system

Abstract

To provide a system capable of providing a meeting with a partner really congenial to a user.SOLUTION: Means for inputting and storing user profile information; means for inputting and storing user personality test and genetic analysis results; and means for collecting user social networking service (SNS) information via an application programming interface (API); A means for storing, a means for integrating the collected data and analyzing the personality and preference of the user by an AI model, a means for analyzing compatibility with another user based on an analysis result by the AI model, a means for transmitting a notification when a suitable partner is found based on the compatibility analysis result, a means for obtaining permission for information disclosure, a means for mutually providing contact information when both parties permit, and a means for providing advice for actually meeting the suitable partner.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] The problem with traditional dating apps is that users often embellish their self-descriptions and photos, providing information that doesn't match their actual personality or appearance, reducing reliability. Another issue is that users themselves don't understand aspects of themselves that they're not aware of, making it difficult to find a truly compatible partner. Furthermore, with the increase in working from home, real-life encounters are decreasing, so the quality of online encounters needs to be improved. [Means for solving the problem]

[0005] This invention provides a system that integrates a wide variety of data, such as user profile information, personality test results, genetic analysis results, social media information, purchase history, and travel history, and analyzes the user's personality and preferences using a generative AI model. Based on the analysis results from the AI ​​model, the system analyzes compatibility with other users and sends notifications to users who are determined to be compatible. Furthermore, by obtaining permission to disclose information and providing contact information to each other if both parties agree, reliable encounters are realized. Furthermore, the system supports users in meeting each other by providing advice on how to actually meet compatible partners. It also includes a setting to hide users who are friends with each other and other measures to protect privacy. This eliminates conventional problems and enables users to meet truly compatible partners.

[0006] "User" means an individual who uses this system to register for the matching service.

[0007] "Profile Information" refers to basic information about a user, such as name, age, gender, interests, and hobbies.

[0008] "Personality test results" is data showing the results of the personality test answered by the user.

[0009] "Genetic analysis results" are data based on a user's genetic testing, and are information that indicates genetic characteristics and tendencies.

[0010] "SNS information" refers to data such as posts, like history, and following relationships collected from a user's SNS account.

[0011] "Purchase history" refers to data that indicates the product purchase history of a user.

[0012] "Movement history" is data showing past movement records based on the user's location information.

[0013] A "generative AI model" is an algorithm and data model that uses artificial intelligence to analyze a user's personality and preferences.

[0014] "Compatibility analysis" is the process of evaluating the degree of compatibility between users' personalities and preferences based on collected data.

[0015] "Notifications" are alerts or messages sent to users who are determined to be compatible.

[0016] "Permission to disclose information" is a user's consent to the other party sharing their contact information.

[0017] "Contact Information" means information provided by a User that enables a User to contact another User (e.g., email address or phone number).

[0018] "Advice" refers to suggestions and guidance provided based on the compatibility analysis results, and is information that serves as a guide when actually meeting.

[0019] "Privacy Safeguards" means the technical and operational measures designed to protect your personal information. [Brief explanation of the drawings]

[0020] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6]FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0021] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0022] First, the terms used in the following description will be explained.

[0023] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0024] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0025] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0026] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0027] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0031] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0032] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0033] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0034] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0035] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0038] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0039] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0040] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0041] The present invention is a system that integrates various user data and analyzes it using a generative AI model to find compatible partners and provide reliable encounters. Specific embodiments are described below.

[0042] Data collection and integration

[0043] 1. User Registration

[0044] A user launches the application and creates an account by entering an email address and password.

[0045] The server stores the entered information in a database and creates a user account.

[0046] 2. Enter your profile information

[0047] Users enter profile information such as name, age, gender, interests and hobbies.

[0048] The server stores and updates this information in a database.

[0049] 3. Personality tests and genetic analysis

[0050] Users answer personality tests provided within the application and provide their results.

[0051] The terminal transmits the results of the personality test to the server, which stores them in a database.

[0052] If a user uploads the results of a genetic analysis that has already been performed, the server stores the results in a database.

[0053] 4. Social media integration and data collection

[0054] Users link their social media accounts to the application.

[0055] The server collects users' SNS data (posts, like history, following relationships, etc.) through the SNS API and stores it in a database.

[0056] AI-based data analysis and compatibility analysis

[0057] 5. Data Integration

[0058] The server integrates the user's profile information, personality test results, genetic analysis results, social media data, purchase history, and travel history to create a single data profile.

[0059] 6. Applying generative AI models

[0060] The server inputs this integrated data into a generative AI model, which then analyzes the user's personality and preferences using deep learning algorithms.

[0061] As a result of the analysis, the server generates a detailed profile of the user, including their personality traits, hobbies, preferences, and behavioral patterns.

[0062] Matching and Notifications

[0063] 7. Compatibility Analysis

[0064] Based on the generated profile, the server analyzes compatibility with other users and calculates a compatibility score.

[0065] 8. Matching Notification

[0066] The server sends notifications to users with pairs that have high compatibility scores.

[0067] User A and User B receive a notification and can choose whether to disclose their information to the other party.

[0068] 9. Information Disclosure and Contact Information

[0069] If the user allows the information to be made public, the servers provide contact information to each other.

[0070] Real-life dating support

[0071] 10. Dating promotion and advice

[0072] Based on the compatibility analysis results, the server suggests meeting places and activities related to common hobbies and interests.

[0073] Users receive suggestions and plan meet-ups through the application.

[0074] Specific Examples

[0075] For example, suppose User A (male, 30 years old, program engineer) registers with the application, takes a personality test, and shares his social media data. The server collects this data and inputs it into a generative AI model to generate a detailed profile. It then analyzes his compatibility with User B (female, 28 years old, designer), who also provided data, and notifies him that their compatibility scores are high. If both parties agree to share their information, the server provides contact information and recommends a shared hobby: watching movies. This allows the two parties to prepare and support each other when they meet in person.

[0076] In this way, the present invention allows users to find the perfect partner based on information they are aware of and information they are not aware of.

[0077] The processing flow will be explained below.

[0078] Step 1:

[0079] Users launch the application and create an account by entering their email address and password.

[0080] Step 2:

[0081] The server receives the entered email address and password, stores them in a database, and creates a user account.

[0082] Step 3:

[0083] Users enter profile information such as name, age, gender, interests and hobbies.

[0084] Step 4:

[0085] The server receives the entered profile information and stores / updates it in a database.

[0086] Step 5:

[0087] Users answer personality tests provided within the application.

[0088] Step 6:

[0089] The terminal transmits the personality test answer data entered by the user to the server.

[0090] Step 7:

[0091] The server receives the personality test response data, calculates the scores, and stores them in a database.

[0092] Step 8:

[0093] Users select the option to upload the results of a genetic analysis they have already performed.

[0094] Step 9:

[0095] The server receives the uploaded genetic analysis results and stores them in a database.

[0096] Step 10:

[0097] Users link their social media accounts (such as Twitter or Facebook) to the application.

[0098] Step 11:

[0099] The server collects data such as user posts, like history, and following relationships through the SNS API.

[0100] Step 12:

[0101] The server stores the collected social media data in a database and adds it to the user profile.

[0102] Step 13:

[0103] The server integrates users' profile information, personality test results, genetic analysis results, social media data, purchase history, and travel history.

[0104] Step 14:

[0105] The server inputs the integrated data into a generative AI model, which analyzes the user's personality and preferences using deep learning algorithms.

[0106] Step 15:

[0107] The server analyzes compatibility with other users based on the detailed profile information generated.

[0108] Step 16:

[0109] The server calculates the compatibility scores between users who are determined to be compatible and stores the results.

[0110] Step 17:

[0111] The server sends a matching notification to users who are determined to be compatible with each other.

[0112] Step 18:

[0113] User A and User B receive the notification and check each other's information.

[0114] Step 19:

[0115] User A and User B choose whether or not to make their contact information public to each other.

[0116] Step 20:

[0117] The terminal transmits the selected permission to disclose information to the server.

[0118] Step 21:

[0119] The server confirms both parties' permission to disclose information and provides each other with contact information.

[0120] Step 22:

[0121] Based on the compatibility analysis results, the server suggests meeting places and activities related to common hobbies and interests.

[0122] Step 23:

[0123] Users receive suggestions and plan meet-ups through the application.

[0124] Step 24:

[0125] The server implements privacy measures to protect users' personal information, including settings to hide users' friend relationships.

[0126] This will create a system that allows users to find the perfect partner in an efficient and reliable way.

[0127] Example 1

[0128] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0129] Conventional dating systems match users based only on their profile information and simple questionnaire results, making it difficult to match users in a way that fully reflects their detailed personality, hobbies, preferences, behavioral patterns, etc. In addition, they often do not provide additional information to improve compatibility or specific advice to support actual encounters, which can result in low user satisfaction.

[0130] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0131] In this invention, the server includes means for registering and storing user attribute information, means for registering and storing the user's psychological test and genetic information results, means for collecting and storing the user's social networking service information via an application programming interface, means for evaluating compatibility with other users based on the analysis results of the generative AI model, means for sending a notification when a highly compatible partner is found based on the compatibility evaluation results, means for obtaining consent for information disclosure, means for mutually exchanging contact information if both parties agree to the disclosure, and means for making recommendations for actually meeting compatible partners. This enables highly accurate matching based on the user's detailed information and specific advice to support actual meetings.

[0132] "User Attribute Information" refers to basic personal information provided by a user, such as name, age, gender, interests, and hobbies.

[0133] A "psychological test" is an assessment tool such as a questionnaire or survey administered to assess a user's personality or psychological characteristics.

[0134] "Genetic Results" means data regarding a user's genetic characteristics and traits based on a genetic analysis of the user.

[0135] "Social networking service information" refers to data such as post content, like history, and following relationships collected from a user's SNS (social networking service) account.

[0136] An "application programming interface" is a defined method of communication that allows different software systems to share information and functionality.

[0137] A "generative AI model" is a machine learning model that uses deep learning algorithms to analyze data and predict and evaluate user characteristics and preferences.

[0138] "Compatibility assessment" is a process in which a generative AI model is used to compare and analyze the personalities, hobbies, and preferences of users, and calculate the degree of compatibility between them.

[0139] "Compatibility evaluation results" refers to information such as compatibility scores and ranks calculated as a result of evaluating the compatibility between users.

[0140] "Notifications" are messages sent to users by the system to inform them that a compatible partner has been found or to inform them of important information.

[0141] "Consent to disclose information" is a user's expression of consent to allow the information they provide to be shared with other users.

[0142] "Contact Information" means information about contact methods, such as email addresses and phone numbers, that allow a user to communicate directly with other users.

[0143] "Recommendations" are specific advice to promote actual encounters, such as suggested meeting locations and activities for users.

[0144] The present invention is a system that integrates various user data and analyzes it with a generative AI model to find the best partner for the user and provide reliable dating. This system involves multiple processing steps, each designed to achieve a specific purpose.

[0145] First, a user launches the application and creates an account by entering their email address and password. The user's device encrypts this information and sends it to the server. The server then stores the received information in a database and creates a new account.

[0146] Next, the user enters their demographic information, such as their name, age, gender, hobbies, interests, etc. The device then sends this information to the server, which stores it in a database.

[0147] The user then answers a personality test within the application and provides the results. The device transmits the test results in real time to the server, which stores the results in a database. Additionally, if the user provides genetic analysis results, the device uploads the results to the server, which stores them in a database.

[0148] Users can also link their social media accounts to the application. The server uses the SNS API to collect users' social media data (posts, likes, and following relationships) and stores them in a database.

[0149] The server retrieves the user's attribute information, personality test results, genetic analysis results, social media data, and other historical data from the database to generate an integrated data profile. This integrated profile is then input into a generative AI model, where the user's personality and preferences are analyzed through a deep learning algorithm. The server then generates a detailed profile based on this information.

[0150] The server then evaluates compatibility with other users based on the analyzed detailed profiles and calculates a compatibility score. When users with high compatibility scores are found, the server sends a notification to the device. The user receives the notification and can choose whether to make the information public. If both parties agree to the disclosure, the server provides each other with their contact information.

[0151] Finally, the server will suggest meeting places and activities to the user based on the compatibility results, and the user will receive the suggestions and make specific plans for the meeting.

[0152] For example, if User A (male, 30 years old, program engineer) creates an account, takes a personality test, and connects his social media accounts, the server collects this data and inputs it into a generative AI model to generate a detailed profile. If the server determines that User B (female, 28 years old, designer), who also provided data, has a high compatibility score, it notifies the user and provides their contact information. If both parties agree to the disclosure of their information, the server can help facilitate a real-life meeting by suggesting a movie to go to.

[0153] An example prompt is:

[0154] Prompt to register:

[0155] Users launch the application and create an account by entering their email address and password.

[0156] Personality Test Prompt:

[0157] Users answer personality tests provided within the application and provide their results.

[0158] Prompt to link social media data:

[0159] Users connect their social media accounts to the application, and the server collects data through the social media API.

[0160] In this way, the embodiments of the invention make it possible to utilize detailed information about users to achieve advanced matching, thereby increasing the chances of encounters that provide high compatibility for users.

[0161] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0162] Step 1:

[0163] User Registration

[0164] Users launch the application and create an account by entering their email address and password.

[0165] Input: Email address, password

[0166] The terminal encrypts the input data and sends it to the server.

[0167] The server stores the received information in a database and creates a new account.

[0168] Output: A confirmation email is sent to the user confirming that their account was created.

[0169] Step 2:

[0170] Enter profile information

[0171] Within the application, users enter profile information such as their name, age, gender, interests and hobbies.

[0172] Input: Name, age, gender, interests and hobbies

[0173] The terminal transmits the entered profile information to the server.

[0174] The server stores and updates this information in a database.

[0175] Output: Sends a notification to the user that their profile information has been saved.

[0176] Step 3:

[0177] Personality tests and genetic analysis

[0178] Users answer personality tests provided within the application and submit their results.

[0179] Input: Personality test answers

[0180] The device transmits the test results to the server in real time.

[0181] The server stores the results of the personality test in a database.

[0182] Output: Sends a notification to the user that the test results have been saved.

[0183] When the user provides the results of the genetic analysis, input: Genetic analysis results

[0184] The terminal uploads the result data to the server.

[0185] The server stores the genetic analysis results in a database.

[0186] Output: Send a notification to the user that the genetic analysis results have been saved.

[0187] Step 4:

[0188] Social media integration and data collection

[0189] Users can link their social media accounts within the application.

[0190] Input: SNS account information (API key, token, etc.)

[0191] The server uses the SNS API to collect users' SNS data (posts, like history, and following relationships).

[0192] The server stores the collected SNS data in a database.

[0193] Output: Sends a notification to the user that the SNS data has been saved.

[0194] Step 5:

[0195] Data integration

[0196] The server retrieves user profile information, personality test results, genetic analysis results, social media data, and other historical data from the database.

[0197] Input: Profile information, personality test results, genetic analysis results, social media data, history data

[0198] The server aggregates the acquired data and generates a single data profile.

[0199] Output: Unified Data Profile

[0200] Step 6:

[0201] Applying generative AI models

[0202] The server inputs the integrated data profile into a generative AI model to analyze the user's personality and preferences.

[0203] Input: Unified Data Profile

[0204] The server uses a generative AI model to generate a detailed profile of the user, including their personality traits, hobbies, preferences, and behavioral patterns.

[0205] Output: Detailed profile

[0206] Step 7:

[0207] Conformity assessment

[0208] The server evaluates your compatibility with other users based on the detailed profile you create.

[0209] Input: Detailed profile

[0210] The server calculates the relevance scores and stores them in a database.

[0211] Output: Relevance score

[0212] Step 8:

[0213] Matching notification

[0214] The server matches users with high compatibility scores and sends a notification to the device.

[0215] Input: Relevance score, matching information

[0216] User A and User B receive a notification and check each other's information within the application.

[0217] Output: Matching notification

[0218] Step 9:

[0219] Disclosure of information and contact details

[0220] If the user allows the information to be made public, the server provides contact information to each other.

[0221] Input: Consent to disclosure of information

[0222] The server retrieves contact information between users from a database and sends it to the terminal.

[0223] Output: Contact information

[0224] Step 10:

[0225] Dating promotion and advice

[0226] Based on the compatibility results, the server suggests meeting places and activities to the user.

[0227] Input: compatibility results, detailed profile

[0228] The server sends the proposal to the terminal.

[0229] Users receive suggestions and plan specific encounters.

[0230] Output: Meeting suggestion notification, planning reminder notification

[0231] (Application example 1)

[0232] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0233] In recent years, online dating and matchmaking platforms have developed rapidly. However, many users find it difficult to find a partner who matches their personality and preferences, or to find suitable products and services. Furthermore, traditional systems are unable to fully utilize diverse user data, making it difficult to provide accurate recommendations. This has led to a decline in user satisfaction and a lack of continued use of the service.

[0234] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0235] In this invention, the server includes means for inputting and saving a user's profile information, means for inputting and saving the user's personality test and genetic analysis results, means for collecting and saving the user's SNS information via an API, means for integrating the collected data and analyzing the user's personality and preferences using a generative AI model, means for analyzing compatibility with other users based on the analysis results of the generative AI model, means for sending a notification when a compatible partner is found based on the compatibility analysis results, means for obtaining permission to disclose information, means for mutually exchanging contact information if both parties give permission, means for providing advice on how to actually meet a compatible partner, means for suggesting optimal products and services based on the user's personality and preferences, and means for notifying the user of product suggestions with the user's permission. This allows users to easily find the partner, products, and services that are best suited for them.

[0236] "User profile information" refers to basic personal information about a user, such as the user's name, age, gender, interests, and hobbies.

[0237] A "personality test" is a questionnaire-based test designed to assess a user's personality traits, the results of which are stored as the user's personality profile.

[0238] "Genetic analysis results" are analysis results based on the user's genetic information, and are data that indicate the user's biological characteristics and tendencies.

[0239] An "API" is an interface for collecting and exchanging data in collaboration with other software applications.

[0240] A "generative AI model" is an artificial intelligence model that uses deep learning algorithms to analyze a user's personality and preferences from a variety of data.

[0241] "Compatibility analysis" is the process of calculating the compatibility between a specific user and other users based on the analysis results of a generative AI model.

[0242] "Notifications" are messages that inform users of compatible partners and product suggestions.

[0243] "Permission to disclose information" means that a user authorizes the disclosure of their personal information or contact information to other users.

[0244] "Contact information" refers to information necessary for contacting users, such as email addresses and telephone numbers.

[0245] "Advice" is guidance that suggests places and activities to meet compatible people in person.

[0246] "Proposing optimal products and services" means recommending products and services that best suit the user's preferences and personality based on the analysis results of the generative AI model.

[0247] A "product suggestion notification" is a message that notifies the user of information about a suggested product.

[0248] This invention allows users to easily find partners, products, and services that suit them. A specific implementation form of a system for carrying out the invention is described below.

[0249] System Configuration

[0250] The system consists of a server, user devices (smartphones, etc.), and various software, including Django (backend framework), MySQL (registered trademark) (database), Pandas (dataframe manipulation library), SciKit-Learn (machine learning library), and TENSORFLOW (registered trademark) (deep learning library).

[0251] Program processing overview

[0252] 1. Data Collection Module

[0253] Using the user's device, the user enters profile information, personality test results, and genetic analysis results. Furthermore, the user's social media accounts are linked via API to collect social media information. All of this data is stored in a MySQL database via the Django framework. The specific data collection interface is the application's registration form and social media API connector.

[0254] 2. Data Processing Module

[0255] The data obtained from each user is integrated using Pandas. For example, personality test results and social media data are combined to create a comprehensive data profile. This process unifies the user's multidimensional data.

[0256] 3. Generative AI model application module

[0257] The combined data profile is then fed into a TensorFlow-powered generative AI model, which uses deep learning algorithms to analyze the user's personality and preferences to generate a detailed profile of the user, including their personality traits, hobbies, and preferences.

[0258] 4. Compatibility Analysis Module

[0259] Based on the profile created, SciKit-Learn's machine learning algorithms are used to analyze compatibility with other users, and if a match is found, a notification is sent to the user.

[0260] 5. Product and service proposal module

[0261] Based on the user's personality and preferences, the system suggests optimal products and services. Specifically, product recommendations are made to the user based on the analysis results of the generative AI model. Notifications of suggested products and services are sent via push notifications using application tokens.

[0262] Examples and Prompts

[0263] For example, if a male user in his 30s is looking for outdoor gear, the system will analyze his preferences based on his personality test, social media posts, and purchase history, and then use a generative AI model to suggest the best products for him. Specific prompt sentence examples are as follows:

[0264] "Male, 30s, loves the outdoors. Please make a list of recommended sports and camping equipment based on the results of a personality test and analysis of social media data."

[0265] In this way, users can easily find products and services that suit their personality and preferences, and it also makes it easier to find a suitable partner, improving user satisfaction.

[0266] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0267] Step 1:

[0268] A user launches an application on a smartphone device and creates an account by entering an email address and password. The entered information (email address and password) is sent to the server, which stores it in a database and creates a user account. At this stage, the input data is the user's basic information, and the output is the user account information stored in the database.

[0269] Step 2:

[0270] The user uses a terminal to input profile information such as name, age, gender, interests, and hobbies. The input profile information is sent to the server and saved and updated in a database. The input data here is the user's personal information, and the output is the updated profile information in the database.

[0271] Step 3:

[0272] The user answers the personality test in the app on their device and sends the results to the server. In addition, if the user's genetic analysis results already exist, the data is uploaded. The personality test results and genetic analysis results are sent to the server and stored in a database. The input is the personality test answers and genetic analysis results, and the output is the analysis results stored in the database.

[0273] Step 4:

[0274] Users connect their SNS accounts to the application on their devices. The server collects the user's SNS data (posts, like history, following relationships, etc.) through the SNS API and stores it in a database. The input data here is the SNS information obtained from the SNS API, and the output is the SNS data stored in the database.

