and the database stores information about educational institutions.

[0825] Collection and transmission of genetic information

[0826] Users use a genetic analysis kit to obtain their child's genetic information, which is then sent to a server via a dedicated application or web portal.

[0827] Receiving and analyzing data

[0828] The server receives the genetic information transmitted from the user terminal.

[0829] Data reception: The server checks the format of the received genetic information and performs error checks.

[0830] Data analysis: Specialized analysis algorithms extract specific traits such as learning ability, athletic ability, and creativity from genetic information.

[0831] Evaluation and Profile Generation

[0832] The server evaluates the child's characteristics based on the analysis results.

[0833] Characteristic evaluation: Generate scores and comments for each characteristic based on the extracted characteristic data.

[0834] Profile generation: Create specific profiles such as "knowledge-seeking" for those with high learning ability, or "sports" for those with high athletic ability.

[0835] Find an educational institution

[0836] The server searches the database and selects the most suitable educational institution based on the evaluated characteristics.

[0837] Database search: Select facilities that fit your characteristics based on information such as the educational institution's program content, features, and location.

[0838] Matching: Match the child's profile with the characteristics of the educational institution to extract the most suitable facility.

[0839] Generating and sending recommendation results

[0840] The server generates a list of suitable educational institutions and compiles details for each facility.

[0841] List Creation: Create a list of recommended facilities and add details about each facility.

[0842] Data transmission: The generated list and detailed information are sent to the user's terminal.

[0843] User Interface

[0844] The terminal displays the received recommendation results to the user.

[0845] List View: Displays a list of facilities in an easy-to-read format, with links to access more information about each facility.

[0846] Specific examples

[0847] For example, suppose a user sends their child's genetic information to a server. The server analyzes the information and evaluates the child's musical ability. Next, the server searches a database for nurseries and schools specializing in music education and creates a list of the most suitable facilities. Finally, the server sends the results to the user's device, where the user can view the list. The user can then select a facility based on the list and make an inquiry or application.

[0848] In this way, the present invention provides an educational environment that maximizes a child's talents by combining scientific evaluation based on genetic information with recommendations for appropriate educational institutions.

[0849] The processing flow will be explained below.

[0850] Step 1:

[0851] Users collect their children's genetic information and send it to a server via a dedicated application or web portal.

[0852] The user analyzes the saliva sample using a genetic analysis kit and obtains the generated genetic data.

[0853] The user enters the genetic data into the application and uploads it to the server.

[0854] Step 2:

[0855] The server receives the genetic information sent from the user.

[0856] The server checks the format of the received genetic data and verifies the integrity of the data.

[0857] The server performs error checking and notifies the user if necessary.

[0858] Step 3:

[0859] The server analyzes the genetic information.

[0860] The server uses specialized analytical algorithms to extract specific traits from the genetic data, such as learning ability, athletic ability, and creativity.

[0861] The server organizes the extracted characteristic data and converts it into a format that can be used in the next step.

[0862] Step 4:

[0863] The server evaluates the child's characteristics and generates a profile.

[0864] The server calculates a score for each characteristic based on the analysis results and generates a comment.

[0865] Based on the evaluation results, the server creates specific profiles such as "knowledge-seeking type" or "sports type."

[0866] Step 5:

[0867] The server searches the educational institution database.

[0868] The server then searches for facilities that offer specific programs or activities based on the evaluated characteristics.

[0869] The server runs a matching algorithm to match genetic characteristics with characteristics of educational institutions.

[0870] Step 6:

[0871] The server generates a recommendation result.

[0872] The server creates a list of suitable educational institutions and adds details about each facility.

[0873] The server converts the recommendation results into a format for transmission to the user.

[0874] Step 7:

[0875] The server transmits the recommendation results to the user's terminal.

[0876] The server sends the list of educational institutions and their details to the user's terminal via a communication protocol.

[0877] Step 8:

[0878] The terminal displays the received recommendation results.

[0879] The device displays the list in an easy-to-read format and provides links to access more information about each facility.

[0880] The user can browse the displayed list to find out more about institutions that interest them.

[0881] Example 1

[0882] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0883] In the current educational environment, there is a lack of a system for scientifically evaluating a child's talents and characteristics and selecting the most suitable educational institution based on that evaluation, which makes it difficult to find an appropriate educational institution that meets individual educational needs.In addition, parents and educators are unable to efficiently search for educational institutions that meet their specific requirements, making it difficult to select an educational institution that will maximize their child's talents.

[0884] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0885] In this invention, the server includes means for acquiring biological information, means for analyzing the acquired biological information and evaluating characteristics, means for searching for suitable educational institutions based on the evaluated characteristics, and means for transmitting the search results to the user's terminal, thereby making it possible to scientifically evaluate a child's characteristics based on the biological information and to suggest the most suitable educational institution based on the evaluation results.

[0886] "Biological information" refers to data obtained from living organisms, such as genetic information.

[0887] "Analysis" refers to the process of analyzing acquired data and extracting characteristics and trends.

[0888] "Characteristics" refers to a child's unique qualities and abilities, such as learning ability, physical ability, and creativity.

[0889] "Educational institutions" refer to facilities that provide educational services, such as schools and nurseries.

[0890] An "algorithm" refers to a procedure or computational method for solving a specific problem.

[0891] "User devices" refers to digital devices such as computers and smartphones used by parents and educators.

[0892] The present invention relates to a system that evaluates a child's characteristics and talents based on their biological information and recommends the most suitable educational institution based on the evaluation results. An embodiment of this system will be described below.

[0893] Overall structure

[0894] The system consists of a user device, a server, and a database. The user device is used by parents and educators, the server provides data analysis and recommendation functions, and the database stores information about educational institutions.

[0895] Collection and transmission of biological information

[0896] Users use a genetic analysis kit to obtain biological information about their children, which is then sent to a server via a dedicated application or web portal.

[0897] Receiving and analyzing data

[0898] The server receives the biological information transmitted from the user terminal.

[0899] Data reception: The server checks the format of the received biological information and performs error checking, for example, checking that the received data is in XML or JSON format and detecting incomplete data or formatting errors.

[0900] Data analysis: The server uses the genetic analysis software "GeneAnalyzer" to extract characteristics such as learning ability, physical ability, and creativity from biological information.

[0901] Evaluation and Profile Generation

[0902] The server evaluates the child's characteristics based on the analysis results and generates a profile.

[0903] Characteristic evaluation: The server generates a score and comment for each characteristic based on the extracted characteristic data. For example, if the learning ability is high, the server evaluates it as "Learning ability: High."

[0904] Profile generation: The server creates specific profiles such as "knowledge seeker," "sportsman," or "artistic," and adds feedback comments and scores.

[0905] Find an educational institution

[0906] The server searches the database for the most suitable educational institution.

[0907] Database search: Find facilities that fit your characteristics based on information such as the program content, features, location, etc. For example, if searching for facilities specializing in music education, filter to find institutions that are suitable for a specific musical ability.

[0908] Matching: The server matches the child's profile with the characteristics of the educational institutions to identify the most suitable facilities. For example, it uses a matching algorithm to compare the profile and the details of the educational institutions, and then creates a ranked list of the most suitable educational institutions.

[0909] Generating and sending recommendation results

[0910] The server generates a list of suitable educational institutions, compiles the details and sends them to the user terminal.

[0911] List Creation: Create a list of recommended facilities and add details about each facility, such as facility name, location, features, and contact information.

[0912] Data transmission: The server encrypts the generated list and transmits it to the user terminal through a secure channel.

[0913] User Interface

[0914] The terminal displays the received recommendation results to the user.

[0915] List view: Displays a list of facilities in an easy-to-read format and provides links to access detailed information. For example, a link to the details page and an inquiry button for "Music Education College A" can be displayed on the device.

[0916] Specific examples

[0917] For example, suppose a user sends their child's genetic information to a server. The server analyzes the information and evaluates the child's musical ability. The server then searches a database for nurseries and schools specializing in music education and creates a list of the most suitable facilities. Finally, the server sends the list of facilities and detailed information to the user's device, where the user can view the list. The user can then select a facility based on the list and make an inquiry or application.

[0918] An example of a prompt for the generative AI model for this system might be:

[0919] "Please explain a system that recommends educational institutions based on a child's genetic information."

[0920] "Please tell me the detailed process of genetic information analysis and the educational institution recommendation system."

[0921] In this way, the present invention provides an educational environment that maximizes children's talents by combining scientific evaluation based on biological information with recommendations for appropriate educational institutions.

[0922] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0923] Step 1: Collecting and sending biological information

[0924] The user uses a genetic analysis kit to obtain biological information about the child. Specifically, the user follows the instructions included in the genetic analysis kit and inserts a cell sample taken from the child's cheek into the kit. Next, the genetic information is scanned using a dedicated application, and after the scan is complete, the application generates genetic information data. This generated data (input) is sent to a server via a dedicated portal (output).

[0925] Step 2: Receiving data and checking for errors

[0926] The server receives biological information sent from the user terminal (input). Specifically, the server verifies that the received data is in the correct format (e.g., XML or JSON format) and performs error checking. If there is an error in the format, the server generates an error message and sends it to the user terminal (output). Only data in the correct format proceeds to the next analysis step.

[0927] Step 3: Data analysis

[0928] The server analyzes the received biological information using an analytical algorithm (input). Specifically, the server uses a software module called "GeneAnalyzer" to analyze the genetic marker information and generate trait scores for learning ability, physical ability, creativity, etc. (output). This process includes data processing to extract various traits from the genetic data.

[0929] Step 4: Characterization and profile generation

[0930] The server evaluates the child's characteristics based on the analysis results and generates a profile (input). Specifically, the server generates scores and comments for each characteristic based on the generated characteristic data. For example, it evaluates the child's learning ability as "high" and "medium" and adds the feedback comments and score to the profile (output).

[0931] Step 5: Find your institution

[0932] The server searches a database for the most suitable educational institutions based on the characteristic evaluation results (input). Specifically, it searches a database called "EducationDB" and filters educational institutions that match the characteristics. For example, when searching for facilities suitable for music education, a specific musical ability score is used as a filter condition. This generates a list of highly suitable educational institutions (output).

[0933] Step 6: Matching and List Generation

[0934] The server compares the generated characteristic profile with the characteristics of educational institutions and extracts highly compatible facilities (input). Specifically, it uses a matching algorithm to compare the profile with the detailed information of educational institutions and creates a list of highly compatible facilities (output). This list includes detailed information such as the facility name, location, features, and contact information.

[0935] Step 7: Send data

[0936] The server encrypts the generated list of educational institutions and sends it to the user terminal through a secure channel (input). Specifically, the server encrypts the generated list and sends it to the user terminal through a secure channel such as SSL / TLS (output).

