System

Abstract

To provide a system.SOLUTION: The system includes: first means for receiving an input of profile information including a name, an age, a hobby, a condition of an ideal partner from a user; second means for generating a prompt sentence for selecting a matching candidate who fits the condition of the user on the basis of the profile information; third means for inputting the generated prompt sentence into the generation Al model and selecting the matching candidate from the database; and fourth means for presenting the selected matching candidate to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional dating services have difficulty accurately understanding users' profiles and preferences to provide appropriate matches, and there is no way to objectively evaluate users' appearances or conversational skills and use that information in matching. [Means for solving the problem]

[0005] This invention combines AI chat support for matching, AI profile diagnosis, a means of connecting with marriage seekers nationwide by adjusting AI to the system of a marriage consultation agency association, AI conversation ability diagnosis, and AI appearance diagnosis to accurately grasp a user's profile and preferences and provide appropriate matching. It also objectively evaluates a user's appearance and conversation ability and uses the results for matching. [Brief explanation of the drawings]

[0006] [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. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] 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.

[0008] First, the terms used in the following description will be explained.

[0009] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] 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.

[0011] 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.

[0012] 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.

[0013] 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."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 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.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] In one embodiment of the present invention, AI chat-based matching support involves AI selecting match candidates based on the profile information and preferences entered by the user. Specifically, if a user enters, "My hobby is watching movies, and I would like a partner with the same hobby," the AI ​​will select match candidates whose hobby is watching movies.

[0029] "Example 2"

[0030] In addition, in the AI ​​profile diagnosis, the AI ​​analyzes the user's personality and values ​​based on the profile information they enter. Specifically, if a user enters, "In my self-introduction, I wrote about my favorite movies and my thoughts on watching movies," the AI ​​will determine from that information that the user has a deep passion for movies.

[0031] "Example 3"

[0032] Furthermore, with the AI ​​Appearance Diagnosis, AI evaluates a user's appearance based on image data provided by the user. Specifically, when a user uploads a photo of themselves, the AI ​​analyzes the user's hairstyle, eyebrow shape, fashion, skin type, etc. from the photo and generates an evaluation for each element.

[0033] The processing flow of each embodiment will be described below.

[0034] "Example 1"

[0035] Step 1: The user accesses the application and enters their profile information and preferences.

[0036] Step 2: The AI ​​analyzes the information entered by the user and selects matching candidates.

[0037] Step 3: AI presents the selected match candidates to the user.

[0038] "Example 2"

[0039] Step 1: The user accesses the application and enters their profile information.

[0040] Step 2: The AI ​​analyzes the profile information entered by the user and analyzes the user's personality and values.

[0041] Step 3: The results of the AI ​​analysis are presented to the user and used as reference information for matching.

[0042] "Example 3"

[0043] Step 1: A user accesses the application and uploads a photo of themselves.

[0044] Step 2: The AI ​​analyzes the photo provided by the user and evaluates the user's appearance. Step 3: The AI ​​presents the results of its evaluation to the user, which serves as reference information for matching.

[0045] Example 1

[0046] Next, a description will be given of Example 1 of Form 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."

[0047] Conventional matching systems did not adequately match users based on their profile information and desired conditions, making it difficult to find suitable candidates. Furthermore, they did not adequately evaluate users' appearance or conversational ability, and did not provide comprehensive matching support. This resulted in low user satisfaction and low system utilization.

[0048] 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.

[0049] In this invention, the server includes a means for the user to input profile information using a terminal, a means for the server to receive and store the user's profile information, a means for the server to send prompt messages to the generating AI model, a means for the generating AI model to analyze and select match candidates, and a means for the server to present the match candidates to the user. This enables highly accurate matching based on the user's detailed profile information and desired conditions, thereby improving user satisfaction. In addition, comprehensive matching support can be provided through AI-based appearance and conversation ability assessments.

[0050] "User" refers to an individual who uses the System to enter profile information and search for potential matches.