[0275] Step 5:

[0276] The server integrates various data, such as user profile information, personality test results, genetic analysis results, social media data, purchase history, and travel history. This integration process uses Pandas to combine each data into a single data frame. The input data is user-related data obtained from multiple sources, and the output is an integrated data profile.

[0277] Step 6:

[0278] The server inputs the integrated data profile into a generative AI model, which uses a deep learning algorithm to analyze the user's personality and preferences. This analysis is performed using TensorFlow, and the profile is generated. The input data is the integrated data profile, and the output is a detailed user profile as a result of the analysis.

[0279] Step 7:

[0280] The server analyzes compatibility with other users based on the generated detailed profile using SciKit-Learn's machine learning algorithm, calculates compatibility scores, and performs optimal matching. The input data is the user's detailed profile, and the output is compatibility scores and matching results.

[0281] Step 8:

[0282] The server sends a notification to users of pairs with high compatibility scores. The notification includes a brief profile of the other person and their compatibility score. Users receive the notification and choose whether to disclose their information to the other person. The input data here is the match result, and the output is the notification to the user and the option to disclose information.

[0283] Step 9:

[0284] Once the user gives permission, the server provides contact information to each other, and the two parties are ready to communicate. The input is the user's permission, and the output is the other party's contact information.

[0285] Step 10:

[0286] The server then suggests meeting places and activities related to common hobbies and interests based on the compatibility analysis results. Users receive the suggestions through the application and plan their meetings. The input data is the compatibility analysis results, and the output is the suggested meeting activities and places.

[0287] Step 11:

[0288] The server uses the analysis results of the generative AI model to suggest optimal products and services based on the user's personality and preferences. The proposals are notified to the user using the application's push notification function. The input data here is the analysis results of the generative AI model, and the output is a product recommendation notification. An example of this prompt is as follows:

[0289] "Male, 30s, loves the outdoors. Please make a list of recommended sports and camping equipment based on the results of a personality test and analysis of social media data."

[0290] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0291] The present invention is a system that uses a variety of user data and an emotion engine to find compatible partners using a generative AI model, providing reliable encounters. Specific embodiments are described below.

[0292] Data collection and integration

[0293] 1. User Registration

[0294] Users launch the application and create an account by entering their email address and password.

[0295] The server stores the entered information in a database and creates a user account.

[0296] 2. Enter your profile information

[0297] Users enter profile information such as name, age, gender, interests and hobbies.

[0298] The server stores and updates this information in a database.

[0299] 3. Personality tests and genetic analysis

[0300] Users answer personality tests provided within the application and provide their results.

[0301] The terminal transmits the results of the personality test to the server, which stores them in a database.

[0302] If a user uploads the results of a genetic analysis that has already been performed, the server stores the results in a database.

[0303] 4. Social media integration and data collection

[0304] Users connect their social media accounts (such as Twitter or Facebook) to the application.

[0305] The server collects data such as user posts, like history, and following relationships through the SNS API.

[0306] The server stores the collected social media data in a database and adds it to the user profile.

[0307] AI-based data analysis and compatibility analysis

[0308] 5. Data Integration

[0309] The server integrates the user's profile information, personality test results, genetic analysis results, social media data, purchase history, and travel history to create a single data profile.

[0310] 6. Applying generative AI models

[0311] The server inputs this integrated data into a generative AI model, which then analyzes the user's personality and preferences using deep learning algorithms.

[0312] As a result of the analysis, the server generates a detailed profile of the user, including their personality traits, hobbies, preferences, and behavioral patterns.

[0313] Use of emotion engine

[0314] 7. Emotional Data Collection and Analysis

[0315] Users allow emotional data to be collected during everyday use.

[0316] The device collects data such as user text input, voice input, and facial expression analysis and sends it to the emotion engine.

[0317] The emotion engine analyzes the user's current emotional state from the collected data and generates emotion data.

[0318] The server stores the generated emotion data in a database and uses it to analyze the AI ​​model.

[0319] Matching and Notifications

[0320] 8. Compatibility Analysis

[0321] The server analyzes compatibility with other users based on the generated user profile and emotional data and calculates a compatibility score.

[0322] 9. Matching Notification

[0323] The server sends a matching notification based on the compatibility scores of users who are determined to be compatible with each other.

[0324] User A and User B receive the notification and check each other's information.

[0325] 10. Information Disclosure and Contact Information

[0326] User A and User B choose whether or not to make their contact information public to each other.

[0327] The terminal transmits the selected permission to disclose information to the server.

[0328] The server confirms both parties' permission to disclose information and provides each other with contact information.

[0329] Real-life dating support

[0330] 11. Dating promotion and advice

[0331] Based on the compatibility analysis results and emotional data, the server suggests meeting places and activities related to common hobbies and interests.

[0332] Users receive suggestions and plan meet-ups through the application.

[0333] Specific Examples

[0334] For example, suppose User A (male, 30 years old, program engineer) registers with the application, connects his personality test and social media data, and allows the use of the emotion engine. The server collects this data and inputs it into a generative AI model to generate a detailed profile. It then analyzes his compatibility with User B (female, 28 years old, designer), who also provided data, and notifies him that their compatibility score is high. If both parties allow their information to be made public, the server provides contact information and recommends a shared hobby: watching movies. It also provides advice based on the emotion engine data.

[0335] In this way, the present invention allows users to find the perfect partner based on both conscious and involuntary information. By using an emotion engine, even more sophisticated and accurate matching can be achieved.

[0336] The processing flow will be explained below.

[0337] Step 1:

[0338] Users launch the application and create an account by entering their email address and password.

[0339] Step 2:

[0340] The server receives the entered email address and password, stores them in a database, and creates a user account.

[0341] Step 3:

[0342] Users enter profile information such as name, age, gender, interests and hobbies.

[0343] Step 4:

[0344] The server receives the entered profile information and stores / updates it in a database.

[0345] Step 5:

[0346] Users answer personality tests provided within the application.

[0347] Step 6:

[0348] The terminal transmits the personality test answer data entered by the user to the server.

[0349] Step 7:

[0350] The server receives the personality test response data, calculates the scores, and stores them in a database.

[0351] Step 8:

[0352] Users upload their genetic analysis results.

[0353] Step 9:

[0354] The server receives the uploaded genetic analysis results and stores them in a database.

[0355] Step 10:

[0356] Users link their social media accounts (such as Twitter or Facebook) to the application.

[0357] Step 11:

[0358] The server collects data such as user posts, like history, and following relationships through the SNS API.

[0359] Step 12:

[0360] The server stores the collected social media data in a database and adds it to the user profile.

[0361] Step 13:

[0362] Users consent to the use of the emotion engine and allow the collection of emotion data.

[0363] Step 14:

[0364] The device collects emotional data from the user's text input, voice input, facial expression recognition, etc.

[0365] Step 15:

[0366] The emotion engine analyzes the collected emotion data and recognizes the user's emotional state.

[0367] Step 16:

[0368] The server receives the emotion data from the emotion engine and stores it in a database.

[0369] Step 17:

[0370] The server integrates the user's profile information, personality test results, genetic analysis results, social media data, purchase history, travel history, and emotional data to create a single data profile.

[0371] Step 18:

[0372] The server inputs the integrated data into a generative AI model, which analyzes the user's personality and preferences using deep learning algorithms.

[0373] Step 19:

[0374] The server analyzes compatibility with other users based on the detailed profile information generated.

[0375] Step 20:

[0376] The server calculates the compatibility scores between users who are determined to be compatible and stores the results.

[0377] Step 21:

[0378] The server sends a matching notification to users who are determined to be compatible with each other.

[0379] Step 22:

[0380] User A and User B receive the notification and check each other's information.

[0381] Step 23:

[0382] User A and User B can review the notification and choose whether or not to disclose their contact information to the other party.

[0383] Step 24:

[0384] The terminal transmits the selected permission to disclose information to the server.

[0385] Step 25:

[0386] The server confirms both parties' permission to disclose information and provides each other with contact information.

[0387] Step 26:

[0388] Based on the compatibility analysis results and emotional data, the server suggests meeting places and activities related to common hobbies and interests.

[0389] Step 27:

[0390] Users receive suggestions and plan meet-ups through the application.

[0391] Step 28:

[0392] The server implements privacy measures to protect users' personal information, including settings to hide users' friend relationships.

[0393] This will enable users to find their ideal partner in an efficient and reliable way, and the use of an emotion engine will provide even more sophisticated and accurate matching.

[0394] Example 2

[0395] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0396] Conventional matching systems suggest partners based on user profile information and simple personality assessment results, but they face the challenge of making matches that fully reflect individual needs and diverse emotional states. Furthermore, because they rely on simple data without utilizing social media data or genetic information, matching accuracy is low and users' true compatibility cannot be determined. Another problem is the lack of actual support for finding a compatible partner.

[0397] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for inputting and saving user profile information, means for inputting and saving the user's personality test and genetic analysis results, means for collecting and saving the user's SNS information via an API, means for integrating the collected data and analyzing the user's personality and preferences using an AI model, means for analyzing compatibility with other users based on the analysis results of the AI ​​model, means for collecting and analyzing the user's emotional data during daily use, means for saving the generated emotional data and using it for compatibility analysis, means for sending a notification when a compatible partner is found based on the compatibility analysis results, means for obtaining permission to disclose information, means for mutually exchanging contact information if both parties give permission, and means for providing advice on how to actually meet a compatible partner. This allows users to find their ideal partner based on detailed and reliable data.

[0398] "Profile Information" refers to basic personal information such as your name, age, gender, interests and hobbies.

[0399] A "personality test" refers to a series of questions or assessments that users answer to assess their personality traits or psychological state.

[0400] "Genetic analysis" refers to the process of analyzing a user's DNA sample to extract genetic characteristics and health information.

[0401] "SNS information" refers to data such as posts, like history, and following relationships collected from a user's SNS services (e.g., Twitter or Facebook).

[0402] "API" stands for Application Programming Interface and refers to the rules and protocols that allow software to communicate with each other.

[0403] An "AI model" refers to a program that uses artificial intelligence algorithms to analyze data and predict user characteristics and behavioral patterns.

[0404] "Emotional Data" refers to data that indicates a user's emotional state, extracted from text input, voice input, facial expression analysis, etc.

[0405] "Compatibility score" refers to the numerical value of the compatibility between users, as a result of analysis by the AI ​​model.

[0406] "Permission to disclose information" refers to a user's consent to providing their contact information to other users.

[0407] "Advice" refers to advice or suggestions offered to facilitate actual encounters with compatible partners.

[0408] "Integrated Data Profile" refers to the creation of unified information about a user from multiple collected data sources.

[0409] The present invention is a system that uses a variety of user data and an emotion engine to find compatible partners using a generative AI model, providing reliable encounters. Specific embodiments are described below.

[0410] Data collection and integration

[0411] User Registration

[0412] A user launches the application and creates an account by entering their email address and password. The server stores the information in a database and creates a user account.

[0413] Enter profile information

[0414] Users enter profile information such as name, age, gender, interests, hobbies, etc. The server stores and updates this information in a database.

[0415] Personality tests and genetic analysis

[0416] The user answers a personality test provided within the application and sends the results to the server, which stores the personality test results in a database. If the user uploads the results of a genetic analysis they have already completed, the server stores these results in a database.

[0417] Social media integration and data collection

[0418] Users connect their social media accounts (e.g., Twitter or Facebook) to the application. The server collects data such as user posts, likes, and following relationships through the social media API. The server stores the collected social media data in a database and adds it to the user profile.

[0419] AI-based data analysis and compatibility analysis

[0420] Data integration

[0421] The server integrates the user's profile information, personality test results, genetic analysis results, social media data, purchase history, and travel history to create a single data profile.

[0422] Applying generative AI models

[0423] The server inputs this integrated data into a generative AI model, which then uses deep learning algorithms to analyze the user's personality and preferences, generating a detailed profile of the user, including their personality traits, hobbies, preferences, and behavioral patterns.

[0424] Use of emotion engine

[0425] Emotion data collection and analysis

[0426] Users allow emotional data to be collected during daily use. The device collects data such as the user's text input, voice input, and facial expression analysis, and sends it to the emotion engine. The emotion engine analyzes the user's current emotional state from the collected data and generates emotional data. The server stores the generated emotional data in a database and uses it for analysis in AI models.

[0427] Matching and Notifications

[0428] Compatibility analysis

[0429] The server analyzes compatibility with other users based on the generated user profile and emotional data and calculates a compatibility score.

[0430] Matching notification

[0431] The server sends a matching notification based on the compatibility scores of users who are determined to be compatible. User A and User B receive the notification and check each other's information.

[0432] Disclosure of information and contact details

[0433] User A and User B choose whether to share their contact information with each other. The device sends the selected permission to share information to the server. The server confirms the permission from both parties and provides the contact information to each other.

[0434] Real-life dating support

[0435] Dating promotion and advice

[0436] The server then suggests meeting places and activities related to shared hobbies and interests based on the compatibility analysis and emotional data. Users receive these suggestions through the app and plan their meetings.

[0437] Specific Examples

[0438] For example, suppose User A (male, 30 years old, program engineer) registers with the application, connects his personality test and social media data, and allows the use of the emotion engine. The server collects this data and inputs it into a generative AI model to generate a detailed profile. It then analyzes his compatibility with User B (female, 28 years old, designer), who also provided data, and notifies him that their compatibility score is high. If both parties allow their information to be made public, the server provides contact information and recommends a shared hobby: watching movies. It also provides advice based on the emotion engine data.

[0439] Example prompts to input to the generative AI model

[0440] "Analyze the personality test results, social media data, and data collected by the emotion engine for User A, a 30-year-old male program engineer, and generate a profile to find the perfect partner for this user."

[0441] As described above, the present invention provides a system that presents optimal partners based on a user's detailed profile information and achieves advanced and highly accurate matching that also takes emotional data into consideration.

[0442] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0443] Step 1:

[0444] A user launches the application and creates an account by entering their email address and password. The input requires the user's email address and password, and the output is a user account created in the database. The server receives this information and saves the new user account in the database. Specifically, the form input data is sent to an endpoint, and a new record is added to the database user table on the server side.

[0445] Step 2:

[0446] The user enters profile information (such as name, age, gender, interests, hobbies, etc.). This profile information is required as input, and the updated user profile is saved in the database as output. The server receives this information and saves and updates it in the database. Specifically, when the user fills out the input form and presses the submit button, the data is sent to the server and the database record is updated.

[0447] Step 3:

[0448] The user takes a personality test and provides the results. The input requires the personality test answer data, and the output is the personality test results stored in the database. The device sends the user's test answers to the server, which stores them in the database. Specifically, when the user answers the test and sends the results, the server receives the results and stores them in the database as a new record.

[0449] Step 4:

[0450] When a user uploads the results of a genetic analysis that has already been conducted, the input required is the genetic analysis result file, and the output is the result stored in the database. The server receives the result and stores it in the database. Specifically, the user uploads a file, and the file is sent to the server and then stored in the database.

[0451] Step 5:

[0452] A user connects their social media account to the application. Social media account authentication information is required as input, and social media data is stored in a database as output. The server uses the social media API to collect data such as the user's posts, like history, and following relationships. Specifically, the user authenticates with the social media account, and then the server calls the API to collect the data and store it in the database.

[0453] Step 6:

[0454] The server integrates profile information, personality test results, genetic analysis results, social media data, etc. These various data are required as input, and an integrated data profile is generated as output. Specifically, the server extracts the necessary information from multiple data tables and compiles it into a single integrated data profile.

[0455] Step 7:

[0456] The server inputs the integrated data into the generative AI model for analysis. The input is an integrated data profile, and the output is a detailed user profile. Specifically, the server sends data to the API of the generative AI model and receives the detailed profile as the analysis result.

[0457] Step 8:

[0458] Collects and analyzes user emotional data. The input requires the user's text input, voice input, and facial expression analysis data, and the output generates emotional data. The device collects this emotional data and sends it to the emotion engine. Specifically, the device uses the camera and microphone to capture data and sends it to the emotion engine in real time.

[0459] Step 9:

[0460] The server stores the collected emotion data in a database and uses it for compatibility analysis. Emotion data is required as input, and an updated user profile is generated as output. The server uses this data for analysis. The specific operation is to update records in the database.

[0461] Step 10:

[0462] The server analyzes the compatibility with other users based on the generated user profile and emotional data. The inputs are the user profile and emotional data, and the output is a compatibility score. Specifically, an algorithm for calculating the compatibility score is executed, and the results are stored in a database.

[0463] Step 11:

[0464] The server sends a matching notification based on the compatibility score between users who are determined to be compatible. The compatibility score is required as input, and a notification is sent to the user as output. Specific actions include sending a push notification or email to the user.

[0465] Step 12:

[0466] User A and User B choose whether to share their contact information with each other. This selection information is required as input, and the permitted contact information is provided to each other as output. The device sends this information to the server, which verifies and provides the information. Specific operation is to display the contact information according to the user's selection.

[0467] Step 13:

[0468] The server suggests meeting places and activities related to common hobbies and interests based on the compatibility analysis results and emotional data. The compatibility analysis results and emotional data are required as input, and suggestions for meeting places and activities are provided to the user as output. Specific operations include notifying the user of recommended places and activities.

[0469] This allows users to find their perfect partner based on detailed profile information and emotional data.

[0470] (Application example 2)

[0471] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0472] Conventional matching systems analyze compatibility based on static data such as user profile information and personality test results, but do not realize matching that reflects the user's real-time emotions and interests. Furthermore, when users shop in physical stores, they lack product recommendations and location suggestions that are optimized for their individual preferences and emotions. This has made improving the user experience a challenge.

[0473] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting and saving user profile information; means for inputting and saving the user's personality test and genetic analysis results; means for collecting and saving the user's SNS information using data communication means; means for integrating the collected data and analyzing the user's personality and preferences using a computational model; means for analyzing compatibility with other users based on the analysis results of the computational model; means for sending a notification when a compatible partner is found based on the compatibility analysis results; means for obtaining permission to disclose information; means for mutually exchanging contact information if both parties give permission; means for making suggestions for actually meeting a compatible partner; means for collecting and analyzing the user's emotional data; means for analyzing purchase and movement history based on the emotional data and suggesting related products and places; and means for suggesting products based on the user's real-time emotional and interest information. This enables matching and product recommendations based on the user's real-time emotional and interest information.

[0474] "User profile information" means basic information about a user, such as the user's name, age, gender, hobbies, and interests.

[0475] "Means for storing" refers to a method of using a database or storage device to permanently store information entered by the user.

[0476] A "personality test" is a series of questions or assessments designed to assess a user's personality or psychological characteristics.

[0477] "Genetic analysis results" are data obtained as a result of analyzing a user's genetic characteristics and constitution.

[0478] "SNS information" refers to data such as posts published by users on social networking services, like history, and following relationships.

[0479] "Data communication means" refers to a method of sending and receiving information using the Internet, wireless communication, etc.

[0480] "Integration" is the process of combining different types and formats of data into one unified data set.

[0481] A "computational model" is a mathematical or statistical algorithm or program used to analyze data or make predictions.

[0482] "Compatibility analysis" is a method of comparing the characteristics and preferences of users and evaluating the degree of compatibility between them.

[0483] A "notification" is a message or alert used to inform a user.

[0484] "Permission to Disclose Information" means that a user consents to the disclosure of their information to others.

[0485] "Contact Information" means contact information for a user, such as a phone number, email address, or postal address.

[0486] "Suggestions" are advice that recommends useful information or actions to the user.

[0487] "Emotional data" refers to data that represents the emotional state of a user, derived from facial expressions, voice, text, etc.

[0488] "Purchase history" refers to records of products and services purchased by a user in the past.

[0489] "Travel history" refers to data about the places a user has visited and the routes they have taken.

[0490] "Means for suggesting products" refers to a system that selects and recommends related products and services based on the user's emotions and interests.

[0491] To implement this invention, it is necessary to build a system in which multiple pieces of hardware and software work together to collect real-time emotional data and interest information from users and perform optimal product recommendations and matching.

[0492] Required Hardware and Software

[0493] 1. Hardware

[0494] Smart glasses (e.g., Google® Glass®)

[0495] Built-in camera and microphone

[0496] Server (capable of high-performance data processing)

[0497] Local data storage (on the user's device)

[0498] 2. Software

[0499] Python libraries (requests, DeepFace)

[0500] Server-side API (user data acquisition API, product recommendation API)

[0501] Database (saves user data and emotion data)

[0502] System Overview

[0503] User registration and data collection

[0504] When a user uses the application for the first time, they enter their profile information (name, age, gender, hobbies, interests, etc.), then provide the results of a personality test and genetic analysis, and link their social media account, which sends and stores this data on the server.

[0505] Collecting Emotional Data

[0506] The smart glasses' built-in camera and microphone are used to collect the user's facial expressions and voice in real time, and a facial expression recognition algorithm (e.g., DeepFace) is used to analyze the user's emotional state from this data.

[0507] Data Integration and Analysis

[0508] The server combines the collected profile information, personality test results, genetic analysis results, social media data, purchase history, travel history, and emotional data, and inputs this into a computational model, which then uses a generative AI algorithm to analyze the user's personality and preferences and generate a detailed user profile.

[0509] Product Recommendation and Matching

[0510] When users shop in physical stores, the smart glasses will suggest relevant products and locations based on collected emotional data and real-time interest information, improving users' shopping experience and providing more personalized services.

[0511] Specific examples

[0512] Suppose a user visits a physical store and puts on a pair of smart glasses. The camera in the glasses recognizes the user's facial expressions and performs emotional analysis. For example, if the user is looking for new work clothes, the system can recommend the most suitable business suit in real time based on the user's personality and past purchasing history. If the user is unsure about a purchase, the server will send appropriate advice to the smart glasses based on the user's emotional data. This process is input into the generative AI model with prompts such as the following:

[0513] Prompt Sentence Examples

[0514] Current emotional state: Joy

[0515] Hobbies: Fashion

[0516] Past purchase history: Suits

[0517] Suggestion: Please recommend the latest business suit.

[0518] This invention enables users to receive optimal product recommendations and matching services based on their real-time emotions and interests.