[0937] Step 8: Displaying the Recommendations

[0938] The terminal displays the received list of educational institutions to the user (input). Specifically, the terminal displays the list of facilities in an easy-to-read format and provides links to access detailed information about each facility. For example, it displays a link to the details page for "Music Education College A" and an inquiry button (output).

[0939] In this way, the entire system processes and analyzes the child's biological information, allowing the most suitable educational institution to be scientifically and efficiently recommended.

[0940] (Application example 1)

[0941] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0942] In the past, parents and educators struggled to understand their children's characteristics and talents and find the right educational facility based on those. Furthermore, there was no system in place that could scientifically evaluate a child's characteristics using genetic information and recommend the most suitable educational facility. Furthermore, there was no established method for easily finding brick-and-mortar stores that offered educational programs and activities suited to a child's characteristics.

[0943] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0944] In this invention, the server includes a means for acquiring a child's genetic information, a means for analyzing the acquired genetic information and evaluating the child's characteristics, a means for searching for suitable educational facilities or activities based on the evaluated characteristics, and a means for transmitting the search results to the user's mobile information terminal. This allows parents and educators to easily find the best educational environment for their children based on scientific evaluation. Furthermore, by using a generative AI model to identify educational facilities suited to the characteristics and generate prompt sentences, more accurate recommendations can be achieved.

[0945] "Child's genetic information" is information obtained from the child's DNA, including gene sequences and specific genetic markers.

[0946] "Trait assessment tools" are algorithms or processes that analyze genetic information to determine a child's learning ability, athletic ability, creativity, or other traits.

[0947] A "means for searching suitable educational facilities or activities" is an algorithm or process for identifying and recommending facilities from a database that offer optimal educational programs or activities based on the evaluated characteristics.

[0948] The "means for sending to the user's mobile information terminal" refers to a communication protocol or interface for sending search results to a mobile information terminal such as a smartphone or tablet held by a parent or educator.

[0949] A "generative AI model" is an algorithm or system that uses artificial intelligence to learn patterns from large amounts of data and perform specific tasks.

[0950] A "prompt sentence" is text generated as an instruction or question to be input into a generative AI model, and serves as a guideline for obtaining the required information.

[0951] The present invention relates to a system that evaluates a child's characteristics and talents based on their genetic information and recommends optimal educational facilities and activities based on the evaluation results. A detailed description of an embodiment of this system is provided below.

[0952] Overall structure

[0953] The system consists of a user's mobile information device, a server, and a database. The user's mobile information device is used by parents or educators, and the server provides data analysis and recommendation functions. The database stores information about educational facilities and activities.

[0954] Collection and transmission of genetic information

[0955] Users obtain their child's genetic information using a dedicated genetic analysis kit. The obtained genetic information is then sent to a server via a smartphone application or web portal using a secure protocol (e.g., HTTPS).

[0956] Receiving and analyzing data

[0957] The server receives the genetic information sent from the user's device. The received data is first checked for formatting and errors. Then, a specialized analysis algorithm (e.g., implemented in Python) is used to analyze the genetic information and extract characteristics such as learning ability, athletic ability, and creativity.

[0958] Evaluation and Profile Generation

[0959] The server evaluates the child's characteristics based on the analysis results. This evaluation includes a score and comments for each characteristic. For example, if a child has a high learning ability, a profile called "Knowledge Seeker" will be generated. Other profiles include "Sports Type" and "Artistic Type."

[0960] Find an educational facility or activity

[0961] The server searches the database to select the educational facility or activity that best suits the assessed characteristics, using an algorithm that selects facilities with specific programs or activities, and uses a generative AI model to generate prompts and analyzes the prompts to identify the facilities that best suit the characteristics.

[0962] Generating and sending recommendation results

[0963] The server generates a list of suitable educational facilities or activities and compiles detailed information about each facility or activity. The generated list and detailed information are sent to the user's mobile information device, which displays the list of facilities and activities in an easy-to-read format and provides links to access detailed information about each facility.

[0964] Specific examples

[0965] For example, suppose a user sends their child's genetic information to a server. The server analyzes the information and evaluates the child's musical ability. Next, the server searches a database for educational facilities and music schools specializing in music education and creates a list of the most suitable facilities. Finally, the server sends the results to the user's mobile information terminal, where the user can view the list. The user can then select a facility based on the list and make an inquiry or application.

[0966] As an example of a prompt sentence using a generative AI model, by generating and analyzing the prompt "Please suggest the best music school for a child with high musical talent," it is possible to identify and recommend specific educational facilities.

[0967] In this way, the present invention can provide an educational environment that maximizes children's talents by combining scientific evaluation based on genetic information with more accurate recommendations.

[0968] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0969] Step 1: Collecting and transmitting genetic information

[0970] Users use a genetic analysis kit to obtain their child's genetic information, which is then sent to a server via a dedicated smartphone application or web portal. The input is the obtained genetic information, and the output is confirmation that transmission to the server has been completed.

[0971] Step 2: Receiving genetic information and checking for errors

[0972] The server receives the genetic information sent from the user terminal. After receiving it, it checks the format of the genetic information and performs an error check. The input is the genetic information received from the user terminal, and the output is the format check and error check status.

[0973] Step 3: Analysis of genetic information

[0974] The server analyzes the genetic information and extracts traits such as learning ability, athletic ability, and creativity. This analysis uses specialized analysis algorithms (e.g., implemented in Python). The input is error-checked genetic information, and the output is the extracted trait data.

[0975] Step 4: Characterization and profile generation

[0976] The server evaluates the child's characteristics based on the extracted characteristic data and generates a profile. For example, if the child has high learning ability, a profile called "knowledge-seeking" is generated. The input is the characteristic data, and the output is the generated profile.

[0977] Step 5: Find an educational facility or activity

[0978] The server searches the database and selects suitable educational facilities or activities based on the assessed characteristics, using algorithms that select facilities with specific programs or activities. The input is the generated profile, and the output is a list of suitable educational facilities or activities.

[0979] Step 6: Generate and analyze prompts using a generative AI model

[0980] The server uses a generative AI model to generate prompts and analyzes them to identify facilities that fit the profile. For example, it generates a prompt such as, "Please suggest the best music school for a child with high musical talent." The input is the profile information, and the output is the analyzed specific educational facility.

[0981] Step 7: Generate and send recommendations

[0982] The server generates a list of suitable educational facilities or activities and compiles detailed information about each facility or activity. The generated list and detailed information are sent to the user's mobile information device. The input is the data of the identified educational facilities or activities, and the output is the recommendation results sent to the user's device.

[0983] Step 8: Display in the user interface

[0984] The user's mobile device displays a list of facilities and activities in an easy-to-read format and provides links to access detailed information about each facility. The input is the recommendation results received from the server, and the output is the information displayed on the device screen.

[0985] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0986] The present invention combines a system that evaluates a child's characteristics and talents based on their genetic information and recommends the most suitable educational institution based on the results with an emotion engine that recognizes the user's emotions. The following describes an embodiment of this system.

[0987] Overall structure

[0988] The system consists of a user device, a server, a database, and an emotion engine. The user device is used by parents and educators, and the server provides data analysis and recommendation functions. The database stores information about educational institutions, and the emotion engine recognizes users' emotions and influences the display of recommendation results.

[0989] Collection and transmission of genetic information

[0990] Users use a genetic analysis kit to obtain their child's genetic information, which is then sent to a server via a dedicated application or web portal.

[0991] Receiving and analyzing data

[0992] The server receives the genetic information transmitted from the user terminal.

[0993] Data reception: The server checks the format of the received genetic data and verifies the data integrity.

[0994] Data analysis: Specialized analysis algorithms extract specific traits such as learning ability, athletic ability, and creativity from genetic information.

[0995] Evaluation and Profile Generation

[0996] The server evaluates the child's characteristics based on the analysis results.

[0997] Characteristic evaluation: Generate scores and comments for each characteristic based on the extracted characteristic data.

[0998] Profile generation: Create specific profiles such as "knowledge-seeking" for those with high learning ability, or "sports" for those with high athletic ability.

[0999] Find an educational institution

[1000] The server searches the database and selects the most suitable educational institution based on the evaluated characteristics.

[1001] Database search: Select facilities that fit your characteristics based on information such as the educational institution's program content, features, and location.

[1002] Matching: Match the child's profile with the characteristics of the educational institution to extract the most suitable facility.

[1003] Generating and sending recommendation results

[1004] The server generates a list of suitable educational institutions and compiles details for each facility.

[1005] List Creation: Create a list of recommended facilities and add details about each facility.

[1006] Data transmission: The generated list and detailed information are sent to the user's terminal.

[1007] User Interface and Emotion Engine

[1008] The terminal displays the received recommendation results to the user.

[1009] List View: Displays a list of facilities in an easy-to-read format, with links to access more information about each facility.

[1010] Emotion recognition: The device uses its built-in camera and microphone to transmit the user's voice and facial expression data to the emotion engine.

[1011] Emotion engine processing

[1012] The server uses an emotion engine to recognize the user's emotional state and adjust the display of recommendation results.

[1013] Emotion Analysis: The emotion engine analyzes the user's voice and facial expression data to identify the user's emotional state.

[1014] Display adjustment: Change the priority of recommendation results and adjust the display method depending on emotional state.

[1015] Feedback: Analyze users' emotional feedback in real time and reflect it in the next recommendation results.

[1016] Specific examples

[1017] For example, suppose a user sends their child's genetic information to a server. The server analyzes the information and evaluates the child's musical ability. Next, the server searches a database for nurseries and schools specializing in music education and creates a list of the most suitable facilities. Finally, the server sends the results to the user's device, where the user can view the list. Furthermore, the emotion engine adjusts the recommendation results based on user feedback, providing information in a form that best suits the user's emotional state. The user can then select a facility from the list and make an inquiry or application.

[1018] In this way, the present invention combines scientific evaluation based on genetic information with user emotion recognition to more effectively provide an educational environment that maximizes children's talents.

[1019] The processing flow will be explained below.

[1020] Step 1:

[1021] Users collect their children's genetic information and send it to a server via a dedicated application or web portal.

[1022] The user collects a saliva sample and analyzes it using a genetic analysis kit.

[1023] The user inputs the genetic data obtained as a result of the analysis into a dedicated application and transmits it to be uploaded to the server.

[1024] Step 2:

[1025] The server receives the genetic information sent from the user.

[1026] The server checks the format of the received genetic data and verifies the consistency and completeness of the data.

[1027] The server detects incomplete data or errors and notifies the user to resend if necessary.