[0051] "Terminal" means the electronic device used by a User to access the System and enter Profile Information.

[0052] "Server" refers to the central processing unit that stores the profile information received from users, sends prompts to the generative AI model, and selects match candidates.

[0053] "Profile information" refers to personal information such as name, age, hobbies, and desired partner criteria that users enter into the system.

[0054] "Generative AI model" refers to an artificial intelligence model that analyzes the database based on prompt statements received from the server and selects matching candidates.

[0055] A "prompt" refers to an instruction that includes the user's profile information and desired conditions, which the server sends to the generative AI model.

[0056] "Matching candidates" refer to other users who meet the user's desired conditions and are analyzed and selected by the generative AI model.

[0057] "Matching support means" refers to a series of processes for selecting appropriate matching candidates based on the user's profile information and presenting them to the user.

[0058] "Profile diagnostic means" refers to a means for analyzing the profile information entered by a user and finding suitable matching candidates.

[0059] "Conversational ability diagnostic means" refers to a means for evaluating a user's conversational ability and using this information to aid in matching.

[0060] "Appearance assessment means" refers to means for analyzing image data provided by a user and generating an assessment of appearance.

[0061] MODE FOR CARRYING OUT THE INVENTION

[0062] This invention is a system in which a user inputs profile information using a terminal, a server receives and stores the information, and a generative AI model is used to select suitable match candidates and present them to the user. Specific embodiments of this system are described below.

[0063] 1. Enter your profile information

[0064] The user accesses the system's interface using a terminal, which displays information such as name, age, hobbies, and desired partner criteria. The user enters this information and clicks the "Submit" button.

[0065] 2. Saving your profile information

[0066] The server receives the profile information sent by the user and stores it in a database, where each user's profile information is organized and stored.

[0067] 3. Selection of matching candidates

[0068] The server generates a prompt based on the user's profile information and sends it to a generative AI model (e.g., GPT-4 (registered trademark)). The prompt includes the user's hobbies and desired conditions.

[0069] As an example of a specific prompt, if User A enters "My hobby is watching movies, and I would like a partner with the same hobby," the server will send the following prompt to the generative AI model:

[0070] User A's profile information:

[0071] Hobbies: Watching movies

[0072] Desired criteria: A partner with the same hobbies

[0073] Analyze other users' information in the database and select matching candidates whose hobby is watching movies.

[0074] 4. Analysis and selection of match candidates

[0075] The generative AI model analyzes the database based on the prompt received from the server, and as a result of the analysis, it lists other users whose hobby is watching movies.

[0076] 5. Presentation of matching candidates

[0077] The server presents the user with a list of match candidates received from the generative AI model, which the user can view via their device.

[0078] As a specific example, the generative AI model analyzes user information in the database and selects matching candidates such as "User ID: 002, Hobby: Watching Movies" and "User ID: 003, Hobby: Watching Movies." For User ID: 001, the server displays information such as "Matching Candidates: User ID: 002, User ID: 003" on the device. Users can review this information and contact candidates they are interested in.

[0079] This system enables highly accurate matching based on the user's detailed profile information and desired conditions, improving user satisfaction. It also provides comprehensive matching support through AI-based appearance and conversation ability assessments.

[0080] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0081] Step 1:

[0082] The user uses the device to enter their profile information.

[0083] Input: Profile information such as the user's name, age, hobbies, and partner preferences.

[0084] Specific operations: The user accesses the terminal interface, enters information in each field, and clicks the "Submit" button.

[0085] Output: The entered profile information is sent from the device to the server.

[0086] Step 2:

[0087] The server receives and stores the user's profile information.

[0088] Input: User profile information sent from the device.

[0089] Specific operation: The server stores the received profile information in a database. For example, it stores it as "User ID: 001, Hobby: Watching movies, Desired conditions: Partner with the same hobby."

[0090] Output: User profile information stored in a database.

