system

Abstract

We provide the system. [Solution] A means for receiving basic user information and generating a profile ID for each user, Means of collecting users' everyday data, A means for analyzing collected data and generating user characteristic profiles, A means of receiving and analyzing genetic test results and adding them to the user profile, A method for using an AI model with all data to calculate compatibility with other users, A means of generating a list of optimal partner candidates based on the calculation results and notifying the user, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a conventional matching system, there is a problem that a user is deceived by a photo with an overly enhanced appearance or a self-introduction different from the facts when the user relies on self-reporting for information about other users. Also, it is difficult to find a really compatible partner based only on a compatibility judgment based on appearance or some self-reported information. Furthermore, there are limited means to actually confirm the compatibility after matching, and there is also a lack of improvement in the system based on the support at the first meeting and the subsequent relationship. As a result, there is a problem that it is difficult for a user to find a satisfactory partner based on a low-reliability compatibility measurement result.

Means for Solving the Problems

[0005] This invention solves the above problems with a system that includes means for receiving basic user information and generating a user-specific profile ID, means for collecting the user's daily data (social media activity, purchase history, travel history, etc.), means for analyzing the collected data and generating the user's characteristic profile, means for receiving and analyzing genetic test results and adding them to the user profile, means for using an AI model with all the data to calculate compatibility with other users, and means for generating a list of optimal partner candidates based on the calculation results and notifying the user. Furthermore, the system also includes means for collecting behavioral data in real time when the user actually meets with a matched partner and continuously improving the AI ​​model, as well as means for analyzing the user's behavioral data and adding it to the generated profile, thereby achieving the provision of highly compatible partner candidates and improving the overall accuracy of the system.

[0006] A "user" is a person who uses the system or a registered individual who is individually identified.

[0007] "Basic information" refers to fundamental data used to identify an individual, such as the user's name, age, gender, and email address.

[0008] A "profile ID" is an identifier generated to uniquely identify information about each user.

[0009] "Everyday data" refers to data based on users' behavior and activities in their daily lives, such as their social media activity, purchase history, and travel history.

[0010] "Data collection methods" refer to the methods, tools, and systems used to collect users' everyday data.

[0011] "Analysis" is the process of processing and analyzing collected data to extract useful information and patterns.

[0012] A "characteristic profile" is a set of information that summarizes a user's behavioral tendencies, interests, personality, etc., based on collected data.

[0013] "Genetic test results" refer to data obtained as a result of analyzing the user's genetic information.

[0014] An "AI model" is an algorithm or system that uses artificial intelligence technology to analyze data and perform compatibility calculations and predictions.

[0015] "Compatibility calculation" is a calculation that compares the characteristic profiles of multiple users and shows the degree of compatibility using numerical values ​​or evaluations.

[0016] The "Partner Candidate List" is a list of potential partners suggested to the user based on the results of a compatibility calculation.

[0017] "Notification methods" refer to methods or tools used to inform users of the results of compatibility calculations and the list of potential partners.

[0018] "Behavioral data from the first meeting" refers to data such as location information, conversation time, and behavioral patterns collected when a user meets a matched partner for the first time.

[0019] "Continuous improvement" is the process of gradually increasing the accuracy of systems and AI models using collected behavioral data.

[0020] "Analysis means" refers to methods, tools, and systems for processing and analyzing collected data and extracting information.

[0021] A "system" refers to an integrated platform that combines the various means and tools described above to provide a set of functions to the user. [Brief explanation of the drawing]

[0022] [Figure 1]It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0023] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0024] First, let's explain the terminology used in the following explanation.

[0025] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0026] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0027] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0028] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0029] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0030] [First Embodiment]

[0031] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0032] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0033] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0034] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0035] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0036] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0037] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0038] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0039] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0040] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0041] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0042] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0043] This invention relates to a system that receives basic user information, identifies each user, collects and analyzes daily data, and uses AI to suggest compatible partners. This system consists of three elements: a server, a terminal, and a user, and is implemented as follows.

[0044] 1. User Registration

[0045] Users access the system's app or website and enter basic information such as their name, age, gender, and email address on the registration page.

[0046] The terminal receives the entered information and sends it to the server.

[0047] The server stores the received information in a database and generates a profile ID for each user.

[0048] 2. Data Collection

[0049] Users link their social media accounts and consent to the sharing of purchase and travel history data.

[0050] The device periodically collects social media activity (tweets, likes, follows, etc.) using an API and sends it to the server.

[0051] The server analyzes the received behavioral data and generates a user characteristic profile.

[0052] 3. Genetic analysis

[0053] Users use genetic testing kits provided by partner companies, perform the tests at home, and then send the samples back.

[0054] The device receives a notification when the genetic test results are ready and uploads the results to the server.

[0055] The server receives the genetic data, processes it using specialized analysis algorithms, and adds it to the user profile.

[0056] 4. Compatibility Calculation

[0057] The server uses all collected data (basic information, behavioral data, and genetic data) to analyze user characteristics with an AI model.

[0058] The server calculates compatibility scores with other registered users and generates a list of optimal partner candidates.

[0059] The server sends the generated list of candidates to the terminal.

[0060] The device displays a list of potential partners who are a good match for the user.

[0061] As a concrete example, let's consider the case where user A uses the system.

[0062] 1. User Registration

[0063] User A accesses the system's app from their smartphone and enters their name, age, gender, and email address. The device sends the entered information to the server, which stores the information in a database and generates a profile ID for User A.

[0064] 2. Data Collection

[0065] User A links their social media accounts and agrees to provide purchase and movement history data. The device collects data such as User A's tweets, likes, and purchase history via an API and sends it to the server. The server analyzes this data and generates a characteristic profile of User A.

[0066] 3. Genetic analysis

[0067] User A uses a genetic testing kit sent by mail and submits the sample. Once the test results are ready, the terminal uploads the results to the server. The server processes the genetic data using an analysis algorithm and adds it to User A's profile.

[0068] 4. Compatibility Calculation

[0069] The server uses an AI model to analyze user A's characteristics based on their basic information, behavioral data, and genetic data. The server then compares user A to other users, calculates a compatibility score, and generates a list of optimal partner candidates. This list is then sent to user A via their device.

[0070] This program is designed to help users find truly compatible partners. When User A meets with a displayed potential partner, the device collects behavioral data in real time and sends it to the server. The server uses this data to continuously improve the AI ​​model, providing more accurate compatibility diagnoses. In this way, the system not only improves overall accuracy but also provides comprehensive support to help User A find the best partner.

[0071] The following describes the processing flow.

[0072] Step 1:

[0073] Users access the system's app or website and enter basic information such as their name, age, gender, and email address.

[0074] Step 2:

[0075] The terminal sends the entered basic information to the server.

[0076] Step 3:

[0077] The server stores the received basic information in a database and generates a profile ID for each user.

[0078] Step 4:

[0079] Users link their social media accounts and consent to the sharing of purchase and travel history data.

[0080] Step 5:

[0081] The device periodically collects social media activity (tweets, likes, follows, etc.) using an API and sends it to the server.

[0082] Step 6:

[0083] The server analyzes the received behavioral data and generates a user characteristic profile.

[0084] Step 7:

[0085] Users use genetic testing kits provided by partner companies, perform the tests at home, and then send the samples back.

[0086] Step 8:

[0087] The device receives a notification when the genetic test results are ready and uploads the results to the server.

[0088] Step 9:

[0089] The server receives the genetic data, processes it using specialized analysis algorithms, and adds it to the user profile.

[0090] Step 10:

[0091] The server uses an AI model to analyze characteristics based on the collected basic information, behavioral data, and genetic data.

[0092] Step 11:

[0093] The server calculates compatibility scores with other registered users and generates a list of optimal partner candidates.

[0094] Step 12:

[0095] The server sends the generated list of candidates to the terminal.

[0096] Step 13:

[0097] The device displays a list of potential partners who are a good match for the user.

[0098] Step 14:

[0099] Users select someone they are interested in from the displayed list of candidates and view their details.

[0100] Step 15:

[0101] The device requests detailed information about the partner candidate selected by the user from the server.

[0102] Step 16:

[0103] Based on the request, the server sends detailed information about the candidate to the terminal.

[0104] Step 17:

[0105] The device displays detailed information about the candidate (name, age, interests, etc.) to the user.

[0106] Step 18:

[0107] Users exchange messages with their chosen potential partners and arrange to meet in person.

[0108] Step 19:

[0109] The device records the date, time, and location of the scheduled meeting and notifies the user with a reminder.

[0110] Step 20:

[0111] The device collects user behavior data (location information, conversation time, etc.) in real time while the user is actually meeting and sends it to the server.

[0112] Step 21:

[0113] The server analyzes the collected behavioral data to evaluate the actual compatibility and relationship of the couple.

[0114] Step 22:

[0115] The server continuously improves its AI model based on collected behavioral data, thereby enhancing the accuracy of compatibility calculations.

[0116] (Example 1)

[0117] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0118] Currently, many matching systems exist, but these systems rely solely on users' basic information and preferences, making it difficult to guarantee long-term compatibility. Furthermore, few systems consider users' genetic characteristics and behavioral history when performing matching. As a result, even when a match is successful, there is a problem in that compatibility necessary for building a lasting relationship is not adequately evaluated.

[0119] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0120] In this invention, the server includes means for receiving user identification information and generating user-specific identification information; means for collecting user behavior history data; means for analyzing the collected data and generating user behavioral characteristics; means for receiving and analyzing genetic information and adding it to the user profile; means for using an AI model with all the data to calculate the optimality with other users; and means for generating an optimal candidate list based on the calculation results and notifying the user. This enables more accurate matching that takes into account the user's behavior data and genetic information.

[0121] "User identification information" refers to information that the system uses to uniquely identify a user, and includes information such as name, age, gender, and email address.

[0122] "Means for generating identification information" refers to a function that collects basic user information and automatically generates a unique identification ID based on that information.

[0123] "Behavioral history data" refers to data obtained from a user's daily activities, including social media activity, purchase history, and travel history.

[0124] "Means for collecting behavioral history data" refers to a function that automatically and periodically retrieves behavioral history from data sources authorized by the user and sends it to the server.

[0125] "Behavioral characteristics" refer to user-specific traits and patterns extracted by analyzing user behavior history data.

[0126] "Means for generating behavioral characteristics" refers to a function that analyzes collected behavioral history data to identify the user's behavioral characteristics.

[0127] "Genetic information" refers to information about the genetic characteristics obtained from the user's genetic test results.

[0128] "Means of receiving, analyzing, and adding genetic information to a user profile" refers to a function that uploads genetic information to a server, analyzes that information, and integrates it into the user's profile.

[0129] "Methods for calculating optimality using AI models" refers to a function that uses AI to quantify the compatibility between users based on collected data and calculates the optimal partner.

[0130] The "optimal candidate list" is a list that ranks the most suitable partners for the user based on compatibility scores calculated by an AI model.

[0131] The "means for generating and notifying candidates" refer to a function that creates a candidate list based on compatibility scores calculated by an AI model and notifies the user of this list.

[0132] This invention relates to a system that receives basic user information, generates user-specific identification information, collects and analyzes daily data, and uses AI to suggest compatible partners. This system consists of three elements: a server, a terminal, and a user, and specifically has the following functions.

[0133] First, the user accesses the system's app or website and enters basic information such as name, age, gender, and email address on the registration page. The device receives the entered information in real time and sends it to the server using the HTTPS protocol. On the server, the received basic information is stored in a database, and a profile ID is assigned to each user using a unique ID generation algorithm (e.g., UUID).

[0134] Next, the user links their social media accounts and consents to providing purchase and movement history data. The device uses OAuth to gain access to the user's social media accounts and periodically collects user behavior data through API endpoints, sending it to the server. The server analyzes this data using text analysis and clustering algorithms to generate a profile that identifies the user's behavioral characteristics.

[0135] Users collect a sample using a genetic testing kit mailed to them by a partner company and return it. When the test results are ready, the testing company's system sends a notification to the user's device, and the device uploads the test result file to the server. Encrypted communication is used to ensure data security during this process. The server processes the received genetic data using a dedicated analysis algorithm and adds the results to the user profile.

[0136] Using all the data (basic information, behavioral data, and genetic data), the server uses a trained generative AI model to calculate compatibility with other registered users. This process utilizes a deep learning matching network to evaluate compatibility between users. Based on the calculation results, the server generates a list of optimal partner candidates and sends it to the terminal. The terminal provides the user with an interface to visually display this list.

[0137] As a concrete example, consider the case where User A uses this system. User A accesses the system's app from their smartphone and enters basic information. The device sends the information to the server via HTTPS, and the server generates a UUID and stores it in the database. Next, User A links their social media accounts and agrees to provide purchase history and travel history data. The device collects data using OAuth and sends it to the server. The server analyzes this data and compiles User A's behavioral characteristics into a profile. User A uses a genetic testing kit and sends a sample. Once the test results are ready, the device uploads the results to the server, and the server processes the genetic data using an analysis algorithm and adds it to the profile. Finally, the server uses a generative AI model to calculate a compatibility score, generates a list of optimal partner candidates, and notifies User A of this.

[0138] An example of a prompt to input into a generative AI model is as follows:

[0139] "Create an AI model that identifies compatible partners using users' basic information, behavioral data, and genetic data. Calculate compatibility scores and generate a list of optimal partner candidates."

[0140] In this way, the system helps users find compatible partners more efficiently and achieves more accurate matching by taking into account the user's behavioral data and genetic information.

[0141] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0142] Step 1: User Registration

[0143] Users access the system's app or website and enter basic information such as their name, age, gender, and email address on the registration page.

[0144] The terminal receives the entered information in real time and sends it to the server using the HTTPS protocol. Specifically, it serializes the data in the form into JSON format and sends it.

[0145] When the server stores the received basic information in the database, it uses a unique ID generation algorithm (e.g., UUID) to assign a profile ID to each user. The input is the user's basic information, and the output is the generated profile ID.

[0146] Step 2: Data Collection

[0147] Users link their social media accounts and consent to the sharing of purchase and travel history data.

[0148] The device uses OAuth to gain access to the user's social media accounts. It collects data such as the user's tweets, likes, and follows at regular intervals via an API endpoint and sends it to the server. Specifically, it calls the API to retrieve the data and sends it to the server in JSON format.

[0149] The server analyzes the received behavioral data using text analysis, clustering algorithms, and other methods to generate a profile that analyzes the user's behavioral characteristics. Inputs include social media activity, purchase history, and travel history, while the output is a profile of behavioral characteristics.

[0150] Step 3: Genetic Analysis

[0151] Users use genetic testing kits provided by partner companies, perform the tests at home, and then send the samples back.

[0152] The device receives a notification when the genetic test results are ready and uploads the results to the server. Specifically, it calls the testing company's API to receive the results file and sends it to the server.

[0153] The server receives genetic data, processes it using a dedicated analysis algorithm, and adds the results to the user profile. The input is test result data, and the output is genetic characteristics added to the user profile.

[0154] Step 4: Compatibility Calculation

[0155] The server integrates basic information, behavioral data, and genetic data, and uses an AI model to calculate compatibility with other registered users. Specifically, it uses a deep learning model to calculate compatibility scores between each user.

[0156] The server creates a list of optimal partner candidates based on the generated compatibility scores. This candidate list is generated as the output of the AI ​​model.

[0157] The server generates a list of optimal partner candidates and sends it to the terminal. The terminal provides the user with an interface that visually displays the list.

[0158] Step 5: Real-time collection of behavioral data and improvement of the AI ​​model

[0159] The device collects real-time behavioral data from when the user actually meets with a candidate. Specifically, the device records the user's location information and behavioral patterns and sends them to the server.

[0160] The server receives this new behavioral data and uses it as feedback to continuously improve the accuracy of the AI ​​model. Real-time collected behavioral data is used as input, and an updated AI model is generated as output.

[0161] Through the processing steps described above, this system helps users efficiently find compatible partners and achieves more accurate matching by taking into account the user's behavioral data and genetic information.

[0162] (Application Example 1)

[0163] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0164] Traditional partner recommendation systems primarily calculate compatibility using only basic user information and behavioral data. However, this approach fails to accurately reflect the user's overall characteristics, resulting in inaccurate recommendations. Furthermore, the development of individually optimized content recommendation systems has been slow, leaving a lack of means to improve compatibility with entertainment content such as movies and dramas that users watch. This highlighted the need for improved user experience.

[0165] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0166] In this invention, the server includes means for receiving basic user information and generating a user-specific profile ID; means for collecting the user's daily data (such as social media activity, purchase history, and travel history); means for analyzing the collected data and generating a user characteristic profile; means for receiving, analyzing, and adding genetic test results to the user profile; means for using an AI model with all the data to calculate compatibility with other users; means for generating a list of optimal partner candidates based on the calculation results and notifying the user; and means for using digital media viewing history and social media activity data to recommend individually optimized content based on the user profile. This enables more accurate reflection of the user's overall characteristics and allows for highly accurate partner recommendations and individually optimized content recommendations.

[0167] "User basic information" refers to information that allows for individual identification of a user, such as their name, age, gender, and email address.

[0168] A "profile ID" is a unique identification code for each user, used to uniquely identify a user within the database.

[0169] "Social media activity" refers to actions that users take on social media platforms, such as tweeting, liking, following, and posting.

[0170] "Purchase history" is a record of a user's online and offline purchasing activities.

[0171] "Travel history" refers to information about places a user has visited and the routes they have traveled.

[0172] A "characteristic profile" is a profile that represents a user's personality, hobbies, and preferences, generated based on the user's behavioral data, basic information, and genetic data.

[0173] "Genetic test results" refer to genetic characteristics and health information obtained by analyzing genetic material collected from the user.

[0174] An "AI model" is an artificial intelligence algorithm that learns from a large amount of data, finds patterns and relationships, and then makes predictions and judgments.

[0175] "Calculating compatibility" means using an AI model to compare a user's characteristic profile with the characteristic profiles of other users and derive a compatibility score based on similarities and differences.

[0176] The "Partner Candidate List" is a list of the most suitable partner candidates determined by compatibility calculations.

[0177] "Digital media viewing history" refers to a record of content such as movies, dramas, and video clips that a user has watched in the past.

[0178] "Personalized content" refers to recommending content that is most likely to interest a user based on their characteristic profile.

[0179] To implement this invention, a system is required that includes three elements: a user, a terminal, and a server. This system starts with receiving the user's basic information and covers the collection and analysis of various data, compatibility calculations using an AI model, and finally the generation of a list of potential partners and the recommendation of individually optimized content.

[0180] 1. User Registration

[0181] Users access the system's app using a device (e.g., a smartphone) and enter basic information such as their name, age, gender, and email address. The device sends this information to the server, which stores the received information in a database and generates a profile ID for each user.

[0182] 2. Data Collection

[0183] Users link their social media accounts and consent to providing purchase and travel history data. The device uses an API to collect social media activity data (tweets, likes, follows, etc.), purchase history, and travel history, and sends it to the server. The server analyzes this data to generate a user profile.

[0184] 3. Genetic analysis

[0185] Users use genetic testing kits provided by partner companies, perform the test at home, and then send the sample back. When the results are ready, the device receives a notification and uploads the results to the server. The server receives the genetic data, processes it using specialized analysis algorithms, and adds it to the user profile.

[0186] 4. Compatibility Calculation

[0187] The server uses an AI model to analyze the user's characteristics based on their basic information, behavioral data, and genetic data. The server calculates compatibility scores with other users and generates a list of optimal partner candidates. Furthermore, based on the user profile, it uses data from digital media viewing history and social media activity to recommend individually optimized content. The calculation results are communicated to the user via their device.

[0188] Hardware and software to use

[0189] Hardware: Smartphone

[0190] Software: Python, TF-IDF vectorizer (scikit-learn library), API interface tools

[0191] Specific example

[0192] This illustrates how User A uses this system. User A accesses the smartphone app and enters their name, age, gender, and email address. They then link their social media accounts and agree to provide data on their purchase and travel history. User A uses a genetic testing kit and sends in a sample. The server analyzes User A's social media activity, viewing history, and genetic data to generate a trait profile. Furthermore, it uses an AI model to generate a list of optimal partner candidates and provides personalized movie and TV show recommendations.

[0193] Example of a prompt

[0194] User ID: u1, Name: Yu, Age: 30, Gender: Male, Email: yu@example.com

[0195] Social media data: action, drama, explosion

[0196] Genetic data: visual_sensitive, auditory_sensitive

[0197] This configuration allows for a detailed understanding of user characteristics, resulting in a system that enables highly accurate partner recommendations and individually optimized content recommendations.

[0198] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0199] Step 1:

[0200] User Registration

[0201] Users access the system's app using their device (smartphone) and enter basic information such as their name, age, gender, and email address. The device sends this information to the server. The server stores the received information in a database and generates a unique profile ID for each user. The input is the user's basic information, and the output is the user's profile ID. In terms of data processing, the user's basic information is integrated into a unique ID.

[0202] Step 2:

[0203] Social media links and data collection

[0204] Users link their social media accounts and consent to providing purchase and travel history data. The device uses an API to collect social media activity data and send it to a server. The server analyzes this data and generates a user profile. Inputs are social media activity, purchase history, and travel history data, while output is the analyzed profile. Data processing includes statistical analysis of collected data and profile generation.

[0205] Step 3:

[0206] Genetic analysis

[0207] Users use genetic testing kits provided by partner companies, perform the test at home, and then send the sample back. When the results are ready, the device receives a notification and uploads the results to the server. The server analyzes the genetic data, processes it using specialized analysis algorithms, and adds it to the user profile. The input is genetic data, and the output is an updated user profile. Data processing involves analyzing genetic information and integrating it into the profile.

[0208] Step 4:

[0209] Compatibility calculation and partner recommendation

[0210] The server uses an AI model to analyze the user's characteristics based on their basic information, behavioral data, and genetic data. The server calculates compatibility scores with other users and generates a list of optimal partner candidates. The calculation results are notified to the user via their terminal. The input is all user data, and the output is a list of partner candidates. Data calculations include the calculation of compatibility scores by the AI ​​model.

[0211] Step 5:

[0212] Personalized content recommendations

[0213] The server recommends personalized content based on the user profile, using data from digital media viewing history and social media activity. The calculation results are notified to the user via the terminal. The input is viewing history and social media data, and the output is a content recommendation list. Specifically, the system performs analysis of viewing history data and content recommendations based on the user's profile.

[0214] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0215] This invention aims to perform more accurate compatibility calculations by combining an emotion engine with a system that analyzes a user's basic information, behavioral data, and genetic data, and uses AI to suggest compatible partners, thereby adding the user's emotional data to their profile. This system consists of three elements: a server, a terminal, and a user, and operates as follows.