[1028] Step 3:

[1029] The server analyzes the genetic information.

[1030] The server runs specialized genetic analysis algorithms to extract traits such as learning ability, athletic ability, and creativity.

[1031] The server compiles the analysis results and generates a score and comment for each characteristic.

[1032] Step 4:

[1033] The server evaluates the child's characteristics and generates a profile.

[1034] The server creates a comment along with a score for each characteristic based on the extracted characteristic data.

[1035] Based on the evaluation results, the server creates specific profiles such as "knowledge-seeking type" or "sports type."

[1036] Step 5:

[1037] The server searches the educational institution database.

[1038] The server conditionally searches the educational institution database based on the evaluated characteristics.

[1039] The server picks facilities that offer specific programs or activities and runs a matching algorithm.

[1040] Step 6:

[1041] The server generates a recommendation result.

[1042] The server creates a list of suitable educational institutions and adds details about each facility (location, programs, features, etc.).

[1043] The server transmits the generated list and detailed information about each facility to the user terminal.

[1044] Step 7:

[1045] The terminal displays the received recommendation results to the user.

[1046] The device displays the recommendation list in an easy-to-read format and provides links to access more information about each establishment.

[1047] Step 8:

[1048] The emotion engine recognizes the user's emotional state.

[1049] The device uses a built-in camera and microphone to collect the user's voice and facial expression data and transmits it to the emotion engine.

[1050] The emotion engine analyzes the collected data to determine the user's emotional state.

[1051] Step 9:

[1052] The server uses feedback from the emotion engine to adjust the recommendation results.

[1053] The server changes the priority of recommendations based on the user's emotional state.

[1054] The server adjusts the display method and content of the suggestions to match the user's emotions and reflects this in the next recommendation.

[1055] Step 10:

[1056] Users can further research educational institutions that interest them based on the recommendations displayed.

[1057] Users can view detailed information about each facility and make inquiries or apply for tours.

[1058] The user selects the most suitable facility and determines the educational environment for their child.

[1059] In this way, the present invention provides a system that effectively recommends educational institutions that are best suited to a child's characteristics by combining scientific evaluation based on genetic information with user emotion recognition.

[1060] Example 2

[1061] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1062] Conventional systems for selecting educational institutions for children only evaluate characteristics based on genetic information and search for educational institutions. However, it is difficult to recognize how users feel about the displayed information and provide recommendation results that reflect that. This can result in the selection of an educational institution that is optimal for the user. In particular, there is a need for a more personalized selection of educational institutions that takes user emotions into account.

[1063] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1064] In this invention, the server includes means for acquiring the child's genetic information, means for analyzing the acquired genetic information and evaluating the child's characteristics, means for searching for suitable educational institutions based on the evaluated characteristics, means for transmitting the search results to the user's terminal, and means for recognizing the user's emotions and adjusting the display of the recommendation results, thereby enabling a more personalized selection of educational institutions that takes the user's emotional state into consideration.

[1065] "Child's genetic information" means information that indicates the child's genetic characteristics, including biological data such as DNA.

[1066] "Means of acquisition" refers to tools and methods for collecting genetic information and storing it electronically as data, such as genetic analysis kits and dedicated applications.

[1067] "Means for analyzing and evaluating characteristics" refers to algorithms and analytical functions for scientifically and digitally evaluating a child's characteristics and abilities based on the acquired genetic information.

[1068] "Learning ability" refers to a child's ability to understand and absorb knowledge.

[1069] "Motoring ability" refers to a child's physical abilities, such as strength, muscle power, and sense of balance, when performing sports.

[1070] "Creativity" refers to a child's ability to come up with new ideas and methods, that is, to generate original thoughts.

[1071] "Educational institutions" refers to facilities such as schools, nurseries, cram schools, and extracurricular classes where children receive an education.

[1072] "Search means" refers to the functions and algorithms used to find and select educational institutions that suit a child's characteristics based on the information in the database.

[1073] "Means for sending" refers to the network communication functions and protocols for sending analysis results and search results to the user's terminal.

[1074] "Means for recognizing emotions" refers to devices or algorithms that analyze the user's voice and facial expression data to identify their emotional state at that time.

[1075] "Means for adjusting the display" refers to a function for changing the display method and priority of recommendation results depending on the user's emotional state.

[1076] The present invention combines a system for evaluating a child's characteristics and talents based on their genetic information, a system for recommending the most suitable educational institution, and an engine for recognizing user emotions. Specific embodiments of the present invention will be described in detail below.

[1077] Overall structure

[1078] This system consists of a user terminal, a server, a database, and an emotion engine.

[1079] The user terminals are used by parents and educators to collect genetic information, send it to a server, and display recommendation results.

[1080] The server provides data analysis and recommendation functions, analyzing genetic information, searching educational institutions, recognizing emotions, and adjusting the display of recommendation results.

[1081] The database holds information about educational institutions and is used when making recommendations.

[1082] The emotion engine recognizes the user's emotions and influences the display of recommendation results.

[1083] Collection and transmission of genetic information

[1084] Users use a genetic analysis kit to obtain their child's genetic information, which is then sent to a server via a dedicated application or web portal. The genetic analysis kit also includes tools for collecting samples such as saliva and hair.

[1085] Receiving and analyzing data

[1086] The server receives the genetic information sent from the user's device. It checks the format of the received data and verifies its integrity. If any inconsistencies or errors are detected, it notifies the user. The server then uses specialized analysis algorithms to extract characteristics such as learning ability, athletic ability, and creativity from the genetic information. The analysis is performed using machine learning models using Python and data analysis tools (e.g., TensorFlow).

[1087] Evaluation and Profile Generation

[1088] The server evaluates the child's characteristics based on the analysis results. Based on the extracted data, it calculates scores for learning ability, athletic ability, creativity, etc., and also generates comments for each characteristic. For example, "highly skilled at learning" or "well-developed athletic ability." Finally, a specific profile is generated based on these scores and comments. The profile is categorized into categories such as "knowledge-seeking type" and "sports type."

[1089] Find an educational institution

[1090] The server searches a database based on the generated profile. The database contains information about each educational institution, including their program content, features, and location. The server compares the profile with the information about the educational institution, selects facilities that fit the characteristics, and extracts the most suitable facilities based on the degree of match between the educational institution and the child's profile.

[1091] Generating and sending recommendation results

[1092] The server generates a list of suitable educational institutions, creates a list of recommended facilities, adds detailed information about each facility, and sends that information to the user's device. The file format used is JSON or XML.

[1093] User interface and emotion engine display

[1094] The device displays the received recommendation results to the user, presenting a list of facilities in an easy-to-read format and providing links to access detailed information about each facility. It also uses the built-in camera and microphone to transmit the user's voice and facial expression data to the emotion engine.

[1095] Emotion engine processing

[1096] The server uses an emotion engine to recognize the user's emotional state and adjust the display of recommendation results. The emotion engine analyzes the user's voice and facial expression data to identify their emotional state at that time. For the analysis, it uses voice recognition tools (e.g., Google Cloud Speech-to-Text) and facial expression analysis tools (e.g., Microsoft Azure Face API). Depending on the user's emotional state, it changes the priority of recommendation results and adjusts the display method. It also analyzes the user's emotional feedback in real time and reflects it in the next recommendation results.

[1097] Specific examples

[1098] For example, when a user sends their child's genetic information to a server, the server analyzes the information and evaluates the child's musical ability. Next, the server searches a database for nurseries and schools specializing in music education and creates a list of the most suitable facilities. Finally, the server sends the results to the user's device, where the user can view the list. Furthermore, the emotion engine adjusts the recommendation results based on user feedback, providing information in a form that best suits the user's emotional state. The user can then select a facility from the list and make an inquiry or application.

[1099] Prompt Sentence Examples

[1100] "Please outline a system that uses a child's genetic information to assess their musical ability and recommend educational institutions that specialize in music education. Also, explain how the results are adjusted using the user's emotional feedback."

[1101] In this way, the present invention combines scientific evaluation based on genetic information with user emotion recognition to more effectively provide an educational environment that maximizes children's talents.

[1102] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1103] Step 1:

[1104] Users collect their child's genetic information using a genetic analysis kit, which includes tools for collecting samples such as saliva and hair. They then log in to a dedicated application or web portal and upload the collected genetic information.

[1105] Input: Child's genetic information (sample data)

[1106] Output: Sending genetic information to the server

[1107] Step 2:

[1108] The server receives the genetic information sent from the user's device, checks the format of the received data, and verifies the data's integrity. If any discrepancies or errors are detected, the server notifies the user.

[1109] Input: Genetic data submitted by the user

[1110] Output: Data integrity check result, notification to user (if there are any errors)

[1111] Step 3:

[1112] The server analyzes the genetic information using specialized analysis algorithms, using Python and TensorFlow to extract characteristics such as learning ability, motor skills, and creativity.

[1113] Input: integrity-checked genetic data

[1114] Output: Trait analysis results (scores for learning ability, motor ability, and creativity)

[1115] Step 4:

[1116] The server evaluates the child's characteristics based on the analysis results, generates a score and comments, and creates a specific profile, such as "knowledge-seeking" if the child has good learning ability, or "sports-oriented" if the child has good athletic ability.

[1117] Input: Analysis result of characteristics

[1118] Output: Child characterization and profile

[1119] Step 5:

[1120] The server then searches a database based on the generated profile. The database contains information about each educational institution, including their program content, features, and location. The server matches the child's profile with the educational institution information to identify the most suitable facilities.

[1121] Input: Child profile, educational institution database

[1122] Output: A list of matching institutions

[1123] Step 6:

[1124] The server generates a list of suitable educational institutions, compiles details about each institution, and sends this information in JSON or XML format to the user's device.

[1125] Input: List of highly relevant institutions

[1126] Output: A list of institutions in JSON or XML format

[1127] Step 7:

[1128] The device displays the received recommendation results to the user, displaying a list of facilities in an easy-to-read format and providing links to each facility.

[1129] Input: List of educational institutions sent from the server

[1130] Output: A list of educational institutions displayed on the user's device

[1131] Step 8:

[1132] The device's built-in camera and microphone capture the user's voice and facial expression data and send it to the emotion engine.

[1133] Input: User's voice and facial expression data

[1134] Output: Sending data to the emotion engine

[1135] Step 9:

[1136] The server's emotion engine analyzes the user's voice and facial expression data to identify their emotional state, and adjusts the priority of recommendation results and changes the way they are displayed based on this information.

[1137] Input: Voice and facial expression data to the emotion engine

[1138] Output: Display of adjusted recommendation results

[1139] Step 10:

[1140] Users can check the recommendation results, select the most suitable educational institution, and make inquiries or applications.