[0091] Step 3:

[0092] The server sends a prompt to the generative AI model.

[0093] Input: User profile information stored in a database.

[0094] Specific operation: The server generates a prompt based on the user's profile information. For example, a prompt such as "User ID: 001's profile information: Hobbies: Watching movies, Desired conditions: Partner with the same hobby" is sent to the AI ​​model.

[0095] Output: The prompt sent to the generative AI model.

[0096] Step 4:

[0097] A generative AI model analyzes and selects match candidates.

[0098] Input: The prompt text sent by the server.

[0099] Specific operation: The generative AI model analyzes the database based on the prompt sentence and lists other users whose hobby is watching movies. For example, it selects matching candidates such as "User ID: 002, Hobby: Watching Movies" and "User ID: 003, Hobby: Watching Movies."

[0100] Output: A list of selected match candidates.

[0101] Step 5:

[0102] The server presents potential matches to the user.

[0103] Input: A list of match candidates received from a generative AI model.

[0104] Specific operation: The server presents a list of match candidates to the user. For example, information such as "Match candidates: User ID: 002, User ID: 003" is displayed on the terminal.

[0105] Output: A list of potential matches displayed on the user's device.

[0106] The above is the specific processing flow of the program of this system.

[0107] (Application example 1)

[0108] Next, a description will be given of Application Example 1 of Embodiment 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."

[0109] Previous matching support systems were mainly limited to online matching and were unable to support matching between users in physical stores. Furthermore, it was difficult to provide opportunities for users with similar hobbies and preferences to meet in physical stores and enjoy shopping together. This limited the user's shopping experience and potentially reduced satisfaction.

[0110] 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.

[0111] In this invention, the server includes a matching support means using AI chat, an AI profile diagnosis means, a means for adjusting the AI ​​to the marriage consultation association's system to connect with people looking for marriage across the country, an AI conversation ability diagnosis means, an AI appearance diagnosis means (hair, eyebrows, fashion, skin type), a means for matching other users with the same preferences in the same store based on the profile information and preferences entered by the user, and a means for matched users to communicate with each other via chat. This enables users with the same hobbies and preferences to meet in a physical store and enjoy shopping together.

[0112] "AI chat matching support means" is a means of using artificial intelligence to select appropriate match candidates based on the profile information and preferences entered by the user, and to support communication between users via chat.

[0113] "AI profile diagnostic means" refers to a means that uses artificial intelligence to analyze the profile information provided by the user and evaluate the user's characteristics and preferences.

[0114] "A means of adjusting AI to the marriage consultation association's system and creating connections with people looking for marriage across the country" means adapting artificial intelligence to the marriage consultation association's system and creating connections with people looking for marriage across the country.

[0115] An "AI conversational ability diagnostic tool" is a tool that uses artificial intelligence to evaluate a user's conversational ability and provide diagnostic results.

[0116] The "AI Appearance Diagnosis (Hair, Eyebrows, Fashion, Skin Type) Method" is a method that uses artificial intelligence to analyze image data provided by users and generate an evaluation of their appearance, such as hair, eyebrows, fashion, and skin type.

[0117] "A means of matching other users with the same preferences within the same store based on the profile information and preferences entered by the user" refers to a means of using artificial intelligence to select and match other users with the same preferences within the same physical store based on the profile information and preferences entered by the user.

[0118] "Means by which matched users can communicate with each other via chat" means means by which matched users can communicate with each other via chat within the application.

[0119] The following system configuration is used as an embodiment of the present invention.

[0120] System Configuration

[0121] The system includes a user's smartphone, a server, and a generative AI model. Users use their smartphone to input their profile information and preferences, and the server selects potential matches based on this information.

[0122] Hardware and software used

[0123] Hardware: Smartphones, servers

[0124] Software: Python (registered trademark), OpenAI (registered trademark) API

[0125] Data processing and calculation

[0126] 1. Enter your profile information:

[0127] Users enter profile information such as hobbies, preferences, and favorite brands through a smartphone application.