[0519] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0520] Step 1:

[0521] A user starts the application and enters their profile information (name, age, gender, hobbies, interests). This input data is sent from the device to the server, which then stores it in a database.

[0522] Step 2:

[0523] The user provides the personality test and genetic analysis results, which are also sent from the device to the server, which stores them in a database.

[0524] Step 3:

[0525] When a user connects their SNS account, the server collects the user's SNS data (posts, like history, following relationships) through the API. This data is also stored in the database.

[0526] Step 4:

[0527] The server combines the collected profile information, personality test results, genetic analysis results, and social media data to create a single user data profile, which is then stored in a database.

[0528] Step 5:

[0529] When a user wears smart glasses and enters a physical store, the smart glasses' built-in camera captures the user's facial expressions in real time. The device inputs this video data into an emotion analysis algorithm (e.g., DeepFace) to analyze the user's current emotional state. This emotion data is then sent from the device to a server.

[0530] Step 6:

[0531] The server inputs data into a computational model based on real-time emotional data and existing user data profiles. A generative AI model then analyzes the user's current interests and needs to predict suitable products. The results are composed of prompt sentences.

[0532] Example prompt sentence:

[0533] Current emotional state: Joy

[0534] Hobbies: Fashion

[0535] Past purchase history: Suits

[0536] Suggestion: Please recommend the latest business suit.

[0537] Step 7:

[0538] The server identifies suitable products based on the output of the generative AI model and sends the recommendation information to the smart glasses, through which the user can view the recommended products in real time.

[0539] Step 8:

[0540] If the user decides to purchase the recommended product, the smart glasses send data on their purchase intention to the server, which stores this data and uses it for future recommendations.

[0541] Step 9:

[0542] If the user is unsure about a purchase, the server will suggest additional advice or related products, which will also be displayed on the smart glasses to assist the user in their purchasing decision.

[0543] Step 10:

[0544] Additionally, users can provide feedback about the proposed products and services through the smart glasses, and this feedback data will also be sent to the server and stored in the database.

[0545] This allows users to receive personalized service and product recommendations in real time, resulting in a more satisfying purchasing experience.

[0546] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0547] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0548] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0549] [Second embodiment]

[0550] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0551] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0552] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0553] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0554] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0555] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0556] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0557] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0558] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0559] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0560] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0561] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0562] The present invention is a system that integrates various user data and analyzes it using a generative AI model to find compatible partners and provide reliable encounters. Specific embodiments are described below.

[0563] Data collection and integration

[0564] 1. User Registration

[0565] A user launches the application and creates an account by entering an email address and password.

[0566] The server stores the entered information in a database and creates a user account.

[0567] 2. Enter your profile information

[0568] Users enter profile information such as name, age, gender, interests and hobbies.

[0569] The server stores and updates this information in a database.

[0570] 3. Personality tests and genetic analysis

[0571] Users answer personality tests provided within the application and provide their results.

[0572] The terminal transmits the results of the personality test to the server, which stores them in a database.

[0573] If a user uploads the results of a genetic analysis that has already been performed, the server stores the results in a database.

[0574] 4. Social media integration and data collection

[0575] Users link their social media accounts to the application.

[0576] The server collects users' SNS data (posts, like history, following relationships, etc.) through the SNS API and stores it in a database.

[0577] AI-based data analysis and compatibility analysis

[0578] 5. Data Integration

[0579] The server integrates the user's profile information, personality test results, genetic analysis results, social media data, purchase history, and travel history to create a single data profile.

[0580] 6. Applying generative AI models

[0581] The server inputs this integrated data into a generative AI model, which then analyzes the user's personality and preferences using deep learning algorithms.

[0582] As a result of the analysis, the server generates a detailed profile of the user, including their personality traits, hobbies, preferences, and behavioral patterns.

[0583] Matching and Notifications

[0584] 7. Compatibility Analysis

[0585] Based on the generated profile, the server analyzes compatibility with other users and calculates a compatibility score.

[0586] 8. Matching Notification

[0587] The server sends notifications to users with pairs that have high compatibility scores.

[0588] User A and User B receive a notification and can choose whether to disclose their information to the other party.

[0589] 9. Information Disclosure and Contact Information

[0590] If the user allows the information to be made public, the servers provide contact information to each other.

[0591] Real-life dating support

[0592] 10. Dating promotion and advice

[0593] Based on the compatibility analysis results, the server suggests meeting places and activities related to common hobbies and interests.

[0594] Users receive suggestions and plan meet-ups through the application.

[0595] Specific Examples

[0596] For example, suppose User A (male, 30 years old, program engineer) registers with the application, takes a personality test, and shares his social media data. The server collects this data and inputs it into a generative AI model to generate a detailed profile. It then analyzes his compatibility with User B (female, 28 years old, designer), who also provided data, and notifies him that their compatibility scores are high. If both parties agree to share their information, the server provides contact information and recommends a shared hobby: watching movies. This allows the two parties to prepare and support each other when they meet in person.

[0597] In this way, the present invention allows users to find the perfect partner based on information they are aware of and information they are not aware of.

[0598] The processing flow will be explained below.

[0599] Step 1:

[0600] Users launch the application and create an account by entering their email address and password.

[0601] Step 2:

[0602] The server receives the entered email address and password, stores them in a database, and creates a user account.

[0603] Step 3:

[0604] Users enter profile information such as name, age, gender, interests and hobbies.

[0605] Step 4:

[0606] The server receives the entered profile information and stores / updates it in a database.

[0607] Step 5:

[0608] Users answer personality tests provided within the application.

[0609] Step 6:

[0610] The terminal transmits the personality test answer data entered by the user to the server.

[0611] Step 7:

[0612] The server receives the personality test response data, calculates the scores, and stores them in a database.

[0613] Step 8:

[0614] Users select the option to upload the results of a genetic analysis they have already performed.

[0615] Step 9:

[0616] The server receives the uploaded genetic analysis results and stores them in a database.

[0617] Step 10:

[0618] Users link their social media accounts (such as Twitter or Facebook) to the application.

[0619] Step 11:

[0620] The server collects data such as user posts, like history, and following relationships through the SNS API.

[0621] Step 12:

[0622] The server stores the collected social media data in a database and adds it to the user profile.

[0623] Step 13:

[0624] The server integrates users' profile information, personality test results, genetic analysis results, social media data, purchase history, and travel history.

[0625] Step 14:

[0626] The server inputs the integrated data into a generative AI model, which analyzes the user's personality and preferences using deep learning algorithms.

[0627] Step 15:

[0628] The server analyzes compatibility with other users based on the detailed profile information generated.

[0629] Step 16:

[0630] The server calculates the compatibility scores between users who are determined to be compatible and stores the results.

[0631] Step 17:

[0632] The server sends a matching notification to users who are determined to be compatible with each other.

[0633] Step 18:

[0634] User A and User B receive the notification and check each other's information.

[0635] Step 19:

[0636] User A and User B choose whether or not to make their contact information public to each other.

[0637] Step 20:

[0638] The terminal transmits the selected permission to disclose information to the server.

[0639] Step 21:

[0640] The server confirms both parties' permission to disclose information and provides each other with contact information.

[0641] Step 22:

[0642] Based on the compatibility analysis results, the server suggests meeting places and activities related to common hobbies and interests.

[0643] Step 23:

[0644] Users receive suggestions and plan meet-ups through the application.

[0645] Step 24:

[0646] The server implements privacy measures to protect users' personal information, including settings to hide users' friend relationships.

[0647] This will create a system that allows users to find the perfect partner in an efficient and reliable way.

[0648] Example 1

[0649] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0650] Conventional dating systems match users based only on their profile information and simple questionnaire results, making it difficult to match users in a way that fully reflects their detailed personality, hobbies, preferences, behavioral patterns, etc. In addition, they often do not provide additional information to improve compatibility or specific advice to support actual encounters, which can result in low user satisfaction.

[0651] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0652] In this invention, the server includes means for registering and storing user attribute information, means for registering and storing the user's psychological test and genetic information results, means for collecting and storing the user's social networking service information via an application programming interface, means for evaluating compatibility with other users based on the analysis results of the generative AI model, means for sending a notification when a highly compatible partner is found based on the compatibility evaluation results, means for obtaining consent for information disclosure, means for mutually exchanging contact information if both parties agree to the disclosure, and means for making recommendations for actually meeting compatible partners. This enables highly accurate matching based on the user's detailed information and specific advice to support actual meetings.

[0653] "User Attribute Information" refers to basic personal information provided by a user, such as name, age, gender, interests, and hobbies.

[0654] A "psychological test" is an assessment tool such as a questionnaire or survey administered to assess a user's personality or psychological characteristics.

[0655] "Genetic Results" means data regarding a user's genetic characteristics and traits based on a genetic analysis of the user.

[0656] "Social networking service information" refers to data such as post content, like history, and following relationships collected from a user's SNS (social networking service) account.

[0657] An "application programming interface" is a defined method of communication that allows different software systems to share information and functionality.

[0658] A "generative AI model" is a machine learning model that uses deep learning algorithms to analyze data and predict and evaluate user characteristics and preferences.

[0659] "Compatibility assessment" is a process in which a generative AI model is used to compare and analyze the personalities, hobbies, and preferences of users, and calculate the degree of compatibility between them.

[0660] "Compatibility evaluation results" refers to information such as compatibility scores and ranks calculated as a result of evaluating the compatibility between users.

[0661] "Notifications" are messages sent to users by the system to inform them that a compatible partner has been found or to inform them of important information.

[0662] "Consent to disclose information" is a user's expression of consent to allow the information they provide to be shared with other users.

[0663] "Contact Information" means information about contact methods, such as email addresses and phone numbers, that allow a user to communicate directly with other users.

[0664] "Recommendations" are specific advice to promote actual encounters, such as suggested meeting locations and activities for users.

[0665] The present invention is a system that integrates various user data and analyzes it with a generative AI model to find the best partner for the user and provide reliable dating. This system involves multiple processing steps, each designed to achieve a specific purpose.

[0666] First, a user launches the application and creates an account by entering their email address and password. The user's device encrypts this information and sends it to the server. The server then stores the received information in a database and creates a new account.

[0667] Next, the user enters their demographic information, such as their name, age, gender, hobbies, interests, etc. The device then sends this information to the server, which stores it in a database.

[0668] The user then answers a personality test within the application and provides the results. The device transmits the test results in real time to the server, which stores the results in a database. Additionally, if the user provides genetic analysis results, the device uploads the results to the server, which stores them in a database.

[0669] Users can also link their social media accounts to the application. The server uses the SNS API to collect users' social media data (posts, likes, and following relationships) and stores them in a database.

[0670] The server retrieves the user's attribute information, personality test results, genetic analysis results, social media data, and other historical data from the database to generate an integrated data profile. This integrated profile is then input into a generative AI model, where the user's personality and preferences are analyzed through a deep learning algorithm. The server then generates a detailed profile based on this information.

[0671] The server then evaluates compatibility with other users based on the analyzed detailed profiles and calculates a compatibility score. When users with high compatibility scores are found, the server sends a notification to the device. The user receives the notification and can choose whether to make the information public. If both parties agree to the disclosure, the server provides each other with their contact information.

[0672] Finally, the server will suggest meeting places and activities to the user based on the compatibility results, and the user will receive the suggestions and make specific plans for the meeting.

[0673] For example, if User A (male, 30 years old, program engineer) creates an account, takes a personality test, and connects his social media accounts, the server collects this data and inputs it into a generative AI model to generate a detailed profile. If the server determines that User B (female, 28 years old, designer), who also provided data, has a high compatibility score, it notifies the user and provides their contact information. If both parties agree to the disclosure of their information, the server can help facilitate a real-life meeting by suggesting a movie to go to.

[0674] An example prompt is:

[0675] Prompt to register:

[0676] Users launch the application and create an account by entering their email address and password.

[0677] Personality Test Prompt:

[0678] Users answer personality tests provided within the application and provide their results.

[0679] Prompt to link social media data:

[0680] Users connect their social media accounts to the application, and the server collects data through the social media API.

[0681] In this way, the embodiments of the invention make it possible to utilize detailed information about users to achieve advanced matching, thereby increasing the chances of encounters that provide high compatibility for users.

[0682] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0683] Step 1:

[0684] User Registration

[0685] Users launch the application and create an account by entering their email address and password.

[0686] Input: Email address, password

[0687] The terminal encrypts the input data and sends it to the server.

[0688] The server stores the received information in a database and creates a new account.

[0689] Output: A confirmation email is sent to the user confirming that their account was created.

[0690] Step 2:

[0691] Enter profile information

[0692] Within the application, users enter profile information such as their name, age, gender, interests and hobbies.

[0693] Input: Name, age, gender, interests and hobbies

[0694] The terminal transmits the entered profile information to the server.

[0695] The server stores and updates this information in a database.

[0696] Output: Sends a notification to the user that their profile information has been saved.

[0697] Step 3:

[0698] Personality tests and genetic analysis

[0699] Users answer personality tests provided within the application and submit their results.

[0700] Input: Personality test answers

[0701] The device transmits the test results to the server in real time.

[0702] The server stores the results of the personality test in a database.

[0703] Output: Sends a notification to the user that the test results have been saved.

[0704] When the user provides the results of the genetic analysis, input: Genetic analysis results

[0705] The terminal uploads the result data to the server.

[0706] The server stores the genetic analysis results in a database.

[0707] Output: Send a notification to the user that the genetic analysis results have been saved.

[0708] Step 4:

[0709] Social media integration and data collection

[0710] Users can link their social media accounts within the application.

[0711] Input: SNS account information (API key, token, etc.)

[0712] The server uses the SNS API to collect users' SNS data (posts, like history, and following relationships).

[0713] The server stores the collected SNS data in a database.

[0714] Output: Sends a notification to the user that the SNS data has been saved.

[0715] Step 5:

[0716] Data integration

[0717] The server retrieves user profile information, personality test results, genetic analysis results, social media data, and other historical data from the database.

[0718] Input: Profile information, personality test results, genetic analysis results, social media data, history data

[0719] The server aggregates the acquired data and generates a single data profile.

[0720] Output: Unified Data Profile

[0721] Step 6:

[0722] Applying generative AI models

[0723] The server inputs the integrated data profile into a generative AI model to analyze the user's personality and preferences.

[0724] Input: Unified Data Profile

[0725] The server uses a generative AI model to generate a detailed profile of the user, including their personality traits, hobbies, preferences, and behavioral patterns.

[0726] Output: Detailed profile

[0727] Step 7:

[0728] Conformity assessment

[0729] The server evaluates your compatibility with other users based on the detailed profile you create.

[0730] Input: Detailed profile

[0731] The server calculates the relevance scores and stores them in a database.

[0732] Output: Relevance score

[0733] Step 8:

[0734] Matching notification

[0735] The server matches users with high compatibility scores and sends a notification to the device.

[0736] Input: Relevance score, matching information

[0737] User A and User B receive a notification and check each other's information within the application.

[0738] Output: Matching notification

[0739] Step 9:

[0740] Disclosure of information and contact details

[0741] If the user allows the information to be made public, the server provides contact information to each other.

[0742] Input: Consent to disclosure of information

[0743] The server retrieves contact information between users from a database and sends it to the terminal.

[0744] Output: Contact information

[0745] Step 10:

[0746] Dating promotion and advice

[0747] Based on the compatibility results, the server suggests meeting places and activities to the user.

[0748] Input: compatibility results, detailed profile

[0749] The server sends the proposal to the terminal.

[0750] Users receive suggestions and plan specific encounters.

[0751] Output: Meeting suggestion notification, planning reminder notification

[0752] (Application example 1)

[0753] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0754] In recent years, online dating and matchmaking platforms have developed rapidly. However, many users find it difficult to find a partner who matches their personality and preferences, or to find suitable products and services. Furthermore, traditional systems are unable to fully utilize diverse user data, making it difficult to provide accurate recommendations. This has led to a decline in user satisfaction and a lack of continued use of the service.

[0755] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0756] In this invention, the server includes means for inputting and saving a user's profile information, means for inputting and saving the user's personality test and genetic analysis results, means for collecting and saving the user's SNS information via an API, means for integrating the collected data and analyzing the user's personality and preferences using a generative AI model, means for analyzing compatibility with other users based on the analysis results of the generative AI model, means for sending a notification when a compatible partner is found based on the compatibility analysis results, means for obtaining permission to disclose information, means for mutually exchanging contact information if both parties give permission, means for providing advice on how to actually meet a compatible partner, means for suggesting optimal products and services based on the user's personality and preferences, and means for notifying the user of product suggestions with the user's permission. This allows users to easily find the partner, products, and services that are best suited for them.

[0757] "User profile information" refers to basic personal information about a user, such as the user's name, age, gender, interests, and hobbies.

[0758] A "personality test" is a questionnaire-based test designed to assess a user's personality traits, the results of which are stored as the user's personality profile.

[0759] "Genetic analysis results" are analysis results based on the user's genetic information, and are data that indicate the user's biological characteristics and tendencies.

[0760] An "API" is an interface for collecting and exchanging data in collaboration with other software applications.

[0761] A "generative AI model" is an artificial intelligence model that uses deep learning algorithms to analyze a user's personality and preferences from a variety of data.

[0762] "Compatibility analysis" is the process of calculating the compatibility between a specific user and other users based on the analysis results of a generative AI model.

[0763] "Notifications" are messages that inform users of compatible partners and product suggestions.

[0764] "Permission to disclose information" means that a user authorizes the disclosure of their personal information or contact information to other users.

[0765] "Contact information" refers to information necessary for contacting users, such as email addresses and telephone numbers.

[0766] "Advice" is guidance that suggests places and activities to meet compatible people in person.

[0767] "Proposing optimal products and services" means recommending products and services that best suit the user's preferences and personality based on the analysis results of the generative AI model.

[0768] A "product suggestion notification" is a message that notifies the user of information about a suggested product.

[0769] This invention allows users to easily find partners, products, and services that suit them. A specific implementation form of a system for carrying out the invention is described below.

[0770] System Configuration

[0771] The system consists of a server, user devices (smartphones, etc.), and various software, including Django (backend framework), MySQL (database), Pandas (dataframe manipulation library), SciKit-Learn (machine learning library), and TensorFlow (deep learning library).

[0772] Program processing overview

[0773] 1. Data Collection Module

[0774] Using the user's device, the user enters profile information, personality test results, and genetic analysis results. Furthermore, the user's social media accounts are linked via API to collect social media information. All of this data is stored in a MySQL database via the Django framework. The specific data collection interface is the application's registration form and social media API connector.

[0775] 2. Data Processing Module

[0776] The data obtained from each user is integrated using Pandas. For example, personality test results and social media data are combined to create a comprehensive data profile. This process unifies the user's multidimensional data.

[0777] 3. Generative AI model application module

[0778] The combined data profile is then fed into a TensorFlow-powered generative AI model, which uses deep learning algorithms to analyze the user's personality and preferences to generate a detailed profile of the user, including their personality traits, hobbies, and preferences.

[0779] 4. Compatibility Analysis Module

[0780] Based on the profile created, SciKit-Learn's machine learning algorithms are used to analyze compatibility with other users, and if a match is found, a notification is sent to the user.

[0781] 5. Product and service proposal module

[0782] Based on the user's personality and preferences, the system suggests optimal products and services. Specifically, product recommendations are made to the user based on the analysis results of the generative AI model. Notifications of suggested products and services are sent via push notifications using application tokens.

[0783] Examples and Prompts

[0784] For example, if a male user in his 30s is looking for outdoor gear, the system will analyze his preferences based on his personality test, social media posts, and purchase history, and then use a generative AI model to suggest the best products for him. Specific prompt sentence examples are as follows:

[0785] "Male, 30s, loves the outdoors. Please make a list of recommended sports and camping equipment based on the results of a personality test and analysis of social media data."

[0786] In this way, users can easily find products and services that suit their personality and preferences, and it also makes it easier to find a suitable partner, improving user satisfaction.

[0787] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0788] Step 1:

[0789] A user launches an application on a smartphone device and creates an account by entering an email address and password. The entered information (email address and password) is sent to the server, which stores it in a database and creates a user account. At this stage, the input data is the user's basic information, and the output is the user account information stored in the database.

[0790] Step 2:

[0791] The user uses a terminal to input profile information such as name, age, gender, interests, and hobbies. The input profile information is sent to the server and saved and updated in a database. The input data here is the user's personal information, and the output is the updated profile information in the database.

[0792] Step 3:

[0793] The user answers the personality test in the app on their device and sends the results to the server. In addition, if the user's genetic analysis results already exist, the data is uploaded. The personality test results and genetic analysis results are sent to the server and stored in a database. The input is the personality test answers and genetic analysis results, and the output is the analysis results stored in the database.

[0794] Step 4:

[0795] Users connect their SNS accounts to the application on their devices. The server collects the user's SNS data (posts, like history, following relationships, etc.) through the SNS API and stores it in a database. The input data here is the SNS information obtained from the SNS API, and the output is the SNS data stored in the database.

[0796] Step 5:

[0797] The server integrates various data, such as user profile information, personality test results, genetic analysis results, social media data, purchase history, and travel history. This integration process uses Pandas to combine each data into a single data frame. The input data is user-related data obtained from multiple sources, and the output is an integrated data profile.

[0798] Step 6:

[0799] The server inputs the integrated data profile into a generative AI model, which uses a deep learning algorithm to analyze the user's personality and preferences. This analysis is performed using TensorFlow, and the profile is generated. The input data is the integrated data profile, and the output is a detailed user profile as a result of the analysis.

[0800] Step 7:

[0801] The server analyzes compatibility with other users based on the generated detailed profile using SciKit-Learn's machine learning algorithm, calculates compatibility scores, and performs optimal matching. The input data is the user's detailed profile, and the output is compatibility scores and matching results.

[0802] Step 8:

[0803] The server sends a notification to users of pairs with high compatibility scores. The notification includes a brief profile of the other person and their compatibility score. Users receive the notification and choose whether to disclose their information to the other person. The input data here is the match result, and the output is the notification to the user and the option to disclose information.