[0216] 1. User Registration

[0217] Users access the system's app or website and enter basic information such as their name, age, gender, and email address.

[0218] The terminal sends the entered basic information to the server.

[0219] The server stores the received basic information in a database and generates a profile ID for each user.

[0220] 2. Data Collection

[0221] Users link their social media accounts and consent to the sharing of purchase and travel history data.

[0222] The device periodically collects social media activity (tweets, likes, follows, etc.) using an API and sends it to the server.

[0223] The server analyzes the received behavioral data and generates a user characteristic profile.

[0224] 3. Genetic analysis

[0225] Users use genetic testing kits provided by partner companies, perform the tests at home, and then send the samples back.

[0226] The device receives a notification when the genetic test results are ready and uploads the results to the server.

[0227] The server receives the genetic data, processes it using specialized analysis algorithms, and adds it to the user profile.

[0228] 4. Collection and analysis of emotional data

[0229] Users utilize messaging and video chat features provided by the system.

[0230] The device analyzes the user's words, actions, and facial expressions, collecting emotional data in real time.

[0231] The server analyzes the received emotion data and adds it to the trait profile.

[0232] 5. Compatibility Calculation

[0233] The server uses all collected data (basic information, behavioral data, genetic data, and emotional data) to analyze the user's characteristics with an AI model.

[0234] The server calculates compatibility scores with other registered users and generates a list of optimal partner candidates.

[0235] The server sends the generated list of candidates to the terminal.

[0236] The device displays a list of potential partners who are a good match for the user.

[0237] As a concrete example, let's consider the case where user A uses the system.

[0238] 1. User Registration

[0239] User A accesses the system's app from their smartphone and enters their name, age, gender, and email address. The device sends the entered information to the server, which stores the information in a database and generates a profile ID for User A.

[0240] 2. Data Collection

[0241] User A links their social media accounts and agrees to provide purchase and movement history data. The device collects data such as User A's tweets, likes, and purchase history via an API and sends it to the server. The server analyzes this data and generates a characteristic profile of User A.

[0242] 3. Genetic analysis

[0243] User A uses a genetic testing kit sent by mail and submits the sample. Once the test results are ready, the terminal uploads the results to the server. The server processes the genetic data using an analysis algorithm and adds it to User A's profile.

[0244] 4. Collection and analysis of emotional data

[0245] While User A is using the messaging and video chat functions within the system, the device analyzes User A's facial expressions and behavior, collecting emotional data. The server analyzes the received emotional data and adds it to a trait profile.

[0246] 5. Compatibility Calculation

[0247] The server uses an AI model to analyze user A's characteristics based on their basic information, behavioral data, genetic data, and emotional data. The server then compares user A to other users, calculates a compatibility score, and generates a list of optimal partner candidates. This list is then communicated to user A via their device.

[0248] This program is designed to help users find truly compatible partners. When User A meets with a displayed partner candidate, the device collects behavioral and emotional data in real time and sends it to the server. The server continuously improves the AI ​​model based on this data, providing more accurate compatibility diagnoses. In this way, the system not only improves the overall accuracy but also provides comprehensive support for User A to find the best partner.

[0249] The following describes the processing flow.

[0250] Step 1:

[0251] Users access the system's app or website and enter basic information such as their name, age, gender, and email address.

[0252] Step 2:

[0253] The terminal sends the entered basic information to the server.

[0254] Step 3:

[0255] The server stores the received basic information in a database and generates a profile ID for each user.

[0256] Step 4:

[0257] Users link their social media accounts and consent to the sharing of purchase and travel history data.

[0258] Step 5:

[0259] The device periodically collects social media activity (tweets, likes, follows, etc.) using an API and sends it to the server.

[0260] Step 6:

[0261] The server analyzes the received behavioral data and generates a user characteristic profile.

[0262] Step 7:

[0263] Users use genetic testing kits provided by partner companies, perform the tests at home, and then send the samples back.

[0264] Step 8:

[0265] The device receives a notification when the genetic test results are ready and uploads the results to the server.

[0266] Step 9:

[0267] The server receives the genetic data, processes it using specialized analysis algorithms, and adds it to the user profile.

[0268] Step 10:

[0269] Users utilize messaging and video chat features provided by the system.

[0270] Step 11:

[0271] The device analyzes the user's facial expressions and behavior to recognize their emotions and collects that data.

[0272] Step 12:

[0273] The server adds the received emotional data to the profile, generating a more detailed trait profile.

[0274] Step 13:

[0275] The server analyzes all collected data (basic information, behavioral data, genetic data, and emotional data) and uses an AI model to evaluate the user's characteristics.

[0276] Step 14:

[0277] The server calculates compatibility scores with other registered users and generates a list of optimal partner candidates.

[0278] Step 15:

[0279] The server sends the generated list of candidates to the terminal.

[0280] Step 16:

[0281] The device displays a list of potential partners who are a good match for the user.

[0282] Step 17:

[0283] Users select someone they are interested in from the displayed list of candidates and view their details.

[0284] Step 18:

[0285] The terminal requests the server for the detailed information of the partner candidate selected by the user.

[0286] Step 19:

[0287] Based on the request, the server sends the detailed information of the candidate to the terminal.

[0288] Step 20:

[0289] The terminal displays the detailed information of the candidate (name, age, interests, etc.) to the user.

[0290] Step 21:

[0291] The user exchanges messages with the selected partner candidate and makes an appointment to actually meet.

[0292] Step 22:

[0293] The terminal records the date and time and location of the planned meeting and notifies the user of the reminder. <00009​​​​​​​​​​​​​​​​​​​​​​

[0300] (Example 2)

[0301] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0302] Traditional matching systems often calculate compatibility based only on basic user information and daily behavioral data, failing to consider deeper data such as the user's emotional state or genetic information, thus limiting the accuracy of compatibility calculations. Furthermore, there is a lack of mechanisms to continuously improve the system's accuracy using data on the user's behavior and emotions when they actually meet their matched partner, resulting in insufficient improvement of the user experience.

[0303] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0304] In this invention, the server includes means for receiving basic user information and generating a profile ID for each user; means for collecting the user's daily information (social media activity, purchase history, travel history, etc.); means for analyzing the collected information and generating a user characteristic profile; means for receiving, analyzing, and adding genetic test results to the user profile; means for collecting emotional data from the user's behavior and facial expressions and adding it to the profile; means for using a generated AI model with all the data to calculate compatibility with other users; and means for generating a list of optimal partner candidates based on the calculation results and notifying the user. This enables comprehensive analysis of diverse user data and more accurate compatibility calculations. Furthermore, by utilizing actual behavior and emotional data after matching, the accuracy of the entire system can be continuously improved, leading to an enhanced user experience.

[0305] "Basic information" refers to identifying information such as the user's name, age, gender, and email address.

[0306] "Daily information" refers to behavioral data in daily life, such as a user's social media activities, purchase history, and movement history.

[0307] "Characteristic profile" refers to a profile of a user's interests, behavioral characteristics, personality, etc. analyzed based on the collected information.

[0308] "Gene test results" refer to the results of genetic information obtained by a user through a gene test.

[0309] "Emotion data" refers to data on the emotional state obtained from a user's behavior and expressions.

[0310] "Generative AI model" refers to an artificial intelligence model for analyzing a user's characteristics and compatibility based on the collected data.

[0311] "Compatibility score" refers to the result of quantifying the compatibility with other users based on a user's characteristic data. [[ID=,22]]

[0312] "Partner candidate list" refers to a list of optimal partner candidates for a user generated based on the compatibility score.

[0313] This invention is a system that analyzes a user's basic information, daily information, genetic information, and emotion data and proposes a compatible partner using a generative AI model. This system consists of three elements: a server, a terminal, and a user.

[0314] 1. User registration

[0315] First, the user accesses the system's app or website and enters basic information such as name, age, gender, email address, etc. The terminal sends the entered basic information to the server, and the server saves the information in the database and generates a profile ID.

[0316] 2. Data collection

[0317] Users link their social media accounts and consent to providing their purchase and travel history. The device periodically collects social media activity (tweets, likes, follows, etc.) using an API and sends it to the server. The server analyzes this behavioral data to generate a user profile.

[0318] 3. Genetic analysis

[0319] Users perform genetic testing at home using a genetic testing kit provided by a partner company. Once the sample is returned, the device notifies the user that the genetic test results are ready and uploads the results to a server. The server processes the genetic data using an analysis algorithm and adds it to the user profile.

[0320] 4. Collection and analysis of emotional data

[0321] As users utilize messaging and video chat features provided by the system, their devices analyze their speech, actions, and facial expressions, collecting emotional data in real time. The server then analyzes the received emotional data and adds it to their characteristic profile.

[0322] 5. Compatibility Calculation

[0323] The server uses all collected data (basic information, behavioral data, genetic data, and emotional data) to analyze the user's characteristics with a generated AI model. The server calculates compatibility scores with other registered users and generates a list of optimal partner candidates. This list is sent to the device, which then displays a list of compatible partner candidates to the user.

[0324] Specific example

[0325] When user A uses the system, user A accesses the system's app from their smartphone and enters their name, age, gender, and email address. The device sends the entered information to the server, which stores the information in a database and generates user A's profile ID.

[0326] Next, User A links their social media accounts and agrees to provide purchase and movement history data. The device collects data such as User A's tweets, likes, and purchase history via an API and sends it to the server. The server analyzes this data and generates a profile of User A's characteristics.

[0327] Furthermore, User A uses the mailed genetic testing kit to send in a sample. Once the test results are ready, the terminal uploads the results to the server. The server processes the genetic data using an analysis algorithm and adds it to User A's profile.

[0328] Next, while User A uses the messaging and video chat functions within the system, the device analyzes User A's facial expressions and behavior, collecting emotional data. The server analyzes the received emotional data and adds it to the characteristic profile.

[0329] Ultimately, the server uses user A's basic information, behavioral data, genetic data, and emotional data to analyze their characteristics with a generative AI model. The server compares them to other users, calculates a compatibility score, and generates a list of optimal partner candidates. This list is then communicated to user A via their device.

[0330] Example of a prompt

[0331] Please analyze the user characteristics based on the following data.

[0332] 1. Basic Information: Name, Age, Gender, Email Address

[0333] 2. Behavioral data: Social media activity, purchase history, travel history

[0334] 3. Genetic data: Genetic test results

[0335] 4. Emotional Data: Facial expression analysis and results obtained through messaging and video chat functions.

[0336] This system is designed to help users find truly compatible partners. When a user meets with a displayed potential partner, the device collects behavioral and emotional data in real time and sends it to the server. The server continuously improves the generated AI model based on this data, providing more accurate compatibility diagnoses. In this way, the system not only improves the overall accuracy but also provides comprehensive support for users to find the best partner for them.

[0337] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0338] Program processing flow

[0339] Step 1: Access the user registration page.

[0340] Subject: User

[0341] Specific steps: The user opens a browser on their smartphone or PC, or a dedicated app, and accesses the system's user registration page.

[0342] Input: The user taps the app icon or enters a URL.

[0343] Output: The registration page is displayed.

[0344] Step 2: Enter basic information

[0345] Subject: User

[0346] Specific actions: The user enters their name, age, gender, and email address on the registration page.

[0347] Input: Enter your "Name," "Age," "Gender," and "Email Address" in the form.

[0348] Output: Basic information is entered.

[0349] Step 3: Submit basic information

[0350] Subject: terminal

[0351] Specific operation: The terminal sends the entered basic information to the server.

[0352] Input: Basic information entered (name, age, gender, email address).

[0353] Output: Generation of data to be sent to the server.

[0354] Step 4: Generating a Profile ID

[0355] Subject: Server

[0356] Specific operation: The server saves the received basic information to the database and generates a profile ID for each user.

[0357] Input: Basic information (name, age, gender, email address).

[0358] Data processing: Create a new entry in the database and generate a unique profile ID.

[0359] Output: Profile ID for each user.

[0360] Step 5: Link your social media accounts

[0361] Subject: User

[0362] Specific actions: Users link their social media accounts and consent to the sharing of their purchase and travel history.

[0363] Input: Enter your login information on the authentication screen.

[0364] Output: Social media accounts are linked.

[0365] Step 6: Start data collection

[0366] Subject: terminal

[0367] Specific operation: The device uses an API to periodically collect social media activity (tweets, likes, follows, etc.) and send it to the server.

[0368] Input: Send a request to the social media API.

[0369] Output: Send the acquired data to the server.

[0370] Step 7: Generating the characteristic profile

[0371] Subject: Server

[0372] Specific operation: The server analyzes the received behavioral data and generates a user characteristic profile.

[0373] Input: Behavioral data (tweets, likes, follows, etc.).

[0374] Data processing: Analyze behavioral data to extract user interests and behavioral characteristics.

[0375] Output: User profile.

[0376] Step 8: Receiving the genetic testing kit

[0377] Subject: User

[0378] Specific operation: The user receives a genetic testing kit provided by a partner company and performs the test at home.

[0379] Input: Collect a saliva sample using the test kit.

[0380] Output: Collected sample.

[0381] Step 9: Sending the sample

[0382] Subject: User

[0383] Specific action: The user places the collected sample in the designated envelope and mails it.

[0384] Input: Place the collected sample in an envelope.

[0385] Output: Sample sent by mail.

[0386] Step 10: Uploading test results

[0387] Subject: terminal

[0388] Specific operation: The device receives a notification when the test results are ready and uploads the results to the server.

[0389] Input: Test result file.

[0390] Output: Test results uploaded to the server.

[0391] Step 11: Analysis of Genetic Data

[0392] Subject: Server

[0393] Specific operation: The server receives genetic data, processes it using specialized analysis algorithms, and adds it to the user profile.

[0394] Input: Genetic data.

[0395] Data processing: Analyze genetic information and extract data based on characteristics.

[0396] Output: Genetic information added to the user profile.

[0397] Step 12: Using the messaging function

[0398] Subject: User

[0399] Specific operation: Users utilize messaging and video chat functions provided by the system.

[0400] Input: Send a message, start a video call.

[0401] Output: Send a message, start a video call.

[0402] Step 13: Collecting emotional data

[0403] Subject: terminal

[0404] Specific operation: The device analyzes the user's words, actions, and facial expressions, and collects emotional data in real time.

[0405] Input: User's facial expressions during messages or video calls.

[0406] Data processing: Facial recognition is performed to obtain emotion data.

[0407] Output: Acquired sentiment data.

[0408] Step 14: Analyzing emotional data

[0409] Subject: Server

[0410] Specific operation: The server analyzes the received emotion data and adds it to the trait profile.

[0411] Input: Sentiment data.

[0412] Data processing: Score emotional data.

[0413] Output: Sentimental information added to the trait profile.

[0414] Step 15: Analysis of all data

[0415] Subject: Server

[0416] Specific operation: The server uses all collected data (basic information, behavioral data, genetic data, emotional data) to generate an AI model that analyzes the user's characteristics.

[0417] Input: Basic information, behavioral data, genetic data, emotional data.

[0418] Data processing: Analyze data using generative AI models.

[0419] Output: Generation of characteristic profile.

[0420] Step 16: Calculate the compatibility score

[0421] Subject: Server

[0422] Specific operation: The server calculates a compatibility score with other registered users.

[0423] Input: Characteristic profile.

[0424] Data processing: Compare the characteristics of each user and calculate a compatibility score.

[0425] Output: Compatibility score.

[0426] Step 17: Generating a list of potential partners

[0427] Subject: Server

[0428] Specific operation: The server generates a list of optimal partner candidates based on the calculation results and notifies the user.

[0429] Input: Compatibility score.

[0430] Data processing: Generate a list of potential partners based on compatibility scores.

[0431] Output: List of potential partners.

[0432] Step 18: Display the list of potential partners

[0433] Subject: terminal

[0434] Specific operation: The device displays a list of compatible partner candidates to the user.

[0435] Input: List of potential partners.

[0436] Output: The list displayed in the user interface.

[0437] (Application Example 2)

[0438] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0439] Traditional matching systems calculate compatibility based on basic user information and behavioral data, but they do not take into account the user's emotional state or mood on any given day. Therefore, they may fail to respond to the user's actual emotions and immediate needs, resulting in low satisfaction. Furthermore, there is no way to suggest what kind of meal is best suited to the user at that moment, limiting the improvement of the user experience. Additionally, there is no means to immediately reflect suggested menus in delivery orders. This makes it difficult to suggest meals that take into account the user's health condition and nutritional balance.

[0440] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0441] In this invention, the server includes means for receiving basic user information and generating a user-specific profile ID; means for collecting the user's daily data (social media activity, purchase history, travel history, etc.); means for analyzing the collected data and generating a user characteristic profile; means for receiving, analyzing, and adding genetic test results to the user profile; means for collecting, analyzing, and adding the user's emotional data in real time to the user profile; means for using an AI model with all the data to calculate compatibility with other users; means for generating a list of optimal partner candidates based on the calculation results and notifying the user; means for suggesting the most suitable meal for the user at that time based on the analysis results; and means for reflecting the suggested menu in the delivery order. This makes it possible to suggest meals and place delivery orders that take into account the user's actual emotional state and instantaneous needs.

[0442] "Basic information" refers to fundamental data used to identify a user, such as their name, age, gender, and email address.

[0443] A "profile ID" is a unique identifier generated for each user, used to link and manage all of that user's data.

[0444] "Everyday data" refers to behavioral data that users generate on a daily basis, such as social media activity, purchase history, and travel history.

[0445] A "trait profile" is a digital profile that provides a detailed description of a user's characteristics, generated by analyzing their behavioral data, genetic data, and emotional data.

[0446] "Genetic test results" refer to information based on the user's genes obtained through genetic testing, including data about their health status and specific traits.

[0447] "Emotional data" refers to data that indicates the user's current emotional state, and is real-time data collected through methods such as facial expression analysis and voice analysis.

[0448] An "AI model" refers to an artificial intelligence algorithm and system that analyzes diverse data and recognizes patterns for a specific purpose.

[0449] The "Partner Candidate List" provides users with a list of suitable partner candidates based on compatibility scores calculated by an AI model.

[0450] "Meal suggestions" refer to the act of suggesting the most suitable meal for a user at a given time, based on their basic information, behavioral data, genetic data, and emotional data.

[0451] "Delivery ordering" is the process in which a user orders a meal based on a suggested menu, and that order is delivered to the user by a delivery service.

[0452] This invention relates to a system that analyzes a user's basic information, daily data, genetic data, and emotional data, and uses AI to suggest a compatible partner. Furthermore, this system can suggest the optimal meal based on the user's mood and nutritional status, and can also instantly place a delivery order.

[0453] System Configuration

[0454] This system consists of three main elements: servers, terminals, and users.

[0455] server

[0456] The server includes the following measures:

[0457] 1. User profile generation means

[0458] The server receives the user's basic information and generates a profile ID. This ensures that all user data is uniquely identified.

[0459] 2. Data Collection Methods

[0460] The server collects everyday data such as social media activity, purchase history, and travel history. This data is sent from the device via an API.

[0461] 3. Data Analysis Methods

[0462] The collected data is analyzed to generate a user profile. This profile includes genetic data and emotional data.

[0463] 4. Genetic data analysis methods

[0464] The server receives the genetic test results and adds them to the user profile.

[0465] 5. Methods for analyzing emotional data

[0466] The server analyzes the sentiment data transmitted from the terminal and adds it to the user profile. Sentiment data is collected and analyzed in real time.

[0467] 6. Compatibility Calculation Method

[0468] The server uses an AI model with all the data to calculate compatibility with other users. Furthermore, it generates a list of optimal partner candidates based on the compatibility score and notifies the device.

[0469] 7. Meal Suggestion Methods

[0470] Based on the analysis results, the server suggests the most suitable meal for the user at that time. The suggested menu is generated taking into account the user's mood and health condition.

[0471] 8. Delivery Ordering Methods

[0472] This provides a means to immediately reflect the suggested menu in delivery orders.

[0473] terminal

[0474] A device refers to a user device such as a smartphone or tablet, and plays the following main roles:

[0475] 1. Data transmission

[0476] The device sends basic user information, behavioral data, emotional data, and other data to the server.

[0477] 2. Collecting emotional data

[0478] The device analyzes the user's facial expressions and voice, collecting emotional data in real time.

[0479] 3. Menu display and ordering

[0480] The terminal displays the menu received from the server to the user and reflects the selected menu in the delivery order.

[0481] User

[0482] Users follow these steps when using the system:

[0483] 1. User registration and basic information entry

[0484] Users enter basic information through a terminal and register it in the system.

[0485] 2. Data Provision

[0486] Users link their social media accounts and consent to the provision of purchase and travel history data. They also provide genetic data using a genetic testing kit.

[0487] 3. Provision of emotional data

[0488] Users provide emotional data through their device's camera and microphone.

[0489] 4. Choosing and ordering your meal

[0490] Choose your meal from the suggested menu and complete your delivery order.

[0491] Hardware and software to use

[0492] Hardware: Smartphones, tablets, and camera-equipped devices

[0493] Software: Emotion Detection API, AI Model API, User Profile API

[0494] Examples of specific cases and prompt statements

[0495] For example, when a user launches the app before lunch, a video feed automatically starts and facial expression data is collected. If the user is feeling stressed, for instance, that emotional data is analyzed, and a meal that helps reduce stress is suggested. Furthermore, a nutritionally balanced menu is provided, taking into account past purchase history and genetic data.

[0496] Example of a prompt:

[0497] "Please suggest nutritionally balanced meal plans that users experiencing stress would prefer, taking into account their past purchase history and genetic data."

[0498] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0499] Step 1:

[0500] User Registration

[0501] Users enter basic information (name, age, gender, email address, etc.) using a terminal. This input data is sent to the terminal and then forwarded from the terminal to the server. Based on the received basic information, the server generates a profile ID for each user and stores it in a database.

[0502] Step 2:

[0503] Collection of everyday data

[0504] Users link their social media accounts using their devices and consent to providing data on their purchase and movement history. The devices use APIs to collect social media activity (e.g., tweets, likes, follows, etc.) and send it to the server. The server receives and analyzes this data to generate a user behavior profile.

[0505] Step 3:

[0506] Collection of genetic data

[0507] The user uses a mailed genetic testing kit to collect a sample, which is then sent back to the testing laboratory. Once the genetic test results are ready, the device receives them and uploads them to the server. The server receives the genetic data and adds it to the user profile using specialized analysis algorithms.