[0128] 2. Submitting Profile Information:

[0129] The smartphone application sends the entered profile information to the server.

[0130] 3. AI-based selection of match candidates:

[0131] The server sends a prompt to OpenAI's generative AI model (text-davinci-003 (registered trademark)) based on the received profile information. The prompt has the following format:

[0132] User profile information: Hobbies: Watching movies, Favorite brand: Brand A, Product category: Casual wear

[0133] Find other users with similar interests.

[0134] 4. Acquiring matching candidates:

[0135] The generative AI model selects other users with similar preferences based on the prompt sentence and returns the results to the server.

[0136] 5. Providing chat functionality:

[0137] The server allows matched users to communicate with each other through chat within the application.

[0138] Specific examples

[0139] For example, if User A inputs "My hobby is watching movies, my favorite brand is Brand A, and my product category is casual wear," the server sends this information to the generative AI model. The generative AI model selects User B who has the same preferences in the same store and returns this information to the server. The server then enables User A and User B to communicate via chat within the application.

[0140] In this way, users with the same hobbies and preferences can meet in physical stores and enjoy shopping together.

[0141] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0142] Step 1:

[0143] A user launches a smartphone application and enters profile information and preferences, such as hobbies, favorite brands, and product categories. The entered information is temporarily stored on the smartphone device.

[0144] Step 2:

[0145] The smartphone device sends the entered profile information to the server. The data sent includes the user's hobbies, favorite brands, product categories, etc. The server analyzes the received data and converts it into an appropriate format.

[0146] Step 3:

[0147] The server creates a prompt for the generative AI model based on the received profile information. The prompt has the following format:

[0148] User profile information: Hobbies: Watching movies, Favorite brand: Brand A, Product category: Casual wear

[0149] Find other users with similar interests.

[0150] This prompt is sent to the generative AI model.

[0151] Step 4:

[0152] The generative AI model selects other users with the same preferences based on the prompt. Specifically, the generative AI model analyzes the input prompt and searches a database for users with the same hobbies and preferences. The search results are returned to the server.

[0153] Step 5:

[0154] The server receives the search results returned by the generative AI model and creates a list of matching candidates. The list includes information on users who share the same preferences in the same store. The server then sends this list to the smartphone device.

[0155] Step 6:

[0156] The smartphone device displays the received list of match candidates to the user, who can then select a match candidate from the displayed list and start chatting.

[0157] Step 7:

[0158] The smartphone device starts a chat session with the selected match candidate. During the chat session, users can exchange messages in real time. Chat data is sent and received via the server.

[0159] Through the above steps, users with the same hobbies and preferences can meet in a physical store and enjoy shopping together.

[0160] Example 2

[0161] Next, a description will be given of Example 2 of Form 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."

[0162] Conventional matching and profile diagnostic systems have difficulty accurately analyzing users' personalities and values, and have also had problems with the accuracy and reliability of the analysis results when providing them to users. Furthermore, there has been a lack of methods for effectively utilizing user profile information to perform matching.

[0163] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for collecting profile information input by the user, a means for preprocessing the collected profile information to input it into the generative AI model, a means for analyzing the user's personality and values ​​using the generative AI model, and a means for providing the analysis results to the user. This makes it possible to analyze the user's personality and values ​​with high accuracy and provide the results to the user.

[0164] "User" refers to an individual who uses the System to enter profile information and receive analysis results.

[0165] "Profile information" refers to the self-introduction and information about interests entered by the user.

[0166] "Generative AI model" refers to an artificial intelligence model used to analyze a user's profile information and infer their personality and values.

[0167] "Preprocessing" refers to the process of converting collected profile information into a format suitable for input into a generative AI model.

[0168] "Analysis results" refers to the results of the generative AI model's estimation of the user's personality and values.

[0169] "Matching score" refers to the score calculated by comparing the user's profile information with the profile information of the person seeking a match.