[1141] Input: Adjusted recommendation results

[1142] Output: Inquiries and applications to educational institutions

[1143] (Application example 2)

[1144] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1145] The present invention relates to a system that analyzes a child's genetic information, evaluates their characteristics, and suggests the most suitable educational institution. However, current systems have the problem that they are unable to take into account the user's emotional state, which limits the acceptability of the recommendation results.

[1146] In addition, there was a problem that the recommendation results ended up being a simple information provision, and the optimal display method according to the user's emotions was not provided.

[1147] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1148] In this invention, the server includes means for acquiring the child's genetic information, means for analyzing the acquired genetic information and evaluating the child's characteristics, means for searching for suitable educational institutions based on the evaluated characteristics, means for transmitting the search results to the user's terminal, and means for recognizing the user's emotional state and adjusting the display of recommendation results, thereby making it possible to provide appropriate recommendation results according to the user's emotional state.

[1149] "Child's genetic information" refers to data about the child's genes obtained using a genetic analysis kit or the like.

[1150] "Traits" refer to factors such as a child's learning ability, athletic ability, and creativity that are extracted from the results of analyzing genetic information.

[1151] "Evaluation" is the process of analyzing genetic information and generating scores and comments for each trait.

[1152] "Educational institutions" is a general term for facilities that provide education and training to children, such as kindergartens, schools, cram schools, and sports clubs.

[1153] "Search" is the process of selecting suitable institutions from the database based on the assessed characteristics.

[1154] "User devices" are devices such as smartphones, tablets, and computers used by parents and educators.

[1155] The "emotional state" is a psychological state that is identified by analyzing the user's voice and facial expression data.

[1156] A "recommendation result" is a list of suitable educational institutions that are searched based on the evaluated characteristics.

[1157] "Display" refers to the process by which the recommendation results are visually presented on the user's device.

[1158] "Adjustment" is the process of changing the priority of recommendation results and changing the display method depending on the user's emotional state.

[1159] The present invention combines a system that evaluates a child's characteristics and talents based on their genetic information and recommends the most suitable educational institution based on the results with an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the present invention will be described below.

[1160] Overall structure

[1161] This system consists of a user device, a server, a database, and an emotion engine. The user device is used by parents and educators, and the server provides data analysis and recommendation functions. The database stores information about educational institutions, and the emotion engine recognizes the user's emotions and influences the display of recommendation results.

[1162] Collection and transmission of genetic information

[1163] Users use a genetic analysis kit to obtain their child's genetic information, which is then sent to a server via a dedicated application or web portal.

[1164] Receiving and analyzing data

[1165] The server receives the genetic information sent from the user's device, checks the format of the received genetic data, verifies the integrity of the data, and then uses specialized analysis algorithms to extract characteristics such as learning ability, athletic ability, and creativity from the genetic information.

[1166] Evaluation and Profile Generation

[1167] The server evaluates the child's characteristics based on the analysis results, generating scores and comments for each characteristic based on the extracted data, and creating specific profiles such as a "knowledge-seeking type" for children with high learning ability, or an "sports type" for children with high athletic ability.

[1168] Find an educational institution

[1169] The server searches the database and selects the most suitable educational institution based on the evaluated characteristics. It picks out facilities that suit the characteristics based on information such as the program content, features, and location of the educational institution, and matches the child's profile with the characteristics of the educational institution to select the most suitable facility.

[1170] Generating and sending recommendation results

[1171] The server generates a list of suitable educational institutions, compiles the details of each institution, and sends the list and details to the user's device.

[1172] User Interface and Emotion Engine

[1173] The user device displays the received recommendation results to the user. The display method is to display a list of facilities in an easy-to-read format and provide links to access detailed information about each facility. The user device also uses its built-in camera and microphone to send the user's voice and facial expression data to the emotion engine.

[1174] Emotion engine processing

[1175] The server uses an emotion engine to recognize the user's emotional state and adjust the display of recommendation results. The emotion engine analyzes the user's voice and facial expression data to identify the user's emotional state, and changes the priority of recommendation results and adjusts the display method according to the user's emotional state. The server analyzes the user's emotional feedback in real time and reflects it in the next recommendation results.

[1176] For example, when a user sends their child's genetic information to a server, the server analyzes the information and evaluates the child's musical ability. It then searches a database for nurseries and schools specializing in music education and creates a list of the most suitable facilities. Finally, the results are sent to the user's device, where the user can view the list. Furthermore, the emotion engine adjusts the recommendation results based on user feedback, providing information in a form that best suits the user's emotional state. The user can then select a facility based on the list and make an inquiry or application.

[1177] Example prompt sentence:

[1178] "Please provide us with an algorithm that recommends the most suitable stores and products based on genetic information and shopping history. Also, please provide us with the logic for recognizing specific emotions and adjusting the priority of recommendations in real time."

[1179] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1180] Step 1:

[1181] Users use a genetic analysis kit to obtain their child's genetic information, which is then sent to a server via a dedicated application or web portal. At this stage, the input data is the child's genetic information, and the output is the data being sent to the server.

[1182] Step 2:

[1183] The server receives the genetic information sent from the user terminal. The format of the received genetic information is confirmed and the data integrity is verified. The input data here is the genetic information, and the output data is the genetic information whose integrity has been confirmed.

[1184] Step 3:

[1185] The server then uses a specialized analysis algorithm to analyze the genetic information whose integrity has been confirmed and extract characteristics such as learning ability, athletic ability, and creativity. This algorithm uses a machine learning model. The input data is the genetic information whose integrity has been confirmed, and the output data is the results of each analyzed characteristic.

[1186] Step 4:

[1187] The server evaluates the child's characteristics based on the analysis results, generates a characteristic score and comments, and then creates a profile and assigns labels such as "knowledge-seeking" or "sports-oriented." The input data here is the analyzed characteristic information, and the output data is the evaluation results and profile information.

[1188] Step 5:

[1189] The server searches the database and selects the most suitable educational institution based on the evaluated characteristics. The database contains information such as the institution's program content, features, and location, and by comparing this information, it extracts the most suitable facilities. The input data is the evaluation results and database information, and the output data is a list of suitable educational institutions.

[1190] Step 6:

[1191] The server generates a list of suitable educational institutions and compiles detailed information about each institution, which is then sent to the user's device. The list includes details about the institution's location, characteristics, admission procedures, etc. The input data is the list of suitable educational institutions and their details, and the output data is the list of recommendation results sent to the user's device.

[1192] Step 7:

[1193] The user terminal displays the recommendation results sent from the server to the user. The recommendation results are displayed as a list of facilities in an easy-to-read format, and links to detailed information about each facility are provided. The input data is the recommendation results sent from the server, and the output data is the displayed recommendation list.

[1194] Step 8:

[1195] The user terminal uses a built-in camera and microphone to transmit the user's voice and facial expression data to the emotion engine. The input data is the user's voice and facial expression data, and the output data is data transmitted to the emotion engine.

[1196] Step 9:

[1197] The server uses an emotion engine to analyze the user's emotional state and adjusts the display of recommendation results based on the results. It changes the priority of recommendation results and adjusts the display method according to the emotional state. The input data is the user's voice and facial expression data, and the output data is the adjusted recommendation display.

[1198] Step 10:

[1199] The server analyzes the user's emotional feedback in real time and reflects it in the next recommendation results. By incorporating the feedback data into the emotion engine, the next recommendation will be more adapted to the user's emotional state. It is a dynamic recommendation algorithm whose input data is the user's emotional feedback and whose output data is reflected in the next recommendation.

[1200] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1201] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1202] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1203] [Fourth embodiment]

[1204] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1205] 7, a 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.

[1206] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1207] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1208] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1209] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1210] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1211] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1212] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1213] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1214] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1215] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1216] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1217] The present invention relates to a system that evaluates a child's characteristics and talents based on their genetic information and recommends the most suitable educational institution based on the evaluation results. An embodiment of this system will be described below.

[1218] Overall structure

[1219] The system consists of a user device, a server, and a database. The user device is used by parents and educators, the server provides data analysis and recommendation functions, and the database stores information about educational institutions.

[1220] Collection and transmission of genetic information

[1221] Users use a genetic analysis kit to obtain their child's genetic information, which is then sent to a server via a dedicated application or web portal.

[1222] Receiving and analyzing data

[1223] The server receives the genetic information transmitted from the user terminal.

[1224] Data reception: The server checks the format of the received genetic information and performs error checks.

[1225] Data analysis: Specialized analysis algorithms extract specific traits such as learning ability, athletic ability, and creativity from genetic information.

[1226] Evaluation and Profile Generation

[1227] The server evaluates the child's characteristics based on the analysis results.

[1228] Characteristic evaluation: Generate scores and comments for each characteristic based on the extracted characteristic data.

[1229] Profile generation: Create specific profiles such as "knowledge-seeking" for those with high learning ability, or "sports" for those with high athletic ability.

[1230] Find an educational institution

[1231] The server searches the database and selects the most suitable educational institution based on the evaluated characteristics.

[1232] Database search: Select facilities that fit your characteristics based on information such as the educational institution's program content, features, and location.

[1233] Matching: Match the child's profile with the characteristics of the educational institution to extract the most suitable facility.

[1234] Generating and sending recommendation results

[1235] The server generates a list of suitable educational institutions and compiles details for each facility.

[1236] List Creation: Create a list of recommended facilities and add details about each facility.

[1237] Data transmission: The generated list and detailed information are sent to the user's terminal.

[1238] User Interface

[1239] The terminal displays the received recommendation results to the user.

[1240] List View: Displays a list of facilities in an easy-to-read format, with links to access more information about each facility.

[1241] Specific examples

[1242] For example, suppose a user sends their child's genetic information to a server. The server analyzes the information and evaluates the child's musical ability. Next, the server searches a database for nurseries and schools specializing in music education and creates a list of the most suitable facilities. Finally, the server sends the results to the user's device, where the user can view the list. The user can then select a facility based on the list and make an inquiry or application.

[1243] In this way, the present invention provides an educational environment that maximizes a child's talents by combining scientific evaluation based on genetic information with recommendations for appropriate educational institutions.

[1244] The processing flow will be explained below.

[1245] Step 1:

[1246] Users collect their children's genetic information and send it to a server via a dedicated application or web portal.

[1247] The user analyzes the saliva sample using a genetic analysis kit and obtains the generated genetic data.

[1248] The user enters the genetic data into the application and uploads it to the server.

[1249] Step 2:

[1250] The server receives the genetic information sent from the user.