[0508] Step 4:

[0509] Collection of emotional data

[0510] Users utilize messaging and video chat functions provided within the system using their devices. The devices analyze the user's facial expressions and voice in real time, collecting emotional data. This collected emotional data is sent to a server and added to the user profile.

[0511] Step 5:

[0512] Compatibility calculation

[0513] The server uses an AI model to analyze user characteristics based on basic information, behavioral data, genetic data, and emotional data. Based on this analysis, it calculates compatibility scores with other users and generates a list of optimal partner candidates. The candidate list is sent to the device and the user is notified.

[0514] Step 6:

[0515] Meal suggestions

[0516] The server comprehensively analyzes collected basic information, behavioral data, genetic data, and emotional data, and proposes an optimal meal plan considering the user's mood and nutritional status at that time. This proposal is sent to and displayed on the device.

[0517] Step 7:

[0518] Delivery Order

[0519] The user reviews the suggested menu on the terminal and orders their selected meal for delivery. The terminal sends the selection to the server, which then works with the delivery service to process the order, and the meal is delivered to the user.

[0520] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0521] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0522] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0523] [Second Embodiment]

[0524] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0525] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0526] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0527] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0528] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0529] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0530] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0531] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0532] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0533] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0534] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0535] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0536] This invention relates to a system that receives basic user information, identifies each user, collects and analyzes daily data, and uses AI to suggest compatible partners. This system consists of three elements: a server, a terminal, and a user, and is implemented as follows.

[0537] 1. User Registration

[0538] Users access the system's app or website and enter basic information such as their name, age, gender, and email address on the registration page.

[0539] The terminal receives the entered information and sends it to the server.

[0540] The server stores the received information in a database and generates a profile ID for each user.

[0541] 2. Data Collection

[0542] Users link their social media accounts and consent to the sharing of purchase and travel history data.

[0543] The device periodically collects social media activity (tweets, likes, follows, etc.) using an API and sends it to the server.

[0544] The server analyzes the received behavioral data and generates a user characteristic profile.

[0545] 3. Genetic analysis

[0546] Users use genetic testing kits provided by partner companies, perform the tests at home, and then send the samples back.

[0547] The device receives a notification when the genetic test results are ready and uploads the results to the server.

[0548] The server receives the genetic data, processes it using specialized analysis algorithms, and adds it to the user profile.

[0549] 4. Compatibility Calculation

[0550] The server uses all collected data (basic information, behavioral data, and genetic data) to analyze user characteristics with an AI model.

[0551] The server calculates compatibility scores with other registered users and generates a list of optimal partner candidates.

[0552] The server sends the generated list of candidates to the terminal.

[0553] The device displays a list of potential partners who are a good match for the user.

[0554] As a concrete example, let's consider the case where user A uses the system.

[0555] 1. User Registration

[0556] User A accesses the system's app from their smartphone and enters their name, age, gender, and email address. The device sends the entered information to the server, which stores the information in a database and generates a profile ID for User A.

[0557] 2. Data Collection

[0558] User A links their social media accounts and agrees to provide purchase and movement history data. The device collects data such as User A's tweets, likes, and purchase history via an API and sends it to the server. The server analyzes this data and generates a characteristic profile of User A.

[0559] 3. Genetic analysis

[0560] User A uses a genetic testing kit sent by mail and submits the sample. Once the test results are ready, the terminal uploads the results to the server. The server processes the genetic data using an analysis algorithm and adds it to User A's profile.

[0561] 4. Compatibility Calculation

[0562] The server uses an AI model to analyze user A's characteristics based on their basic information, behavioral data, and genetic data. The server then compares user A to other users, calculates a compatibility score, and generates a list of optimal partner candidates. This list is then sent to user A via their device.

[0563] This program is designed to help users find truly compatible partners. When User A meets with a displayed potential partner, the device collects behavioral data in real time and sends it to the server. The server uses this data to continuously improve the AI ​​model, providing more accurate compatibility diagnoses. In this way, the system not only improves overall accuracy but also provides comprehensive support to help User A find the best partner.

[0564] The following describes the processing flow.

[0565] Step 1:

[0566] Users access the system's app or website and enter basic information such as their name, age, gender, and email address.

[0567] Step 2:

[0568] The terminal sends the entered basic information to the server.

[0569] Step 3:

[0570] The server stores the received basic information in a database and generates a profile ID for each user.

[0571] Step 4:

[0572] Users link their social media accounts and consent to the sharing of purchase and travel history data.

[0573] Step 5:

[0574] The device periodically collects social media activity (tweets, likes, follows, etc.) using an API and sends it to the server.

[0575] Step 6:

[0576] The server analyzes the received behavioral data and generates a user characteristic profile.

[0577] Step 7:

[0578] Users use genetic testing kits provided by partner companies, perform the tests at home, and then send the samples back.

[0579] Step 8:

[0580] The device receives a notification when the genetic test results are ready and uploads the results to the server.

[0581] Step 9:

[0582] The server receives the genetic data, processes it using specialized analysis algorithms, and adds it to the user profile.

[0583] Step 10:

[0584] The server uses an AI model to analyze characteristics based on the collected basic information, behavioral data, and genetic data.

[0585] Step 11:

[0586] The server calculates compatibility scores with other registered users and generates a list of optimal partner candidates.

[0587] Step 12:

[0588] The server sends the generated list of candidates to the terminal.

[0589] Step 13:

[0590] The device displays a list of potential partners who are a good match for the user.

[0591] Step 14:

[0592] Users select someone they are interested in from the displayed list of candidates and view their details.

[0593] Step 15:

[0594] The device requests detailed information about the partner candidate selected by the user from the server.

[0595] Step 16:

[0596] Based on the request, the server sends detailed information about the candidate to the terminal.

[0597] Step 17:

[0598] The device displays detailed information about the candidate (name, age, interests, etc.) to the user.

[0599] Step 18:

[0600] Users exchange messages with their chosen potential partners and arrange to meet in person.

[0601] Step 19:

[0602] The device records the date, time, and location of the scheduled meeting and notifies the user with a reminder.

[0603] Step 20:

[0604] The device collects user behavior data (location information, conversation time, etc.) in real time while the user is actually meeting and sends it to the server.

[0605] Step 21:

[0606] The server analyzes the collected behavioral data to evaluate the actual compatibility and relationship of the couple.

[0607] Step 22:

[0608] The server continuously improves its AI model based on collected behavioral data, thereby enhancing the accuracy of compatibility calculations.

[0609] (Example 1)

[0610] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0611] Currently, many matching systems exist, but these systems rely solely on users' basic information and preferences, making it difficult to guarantee long-term compatibility. Furthermore, few systems consider users' genetic characteristics and behavioral history when performing matching. As a result, even when a match is successful, there is a problem in that compatibility necessary for building a lasting relationship is not adequately evaluated.

[0612] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0613] In this invention, the server includes means for receiving user identification information and generating user-specific identification information; means for collecting user behavior history data; means for analyzing the collected data and generating user behavioral characteristics; means for receiving and analyzing genetic information and adding it to the user profile; means for using an AI model with all the data to calculate the optimality with other users; and means for generating an optimal candidate list based on the calculation results and notifying the user. This enables more accurate matching that takes into account the user's behavior data and genetic information.

[0614] "User identification information" refers to information that the system uses to uniquely identify a user, and includes information such as name, age, gender, and email address.

[0615] "Means for generating identification information" refers to a function that collects basic user information and automatically generates a unique identification ID based on that information.

[0616] "Behavioral history data" refers to data obtained from a user's daily activities, including social media activity, purchase history, and travel history.

[0617] "Means for collecting behavioral history data" refers to a function that automatically and periodically retrieves behavioral history from data sources authorized by the user and sends it to the server.

[0618] "Behavioral characteristics" refer to user-specific traits and patterns extracted by analyzing user behavior history data.

[0619] "Means for generating behavioral characteristics" refers to a function that analyzes collected behavioral history data to identify the user's behavioral characteristics.

[0620] "Genetic information" refers to information about the genetic characteristics obtained from the user's genetic test results.

[0621] "Means of receiving, analyzing, and adding genetic information to a user profile" refers to a function that uploads genetic information to a server, analyzes that information, and integrates it into the user's profile.

[0622] "Methods for calculating optimality using AI models" refers to a function that uses AI to quantify the compatibility between users based on collected data and calculates the optimal partner.

[0623] The "optimal candidate list" is a list that ranks the most suitable partners for the user based on compatibility scores calculated by an AI model.

[0624] The "means for generating and notifying candidates" refer to a function that creates a candidate list based on compatibility scores calculated by an AI model and notifies the user of this list.

[0625] This invention relates to a system that receives basic user information, generates user-specific identification information, collects and analyzes daily data, and uses AI to suggest compatible partners. This system consists of three elements: a server, a terminal, and a user, and specifically has the following functions.

[0626] First, the user accesses the system's app or website and enters basic information such as name, age, gender, and email address on the registration page. The device receives the entered information in real time and sends it to the server using the HTTPS protocol. On the server, the received basic information is stored in a database, and a profile ID is assigned to each user using a unique ID generation algorithm (e.g., UUID).

[0627] Next, the user links their social media accounts and consents to providing purchase and movement history data. The device uses OAuth to gain access to the user's social media accounts and periodically collects user behavior data through API endpoints, sending it to the server. The server analyzes this data using text analysis and clustering algorithms to generate a profile that identifies the user's behavioral characteristics.

[0628] Users collect a sample using a genetic testing kit mailed to them by a partner company and return it. When the test results are ready, the testing company's system sends a notification to the user's device, and the device uploads the test result file to the server. Encrypted communication is used to ensure data security during this process. The server processes the received genetic data using a dedicated analysis algorithm and adds the results to the user profile.

[0629] Using all the data (basic information, behavioral data, and genetic data), the server uses a trained generative AI model to calculate compatibility with other registered users. This process utilizes a deep learning matching network to evaluate compatibility between users. Based on the calculation results, the server generates a list of optimal partner candidates and sends it to the terminal. The terminal provides the user with an interface to visually display this list.

[0630] As a concrete example, consider the case where User A uses this system. User A accesses the system's app from their smartphone and enters basic information. The device sends the information to the server via HTTPS, and the server generates a UUID and stores it in the database. Next, User A links their social media accounts and agrees to provide purchase history and travel history data. The device collects data using OAuth and sends it to the server. The server analyzes this data and compiles User A's behavioral characteristics into a profile. User A uses a genetic testing kit and sends a sample. Once the test results are ready, the device uploads the results to the server, and the server processes the genetic data using an analysis algorithm and adds it to the profile. Finally, the server uses a generative AI model to calculate a compatibility score, generates a list of optimal partner candidates, and notifies User A of this.

[0631] An example of a prompt to input into a generative AI model is as follows:

[0632] "Create an AI model that identifies compatible partners using users' basic information, behavioral data, and genetic data. Calculate compatibility scores and generate a list of optimal partner candidates."

[0633] In this way, the system helps users find compatible partners more efficiently and achieves more accurate matching by taking into account the user's behavioral data and genetic information.

[0634] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0635] Step 1: User Registration

[0636] Users access the system's app or website and enter basic information such as their name, age, gender, and email address on the registration page.

[0637] The terminal receives the entered information in real time and sends it to the server using the HTTPS protocol. Specifically, it serializes the data in the form into JSON format and sends it.

[0638] When the server stores the received basic information in the database, it uses a unique ID generation algorithm (e.g., UUID) to assign a profile ID to each user. The input is the user's basic information, and the output is the generated profile ID.

[0639] Step 2: Data Collection

[0640] Users link their social media accounts and consent to the sharing of purchase and travel history data.

[0641] The device uses OAuth to gain access to the user's social media accounts. It collects data such as the user's tweets, likes, and follows at regular intervals via an API endpoint and sends it to the server. Specifically, it calls the API to retrieve the data and sends it to the server in JSON format.

[0642] The server analyzes the received behavioral data using text analysis, clustering algorithms, and other methods to generate a profile that analyzes the user's behavioral characteristics. Inputs include social media activity, purchase history, and travel history, while the output is a profile of behavioral characteristics.

[0643] Step 3: Genetic Analysis

[0644] Users use genetic testing kits provided by partner companies, perform the tests at home, and then send the samples back.

[0645] The device receives a notification when the genetic test results are ready and uploads the results to the server. Specifically, it calls the testing company's API to receive the results file and sends it to the server.

[0646] The server receives genetic data, processes it using a dedicated analysis algorithm, and adds the results to the user profile. The input is test result data, and the output is genetic characteristics added to the user profile.

[0647] Step 4: Compatibility Calculation

[0648] The server integrates basic information, behavioral data, and genetic data, and uses an AI model to calculate compatibility with other registered users. Specifically, it uses a deep learning model to calculate compatibility scores between each user.

[0649] The server creates a list of optimal partner candidates based on the generated compatibility scores. This candidate list is generated as the output of the AI ​​model.

[0650] The server generates a list of optimal partner candidates and sends it to the terminal. The terminal provides the user with an interface that visually displays the list.

[0651] Step 5: Real-time collection of behavioral data and improvement of the AI ​​model

[0652] The device collects real-time behavioral data from when the user actually meets with a candidate. Specifically, the device records the user's location information and behavioral patterns and sends them to the server.

[0653] The server receives this new behavioral data and uses it as feedback to continuously improve the accuracy of the AI ​​model. Real-time collected behavioral data is used as input, and an updated AI model is generated as output.

[0654] Through the processing steps described above, this system helps users efficiently find compatible partners and achieves more accurate matching by taking into account the user's behavioral data and genetic information.

[0655] (Application Example 1)

[0656] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0657] Traditional partner recommendation systems primarily calculate compatibility using only basic user information and behavioral data. However, this approach fails to accurately reflect the user's overall characteristics, resulting in inaccurate recommendations. Furthermore, the development of individually optimized content recommendation systems has been slow, leaving a lack of means to improve compatibility with entertainment content such as movies and dramas that users watch. This highlighted the need for improved user experience.

[0658] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0659] In this invention, the server includes means for receiving basic user information and generating a user-specific profile ID; means for collecting the user's daily data (such as social media activity, purchase history, and travel history); means for analyzing the collected data and generating a user characteristic profile; means for receiving, analyzing, and adding genetic test results to the user profile; means for using an AI model with all the data to calculate compatibility with other users; means for generating a list of optimal partner candidates based on the calculation results and notifying the user; and means for using digital media viewing history and social media activity data to recommend individually optimized content based on the user profile. This enables more accurate reflection of the user's overall characteristics and allows for highly accurate partner recommendations and individually optimized content recommendations.

[0660] "User basic information" refers to information that allows for individual identification of a user, such as their name, age, gender, and email address.

[0661] A "profile ID" is a unique identification code for each user, used to uniquely identify a user within the database.

[0662] "Social media activity" refers to actions that users take on social media platforms, such as tweeting, liking, following, and posting.

[0663] "Purchase history" is a record of a user's online and offline purchasing activities.

[0664] "Travel history" refers to information about places a user has visited and the routes they have traveled.

[0665] A "characteristic profile" is a profile that represents a user's personality, hobbies, and preferences, generated based on the user's behavioral data, basic information, and genetic data.

[0666] "Genetic test results" refer to genetic characteristics and health information obtained by analyzing genetic material collected from the user.

[0667] An "AI model" is an artificial intelligence algorithm that learns from a large amount of data, finds patterns and relationships, and then makes predictions and judgments.

[0668] "Calculating compatibility" means using an AI model to compare a user's characteristic profile with the characteristic profiles of other users and derive a compatibility score based on similarities and differences.

[0669] The "Partner Candidate List" is a list of the most suitable partner candidates determined by compatibility calculations.

[0670] "Digital media viewing history" refers to a record of content such as movies, dramas, and video clips that a user has watched in the past.

[0671] "Personalized content" refers to recommending content that is most likely to interest a user based on their characteristic profile.

[0672] To implement this invention, a system is required that includes three elements: a user, a terminal, and a server. This system starts with receiving the user's basic information and covers the collection and analysis of various data, compatibility calculations using an AI model, and finally the generation of a list of potential partners and the recommendation of individually optimized content.

[0673] 1. User Registration

[0674] Users access the system's app using a device (e.g., a smartphone) and enter basic information such as their name, age, gender, and email address. The device sends this information to the server, which stores the received information in a database and generates a profile ID for each user.

[0675] 2. Data Collection

[0676] Users link their social media accounts and consent to providing purchase and travel history data. The device uses an API to collect social media activity data (tweets, likes, follows, etc.), purchase history, and travel history, and sends it to the server. The server analyzes this data to generate a user profile.

[0677] 3. Genetic analysis

[0678] Users use genetic testing kits provided by partner companies, perform the test at home, and then send the sample back. When the results are ready, the device receives a notification and uploads the results to the server. The server receives the genetic data, processes it using specialized analysis algorithms, and adds it to the user profile.

[0679] 4. Compatibility Calculation

[0680] The server uses an AI model to analyze the user's characteristics based on their basic information, behavioral data, and genetic data. The server calculates compatibility scores with other users and generates a list of optimal partner candidates. Furthermore, based on the user profile, it uses data from digital media viewing history and social media activity to recommend individually optimized content. The calculation results are communicated to the user via their device.

[0681] Hardware and software to use

[0682] Hardware: Smartphone

[0683] Software: Python, TF-IDF vectorizer (scikit-learn library), API interface tools

[0684] Specific example

[0685] This illustrates how User A uses this system. User A accesses the smartphone app and enters their name, age, gender, and email address. They then link their social media accounts and agree to provide data on their purchase and travel history. User A uses a genetic testing kit and sends in a sample. The server analyzes User A's social media activity, viewing history, and genetic data to generate a trait profile. Furthermore, it uses an AI model to generate a list of optimal partner candidates and provides personalized movie and TV show recommendations.

[0686] Example of a prompt

[0687] User ID: u1, Name: Yu, Age: 30, Gender: Male, Email: yu@example.com

[0688] Social media data: action, drama, explosion

[0689] Genetic data: visual_sensitive, auditory_sensitive

[0690] This configuration allows for a detailed understanding of user characteristics, resulting in a system that enables highly accurate partner recommendations and individually optimized content recommendations.

[0691] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0692] Step 1:

[0693] User Registration

[0694] Users access the system's app using their device (smartphone) and enter basic information such as their name, age, gender, and email address. The device sends this information to the server. The server stores the received information in a database and generates a unique profile ID for each user. The input is the user's basic information, and the output is the user's profile ID. In terms of data processing, the user's basic information is integrated into a unique ID.

[0695] Step 2:

[0696] Social media links and data collection

[0697] Users link their social media accounts and consent to providing purchase and travel history data. The device uses an API to collect social media activity data and send it to a server. The server analyzes this data and generates a user profile. Inputs are social media activity, purchase history, and travel history data, while output is the analyzed profile. Data processing includes statistical analysis of collected data and profile generation.

[0698] Step 3:

[0699] Genetic analysis

[0700] Users use genetic testing kits provided by partner companies, perform the test at home, and then send the sample back. When the results are ready, the device receives a notification and uploads the results to the server. The server analyzes the genetic data, processes it using specialized analysis algorithms, and adds it to the user profile. The input is genetic data, and the output is an updated user profile. Data processing involves analyzing genetic information and integrating it into the profile.

[0701] Step 4:

[0702] Compatibility calculation and partner recommendation

[0703] The server uses an AI model to analyze the user's characteristics based on their basic information, behavioral data, and genetic data. The server calculates compatibility scores with other users and generates a list of optimal partner candidates. The calculation results are notified to the user via their terminal. The input is all user data, and the output is a list of partner candidates. Data calculations include the calculation of compatibility scores by the AI ​​model.

[0704] Step 5:

[0705] Personalized content recommendations

[0706] The server recommends personalized content based on the user profile, using data from digital media viewing history and social media activity. The calculation results are notified to the user via the terminal. The input is viewing history and social media data, and the output is a content recommendation list. Specifically, the system performs analysis of viewing history data and content recommendations based on the user's profile.

[0707] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0708] This invention aims to perform more accurate compatibility calculations by combining an emotion engine with a system that analyzes a user's basic information, behavioral data, and genetic data, and uses AI to suggest compatible partners, thereby adding the user's emotional data to their profile. This system consists of three elements: a server, a terminal, and a user, and operates as follows.

[0709] 1. User Registration

[0710] Users access the system's app or website and enter basic information such as their name, age, gender, and email address.

[0711] The terminal sends the entered basic information to the server.

[0712] The server stores the received basic information in a database and generates a profile ID for each user.

[0713] 2. Data Collection

[0714] Users link their social media accounts and consent to the sharing of purchase and travel history data.

[0715] The device periodically collects social media activity (tweets, likes, follows, etc.) using an API and sends it to the server.

[0716] The server analyzes the received behavioral data and generates a user characteristic profile.

[0717] 3. Genetic analysis

[0718] Users use genetic testing kits provided by partner companies, perform the tests at home, and then send the samples back.

[0719] The device receives a notification when the genetic test results are ready and uploads the results to the server.

[0720] The server receives the genetic data, processes it using specialized analysis algorithms, and adds it to the user profile.

[0721] 4. Collection and analysis of emotional data

[0722] Users utilize messaging and video chat features provided by the system.

[0723] The device analyzes the user's words, actions, and facial expressions, collecting emotional data in real time.

[0724] The server analyzes the received emotion data and adds it to the trait profile.

[0725] 5. Compatibility Calculation

[0726] The server uses all collected data (basic information, behavioral data, genetic data, and emotional data) to analyze the user's characteristics with an AI model.

[0727] The server calculates compatibility scores with other registered users and generates a list of optimal partner candidates.

[0728] The server sends the generated list of candidates to the terminal.

[0729] The device displays a list of potential partners who are a good match for the user.

[0730] As a concrete example, let's consider the case where user A uses the system.

[0731] 1. User Registration

[0732] User A accesses the system's app from their smartphone and enters their name, age, gender, and email address. The device sends the entered information to the server, which stores the information in a database and generates a profile ID for User A.

[0733] 2. Data Collection

[0734] User A links their social media accounts and agrees to provide purchase and movement history data. The device collects data such as User A's tweets, likes, and purchase history via an API and sends it to the server. The server analyzes this data and generates a characteristic profile of User A.

[0735] 3. Genetic analysis

[0736] User A uses a genetic testing kit sent by mail and submits the sample. Once the test results are ready, the terminal uploads the results to the server. The server processes the genetic data using an analysis algorithm and adds it to User A's profile.

[0737] 4. Collection and analysis of emotional data

[0738] While User A is using the messaging and video chat functions within the system, the device analyzes User A's facial expressions and behavior, collecting emotional data. The server analyzes the received emotional data and adds it to a trait profile.