[0170] "Image data" refers to photographs and image files provided by users.

[0171] "Evaluation of appearance" refers to the evaluation results of a user's appearance obtained by analyzing image data.

[0172] MODE FOR CARRYING OUT THE INVENTION

[0173] This invention is a system that uses a generative AI model to analyze a user's personality and values ​​based on profile information entered by the user, and provides the results to the user. Specific embodiments of this system are described below.

[0174] 1. Enter your profile information

[0175] Users use their devices to enter their profile information. The devices provide an interface through a web browser or dedicated application that allows users to enter information about themselves and their interests. For example, a user might enter, "I love movies, and I'm particularly interested in independent films. I always go to the cinema on the weekends to check out new releases."

[0176] 2. Data transmission

[0177] The device sends the profile information entered by the user to the server using the HTTPS protocol to ensure data security, thereby protecting the user's privacy.

[0178] 3. Data preparation for analysis

[0179] The server converts the received profile information into a format suitable for the generative AI model to analyze it. Specifically, it tokenizes the text data and performs preprocessing as needed. For example, it uses the Python NLTK library to tokenize the text.

[0180] 4. Analysis using generative AI models

[0181] The server inputs the preprocessed data into a generative AI model, such as OpenAI's GPT-4, which analyzes the user's personality and values ​​from the input text and generates inferences.

[0182] 5. Obtaining analysis results

[0183] The server receives the analysis results from the generative AI model, which include information about the user's personality and values. For example, it may learn that the user has a strong passion for movies and is particularly interested in independent films.

[0184] 6. Providing Results

[0185] The server provides the analysis results to the user, who can then check them through their device. For example, a message such as "You have a strong passion for film, and are particularly interested in independent films" will appear on the screen of a web browser or dedicated application.

[0186] Examples of concrete examples and prompts

[0187] Specific examples

[0188] Suppose a user enters the following self-description:

[0189] "I love movies, and I'm especially interested in independent films. I always go to the cinema on the weekends to check out new releases."

[0190] In this case, the generative AI model analyzes it as follows:

[0191] Users have a strong passion for movies.

[0192] I'm particularly interested in independent films.

[0193] He places great importance on watching movies as a way to spend his weekends.

[0194] Prompt Sentence Examples

[0195] An example of a prompt to input to the generative AI model is:

[0196] "A user has entered the following self-introduction: 'I love movies, and I'm particularly interested in independent films. I always go to the cinema on the weekends to check out new releases.' Use this information to analyze the user's personality and values."

[0197] Using this prompt, the generative AI model can perform a detailed analysis of the user's personality and values ​​and provide appropriate feedback.

[0198] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0199] Step 1:

[0200] Users enter their profile information using their device. Specifically, they enter a self-introduction and information about their interests through the device's web browser or a dedicated application. The entered information is stored in text format on the device.

[0201] Input: Profile information you enter (e.g., "I love movies, especially independent films.")

[0202] Output: Text data saved on the device

[0203] Step 2:

[0204] The device sends the profile information entered by the user to the server using the HTTPS protocol to ensure data security, and the sent data is stored on the server.

[0205] Input: Text data stored on the device

[0206] Output: Text data sent to the server

[0207] Step 3:

[0208] The server converts the received profile information into a format suitable for the generative AI model to analyze it. Specifically, it tokenizes the text data and performs preprocessing as needed. For example, it uses the Python NLTK library to tokenize the text.

[0209] Input: Text data stored on the server

[0210] Output: Tokenized data

[0211] Step 4:

[0212] The server inputs the preprocessed data into a generative AI model, such as OpenAI's GPT-4. The generative AI model analyzes the user's personality and values ​​from the input text and generates an inference.

[0213] Input: Tokenized data

[0214] Output: Analysis results from the generative AI model

[0215] Step 5:

[0216] The server receives the analysis results from the generative AI model. The analysis results include information about the user's personality and values. For example, information such as "The user has a strong passion for movies" and "They are particularly interested in independent films" may be obtained.