[1251] The server checks the format of the received genetic data and verifies the integrity of the data.

[1252] The server performs error checking and notifies the user if necessary.

[1253] Step 3:

[1254] The server analyzes the genetic information.

[1255] The server uses specialized analytical algorithms to extract specific traits from the genetic data, such as learning ability, athletic ability, and creativity.

[1256] The server organizes the extracted characteristic data and converts it into a format that can be used in the next step.

[1257] Step 4:

[1258] The server evaluates the child's characteristics and generates a profile.

[1259] The server calculates a score for each characteristic based on the analysis results and generates a comment.

[1260] Based on the evaluation results, the server creates specific profiles such as "knowledge-seeking type" or "sports type."

[1261] Step 5:

[1262] The server searches the educational institution database.

[1263] The server then searches for facilities that offer specific programs or activities based on the evaluated characteristics.

[1264] The server runs a matching algorithm to match genetic characteristics with characteristics of educational institutions.

[1265] Step 6:

[1266] The server generates a recommendation result.

[1267] The server creates a list of suitable educational institutions and adds details about each facility.

[1268] The server converts the recommendation results into a format for transmission to the user.

[1269] Step 7:

[1270] The server transmits the recommendation results to the user's terminal.

[1271] The server sends the list of educational institutions and their details to the user's terminal via a communication protocol.

[1272] Step 8:

[1273] The terminal displays the received recommendation results.

[1274] The device displays the list in an easy-to-read format and provides links to access more information about each facility.

[1275] The user can browse the displayed list to find out more about institutions that interest them.

[1276] Example 1

[1277] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1278] In the current educational environment, there is a lack of a system for scientifically evaluating a child's talents and characteristics and selecting the most suitable educational institution based on that evaluation, which makes it difficult to find an appropriate educational institution that meets individual educational needs.In addition, parents and educators are unable to efficiently search for educational institutions that meet their specific requirements, making it difficult to select an educational institution that will maximize their child's talents.

[1279] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1280] In this invention, the server includes means for acquiring biological information, means for analyzing the acquired biological information and evaluating characteristics, means for searching for suitable educational institutions based on the evaluated characteristics, and means for transmitting the search results to the user's terminal, thereby making it possible to scientifically evaluate a child's characteristics based on the biological information and to suggest the most suitable educational institution based on the evaluation results.

[1281] "Biological information" refers to data obtained from living organisms, such as genetic information.

[1282] "Analysis" refers to the process of analyzing acquired data and extracting characteristics and trends.

[1283] "Characteristics" refers to a child's unique qualities and abilities, such as learning ability, physical ability, and creativity.

[1284] "Educational institutions" refer to facilities that provide educational services, such as schools and nurseries.

[1285] An "algorithm" refers to a procedure or computational method for solving a specific problem.

[1286] "User devices" refers to digital devices such as computers and smartphones used by parents and educators.

[1287] The present invention relates to a system that evaluates a child's characteristics and talents based on their biological information and recommends the most suitable educational institution based on the evaluation results. An embodiment of this system will be described below.

[1288] Overall structure

[1289] The system consists of a user device, a server, and a database. The user device is used by parents and educators, the server provides data analysis and recommendation functions, and the database stores information about educational institutions.

[1290] Collection and transmission of biological information

[1291] Users use a genetic analysis kit to obtain biological information about their children, which is then sent to a server via a dedicated application or web portal.

[1292] Receiving and analyzing data

[1293] The server receives the biological information transmitted from the user terminal.

[1294] Data reception: The server checks the format of the received biological information and performs error checking, for example, checking that the received data is in XML or JSON format and detecting incomplete data or formatting errors.

[1295] Data analysis: The server uses the genetic analysis software "GeneAnalyzer" to extract characteristics such as learning ability, physical ability, and creativity from biological information.

[1296] Evaluation and Profile Generation

[1297] The server evaluates the child's characteristics based on the analysis results and generates a profile.

[1298] Characteristic evaluation: The server generates a score and comment for each characteristic based on the extracted characteristic data. For example, if the learning ability is high, the server evaluates it as "Learning ability: High."

[1299] Profile generation: The server creates specific profiles such as "knowledge seeker," "sportsman," or "artistic," and adds feedback comments and scores.

[1300] Find an educational institution

[1301] The server searches the database for the most suitable educational institution.

[1302] Database search: Find facilities that fit your characteristics based on information such as the program content, features, location, etc. For example, if searching for facilities specializing in music education, filter to find institutions that are suitable for a specific musical ability.

[1303] Matching: The server matches the child's profile with the characteristics of the educational institutions to identify the most suitable facilities. For example, it uses a matching algorithm to compare the profile and the details of the educational institutions, and then creates a ranked list of the most suitable educational institutions.

[1304] Generating and sending recommendation results

[1305] The server generates a list of suitable educational institutions, compiles the details and sends them to the user terminal.

[1306] List Creation: Create a list of recommended facilities and add details about each facility, such as facility name, location, features, and contact information.

[1307] Data transmission: The server encrypts the generated list and transmits it to the user terminal through a secure channel.

[1308] User Interface

[1309] The terminal displays the received recommendation results to the user.

[1310] List view: Displays a list of facilities in an easy-to-read format and provides links to access detailed information. For example, a link to the details page and an inquiry button for "Music Education College A" can be displayed on the device.

[1311] Specific examples

[1312] For example, suppose a user sends their child's genetic information to a server. The server analyzes the information and evaluates the child's musical ability. The server then searches a database for nurseries and schools specializing in music education and creates a list of the most suitable facilities. Finally, the server sends the list of facilities and detailed information to the user's device, where the user can view the list. The user can then select a facility based on the list and make an inquiry or application.

[1313] An example of a prompt for the generative AI model for this system might be:

[1314] "Please explain a system that recommends educational institutions based on a child's genetic information."

[1315] "Please tell me the detailed process of genetic information analysis and the educational institution recommendation system."

[1316] In this way, the present invention provides an educational environment that maximizes children's talents by combining scientific evaluation based on biological information with recommendations for appropriate educational institutions.

[1317] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1318] Step 1: Collecting and sending biological information

[1319] The user uses a genetic analysis kit to obtain biological information about the child. Specifically, the user follows the instructions included in the genetic analysis kit and inserts a cell sample taken from the child's cheek into the kit. Next, the genetic information is scanned using a dedicated application, and after the scan is complete, the application generates genetic information data. This generated data (input) is sent to a server via a dedicated portal (output).

[1320] Step 2: Receiving data and checking for errors

[1321] The server receives biological information sent from the user terminal (input). Specifically, the server verifies that the received data is in the correct format (e.g., XML or JSON format) and performs error checking. If there is an error in the format, the server generates an error message and sends it to the user terminal (output). Only data in the correct format proceeds to the next analysis step.

[1322] Step 3: Data analysis

[1323] The server analyzes the received biological information using an analytical algorithm (input). Specifically, the server uses a software module called "GeneAnalyzer" to analyze the genetic marker information and generate trait scores for learning ability, physical ability, creativity, etc. (output). This process includes data processing to extract various traits from the genetic data.

[1324] Step 4: Characterization and profile generation

[1325] The server evaluates the child's characteristics based on the analysis results and generates a profile (input). Specifically, the server generates scores and comments for each characteristic based on the generated characteristic data. For example, it evaluates the child's learning ability as "high" and "medium" and adds the feedback comments and score to the profile (output).

[1326] Step 5: Find your institution

[1327] The server searches a database for the most suitable educational institutions based on the characteristic evaluation results (input). Specifically, it searches a database called "EducationDB" and filters educational institutions that match the characteristics. For example, when searching for facilities suitable for music education, a specific musical ability score is used as a filter condition. This generates a list of highly suitable educational institutions (output).

[1328] Step 6: Matching and List Generation

[1329] The server compares the generated characteristic profile with the characteristics of educational institutions and extracts highly compatible facilities (input). Specifically, it uses a matching algorithm to compare the profile with the detailed information of educational institutions and creates a list of highly compatible facilities (output). This list includes detailed information such as the facility name, location, features, and contact information.

[1330] Step 7: Send data

[1331] The server encrypts the generated list of educational institutions and sends it to the user terminal through a secure channel (input). Specifically, the server encrypts the generated list and sends it to the user terminal through a secure channel such as SSL / TLS (output).

[1332] Step 8: Displaying the Recommendations

[1333] The terminal displays the received list of educational institutions to the user (input). Specifically, the terminal displays the list of facilities in an easy-to-read format and provides links to access detailed information about each facility. For example, it displays a link to the details page for "Music Education College A" and an inquiry button (output).

[1334] In this way, the entire system processes and analyzes the child's biological information, allowing the most suitable educational institution to be scientifically and efficiently recommended.

[1335] (Application example 1)

[1336] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1337] In the past, parents and educators struggled to understand their children's characteristics and talents and find the right educational facility based on those. Furthermore, there was no system in place that could scientifically evaluate a child's characteristics using genetic information and recommend the most suitable educational facility. Furthermore, there was no established method for easily finding brick-and-mortar stores that offered educational programs and activities suited to a child's characteristics.

[1338] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1339] In this invention, the server includes a means for acquiring a child's genetic information, a means for analyzing the acquired genetic information and evaluating the child's characteristics, a means for searching for suitable educational facilities or activities based on the evaluated characteristics, and a means for transmitting the search results to the user's mobile information terminal. This allows parents and educators to easily find the best educational environment for their children based on scientific evaluation. Furthermore, by using a generative AI model to identify educational facilities suited to the characteristics and generate prompt sentences, more accurate recommendations can be achieved.

[1340] "Child's genetic information" is information obtained from the child's DNA, including gene sequences and specific genetic markers.

[1341] "Trait assessment tools" are algorithms or processes that analyze genetic information to determine a child's learning ability, athletic ability, creativity, or other traits.

[1342] A "means for searching suitable educational facilities or activities" is an algorithm or process for identifying and recommending facilities from a database that offer optimal educational programs or activities based on the evaluated characteristics.

[1343] The "means for sending to the user's mobile information terminal" refers to a communication protocol or interface for sending search results to a mobile information terminal such as a smartphone or tablet held by a parent or educator.

[1344] A "generative AI model" is an algorithm or system that uses artificial intelligence to learn patterns from large amounts of data and perform specific tasks.

[1345] A "prompt sentence" is text generated as an instruction or question to be input into a generative AI model, and serves as a guideline for obtaining the required information.

[1346] The present invention relates to a system that evaluates a child's characteristics and talents based on their genetic information and recommends optimal educational facilities and activities based on the evaluation results. A detailed description of an embodiment of this system is provided below.