[0739] 5. Compatibility Calculation

[0740] The server uses an AI model to analyze user A's characteristics based on their basic information, behavioral data, genetic data, and emotional data. The server then compares user A to other users, calculates a compatibility score, and generates a list of optimal partner candidates. This list is then communicated to user A via their device.

[0741] This program is designed to help users find truly compatible partners. When User A meets with a displayed partner candidate, the device collects behavioral and emotional data in real time and sends it to the server. The server continuously improves the AI ​​model based on this data, providing more accurate compatibility diagnoses. In this way, the system not only improves the overall accuracy but also provides comprehensive support for User A to find the best partner.

[0742] The following describes the processing flow.

[0743] Step 1:

[0744] Users access the system's app or website and enter basic information such as their name, age, gender, and email address.

[0745] Step 2:

[0746] The terminal sends the entered basic information to the server.

[0747] Step 3:

[0748] The server stores the received basic information in a database and generates a profile ID for each user.

[0749] Step 4:

[0750] Users link their social media accounts and consent to the sharing of purchase and travel history data.

[0751] Step 5:

[0752] The device periodically collects social media activity (tweets, likes, follows, etc.) using an API and sends it to the server.

[0753] Step 6:

[0754] The server analyzes the received behavioral data and generates a user characteristic profile.

[0755] Step 7:

[0756] Users use genetic testing kits provided by partner companies, perform the tests at home, and then send the samples back.

[0757] Step 8:

[0758] The device receives a notification when the genetic test results are ready and uploads the results to the server.

[0759] Step 9:

[0760] The server receives the genetic data, processes it using specialized analysis algorithms, and adds it to the user profile.

[0761] Step 10:

[0762] Users utilize messaging and video chat features provided by the system.

[0763] Step 11:

[0764] The device analyzes the user's facial expressions and behavior to recognize their emotions and collects that data.

[0765] Step 12:

[0766] The server adds the received emotional data to the profile, generating a more detailed trait profile.

[0767] Step 13:

[0768] The server analyzes all collected data (basic information, behavioral data, genetic data, and emotional data) and uses an AI model to evaluate the user's characteristics.

[0769] Step 14:

[0770] The server calculates compatibility scores with other registered users and generates a list of optimal partner candidates.

[0771] Step 15:

[0772] The server sends the generated list of candidates to the terminal.

[0773] Step 16:

[0774] The device displays a list of potential partners who are a good match for the user.

[0775] Step 17:

[0776] Users select someone they are interested in from the displayed list of candidates and view their details.

[0777] Step 18:

[0778] The device requests detailed information about the partner candidate selected by the user from the server.

[0779] Step 19:

[0780] Based on the request, the server sends detailed information about the candidate to the terminal.

[0781] Step 20:

[0782] The device displays detailed information about the candidate (name, age, interests, etc.) to the user.

[0783] Step 21:

[0784] Users exchange messages with their chosen potential partners and arrange to meet in person.

[0785] Step 22:

[0786] The device records the date, time, and location of the scheduled meeting and notifies the user with a reminder.

[0787] Step 23:

[0788] The device collects user behavior data (location information, conversation time, etc.) and emotional data in real time while the user is actually meeting, and sends this data to the server.

[0789] Step 24:

[0790] The server analyzes the collected behavioral and emotional data to evaluate the couple's actual compatibility and relationship.

[0791] Step 25:

[0792] The server continuously improves the AI ​​model based on the collected data, thereby increasing the accuracy of compatibility calculations.

[0793] (Example 2)

[0794] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0795] Traditional matching systems often calculate compatibility based only on basic user information and daily behavioral data, failing to consider deeper data such as the user's emotional state or genetic information, thus limiting the accuracy of compatibility calculations. Furthermore, there is a lack of mechanisms to continuously improve the system's accuracy using data on the user's behavior and emotions when they actually meet their matched partner, resulting in insufficient improvement of the user experience.

[0796] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0797] In this invention, the server includes means for receiving basic user information and generating a profile ID for each user; means for collecting the user's daily information (social media activity, purchase history, travel history, etc.); means for analyzing the collected information and generating a user characteristic profile; means for receiving, analyzing, and adding genetic test results to the user profile; means for collecting emotional data from the user's behavior and facial expressions and adding it to the profile; means for using a generated AI model with all the data to calculate compatibility with other users; and means for generating a list of optimal partner candidates based on the calculation results and notifying the user. This enables comprehensive analysis of diverse user data and more accurate compatibility calculations. Furthermore, by utilizing actual behavior and emotional data after matching, the accuracy of the entire system can be continuously improved, leading to an enhanced user experience.

[0798] "Basic information" refers to identifying information such as the user's name, age, gender, and email address.

[0799] "Everyday information" refers to behavioral data from users' daily lives, such as their social media activity, purchase history, and travel history.

[0800] A "characteristic profile" refers to a profile of a user's interests, behavioral characteristics, personality, and other traits, which is analyzed based on collected information.

[0801] "Genetic test results" refers to the results of the genetic information obtained by the user through genetic testing.

[0802] "Emotional data" refers to data on the emotional state obtained from the user's behavior and facial expressions.

[0803] A "generative AI model" refers to an artificial intelligence model used to analyze user characteristics and compatibility based on collected data.

[0804] A "compatibility score" refers to the result of quantifying the compatibility between a user and other users based on the user's characteristic data.

[0805] The "Partner Candidate List" refers to a list of potential partners that are best suited to the user, generated based on compatibility scores.

[0806] This invention is a system that analyzes a user's basic information, daily information, genetic information, and emotional data, and uses a generative AI model to suggest a compatible partner. This system consists of three elements: a server, a terminal, and the user.

[0807] 1. User Registration

[0808] The user first accesses the system's app or website and enters basic information such as their name, age, gender, and email address. The device sends this information to the server, which stores it in a database and generates a profile ID.

[0809] 2. Data Collection

[0810] Users link their social media accounts and consent to providing their purchase and travel history. The device periodically collects social media activity (tweets, likes, follows, etc.) using an API and sends it to the server. The server analyzes this behavioral data to generate a user profile.

[0811] 3. Genetic analysis

[0812] Users perform genetic testing at home using a genetic testing kit provided by a partner company. Once the sample is returned, the device notifies the user that the genetic test results are ready and uploads the results to a server. The server processes the genetic data using an analysis algorithm and adds it to the user profile.

[0813] 4. Collection and analysis of emotional data

[0814] As users utilize messaging and video chat features provided by the system, their devices analyze their speech, actions, and facial expressions, collecting emotional data in real time. The server then analyzes the received emotional data and adds it to their characteristic profile.

[0815] 5. Compatibility Calculation

[0816] The server uses all collected data (basic information, behavioral data, genetic data, and emotional data) to analyze the user's characteristics with a generated AI model. The server calculates compatibility scores with other registered users and generates a list of optimal partner candidates. This list is sent to the device, which then displays a list of compatible partner candidates to the user.

[0817] Specific example

[0818] When user A uses the system, user A accesses the system's app from their smartphone and enters their name, age, gender, and email address. The device sends the entered information to the server, which stores the information in a database and generates user A's profile ID.

[0819] Next, User A links their social media accounts and agrees to provide purchase and movement history data. The device collects data such as User A's tweets, likes, and purchase history via an API and sends it to the server. The server analyzes this data and generates a profile of User A's characteristics.

[0820] Furthermore, User A uses the mailed genetic testing kit to send in a sample. Once the test results are ready, the terminal uploads the results to the server. The server processes the genetic data using an analysis algorithm and adds it to User A's profile.

[0821] Next, while User A uses the messaging and video chat functions within the system, the device analyzes User A's facial expressions and behavior, collecting emotional data. The server analyzes the received emotional data and adds it to the characteristic profile.

[0822] Ultimately, the server uses user A's basic information, behavioral data, genetic data, and emotional data to analyze their characteristics with a generative AI model. The server compares them to other users, calculates a compatibility score, and generates a list of optimal partner candidates. This list is then communicated to user A via their device.

[0823] Example of a prompt

[0824] Please analyze the user characteristics based on the following data.

[0825] 1. Basic Information: Name, Age, Gender, Email Address

[0826] 2. Behavioral data: Social media activity, purchase history, travel history

[0827] 3. Genetic data: Genetic test results

[0828] 4. Emotional Data: Facial expression analysis and results obtained through messaging and video chat functions.

[0829] This system is designed to help users find truly compatible partners. When a user meets with a displayed potential partner, the device collects behavioral and emotional data in real time and sends it to the server. The server continuously improves the generated AI model based on this data, providing more accurate compatibility diagnoses. In this way, the system not only improves the overall accuracy but also provides comprehensive support for users to find the best partner for them.

[0830] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0831] Program processing flow

[0832] Step 1: Access the user registration page.

[0833] Subject: User

[0834] Specific steps: The user opens a browser on their smartphone or PC, or a dedicated app, and accesses the system's user registration page.

[0835] Input: The user taps the app icon or enters a URL.

[0836] Output: The registration page is displayed.

[0837] Step 2: Enter basic information

[0838] Subject: User

[0839] Specific actions: The user enters their name, age, gender, and email address on the registration page.

[0840] Input: Enter your "Name," "Age," "Gender," and "Email Address" in the form.

[0841] Output: Basic information is entered.

[0842] Step 3: Submit basic information

[0843] Subject: terminal

[0844] Specific operation: The terminal sends the entered basic information to the server.

[0845] Input: Basic information entered (name, age, gender, email address).

[0846] Output: Generation of data to be sent to the server.

[0847] Step 4: Generating a Profile ID

[0848] Subject: Server

[0849] Specific operation: The server saves the received basic information to the database and generates a profile ID for each user.

[0850] Input: Basic information (name, age, gender, email address).

[0851] Data processing: Create a new entry in the database and generate a unique profile ID.

[0852] Output: Profile ID for each user.

[0853] Step 5: Link your social media accounts

[0854] Subject: User

[0855] Specific actions: Users link their social media accounts and consent to the sharing of their purchase and travel history.

[0856] Input: Enter your login information on the authentication screen.

[0857] Output: Social media accounts are linked.

[0858] Step 6: Start data collection

[0859] Subject: terminal

[0860] Specific operation: The device uses an API to periodically collect social media activity (tweets, likes, follows, etc.) and send it to the server.

[0861] Input: Send a request to the social media API.

[0862] Output: Send the acquired data to the server.

[0863] Step 7: Generating the characteristic profile

[0864] Subject: Server

[0865] Specific operation: The server analyzes the received behavioral data and generates a user characteristic profile.

[0866] Input: Behavioral data (tweets, likes, follows, etc.).

[0867] Data processing: Analyze behavioral data to extract user interests and behavioral characteristics.

[0868] Output: User profile.

[0869] Step 8: Receiving the genetic testing kit

[0870] Subject: User

[0871] Specific operation: The user receives a genetic testing kit provided by a partner company and performs the test at home.

[0872] Input: Collect a saliva sample using the test kit.

[0873] Output: Collected sample.

[0874] Step 9: Sending the sample

[0875] Subject: User

[0876] Specific action: The user places the collected sample in the designated envelope and mails it.

[0877] Input: Place the collected sample in an envelope.

[0878] Output: Sample sent by mail.

[0879] Step 10: Uploading test results

[0880] Subject: terminal

[0881] Specific operation: The device receives a notification when the test results are ready and uploads the results to the server.

[0882] Input: Test result file.

[0883] Output: Test results uploaded to the server.

[0884] Step 11: Analysis of Genetic Data

[0885] Subject: Server

[0886] Specific operation: The server receives genetic data, processes it using specialized analysis algorithms, and adds it to the user profile.

[0887] Input: Genetic data.

[0888] Data processing: Analyze genetic information and extract data based on characteristics.

[0889] Output: Genetic information added to the user profile.

[0890] Step 12: Using the messaging function

[0891] Subject: User

[0892] Specific operation: Users utilize messaging and video chat functions provided by the system.

[0893] Input: Send a message, start a video call.

[0894] Output: Send a message, start a video call.

[0895] Step 13: Collecting emotional data

[0896] Subject: terminal

[0897] Specific operation: The device analyzes the user's words, actions, and facial expressions, and collects emotional data in real time.

[0898] Input: User's facial expressions during messages or video calls.

[0899] Data processing: Facial recognition is performed to obtain emotion data.

[0900] Output: Acquired sentiment data.

[0901] Step 14: Analyzing emotional data

[0902] Subject: Server

[0903] Specific operation: The server analyzes the received emotion data and adds it to the trait profile.

[0904] Input: Sentiment data.

[0905] Data processing: Score emotional data.

[0906] Output: Sentimental information added to the trait profile.

[0907] Step 15: Analysis of all data

[0908] Subject: Server

[0909] Specific operation: The server uses all collected data (basic information, behavioral data, genetic data, emotional data) to generate an AI model that analyzes the user's characteristics.

[0910] Input: Basic information, behavioral data, genetic data, emotional data.

[0911] Data processing: Analyze data using generative AI models.

[0912] Output: Generation of characteristic profile.

[0913] Step 16: Calculate the compatibility score

[0914] Subject: Server

[0915] Specific operation: The server calculates a compatibility score with other registered users.

[0916] Input: Characteristic profile.

[0917] Data processing: Compare the characteristics of each user and calculate a compatibility score.

[0918] Output: Compatibility score.

[0919] Step 17: Generating a list of potential partners

[0920] Subject: Server

[0921] Specific operation: The server generates a list of optimal partner candidates based on the calculation results and notifies the user.

[0922] Input: Compatibility score.

[0923] Data processing: Generate a list of potential partners based on compatibility scores.

[0924] Output: List of potential partners.

[0925] Step 18: Display the list of potential partners

[0926] Subject: terminal

[0927] Specific operation: The device displays a list of compatible partner candidates to the user.

[0928] Input: List of potential partners.

[0929] Output: The list displayed in the user interface.

[0930] (Application Example 2)

[0931] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0932] Traditional matching systems calculate compatibility based on basic user information and behavioral data, but they do not take into account the user's emotional state or mood on any given day. Therefore, they may fail to respond to the user's actual emotions and immediate needs, resulting in low satisfaction. Furthermore, there is no way to suggest what kind of meal is best suited to the user at that moment, limiting the improvement of the user experience. Additionally, there is no means to immediately reflect suggested menus in delivery orders. This makes it difficult to suggest meals that take into account the user's health condition and nutritional balance.

[0933] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0934] In this invention, the server includes means for receiving basic user information and generating a user-specific profile ID; means for collecting the user's daily data (social media activity, purchase history, travel history, etc.); means for analyzing the collected data and generating a user characteristic profile; means for receiving, analyzing, and adding genetic test results to the user profile; means for collecting, analyzing, and adding the user's emotional data in real time to the user profile; means for using an AI model with all the data to calculate compatibility with other users; means for generating a list of optimal partner candidates based on the calculation results and notifying the user; means for suggesting the most suitable meal for the user at that time based on the analysis results; and means for reflecting the suggested menu in the delivery order. This makes it possible to suggest meals and place delivery orders that take into account the user's actual emotional state and instantaneous needs.

[0935] "Basic information" refers to fundamental data used to identify a user, such as their name, age, gender, and email address.

[0936] A "profile ID" is a unique identifier generated for each user, used to link and manage all of that user's data.

[0937] "Everyday data" refers to behavioral data that users generate on a daily basis, such as social media activity, purchase history, and travel history.

[0938] A "trait profile" is a digital profile that provides a detailed description of a user's characteristics, generated by analyzing their behavioral data, genetic data, and emotional data.

[0939] "Genetic test results" refer to information based on the user's genes obtained through genetic testing, including data about their health status and specific traits.

[0940] "Emotional data" refers to data that indicates the user's current emotional state, and is real-time data collected through methods such as facial expression analysis and voice analysis.

[0941] An "AI model" refers to an artificial intelligence algorithm and system that analyzes diverse data and recognizes patterns for a specific purpose.

[0942] The "Partner Candidate List" provides users with a list of suitable partner candidates based on compatibility scores calculated by an AI model.

[0943] "Meal suggestions" refer to the act of suggesting the most suitable meal for a user at a given time, based on their basic information, behavioral data, genetic data, and emotional data.

[0944] "Delivery ordering" is the process in which a user orders a meal based on a suggested menu, and that order is delivered to the user by a delivery service.

[0945] This invention relates to a system that analyzes a user's basic information, daily data, genetic data, and emotional data, and uses AI to suggest a compatible partner. Furthermore, this system can suggest the optimal meal based on the user's mood and nutritional status, and can also instantly place a delivery order.

[0946] System Configuration

[0947] This system consists of three main elements: servers, terminals, and users.

[0948] server

[0949] The server includes the following measures:

[0950] 1. User profile generation means

[0951] The server receives the user's basic information and generates a profile ID. This ensures that all user data is uniquely identified.

[0952] 2. Data Collection Methods

[0953] The server collects everyday data such as social media activity, purchase history, and travel history. This data is sent from the device via an API.

[0954] 3. Data Analysis Methods

[0955] The collected data is analyzed to generate a user profile. This profile includes genetic data and emotional data.

[0956] 4. Genetic data analysis methods

[0957] The server receives the genetic test results and adds them to the user profile.

[0958] 5. Methods for analyzing emotional data

[0959] The server analyzes the sentiment data transmitted from the terminal and adds it to the user profile. Sentiment data is collected and analyzed in real time.

[0960] 6. Compatibility Calculation Method

[0961] The server uses an AI model with all the data to calculate compatibility with other users. Furthermore, it generates a list of optimal partner candidates based on the compatibility score and notifies the device.

[0962] 7. Meal Suggestion Methods

[0963] Based on the analysis results, the server suggests the most suitable meal for the user at that time. The suggested menu is generated taking into account the user's mood and health condition.

[0964] 8. Delivery Ordering Methods

[0965] This provides a means to immediately reflect the suggested menu in delivery orders.

[0966] terminal

[0967] A device refers to a user device such as a smartphone or tablet, and plays the following main roles:

[0968] 1. Data transmission

[0969] The device sends basic user information, behavioral data, emotional data, and other data to the server.

[0970] 2. Collecting emotional data

[0971] The device analyzes the user's facial expressions and voice, collecting emotional data in real time.

[0972] 3. Menu display and ordering

[0973] The terminal displays the menu received from the server to the user and reflects the selected menu in the delivery order.

[0974] User

[0975] Users follow these steps when using the system:

[0976] 1. User registration and basic information entry

[0977] Users enter basic information through a terminal and register it in the system.

[0978] 2. Data Provision

[0979] Users link their social media accounts and consent to the provision of purchase and travel history data. They also provide genetic data using a genetic testing kit.

[0980] 3. Provision of emotional data

[0981] Users provide emotional data through their device's camera and microphone.

[0982] 4. Choosing and ordering your meal

[0983] Choose your meal from the suggested menu and complete your delivery order.

[0984] Hardware and software to use

[0985] Hardware: Smartphones, tablets, and camera-equipped devices

[0986] Software: Emotion Detection API, AI Model API, User Profile API

[0987] Examples of specific cases and prompt statements

[0988] For example, when a user launches the app before lunch, a video feed automatically starts and facial expression data is collected. If the user is feeling stressed, for instance, that emotional data is analyzed, and a meal that helps reduce stress is suggested. Furthermore, a nutritionally balanced menu is provided, taking into account past purchase history and genetic data.

[0989] Example of a prompt:

[0990] "Please suggest nutritionally balanced meal plans that users experiencing stress would prefer, taking into account their past purchase history and genetic data."

[0991] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0992] Step 1:

[0993] User Registration

[0994] Users enter basic information (name, age, gender, email address, etc.) using a terminal. This input data is sent to the terminal and then forwarded from the terminal to the server. Based on the received basic information, the server generates a profile ID for each user and stores it in a database.

[0995] Step 2:

[0996] Collection of everyday data

[0997] Users link their social media accounts using their devices and consent to providing data on their purchase and movement history. The devices use APIs to collect social media activity (e.g., tweets, likes, follows, etc.) and send it to the server. The server receives and analyzes this data to generate a user behavior profile.

[0998] Step 3:

[0999] Collection of genetic data

[1000] The user uses a mailed genetic testing kit to collect a sample, which is then sent back to the testing laboratory. Once the genetic test results are ready, the device receives them and uploads them to the server. The server receives the genetic data and adds it to the user profile using specialized analysis algorithms.

[1001] Step 4:

[1002] Collection of emotional data

[1003] Users utilize messaging and video chat functions provided within the system using their devices. The devices analyze the user's facial expressions and voice in real time, collecting emotional data. This collected emotional data is sent to a server and added to the user profile.

[1004] Step 5:

[1005] Compatibility calculation

[1006] The server uses an AI model to analyze user characteristics based on basic information, behavioral data, genetic data, and emotional data. Based on this analysis, it calculates compatibility scores with other users and generates a list of optimal partner candidates. The candidate list is sent to the device and the user is notified.

[1007] Step 6:

[1008] Meal suggestions

[1009] The server comprehensively analyzes collected basic information, behavioral data, genetic data, and emotional data, and proposes an optimal meal plan considering the user's mood and nutritional status at that time. This proposal is sent to and displayed on the device.

[1010] Step 7:

[1011] Delivery Order

[1012] The user reviews the suggested menu on the terminal and orders their selected meal for delivery. The terminal sends the selection to the server, which then works with the delivery service to process the order, and the meal is delivered to the user.

[1013] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1014] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1015] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[1016] [Third Embodiment]

[1017] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[1018] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1019] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1020] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[1021] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1022] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1023] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1024] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1025] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1026] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1027] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1028] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[1029] This invention relates to a system that receives basic user information, identifies each user, collects and analyzes daily data, and uses AI to suggest compatible partners. This system consists of three elements: a server, a terminal, and a user, and is implemented as follows.

[1030] 1. User Registration

[1031] Users access the system's app or website and enter basic information such as their name, age, gender, and email address on the registration page.

[1032] The terminal receives the entered information and sends it to the server.

[1033] The server stores the received information in a database and generates a profile ID for each user.

[1034] 2. Data Collection

[1035] Users link their social media accounts and consent to the sharing of purchase and travel history data.

[1036] The device periodically collects social media activity (tweets, likes, follows, etc.) using an API and sends it to the server.

[1037] The server analyzes the received behavioral data and generates a user characteristic profile.

[1038] 3. Genetic analysis

[1039] Users use genetic testing kits provided by partner companies, perform the tests at home, and then send the samples back.

[1040] The device receives a notification when the genetic test results are ready and uploads the results to the server.