[0804] Step 9:

[0805] Once the user gives permission, the server provides contact information to each other, and the two parties are ready to communicate. The input is the user's permission, and the output is the other party's contact information.

[0806] Step 10:

[0807] The server then suggests meeting places and activities related to common hobbies and interests based on the compatibility analysis results. Users receive the suggestions through the application and plan their meetings. The input data is the compatibility analysis results, and the output is the suggested meeting activities and places.

[0808] Step 11:

[0809] The server uses the analysis results of the generative AI model to suggest optimal products and services based on the user's personality and preferences. The proposals are notified to the user using the application's push notification function. The input data here is the analysis results of the generative AI model, and the output is a product recommendation notification. An example of this prompt is as follows:

[0810] "Male, 30s, loves the outdoors. Please make a list of recommended sports and camping equipment based on the results of a personality test and analysis of social media data."

[0811] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0812] The present invention is a system that uses a variety of user data and an emotion engine to find compatible partners using a generative AI model, providing reliable encounters. Specific embodiments are described below.

[0813] Data collection and integration

[0814] 1. User Registration

[0815] Users launch the application and create an account by entering their email address and password.

[0816] The server stores the entered information in a database and creates a user account.

[0817] 2. Enter your profile information

[0818] Users enter profile information such as name, age, gender, interests and hobbies.

[0819] The server stores and updates this information in a database.

[0820] 3. Personality tests and genetic analysis

[0821] Users answer personality tests provided within the application and provide their results.

[0822] The terminal transmits the results of the personality test to the server, which stores them in a database.

[0823] If a user uploads the results of a genetic analysis that has already been performed, the server stores the results in a database.

[0824] 4. Social media integration and data collection

[0825] Users connect their social media accounts (such as Twitter or Facebook) to the application.

[0826] The server collects data such as user posts, like history, and following relationships through the SNS API.

[0827] The server stores the collected social media data in a database and adds it to the user profile.

[0828] AI-based data analysis and compatibility analysis

[0829] 5. Data Integration

[0830] The server integrates the user's profile information, personality test results, genetic analysis results, social media data, purchase history, and travel history to create a single data profile.

[0831] 6. Applying generative AI models

[0832] The server inputs this integrated data into a generative AI model, which then analyzes the user's personality and preferences using deep learning algorithms.

[0833] As a result of the analysis, the server generates a detailed profile of the user, including their personality traits, hobbies, preferences, and behavioral patterns.

[0834] Use of emotion engine

[0835] 7. Emotional Data Collection and Analysis

[0836] Users allow emotional data to be collected during everyday use.

[0837] The device collects data such as user text input, voice input, and facial expression analysis and sends it to the emotion engine.

[0838] The emotion engine analyzes the user's current emotional state from the collected data and generates emotion data.

[0839] The server stores the generated emotion data in a database and uses it to analyze the AI ​​model.

[0840] Matching and Notifications

[0841] 8. Compatibility Analysis

[0842] The server analyzes compatibility with other users based on the generated user profile and emotional data and calculates a compatibility score.

[0843] 9. Matching Notification

[0844] The server sends a matching notification based on the compatibility scores of users who are determined to be compatible with each other.

[0845] User A and User B receive the notification and check each other's information.

[0846] 10. Information Disclosure and Contact Information

[0847] User A and User B choose whether or not to make their contact information public to each other.

[0848] The terminal transmits the selected permission to disclose information to the server.

[0849] The server confirms both parties' permission to disclose information and provides each other with contact information.

[0850] Real-life dating support

[0851] 11. Dating promotion and advice

[0852] Based on the compatibility analysis results and emotional data, the server suggests meeting places and activities related to common hobbies and interests.

[0853] Users receive suggestions and plan meet-ups through the application.

[0854] Specific Examples

[0855] For example, suppose User A (male, 30 years old, program engineer) registers with the application, connects his personality test and social media data, and allows the use of the emotion engine. The server collects this data and inputs it into a generative AI model to generate a detailed profile. It then analyzes his compatibility with User B (female, 28 years old, designer), who also provided data, and notifies him that their compatibility score is high. If both parties allow their information to be made public, the server provides contact information and recommends a shared hobby: watching movies. It also provides advice based on the emotion engine data.

[0856] In this way, the present invention allows users to find the perfect partner based on both conscious and involuntary information. By using an emotion engine, even more sophisticated and accurate matching can be achieved.

[0857] The processing flow will be explained below.

[0858] Step 1:

[0859] Users launch the application and create an account by entering their email address and password.

[0860] Step 2:

[0861] The server receives the entered email address and password, stores them in a database, and creates a user account.

[0862] Step 3:

[0863] Users enter profile information such as name, age, gender, interests and hobbies.

[0864] Step 4:

[0865] The server receives the entered profile information and stores / updates it in a database.

[0866] Step 5:

[0867] Users answer personality tests provided within the application.

[0868] Step 6:

[0869] The terminal transmits the personality test answer data entered by the user to the server.

[0870] Step 7:

[0871] The server receives the personality test response data, calculates the scores, and stores them in a database.

[0872] Step 8:

[0873] Users upload their genetic analysis results.

[0874] Step 9:

[0875] The server receives the uploaded genetic analysis results and stores them in a database.

[0876] Step 10:

[0877] Users link their social media accounts (such as Twitter or Facebook) to the application.

[0878] Step 11:

[0879] The server collects data such as user posts, like history, and following relationships through the SNS API.

[0880] Step 12:

[0881] The server stores the collected social media data in a database and adds it to the user profile.

[0882] Step 13:

[0883] Users consent to the use of the emotion engine and allow the collection of emotion data.

[0884] Step 14:

[0885] The device collects emotional data from the user's text input, voice input, facial expression recognition, etc.

[0886] Step 15:

[0887] The emotion engine analyzes the collected emotion data and recognizes the user's emotional state.

[0888] Step 16:

[0889] The server receives the emotion data from the emotion engine and stores it in a database.

[0890] Step 17:

[0891] The server integrates the user's profile information, personality test results, genetic analysis results, social media data, purchase history, travel history, and emotional data to create a single data profile.

[0892] Step 18:

[0893] The server inputs the integrated data into a generative AI model, which analyzes the user's personality and preferences using deep learning algorithms.

[0894] Step 19:

[0895] The server analyzes compatibility with other users based on the detailed profile information generated.

[0896] Step 20:

[0897] The server calculates the compatibility scores between users who are determined to be compatible and stores the results.

[0898] Step 21:

[0899] The server sends a matching notification to users who are determined to be compatible with each other.

[0900] Step 22:

[0901] User A and User B receive the notification and check each other's information.

[0902] Step 23:

[0903] User A and User B can review the notification and choose whether or not to disclose their contact information to the other party.

[0904] Step 24:

[0905] The terminal transmits the selected permission to disclose information to the server.

[0906] Step 25:

[0907] The server confirms both parties' permission to disclose information and provides each other with contact information.

[0908] Step 26:

[0909] Based on the compatibility analysis results and emotional data, the server suggests meeting places and activities related to common hobbies and interests.

[0910] Step 27:

[0911] Users receive suggestions and plan meet-ups through the application.

[0912] Step 28:

[0913] The server implements privacy measures to protect users' personal information, including settings to hide users' friend relationships.

[0914] This will enable users to find their ideal partner in an efficient and reliable way, and the use of an emotion engine will provide even more sophisticated and accurate matching.

[0915] Example 2

[0916] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0917] Conventional matching systems suggest partners based on user profile information and simple personality assessment results, but they face the challenge of making matches that fully reflect individual needs and diverse emotional states. Furthermore, because they rely on simple data without utilizing social media data or genetic information, matching accuracy is low and users' true compatibility cannot be determined. Another problem is the lack of actual support for finding a compatible partner.

[0918] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for inputting and saving user profile information, means for inputting and saving the user's personality test and genetic analysis results, means for collecting and saving the user's SNS information via an API, means for integrating the collected data and analyzing the user's personality and preferences using an AI model, means for analyzing compatibility with other users based on the analysis results of the AI ​​model, means for collecting and analyzing the user's emotional data during daily use, means for saving the generated emotional data and using it for compatibility analysis, means for sending a notification when a compatible partner is found based on the compatibility analysis results, means for obtaining permission to disclose information, means for mutually exchanging contact information if both parties give permission, and means for providing advice on how to actually meet a compatible partner. This allows users to find their ideal partner based on detailed and reliable data.

[0919] "Profile Information" refers to basic personal information such as your name, age, gender, interests and hobbies.

[0920] A "personality test" refers to a series of questions or assessments that users answer to assess their personality traits or psychological state.

[0921] "Genetic analysis" refers to the process of analyzing a user's DNA sample to extract genetic characteristics and health information.

[0922] "SNS information" refers to data such as posts, like history, and following relationships collected from a user's SNS services (e.g., Twitter or Facebook).

[0923] "API" stands for Application Programming Interface and refers to the rules and protocols that allow software to communicate with each other.

[0924] An "AI model" refers to a program that uses artificial intelligence algorithms to analyze data and predict user characteristics and behavioral patterns.

[0925] "Emotional Data" refers to data that indicates a user's emotional state, extracted from text input, voice input, facial expression analysis, etc.

[0926] "Compatibility score" refers to the numerical value of the compatibility between users, as a result of analysis by the AI ​​model.

[0927] "Permission to disclose information" refers to a user's consent to providing their contact information to other users.

[0928] "Advice" refers to advice or suggestions offered to facilitate actual encounters with compatible partners.

[0929] "Integrated Data Profile" refers to the creation of unified information about a user from multiple collected data sources.

[0930] The present invention is a system that uses a variety of user data and an emotion engine to find compatible partners using a generative AI model, providing reliable encounters. Specific embodiments are described below.

[0931] Data collection and integration

[0932] User Registration

[0933] A user launches the application and creates an account by entering their email address and password. The server stores the information in a database and creates a user account.

[0934] Enter profile information

[0935] Users enter profile information such as name, age, gender, interests, hobbies, etc. The server stores and updates this information in a database.

[0936] Personality tests and genetic analysis

[0937] The user answers a personality test provided within the application and sends the results to the server, which stores the personality test results in a database. If the user uploads the results of a genetic analysis they have already completed, the server stores these results in a database.

[0938] Social media integration and data collection

[0939] Users connect their social media accounts (e.g., Twitter or Facebook) to the application. The server collects data such as user posts, likes, and following relationships through the social media API. The server stores the collected social media data in a database and adds it to the user profile.

[0940] AI-based data analysis and compatibility analysis

[0941] Data integration

[0942] The server integrates the user's profile information, personality test results, genetic analysis results, social media data, purchase history, and travel history to create a single data profile.

[0943] Applying generative AI models

[0944] The server inputs this integrated data into a generative AI model, which then uses deep learning algorithms to analyze the user's personality and preferences, generating a detailed profile of the user, including their personality traits, hobbies, preferences, and behavioral patterns.

[0945] Use of emotion engine

[0946] Emotion data collection and analysis

[0947] Users allow emotional data to be collected during daily use. The device collects data such as the user's text input, voice input, and facial expression analysis, and sends it to the emotion engine. The emotion engine analyzes the user's current emotional state from the collected data and generates emotional data. The server stores the generated emotional data in a database and uses it for analysis in AI models.

[0948] Matching and Notifications

[0949] Compatibility analysis

[0950] The server analyzes compatibility with other users based on the generated user profile and emotional data and calculates a compatibility score.

[0951] Matching notification

[0952] The server sends a matching notification based on the compatibility scores of users who are determined to be compatible. User A and User B receive the notification and check each other's information.

[0953] Disclosure of information and contact details

[0954] User A and User B choose whether to share their contact information with each other. The device sends the selected permission to share information to the server. The server confirms the permission from both parties and provides the contact information to each other.

[0955] Real-life dating support

[0956] Dating promotion and advice

[0957] The server then suggests meeting places and activities related to shared hobbies and interests based on the compatibility analysis and emotional data. Users receive these suggestions through the app and plan their meetings.

[0958] Specific Examples

[0959] For example, suppose User A (male, 30 years old, program engineer) registers with the application, connects his personality test and social media data, and allows the use of the emotion engine. The server collects this data and inputs it into a generative AI model to generate a detailed profile. It then analyzes his compatibility with User B (female, 28 years old, designer), who also provided data, and notifies him that their compatibility score is high. If both parties allow their information to be made public, the server provides contact information and recommends a shared hobby: watching movies. It also provides advice based on the emotion engine data.

[0960] Example prompts to input to the generative AI model

[0961] "Analyze the personality test results, social media data, and data collected by the emotion engine for User A, a 30-year-old male program engineer, and generate a profile to find the perfect partner for this user."

[0962] As described above, the present invention provides a system that presents optimal partners based on a user's detailed profile information and achieves advanced and highly accurate matching that also takes emotional data into consideration.

[0963] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0964] Step 1:

[0965] A user launches the application and creates an account by entering their email address and password. The input requires the user's email address and password, and the output is a user account created in the database. The server receives this information and saves the new user account in the database. Specifically, the form input data is sent to an endpoint, and a new record is added to the database user table on the server side.

[0966] Step 2:

[0967] The user enters profile information (such as name, age, gender, interests, hobbies, etc.). This profile information is required as input, and the updated user profile is saved in the database as output. The server receives this information and saves and updates it in the database. Specifically, when the user fills out the input form and presses the submit button, the data is sent to the server and the database record is updated.

[0968] Step 3:

[0969] The user takes a personality test and provides the results. The input requires the personality test answer data, and the output is the personality test results stored in the database. The device sends the user's test answers to the server, which stores them in the database. Specifically, when the user answers the test and sends the results, the server receives the results and stores them in the database as a new record.

[0970] Step 4:

[0971] When a user uploads the results of a genetic analysis that has already been conducted, the input required is the genetic analysis result file, and the output is the result stored in the database. The server receives the result and stores it in the database. Specifically, the user uploads a file, and the file is sent to the server and then stored in the database.

[0972] Step 5:

[0973] A user connects their social media account to the application. Social media account authentication information is required as input, and social media data is stored in a database as output. The server uses the social media API to collect data such as the user's posts, like history, and following relationships. Specifically, the user authenticates with the social media account, and then the server calls the API to collect the data and store it in the database.

[0974] Step 6:

[0975] The server integrates profile information, personality test results, genetic analysis results, social media data, etc. These various data are required as input, and an integrated data profile is generated as output. Specifically, the server extracts the necessary information from multiple data tables and compiles it into a single integrated data profile.

[0976] Step 7:

[0977] The server inputs the integrated data into the generative AI model for analysis. The input is an integrated data profile, and the output is a detailed user profile. Specifically, the server sends data to the API of the generative AI model and receives the detailed profile as the analysis result.

[0978] Step 8:

[0979] Collects and analyzes user emotional data. The input requires the user's text input, voice input, and facial expression analysis data, and the output generates emotional data. The device collects this emotional data and sends it to the emotion engine. Specifically, the device uses the camera and microphone to capture data and sends it to the emotion engine in real time.

[0980] Step 9:

[0981] The server stores the collected emotion data in a database and uses it for compatibility analysis. Emotion data is required as input, and an updated user profile is generated as output. The server uses this data for analysis. The specific operation is to update records in the database.

[0982] Step 10:

[0983] The server analyzes the compatibility with other users based on the generated user profile and emotional data. The inputs are the user profile and emotional data, and the output is a compatibility score. Specifically, an algorithm for calculating the compatibility score is executed, and the results are stored in a database.

[0984] Step 11:

[0985] The server sends a matching notification based on the compatibility score between users who are determined to be compatible. The compatibility score is required as input, and a notification is sent to the user as output. Specific actions include sending a push notification or email to the user.

[0986] Step 12:

[0987] User A and User B choose whether to share their contact information with each other. This selection information is required as input, and the permitted contact information is provided to each other as output. The device sends this information to the server, which verifies and provides the information. Specific operation is to display the contact information according to the user's selection.

[0988] Step 13:

[0989] The server suggests meeting places and activities related to common hobbies and interests based on the compatibility analysis results and emotional data. The compatibility analysis results and emotional data are required as input, and suggestions for meeting places and activities are provided to the user as output. Specific operations include notifying the user of recommended places and activities.

[0990] This allows users to find their perfect partner based on detailed profile information and emotional data.

[0991] (Application example 2)

[0992] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0993] Conventional matching systems analyze compatibility based on static data such as user profile information and personality test results, but do not realize matching that reflects the user's real-time emotions and interests. Furthermore, when users shop in physical stores, they lack product recommendations and location suggestions that are optimized for their individual preferences and emotions. This has made improving the user experience a challenge.

[0994] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting and saving user profile information; means for inputting and saving the user's personality test and genetic analysis results; means for collecting and saving the user's SNS information using data communication means; means for integrating the collected data and analyzing the user's personality and preferences using a computational model; means for analyzing compatibility with other users based on the analysis results of the computational model; means for sending a notification when a compatible partner is found based on the compatibility analysis results; means for obtaining permission to disclose information; means for mutually exchanging contact information if both parties give permission; means for making suggestions for actually meeting a compatible partner; means for collecting and analyzing the user's emotional data; means for analyzing purchase and movement history based on the emotional data and suggesting related products and places; and means for suggesting products based on the user's real-time emotional and interest information. This enables matching and product recommendations based on the user's real-time emotional and interest information.

[0995] "User profile information" means basic information about a user, such as the user's name, age, gender, hobbies, and interests.

[0996] "Means for storing" refers to a method of using a database or storage device to permanently store information entered by the user.

[0997] A "personality test" is a series of questions or assessments designed to assess a user's personality or psychological characteristics.

[0998] "Genetic analysis results" are data obtained as a result of analyzing a user's genetic characteristics and constitution.

[0999] "SNS information" refers to data such as posts published by users on social networking services, like history, and following relationships.

[1000] "Data communication means" refers to a method of sending and receiving information using the Internet, wireless communication, etc.

[1001] "Integration" is the process of combining different types and formats of data into one unified data set.

[1002] A "computational model" is a mathematical or statistical algorithm or program used to analyze data or make predictions.

[1003] "Compatibility analysis" is a method of comparing the characteristics and preferences of users and evaluating the degree of compatibility between them.

[1004] A "notification" is a message or alert used to inform a user.

[1005] "Permission to Disclose Information" means that a user consents to the disclosure of their information to others.

[1006] "Contact Information" means contact information for a user, such as a phone number, email address, or postal address.

[1007] "Suggestions" are advice that recommends useful information or actions to the user.

[1008] "Emotional data" refers to data that represents the emotional state of a user, derived from facial expressions, voice, text, etc.

[1009] "Purchase history" refers to records of products and services purchased by a user in the past.

[1010] "Travel history" refers to data about the places a user has visited and the routes they have taken.

[1011] "Means for suggesting products" refers to a system that selects and recommends related products and services based on the user's emotions and interests.

[1012] To implement this invention, it is necessary to build a system in which multiple pieces of hardware and software work together to collect real-time emotional data and interest information from users and perform optimal product recommendations and matching.

[1013] Required Hardware and Software

[1014] 1. Hardware

[1015] Smart glasses (e.g. Google Glass)

[1016] Built-in camera and microphone

[1017] Server (capable of high-performance data processing)

[1018] Local data storage (on the user's device)

[1019] 2. Software

[1020] Python libraries (requests, DeepFace)

[1021] Server-side API (user data acquisition API, product recommendation API)

[1022] Database (saves user data and emotion data)

[1023] System Overview

[1024] User registration and data collection

[1025] When a user uses the application for the first time, they enter their profile information (name, age, gender, hobbies, interests, etc.), then provide the results of a personality test and genetic analysis, and link their social media account, which sends and stores this data on the server.

[1026] Collecting Emotional Data

[1027] The smart glasses' built-in camera and microphone are used to collect the user's facial expressions and voice in real time, and a facial expression recognition algorithm (e.g., DeepFace) is used to analyze the user's emotional state from this data.

[1028] Data Integration and Analysis

[1029] The server combines the collected profile information, personality test results, genetic analysis results, social media data, purchase history, travel history, and emotional data, and inputs this into a computational model, which then uses a generative AI algorithm to analyze the user's personality and preferences and generate a detailed user profile.

[1030] Product Recommendation and Matching

[1031] When users shop in physical stores, the smart glasses will suggest relevant products and locations based on collected emotional data and real-time interest information, improving users' shopping experience and providing more personalized services.

[1032] Specific examples

[1033] Suppose a user visits a physical store and puts on a pair of smart glasses. The camera in the glasses recognizes the user's facial expressions and performs emotional analysis. For example, if the user is looking for new work clothes, the system can recommend the most suitable business suit in real time based on the user's personality and past purchasing history. If the user is unsure about a purchase, the server will send appropriate advice to the smart glasses based on the user's emotional data. This process is input into the generative AI model with prompts such as the following:

[1034] Prompt Sentence Examples

[1035] Current emotional state: Joy

[1036] Hobbies: Fashion

[1037] Past purchase history: Suits

[1038] Suggestion: Please recommend the latest business suit.

[1039] This invention enables users to receive optimal product recommendations and matching services based on their real-time emotions and interests.

[1040] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1041] Step 1:

[1042] A user starts the application and enters their profile information (name, age, gender, hobbies, interests). This input data is sent from the device to the server, which then stores it in a database.

[1043] Step 2:

[1044] The user provides the personality test and genetic analysis results, which are also sent from the device to the server, which stores them in a database.

[1045] Step 3:

[1046] When a user connects their SNS account, the server collects the user's SNS data (posts, like history, following relationships) through the API. This data is also stored in the database.

[1047] Step 4:

[1048] The server combines the collected profile information, personality test results, genetic analysis results, and social media data to create a single user data profile, which is then stored in a database.

[1049] Step 5:

[1050] When a user wears smart glasses and enters a physical store, the smart glasses' built-in camera captures the user's facial expressions in real time. The device inputs this video data into an emotion analysis algorithm (e.g., DeepFace) to analyze the user's current emotional state. This emotion data is then sent from the device to a server.