[1347] Overall structure

[1348] The system consists of a user's mobile information device, a server, and a database. The user's mobile information device is used by parents or educators, and the server provides data analysis and recommendation functions. The database stores information about educational facilities and activities.

[1349] Collection and transmission of genetic information

[1350] Users obtain their child's genetic information using a dedicated genetic analysis kit. The obtained genetic information is then sent to a server via a smartphone application or web portal using a secure protocol (e.g., HTTPS).

[1351] Receiving and analyzing data

[1352] The server receives the genetic information sent from the user's device. The received data is first checked for formatting and errors. Then, a specialized analysis algorithm (e.g., implemented in Python) is used to analyze the genetic information and extract characteristics such as learning ability, athletic ability, and creativity.

[1353] Evaluation and Profile Generation

[1354] The server evaluates the child's characteristics based on the analysis results. This evaluation includes a score and comments for each characteristic. For example, if a child has a high learning ability, a profile called "Knowledge Seeker" will be generated. Other profiles include "Sports Type" and "Artistic Type."

[1355] Find an educational facility or activity

[1356] The server searches the database to select the educational facility or activity that best suits the assessed characteristics, using an algorithm that selects facilities with specific programs or activities, and uses a generative AI model to generate prompts and analyzes the prompts to identify the facilities that best suit the characteristics.

[1357] Generating and sending recommendation results

[1358] The server generates a list of suitable educational facilities or activities and compiles detailed information about each facility or activity. The generated list and detailed information are sent to the user's mobile information device, which displays the list of facilities and activities in an easy-to-read format and provides links to access detailed information about each facility.

[1359] Specific examples

[1360] For example, suppose a user sends their child's genetic information to a server. The server analyzes the information and evaluates the child's musical ability. Next, the server searches a database for educational facilities and music schools specializing in music education and creates a list of the most suitable facilities. Finally, the server sends the results to the user's mobile information terminal, where the user can view the list. The user can then select a facility based on the list and make an inquiry or application.

[1361] As an example of a prompt sentence using a generative AI model, by generating and analyzing the prompt "Please suggest the best music school for a child with high musical talent," it is possible to identify and recommend specific educational facilities.

[1362] In this way, the present invention can provide an educational environment that maximizes children's talents by combining scientific evaluation based on genetic information with more accurate recommendations.

[1363] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1364] Step 1: Collecting and transmitting genetic information

[1365] Users use a genetic analysis kit to obtain their child's genetic information, which is then sent to a server via a dedicated smartphone application or web portal. The input is the obtained genetic information, and the output is confirmation that transmission to the server has been completed.

[1366] Step 2: Receiving genetic information and checking for errors

[1367] The server receives the genetic information sent from the user terminal. After receiving it, it checks the format of the genetic information and performs an error check. The input is the genetic information received from the user terminal, and the output is the format check and error check status.

[1368] Step 3: Analysis of genetic information

[1369] The server analyzes the genetic information and extracts traits such as learning ability, athletic ability, and creativity. This analysis uses specialized analysis algorithms (e.g., implemented in Python). The input is error-checked genetic information, and the output is the extracted trait data.

[1370] Step 4: Characterization and profile generation

[1371] The server evaluates the child's characteristics based on the extracted characteristic data and generates a profile. For example, if the child has high learning ability, a profile called "knowledge-seeking" is generated. The input is the characteristic data, and the output is the generated profile.

[1372] Step 5: Find an educational facility or activity

[1373] The server searches the database and selects suitable educational facilities or activities based on the assessed characteristics, using algorithms that select facilities with specific programs or activities. The input is the generated profile, and the output is a list of suitable educational facilities or activities.

[1374] Step 6: Generate and analyze prompts using a generative AI model

[1375] The server uses a generative AI model to generate prompts and analyzes them to identify facilities that fit the profile. For example, it generates a prompt such as, "Please suggest the best music school for a child with high musical talent." The input is the profile information, and the output is the analyzed specific educational facility.

[1376] Step 7: Generate and send recommendations

[1377] The server generates a list of suitable educational facilities or activities and compiles detailed information about each facility or activity. The generated list and detailed information are sent to the user's mobile information device. The input is the data of the identified educational facilities or activities, and the output is the recommendation results sent to the user's device.

[1378] Step 8: Display in the user interface

[1379] The user's mobile device displays a list of facilities and activities in an easy-to-read format and provides links to access detailed information about each facility. The input is the recommendation results received from the server, and the output is the information displayed on the device screen.

[1380] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1381] The present invention combines a system that evaluates a child's characteristics and talents based on their genetic information and recommends the most suitable educational institution based on the results with an emotion engine that recognizes the user's emotions. The following describes an embodiment of this system.

[1382] Overall structure

[1383] The system consists of a user device, a server, a database, and an emotion engine. The user device is used by parents and educators, and the server provides data analysis and recommendation functions. The database stores information about educational institutions, and the emotion engine recognizes users' emotions and influences the display of recommendation results.

[1384] Collection and transmission of genetic information

[1385] Users use a genetic analysis kit to obtain their child's genetic information, which is then sent to a server via a dedicated application or web portal.

[1386] Receiving and analyzing data

[1387] The server receives the genetic information transmitted from the user terminal.

[1388] Data reception: The server checks the format of the received genetic data and verifies the data integrity.

[1389] Data analysis: Specialized analysis algorithms extract specific traits such as learning ability, athletic ability, and creativity from genetic information.

[1390] Evaluation and Profile Generation

[1391] The server evaluates the child's characteristics based on the analysis results.

[1392] Characteristic evaluation: Generate scores and comments for each characteristic based on the extracted characteristic data.

[1393] Profile generation: Create specific profiles such as "knowledge-seeking" for those with high learning ability, or "sports" for those with high athletic ability.

[1394] Find an educational institution

[1395] The server searches the database and selects the most suitable educational institution based on the evaluated characteristics.

[1396] Database search: Select facilities that fit your characteristics based on information such as the educational institution's program content, features, and location.

[1397] Matching: Match the child's profile with the characteristics of the educational institution to extract the most suitable facility.

[1398] Generating and sending recommendation results

[1399] The server generates a list of suitable educational institutions and compiles details for each facility.

[1400] List Creation: Create a list of recommended facilities and add details about each facility.

[1401] Data transmission: The generated list and detailed information are sent to the user's terminal.

[1402] User Interface and Emotion Engine

[1403] The terminal displays the received recommendation results to the user.

[1404] List View: Displays a list of facilities in an easy-to-read format, with links to access more information about each facility.

[1405] Emotion recognition: The device uses its built-in camera and microphone to transmit the user's voice and facial expression data to the emotion engine.

[1406] Emotion engine processing

[1407] The server uses an emotion engine to recognize the user's emotional state and adjust the display of recommendation results.

[1408] Emotion Analysis: The emotion engine analyzes the user's voice and facial expression data to identify the user's emotional state.

[1409] Display adjustment: Change the priority of recommendation results and adjust the display method depending on emotional state.

[1410] Feedback: Analyze users' emotional feedback in real time and reflect it in the next recommendation results.

[1411] Specific examples

[1412] For example, suppose a user sends their child's genetic information to a server. The server analyzes the information and evaluates the child's musical ability. Next, the server searches a database for nurseries and schools specializing in music education and creates a list of the most suitable facilities. Finally, the server sends the results to the user's device, where the user can view the list. Furthermore, the emotion engine adjusts the recommendation results based on user feedback, providing information in a form that best suits the user's emotional state. The user can then select a facility from the list and make an inquiry or application.

[1413] In this way, the present invention combines scientific evaluation based on genetic information with user emotion recognition to more effectively provide an educational environment that maximizes children's talents.

[1414] The processing flow will be explained below.

[1415] Step 1:

[1416] Users collect their children's genetic information and send it to a server via a dedicated application or web portal.

[1417] The user collects a saliva sample and analyzes it using a genetic analysis kit.

[1418] The user inputs the genetic data obtained as a result of the analysis into a dedicated application and transmits it to be uploaded to the server.

[1419] Step 2:

[1420] The server receives the genetic information sent from the user.

[1421] The server checks the format of the received genetic data and verifies the consistency and completeness of the data.

[1422] The server detects incomplete data or errors and notifies the user to resend if necessary.

[1423] Step 3:

[1424] The server analyzes the genetic information.

[1425] The server runs specialized genetic analysis algorithms to extract traits such as learning ability, athletic ability, and creativity.

[1426] The server compiles the analysis results and generates a score and comment for each characteristic.

[1427] Step 4:

[1428] The server evaluates the child's characteristics and generates a profile.

[1429] The server creates a comment along with a score for each characteristic based on the extracted characteristic data.

[1430] Based on the evaluation results, the server creates specific profiles such as "knowledge-seeking type" or "sports type."

[1431] Step 5:

[1432] The server searches the educational institution database.

[1433] The server conditionally searches the educational institution database based on the evaluated characteristics.

[1434] The server picks facilities that offer specific programs or activities and runs a matching algorithm.

[1435] Step 6:

[1436] The server generates a recommendation result.

[1437] The server creates a list of suitable educational institutions and adds details about each facility (location, programs, features, etc.).

[1438] The server transmits the generated list and detailed information about each facility to the user terminal.

[1439] Step 7:

[1440] The terminal displays the received recommendation results to the user.

[1441] The device displays the recommendation list in an easy-to-read format and provides links to access more information about each establishment.

[1442] Step 8:

[1443] The emotion engine recognizes the user's emotional state.

[1444] The device uses a built-in camera and microphone to collect the user's voice and facial expression data and transmits it to the emotion engine.

[1445] The emotion engine analyzes the collected data to determine the user's emotional state.

[1446] Step 9:

[1447] The server uses feedback from the emotion engine to adjust the recommendation results.

[1448] The server changes the priority of recommendations based on the user's emotional state.

[1449] The server adjusts the display method and content of the suggestions to match the user's emotions and reflects this in the next recommendation.

[1450] Step 10:

[1451] Users can further research educational institutions that interest them based on the recommendations displayed.

[1452] Users can view detailed information about each facility and make inquiries or apply for tours.

[1453] The user selects the most suitable facility and determines the educational environment for their child.

[1454] In this way, the present invention provides a system that effectively recommends educational institutions that are best suited to a child's characteristics by combining scientific evaluation based on genetic information with user emotion recognition.

[1455] Example 2

[1456] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1457] Conventional systems for selecting educational institutions for children only evaluate characteristics based on genetic information and search for educational institutions. However, it is difficult to recognize how users feel about the displayed information and provide recommendation results that reflect that. This can result in the selection of an educational institution that is optimal for the user. In particular, there is a need for a more personalized selection of educational institutions that takes user emotions into account.