[1041] The server receives the genetic data, processes it using specialized analysis algorithms, and adds it to the user profile.

[1042] 4. Compatibility Calculation

[1043] The server uses all collected data (basic information, behavioral data, and genetic data) to analyze user characteristics with an AI model.

[1044] The server calculates compatibility scores with other registered users and generates a list of optimal partner candidates.

[1045] The server sends the generated list of candidates to the terminal.

[1046] The device displays a list of potential partners who are a good match for the user.

[1047] As a concrete example, let's consider the case where user A uses the system.

[1048] 1. User Registration

[1049] User A accesses the system's app from their smartphone and enters their name, age, gender, and email address. The device sends the entered information to the server, which stores the information in a database and generates a profile ID for User A.

[1050] 2. Data Collection

[1051] User A links their social media accounts and agrees to provide purchase and movement history data. The device collects data such as User A's tweets, likes, and purchase history via an API and sends it to the server. The server analyzes this data and generates a characteristic profile of User A.

[1052] 3. Genetic analysis

[1053] User A uses a genetic testing kit sent by mail and submits the sample. Once the test results are ready, the terminal uploads the results to the server. The server processes the genetic data using an analysis algorithm and adds it to User A's profile.

[1054] 4. Compatibility Calculation

[1055] The server uses an AI model to analyze user A's characteristics based on their basic information, behavioral data, and genetic data. The server then compares user A to other users, calculates a compatibility score, and generates a list of optimal partner candidates. This list is then sent to user A via their device.

[1056] This program is designed to help users find truly compatible partners. When User A meets with a displayed potential partner, the device collects behavioral data in real time and sends it to the server. The server uses this data to continuously improve the AI ​​model, providing more accurate compatibility diagnoses. In this way, the system not only improves overall accuracy but also provides comprehensive support to help User A find the best partner.

[1057] The following describes the processing flow.

[1058] Step 1:

[1059] Users access the system's app or website and enter basic information such as their name, age, gender, and email address.

[1060] Step 2:

[1061] The terminal sends the entered basic information to the server.

[1062] Step 3:

[1063] The server stores the received basic information in a database and generates a profile ID for each user.

[1064] Step 4:

[1065] Users link their social media accounts and consent to the sharing of purchase and travel history data.

[1066] Step 5:

[1067] The device periodically collects social media activity (tweets, likes, follows, etc.) using an API and sends it to the server.

[1068] Step 6:

[1069] The server analyzes the received behavioral data and generates a user characteristic profile.

[1070] Step 7:

[1071] Users use genetic testing kits provided by partner companies, perform the tests at home, and then send the samples back.

[1072] Step 8:

[1073] The device receives a notification when the genetic test results are ready and uploads the results to the server.

[1074] Step 9:

[1075] The server receives the genetic data, processes it using specialized analysis algorithms, and adds it to the user profile.

[1076] Step 10:

[1077] The server uses an AI model to analyze characteristics based on the collected basic information, behavioral data, and genetic data.

[1078] Step 11:

[1079] The server calculates compatibility scores with other registered users and generates a list of optimal partner candidates.

[1080] Step 12:

[1081] The server sends the generated list of candidates to the terminal.

[1082] Step 13:

[1083] The device displays a list of potential partners who are a good match for the user.

[1084] Step 14:

[1085] Users select someone they are interested in from the displayed list of candidates and view their details.

[1086] Step 15:

[1087] The device requests detailed information about the partner candidate selected by the user from the server.

[1088] Step 16:

[1089] Based on the request, the server sends detailed information about the candidate to the terminal.

[1090] Step 17:

[1091] The device displays detailed information about the candidate (name, age, interests, etc.) to the user.

[1092] Step 18:

[1093] Users exchange messages with their chosen potential partners and arrange to meet in person.

[1094] Step 19:

[1095] The device records the date, time, and location of the scheduled meeting and notifies the user with a reminder.

[1096] Step 20:

[1097] The device collects user behavior data (location information, conversation time, etc.) in real time while the user is actually meeting and sends it to the server.

[1098] Step 21:

[1099] The server analyzes the collected behavioral data to evaluate the actual compatibility and relationship of the couple.

[1100] Step 22:

[1101] The server continuously improves its AI model based on collected behavioral data, thereby enhancing the accuracy of compatibility calculations.

[1102] (Example 1)

[1103] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1104] Currently, many matching systems exist, but these systems rely solely on users' basic information and preferences, making it difficult to guarantee long-term compatibility. Furthermore, few systems consider users' genetic characteristics and behavioral history when performing matching. As a result, even when a match is successful, there is a problem in that compatibility necessary for building a lasting relationship is not adequately evaluated.

[1105] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1106] In this invention, the server includes means for receiving user identification information and generating user-specific identification information; means for collecting user behavior history data; means for analyzing the collected data and generating user behavioral characteristics; means for receiving and analyzing genetic information and adding it to the user profile; means for using an AI model with all the data to calculate the optimality with other users; and means for generating an optimal candidate list based on the calculation results and notifying the user. This enables more accurate matching that takes into account the user's behavior data and genetic information.

[1107] "User identification information" refers to information that the system uses to uniquely identify a user, and includes information such as name, age, gender, and email address.

[1108] "Means for generating identification information" refers to a function that collects basic user information and automatically generates a unique identification ID based on that information.

[1109] "Behavioral history data" refers to data obtained from a user's daily activities, including social media activity, purchase history, and travel history.

[1110] "Means for collecting behavioral history data" refers to a function that automatically and periodically retrieves behavioral history from data sources authorized by the user and sends it to the server.

[1111] "Behavioral characteristics" refer to user-specific traits and patterns extracted by analyzing user behavior history data.

[1112] "Means for generating behavioral characteristics" refers to a function that analyzes collected behavioral history data to identify the user's behavioral characteristics.

[1113] "Genetic information" refers to information about the genetic characteristics obtained from the user's genetic test results.

[1114] "Means of receiving, analyzing, and adding genetic information to a user profile" refers to a function that uploads genetic information to a server, analyzes that information, and integrates it into the user's profile.

[1115] "Methods for calculating optimality using AI models" refers to a function that uses AI to quantify the compatibility between users based on collected data and calculates the optimal partner.

[1116] The "optimal candidate list" is a list that ranks the most suitable partners for the user based on compatibility scores calculated by an AI model.

[1117] The "means for generating and notifying candidates" refer to a function that creates a candidate list based on compatibility scores calculated by an AI model and notifies the user of this list.

[1118] This invention relates to a system that receives basic user information, generates user-specific identification information, collects and analyzes daily data, and uses AI to suggest compatible partners. This system consists of three elements: a server, a terminal, and a user, and specifically has the following functions.

[1119] First, the user accesses the system's app or website and enters basic information such as name, age, gender, and email address on the registration page. The device receives the entered information in real time and sends it to the server using the HTTPS protocol. On the server, the received basic information is stored in a database, and a profile ID is assigned to each user using a unique ID generation algorithm (e.g., UUID).

[1120] Next, the user links their social media accounts and consents to providing purchase and movement history data. The device uses OAuth to gain access to the user's social media accounts and periodically collects user behavior data through API endpoints, sending it to the server. The server analyzes this data using text analysis and clustering algorithms to generate a profile that identifies the user's behavioral characteristics.

[1121] Users collect a sample using a genetic testing kit mailed to them by a partner company and return it. When the test results are ready, the testing company's system sends a notification to the user's device, and the device uploads the test result file to the server. Encrypted communication is used to ensure data security during this process. The server processes the received genetic data using a dedicated analysis algorithm and adds the results to the user profile.

[1122] Using all the data (basic information, behavioral data, and genetic data), the server uses a trained generative AI model to calculate compatibility with other registered users. This process utilizes a deep learning matching network to evaluate compatibility between users. Based on the calculation results, the server generates a list of optimal partner candidates and sends it to the terminal. The terminal provides the user with an interface to visually display this list.

[1123] As a concrete example, consider the case where User A uses this system. User A accesses the system's app from their smartphone and enters basic information. The device sends the information to the server via HTTPS, and the server generates a UUID and stores it in the database. Next, User A links their social media accounts and agrees to provide purchase history and travel history data. The device collects data using OAuth and sends it to the server. The server analyzes this data and compiles User A's behavioral characteristics into a profile. User A uses a genetic testing kit and sends a sample. Once the test results are ready, the device uploads the results to the server, and the server processes the genetic data using an analysis algorithm and adds it to the profile. Finally, the server uses a generative AI model to calculate a compatibility score, generates a list of optimal partner candidates, and notifies User A of this.

[1124] An example of a prompt to input into a generative AI model is as follows:

[1125] "Create an AI model that identifies compatible partners using users' basic information, behavioral data, and genetic data. Calculate compatibility scores and generate a list of optimal partner candidates."

[1126] In this way, the system helps users find compatible partners more efficiently and achieves more accurate matching by taking into account the user's behavioral data and genetic information.

[1127] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1128] Step 1: User Registration

[1129] Users access the system's app or website and enter basic information such as their name, age, gender, and email address on the registration page.

[1130] The terminal receives the entered information in real time and sends it to the server using the HTTPS protocol. Specifically, it serializes the data in the form into JSON format and sends it.

[1131] When the server stores the received basic information in the database, it uses a unique ID generation algorithm (e.g., UUID) to assign a profile ID to each user. The input is the user's basic information, and the output is the generated profile ID.

[1132] Step 2: Data Collection

[1133] Users link their social media accounts and consent to the sharing of purchase and travel history data.

[1134] The device uses OAuth to gain access to the user's social media accounts. It collects data such as the user's tweets, likes, and follows at regular intervals via an API endpoint and sends it to the server. Specifically, it calls the API to retrieve the data and sends it to the server in JSON format.

[1135] The server analyzes the received behavioral data using text analysis, clustering algorithms, and other methods to generate a profile that analyzes the user's behavioral characteristics. Inputs include social media activity, purchase history, and travel history, while the output is a profile of behavioral characteristics.

[1136] Step 3: Genetic Analysis

[1137] Users use genetic testing kits provided by partner companies, perform the tests at home, and then send the samples back.

[1138] The device receives a notification when the genetic test results are ready and uploads the results to the server. Specifically, it calls the testing company's API to receive the results file and sends it to the server.

[1139] The server receives genetic data, processes it using a dedicated analysis algorithm, and adds the results to the user profile. The input is test result data, and the output is genetic characteristics added to the user profile.

[1140] Step 4: Compatibility Calculation

[1141] The server integrates basic information, behavioral data, and genetic data, and uses an AI model to calculate compatibility with other registered users. Specifically, it uses a deep learning model to calculate compatibility scores between each user.

[1142] The server creates a list of optimal partner candidates based on the generated compatibility scores. This candidate list is generated as the output of the AI ​​model.

[1143] The server generates a list of optimal partner candidates and sends it to the terminal. The terminal provides the user with an interface that visually displays the list.

[1144] Step 5: Real-time collection of behavioral data and improvement of the AI ​​model

[1145] The device collects real-time behavioral data from when the user actually meets with a candidate. Specifically, the device records the user's location information and behavioral patterns and sends them to the server.

[1146] The server receives this new behavioral data and uses it as feedback to continuously improve the accuracy of the AI ​​model. Real-time collected behavioral data is used as input, and an updated AI model is generated as output.

[1147] Through the processing steps described above, this system helps users efficiently find compatible partners and achieves more accurate matching by taking into account the user's behavioral data and genetic information.

[1148] (Application Example 1)

[1149] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1150] Traditional partner recommendation systems primarily calculate compatibility using only basic user information and behavioral data. However, this approach fails to accurately reflect the user's overall characteristics, resulting in inaccurate recommendations. Furthermore, the development of individually optimized content recommendation systems has been slow, leaving a lack of means to improve compatibility with entertainment content such as movies and dramas that users watch. This highlighted the need for improved user experience.

[1151] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1152] In this invention, the server includes means for receiving basic user information and generating a user-specific profile ID; means for collecting the user's daily data (such as social media activity, purchase history, and travel history); means for analyzing the collected data and generating a user characteristic profile; means for receiving, analyzing, and adding genetic test results to the user profile; means for using an AI model with all the data to calculate compatibility with other users; means for generating a list of optimal partner candidates based on the calculation results and notifying the user; and means for using digital media viewing history and social media activity data to recommend individually optimized content based on the user profile. This enables more accurate reflection of the user's overall characteristics and allows for highly accurate partner recommendations and individually optimized content recommendations.

[1153] "User basic information" refers to information that allows for individual identification of a user, such as their name, age, gender, and email address.

[1154] A "profile ID" is a unique identification code for each user, used to uniquely identify a user within the database.

[1155] "Social media activity" refers to actions that users take on social media platforms, such as tweeting, liking, following, and posting.

[1156] "Purchase history" is a record of a user's online and offline purchasing activities.

[1157] "Travel history" refers to information about places a user has visited and the routes they have traveled.

[1158] A "characteristic profile" is a profile that represents a user's personality, hobbies, and preferences, generated based on the user's behavioral data, basic information, and genetic data.

[1159] "Genetic test results" refer to genetic characteristics and health information obtained by analyzing genetic material collected from the user.

[1160] An "AI model" is an artificial intelligence algorithm that learns from a large amount of data, finds patterns and relationships, and then makes predictions and judgments.

[1161] "Calculating compatibility" means using an AI model to compare a user's characteristic profile with the characteristic profiles of other users and derive a compatibility score based on similarities and differences.

[1162] The "Partner Candidate List" is a list of the most suitable partner candidates determined by compatibility calculations.

[1163] "Digital media viewing history" refers to a record of content such as movies, dramas, and video clips that a user has watched in the past.

[1164] "Personalized content" refers to recommending content that is most likely to interest a user based on their characteristic profile.

[1165] To implement this invention, a system is required that includes three elements: a user, a terminal, and a server. This system starts with receiving the user's basic information and covers the collection and analysis of various data, compatibility calculations using an AI model, and finally the generation of a list of potential partners and the recommendation of individually optimized content.

[1166] 1. User Registration

[1167] Users access the system's app using a device (e.g., a smartphone) and enter basic information such as their name, age, gender, and email address. The device sends this information to the server, which stores the received information in a database and generates a profile ID for each user.

[1168] 2. Data Collection

[1169] Users link their social media accounts and consent to providing purchase and travel history data. The device uses an API to collect social media activity data (tweets, likes, follows, etc.), purchase history, and travel history, and sends it to the server. The server analyzes this data to generate a user profile.

[1170] 3. Genetic analysis

[1171] Users use genetic testing kits provided by partner companies, perform the test at home, and then send the sample back. When the results are ready, the device receives a notification and uploads the results to the server. The server receives the genetic data, processes it using specialized analysis algorithms, and adds it to the user profile.

[1172] 4. Compatibility Calculation

[1173] The server uses an AI model to analyze the user's characteristics based on their basic information, behavioral data, and genetic data. The server calculates compatibility scores with other users and generates a list of optimal partner candidates. Furthermore, based on the user profile, it uses data from digital media viewing history and social media activity to recommend individually optimized content. The calculation results are communicated to the user via their device.

[1174] Hardware and software to use

[1175] Hardware: Smartphone

[1176] Software: Python, TF-IDF vectorizer (scikit-learn library), API interface tools

[1177] Specific example

[1178] This illustrates how User A uses this system. User A accesses the smartphone app and enters their name, age, gender, and email address. They then link their social media accounts and agree to provide data on their purchase and travel history. User A uses a genetic testing kit and sends in a sample. The server analyzes User A's social media activity, viewing history, and genetic data to generate a trait profile. Furthermore, it uses an AI model to generate a list of optimal partner candidates and provides personalized movie and TV show recommendations.

[1179] Example of a prompt

[1180] User ID: u1, Name: Yu, Age: 30, Gender: Male, Email: yu@example.com

[1181] Social media data: action, drama, explosion

[1182] Genetic data: visual_sensitive, auditory_sensitive

[1183] This configuration allows for a detailed understanding of user characteristics, resulting in a system that enables highly accurate partner recommendations and individually optimized content recommendations.

[1184] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1185] Step 1:

[1186] User Registration

[1187] Users access the system's app using their device (smartphone) and enter basic information such as their name, age, gender, and email address. The device sends this information to the server. The server stores the received information in a database and generates a unique profile ID for each user. The input is the user's basic information, and the output is the user's profile ID. In terms of data processing, the user's basic information is integrated into a unique ID.

[1188] Step 2:

[1189] Social media links and data collection

[1190] Users link their social media accounts and consent to providing purchase and travel history data. The device uses an API to collect social media activity data and send it to a server. The server analyzes this data and generates a user profile. Inputs are social media activity, purchase history, and travel history data, while output is the analyzed profile. Data processing includes statistical analysis of collected data and profile generation.

[1191] Step 3:

[1192] Genetic analysis

[1193] Users use genetic testing kits provided by partner companies, perform the test at home, and then send the sample back. When the results are ready, the device receives a notification and uploads the results to the server. The server analyzes the genetic data, processes it using specialized analysis algorithms, and adds it to the user profile. The input is genetic data, and the output is an updated user profile. Data processing involves analyzing genetic information and integrating it into the profile.

[1194] Step 4:

[1195] Compatibility calculation and partner recommendation

[1196] The server uses an AI model to analyze the user's characteristics based on their basic information, behavioral data, and genetic data. The server calculates compatibility scores with other users and generates a list of optimal partner candidates. The calculation results are notified to the user via their terminal. The input is all user data, and the output is a list of partner candidates. Data calculations include the calculation of compatibility scores by the AI ​​model.

[1197] Step 5:

[1198] Personalized content recommendations

[1199] The server recommends personalized content based on the user profile, using data from digital media viewing history and social media activity. The calculation results are notified to the user via the terminal. The input is viewing history and social media data, and the output is a content recommendation list. Specifically, the system performs analysis of viewing history data and content recommendations based on the user's profile.

[1200] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1201] This invention aims to perform more accurate compatibility calculations by combining an emotion engine with a system that analyzes a user's basic information, behavioral data, and genetic data, and uses AI to suggest compatible partners, thereby adding the user's emotional data to their profile. This system consists of three elements: a server, a terminal, and a user, and operates as follows.

[1202] 1. User Registration

[1203] Users access the system's app or website and enter basic information such as their name, age, gender, and email address.

[1204] The terminal sends the entered basic information to the server.

[1205] The server stores the received basic information in a database and generates a profile ID for each user.

[1206] 2. Data Collection

[1207] Users link their social media accounts and consent to the sharing of purchase and travel history data.

[1208] The device periodically collects social media activity (tweets, likes, follows, etc.) using an API and sends it to the server.

[1209] The server analyzes the received behavioral data and generates a user characteristic profile.

[1210] 3. Genetic analysis

[1211] Users use genetic testing kits provided by partner companies, perform the tests at home, and then send the samples back.

[1212] The device receives a notification when the genetic test results are ready and uploads the results to the server.

[1213] The server receives the genetic data, processes it using specialized analysis algorithms, and adds it to the user profile.

[1214] 4. Collection and analysis of emotional data

[1215] Users utilize messaging and video chat features provided by the system.

[1216] The device analyzes the user's words, actions, and facial expressions, collecting emotional data in real time.

[1217] The server analyzes the received emotion data and adds it to the trait profile.

[1218] 5. Compatibility Calculation

[1219] The server uses all collected data (basic information, behavioral data, genetic data, and emotional data) to analyze the user's characteristics with an AI model.

[1220] The server calculates compatibility scores with other registered users and generates a list of optimal partner candidates.

[1221] The server sends the generated list of candidates to the terminal.

[1222] The device displays a list of potential partners who are a good match for the user.

[1223] As a concrete example, let's consider the case where user A uses the system.

[1224] 1. User Registration

[1225] User A accesses the system's app from their smartphone and enters their name, age, gender, and email address. The device sends the entered information to the server, which stores the information in a database and generates a profile ID for User A.

[1226] 2. Data Collection

[1227] User A links their social media accounts and agrees to provide purchase and movement history data. The device collects data such as User A's tweets, likes, and purchase history via an API and sends it to the server. The server analyzes this data and generates a characteristic profile of User A.

[1228] 3. Genetic analysis

[1229] User A uses a genetic testing kit sent by mail and submits the sample. Once the test results are ready, the terminal uploads the results to the server. The server processes the genetic data using an analysis algorithm and adds it to User A's profile.

[1230] 4. Collection and analysis of emotional data

[1231] While User A is using the messaging and video chat functions within the system, the device analyzes User A's facial expressions and behavior, collecting emotional data. The server analyzes the received emotional data and adds it to a trait profile.

[1232] 5. Compatibility Calculation

[1233] The server uses an AI model to analyze user A's characteristics based on their basic information, behavioral data, genetic data, and emotional data. The server then compares user A to other users, calculates a compatibility score, and generates a list of optimal partner candidates. This list is then communicated to user A via their device.

[1234] This program is designed to help users find truly compatible partners. When User A meets with a displayed partner candidate, the device collects behavioral and emotional data in real time and sends it to the server. The server continuously improves the AI ​​model based on this data, providing more accurate compatibility diagnoses. In this way, the system not only improves the overall accuracy but also provides comprehensive support for User A to find the best partner.

[1235] The following describes the processing flow.

[1236] Step 1:

[1237] Users access the system's app or website and enter basic information such as their name, age, gender, and email address.

[1238] Step 2:

[1239] The terminal sends the entered basic information to the server.

[1240] Step 3:

[1241] The server stores the received basic information in a database and generates a profile ID for each user.

[1242] Step 4:

[1243] Users link their social media accounts and consent to the sharing of purchase and travel history data.

[1244] Step 5:

[1245] The device periodically collects social media activity (tweets, likes, follows, etc.) using an API and sends it to the server.

[1246] Step 6:

[1247] The server analyzes the received behavioral data and generates a user characteristic profile.

[1248] Step 7:

[1249] Users use genetic testing kits provided by partner companies, perform the tests at home, and then send the samples back.

[1250] Step 8:

[1251] The device receives a notification when the genetic test results are ready and uploads the results to the server.

[1252] Step 9:

[1253] The server receives the genetic data, processes it using specialized analysis algorithms, and adds it to the user profile.

[1254] Step 10:

[1255] Users utilize messaging and video chat features provided by the system.

[1256] Step 11:

[1257] The device analyzes the user's facial expressions and behavior to recognize their emotions and collects that data.

[1258] Step 12:

[1259] The server adds the received emotional data to the profile, generating a more detailed trait profile.

[1260] Step 13:

[1261] The server analyzes all collected data (basic information, behavioral data, genetic data, and emotional data) and uses an AI model to evaluate the user's characteristics.