[1051] Step 6:

[1052] The server inputs data into a computational model based on real-time emotional data and existing user data profiles. A generative AI model then analyzes the user's current interests and needs to predict suitable products. The results are composed of prompt sentences.

[1053] Example prompt sentence:

[1054] Current emotional state: Joy

[1055] Hobbies: Fashion

[1056] Past purchase history: Suits

[1057] Suggestion: Please recommend the latest business suit.

[1058] Step 7:

[1059] The server identifies suitable products based on the output of the generative AI model and sends the recommendation information to the smart glasses, through which the user can view the recommended products in real time.

[1060] Step 8:

[1061] If the user decides to purchase the recommended product, the smart glasses send data on their purchase intention to the server, which stores this data and uses it for future recommendations.

[1062] Step 9:

[1063] If the user is unsure about a purchase, the server will suggest additional advice or related products, which will also be displayed on the smart glasses to assist the user in their purchasing decision.

[1064] Step 10:

[1065] Additionally, users can provide feedback about the proposed products and services through the smart glasses, and this feedback data will also be sent to the server and stored in the database.

[1066] This allows users to receive personalized service and product recommendations in real time, resulting in a more satisfying purchasing experience.

[1067] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1068] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1069] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1070] [Third embodiment]

[1071] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1072] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1073] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1074] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1075] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1076] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1077] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1078] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1079] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1080] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1081] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1082] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1083] The present invention is a system that integrates various user data and analyzes it using a generative AI model to find compatible partners and provide reliable encounters. Specific embodiments are described below.

[1084] Data collection and integration

[1085] 1. User Registration

[1086] A user launches the application and creates an account by entering an email address and password.

[1087] The server stores the entered information in a database and creates a user account.

[1088] 2. Enter your profile information

[1089] Users enter profile information such as name, age, gender, interests and hobbies.

[1090] The server stores and updates this information in a database.

[1091] 3. Personality tests and genetic analysis

[1092] Users answer personality tests provided within the application and provide their results.

[1093] The terminal transmits the results of the personality test to the server, which stores them in a database.

[1094] If a user uploads the results of a genetic analysis that has already been performed, the server stores the results in a database.

[1095] 4. Social media integration and data collection

[1096] Users link their social media accounts to the application.

[1097] The server collects users' SNS data (posts, like history, following relationships, etc.) through the SNS API and stores it in a database.

[1098] AI-based data analysis and compatibility analysis

[1099] 5. Data Integration

[1100] The server integrates the user's profile information, personality test results, genetic analysis results, social media data, purchase history, and travel history to create a single data profile.

[1101] 6. Applying generative AI models

[1102] The server inputs this integrated data into a generative AI model, which then analyzes the user's personality and preferences using deep learning algorithms.

[1103] As a result of the analysis, the server generates a detailed profile of the user, including their personality traits, hobbies, preferences, and behavioral patterns.

[1104] Matching and Notifications

[1105] 7. Compatibility Analysis

[1106] Based on the generated profile, the server analyzes compatibility with other users and calculates a compatibility score.

[1107] 8. Matching Notification

[1108] The server sends notifications to users with pairs that have high compatibility scores.

[1109] User A and User B receive a notification and can choose whether to disclose their information to the other party.

[1110] 9. Information Disclosure and Contact Information

[1111] If the user allows the information to be made public, the servers provide contact information to each other.

[1112] Real-life dating support

[1113] 10. Dating promotion and advice

[1114] Based on the compatibility analysis results, the server suggests meeting places and activities related to common hobbies and interests.

[1115] Users receive suggestions and plan meet-ups through the application.

[1116] Specific Examples

[1117] For example, suppose User A (male, 30 years old, program engineer) registers with the application, takes a personality test, and shares his social media data. The server collects this data and inputs it into a generative AI model to generate a detailed profile. It then analyzes his compatibility with User B (female, 28 years old, designer), who also provided data, and notifies him that their compatibility scores are high. If both parties agree to share their information, the server provides contact information and recommends a shared hobby: watching movies. This allows the two parties to prepare and support each other when they meet in person.

[1118] In this way, the present invention allows users to find the perfect partner based on information they are aware of and information they are not aware of.

[1119] The processing flow will be explained below.

[1120] Step 1:

[1121] Users launch the application and create an account by entering their email address and password.

[1122] Step 2:

[1123] The server receives the entered email address and password, stores them in a database, and creates a user account.

[1124] Step 3:

[1125] Users enter profile information such as name, age, gender, interests and hobbies.

[1126] Step 4:

[1127] The server receives the entered profile information and stores / updates it in a database.

[1128] Step 5:

[1129] Users answer personality tests provided within the application.

[1130] Step 6:

[1131] The terminal transmits the personality test answer data entered by the user to the server.

[1132] Step 7:

[1133] The server receives the personality test response data, calculates the scores, and stores them in a database.

[1134] Step 8:

[1135] Users select the option to upload the results of a genetic analysis they have already performed.

[1136] Step 9:

[1137] The server receives the uploaded genetic analysis results and stores them in a database.

[1138] Step 10:

[1139] Users link their social media accounts (such as Twitter or Facebook) to the application.

[1140] Step 11:

[1141] The server collects data such as user posts, like history, and following relationships through the SNS API.

[1142] Step 12:

[1143] The server stores the collected social media data in a database and adds it to the user profile.

[1144] Step 13:

[1145] The server integrates users' profile information, personality test results, genetic analysis results, social media data, purchase history, and travel history.

[1146] Step 14:

[1147] The server inputs the integrated data into a generative AI model, which analyzes the user's personality and preferences using deep learning algorithms.

[1148] Step 15:

[1149] The server analyzes compatibility with other users based on the detailed profile information generated.

[1150] Step 16:

[1151] The server calculates the compatibility scores between users who are determined to be compatible and stores the results.

[1152] Step 17:

[1153] The server sends a matching notification to users who are determined to be compatible with each other.

[1154] Step 18:

[1155] User A and User B receive the notification and check each other's information.

[1156] Step 19:

[1157] User A and User B choose whether or not to make their contact information public to each other.

[1158] Step 20:

[1159] The terminal transmits the selected permission to disclose information to the server.

[1160] Step 21:

[1161] The server confirms both parties' permission to disclose information and provides each other with contact information.

[1162] Step 22:

[1163] Based on the compatibility analysis results, the server suggests meeting places and activities related to common hobbies and interests.

[1164] Step 23:

[1165] Users receive suggestions and plan meet-ups through the application.

[1166] Step 24:

[1167] The server implements privacy measures to protect users' personal information, including settings to hide users' friend relationships.

[1168] This will create a system that allows users to find the perfect partner in an efficient and reliable way.

[1169] Example 1

[1170] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1171] Conventional dating systems match users based only on their profile information and simple questionnaire results, making it difficult to match users in a way that fully reflects their detailed personality, hobbies, preferences, behavioral patterns, etc. In addition, they often do not provide additional information to improve compatibility or specific advice to support actual encounters, which can result in low user satisfaction.

[1172] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1173] In this invention, the server includes means for registering and storing user attribute information, means for registering and storing the user's psychological test and genetic information results, means for collecting and storing the user's social networking service information via an application programming interface, means for evaluating compatibility with other users based on the analysis results of the generative AI model, means for sending a notification when a highly compatible partner is found based on the compatibility evaluation results, means for obtaining consent for information disclosure, means for mutually exchanging contact information if both parties agree to the disclosure, and means for making recommendations for actually meeting compatible partners. This enables highly accurate matching based on the user's detailed information and specific advice to support actual meetings.

[1174] "User Attribute Information" refers to basic personal information provided by a user, such as name, age, gender, interests, and hobbies.

[1175] A "psychological test" is an assessment tool such as a questionnaire or survey administered to assess a user's personality or psychological characteristics.

[1176] "Genetic Results" means data regarding a user's genetic characteristics and traits based on a genetic analysis of the user.

[1177] "Social networking service information" refers to data such as post content, like history, and following relationships collected from a user's SNS (social networking service) account.

[1178] An "application programming interface" is a defined method of communication that allows different software systems to share information and functionality.

[1179] A "generative AI model" is a machine learning model that uses deep learning algorithms to analyze data and predict and evaluate user characteristics and preferences.

[1180] "Compatibility assessment" is a process in which a generative AI model is used to compare and analyze the personalities, hobbies, and preferences of users, and calculate the degree of compatibility between them.

[1181] "Compatibility evaluation results" refers to information such as compatibility scores and ranks calculated as a result of evaluating the compatibility between users.

[1182] "Notifications" are messages sent to users by the system to inform them that a compatible partner has been found or to inform them of important information.

[1183] "Consent to disclose information" is a user's expression of consent to allow the information they provide to be shared with other users.

[1184] "Contact Information" means information about contact methods, such as email addresses and phone numbers, that allow a user to communicate directly with other users.

[1185] "Recommendations" are specific advice to promote actual encounters, such as suggested meeting locations and activities for users.

[1186] The present invention is a system that integrates various user data and analyzes it with a generative AI model to find the best partner for the user and provide reliable dating. This system involves multiple processing steps, each designed to achieve a specific purpose.

[1187] First, a user launches the application and creates an account by entering their email address and password. The user's device encrypts this information and sends it to the server. The server then stores the received information in a database and creates a new account.

[1188] Next, the user enters their demographic information, such as their name, age, gender, hobbies, interests, etc. The device then sends this information to the server, which stores it in a database.

[1189] The user then answers a personality test within the application and provides the results. The device transmits the test results in real time to the server, which stores the results in a database. Additionally, if the user provides genetic analysis results, the device uploads the results to the server, which stores them in a database.

[1190] Users can also link their social media accounts to the application. The server uses the SNS API to collect users' social media data (posts, likes, and following relationships) and stores them in a database.

[1191] The server retrieves the user's attribute information, personality test results, genetic analysis results, social media data, and other historical data from the database to generate an integrated data profile. This integrated profile is then input into a generative AI model, where the user's personality and preferences are analyzed through a deep learning algorithm. The server then generates a detailed profile based on this information.

[1192] The server then evaluates compatibility with other users based on the analyzed detailed profiles and calculates a compatibility score. When users with high compatibility scores are found, the server sends a notification to the device. The user receives the notification and can choose whether to make the information public. If both parties agree to the disclosure, the server provides each other with their contact information.

[1193] Finally, the server will suggest meeting places and activities to the user based on the compatibility results, and the user will receive the suggestions and make specific plans for the meeting.

[1194] For example, if User A (male, 30 years old, program engineer) creates an account, takes a personality test, and connects his social media accounts, the server collects this data and inputs it into a generative AI model to generate a detailed profile. If the server determines that User B (female, 28 years old, designer), who also provided data, has a high compatibility score, it notifies the user and provides their contact information. If both parties agree to the disclosure of their information, the server can help facilitate a real-life meeting by suggesting a movie to go to.

[1195] An example prompt is:

[1196] Prompt to register:

[1197] Users launch the application and create an account by entering their email address and password.

[1198] Personality Test Prompt:

[1199] Users answer personality tests provided within the application and provide their results.

[1200] Prompt to link social media data:

[1201] Users connect their social media accounts to the application, and the server collects data through the social media API.

[1202] In this way, the embodiments of the invention make it possible to utilize detailed information about users to achieve advanced matching, thereby increasing the chances of encounters that provide high compatibility for users.

[1203] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1204] Step 1:

[1205] User Registration

[1206] Users launch the application and create an account by entering their email address and password.

[1207] Input: Email address, password

[1208] The terminal encrypts the input data and sends it to the server.

[1209] The server stores the received information in a database and creates a new account.

[1210] Output: A confirmation email is sent to the user confirming that their account was created.

[1211] Step 2:

[1212] Enter profile information

[1213] Within the application, users enter profile information such as their name, age, gender, interests and hobbies.

[1214] Input: Name, age, gender, interests and hobbies

[1215] The terminal transmits the entered profile information to the server.

[1216] The server stores and updates this information in a database.

[1217] Output: Sends a notification to the user that their profile information has been saved.

[1218] Step 3:

[1219] Personality tests and genetic analysis

[1220] Users answer personality tests provided within the application and submit their results.

[1221] Input: Personality test answers

[1222] The device transmits the test results to the server in real time.

[1223] The server stores the results of the personality test in a database.

[1224] Output: Sends a notification to the user that the test results have been saved.

[1225] When the user provides the results of the genetic analysis, input: Genetic analysis results

[1226] The terminal uploads the result data to the server.

[1227] The server stores the genetic analysis results in a database.

[1228] Output: Send a notification to the user that the genetic analysis results have been saved.

[1229] Step 4:

[1230] Social media integration and data collection

[1231] Users can link their social media accounts within the application.

[1232] Input: SNS account information (API key, token, etc.)

[1233] The server uses the SNS API to collect users' SNS data (posts, like history, and following relationships).

[1234] The server stores the collected SNS data in a database.

[1235] Output: Sends a notification to the user that the SNS data has been saved.

[1236] Step 5:

[1237] Data integration

[1238] The server retrieves user profile information, personality test results, genetic analysis results, social media data, and other historical data from the database.

[1239] Input: Profile information, personality test results, genetic analysis results, social media data, history data

[1240] The server aggregates the acquired data and generates a single data profile.

[1241] Output: Unified Data Profile

[1242] Step 6:

[1243] Applying generative AI models

[1244] The server inputs the integrated data profile into a generative AI model to analyze the user's personality and preferences.

[1245] Input: Unified Data Profile

[1246] The server uses a generative AI model to generate a detailed profile of the user, including their personality traits, hobbies, preferences, and behavioral patterns.

[1247] Output: Detailed profile

[1248] Step 7:

[1249] Conformity assessment

[1250] The server evaluates your compatibility with other users based on the detailed profile you create.

[1251] Input: Detailed profile

[1252] The server calculates the relevance scores and stores them in a database.

[1253] Output: Relevance score

[1254] Step 8:

[1255] Matching notification

[1256] The server matches users with high compatibility scores and sends a notification to the device.

[1257] Input: Relevance score, matching information

[1258] User A and User B receive a notification and check each other's information within the application.

[1259] Output: Matching notification

[1260] Step 9:

[1261] Disclosure of information and contact information

[1262] If the user allows the information to be made public, the server provides contact information to each other.

[1263] Input: Consent to disclosure of information

[1264] The server retrieves contact information between users from a database and sends it to the terminal.

[1265] Output: Contact information

[1266] Step 10:

[1267] Dating promotion and advice

[1268] Based on the compatibility results, the server suggests meeting places and activities to the user.

[1269] Input: compatibility results, detailed profile

[1270] The server sends the proposal to the terminal.

[1271] Users receive suggestions and plan specific encounters.

[1272] Output: Meeting suggestion notification, planning reminder notification

[1273] (Application example 1)

[1274] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1275] In recent years, online dating and matchmaking platforms have developed rapidly. However, many users find it difficult to find a partner who matches their personality and preferences, or to find suitable products and services. Furthermore, traditional systems are unable to fully utilize diverse user data, making it difficult to provide accurate recommendations. This has led to a decline in user satisfaction and a lack of continued use of the service.

[1276] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1277] In this invention, the server includes means for inputting and saving a user's profile information, means for inputting and saving the user's personality test and genetic analysis results, means for collecting and saving the user's SNS information via an API, means for integrating the collected data and analyzing the user's personality and preferences using a generative AI model, means for analyzing compatibility with other users based on the analysis results of the generative AI model, means for sending a notification when a compatible partner is found based on the compatibility analysis results, means for obtaining permission to disclose information, means for mutually exchanging contact information if both parties give permission, means for providing advice on how to actually meet a compatible partner, means for suggesting optimal products and services based on the user's personality and preferences, and means for notifying the user of product suggestions with the user's permission. This allows users to easily find the partner, products, and services that are best suited for them.

[1278] "User profile information" refers to basic personal information about a user, such as the user's name, age, gender, interests, and hobbies.

[1279] A "personality test" is a questionnaire-based test designed to assess a user's personality traits, the results of which are stored as the user's personality profile.

[1280] "Genetic analysis results" are analysis results based on the user's genetic information, and are data that indicate the user's biological characteristics and tendencies.

[1281] An "API" is an interface for collecting and exchanging data in collaboration with other software applications.

[1282] A "generative AI model" is an artificial intelligence model that uses deep learning algorithms to analyze a user's personality and preferences from a variety of data.

[1283] "Compatibility analysis" is the process of calculating the compatibility between a specific user and other users based on the analysis results of a generative AI model.

[1284] "Notifications" are messages that inform users of compatible partners and product suggestions.

[1285] "Permission to disclose information" means that a user authorizes the disclosure of their personal information or contact information to other users.

[1286] "Contact information" refers to information necessary for contacting users, such as email addresses and telephone numbers.

[1287] "Advice" is guidance that suggests places and activities to meet compatible people in person.

[1288] "Proposing optimal products and services" means recommending products and services that best suit the user's preferences and personality based on the analysis results of the generative AI model.

[1289] A "product suggestion notification" is a message that notifies the user of information about a suggested product.

[1290] This invention allows users to easily find partners, products, and services that suit them. A specific implementation form of a system for carrying out the invention is described below.

[1291] System Configuration

[1292] The system consists of a server, user devices (smartphones, etc.), and various software, including Django (backend framework), MySQL (database), Pandas (dataframe manipulation library), SciKit-Learn (machine learning library), and TensorFlow (deep learning library).

[1293] Program processing overview

[1294] 1. Data Collection Module

[1295] Using the user's device, the user enters profile information, personality test results, and genetic analysis results. Furthermore, the user's social media accounts are linked via API to collect social media information. All of this data is stored in a MySQL database via the Django framework. The specific data collection interface is the application's registration form and social media API connector.

[1296] 2. Data Processing Module

[1297] The data obtained from each user is integrated using Pandas. For example, personality test results and social media data are combined to create a comprehensive data profile. This process unifies the user's multidimensional data.

[1298] 3. Generative AI model application module

[1299] The combined data profile is then fed into a TensorFlow-powered generative AI model, which uses deep learning algorithms to analyze the user's personality and preferences to generate a detailed profile of the user, including their personality traits, interests, and preferences.

[1300] 4. Compatibility Analysis Module

[1301] Based on the profile created, SciKit-Learn's machine learning algorithms are used to analyze compatibility with other users, and if a match is found, a notification is sent to the user.

[1302] 5. Product and service proposal module

[1303] Based on the user's personality and preferences, the system suggests optimal products and services. Specifically, product recommendations are made to the user based on the analysis results of the generative AI model. Notifications of suggested products and services are sent via push notifications using application tokens.

[1304] Examples and Prompts

[1305] For example, if a male user in his 30s is looking for outdoor gear, the system will analyze his preferences based on his personality test, social media posts, and purchase history, and then use a generative AI model to suggest the best products for him. Specific prompt sentence examples are as follows:

[1306] "Male, 30s, loves the outdoors. Please make a list of recommended sports and camping equipment based on the results of a personality test and analysis of social media data."

[1307] In this way, users can easily find products and services that suit their personality and preferences, and it also makes it easier to find a suitable partner, improving user satisfaction.

[1308] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1309] Step 1:

[1310] A user launches an application on a smartphone device and creates an account by entering an email address and password. The entered information (email address and password) is sent to the server, which stores it in a database and creates a user account. At this stage, the input data is the user's basic information, and the output is the user account information stored in the database.

[1311] Step 2:

[1312] The user uses a terminal to input profile information such as name, age, gender, interests, and hobbies. The input profile information is sent to the server and saved and updated in a database. The input data here is the user's personal information, and the output is the updated profile information in the database.

[1313] Step 3:

[1314] The user answers the personality test in the app on their device and sends the results to the server. In addition, if the user's genetic analysis results already exist, the data is uploaded. The personality test results and genetic analysis results are sent to the server and stored in a database. The input is the personality test answers and genetic analysis results, and the output is the analysis results stored in the database.

[1315] Step 4:

[1316] Users connect their SNS accounts to the application on their devices. The server collects the user's SNS data (posts, like history, following relationships, etc.) through the SNS API and stores it in a database. The input data here is the SNS information obtained from the SNS API, and the output is the SNS data stored in the database.

[1317] Step 5:

[1318] The server integrates various data, such as user profile information, personality test results, genetic analysis results, social media data, purchase history, and travel history. This integration process uses Pandas to combine each data into a single data frame. The input data is user-related data obtained from multiple sources, and the output is an integrated data profile.

[1319] Step 6:

[1320] The server inputs the integrated data profile into a generative AI model, which uses a deep learning algorithm to analyze the user's personality and preferences. This analysis is performed using TensorFlow, and the profile is generated. The input data is the integrated data profile, and the output is a detailed user profile as a result of the analysis.

[1321] Step 7:

[1322] The server analyzes compatibility with other users based on the generated detailed profile using SciKit-Learn's machine learning algorithm, calculates compatibility scores, and performs optimal matching. The input data is the user's detailed profile, and the output is compatibility scores and matching results.

[1323] Step 8:

[1324] The server sends a notification to users of pairs with high compatibility scores. The notification includes a brief profile of the other person and their compatibility score. Users receive the notification and choose whether to disclose their information to the other person. The input data here is the match result, and the output is the notification to the user and the option to disclose information.

[1325] Step 9:

[1326] Once the user gives permission, the server provides contact information to each other, and the two parties are ready to communicate. The input is the user's permission, and the output is the other party's contact information.

[1327] Step 10:

[1328] The server then suggests meeting places and activities related to common hobbies and interests based on the compatibility analysis results. Users receive the suggestions through the application and plan their meetings. The input data is the compatibility analysis results, and the output is the suggested meeting activities and places.

[1329] Step 11:

[1330] The server uses the analysis results of the generative AI model to suggest optimal products and services based on the user's personality and preferences. The proposals are notified to the user using the application's push notification function. The input data here is the analysis results of the generative AI model, and the output is a product recommendation notification. An example of this prompt is as follows:

[1331] "Male, 30s, loves the outdoors. Please make a list of recommended sports and camping equipment based on the results of a personality test and analysis of social media data."