[1458] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1459] In this invention, the server includes means for acquiring the child's genetic information, means for analyzing the acquired genetic information and evaluating the child's characteristics, means for searching for suitable educational institutions based on the evaluated characteristics, means for transmitting the search results to the user's terminal, and means for recognizing the user's emotions and adjusting the display of the recommendation results, thereby enabling a more personalized selection of educational institutions that takes the user's emotional state into consideration.

[1460] "Child's genetic information" means information that indicates the child's genetic characteristics, including biological data such as DNA.

[1461] "Means of acquisition" refers to tools and methods for collecting genetic information and storing it electronically as data, such as genetic analysis kits and dedicated applications.

[1462] "Means for analyzing and evaluating characteristics" refers to algorithms and analytical functions for scientifically and digitally evaluating a child's characteristics and abilities based on the acquired genetic information.

[1463] "Learning ability" refers to a child's ability to understand and absorb knowledge.

[1464] "Motoring ability" refers to a child's physical abilities, such as strength, muscle power, and sense of balance, when performing sports.

[1465] "Creativity" refers to a child's ability to come up with new ideas and methods, that is, to generate original thoughts.

[1466] "Educational institutions" refers to facilities such as schools, nurseries, cram schools, and extracurricular classes where children receive an education.

[1467] "Search means" refers to the functions and algorithms used to find and select educational institutions that suit a child's characteristics based on the information in the database.

[1468] "Means for sending" refers to the network communication functions and protocols for sending analysis results and search results to the user's terminal.

[1469] "Means for recognizing emotions" refers to devices or algorithms that analyze the user's voice and facial expression data to identify their emotional state at that time.

[1470] "Means for adjusting the display" refers to a function for changing the display method and priority of recommendation results depending on the user's emotional state.

[1471] The present invention combines a system for evaluating a child's characteristics and talents based on their genetic information, a system for recommending the most suitable educational institution, and an engine for recognizing user emotions. Specific embodiments of the present invention will be described in detail below.

[1472] Overall structure

[1473] This system consists of a user terminal, a server, a database, and an emotion engine.

[1474] The user terminals are used by parents and educators to collect genetic information, send it to a server, and display recommendation results.

[1475] The server provides data analysis and recommendation functions, analyzing genetic information, searching educational institutions, recognizing emotions, and adjusting the display of recommendation results.

[1476] The database holds information about educational institutions and is used when making recommendations.

[1477] The emotion engine recognizes the user's emotions and influences the display of recommendation results.

[1478] Collection and transmission of genetic information

[1479] Users use a genetic analysis kit to obtain their child's genetic information, which is then sent to a server via a dedicated application or web portal. The genetic analysis kit also includes tools for collecting samples such as saliva and hair.

[1480] Receiving and analyzing data

[1481] The server receives the genetic information sent from the user's device. It checks the format of the received data and verifies its integrity. If any inconsistencies or errors are detected, it notifies the user. The server then uses specialized analysis algorithms to extract characteristics such as learning ability, athletic ability, and creativity from the genetic information. The analysis is performed using machine learning models using Python and data analysis tools (e.g., TensorFlow).

[1482] Evaluation and Profile Generation

[1483] The server evaluates the child's characteristics based on the analysis results. Based on the extracted data, it calculates scores for learning ability, athletic ability, creativity, etc., and also generates comments for each characteristic. For example, "highly skilled at learning" or "well-developed athletic ability." Finally, a specific profile is generated based on these scores and comments. The profile is categorized into categories such as "knowledge-seeking type" and "sports type."

[1484] Find an educational institution

[1485] The server searches a database based on the generated profile. The database contains information about each educational institution, including their program content, features, and location. The server compares the profile with the information about the educational institution, selects facilities that fit the characteristics, and extracts the most suitable facilities based on the degree of match between the educational institution and the child's profile.

[1486] Generating and sending recommendation results

[1487] The server generates a list of suitable educational institutions, creates a list of recommended facilities, adds detailed information about each facility, and sends that information to the user's device. The file format used is JSON or XML.

[1488] User interface and emotion engine display

[1489] The device displays the received recommendation results to the user, presenting a list of facilities in an easy-to-read format and providing links to access detailed information about each facility. It also uses the built-in camera and microphone to transmit the user's voice and facial expression data to the emotion engine.

[1490] Emotion engine processing

[1491] The server uses an emotion engine to recognize the user's emotional state and adjust the display of recommendation results. The emotion engine analyzes the user's voice and facial expression data to identify their emotional state at that time. For the analysis, it uses voice recognition tools (e.g., Google Cloud Speech-to-Text) and facial expression analysis tools (e.g., Microsoft Azure Face API). Depending on the user's emotional state, it changes the priority of recommendation results and adjusts the display method. It also analyzes the user's emotional feedback in real time and reflects it in the next recommendation results.

[1492] Specific examples

[1493] For example, when a user sends their child's genetic information to a server, the server analyzes the information and evaluates the child's musical ability. Next, the server searches a database for nurseries and schools specializing in music education and creates a list of the most suitable facilities. Finally, the server sends the results to the user's device, where the user can view the list. Furthermore, the emotion engine adjusts the recommendation results based on user feedback, providing information in a form that best suits the user's emotional state. The user can then select a facility from the list and make an inquiry or application.

[1494] Prompt Sentence Examples

[1495] "Please outline a system that uses a child's genetic information to assess their musical ability and recommend educational institutions that specialize in music education. Also, explain how the results are adjusted using the user's emotional feedback."

[1496] In this way, the present invention combines scientific evaluation based on genetic information with user emotion recognition to more effectively provide an educational environment that maximizes children's talents.

[1497] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1498] Step 1:

[1499] Users collect their child's genetic information using a genetic analysis kit, which includes tools for collecting samples such as saliva and hair. They then log in to a dedicated application or web portal and upload the collected genetic information.

[1500] Input: Child's genetic information (sample data)

[1501] Output: Sending genetic information to the server

[1502] Step 2:

[1503] The server receives the genetic information sent from the user's device, checks the format of the received data, and verifies the data's integrity. If any discrepancies or errors are detected, the server notifies the user.

[1504] Input: Genetic data submitted by the user

[1505] Output: Data integrity check result, notification to user (if there are any errors)

[1506] Step 3:

[1507] The server analyzes the genetic information using specialized analysis algorithms, using Python and TensorFlow to extract characteristics such as learning ability, motor skills, and creativity.

[1508] Input: integrity-checked genetic data

[1509] Output: Trait analysis results (scores for learning ability, motor ability, and creativity)

[1510] Step 4:

[1511] The server evaluates the child's characteristics based on the analysis results, generates a score and comments, and creates a specific profile, such as "knowledge-seeking" if the child has good learning ability, or "sports-oriented" if the child has good athletic ability.

[1512] Input: Analysis result of characteristics

[1513] Output: Child characterization and profile

[1514] Step 5:

[1515] The server then searches a database based on the generated profile. The database contains information about each educational institution, including their program content, features, and location. The server matches the child's profile with the educational institution information to identify the most suitable facilities.

[1516] Input: Child profile, educational institution database

[1517] Output: A list of matching institutions

[1518] Step 6:

[1519] The server generates a list of suitable educational institutions, compiles details about each institution, and sends this information in JSON or XML format to the user's device.

[1520] Input: List of highly relevant institutions

[1521] Output: A list of institutions in JSON or XML format

[1522] Step 7:

[1523] The device displays the received recommendation results to the user, displaying a list of facilities in an easy-to-read format and providing links to each facility.

[1524] Input: List of educational institutions sent from the server

[1525] Output: A list of educational institutions displayed on the user's device

[1526] Step 8:

[1527] The device's built-in camera and microphone capture the user's voice and facial expression data and send it to the emotion engine.

[1528] Input: User's voice and facial expression data

[1529] Output: Sending data to the emotion engine

[1530] Step 9:

[1531] The server's emotion engine analyzes the user's voice and facial expression data to identify their emotional state, and adjusts the priority of recommendation results and changes the way they are displayed based on this information.

[1532] Input: Voice and facial expression data to the emotion engine

[1533] Output: Display of adjusted recommendation results

[1534] Step 10:

[1535] Users can check the recommendation results, select the most suitable educational institution, and make inquiries or applications.

[1536] Input: Adjusted recommendation results

[1537] Output: Inquiries and applications to educational institutions

[1538] (Application example 2)

[1539] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1540] The present invention relates to a system that analyzes a child's genetic information, evaluates their characteristics, and suggests the most suitable educational institution. However, current systems have the problem that they are unable to take into account the user's emotional state, which limits the acceptability of the recommendation results.

[1541] In addition, there was a problem that the recommendation results ended up being a simple information provision, and the optimal display method according to the user's emotions was not provided.

[1542] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1543] In this invention, the server includes means for acquiring the child's genetic information, means for analyzing the acquired genetic information and evaluating the child's characteristics, means for searching for suitable educational institutions based on the evaluated characteristics, means for transmitting the search results to the user's terminal, and means for recognizing the user's emotional state and adjusting the display of recommendation results, thereby making it possible to provide appropriate recommendation results according to the user's emotional state.

[1544] "Child's genetic information" refers to data about the child's genes obtained using a genetic analysis kit or the like.

[1545] "Traits" refer to factors such as a child's learning ability, athletic ability, and creativity that are extracted from the results of analyzing genetic information.

[1546] "Evaluation" is the process of analyzing genetic information and generating scores and comments for each trait.

[1547] "Educational institutions" is a general term for facilities that provide education and training to children, such as kindergartens, schools, cram schools, and sports clubs.

[1548] "Search" is the process of selecting suitable institutions from the database based on the assessed characteristics.

[1549] "User devices" are devices such as smartphones, tablets, and computers used by parents and educators.

[1550] The "emotional state" is a psychological state that is identified by analyzing the user's voice and facial expression data.

[1551] A "recommendation result" is a list of suitable educational institutions that are searched based on the evaluated characteristics.

[1552] "Display" refers to the process by which the recommendation results are visually presented on the user's device.

[1553] "Adjustment" is the process of changing the priority of recommendation results and changing the display method depending on the user's emotional state.

[1554] The present invention combines a system that evaluates a child's characteristics and talents based on their genetic information and recommends the most suitable educational institution based on the results with an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the present invention will be described below.

[1555] Overall structure

[1556] This system consists of a user device, a server, a database, and an emotion engine. The user device is used by parents and educators, and the server provides data analysis and recommendation functions. The database stores information about educational institutions, and the emotion engine recognizes the user's emotions and influences the display of recommendation results.