[1262] Step 14:

[1263] The server calculates compatibility scores with other registered users and generates a list of optimal partner candidates.

[1264] Step 15:

[1265] The server sends the generated list of candidates to the terminal.

[1266] Step 16:

[1267] The device displays a list of potential partners who are a good match for the user.

[1268] Step 17:

[1269] Users select someone they are interested in from the displayed list of candidates and view their details.

[1270] Step 18:

[1271] The device requests detailed information about the partner candidate selected by the user from the server.

[1272] Step 19:

[1273] Based on the request, the server sends detailed information about the candidate to the terminal.

[1274] Step 20:

[1275] The device displays detailed information about the candidate (name, age, interests, etc.) to the user.

[1276] Step 21:

[1277] Users exchange messages with their chosen potential partners and arrange to meet in person.

[1278] Step 22:

[1279] The device records the date, time, and location of the scheduled meeting and notifies the user with a reminder.

[1280] Step 23:

[1281] The device collects user behavior data (location information, conversation time, etc.) and emotional data in real time while the user is actually meeting, and sends this data to the server.

[1282] Step 24:

[1283] The server analyzes the collected behavioral and emotional data to evaluate the couple's actual compatibility and relationship.

[1284] Step 25:

[1285] The server continuously improves the AI ​​model based on the collected data, thereby increasing the accuracy of compatibility calculations.

[1286] (Example 2)

[1287] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1288] Traditional matching systems often calculate compatibility based only on basic user information and daily behavioral data, failing to consider deeper data such as the user's emotional state or genetic information, thus limiting the accuracy of compatibility calculations. Furthermore, there is a lack of mechanisms to continuously improve the system's accuracy using data on the user's behavior and emotions when they actually meet their matched partner, resulting in insufficient improvement of the user experience.

[1289] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1290] In this invention, the server includes means for receiving basic user information and generating a profile ID for each user; means for collecting the user's daily information (social media activity, purchase history, travel history, etc.); means for analyzing the collected information and generating a user characteristic profile; means for receiving, analyzing, and adding genetic test results to the user profile; means for collecting emotional data from the user's behavior and facial expressions and adding it to the profile; means for using a generated AI model with all the data to calculate compatibility with other users; and means for generating a list of optimal partner candidates based on the calculation results and notifying the user. This enables comprehensive analysis of diverse user data and more accurate compatibility calculations. Furthermore, by utilizing actual behavior and emotional data after matching, the accuracy of the entire system can be continuously improved, leading to an enhanced user experience.

[1291] "Basic information" refers to identifying information such as the user's name, age, gender, and email address.

[1292] "Everyday information" refers to behavioral data from users' daily lives, such as their social media activity, purchase history, and travel history.

[1293] A "characteristic profile" refers to a profile of a user's interests, behavioral characteristics, personality, and other traits, which is analyzed based on collected information.

[1294] "Genetic test results" refers to the results of the genetic information obtained by the user through genetic testing.

[1295] "Emotional data" refers to data on the emotional state obtained from the user's behavior and facial expressions.

[1296] A "generative AI model" refers to an artificial intelligence model used to analyze user characteristics and compatibility based on collected data.

[1297] A "compatibility score" refers to the result of quantifying the compatibility between a user and other users based on the user's characteristic data.

[1298] The "Partner Candidate List" refers to a list of potential partners that are best suited to the user, generated based on compatibility scores.

[1299] This invention is a system that analyzes a user's basic information, daily information, genetic information, and emotional data, and uses a generative AI model to suggest a compatible partner. This system consists of three elements: a server, a terminal, and the user.

[1300] 1. User Registration

[1301] The user first accesses the system's app or website and enters basic information such as their name, age, gender, and email address. The device sends this information to the server, which stores it in a database and generates a profile ID.

[1302] 2. Data Collection

[1303] Users link their social media accounts and consent to providing their purchase and travel history. The device periodically collects social media activity (tweets, likes, follows, etc.) using an API and sends it to the server. The server analyzes this behavioral data to generate a user profile.

[1304] 3. Genetic analysis

[1305] Users perform genetic testing at home using a genetic testing kit provided by a partner company. Once the sample is returned, the device notifies the user that the genetic test results are ready and uploads the results to a server. The server processes the genetic data using an analysis algorithm and adds it to the user profile.

[1306] 4. Collection and analysis of emotional data

[1307] As users utilize messaging and video chat features provided by the system, their devices analyze their speech, actions, and facial expressions, collecting emotional data in real time. The server then analyzes the received emotional data and adds it to their characteristic profile.

[1308] 5. Compatibility Calculation

[1309] The server uses all collected data (basic information, behavioral data, genetic data, and emotional data) to analyze the user's characteristics with a generated AI model. The server calculates compatibility scores with other registered users and generates a list of optimal partner candidates. This list is sent to the device, which then displays a list of compatible partner candidates to the user.

[1310] Specific example

[1311] When user A uses the system, user A accesses the system's app from their smartphone and enters their name, age, gender, and email address. The device sends the entered information to the server, which stores the information in a database and generates user A's profile ID.

[1312] Next, User A links their social media accounts and agrees to provide purchase and movement history data. The device collects data such as User A's tweets, likes, and purchase history via an API and sends it to the server. The server analyzes this data and generates a profile of User A's characteristics.

[1313] Furthermore, User A uses the mailed genetic testing kit to send in a sample. Once the test results are ready, the terminal uploads the results to the server. The server processes the genetic data using an analysis algorithm and adds it to User A's profile.

[1314] Next, while User A uses the messaging and video chat functions within the system, the device analyzes User A's facial expressions and behavior, collecting emotional data. The server analyzes the received emotional data and adds it to the characteristic profile.

[1315] Ultimately, the server uses user A's basic information, behavioral data, genetic data, and emotional data to analyze their characteristics with a generative AI model. The server compares them to other users, calculates a compatibility score, and generates a list of optimal partner candidates. This list is then communicated to user A via their device.

[1316] Example of a prompt

[1317] Please analyze the user characteristics based on the following data.

[1318] 1. Basic Information: Name, Age, Gender, Email Address

[1319] 2. Behavioral data: Social media activity, purchase history, travel history

[1320] 3. Genetic data: Genetic test results

[1321] 4. Emotional Data: Facial expression analysis and results obtained through messaging and video chat functions.

[1322] This system is designed to help users find truly compatible partners. When a user meets with a displayed potential partner, the device collects behavioral and emotional data in real time and sends it to the server. The server continuously improves the generated AI model based on this data, providing more accurate compatibility diagnoses. In this way, the system not only improves the overall accuracy but also provides comprehensive support for users to find the best partner for them.

[1323] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1324] Program processing flow

[1325] Step 1: Access the user registration page.

[1326] Subject: User

[1327] Specific steps: The user opens a browser on their smartphone or PC, or a dedicated app, and accesses the system's user registration page.

[1328] Input: The user taps the app icon or enters a URL.

[1329] Output: The registration page is displayed.

[1330] Step 2: Enter basic information

[1331] Subject: User

[1332] Specific actions: The user enters their name, age, gender, and email address on the registration page.

[1333] Input: Enter your "Name," "Age," "Gender," and "Email Address" in the form.

[1334] Output: Basic information is entered.

[1335] Step 3: Submit basic information

[1336] Subject: terminal

[1337] Specific operation: The terminal sends the entered basic information to the server.

[1338] Input: Basic information entered (name, age, gender, email address).

[1339] Output: Generation of data to be sent to the server.

[1340] Step 4: Generating a Profile ID

[1341] Subject: Server

[1342] Specific operation: The server saves the received basic information to the database and generates a profile ID for each user.

[1343] Input: Basic information (name, age, gender, email address).

[1344] Data processing: Create a new entry in the database and generate a unique profile ID.

[1345] Output: Profile ID for each user.

[1346] Step 5: Link your social media accounts

[1347] Subject: User

[1348] Specific actions: Users link their social media accounts and consent to the sharing of their purchase and travel history.

[1349] Input: Enter your login information on the authentication screen.

[1350] Output: Social media accounts are linked.

[1351] Step 6: Start data collection

[1352] Subject: terminal

[1353] Specific operation: The device uses an API to periodically collect social media activity (tweets, likes, follows, etc.) and send it to the server.

[1354] Input: Send a request to the social media API.

[1355] Output: Send the acquired data to the server.

[1356] Step 7: Generating the characteristic profile

[1357] Subject: Server

[1358] Specific operation: The server analyzes the received behavioral data and generates a user characteristic profile.

[1359] Input: Behavioral data (tweets, likes, follows, etc.).

[1360] Data processing: Analyze behavioral data to extract user interests and behavioral characteristics.

[1361] Output: User profile.

[1362] Step 8: Receiving the genetic testing kit

[1363] Subject: User

[1364] Specific operation: The user receives a genetic testing kit provided by a partner company and performs the test at home.

[1365] Input: Collect a saliva sample using the test kit.

[1366] Output: Collected sample.

[1367] Step 9: Sending the sample

[1368] Subject: User

[1369] Specific action: The user places the collected sample in the designated envelope and mails it.

[1370] Input: Place the collected sample in an envelope.

[1371] Output: Sample sent by mail.

[1372] Step 10: Uploading test results

[1373] Subject: terminal

[1374] Specific operation: The device receives a notification when the test results are ready and uploads the results to the server.

[1375] Input: Test result file.

[1376] Output: Test results uploaded to the server.

[1377] Step 11: Analysis of Genetic Data

[1378] Subject: Server

[1379] Specific operation: The server receives genetic data, processes it using specialized analysis algorithms, and adds it to the user profile.

[1380] Input: Genetic data.

[1381] Data processing: Analyze genetic information and extract data based on characteristics.

[1382] Output: Genetic information added to the user profile.

[1383] Step 12: Using the messaging function

[1384] Subject: User

[1385] Specific operation: Users utilize messaging and video chat functions provided by the system.

[1386] Input: Send a message, start a video call.

[1387] Output: Send a message, start a video call.

[1388] Step 13: Collecting emotional data

[1389] Subject: terminal

[1390] Specific operation: The device analyzes the user's words, actions, and facial expressions, and collects emotional data in real time.

[1391] Input: User's facial expressions during messages or video calls.

[1392] Data processing: Facial recognition is performed to obtain emotion data.

[1393] Output: Acquired sentiment data.

[1394] Step 14: Analyzing emotional data

[1395] Subject: Server

[1396] Specific operation: The server analyzes the received emotion data and adds it to the trait profile.

[1397] Input: Sentiment data.

[1398] Data processing: Score emotional data.

[1399] Output: Sentimental information added to the trait profile.

[1400] Step 15: Analysis of all data

[1401] Subject: Server

[1402] Specific operation: The server uses all collected data (basic information, behavioral data, genetic data, emotional data) to generate an AI model that analyzes the user's characteristics.

[1403] Input: Basic information, behavioral data, genetic data, emotional data.

[1404] Data processing: Analyze data using generative AI models.

[1405] Output: Generation of characteristic profile.

[1406] Step 16: Calculate the compatibility score

[1407] Subject: Server

[1408] Specific operation: The server calculates a compatibility score with other registered users.

[1409] Input: Characteristic profile.

[1410] Data processing: Compare the characteristics of each user and calculate a compatibility score.

[1411] Output: Compatibility score.

[1412] Step 17: Generating a list of potential partners

[1413] Subject: Server

[1414] Specific operation: The server generates a list of optimal partner candidates based on the calculation results and notifies the user.

[1415] Input: Compatibility score.

[1416] Data processing: Generate a list of potential partners based on compatibility scores.

[1417] Output: List of potential partners.

[1418] Step 18: Display the list of potential partners

[1419] Subject: terminal

[1420] Specific operation: The device displays a list of compatible partner candidates to the user.

[1421] Input: List of potential partners.

[1422] Output: The list displayed in the user interface.

[1423] (Application Example 2)

[1424] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1425] Traditional matching systems calculate compatibility based on basic user information and behavioral data, but they do not take into account the user's emotional state or mood on any given day. Therefore, they may fail to respond to the user's actual emotions and immediate needs, resulting in low satisfaction. Furthermore, there is no way to suggest what kind of meal is best suited to the user at that moment, limiting the improvement of the user experience. Additionally, there is no means to immediately reflect suggested menus in delivery orders. This makes it difficult to suggest meals that take into account the user's health condition and nutritional balance.

[1426] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1427] In this invention, the server includes means for receiving basic user information and generating a user-specific profile ID; means for collecting the user's daily data (social media activity, purchase history, travel history, etc.); means for analyzing the collected data and generating a user characteristic profile; means for receiving, analyzing, and adding genetic test results to the user profile; means for collecting, analyzing, and adding the user's emotional data in real time to the user profile; means for using an AI model with all the data to calculate compatibility with other users; means for generating a list of optimal partner candidates based on the calculation results and notifying the user; means for suggesting the most suitable meal for the user at that time based on the analysis results; and means for reflecting the suggested menu in the delivery order. This makes it possible to suggest meals and place delivery orders that take into account the user's actual emotional state and instantaneous needs.

[1428] "Basic information" refers to fundamental data used to identify a user, such as their name, age, gender, and email address.

[1429] A "profile ID" is a unique identifier generated for each user, used to link and manage all of that user's data.

[1430] "Everyday data" refers to behavioral data that users generate on a daily basis, such as social media activity, purchase history, and travel history.

[1431] A "trait profile" is a digital profile that provides a detailed description of a user's characteristics, generated by analyzing their behavioral data, genetic data, and emotional data.

[1432] "Genetic test results" refer to information based on the user's genes obtained through genetic testing, including data about their health status and specific traits.

[1433] "Emotional data" refers to data that indicates the user's current emotional state, and is real-time data collected through methods such as facial expression analysis and voice analysis.

[1434] An "AI model" refers to an artificial intelligence algorithm and system that analyzes diverse data and recognizes patterns for a specific purpose.

[1435] The "Partner Candidate List" provides users with a list of suitable partner candidates based on compatibility scores calculated by an AI model.

[1436] "Meal suggestions" refer to the act of suggesting the most suitable meal for a user at a given time, based on their basic information, behavioral data, genetic data, and emotional data.

[1437] "Delivery ordering" is the process in which a user orders a meal based on a suggested menu, and that order is delivered to the user by a delivery service.

[1438] This invention relates to a system that analyzes a user's basic information, daily data, genetic data, and emotional data, and uses AI to suggest a compatible partner. Furthermore, this system can suggest the optimal meal based on the user's mood and nutritional status, and can also instantly place a delivery order.

[1439] System Configuration

[1440] This system consists of three main elements: servers, terminals, and users.

[1441] server

[1442] The server includes the following measures:

[1443] 1. User profile generation means

[1444] The server receives the user's basic information and generates a profile ID. This ensures that all user data is uniquely identified.

[1445] 2. Data Collection Methods

[1446] The server collects everyday data such as social media activity, purchase history, and travel history. This data is sent from the device via an API.

[1447] 3. Data Analysis Methods

[1448] The collected data is analyzed to generate a user profile. This profile includes genetic data and emotional data.

[1449] 4. Genetic data analysis methods

[1450] The server receives the genetic test results and adds them to the user profile.

[1451] 5. Methods for analyzing emotional data

[1452] The server analyzes the sentiment data transmitted from the terminal and adds it to the user profile. Sentiment data is collected and analyzed in real time.

[1453] 6. Compatibility Calculation Method

[1454] The server uses an AI model with all the data to calculate compatibility with other users. Furthermore, it generates a list of optimal partner candidates based on the compatibility score and notifies the device.

[1455] 7. Meal Suggestion Methods

[1456] Based on the analysis results, the server suggests the most suitable meal for the user at that time. The suggested menu is generated taking into account the user's mood and health condition.

[1457] 8. Delivery Ordering Methods

[1458] This provides a means to immediately reflect the suggested menu in delivery orders.

[1459] terminal

[1460] A device refers to a user device such as a smartphone or tablet, and plays the following main roles:

[1461] 1. Data transmission

[1462] The device sends basic user information, behavioral data, emotional data, and other data to the server.

[1463] 2. Collecting emotional data

[1464] The device analyzes the user's facial expressions and voice, collecting emotional data in real time.

[1465] 3. Menu display and ordering

[1466] The terminal displays the menu received from the server to the user and reflects the selected menu in the delivery order.

[1467] User

[1468] Users follow these steps when using the system:

[1469] 1. User registration and basic information entry

[1470] Users enter basic information through a terminal and register it in the system.

[1471] 2. Data Provision

[1472] Users link their social media accounts and consent to the provision of purchase and travel history data. They also provide genetic data using a genetic testing kit.

[1473] 3. Provision of emotional data

[1474] Users provide emotional data through their device's camera and microphone.

[1475] 4. Choosing and ordering your meal

[1476] Choose your meal from the suggested menu and complete your delivery order.

[1477] Hardware and software to use

[1478] Hardware: Smartphones, tablets, and camera-equipped devices

[1479] Software: Emotion Detection API, AI Model API, User Profile API

[1480] Examples of specific cases and prompt statements

[1481] For example, when a user launches the app before lunch, a video feed automatically starts and facial expression data is collected. If the user is feeling stressed, for instance, that emotional data is analyzed, and a meal that helps reduce stress is suggested. Furthermore, a nutritionally balanced menu is provided, taking into account past purchase history and genetic data.

[1482] Example of a prompt:

[1483] "Please suggest nutritionally balanced meal plans that users experiencing stress would prefer, taking into account their past purchase history and genetic data."

[1484] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1485] Step 1:

[1486] User Registration

[1487] Users enter basic information (name, age, gender, email address, etc.) using a terminal. This input data is sent to the terminal and then forwarded from the terminal to the server. Based on the received basic information, the server generates a profile ID for each user and stores it in a database.

[1488] Step 2:

[1489] Collection of everyday data

[1490] Users link their social media accounts using their devices and consent to providing data on their purchase and movement history. The devices use APIs to collect social media activity (e.g., tweets, likes, follows, etc.) and send it to the server. The server receives and analyzes this data to generate a user behavior profile.

[1491] Step 3:

[1492] Collection of genetic data

[1493] The user uses a mailed genetic testing kit to collect a sample, which is then sent back to the testing laboratory. Once the genetic test results are ready, the device receives them and uploads them to the server. The server receives the genetic data and adds it to the user profile using specialized analysis algorithms.

[1494] Step 4:

[1495] Collection of emotional data

[1496] Users utilize messaging and video chat functions provided within the system using their devices. The devices analyze the user's facial expressions and voice in real time, collecting emotional data. This collected emotional data is sent to a server and added to the user profile.

[1497] Step 5:

[1498] Compatibility calculation

[1499] The server uses an AI model to analyze user characteristics based on basic information, behavioral data, genetic data, and emotional data. Based on this analysis, it calculates compatibility scores with other users and generates a list of optimal partner candidates. The candidate list is sent to the device and the user is notified.

[1500] Step 6:

[1501] Meal suggestions

[1502] The server comprehensively analyzes collected basic information, behavioral data, genetic data, and emotional data, and proposes an optimal meal plan considering the user's mood and nutritional status at that time. This proposal is sent to and displayed on the device.

[1503] Step 7:

[1504] Delivery Order

[1505] The user reviews the suggested menu on the terminal and orders their selected meal for delivery. The terminal sends the selection to the server, which then works with the delivery service to process the order, and the meal is delivered to the user.

[1506] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1507] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1508] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1509] [Fourth Embodiment]

[1510] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1511] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1512] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1513] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1514] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1515] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1516] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1517] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1518] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1519] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1520] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1521] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1522] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1523] This invention relates to a system that receives basic user information, identifies each user, collects and analyzes daily data, and uses AI to suggest compatible partners. This system consists of three elements: a server, a terminal, and a user, and is implemented as follows.

[1524] 1. User Registration

[1525] Users access the system's app or website and enter basic information such as their name, age, gender, and email address on the registration page.

[1526] The terminal receives the entered information and sends it to the server.

[1527] The server stores the received information in a database and generates a profile ID for each user.

[1528] 2. Data Collection

[1529] Users link their social media accounts and consent to the sharing of purchase and travel history data.

[1530] The device periodically collects social media activity (tweets, likes, follows, etc.) using an API and sends it to the server.

[1531] The server analyzes the received behavioral data and generates a user characteristic profile.

[1532] 3. Genetic analysis

[1533] Users use genetic testing kits provided by partner companies, perform the tests at home, and then send the samples back.

[1534] The device receives a notification when the genetic test results are ready and uploads the results to the server.

[1535] The server receives the genetic data, processes it using specialized analysis algorithms, and adds it to the user profile.

[1536] 4. Compatibility Calculation

[1537] The server uses all collected data (basic information, behavioral data, and genetic data) to analyze user characteristics with an AI model.

[1538] The server calculates compatibility scores with other registered users and generates a list of optimal partner candidates.

[1539] The server sends the generated list of candidates to the terminal.

[1540] The device displays a list of potential partners who are a good match for the user.

[1541] As a concrete example, let's consider the case where user A uses the system.

[1542] 1. User Registration

[1543] User A accesses the system's app from their smartphone and enters their name, age, gender, and email address. The device sends the entered information to the server, which stores the information in a database and generates a profile ID for User A.

[1544] 2. Data Collection

[1545] User A links their social media accounts and agrees to provide purchase and movement history data. The device collects data such as User A's tweets, likes, and purchase history via an API and sends it to the server. The server analyzes this data and generates a characteristic profile of User A.

[1546] 3. Genetic analysis

[1547] User A uses a genetic testing kit sent by mail and submits the sample. Once the test results are ready, the terminal uploads the results to the server. The server processes the genetic data using an analysis algorithm and adds it to User A's profile.

[1548] 4. Compatibility Calculation

[1549] The server uses an AI model to analyze user A's characteristics based on their basic information, behavioral data, and genetic data. The server then compares user A to other users, calculates a compatibility score, and generates a list of optimal partner candidates. This list is then sent to user A via their device.

[1550] This program is designed to help users find truly compatible partners. When User A meets with a displayed potential partner, the device collects behavioral data in real time and sends it to the server. The server uses this data to continuously improve the AI ​​model, providing more accurate compatibility diagnoses. In this way, the system not only improves overall accuracy but also provides comprehensive support to help User A find the best partner.

[1551] The following describes the processing flow.

[1552] Step 1:

[1553] Users access the system's app or website and enter basic information such as their name, age, gender, and email address.

[1554] Step 2:

[1555] The terminal sends the entered basic information to the server.

[1556] Step 3:

[1557] The server stores the received basic information in a database and generates a profile ID for each user.