[1332] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1333] The present invention is a system that uses a variety of user data and an emotion engine to find compatible partners using a generative AI model, providing reliable encounters. Specific embodiments are described below.

[1334] Data collection and integration

[1335] 1. User Registration

[1336] Users launch the application and create an account by entering their email address and password.

[1337] The server stores the entered information in a database and creates a user account.

[1338] 2. Enter your profile information

[1339] Users enter profile information such as name, age, gender, interests and hobbies.

[1340] The server stores and updates this information in a database.

[1341] 3. Personality tests and genetic analysis

[1342] Users answer personality tests provided within the application and provide their results.

[1343] The terminal transmits the results of the personality test to the server, which stores them in a database.

[1344] If a user uploads the results of a genetic analysis that has already been performed, the server stores the results in a database.

[1345] 4. Social media integration and data collection

[1346] Users connect their social media accounts (such as Twitter or Facebook) to the application.

[1347] The server collects data such as user posts, like history, and following relationships through the SNS API.

[1348] The server stores the collected social media data in a database and adds it to the user profile.

[1349] AI-based data analysis and compatibility analysis

[1350] 5. Data Integration

[1351] The server integrates the user's profile information, personality test results, genetic analysis results, social media data, purchase history, and travel history to create a single data profile.

[1352] 6. Applying generative AI models

[1353] The server inputs this integrated data into a generative AI model, which then analyzes the user's personality and preferences using deep learning algorithms.

[1354] As a result of the analysis, the server generates a detailed profile of the user, including their personality traits, hobbies, preferences, and behavioral patterns.

[1355] Use of emotion engine

[1356] 7. Emotional Data Collection and Analysis

[1357] Users allow emotional data to be collected during everyday use.

[1358] The device collects data such as user text input, voice input, and facial expression analysis and sends it to the emotion engine.

[1359] The emotion engine analyzes the user's current emotional state from the collected data and generates emotion data.

[1360] The server stores the generated emotion data in a database and uses it to analyze the AI ​​model.

[1361] Matching and Notifications

[1362] 8. Compatibility Analysis

[1363] The server analyzes compatibility with other users based on the generated user profile and emotional data and calculates a compatibility score.

[1364] 9. Matching Notification

[1365] The server sends a matching notification based on the compatibility scores of users who are determined to be compatible with each other.

[1366] User A and User B receive the notification and check each other's information.

[1367] 10. Information Disclosure and Contact Information

[1368] User A and User B choose whether or not to make their contact information public to each other.

[1369] The terminal transmits the selected permission to disclose information to the server.

[1370] The server confirms both parties' permission to disclose information and provides each other with contact information.

[1371] Real-life dating support

[1372] 11. Dating promotion and advice

[1373] Based on the compatibility analysis results and emotional data, the server suggests meeting places and activities related to common hobbies and interests.

[1374] Users receive suggestions and plan meet-ups through the application.

[1375] Specific Examples

[1376] For example, suppose User A (male, 30 years old, program engineer) registers with the application, connects his personality test and social media data, and allows the use of the emotion engine. The server collects this data and inputs it into a generative AI model to generate a detailed profile. It then analyzes his compatibility with User B (female, 28 years old, designer), who also provided data, and notifies him that their compatibility score is high. If both parties allow their information to be made public, the server provides contact information and recommends a shared hobby: watching movies. It also provides advice based on the emotion engine data.

[1377] In this way, the present invention allows users to find the perfect partner based on both conscious and involuntary information. By using an emotion engine, even more sophisticated and accurate matching can be achieved.

[1378] The processing flow will be explained below.

[1379] Step 1:

[1380] Users launch the application and create an account by entering their email address and password.

[1381] Step 2:

[1382] The server receives the entered email address and password, stores them in a database, and creates a user account.

[1383] Step 3:

[1384] Users enter profile information such as name, age, gender, interests and hobbies.

[1385] Step 4:

[1386] The server receives the entered profile information and stores / updates it in a database.

[1387] Step 5:

[1388] Users answer personality tests provided within the application.

[1389] Step 6:

[1390] The terminal transmits the personality test answer data entered by the user to the server.

[1391] Step 7:

[1392] The server receives the personality test response data, calculates the scores, and stores them in a database.

[1393] Step 8:

[1394] Users upload their genetic analysis results.

[1395] Step 9:

[1396] The server receives the uploaded genetic analysis results and stores them in a database.

[1397] Step 10:

[1398] Users link their social media accounts (such as Twitter or Facebook) to the application.

[1399] Step 11:

[1400] The server collects data such as user posts, like history, and following relationships through the SNS API.

[1401] Step 12:

[1402] The server stores the collected social media data in a database and adds it to the user profile.

[1403] Step 13:

[1404] Users consent to the use of the emotion engine and allow the collection of emotion data.

[1405] Step 14:

[1406] The device collects emotional data from the user's text input, voice input, facial expression recognition, etc.

[1407] Step 15:

[1408] The emotion engine analyzes the collected emotion data and recognizes the user's emotional state.

[1409] Step 16:

[1410] The server receives the emotion data from the emotion engine and stores it in a database.

[1411] Step 17:

[1412] The server integrates the user's profile information, personality test results, genetic analysis results, social media data, purchase history, travel history, and emotional data to create a single data profile.

[1413] Step 18:

[1414] The server inputs the integrated data into a generative AI model, which analyzes the user's personality and preferences using deep learning algorithms.

[1415] Step 19:

[1416] The server analyzes compatibility with other users based on the detailed profile information generated.

[1417] Step 20:

[1418] The server calculates the compatibility scores between users who are determined to be compatible and stores the results.

[1419] Step 21:

[1420] The server sends a matching notification to users who are determined to be compatible with each other.

[1421] Step 22:

[1422] User A and User B receive the notification and check each other's information.

[1423] Step 23:

[1424] User A and User B can review the notification and choose whether or not to disclose their contact information to the other party.

[1425] Step 24:

[1426] The terminal transmits the selected permission to disclose information to the server.

[1427] Step 25:

[1428] The server confirms both parties' permission to disclose information and provides each other with contact information.

[1429] Step 26:

[1430] Based on the compatibility analysis results and emotional data, the server suggests meeting places and activities related to common hobbies and interests.

[1431] Step 27:

[1432] Users receive suggestions and plan meet-ups through the application.

[1433] Step 28:

[1434] The server implements privacy measures to protect users' personal information, including settings to hide users' friend relationships.

[1435] This will enable users to find their ideal partner in an efficient and reliable way, and the use of an emotion engine will provide even more sophisticated and accurate matching.

[1436] Example 2

[1437] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1438] Conventional matching systems suggest partners based on user profile information and simple personality assessment results, but they face the challenge of making matches that fully reflect individual needs and diverse emotional states. Furthermore, because they rely on simple data without utilizing social media data or genetic information, matching accuracy is low and users' true compatibility cannot be determined. Another problem is the lack of actual support for finding a compatible partner.

[1439] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for inputting and saving user profile information, means for inputting and saving the user's personality test and genetic analysis results, means for collecting and saving the user's SNS information via an API, means for integrating the collected data and analyzing the user's personality and preferences using an AI model, means for analyzing compatibility with other users based on the analysis results of the AI ​​model, means for collecting and analyzing the user's emotional data during daily use, means for saving the generated emotional data and using it for compatibility analysis, means for sending a notification when a compatible partner is found based on the compatibility analysis results, means for obtaining permission to disclose information, means for mutually exchanging contact information if both parties give permission, and means for providing advice on how to actually meet a compatible partner. This allows users to find their ideal partner based on detailed and reliable data.

[1440] "Profile Information" refers to basic personal information such as your name, age, gender, interests and hobbies.

[1441] A "personality test" refers to a series of questions or assessments that users answer to assess their personality traits or psychological state.

[1442] "Genetic analysis" refers to the process of analyzing a user's DNA sample to extract genetic characteristics and health information.

[1443] "SNS information" refers to data such as posts, like history, and following relationships collected from a user's SNS services (e.g., Twitter or Facebook).

[1444] "API" stands for Application Programming Interface and refers to the rules and protocols that allow software to communicate with each other.

[1445] An "AI model" refers to a program that uses artificial intelligence algorithms to analyze data and predict user characteristics and behavioral patterns.

[1446] "Emotional Data" refers to data that indicates a user's emotional state, extracted from text input, voice input, facial expression analysis, etc.

[1447] "Compatibility score" refers to the numerical value of the compatibility between users, as a result of analysis by the AI ​​model.

[1448] "Permission to disclose information" refers to a user's consent to providing their contact information to other users.

[1449] "Advice" refers to advice or suggestions offered to facilitate actual encounters with compatible partners.

[1450] "Integrated Data Profile" refers to the creation of unified information about a user from multiple collected data sources.

[1451] The present invention is a system that uses a variety of user data and an emotion engine to find compatible partners using a generative AI model, providing reliable encounters. Specific embodiments are described below.

[1452] Data collection and integration

[1453] User Registration

[1454] A user launches the application and creates an account by entering their email address and password. The server stores the information in a database and creates a user account.

[1455] Enter profile information

[1456] Users enter profile information such as name, age, gender, interests, hobbies, etc. The server stores and updates this information in a database.

[1457] Personality tests and genetic analysis

[1458] The user answers a personality test provided within the application and sends the results to the server, which stores the personality test results in a database. If the user uploads the results of a genetic analysis they have already completed, the server stores these results in a database.

[1459] Social media integration and data collection

[1460] Users connect their social media accounts (e.g., Twitter or Facebook) to the application. The server collects data such as user posts, likes, and following relationships through the social media API. The server stores the collected social media data in a database and adds it to the user profile.

[1461] AI-based data analysis and compatibility analysis

[1462] Data integration

[1463] The server integrates the user's profile information, personality test results, genetic analysis results, social media data, purchase history, and travel history to create a single data profile.

[1464] Applying generative AI models

[1465] The server inputs this integrated data into a generative AI model, which then uses deep learning algorithms to analyze the user's personality and preferences, generating a detailed profile of the user, including their personality traits, hobbies, preferences, and behavioral patterns.

[1466] Use of emotion engine

[1467] Emotion data collection and analysis

[1468] Users allow emotional data to be collected during daily use. The device collects data such as the user's text input, voice input, and facial expression analysis, and sends it to the emotion engine. The emotion engine analyzes the user's current emotional state from the collected data and generates emotional data. The server stores the generated emotional data in a database and uses it for analysis in AI models.

[1469] Matching and Notifications

[1470] Compatibility analysis

[1471] The server analyzes compatibility with other users based on the generated user profile and emotional data and calculates a compatibility score.

[1472] Matching notification

[1473] The server sends a matching notification based on the compatibility scores of users who are determined to be compatible. User A and User B receive the notification and check each other's information.

[1474] Disclosure of information and contact details

[1475] User A and User B choose whether to share their contact information with each other. The device sends the selected permission to share information to the server. The server confirms the permission from both parties and provides the contact information to each other.

[1476] Real-life dating support

[1477] Dating promotion and advice

[1478] The server then suggests meeting places and activities related to shared hobbies and interests based on the compatibility analysis and emotional data. Users receive these suggestions through the app and plan their meetings.

[1479] Specific Examples

[1480] For example, suppose User A (male, 30 years old, program engineer) registers with the application, connects his personality test and social media data, and allows the use of the emotion engine. The server collects this data and inputs it into a generative AI model to generate a detailed profile. It then analyzes his compatibility with User B (female, 28 years old, designer), who also provided data, and notifies him that their compatibility score is high. If both parties allow their information to be made public, the server provides contact information and recommends a shared hobby: watching movies. It also provides advice based on the emotion engine data.

[1481] Example prompts to input to the generative AI model

[1482] "Analyze the personality test results, social media data, and data collected by the emotion engine for User A, a 30-year-old male program engineer, and generate a profile to find the perfect partner for this user."

[1483] As described above, the present invention provides a system that presents optimal partners based on a user's detailed profile information and achieves advanced and highly accurate matching that also takes emotional data into consideration.

[1484] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1485] Step 1:

[1486] A user launches the application and creates an account by entering their email address and password. The input requires the user's email address and password, and the output is a user account created in the database. The server receives this information and saves the new user account in the database. Specifically, the form input data is sent to an endpoint, and a new record is added to the database user table on the server side.

[1487] Step 2:

[1488] The user enters profile information (such as name, age, gender, interests, hobbies, etc.). This profile information is required as input, and the updated user profile is saved in the database as output. The server receives this information and saves and updates it in the database. Specifically, when the user fills out the input form and presses the submit button, the data is sent to the server and the database record is updated.

[1489] Step 3:

[1490] The user takes a personality test and provides the results. The input requires the personality test answer data, and the output is the personality test results stored in the database. The device sends the user's test answers to the server, which stores them in the database. Specifically, when the user answers the test and sends the results, the server receives the results and stores them in the database as a new record.

[1491] Step 4:

[1492] When a user uploads the results of a genetic analysis that has already been conducted, the input required is the genetic analysis result file, and the output is the result stored in the database. The server receives the result and stores it in the database. Specifically, the user uploads a file, and the file is sent to the server and then stored in the database.

[1493] Step 5:

[1494] A user connects their social media account to the application. Social media account authentication information is required as input, and social media data is stored in a database as output. The server uses the social media API to collect data such as the user's posts, like history, and following relationships. Specifically, the user authenticates with the social media account, and then the server calls the API to collect the data and store it in the database.

[1495] Step 6:

[1496] The server integrates profile information, personality test results, genetic analysis results, social media data, etc. These various data are required as input, and an integrated data profile is generated as output. Specifically, the server extracts the necessary information from multiple data tables and compiles it into a single integrated data profile.

[1497] Step 7:

[1498] The server inputs the integrated data into the generative AI model for analysis. The input is an integrated data profile, and the output is a detailed user profile. Specifically, the server sends data to the API of the generative AI model and receives the detailed profile as the analysis result.

[1499] Step 8:

[1500] Collects and analyzes user emotional data. The input requires the user's text input, voice input, and facial expression analysis data, and the output generates emotional data. The device collects this emotional data and sends it to the emotion engine. Specifically, the device uses the camera and microphone to capture data and sends it to the emotion engine in real time.

[1501] Step 9:

[1502] The server stores the collected emotion data in a database and uses it for compatibility analysis. Emotion data is required as input, and an updated user profile is generated as output. The server uses this data for analysis. The specific operation is to update records in the database.

[1503] Step 10:

[1504] The server analyzes the compatibility with other users based on the generated user profile and emotional data. The inputs are the user profile and emotional data, and the output is a compatibility score. Specifically, an algorithm for calculating the compatibility score is executed, and the results are stored in a database.

[1505] Step 11:

[1506] The server sends a matching notification based on the compatibility score between users who are determined to be compatible. The compatibility score is required as input, and a notification is sent to the user as output. Specific actions include sending a push notification or email to the user.

[1507] Step 12:

[1508] User A and User B choose whether to share their contact information with each other. This selection information is required as input, and the permitted contact information is provided to each other as output. The device sends this information to the server, which verifies and provides the information. Specific operation is to display the contact information according to the user's selection.

[1509] Step 13:

[1510] The server suggests meeting places and activities related to common hobbies and interests based on the compatibility analysis results and emotional data. The compatibility analysis results and emotional data are required as input, and suggestions for meeting places and activities are provided to the user as output. Specific operations include notifying the user of recommended places and activities.

[1511] This allows users to find their perfect partner based on detailed profile information and emotional data.

[1512] (Application example 2)

[1513] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1514] Conventional matching systems analyze compatibility based on static data such as user profile information and personality test results, but do not realize matching that reflects the user's real-time emotions and interests. Furthermore, when users shop in physical stores, they lack product recommendations and location suggestions that are optimized for their individual preferences and emotions. This has made improving the user experience a challenge.

[1515] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting and saving user profile information; means for inputting and saving the user's personality test and genetic analysis results; means for collecting and saving the user's SNS information using data communication means; means for integrating the collected data and analyzing the user's personality and preferences using a computational model; means for analyzing compatibility with other users based on the analysis results of the computational model; means for sending a notification when a compatible partner is found based on the compatibility analysis results; means for obtaining permission to disclose information; means for mutually exchanging contact information if both parties give permission; means for making suggestions for actually meeting a compatible partner; means for collecting and analyzing the user's emotional data; means for analyzing purchase and movement history based on the emotional data and suggesting related products and places; and means for suggesting products based on the user's real-time emotional and interest information. This enables matching and product recommendations based on the user's real-time emotional and interest information.

[1516] "User profile information" means basic information about a user, such as the user's name, age, gender, hobbies, and interests.

[1517] "Means for storing" refers to a method of using a database or storage device to permanently store information entered by the user.

[1518] A "personality test" is a series of questions or assessments designed to assess a user's personality or psychological characteristics.

[1519] "Genetic analysis results" are data obtained as a result of analyzing a user's genetic characteristics and constitution.

[1520] "SNS information" refers to data such as posts published by users on social networking services, like history, and following relationships.

[1521] "Data communication means" refers to a method of sending and receiving information using the Internet, wireless communication, etc.

[1522] "Integration" is the process of combining different types and formats of data into one unified data set.

[1523] A "computational model" is a mathematical or statistical algorithm or program used to analyze data or make predictions.

[1524] "Compatibility analysis" is a method of comparing the characteristics and preferences of users and evaluating the degree of compatibility between them.

[1525] A "notification" is a message or alert used to inform a user.

[1526] "Permission to Disclose Information" means that a user consents to the disclosure of their information to others.

[1527] "Contact Information" means contact information for a user, such as a phone number, email address, or postal address.

[1528] "Suggestions" are advice that recommends useful information or actions to the user.

[1529] "Emotional data" refers to data that represents the emotional state of a user, derived from facial expressions, voice, text, etc.

[1530] "Purchase history" refers to records of products and services purchased by a user in the past.

[1531] "Travel history" refers to data about the places a user has visited and the routes they have taken.

[1532] "Means for suggesting products" refers to a system that selects and recommends related products and services based on the user's emotions and interests.

[1533] To implement this invention, it is necessary to build a system in which multiple pieces of hardware and software work together to collect real-time emotional data and interest information from users and perform optimal product recommendations and matching.

[1534] Required Hardware and Software

[1535] 1. Hardware

[1536] Smart glasses (e.g. Google Glass)

[1537] Built-in camera and microphone

[1538] Server (capable of high-performance data processing)

[1539] Local data storage (on the user's device)

[1540] 2. Software

[1541] Python libraries (requests, DeepFace)

[1542] Server-side API (user data acquisition API, product recommendation API)

[1543] Database (saves user data and emotion data)

[1544] System Overview

[1545] User registration and data collection

[1546] When a user uses the application for the first time, they enter their profile information (name, age, gender, hobbies, interests, etc.), then provide the results of a personality test and genetic analysis, and link their social media account, which sends and stores this data on the server.

[1547] Collecting Emotional Data

[1548] The smart glasses' built-in camera and microphone are used to collect the user's facial expressions and voice in real time, and a facial expression recognition algorithm (e.g., DeepFace) is used to analyze the user's emotional state from this data.

[1549] Data Integration and Analysis

[1550] The server combines the collected profile information, personality test results, genetic analysis results, social media data, purchase history, travel history, and emotional data, and inputs this into a computational model, which then uses a generative AI algorithm to analyze the user's personality and preferences and generate a detailed user profile.

[1551] Product Recommendation and Matching

[1552] When users shop in physical stores, the smart glasses will suggest relevant products and locations based on collected emotional data and real-time interest information, improving users' shopping experience and providing more personalized services.

[1553] Specific examples

[1554] Suppose a user visits a brick-and-mortar store and puts on a pair of smart glasses. The camera in the glasses recognizes the user's facial expressions and performs emotional analysis. For example, if the user is looking for new work clothes, the system can recommend the most suitable business suit in real time based on the user's personality and past purchasing history. If the user is unsure about a purchase, the server will send appropriate advice to the smart glasses based on the user's emotional data. This process is input into the generative AI model with prompts such as the following:

[1555] Prompt Sentence Examples

[1556] Current emotional state: Joy

[1557] Hobbies: Fashion

[1558] Past purchase history: Suits

[1559] Suggestion: Please recommend the latest business suit.

[1560] This invention enables users to receive optimal product recommendations and matching services based on their real-time emotions and interests.

[1561] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1562] Step 1:

[1563] A user starts the application and enters their profile information (name, age, gender, hobbies, interests). This input data is sent from the device to the server, which then stores it in a database.

[1564] Step 2:

[1565] The user provides the personality test and genetic analysis results, which are also sent from the device to the server, which stores them in a database.

[1566] Step 3:

[1567] When a user connects their SNS account, the server collects the user's SNS data (posts, like history, following relationships) through the API. This data is also stored in the database.

[1568] Step 4:

[1569] The server combines the collected profile information, personality test results, genetic analysis results, and social media data to create a single user data profile, which is then stored in a database.

[1570] Step 5:

[1571] When a user wears smart glasses and enters a physical store, the smart glasses' built-in camera captures the user's facial expressions in real time. The device inputs this video data into an emotion analysis algorithm (e.g., DeepFace) to analyze the user's current emotional state. This emotion data is then sent from the device to a server.

[1572] Step 6:

[1573] The server inputs data into a computational model based on real-time emotional data and existing user data profiles. A generative AI model then analyzes the user's current interests and needs to predict suitable products. The results are composed of prompt sentences.

[1574] Example prompt sentence:

[1575] Current emotional state: Joy

[1576] Hobbies: Fashion

[1577] Past purchase history: Suits

[1578] Suggestion: Please recommend the latest business suit.

[1579] Step 7:

[1580] The server identifies suitable products based on the output of the generative AI model and sends the recommendation information to the smart glasses, through which the user can view the recommended products in real time.

[1581] Step 8:

[1582] If the user decides to purchase the recommended product, the smart glasses send data on their purchase intention to the server, which stores this data and uses it for future recommendations.

[1583] Step 9:

[1584] If the user is unsure about a purchase, the server will suggest additional advice or related products, which will also be displayed on the smart glasses to assist the user in their purchasing decision.