[1557] Collection and transmission of genetic information

[1558] Users use a genetic analysis kit to obtain their child's genetic information, which is then sent to a server via a dedicated application or web portal.

[1559] Receiving and analyzing data

[1560] The server receives the genetic information sent from the user's device, checks the format of the received genetic data, verifies the integrity of the data, and then uses specialized analysis algorithms to extract characteristics such as learning ability, athletic ability, and creativity from the genetic information.

[1561] Evaluation and Profile Generation

[1562] The server evaluates the child's characteristics based on the analysis results, generating scores and comments for each characteristic based on the extracted data, and creating specific profiles such as a "knowledge-seeking type" for children with high learning ability, or an "sports type" for children with high athletic ability.

[1563] Find an educational institution

[1564] The server searches the database and selects the most suitable educational institution based on the evaluated characteristics. It picks out facilities that suit the characteristics based on information such as the program content, features, and location of the educational institution, and matches the child's profile with the characteristics of the educational institution to select the most suitable facility.

[1565] Generating and sending recommendation results

[1566] The server generates a list of suitable educational institutions, compiles the details of each institution, and sends the list and details to the user's device.

[1567] User Interface and Emotion Engine

[1568] The user device displays the received recommendation results to the user. The display method is to display a list of facilities in an easy-to-read format and provide links to access detailed information about each facility. The user device also uses its built-in camera and microphone to send the user's voice and facial expression data to the emotion engine.

[1569] Emotion engine processing

[1570] The server uses an emotion engine to recognize the user's emotional state and adjust the display of recommendation results. The emotion engine analyzes the user's voice and facial expression data to identify the user's emotional state, and changes the priority of recommendation results and adjusts the display method according to the user's emotional state. The server analyzes the user's emotional feedback in real time and reflects it in the next recommendation results.

[1571] For example, when a user sends their child's genetic information to a server, the server analyzes the information and evaluates the child's musical ability. It then searches a database for nurseries and schools specializing in music education and creates a list of the most suitable facilities. Finally, the results are sent to the user's device, where the user can view the list. Furthermore, the emotion engine adjusts the recommendation results based on user feedback, providing information in a form that best suits the user's emotional state. The user can then select a facility based on the list and make an inquiry or application.

[1572] Example prompt sentence:

[1573] "Please provide us with an algorithm that recommends the most suitable stores and products based on genetic information and shopping history. Also, please provide us with the logic for recognizing specific emotions and adjusting the priority of recommendations in real time."

[1574] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1575] Step 1:

[1576] Users use a genetic analysis kit to obtain their child's genetic information, which is then sent to a server via a dedicated application or web portal. At this stage, the input data is the child's genetic information, and the output is the data being sent to the server.

[1577] Step 2:

[1578] The server receives the genetic information sent from the user terminal. The format of the received genetic information is confirmed and the data integrity is verified. The input data here is the genetic information, and the output data is the genetic information whose integrity has been confirmed.

[1579] Step 3:

[1580] The server then uses a specialized analysis algorithm to analyze the genetic information whose integrity has been confirmed and extract characteristics such as learning ability, athletic ability, and creativity. This algorithm uses a machine learning model. The input data is the genetic information whose integrity has been confirmed, and the output data is the results of each analyzed characteristic.

[1581] Step 4:

[1582] The server evaluates the child's characteristics based on the analysis results, generates a characteristic score and comments, and then creates a profile and assigns labels such as "knowledge-seeking" or "sports-oriented." The input data here is the analyzed characteristic information, and the output data is the evaluation results and profile information.

[1583] Step 5:

[1584] The server searches the database and selects the most suitable educational institution based on the evaluated characteristics. The database contains information such as the institution's program content, features, and location, and by comparing this information, it extracts the most suitable facilities. The input data is the evaluation results and database information, and the output data is a list of suitable educational institutions.

[1585] Step 6:

[1586] The server generates a list of suitable educational institutions and compiles detailed information about each institution, which is then sent to the user's device. The list includes details about the institution's location, characteristics, admission procedures, etc. The input data is the list of suitable educational institutions and their details, and the output data is the list of recommendation results sent to the user's device.

[1587] Step 7:

[1588] The user terminal displays the recommendation results sent from the server to the user. The recommendation results are displayed as a list of facilities in an easy-to-read format, and links to detailed information about each facility are provided. The input data is the recommendation results sent from the server, and the output data is the displayed recommendation list.

[1589] Step 8:

[1590] The user terminal uses a built-in camera and microphone to transmit the user's voice and facial expression data to the emotion engine. The input data is the user's voice and facial expression data, and the output data is data transmitted to the emotion engine.

[1591] Step 9:

[1592] The server uses an emotion engine to analyze the user's emotional state and adjusts the display of recommendation results based on the results. It changes the priority of recommendation results and adjusts the display method according to the emotional state. The input data is the user's voice and facial expression data, and the output data is the adjusted recommendation display.

[1593] Step 10:

[1594] The server analyzes the user's emotional feedback in real time and reflects it in the next recommendation results. By incorporating the feedback data into the emotion engine, the next recommendation will be more adapted to the user's emotional state. It is a dynamic recommendation algorithm whose input data is the user's emotional feedback and whose output data is reflected in the next recommendation.

[1595] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1596] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1597] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1598] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1599] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1600] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1601] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1602] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1603] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1604] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1605] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1606] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1607] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1608] 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.

[1609] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1610] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1611] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1612] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1613] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1614] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1615] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1616] The following is further disclosed regarding the above embodiment.

[1617] (Claim 1)

[1618] A means of obtaining genetic information about the child;

[1619] A means for analyzing the acquired genetic information and evaluating the characteristics of the child;

[1620] A means of searching for suitable educational institutions based on the assessed characteristics;

[1621] means for transmitting search results to a user's terminal;

[1622] A system including:

[1623] (Claim 2)

[1624] 2. The system according to claim 1, wherein the genetic information analysis means includes an algorithm for extracting characteristics such as learning ability, athletic ability, and creativity.

[1625] (Claim 3)

[1626] 10. The system of claim 1, wherein the educational institution search means includes an algorithm that selects institutions that offer specific programs or activities based on evaluated characteristics.

[1627] (Claim 4)

[1628] 2. The system of claim 1, wherein the means for transmitting the search results includes a function for displaying a list of suitable educational institutions and detailed facility information on the user's terminal.

[1629] (Claim 5)

[1630] 2. The system of claim 1, wherein the means for evaluating the child's characteristics includes a function for generating a score and comments for each characteristic from genetic data.

[1631] "Example 1"

[1632] (Claim 1)

[1633] a means for obtaining the child's biological information;

[1634] A means for analyzing the acquired biological information and evaluating the characteristics of the child;

[1635] A means of searching for suitable educational institutions based on the assessed characteristics;

[1636] means for transmitting search results to a user's terminal;

[1637] A system including:

[1638] (Claim 2)

[1639] 2. The system according to claim 1, wherein the biological information analysis means includes an algorithm for extracting characteristics such as learning ability, physical ability, and creativity.

[1640] (Claim 3)

[1641] 10. The system of claim 1, wherein the educational institution search means includes an algorithm that selects facilities that offer specific educational programs or activities based on the evaluated characteristics.

[1642] "Application Example 1"

[1643] (Claim 1)

[1644] A means of obtaining genetic information about the child;

[1645] A means for analyzing the acquired genetic information and evaluating the characteristics of the child;

[1646] a means of searching for suitable educational facilities or activities based on the assessed characteristics;

[1647] means for transmitting search results to a user's mobile information terminal;

[1648] A system including:

[1649] (Claim 2)

[1650] 2. The system according to claim 1, wherein the genetic information analysis means includes an algorithm for extracting characteristics such as learning ability, athletic ability, and creativity.

[1651] (Claim 3)

[1652] 10. The system of claim 1, wherein the educational facility or activity search means includes an algorithm that selects facilities offering specific programs or activities based on the evaluated characteristics.

[1653] (Claim 4)

[1654] The system of claim 1, wherein the search means includes an algorithm that uses a generative AI model to generate a prompt sentence and analyzes the prompt sentence to identify facilities that are suitable for the characteristics.

[1655] "Example 2: Combining Emotion Engines"

[1656] (Claim 1)

[1657] A means of obtaining genetic information about the child;

[1658] A means for analyzing the acquired genetic information and evaluating the characteristics of the child;

[1659] A means of searching for suitable educational institutions based on the assessed characteristics;

[1660] means for transmitting search results to a user's terminal;

[1661] A means for recognizing a user's emotions and adjusting the display of recommendation results;

[1662] A system including:

[1663] (Claim 2)

[1664] 2. The system according to claim 1, wherein the genetic information analysis means includes an algorithm for extracting characteristics such as learning ability, athletic ability, and creativity.

[1665] (Claim 3)

[1666] 10. The system of claim 1, wherein the educational institution search means includes an algorithm that selects institutions that offer specific programs or activities based on evaluated characteristics.

[1667] (Claim 4)

[1668] 10. The system of claim 1, wherein the emotion recognition means includes a device for analyzing the user's voice and facial expression data.

[1669] "Application example 2 when combining emotion engines"

[1670] (Claim 1)

[1671] A means of obtaining genetic information about the child;

[1672] A means for analyzing the acquired genetic information and evaluating the characteristics of the child;

[1673] A means of searching for suitable educational institutions based on the assessed characteristics;

[1674] means for transmitting search results to a user's terminal;

[1675] means for recognizing a user's emotional state and adjusting the display of recommendation results;

[1676] A system including:

[1677] (Claim 2)

[1678] 2. The system according to claim 1, wherein the genetic information analysis means includes an algorithm for extracting characteristics such as learning ability, athletic ability, and creativity.

[1679] (Claim 3)

[1680] 10. The system of claim 1, wherein the educational institution search means includes an algorithm that selects institutions that offer specific programs or activities based on evaluated characteristics. [Explanation of symbols]

[1681] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of obtaining genetic information about the child; A means for analyzing the acquired genetic information and evaluating the characteristics of the child; A means of searching for suitable educational institutions based on the assessed characteristics; means for transmitting search results to a user's terminal; A system including:

2. 2. The system according to claim 1, wherein said genetic information analyzing means includes an algorithm for extracting characteristics such as learning ability, athletic ability, and creativity.

3. 10. The system of claim 1, wherein the educational institution search means includes an algorithm that selects institutions offering specific programs or activities based on evaluated characteristics.

4. 2. The system according to claim 1, wherein the means for transmitting the search results includes a function for displaying a list of suitable educational institutions and detailed facility information on the user's terminal.

5. The system according to claim 1 , wherein the means for evaluating the child's characteristics includes a function for generating a score and comments for each characteristic from genetic data.

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