[1558] Step 4:

[1559] Users link their social media accounts and consent to the sharing of purchase and travel history data.

[1560] Step 5:

[1561] The device periodically collects social media activity (tweets, likes, follows, etc.) using an API and sends it to the server.

[1562] Step 6:

[1563] The server analyzes the received behavioral data and generates a user characteristic profile.

[1564] Step 7:

[1565] Users use genetic testing kits provided by partner companies, perform the tests at home, and then send the samples back.

[1566] Step 8:

[1567] The device receives a notification when the genetic test results are ready and uploads the results to the server.

[1568] Step 9:

[1569] The server receives the genetic data, processes it using specialized analysis algorithms, and adds it to the user profile.

[1570] Step 10:

[1571] The server uses an AI model to analyze characteristics based on the collected basic information, behavioral data, and genetic data.

[1572] Step 11:

[1573] The server calculates compatibility scores with other registered users and generates a list of optimal partner candidates.

[1574] Step 12:

[1575] The server sends the generated list of candidates to the terminal.

[1576] Step 13:

[1577] The device displays a list of potential partners who are a good match for the user.

[1578] Step 14:

[1579] Users select someone they are interested in from the displayed list of candidates and view their details.

[1580] Step 15:

[1581] The device requests detailed information about the partner candidate selected by the user from the server.

[1582] Step 16:

[1583] Based on the request, the server sends detailed information about the candidate to the terminal.

[1584] Step 17:

[1585] The device displays detailed information about the candidate (name, age, interests, etc.) to the user.

[1586] Step 18:

[1587] Users exchange messages with their chosen potential partners and arrange to meet in person.

[1588] Step 19:

[1589] The device records the date, time, and location of the scheduled meeting and notifies the user with a reminder.

[1590] Step 20:

[1591] The device collects user behavior data (location information, conversation time, etc.) in real time while the user is actually meeting and sends it to the server.

[1592] Step 21:

[1593] The server analyzes the collected behavioral data to evaluate the actual compatibility and relationship of the couple.

[1594] Step 22:

[1595] The server continuously improves its AI model based on collected behavioral data, thereby enhancing the accuracy of compatibility calculations.

[1596] (Example 1)

[1597] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1598] Currently, many matching systems exist, but these systems rely solely on users' basic information and preferences, making it difficult to guarantee long-term compatibility. Furthermore, few systems consider users' genetic characteristics and behavioral history when performing matching. As a result, even when a match is successful, there is a problem in that compatibility necessary for building a lasting relationship is not adequately evaluated.

[1599] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1600] In this invention, the server includes means for receiving user identification information and generating user-specific identification information; means for collecting user behavior history data; means for analyzing the collected data and generating user behavioral characteristics; means for receiving and analyzing genetic information and adding it to the user profile; means for using an AI model with all the data to calculate the optimality with other users; and means for generating an optimal candidate list based on the calculation results and notifying the user. This enables more accurate matching that takes into account the user's behavior data and genetic information.

[1601] "User identification information" refers to information that the system uses to uniquely identify a user, and includes information such as name, age, gender, and email address.

[1602] "Means for generating identification information" refers to a function that collects basic user information and automatically generates a unique identification ID based on that information.

[1603] "Behavioral history data" refers to data obtained from a user's daily activities, including social media activity, purchase history, and travel history.

[1604] "Means for collecting behavioral history data" refers to a function that automatically and periodically retrieves behavioral history from data sources authorized by the user and sends it to the server.

[1605] "Behavioral characteristics" refer to user-specific traits and patterns extracted by analyzing user behavior history data.

[1606] "Means for generating behavioral characteristics" refers to a function that analyzes collected behavioral history data to identify the user's behavioral characteristics.

[1607] "Genetic information" refers to information about the genetic characteristics obtained from the user's genetic test results.

[1608] "Means of receiving, analyzing, and adding genetic information to a user profile" refers to a function that uploads genetic information to a server, analyzes that information, and integrates it into the user's profile.

[1609] "Methods for calculating optimality using AI models" refers to a function that uses AI to quantify the compatibility between users based on collected data and calculates the optimal partner.

[1610] The "optimal candidate list" is a list that ranks the most suitable partners for the user based on compatibility scores calculated by an AI model.

[1611] The "means for generating and notifying candidates" refer to a function that creates a candidate list based on compatibility scores calculated by an AI model and notifies the user of this list.

[1612] This invention relates to a system that receives basic user information, generates user-specific identification information, collects and analyzes daily data, and uses AI to suggest compatible partners. This system consists of three elements: a server, a terminal, and a user, and specifically has the following functions.

[1613] First, the user accesses the system's app or website and enters basic information such as name, age, gender, and email address on the registration page. The device receives the entered information in real time and sends it to the server using the HTTPS protocol. On the server, the received basic information is stored in a database, and a profile ID is assigned to each user using a unique ID generation algorithm (e.g., UUID).

[1614] Next, the user links their social media accounts and consents to providing purchase and movement history data. The device uses OAuth to gain access to the user's social media accounts and periodically collects user behavior data through API endpoints, sending it to the server. The server analyzes this data using text analysis and clustering algorithms to generate a profile that identifies the user's behavioral characteristics.

[1615] Users collect a sample using a genetic testing kit mailed to them by a partner company and return it. When the test results are ready, the testing company's system sends a notification to the user's device, and the device uploads the test result file to the server. Encrypted communication is used to ensure data security during this process. The server processes the received genetic data using a dedicated analysis algorithm and adds the results to the user profile.

[1616] Using all the data (basic information, behavioral data, and genetic data), the server uses a trained generative AI model to calculate compatibility with other registered users. This process utilizes a deep learning matching network to evaluate compatibility between users. Based on the calculation results, the server generates a list of optimal partner candidates and sends it to the terminal. The terminal provides the user with an interface to visually display this list.

[1617] As a concrete example, consider the case where User A uses this system. User A accesses the system's app from their smartphone and enters basic information. The device sends the information to the server via HTTPS, and the server generates a UUID and stores it in the database. Next, User A links their social media accounts and agrees to provide purchase history and travel history data. The device collects data using OAuth and sends it to the server. The server analyzes this data and compiles User A's behavioral characteristics into a profile. User A uses a genetic testing kit and sends a sample. Once the test results are ready, the device uploads the results to the server, and the server processes the genetic data using an analysis algorithm and adds it to the profile. Finally, the server uses a generative AI model to calculate a compatibility score, generates a list of optimal partner candidates, and notifies User A of this.

[1618] An example of a prompt to input into a generative AI model is as follows:

[1619] "Create an AI model that identifies compatible partners using users' basic information, behavioral data, and genetic data. Calculate compatibility scores and generate a list of optimal partner candidates."

[1620] In this way, the system helps users find compatible partners more efficiently and achieves more accurate matching by taking into account the user's behavioral data and genetic information.

[1621] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1622] Step 1: User Registration

[1623] Users access the system's app or website and enter basic information such as their name, age, gender, and email address on the registration page.

[1624] The terminal receives the entered information in real time and sends it to the server using the HTTPS protocol. Specifically, it serializes the data in the form into JSON format and sends it.

[1625] When the server stores the received basic information in the database, it uses a unique ID generation algorithm (e.g., UUID) to assign a profile ID to each user. The input is the user's basic information, and the output is the generated profile ID.

[1626] Step 2: Data Collection

[1627] Users link their social media accounts and consent to the sharing of purchase and travel history data.

[1628] The device uses OAuth to gain access to the user's social media accounts. It collects data such as the user's tweets, likes, and follows at regular intervals via an API endpoint and sends it to the server. Specifically, it calls the API to retrieve the data and sends it to the server in JSON format.

[1629] The server analyzes the received behavioral data using text analysis, clustering algorithms, and other methods to generate a profile that analyzes the user's behavioral characteristics. Inputs include social media activity, purchase history, and travel history, while the output is a profile of behavioral characteristics.

[1630] Step 3: Genetic Analysis

[1631] Users use genetic testing kits provided by partner companies, perform the tests at home, and then send the samples back.

[1632] The device receives a notification when the genetic test results are ready and uploads the results to the server. Specifically, it calls the testing company's API to receive the results file and sends it to the server.

[1633] The server receives genetic data, processes it using a dedicated analysis algorithm, and adds the results to the user profile. The input is test result data, and the output is genetic characteristics added to the user profile.

[1634] Step 4: Compatibility Calculation

[1635] The server integrates basic information, behavioral data, and genetic data, and uses an AI model to calculate compatibility with other registered users. Specifically, it uses a deep learning model to calculate compatibility scores between each user.

[1636] The server creates a list of optimal partner candidates based on the generated compatibility scores. This candidate list is generated as the output of the AI ​​model.

[1637] The server generates a list of optimal partner candidates and sends it to the terminal. The terminal provides the user with an interface that visually displays the list.

[1638] Step 5: Real-time collection of behavioral data and improvement of the AI ​​model

[1639] The device collects real-time behavioral data from when the user actually meets with a candidate. Specifically, the device records the user's location information and behavioral patterns and sends them to the server.

[1640] The server receives this new behavioral data and uses it as feedback to continuously improve the accuracy of the AI ​​model. Real-time collected behavioral data is used as input, and an updated AI model is generated as output.

[1641] Through the processing steps described above, this system helps users efficiently find compatible partners and achieves more accurate matching by taking into account the user's behavioral data and genetic information.

[1642] (Application Example 1)

[1643] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1644] Traditional partner recommendation systems primarily calculate compatibility using only basic user information and behavioral data. However, this approach fails to accurately reflect the user's overall characteristics, resulting in inaccurate recommendations. Furthermore, the development of individually optimized content recommendation systems has been slow, leaving a lack of means to improve compatibility with entertainment content such as movies and dramas that users watch. This highlighted the need for improved user experience.

[1645] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1646] In this invention, the server includes means for receiving basic user information and generating a user-specific profile ID; means for collecting the user's daily data (such as social media activity, purchase history, and travel history); means for analyzing the collected data and generating a user characteristic profile; means for receiving, analyzing, and adding genetic test results to the user profile; means for using an AI model with all the data to calculate compatibility with other users; means for generating a list of optimal partner candidates based on the calculation results and notifying the user; and means for using digital media viewing history and social media activity data to recommend individually optimized content based on the user profile. This enables more accurate reflection of the user's overall characteristics and allows for highly accurate partner recommendations and individually optimized content recommendations.

[1647] "User basic information" refers to information that allows for individual identification of a user, such as their name, age, gender, and email address.

[1648] A "profile ID" is a unique identification code for each user, used to uniquely identify a user within the database.

[1649] "Social media activity" refers to actions that users take on social media platforms, such as tweeting, liking, following, and posting.

[1650] "Purchase history" is a record of a user's online and offline purchasing activities.

[1651] "Travel history" refers to information about places a user has visited and the routes they have traveled.

[1652] A "characteristic profile" is a profile that represents a user's personality, hobbies, and preferences, generated based on the user's behavioral data, basic information, and genetic data.

[1653] "Genetic test results" refer to genetic characteristics and health information obtained by analyzing genetic material collected from the user.

[1654] An "AI model" is an artificial intelligence algorithm that learns from a large amount of data, finds patterns and relationships, and then makes predictions and judgments.

[1655] "Calculating compatibility" means using an AI model to compare a user's characteristic profile with the characteristic profiles of other users and derive a compatibility score based on similarities and differences.

[1656] The "Partner Candidate List" is a list of the most suitable partner candidates determined by compatibility calculations.

[1657] "Digital media viewing history" refers to a record of content such as movies, dramas, and video clips that a user has watched in the past.

[1658] "Personalized content" refers to recommending content that is most likely to interest a user based on their characteristic profile.

[1659] To implement this invention, a system is required that includes three elements: a user, a terminal, and a server. This system starts with receiving the user's basic information and covers the collection and analysis of various data, compatibility calculations using an AI model, and finally the generation of a list of potential partners and the recommendation of individually optimized content.

[1660] 1. User Registration

[1661] Users access the system's app using a device (e.g., a smartphone) and enter basic information such as their name, age, gender, and email address. The device sends this information to the server, which stores the received information in a database and generates a profile ID for each user.

[1662] 2. Data Collection

[1663] Users link their social media accounts and consent to providing purchase and travel history data. The device uses an API to collect social media activity data (tweets, likes, follows, etc.), purchase history, and travel history, and sends it to the server. The server analyzes this data to generate a user profile.

[1664] 3. Genetic analysis

[1665] Users use genetic testing kits provided by partner companies, perform the test at home, and then send the sample back. When the results are ready, the device receives a notification and uploads the results to the server. The server receives the genetic data, processes it using specialized analysis algorithms, and adds it to the user profile.

[1666] 4. Compatibility Calculation

[1667] The server uses an AI model to analyze the user's characteristics based on their basic information, behavioral data, and genetic data. The server calculates compatibility scores with other users and generates a list of optimal partner candidates. Furthermore, based on the user profile, it uses data from digital media viewing history and social media activity to recommend individually optimized content. The calculation results are communicated to the user via their device.

[1668] Hardware and software to use

[1669] Hardware: Smartphone

[1670] Software: Python, TF-IDF vectorizer (scikit-learn library), API interface tools

[1671] Specific example

[1672] This illustrates how User A uses this system. User A accesses the smartphone app and enters their name, age, gender, and email address. They then link their social media accounts and agree to provide data on their purchase and travel history. User A uses a genetic testing kit and sends in a sample. The server analyzes User A's social media activity, viewing history, and genetic data to generate a trait profile. Furthermore, it uses an AI model to generate a list of optimal partner candidates and provides personalized movie and TV show recommendations.

[1673] Example of a prompt

[1674] User ID: u1, Name: Yu, Age: 30, Gender: Male, Email: yu@example.com

[1675] Social media data: action, drama, explosion

[1676] Genetic data: visual_sensitive, auditory_sensitive

[1677] This configuration allows for a detailed understanding of user characteristics, resulting in a system that enables highly accurate partner recommendations and individually optimized content recommendations.

[1678] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1679] Step 1:

[1680] User Registration

[1681] Users access the system's app using their device (smartphone) and enter basic information such as their name, age, gender, and email address. The device sends this information to the server. The server stores the received information in a database and generates a unique profile ID for each user. The input is the user's basic information, and the output is the user's profile ID. In terms of data processing, the user's basic information is integrated into a unique ID.

[1682] Step 2:

[1683] Social media links and data collection

[1684] Users link their social media accounts and consent to providing purchase and travel history data. The device uses an API to collect social media activity data and send it to a server. The server analyzes this data and generates a user profile. Inputs are social media activity, purchase history, and travel history data, while output is the analyzed profile. Data processing includes statistical analysis of collected data and profile generation.

[1685] Step 3:

[1686] Genetic analysis

[1687] Users use genetic testing kits provided by partner companies, perform the test at home, and then send the sample back. When the results are ready, the device receives a notification and uploads the results to the server. The server analyzes the genetic data, processes it using specialized analysis algorithms, and adds it to the user profile. The input is genetic data, and the output is an updated user profile. Data processing involves analyzing genetic information and integrating it into the profile.

[1688] Step 4:

[1689] Compatibility calculation and partner recommendation

[1690] The server uses an AI model to analyze the user's characteristics based on their basic information, behavioral data, and genetic data. The server calculates compatibility scores with other users and generates a list of optimal partner candidates. The calculation results are notified to the user via their terminal. The input is all user data, and the output is a list of partner candidates. Data calculations include the calculation of compatibility scores by the AI ​​model.

[1691] Step 5:

[1692] Personalized content recommendations

[1693] The server recommends personalized content based on the user profile, using data from digital media viewing history and social media activity. The calculation results are notified to the user via the terminal. The input is viewing history and social media data, and the output is a content recommendation list. Specifically, the system performs analysis of viewing history data and content recommendations based on the user's profile.

[1694] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1695] This invention aims to perform more accurate compatibility calculations by combining an emotion engine with a system that analyzes a user's basic information, behavioral data, and genetic data, and uses AI to suggest compatible partners, thereby adding the user's emotional data to their profile. This system consists of three elements: a server, a terminal, and a user, and operates as follows.

[1696] 1. User Registration

[1697] Users access the system's app or website and enter basic information such as their name, age, gender, and email address.

[1698] The terminal sends the entered basic information to the server.

[1699] The server stores the received basic information in a database and generates a profile ID for each user.

[1700] 2. Data Collection

[1701] Users link their social media accounts and consent to the sharing of purchase and travel history data.

[1702] The device periodically collects social media activity (tweets, likes, follows, etc.) using an API and sends it to the server.

[1703] The server analyzes the received behavioral data and generates a user characteristic profile.

[1704] 3. Genetic analysis

[1705] Users use genetic testing kits provided by partner companies, perform the tests at home, and then send the samples back.

[1706] The device receives a notification when the genetic test results are ready and uploads the results to the server.

[1707] The server receives the genetic data, processes it using specialized analysis algorithms, and adds it to the user profile.

[1708] 4. Collection and analysis of emotional data

[1709] Users utilize messaging and video chat features provided by the system.

[1710] The device analyzes the user's words, actions, and facial expressions, collecting emotional data in real time.

[1711] The server analyzes the received emotion data and adds it to the trait profile.

[1712] 5. Compatibility Calculation

[1713] The server uses all collected data (basic information, behavioral data, genetic data, and emotional data) to analyze the user's characteristics with an AI model.

[1714] The server calculates compatibility scores with other registered users and generates a list of optimal partner candidates.

[1715] The server sends the generated list of candidates to the terminal.

[1716] The device displays a list of potential partners who are a good match for the user.

[1717] As a concrete example, let's consider the case where user A uses the system.

[1718] 1. User Registration

[1719] User A accesses the system's app from their smartphone and enters their name, age, gender, and email address. The device sends the entered information to the server, which stores the information in a database and generates a profile ID for User A.

[1720] 2. Data Collection

[1721] User A links their social media accounts and agrees to provide purchase and movement history data. The device collects data such as User A's tweets, likes, and purchase history via an API and sends it to the server. The server analyzes this data and generates a characteristic profile of User A.

[1722] 3. Genetic analysis

[1723] User A uses a genetic testing kit sent by mail and submits the sample. Once the test results are ready, the terminal uploads the results to the server. The server processes the genetic data using an analysis algorithm and adds it to User A's profile.

[1724] 4. Collection and analysis of emotional data

[1725] While User A is using the messaging and video chat functions within the system, the device analyzes User A's facial expressions and behavior, collecting emotional data. The server analyzes the received emotional data and adds it to a trait profile.

[1726] 5. Compatibility Calculation

[1727] The server uses an AI model to analyze user A's characteristics based on their basic information, behavioral data, genetic data, and emotional data. The server then compares user A to other users, calculates a compatibility score, and generates a list of optimal partner candidates. This list is then communicated to user A via their device.

[1728] This program is designed to help users find truly compatible partners. When User A meets with a displayed partner candidate, the device collects behavioral and emotional data in real time and sends it to the server. The server continuously improves the AI ​​model based on this data, providing more accurate compatibility diagnoses. In this way, the system not only improves the overall accuracy but also provides comprehensive support for User A to find the best partner.

[1729] The following describes the processing flow.

[1730] Step 1:

[1731] Users access the system's app or website and enter basic information such as their name, age, gender, and email address.

[1732] Step 2:

[1733] The terminal sends the entered basic information to the server.

[1734] Step 3:

[1735] The server stores the received basic information in a database and generates a profile ID for each user.

[1736] Step 4:

[1737] Users link their social media accounts and consent to the sharing of purchase and travel history data.

[1738] Step 5:

[1739] The device periodically collects social media activity (tweets, likes, follows, etc.) using an API and sends it to the server.

[1740] Step 6:

[1741] The server analyzes the received behavioral data and generates a user characteristic profile.

[1742] Step 7:

[1743] Users use genetic testing kits provided by partner companies, perform the tests at home, and then send the samples back.

[1744] Step 8:

[1745] The device receives a notification when the genetic test results are ready and uploads the results to the server.

[1746] Step 9:

[1747] The server receives the genetic data, processes it using specialized analysis algorithms, and adds it to the user profile.

[1748] Step 10:

[1749] Users utilize messaging and video chat features provided by the system.

[1750] Step 11:

[1751] The device analyzes the user's facial expressions and behavior to recognize their emotions and collects that data.

[1752] Step 12:

[1753] The server adds the received emotional data to the profile, generating a more detailed trait profile.

[1754] Step 13:

[1755] The server analyzes all collected data (basic information, behavioral data, genetic data, and emotional data) and uses an AI model to evaluate the user's characteristics.

[1756] Step 14:

[1757] The server calculates compatibility scores with other registered users and generates a list of optimal partner candidates.

[1758] Step 15:

[1759] The server sends the generated list of candidates to the terminal.

[1760] Step 16:

[1761] The device displays a list of potential partners who are a good match for the user.

[1762] Step 17:

[1763] Users select someone they are interested in from the displayed list of candidates and view their details.

[1764] Step 18:

[1765] The device requests detailed information about the partner candidate selected by the user from the server.

[1766] Step 19:

[1767] Based on the request, the server sends detailed information about the candidate to the terminal.

[1768] Step 20:

[1769] The device displays detailed information about the candidate (name, age, interests, etc.) to the user.

[1770] Step 21:

[1771] Users exchange messages with their chosen potential partners and arrange to meet in person.

[1772] Step 22:

[1773] The device records the date, time, and location of the scheduled meeting and notifies the user with a reminder.

[1774] Step 23:

[1775] The device collects user behavior data (location information, conversation time, etc.) and emotional data in real time while the user is actually meeting, and sends this data to the server.

[1776] Step 24:

[1777] The server analyzes the collected behavioral and emotional data to evaluate the couple's actual compatibility and relationship.

[1778] Step 25:

[1779] The server continuously improves the AI ​​model based on the collected data, thereby increasing the accuracy of compatibility calculations.

[1780] (Example 2)

[1781] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1782] Traditional matching systems often calculate compatibility based only on basic user information and daily behavioral data, failing to consider deeper data such as the user's emotional state or genetic information, thus limiting the accuracy of compatibility calculations. Furthermore, there is a lack of mechanisms to continuously improve the system's accuracy using data on the user's behavior and emotions when they actually meet their matched partner, resulting in insufficient improvement of the user experience.