[1585] Step 10:

[1586] Additionally, users can provide feedback about the proposed products and services through the smart glasses, and this feedback data will also be sent to the server and stored in the database.

[1587] This allows users to receive personalized service and product recommendations in real time, resulting in a more satisfying purchasing experience.

[1588] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1589] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1590] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1591] [Fourth embodiment]

[1592] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1593] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1594] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1595] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1596] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1597] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1598] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1599] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1600] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1601] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1602] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1603] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1604] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1605] The present invention is a system that integrates various user data and analyzes it using a generative AI model to find compatible partners and provide reliable encounters. Specific embodiments are described below.

[1606] Data collection and integration

[1607] 1. User Registration

[1608] A user launches the application and creates an account by entering an email address and password.

[1609] The server stores the entered information in a database and creates a user account.

[1610] 2. Enter your profile information

[1611] Users enter profile information such as name, age, gender, interests and hobbies.

[1612] The server stores and updates this information in a database.

[1613] 3. Personality tests and genetic analysis

[1614] Users answer personality tests provided within the application and provide their results.

[1615] The terminal transmits the results of the personality test to the server, which stores them in a database.

[1616] If a user uploads the results of a genetic analysis that has already been performed, the server stores the results in a database.

[1617] 4. Social media integration and data collection

[1618] Users link their social media accounts to the application.

[1619] The server collects users' SNS data (posts, like history, following relationships, etc.) through the SNS API and stores it in a database.

[1620] AI-based data analysis and compatibility analysis

[1621] 5. Data Integration

[1622] The server integrates the user's profile information, personality test results, genetic analysis results, social media data, purchase history, and travel history to create a single data profile.

[1623] 6. Applying generative AI models

[1624] The server inputs this integrated data into a generative AI model, which then analyzes the user's personality and preferences using deep learning algorithms.

[1625] As a result of the analysis, the server generates a detailed profile of the user, including their personality traits, hobbies, preferences, and behavioral patterns.

[1626] Matching and Notifications

[1627] 7. Compatibility Analysis

[1628] Based on the generated profile, the server analyzes compatibility with other users and calculates a compatibility score.

[1629] 8. Matching Notification

[1630] The server sends notifications to users with pairs that have high compatibility scores.

[1631] User A and User B receive a notification and can choose whether to disclose their information to the other party.

[1632] 9. Information Disclosure and Contact Information

[1633] If the user allows the information to be made public, the servers provide contact information to each other.

[1634] Real-life dating support

[1635] 10. Dating promotion and advice

[1636] Based on the compatibility analysis results, the server suggests meeting places and activities related to common hobbies and interests.

[1637] Users receive suggestions and plan meet-ups through the application.

[1638] Specific Examples

[1639] For example, suppose User A (male, 30 years old, program engineer) registers with the application, takes a personality test, and shares his social media data. The server collects this data and inputs it into a generative AI model to generate a detailed profile. It then analyzes his compatibility with User B (female, 28 years old, designer), who also provided data, and notifies him that their compatibility scores are high. If both parties agree to share their information, the server provides contact information and recommends a shared hobby: watching movies. This allows the two parties to prepare and support each other when they meet in person.

[1640] In this way, the present invention allows users to find the perfect partner based on information they are aware of and information they are not aware of.

[1641] The processing flow will be explained below.

[1642] Step 1:

[1643] Users launch the application and create an account by entering their email address and password.

[1644] Step 2:

[1645] The server receives the entered email address and password, stores them in a database, and creates a user account.

[1646] Step 3:

[1647] Users enter profile information such as name, age, gender, interests and hobbies.

[1648] Step 4:

[1649] The server receives the entered profile information and stores / updates it in a database.

[1650] Step 5:

[1651] Users answer personality tests provided within the application.

[1652] Step 6:

[1653] The terminal transmits the personality test answer data entered by the user to the server.

[1654] Step 7:

[1655] The server receives the personality test response data, calculates the scores, and stores them in a database.

[1656] Step 8:

[1657] Users select the option to upload the results of a genetic analysis they have already performed.

[1658] Step 9:

[1659] The server receives the uploaded genetic analysis results and stores them in a database.

[1660] Step 10:

[1661] Users link their social media accounts (such as Twitter or Facebook) to the application.

[1662] Step 11:

[1663] The server collects data such as user posts, like history, and following relationships through the SNS API.

[1664] Step 12:

[1665] The server stores the collected social media data in a database and adds it to the user profile.

[1666] Step 13:

[1667] The server integrates users' profile information, personality test results, genetic analysis results, social media data, purchase history, and travel history.

[1668] Step 14:

[1669] The server inputs the integrated data into a generative AI model, which analyzes the user's personality and preferences using deep learning algorithms.

[1670] Step 15:

[1671] The server analyzes compatibility with other users based on the detailed profile information generated.

[1672] Step 16:

[1673] The server calculates the compatibility scores between users who are determined to be compatible and stores the results.

[1674] Step 17:

[1675] The server sends a matching notification to users who are determined to be compatible with each other.

[1676] Step 18:

[1677] User A and User B receive the notification and check each other's information.

[1678] Step 19:

[1679] User A and User B choose whether or not to make their contact information public to each other.

[1680] Step 20:

[1681] The terminal transmits the selected permission to disclose information to the server.

[1682] Step 21:

[1683] The server confirms both parties' permission to disclose information and provides each other with contact information.

[1684] Step 22:

[1685] Based on the compatibility analysis results, the server suggests meeting places and activities related to common hobbies and interests.

[1686] Step 23:

[1687] Users receive suggestions and plan meet-ups through the application.

[1688] Step 24:

[1689] The server implements privacy measures to protect users' personal information, including settings to hide users' friend relationships.

[1690] This will create a system that allows users to find the perfect partner in an efficient and reliable way.

[1691] Example 1

[1692] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1693] Conventional dating systems match users based only on their profile information and simple questionnaire results, making it difficult to match users in a way that fully reflects their detailed personality, hobbies, preferences, behavioral patterns, etc. In addition, they often do not provide additional information to improve compatibility or specific advice to support actual encounters, which can result in low user satisfaction.

[1694] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1695] In this invention, the server includes means for registering and storing user attribute information, means for registering and storing the user's psychological test and genetic information results, means for collecting and storing the user's social networking service information via an application programming interface, means for evaluating compatibility with other users based on the analysis results of the generative AI model, means for sending a notification when a highly compatible partner is found based on the compatibility evaluation results, means for obtaining consent for information disclosure, means for mutually exchanging contact information if both parties agree to the disclosure, and means for making recommendations for actually meeting compatible partners. This enables highly accurate matching based on the user's detailed information and specific advice to support actual meetings.

[1696] "User Attribute Information" refers to basic personal information provided by a user, such as name, age, gender, interests, and hobbies.

[1697] A "psychological test" is an assessment tool such as a questionnaire or survey administered to assess a user's personality or psychological characteristics.

[1698] "Genetic Results" means data regarding a user's genetic characteristics and traits based on a genetic analysis of the user.

[1699] "Social networking service information" refers to data such as post content, like history, and following relationships collected from a user's SNS (social networking service) account.

[1700] An "application programming interface" is a defined method of communication that allows different software systems to share information and functionality.

[1701] A "generative AI model" is a machine learning model that uses deep learning algorithms to analyze data and predict and evaluate user characteristics and preferences.

[1702] "Compatibility assessment" is a process in which a generative AI model is used to compare and analyze the personalities, hobbies, and preferences of users, and calculate the degree of compatibility between them.

[1703] "Compatibility evaluation results" refers to information such as compatibility scores and ranks calculated as a result of evaluating the compatibility between users.

[1704] "Notifications" are messages sent to users by the system to inform them that a compatible partner has been found or to inform them of important information.

[1705] "Consent to disclose information" is a user's expression of consent to allow the information they provide to be shared with other users.

[1706] "Contact Information" means information about contact methods, such as email addresses and phone numbers, that allow a user to communicate directly with other users.

[1707] "Recommendations" are specific advice to promote actual encounters, such as suggested meeting locations and activities for users.

[1708] The present invention is a system that integrates various user data and analyzes it with a generative AI model to find the best partner for the user and provide reliable dating. This system involves multiple processing steps, each designed to achieve a specific purpose.

[1709] First, a user launches the application and creates an account by entering their email address and password. The user's device encrypts this information and sends it to the server. The server then stores the received information in a database and creates a new account.

[1710] Next, the user enters their demographic information, such as their name, age, gender, hobbies, interests, etc. The device then sends this information to the server, which stores it in a database.

[1711] The user then answers a personality test within the application and provides the results. The device transmits the test results in real time to the server, which stores the results in a database. Additionally, if the user provides genetic analysis results, the device uploads the results to the server, which stores them in a database.

[1712] Users can also link their social media accounts to the application. The server uses the SNS API to collect users' social media data (posts, likes, and following relationships) and stores them in a database.

[1713] The server retrieves the user's attribute information, personality test results, genetic analysis results, social media data, and other historical data from the database to generate an integrated data profile. This integrated profile is then input into a generative AI model, where the user's personality and preferences are analyzed through a deep learning algorithm. The server then generates a detailed profile based on this information.

[1714] The server then evaluates compatibility with other users based on the analyzed detailed profiles and calculates a compatibility score. When users with high compatibility scores are found, the server sends a notification to the device. The user receives the notification and can choose whether to make the information public. If both parties agree to the disclosure, the server provides each other with their contact information.

[1715] Finally, the server will suggest meeting places and activities to the user based on the compatibility results, and the user will receive the suggestions and make specific plans for the meeting.

[1716] For example, if User A (male, 30 years old, program engineer) creates an account, takes a personality test, and connects his social media accounts, the server collects this data and inputs it into a generative AI model to generate a detailed profile. If the server determines that User B (female, 28 years old, designer), who also provided data, has a high compatibility score, it notifies the user and provides their contact information. If both parties agree to the disclosure of their information, the server can help facilitate a real-life meeting by suggesting a movie to go to.

[1717] An example prompt is:

[1718] Prompt to register:

[1719] Users launch the application and create an account by entering their email address and password.

[1720] Personality Test Prompt:

[1721] Users answer personality tests provided within the application and provide their results.

[1722] Prompt to link social media data:

[1723] Users connect their social media accounts to the application, and the server collects data through the social media API.

[1724] In this way, the embodiments of the invention make it possible to utilize detailed information about users to achieve advanced matching, thereby increasing the chances of encounters that provide high compatibility for users.

[1725] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1726] Step 1:

[1727] User Registration

[1728] Users launch the application and create an account by entering their email address and password.

[1729] Input: Email address, password

[1730] The terminal encrypts the input data and sends it to the server.

[1731] The server stores the received information in a database and creates a new account.

[1732] Output: A confirmation email is sent to the user confirming that their account was created.

[1733] Step 2:

[1734] Enter profile information

[1735] Within the application, users enter profile information such as their name, age, gender, interests and hobbies.

[1736] Input: Name, age, gender, interests and hobbies

[1737] The terminal transmits the entered profile information to the server.

[1738] The server stores and updates this information in a database.

[1739] Output: Sends a notification to the user that their profile information has been saved.

[1740] Step 3:

[1741] Personality tests and genetic analysis

[1742] Users answer personality tests provided within the application and submit their results.

[1743] Input: Personality test answers

[1744] The device transmits the test results to the server in real time.

[1745] The server stores the results of the personality test in a database.

[1746] Output: Sends a notification to the user that the test results have been saved.

[1747] When the user provides the results of the genetic analysis, input: Genetic analysis results

[1748] The terminal uploads the result data to the server.

[1749] The server stores the genetic analysis results in a database.

[1750] Output: Send a notification to the user that the genetic analysis results have been saved.

[1751] Step 4:

[1752] Social media integration and data collection

[1753] Users can link their social media accounts within the application.

[1754] Input: SNS account information (API key, token, etc.)

[1755] The server uses the SNS API to collect users' SNS data (posts, like history, and following relationships).

[1756] The server stores the collected SNS data in a database.

[1757] Output: Sends a notification to the user that the SNS data has been saved.

[1758] Step 5:

[1759] Data integration

[1760] The server retrieves user profile information, personality test results, genetic analysis results, social media data, and other historical data from the database.

[1761] Input: Profile information, personality test results, genetic analysis results, social media data, history data

[1762] The server aggregates the acquired data and generates a single data profile.

[1763] Output: Unified Data Profile

[1764] Step 6:

[1765] Applying generative AI models

[1766] The server inputs the integrated data profile into a generative AI model to analyze the user's personality and preferences.

[1767] Input: Unified Data Profile

[1768] The server uses a generative AI model to generate a detailed profile of the user, including their personality traits, hobbies, preferences, and behavioral patterns.

[1769] Output: Detailed profile

[1770] Step 7:

[1771] Conformity assessment

[1772] The server evaluates your compatibility with other users based on the detailed profile you create.

[1773] Input: Detailed profile

[1774] The server calculates the relevance scores and stores them in a database.

[1775] Output: Relevance score

[1776] Step 8:

[1777] Matching notification

[1778] The server matches users with high compatibility scores and sends a notification to the device.

[1779] Input: Relevance score, matching information

[1780] User A and User B receive a notification and check each other's information within the application.

[1781] Output: Matching notification

[1782] Step 9:

[1783] Disclosure of information and contact information

[1784] If the user allows the information to be made public, the server provides contact information to each other.

[1785] Input: Consent to disclosure of information

[1786] The server retrieves contact information between users from a database and sends it to the terminal.

[1787] Output: Contact information

[1788] Step 10:

[1789] Dating promotion and advice

[1790] Based on the compatibility results, the server suggests meeting places and activities to the user.

[1791] Input: compatibility results, detailed profile

[1792] The server sends the proposal to the terminal.

[1793] Users receive suggestions and plan specific encounters.

[1794] Output: Meeting suggestion notification, planning reminder notification

[1795] (Application example 1)

[1796] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1797] In recent years, online dating and matchmaking platforms have developed rapidly. However, many users find it difficult to find a partner who matches their personality and preferences, or to find suitable products and services. Furthermore, traditional systems are unable to fully utilize diverse user data, making it difficult to provide accurate recommendations. This has led to a decline in user satisfaction and a lack of continued use of the service.

[1798] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1799] In this invention, the server includes means for inputting and saving a user's profile information, means for inputting and saving the user's personality test and genetic analysis results, means for collecting and saving the user's SNS information via an API, means for integrating the collected data and analyzing the user's personality and preferences using a generative AI model, means for analyzing compatibility with other users based on the analysis results of the generative AI model, means for sending a notification when a compatible partner is found based on the compatibility analysis results, means for obtaining permission to disclose information, means for mutually exchanging contact information if both parties give permission, means for providing advice on how to actually meet a compatible partner, means for suggesting optimal products and services based on the user's personality and preferences, and means for notifying the user of product suggestions with the user's permission. This allows users to easily find the partner, products, and services that are best suited for them.

[1800] "User profile information" refers to basic personal information about a user, such as the user's name, age, gender, interests, and hobbies.

[1801] A "personality test" is a questionnaire-based test designed to assess a user's personality traits, the results of which are stored as the user's personality profile.

[1802] "Genetic analysis results" are analysis results based on the user's genetic information, and are data that indicate the user's biological characteristics and tendencies.

[1803] An "API" is an interface for collecting and exchanging data in collaboration with other software applications.

[1804] A "generative AI model" is an artificial intelligence model that uses deep learning algorithms to analyze a user's personality and preferences from a variety of data.

[1805] "Compatibility analysis" is the process of calculating the compatibility between a specific user and other users based on the analysis results of a generative AI model.

[1806] "Notifications" are messages that inform users of compatible partners and product suggestions.

[1807] "Permission to disclose information" means that a user authorizes the disclosure of their personal information or contact information to other users.

[1808] "Contact information" refers to information necessary for contacting users, such as email addresses and telephone numbers.

[1809] "Advice" is guidance that suggests places and activities to meet compatible people in person.

[1810] "Proposing optimal products and services" means recommending products and services that best suit the user's preferences and personality based on the analysis results of the generative AI model.

[1811] A "product suggestion notification" is a message that notifies the user of information about a suggested product.

[1812] This invention allows users to easily find partners, products, and services that suit them. A specific implementation form of a system for carrying out the invention is described below.

[1813] System Configuration

[1814] The system consists of a server, user devices (smartphones, etc.), and various software, including Django (backend framework), MySQL (database), Pandas (dataframe manipulation library), SciKit-Learn (machine learning library), and TensorFlow (deep learning library).

[1815] Program processing overview

[1816] 1. Data Collection Module

[1817] Using the user's device, the user enters profile information, personality test results, and genetic analysis results. Furthermore, the user's social media accounts are linked via API to collect social media information. All of this data is stored in a MySQL database via the Django framework. The specific data collection interface is the application's registration form and social media API connector.

[1818] 2. Data Processing Module

[1819] The data obtained from each user is integrated using Pandas. For example, personality test results and social media data are combined to create a comprehensive data profile. This process unifies the user's multidimensional data.

[1820] 3. Generative AI model application module

[1821] The combined data profile is then fed into a TensorFlow-powered generative AI model, which uses deep learning algorithms to analyze the user's personality and preferences to generate a detailed profile of the user, including their personality traits, interests, and preferences.

[1822] 4. Compatibility Analysis Module

[1823] Based on the profile created, SciKit-Learn's machine learning algorithms are used to analyze compatibility with other users, and if a match is found, a notification is sent to the user.

[1824] 5. Product and service proposal module

[1825] Based on the user's personality and preferences, the system suggests optimal products and services. Specifically, product recommendations are made to the user based on the analysis results of the generative AI model. Notifications of suggested products and services are sent via push notifications using application tokens.

[1826] Examples and Prompts

[1827] For example, if a male user in his 30s is looking for outdoor gear, the system will analyze his preferences based on his personality test, social media posts, and purchase history, and then use a generative AI model to suggest the best products for him. Specific prompt sentence examples are as follows:

[1828] "Male, 30s, loves the outdoors. Please make a list of recommended sports and camping equipment based on the results of a personality test and analysis of social media data."

[1829] In this way, users can easily find products and services that suit their personality and preferences, and it also makes it easier to find a suitable partner, improving user satisfaction.

[1830] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1831] Step 1:

[1832] A user launches an application on a smartphone device and creates an account by entering an email address and password. The entered information (email address and password) is sent to the server, which stores it in a database and creates a user account. At this stage, the input data is the user's basic information, and the output is the user account information stored in the database.

[1833] Step 2:

[1834] The user uses a terminal to input profile information such as name, age, gender, interests, and hobbies. The input profile information is sent to the server and saved and updated in a database. The input data here is the user's personal information, and the output is the updated profile information in the database.

[1835] Step 3:

[1836] The user answers the personality test in the app on their device and sends the results to the server. In addition, if the user's genetic analysis results already exist, the data is uploaded. The personality test results and genetic analysis results are sent to the server and stored in a database. The input is the personality test answers and genetic analysis results, and the output is the analysis results stored in the database.

[1837] Step 4:

[1838] Users connect their SNS accounts to the application on their devices. The server collects the user's SNS data (posts, like history, following relationships, etc.) through the SNS API and stores it in a database. The input data here is the SNS information obtained from the SNS API, and the output is the SNS data stored in the database.

[1839] Step 5:

[1840] The server integrates various data, such as user profile information, personality test results, genetic analysis results, social media data, purchase history, and travel history. This integration process uses Pandas to combine each data into a single data frame. The input data is user-related data obtained from multiple sources, and the output is an integrated data profile.

[1841] Step 6:

[1842] The server inputs the integrated data profile into a generative AI model, which uses a deep learning algorithm to analyze the user's personality and preferences. This analysis is performed using TensorFlow, and the profile is generated. The input data is the integrated data profile, and the output is a detailed user profile as a result of the analysis.

[1843] Step 7:

[1844] The server analyzes compatibility with other users based on the generated detailed profile using SciKit-Learn's machine learning algorithm, calculates compatibility scores, and performs optimal matching. The input data is the user's detailed profile, and the output is compatibility scores and matching results.

[1845] Step 8:

[1846] The server sends a notification to users of pairs with high compatibility scores. The notification includes a brief profile of the other person and their compatibility score. Users receive the notification and choose whether to disclose their information to the other person. The input data here is the match result, and the output is the notification to the user and the option to disclose information.

[1847] Step 9:

[1848] Once the user gives permission, the server provides contact information to each other, and the two parties are ready to communicate. The input is the user's permission, and the output is the other party's contact information.

[1849] Step 10:

[1850] The server then suggests meeting places and activities related to common hobbies and interests based on the compatibility analysis results. Users receive the suggestions through the application and plan their meetings. The input data is the compatibility analysis results, and the output is the suggested meeting activities and places.

[1851] Step 11:

[1852] The server uses the analysis results of the generative AI model to suggest optimal products and services based on the user's personality and preferences. The proposals are notified to the user using the application's push notification function. The input data here is the analysis results of the generative AI model, and the output is a product recommendation notification. An example of this prompt is as follows:

[1853] "Male, 30s, loves the outdoors. Please make a list of recommended sports and camping equipment based on the results of a personality test and analysis of social media data."

[1854] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1855] The present invention is a system that uses a variety of user data and an emotion engine to find compatible partners using a generative AI model, providing reliable encounters. Specific embodiments are described below.

[1856] Data collection and integration

[1857] 1. User Registration

[1858] Users launch the application and create an account by entering their email address and password.

[1859] The server stores the entered information in a database and creates a user account.

[1860] 2. Enter your profile information

[1861] Users enter profile information such as name, age, gender, interests and hobbies.

[1862] The server stores and updates this information in a database.

[1863] 3. Personality tests and genetic analysis

[1864] Users answer personality tests provided within the application and provide their results.

[1865] The terminal transmits the results of the personality test to the server, which stores them in a database.

[1866] If a user uploads the results of a genetic analysis that has already been performed, the server stores the results in a database.

[1867] 4. Social media integration and data collection

[1868] Users connect their social media accounts (such as Twitter or Facebook) to the application.

[1869] The server collects data such as user posts, like history, and following relationships through the SNS API.

[1870] The server stores the collected social media data in a database and adds it to the user profile.