[1783] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1784] In this invention, the server includes means for receiving basic user information and generating a profile ID for each user; means for collecting the user's daily information (social media activity, purchase history, travel history, etc.); means for analyzing the collected information and generating a user characteristic profile; means for receiving, analyzing, and adding genetic test results to the user profile; means for collecting emotional data from the user's behavior and facial expressions and adding it to the profile; means for using a generated AI model with all the data to calculate compatibility with other users; and means for generating a list of optimal partner candidates based on the calculation results and notifying the user. This enables comprehensive analysis of diverse user data and more accurate compatibility calculations. Furthermore, by utilizing actual behavior and emotional data after matching, the accuracy of the entire system can be continuously improved, leading to an enhanced user experience.

[1785] "Basic information" refers to identifying information such as the user's name, age, gender, and email address.

[1786] "Everyday information" refers to behavioral data from users' daily lives, such as their social media activity, purchase history, and travel history.

[1787] A "characteristic profile" refers to a profile of a user's interests, behavioral characteristics, personality, and other traits, which is analyzed based on collected information.

[1788] "Genetic test results" refers to the results of the genetic information obtained by the user through genetic testing.

[1789] "Emotional data" refers to data on the emotional state obtained from the user's behavior and facial expressions.

[1790] A "generative AI model" refers to an artificial intelligence model used to analyze user characteristics and compatibility based on collected data.

[1791] A "compatibility score" refers to the result of quantifying the compatibility between a user and other users based on the user's characteristic data.

[1792] The "Partner Candidate List" refers to a list of potential partners that are best suited to the user, generated based on compatibility scores.

[1793] This invention is a system that analyzes a user's basic information, daily information, genetic information, and emotional data, and uses a generative AI model to suggest a compatible partner. This system consists of three elements: a server, a terminal, and the user.

[1794] 1. User Registration

[1795] The user first accesses the system's app or website and enters basic information such as their name, age, gender, and email address. The device sends this information to the server, which stores it in a database and generates a profile ID.

[1796] 2. Data Collection

[1797] Users link their social media accounts and consent to providing their purchase and travel history. The device periodically collects social media activity (tweets, likes, follows, etc.) using an API and sends it to the server. The server analyzes this behavioral data to generate a user profile.

[1798] 3. Genetic analysis

[1799] Users perform genetic testing at home using a genetic testing kit provided by a partner company. Once the sample is returned, the device notifies the user that the genetic test results are ready and uploads the results to a server. The server processes the genetic data using an analysis algorithm and adds it to the user profile.

[1800] 4. Collection and analysis of emotional data

[1801] As users utilize messaging and video chat features provided by the system, their devices analyze their speech, actions, and facial expressions, collecting emotional data in real time. The server then analyzes the received emotional data and adds it to their characteristic profile.

[1802] 5. Compatibility Calculation

[1803] The server uses all collected data (basic information, behavioral data, genetic data, and emotional data) to analyze the user's characteristics with a generated AI model. The server calculates compatibility scores with other registered users and generates a list of optimal partner candidates. This list is sent to the device, which then displays a list of compatible partner candidates to the user.

[1804] Specific example

[1805] When user A uses the system, user A accesses the system's app from their smartphone and enters their name, age, gender, and email address. The device sends the entered information to the server, which stores the information in a database and generates user A's profile ID.

[1806] Next, User A links their social media accounts and agrees to provide purchase and movement history data. The device collects data such as User A's tweets, likes, and purchase history via an API and sends it to the server. The server analyzes this data and generates a profile of User A's characteristics.

[1807] Furthermore, User A uses the mailed genetic testing kit to send in a sample. Once the test results are ready, the terminal uploads the results to the server. The server processes the genetic data using an analysis algorithm and adds it to User A's profile.

[1808] Next, while User A uses the messaging and video chat functions within the system, the device analyzes User A's facial expressions and behavior, collecting emotional data. The server analyzes the received emotional data and adds it to the characteristic profile.

[1809] Ultimately, the server uses user A's basic information, behavioral data, genetic data, and emotional data to analyze their characteristics with a generative AI model. The server compares them to other users, calculates a compatibility score, and generates a list of optimal partner candidates. This list is then communicated to user A via their device.

[1810] Example of a prompt

[1811] Please analyze the user characteristics based on the following data.

[1812] 1. Basic Information: Name, Age, Gender, Email Address

[1813] 2. Behavioral data: Social media activity, purchase history, travel history

[1814] 3. Genetic data: Genetic test results

[1815] 4. Emotional Data: Facial expression analysis and results obtained through messaging and video chat functions.

[1816] This system is designed to help users find truly compatible partners. When a user meets with a displayed potential partner, the device collects behavioral and emotional data in real time and sends it to the server. The server continuously improves the generated AI model based on this data, providing more accurate compatibility diagnoses. In this way, the system not only improves the overall accuracy but also provides comprehensive support for users to find the best partner for them.

[1817] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1818] Program processing flow

[1819] Step 1: Access the user registration page.

[1820] Subject: User

[1821] Specific steps: The user opens a browser on their smartphone or PC, or a dedicated app, and accesses the system's user registration page.

[1822] Input: The user taps the app icon or enters a URL.

[1823] Output: The registration page is displayed.

[1824] Step 2: Enter basic information

[1825] Subject: User

[1826] Specific actions: The user enters their name, age, gender, and email address on the registration page.

[1827] Input: Enter your "Name," "Age," "Gender," and "Email Address" in the form.

[1828] Output: Basic information is entered.

[1829] Step 3: Submit basic information

[1830] Subject: terminal

[1831] Specific operation: The terminal sends the entered basic information to the server.

[1832] Input: Basic information entered (name, age, gender, email address).

[1833] Output: Generation of data to be sent to the server.

[1834] Step 4: Generating a Profile ID

[1835] Subject: Server

[1836] Specific operation: The server saves the received basic information to the database and generates a profile ID for each user.

[1837] Input: Basic information (name, age, gender, email address).

[1838] Data processing: Create a new entry in the database and generate a unique profile ID.

[1839] Output: Profile ID for each user.

[1840] Step 5: Link your social media accounts

[1841] Subject: User

[1842] Specific actions: Users link their social media accounts and consent to the sharing of their purchase and travel history.

[1843] Input: Enter your login information on the authentication screen.

[1844] Output: Social media accounts are linked.

[1845] Step 6: Start data collection

[1846] Subject: terminal

[1847] Specific operation: The device uses an API to periodically collect social media activity (tweets, likes, follows, etc.) and send it to the server.

[1848] Input: Send a request to the social media API.

[1849] Output: Send the acquired data to the server.

[1850] Step 7: Generating the characteristic profile

[1851] Subject: Server

[1852] Specific operation: The server analyzes the received behavioral data and generates a user characteristic profile.

[1853] Input: Behavioral data (tweets, likes, follows, etc.).

[1854] Data processing: Analyze behavioral data to extract user interests and behavioral characteristics.

[1855] Output: User profile.

[1856] Step 8: Receiving the genetic testing kit

[1857] Subject: User

[1858] Specific operation: The user receives a genetic testing kit provided by a partner company and performs the test at home.

[1859] Input: Collect a saliva sample using the test kit.

[1860] Output: Collected sample.

[1861] Step 9: Sending the sample

[1862] Subject: User

[1863] Specific action: The user places the collected sample in the designated envelope and mails it.

[1864] Input: Place the collected sample in an envelope.

[1865] Output: Sample sent by mail.

[1866] Step 10: Uploading test results

[1867] Subject: terminal

[1868] Specific operation: The device receives a notification when the test results are ready and uploads the results to the server.

[1869] Input: Test result file.

[1870] Output: Test results uploaded to the server.

[1871] Step 11: Analysis of Genetic Data

[1872] Subject: Server

[1873] Specific operation: The server receives genetic data, processes it using specialized analysis algorithms, and adds it to the user profile.

[1874] Input: Genetic data.

[1875] Data processing: Analyze genetic information and extract data based on characteristics.

[1876] Output: Genetic information added to the user profile.

[1877] Step 12: Using the messaging function

[1878] Subject: User

[1879] Specific operation: Users utilize messaging and video chat functions provided by the system.

[1880] Input: Send a message, start a video call.

[1881] Output: Send a message, start a video call.

[1882] Step 13: Collecting emotional data

[1883] Subject: terminal

[1884] Specific operation: The device analyzes the user's words, actions, and facial expressions, and collects emotional data in real time.

[1885] Input: User's facial expressions during messages or video calls.

[1886] Data processing: Facial recognition is performed to obtain emotion data.

[1887] Output: Acquired sentiment data.

[1888] Step 14: Analyzing emotional data

[1889] Subject: Server

[1890] Specific operation: The server analyzes the received emotion data and adds it to the trait profile.

[1891] Input: Sentiment data.

[1892] Data processing: Score emotional data.

[1893] Output: Sentimental information added to the trait profile.

[1894] Step 15: Analysis of all data

[1895] Subject: Server

[1896] Specific operation: The server uses all collected data (basic information, behavioral data, genetic data, emotional data) to generate an AI model that analyzes the user's characteristics.

[1897] Input: Basic information, behavioral data, genetic data, emotional data.

[1898] Data processing: Analyze data using generative AI models.

[1899] Output: Generation of characteristic profile.

[1900] Step 16: Calculate the compatibility score

[1901] Subject: Server

[1902] Specific operation: The server calculates a compatibility score with other registered users.

[1903] Input: Characteristic profile.

[1904] Data processing: Compare the characteristics of each user and calculate a compatibility score.

[1905] Output: Compatibility score.

[1906] Step 17: Generating a list of potential partners

[1907] Subject: Server

[1908] Specific operation: The server generates a list of optimal partner candidates based on the calculation results and notifies the user.

[1909] Input: Compatibility score.

[1910] Data processing: Generate a list of potential partners based on compatibility scores.

[1911] Output: List of potential partners.

[1912] Step 18: Display the list of potential partners

[1913] Subject: terminal

[1914] Specific operation: The device displays a list of compatible partner candidates to the user.

[1915] Input: List of potential partners.

[1916] Output: The list displayed in the user interface.

[1917] (Application Example 2)

[1918] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1919] Traditional matching systems calculate compatibility based on basic user information and behavioral data, but they do not take into account the user's emotional state or mood on any given day. Therefore, they may fail to respond to the user's actual emotions and immediate needs, resulting in low satisfaction. Furthermore, there is no way to suggest what kind of meal is best suited to the user at that moment, limiting the improvement of the user experience. Additionally, there is no means to immediately reflect suggested menus in delivery orders. This makes it difficult to suggest meals that take into account the user's health condition and nutritional balance.

[1920] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1921] In this invention, the server includes means for receiving basic user information and generating a user-specific profile ID; means for collecting the user's daily data (social media activity, purchase history, travel history, etc.); means for analyzing the collected data and generating a user characteristic profile; means for receiving, analyzing, and adding genetic test results to the user profile; means for collecting, analyzing, and adding the user's emotional data in real time to the user profile; means for using an AI model with all the data to calculate compatibility with other users; means for generating a list of optimal partner candidates based on the calculation results and notifying the user; means for suggesting the most suitable meal for the user at that time based on the analysis results; and means for reflecting the suggested menu in the delivery order. This makes it possible to suggest meals and place delivery orders that take into account the user's actual emotional state and instantaneous needs.

[1922] "Basic information" refers to fundamental data used to identify a user, such as their name, age, gender, and email address.

[1923] A "profile ID" is a unique identifier generated for each user, used to link and manage all of that user's data.

[1924] "Everyday data" refers to behavioral data that users generate on a daily basis, such as social media activity, purchase history, and travel history.

[1925] A "trait profile" is a digital profile that provides a detailed description of a user's characteristics, generated by analyzing their behavioral data, genetic data, and emotional data.

[1926] "Genetic test results" refer to information based on the user's genes obtained through genetic testing, including data about their health status and specific traits.

[1927] "Emotional data" refers to data that indicates the user's current emotional state, and is real-time data collected through methods such as facial expression analysis and voice analysis.

[1928] An "AI model" refers to an artificial intelligence algorithm and system that analyzes diverse data and recognizes patterns for a specific purpose.

[1929] The "Partner Candidate List" provides users with a list of suitable partner candidates based on compatibility scores calculated by an AI model.

[1930] "Meal suggestions" refer to the act of suggesting the most suitable meal for a user at a given time, based on their basic information, behavioral data, genetic data, and emotional data.

[1931] "Delivery ordering" is the process in which a user orders a meal based on a suggested menu, and that order is delivered to the user by a delivery service.

[1932] This invention relates to a system that analyzes a user's basic information, daily data, genetic data, and emotional data, and uses AI to suggest a compatible partner. Furthermore, this system can suggest the optimal meal based on the user's mood and nutritional status, and can also instantly place a delivery order.

[1933] System Configuration

[1934] This system consists of three main elements: servers, terminals, and users.

[1935] server

[1936] The server includes the following measures:

[1937] 1. User profile generation means

[1938] The server receives the user's basic information and generates a profile ID. This ensures that all user data is uniquely identified.

[1939] 2. Data Collection Methods

[1940] The server collects everyday data such as social media activity, purchase history, and travel history. This data is sent from the device via an API.

[1941] 3. Data Analysis Methods

[1942] The collected data is analyzed to generate a user profile. This profile includes genetic data and emotional data.

[1943] 4. Genetic data analysis methods

[1944] The server receives the genetic test results and adds them to the user profile.

[1945] 5. Methods for analyzing emotional data

[1946] The server analyzes the sentiment data transmitted from the terminal and adds it to the user profile. Sentiment data is collected and analyzed in real time.

[1947] 6. Compatibility Calculation Method

[1948] The server uses an AI model with all the data to calculate compatibility with other users. Furthermore, it generates a list of optimal partner candidates based on the compatibility score and notifies the device.

[1949] 7. Meal Suggestion Methods

[1950] Based on the analysis results, the server suggests the most suitable meal for the user at that time. The suggested menu is generated taking into account the user's mood and health condition.

[1951] 8. Delivery Ordering Methods

[1952] This provides a means to immediately reflect the suggested menu in delivery orders.

[1953] terminal

[1954] A device refers to a user device such as a smartphone or tablet, and plays the following main roles:

[1955] 1. Data transmission

[1956] The device sends basic user information, behavioral data, emotional data, and other data to the server.

[1957] 2. Collecting emotional data

[1958] The device analyzes the user's facial expressions and voice, collecting emotional data in real time.

[1959] 3. Menu display and ordering

[1960] The terminal displays the menu received from the server to the user and reflects the selected menu in the delivery order.

[1961] User

[1962] Users follow these steps when using the system:

[1963] 1. User registration and basic information entry

[1964] Users enter basic information through a terminal and register it in the system.

[1965] 2. Data Provision

[1966] Users link their social media accounts and consent to the provision of purchase and travel history data. They also provide genetic data using a genetic testing kit.

[1967] 3. Provision of emotional data

[1968] Users provide emotional data through their device's camera and microphone.

[1969] 4. Choosing and ordering your meal

[1970] Choose your meal from the suggested menu and complete your delivery order.

[1971] Hardware and software to use

[1972] Hardware: Smartphones, tablets, and camera-equipped devices

[1973] Software: Emotion Detection API, AI Model API, User Profile API

[1974] Examples of specific cases and prompt statements

[1975] For example, when a user launches the app before lunch, a video feed automatically starts and facial expression data is collected. If the user is feeling stressed, for instance, that emotional data is analyzed, and a meal that helps reduce stress is suggested. Furthermore, a nutritionally balanced menu is provided, taking into account past purchase history and genetic data.

[1976] Example of a prompt:

[1977] "Please suggest nutritionally balanced meal plans that users experiencing stress would prefer, taking into account their past purchase history and genetic data."

[1978] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1979] Step 1:

[1980] User Registration

[1981] Users enter basic information (name, age, gender, email address, etc.) using a terminal. This input data is sent to the terminal and then forwarded from the terminal to the server. Based on the received basic information, the server generates a profile ID for each user and stores it in a database.

[1982] Step 2:

[1983] Collection of everyday data

[1984] Users link their social media accounts using their devices and consent to providing data on their purchase and movement history. The devices use APIs to collect social media activity (e.g., tweets, likes, follows, etc.) and send it to the server. The server receives and analyzes this data to generate a user behavior profile.

[1985] Step 3:

[1986] Collection of genetic data

[1987] The user uses a mailed genetic testing kit to collect a sample, which is then sent back to the testing laboratory. Once the genetic test results are ready, the device receives them and uploads them to the server. The server receives the genetic data and adds it to the user profile using specialized analysis algorithms.

[1988] Step 4:

[1989] Collection of emotional data

[1990] Users utilize messaging and video chat functions provided within the system using their devices. The devices analyze the user's facial expressions and voice in real time, collecting emotional data. This collected emotional data is sent to a server and added to the user profile.

[1991] Step 5:

[1992] Compatibility calculation

[1993] The server uses an AI model to analyze user characteristics based on basic information, behavioral data, genetic data, and emotional data. Based on this analysis, it calculates compatibility scores with other users and generates a list of optimal partner candidates. The candidate list is sent to the device and the user is notified.

[1994] Step 6:

[1995] Meal suggestions

[1996] The server comprehensively analyzes collected basic information, behavioral data, genetic data, and emotional data, and proposes an optimal meal plan considering the user's mood and nutritional status at that time. This proposal is sent to and displayed on the device.

[1997] Step 7:

[1998] Delivery Order

[1999] The user reviews the suggested menu on the terminal and orders their selected meal for delivery. The terminal sends the selection to the server, which then works with the delivery service to process the order, and the meal is delivered to the user.

[2000] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[2001] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2002] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[2003] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2004] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[2005] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[2006] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[2007] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[2008] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[2009] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[2010] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[2011] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[2012] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[2013] 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.

[2014] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[2015] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[2016] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[2017] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[2018] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[2019] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[2020] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[2021] The following is further disclosed regarding the embodiments described above.

[2022] (Claim 1)

[2023] A means for receiving basic user information and generating a profile ID for each user,

[2024] Means of collecting users' everyday data (social media activity, purchase history, travel history, etc.),

[2025] A means for analyzing collected data and generating user characteristic profiles,

[2026] A means of receiving and analyzing genetic test results and adding them to the user profile,

[2027] A method for using an AI model with all data to calculate compatibility with other users,

[2028] A means of generating a list of optimal partner candidates based on the calculation results and notifying the user,

[2029] A system that includes this.

[2030] (Claim 2)

[2031] The system according to claim 1, comprising means for collecting real-time behavioral data when a user actually meets with a matched partner and for continuously improving the AI ​​model.

[2032] (Claim 3)

[2033] The system according to claim 1, comprising means for a process that includes analyzing user behavior data and adding it to a generated profile.

[2034] "Example 1"

[2035] (Claim 1)

[2036] A means for receiving user identification information and generating user-specific identification information,

[2037] Means for collecting user behavior history data,

[2038] A means of analyzing collected data and generating user behavioral characteristics,

[2039] A means of receiving and analyzing genetic information and adding it to a user profile,

[2040] A method for using an AI model with all data to calculate optimality compared to other users,

[2041] A means of generating an optimal candidate list based on the calculation results and notifying the user,

[2042] A system that includes this.

[2043] (Claim 2)

[2044] The system according to claim 1, comprising means for collecting user behavior data in real time when a user actually meets with a candidate and for continuously improving the AI ​​model.

[2045] (Claim 3)

[2046] The system according to claim 1, comprising means for a process that includes analyzing user behavior data and adding it to generated behavioral characteristics.

[2047] "Application Example 1"

[2048] (Claim 1)

[2049] A means for receiving basic user information and generating a profile ID for each user,

[2050] Means of collecting users' everyday data (social media activity, purchase history, travel history, etc.),

[2051] A means for analyzing collected data and generating user characteristic profiles,

[2052] A means of receiving and analyzing genetic test results and adding them to the user profile,

[2053] A method for using an AI model with all data to calculate compatibility with other users,

[2054] A means of generating a list of optimal partner candidates based on the calculation results and notifying the user,

[2055] Means of using data on digital media viewing history and social media activity to recommend individually optimized content based on user profiles,

[2056] A system that includes this.

[2057] (Claim 2)

[2058] The system according to claim 1, comprising means for collecting real-time behavioral data when a user actually meets with a matched partner and for continuously improving the AI ​​model.

[2059] (Claim 3)

[2060] The system according to claim 1, comprising means for a process that includes analyzing user behavior data and viewing history data and adding them to a generated profile.

[2061] "Example 2 of combining an emotion engine"

[2062] (Claim 1)

[2063] A means for receiving basic user information and generating a profile ID for each user,

[2064] Means of collecting users' everyday information (social media activity, purchase history, travel history, etc.),

[2065] A means for analyzing collected information and generating a user characteristic profile,

[2066] A means of receiving and analyzing genetic test results and adding them to the user profile,

[2067] A means of collecting emotional data from user behavior and facial expressions and adding it to a profile,

[2068] A method for calculating compatibility with other users using a generated AI model with all data,

[2069] A means of generating a list of optimal partner candidates based on the calculation results and notifying the user,

[2070] A system that includes this.

[2071] (Claim 2)

[2072] The system according to claim 1, comprising means for collecting behavioral and emotional data in real time when a user actually meets with a matched partner, and for continuously improving the generated AI model.

[2073] (Claim 3)

[2074] The system according to claim 1, comprising means for a process of analyzing user behavioral data and emotional data and adding them to a generated profile.

[2075] "Application example 2 when combining with an emotional engine"

[2076] (Claim 1)

[2077] A means for receiving basic user information and generating a profile ID for each user,

[2078] Means of collecting users' everyday data (social media activity, purchase history, travel history, etc.),

[2079] A means for analyzing collected data and generating user characteristic profiles,

[2080] A means of receiving and analyzing genetic test results and adding them to the user profile,

[2081] A means of collecting and analyzing user sentiment data in real time and adding it to the user profile,

[2082] A method for using an AI model with all data to calculate compatibility with other users,

[2083] A means of generating a list of optimal partner candidates based on the calculation results and notifying the user,

[2084] Based on the analysis results, a means of suggesting the optimal meal for the user at that time,

[2085] A means of reflecting the proposed menu in delivery orders,

[2086] A system that includes this.

[2087] (Claim 2)

[2088] The system according to claim ...

Claims

1. A means for receiving basic user information and generating a profile ID for each user, Means of collecting users' everyday data, A means for analyzing collected data and generating user characteristic profiles, A means of receiving and analyzing genetic test results and adding them to the user profile, A method for using an AI model with all data to calculate compatibility with other users, A means of generating a list of optimal partner candidates based on the calculation results and notifying the user, A system that includes this.

2. The system according to claim 1, comprising means for collecting real-time behavioral data when a user actually meets with a matched partner and for continuously improving the AI ​​model.

3. The system according to claim 1, comprising means for a process that includes analyzing user behavior data and adding it to a generated profile.

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