[1871] AI-based data analysis and compatibility analysis

[1872] 5. Data Integration

[1873] The server integrates the user's profile information, personality test results, genetic analysis results, social media data, purchase history, and travel history to create a single data profile.

[1874] 6. Applying generative AI models

[1875] The server inputs this integrated data into a generative AI model, which then analyzes the user's personality and preferences using deep learning algorithms.

[1876] As a result of the analysis, the server generates a detailed profile of the user, including their personality traits, hobbies, preferences, and behavioral patterns.

[1877] Use of emotion engine

[1878] 7. Emotional Data Collection and Analysis

[1879] Users allow emotional data to be collected during everyday use.

[1880] The device collects data such as user text input, voice input, and facial expression analysis and sends it to the emotion engine.

[1881] The emotion engine analyzes the user's current emotional state from the collected data and generates emotion data.

[1882] The server stores the generated emotion data in a database and uses it to analyze the AI ​​model.

[1883] Matching and Notifications

[1884] 8. Compatibility Analysis

[1885] The server analyzes compatibility with other users based on the generated user profile and emotional data and calculates a compatibility score.

[1886] 9. Matching Notification

[1887] The server sends a matching notification based on the compatibility scores of users who are determined to be compatible with each other.

[1888] User A and User B receive the notification and check each other's information.

[1889] 10. Information Disclosure and Contact Information

[1890] User A and User B choose whether or not to make their contact information public to each other.

[1891] The terminal transmits the selected permission to disclose information to the server.

[1892] The server confirms both parties' permission to disclose information and provides each other with contact information.

[1893] Real-life dating support

[1894] 11. Dating promotion and advice

[1895] Based on the compatibility analysis results and emotional data, the server suggests meeting places and activities related to common hobbies and interests.

[1896] Users receive suggestions and plan meet-ups through the application.

[1897] Specific Examples

[1898] For example, suppose User A (male, 30 years old, program engineer) registers with the application, connects his personality test and social media data, and allows the use of the emotion engine. The server collects this data and inputs it into a generative AI model to generate a detailed profile. It then analyzes his compatibility with User B (female, 28 years old, designer), who also provided data, and notifies him that their compatibility score is high. If both parties allow their information to be made public, the server provides contact information and recommends a shared hobby: watching movies. It also provides advice based on the emotion engine data.

[1899] In this way, the present invention allows users to find the perfect partner based on both conscious and involuntary information. By using an emotion engine, even more sophisticated and accurate matching can be achieved.

[1900] The processing flow will be explained below.

[1901] Step 1:

[1902] Users launch the application and create an account by entering their email address and password.

[1903] Step 2:

[1904] The server receives the entered email address and password, stores them in a database, and creates a user account.

[1905] Step 3:

[1906] Users enter profile information such as name, age, gender, interests and hobbies.

[1907] Step 4:

[1908] The server receives the entered profile information and stores / updates it in a database.

[1909] Step 5:

[1910] Users answer personality tests provided within the application.

[1911] Step 6:

[1912] The terminal transmits the personality test answer data entered by the user to the server.

[1913] Step 7:

[1914] The server receives the personality test response data, calculates the scores, and stores them in a database.

[1915] Step 8:

[1916] Users upload their genetic analysis results.

[1917] Step 9:

[1918] The server receives the uploaded genetic analysis results and stores them in a database.

[1919] Step 10:

[1920] Users link their social media accounts (such as Twitter or Facebook) to the application.

[1921] Step 11:

[1922] The server collects data such as user posts, like history, and following relationships through the SNS API.

[1923] Step 12:

[1924] The server stores the collected social media data in a database and adds it to the user profile.

[1925] Step 13:

[1926] Users consent to the use of the emotion engine and allow the collection of emotion data.

[1927] Step 14:

[1928] The device collects emotional data from the user's text input, voice input, facial expression recognition, etc.

[1929] Step 15:

[1930] The emotion engine analyzes the collected emotion data and recognizes the user's emotional state.

[1931] Step 16:

[1932] The server receives the emotion data from the emotion engine and stores it in a database.

[1933] Step 17:

[1934] The server integrates the user's profile information, personality test results, genetic analysis results, social media data, purchase history, travel history, and emotional data to create a single data profile.

[1935] Step 18:

[1936] The server inputs the integrated data into a generative AI model, which analyzes the user's personality and preferences using deep learning algorithms.

[1937] Step 19:

[1938] The server analyzes compatibility with other users based on the detailed profile information generated.

[1939] Step 20:

[1940] The server calculates the compatibility scores between users who are determined to be compatible and stores the results.

[1941] Step 21:

[1942] The server sends a matching notification to users who are determined to be compatible with each other.

[1943] Step 22:

[1944] User A and User B receive the notification and check each other's information.

[1945] Step 23:

[1946] User A and User B can review the notification and choose whether or not to disclose their contact information to the other party.

[1947] Step 24:

[1948] The terminal transmits the selected permission to disclose information to the server.

[1949] Step 25:

[1950] The server confirms both parties' permission to disclose information and provides each other with contact information.

[1951] Step 26:

[1952] Based on the compatibility analysis results and emotional data, the server suggests meeting places and activities related to common hobbies and interests.

[1953] Step 27:

[1954] Users receive suggestions and plan meet-ups through the application.

[1955] Step 28:

[1956] The server implements privacy measures to protect users' personal information, including settings to hide users' friend relationships.

[1957] This will enable users to find their ideal partner in an efficient and reliable way, and the use of an emotion engine will provide even more sophisticated and accurate matching.

[1958] Example 2

[1959] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1960] Conventional matching systems suggest partners based on user profile information and simple personality assessment results, but they face the challenge of making matches that fully reflect individual needs and diverse emotional states. Furthermore, because they rely on simple data without utilizing social media data or genetic information, matching accuracy is low and users' true compatibility cannot be determined. Another problem is the lack of actual support for finding a compatible partner.

[1961] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for inputting and saving user profile information, means for inputting and saving the user's personality test and genetic analysis results, means for collecting and saving the user's SNS information via an API, means for integrating the collected data and analyzing the user's personality and preferences using an AI model, means for analyzing compatibility with other users based on the analysis results of the AI ​​model, means for collecting and analyzing the user's emotional data during daily use, means for saving the generated emotional data and using it for compatibility analysis, means for sending a notification when a compatible partner is found based on the compatibility analysis results, means for obtaining permission to disclose information, means for mutually exchanging contact information if both parties give permission, and means for providing advice on how to actually meet a compatible partner. This allows users to find their ideal partner based on detailed and reliable data.

[1962] "Profile Information" refers to basic personal information such as your name, age, gender, interests and hobbies.

[1963] A "personality test" refers to a series of questions or assessments that users answer to assess their personality traits or psychological state.

[1964] "Genetic analysis" refers to the process of analyzing a user's DNA sample to extract genetic characteristics and health information.

[1965] "SNS information" refers to data such as posts, like history, and following relationships collected from a user's SNS services (e.g., Twitter or Facebook).

[1966] "API" stands for Application Programming Interface and refers to the rules and protocols that allow software to communicate with each other.

[1967] An "AI model" refers to a program that uses artificial intelligence algorithms to analyze data and predict user characteristics and behavioral patterns.

[1968] "Emotional Data" refers to data that indicates a user's emotional state, extracted from text input, voice input, facial expression analysis, etc.

[1969] "Compatibility score" refers to the numerical value of the compatibility between users, as a result of analysis by the AI ​​model.

[1970] "Permission to disclose information" refers to a user's consent to providing their contact information to other users.

[1971] "Advice" refers to advice or suggestions offered to facilitate actual encounters with compatible partners.

[1972] "Integrated Data Profile" refers to the creation of unified information about a user from multiple collected data sources.

[1973] The present invention is a system that uses a variety of user data and an emotion engine to find compatible partners using a generative AI model, providing reliable encounters. Specific embodiments are described below.

[1974] Data collection and integration

[1975] User Registration

[1976] A user launches the application and creates an account by entering their email address and password. The server stores the information in a database and creates a user account.

[1977] Enter profile information

[1978] Users enter profile information such as name, age, gender, interests, hobbies, etc. The server stores and updates this information in a database.

[1979] Personality tests and genetic analysis

[1980] The user answers a personality test provided within the application and sends the results to the server, which stores the personality test results in a database. If the user uploads the results of a genetic analysis they have already completed, the server stores these results in a database.

[1981] Social media integration and data collection

[1982] Users connect their social media accounts (e.g., Twitter or Facebook) to the application. The server collects data such as user posts, likes, and following relationships through the social media API. The server stores the collected social media data in a database and adds it to the user profile.

[1983] AI-based data analysis and compatibility analysis

[1984] Data integration

[1985] The server integrates the user's profile information, personality test results, genetic analysis results, social media data, purchase history, and travel history to create a single data profile.

[1986] Applying generative AI models

[1987] The server inputs this integrated data into a generative AI model, which then uses deep learning algorithms to analyze the user's personality and preferences, generating a detailed profile of the user, including their personality traits, hobbies, preferences, and behavioral patterns.

[1988] Use of emotion engine

[1989] Emotion data collection and analysis

[1990] Users allow emotional data to be collected during daily use. The device collects data such as the user's text input, voice input, and facial expression analysis, and sends it to the emotion engine. The emotion engine analyzes the user's current emotional state from the collected data and generates emotional data. The server stores the generated emotional data in a database and uses it for analysis in AI models.

[1991] Matching and Notifications

[1992] Compatibility analysis

[1993] The server analyzes compatibility with other users based on the generated user profile and emotional data and calculates a compatibility score.

[1994] Matching notification

[1995] The server sends a matching notification based on the compatibility scores of users who are determined to be compatible. User A and User B receive the notification and check each other's information.

[1996] Disclosure of information and contact details

[1997] User A and User B choose whether to share their contact information with each other. The device sends the selected permission to share information to the server. The server confirms the permission from both parties and provides the contact information to each other.

[1998] Real-life dating support

[1999] Dating promotion and advice

[2000] The server then suggests meeting places and activities related to shared hobbies and interests based on the compatibility analysis and emotional data. Users receive these suggestions through the app and plan their meetings.

[2001] Specific Examples

[2002] For example, suppose User A (male, 30 years old, program engineer) registers with the application, connects his personality test and social media data, and allows the use of the emotion engine. The server collects this data and inputs it into a generative AI model to generate a detailed profile. It then analyzes his compatibility with User B (female, 28 years old, designer), who also provided data, and notifies him that their compatibility score is high. If both parties allow their information to be made public, the server provides contact information and recommends a shared hobby: watching movies. It also provides advice based on the emotion engine data.

[2003] Example prompts to input to the generative AI model

[2004] "Analyze the personality test results, social media data, and data collected by the emotion engine for User A, a 30-year-old male program engineer, and generate a profile to find the perfect partner for this user."

[2005] As described above, the present invention provides a system that presents optimal partners based on a user's detailed profile information and achieves advanced and highly accurate matching that also takes emotional data into consideration.

[2006] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2007] Step 1:

[2008] A user launches the application and creates an account by entering their email address and password. The input requires the user's email address and password, and the output is a user account created in the database. The server receives this information and saves the new user account in the database. Specifically, the form input data is sent to an endpoint, and a new record is added to the database user table on the server side.

[2009] Step 2:

[2010] The user enters profile information (such as name, age, gender, interests, hobbies, etc.). This profile information is required as input, and the updated user profile is saved in the database as output. The server receives this information and saves and updates it in the database. Specifically, when the user fills out the input form and presses the submit button, the data is sent to the server and the database record is updated.

[2011] Step 3:

[2012] The user takes a personality test and provides the results. The input requires the personality test answer data, and the output is the personality test results stored in the database. The device sends the user's test answers to the server, which stores them in the database. Specifically, when the user answers the test and sends the results, the server receives the results and stores them in the database as a new record.

[2013] Step 4:

[2014] When a user uploads the results of a genetic analysis that has already been conducted, the input required is the genetic analysis result file, and the output is the result stored in the database. The server receives the result and stores it in the database. Specifically, the user uploads a file, and the file is sent to the server and then stored in the database.

[2015] Step 5:

[2016] A user connects their social media account to the application. Social media account authentication information is required as input, and social media data is stored in a database as output. The server uses the social media API to collect data such as the user's posts, like history, and following relationships. Specifically, the user authenticates with the social media account, and then the server calls the API to collect the data and store it in the database.

[2017] Step 6:

[2018] The server integrates profile information, personality test results, genetic analysis results, social media data, etc. These various data are required as input, and an integrated data profile is generated as output. Specifically, the server extracts the necessary information from multiple data tables and compiles it into a single integrated data profile.

[2019] Step 7:

[2020] The server inputs the integrated data into the generative AI model for analysis. The input is an integrated data profile, and the output is a detailed user profile. Specifically, the server sends data to the API of the generative AI model and receives the detailed profile as the analysis result.

[2021] Step 8:

[2022] Collects and analyzes user emotional data. The input requires the user's text input, voice input, and facial expression analysis data, and the output generates emotional data. The device collects this emotional data and sends it to the emotion engine. Specifically, the device uses the camera and microphone to capture data and sends it to the emotion engine in real time.

[2023] Step 9:

[2024] The server stores the collected emotion data in a database and uses it for compatibility analysis. Emotion data is required as input, and an updated user profile is generated as output. The server uses this data for analysis. The specific operation is to update records in the database.

[2025] Step 10:

[2026] The server analyzes the compatibility with other users based on the generated user profile and emotional data. The inputs are the user profile and emotional data, and the output is a compatibility score. Specifically, an algorithm for calculating the compatibility score is executed, and the results are stored in a database.

[2027] Step 11:

[2028] The server sends a matching notification based on the compatibility score between users who are determined to be compatible. The compatibility score is required as input, and a notification is sent to the user as output. Specific actions include sending a push notification or email to the user.

[2029] Step 12:

[2030] User A and User B choose whether to share their contact information with each other. This selection information is required as input, and the permitted contact information is provided to each other as output. The device sends this information to the server, which verifies and provides the information. Specific operation is to display the contact information according to the user's selection.

[2031] Step 13:

[2032] The server suggests meeting places and activities related to common hobbies and interests based on the compatibility analysis results and emotional data. The compatibility analysis results and emotional data are required as input, and suggestions for meeting places and activities are provided to the user as output. Specific operations include notifying the user of recommended places and activities.

[2033] This allows users to find their perfect partner based on detailed profile information and emotional data.

[2034] (Application example 2)

[2035] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2036] Conventional matching systems analyze compatibility based on static data such as user profile information and personality test results, but do not realize matching that reflects the user's real-time emotions and interests. Furthermore, when users shop in physical stores, they lack product recommendations and location suggestions that are optimized for their individual preferences and emotions. This has made improving the user experience a challenge.

[2037] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting and saving user profile information; means for inputting and saving the user's personality test and genetic analysis results; means for collecting and saving the user's SNS information using data communication means; means for integrating the collected data and analyzing the user's personality and preferences using a computational model; means for analyzing compatibility with other users based on the analysis results of the computational model; means for sending a notification when a compatible partner is found based on the compatibility analysis results; means for obtaining permission to disclose information; means for mutually exchanging contact information if both parties give permission; means for making suggestions for actually meeting a compatible partner; means for collecting and analyzing the user's emotional data; means for analyzing purchase and movement history based on the emotional data and suggesting related products and places; and means for suggesting products based on the user's real-time emotional and interest information. This enables matching and product recommendations based on the user's real-time emotional and interest information.

[2038] "User profile information" means basic information about a user, such as the user's name, age, gender, hobbies, and interests.

[2039] "Means for storing" refers to a method of using a database or storage device to permanently store information entered by the user.

[2040] A "personality test" is a series of questions or assessments designed to assess a user's personality or psychological characteristics.

[2041] "Genetic analysis results" are data obtained as a result of analyzing a user's genetic characteristics and constitution.

[2042] "SNS information" refers to data such as posts published by users on social networking services, like history, and following relationships.

[2043] "Data communication means" refers to a method of sending and receiving information using the Internet, wireless communication, etc.

[2044] "Integration" is the process of combining different types and formats of data into one unified data set.

[2045] A "computational model" is a mathematical or statistical algorithm or program used to analyze data or make predictions.

[2046] "Compatibility analysis" is a method of comparing the characteristics and preferences of users and evaluating the degree of compatibility between them.

[2047] A "notification" is a message or alert used to inform a user.

[2048] "Permission to Disclose Information" means that a user consents to the disclosure of their information to others.

[2049] "Contact Information" means contact information for a user, such as a phone number, email address, or postal address.

[2050] "Suggestions" are advice that recommends useful information or actions to the user.

[2051] "Emotional data" refers to data that represents the emotional state of a user, derived from facial expressions, voice, text, etc.

[2052] "Purchase history" refers to records of products and services purchased by a user in the past.

[2053] "Travel history" refers to data about the places a user has visited and the routes they have taken.

[2054] "Means for suggesting products" refers to a system that selects and recommends related products and services based on the user's emotions and interests.

[2055] To implement this invention, it is necessary to build a system in which multiple pieces of hardware and software work together to collect real-time emotional data and interest information from users and perform optimal product recommendations and matching.

[2056] Required Hardware and Software

[2057] 1. Hardware

[2058] Smart glasses (e.g. Google Glass)

[2059] Built-in camera and microphone

[2060] Server (capable of high-performance data processing)

[2061] Local data storage (on the user's device)

[2062] 2. Software

[2063] Python libraries (requests, DeepFace)

[2064] Server-side API (user data acquisition API, product recommendation API)

[2065] Database (saves user data and emotion data)

[2066] System Overview

[2067] User registration and data collection

[2068] When a user uses the application for the first time, they enter their profile information (name, age, gender, hobbies, interests, etc.), then provide the results of a personality test and genetic analysis, and link their social media account, which sends and stores this data on the server.

[2069] Collecting Emotional Data

[2070] The smart glasses' built-in camera and microphone are used to collect the user's facial expressions and voice in real time, and a facial expression recognition algorithm (e.g., DeepFace) is used to analyze the user's emotional state from this data.

[2071] Data Integration and Analysis

[2072] The server combines the collected profile information, personality test results, genetic analysis results, social media data, purchase history, travel history, and emotional data, and inputs this into a computational model, which then uses a generative AI algorithm to analyze the user's personality and preferences and generate a detailed user profile.

[2073] Product Recommendation and Matching

[2074] When users shop in physical stores, the smart glasses will suggest relevant products and locations based on collected emotional data and real-time interest information, improving users' shopping experience and providing more personalized services.

[2075] Specific examples

[2076] Suppose a user visits a brick-and-mortar store and puts on a pair of smart glasses. The camera in the glasses recognizes the user's facial expressions and performs emotional analysis. For example, if the user is looking for new work clothes, the system can recommend the most suitable business suit in real time based on the user's personality and past purchasing history. If the user is unsure about a purchase, the server will send appropriate advice to the smart glasses based on the user's emotional data. This process is input into the generative AI model with prompts such as the following:

[2077] Prompt Sentence Examples

[2078] Current emotional state: Joy

[2079] Hobbies: Fashion

[2080] Past purchase history: Suits

[2081] Suggestion: Please recommend the latest business suit.

[2082] This invention enables users to receive optimal product recommendations and matching services based on their real-time emotions and interests.

[2083] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2084] Step 1:

[2085] A user starts the application and enters their profile information (name, age, gender, hobbies, interests). This input data is sent from the device to the server, which then stores it in a database.

[2086] Step 2:

[2087] The user provides the personality test and genetic analysis results, which are also sent from the device to the server, which stores them in a database.

[2088] Step 3:

[2089] When a user connects their SNS account, the server collects the user's SNS data (posts, like history, following relationships) through the API. This data is also stored in the database.

[2090] Step 4:

[2091] The server combines the collected profile information, personality test results, genetic analysis results, and social media data to create a single user data profile, which is then stored in a database.

[2092] Step 5:

[2093] When a user wears smart glasses and enters a physical store, the smart glasses' built-in camera captures the user's facial expressions in real time. The device inputs this video data into an emotion analysis algorithm (e.g., DeepFace) to analyze the user's current emotional state. This emotion data is then sent from the device to a server.

[2094] Step 6:

[2095] The server inputs data into a computational model based on real-time emotional data and existing user data profiles. A generative AI model then analyzes the user's current interests and needs to predict suitable products. The results are composed of prompt sentences.

[2096] Example prompt sentence:

[2097] Current emotional state: Joy

[2098] Hobbies: Fashion

[2099] Past purchase history: Suits

[2100] Suggestion: Please recommend the latest business suit.

[2101] Step 7:

[2102] The server identifies suitable products based on the output of the generative AI model and sends the recommendation information to the smart glasses, through which the user can view the recommended products in real time.

[2103] Step 8:

[2104] If the user decides to purchase the recommended product, the smart glasses send data on their purchase intention to the server, which stores this data and uses it for future recommendations.

[2105] Step 9:

[2106] If the user is unsure about a purchase, the server will suggest additional advice or related products, which will also be displayed on the smart glasses to assist the user in their purchasing decision.

[2107] Step 10:

[2108] Additionally, users can provide feedback about the proposed products and services through the smart glasses, and this feedback data will also be sent to the server and stored in the database.

[2109] This allows users to receive personalized service and product recommendations in real time, resulting in a more satisfying purchasing experience.

[2110] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2111] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2112] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2113] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2114] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2115] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2116] ...

Claims

1. a means for entering and storing user profile information; a means for inputting and storing the user's personality test and genetic analysis results; A means of collecting and storing users' SNS information via API, A means of integrating collected data and analyzing user characteristics and preferences through AI models; A means to analyze compatibility with other users based on the analysis results of the AI ​​model, A means for sending a notification when a compatible partner is found based on the compatibility analysis results; How to obtain permission to release information; A means of providing contact information to each other when both parties permit it; A means of providing advice on how to actually meet compatible partners, A system including:

2. The system of claim 1 further comprising means for collecting and storing a user's purchasing history and travel history.

3. The system according to claim 1, further comprising means for setting friends to not be displayed.

Images