[0217] Input: Analysis results from generative AI model

[0218] Output: Analysis result data

[0219] Step 6:

[0220] The server provides the analysis results to the user, who can then check them through their device. For example, a message such as "You have a strong passion for movies, and are particularly interested in independent films" will appear on the screen of a web browser or dedicated application.

[0221] Input: Analysis result data

[0222] Output: Analysis results provided to the user

[0223] (Application example 2)

[0224] Next, a description will be given of Application Example 2 of Form 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."

[0225] Conventional matching systems and profile diagnostic systems have the problem of being unable to recommend products that fully reflect the user's personality and values. In addition, product recommendations are rarely based on the user's profile information, making it difficult to find the best product for the user.

[0226] The specific processing by the specific 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 a matching support means using AI chat, an AI profile diagnosis means, a means for adjusting the AI ​​to the marriage consultation association's system to connect with marriage-seeking people nationwide, an AI conversation ability diagnosis means, an AI appearance diagnosis means (hair, eyebrows, fashion, skin type), a means for recommending products based on user profile information, a means for analyzing the user's personality and values ​​using a generative AI model, and a means for generating prompt sentences. This enables product recommendations that reflect the user's personality and values.

[0227] "AI chat matching support means" is a means of using artificial intelligence to support communication between users and make appropriate matches.

[0228] "AI profile diagnostic means" is a method in which artificial intelligence analyzes a user's personality and values ​​based on the profile information entered by the user.

[0229] "A means of adjusting AI to the marriage consultation association's system and creating connections with people looking for marriage across the country" refers to a means of adapting artificial intelligence to the marriage consultation association's system and creating connections with people looking for marriage across the country.

[0230] An "AI conversational ability diagnostic tool" is a tool that uses artificial intelligence to diagnose a user's conversational ability.

[0231] "AI appearance diagnosis (hair, eyebrows, fashion, skin type) method" is a method that uses artificial intelligence to diagnose a user's appearance (hair, eyebrows, fashion, skin type).

[0232] "Means for recommending products based on user profile information" refers to means for recommending appropriate products based on the profile information entered by the user.

[0233] "Means for analyzing a user's personality and values ​​using a generative AI model" means means for analyzing a user's personality and values ​​using a generative artificial intelligence model.

[0234] "Means for generating prompt sentences" means means for using artificial intelligence to generate prompt sentences based on the user's profile information and other data.

[0235] A system for carrying out this invention has the following configuration: The server includes a matching support means using AI chat, an AI profile diagnosis means, a means for adjusting the AI ​​to the marriage consultation association's system to connect with people looking for marriage across the country, an AI conversation ability diagnosis means, an AI appearance diagnosis means (hair, eyebrows, fashion, skin type), a means for recommending products based on user profile information, a means for analyzing the user's personality and values ​​using a generative AI model, and a means for generating prompt sentences.

[0236] Hardware and Software

[0237] This system uses the following hardware and software:

[0238] Hardware: Servers, smartphones

[0239] Software: Python, OpenAI API

[0240] Data processing and calculation

[0241] The server receives the profile information entered by the user and analyzes it using a generative AI model. Specifically, it generates the following prompt text and performs the analysis using OpenAI's generative AI model (text-davinci-003).

[0242] Example prompt sentence:

[0243] User profile information: In the self-introduction, users wrote about their favorite movies and their thoughts on watching movies.

[0244] Use this information to analyze your users' personalities and values.

[0245] Based on the generated prompt, the generative AI model analyzes the user's personality and values ​​and returns the results to the server, which then uses the analysis results to recommend the most suitable products for the user.

[0246] Specific examples

[0247] For example, if a user writes, "In my self-introduction, I wrote about my favorite movies and my thoughts on watching movies," the generative AI model can determine from that information that the user has a deep passion for movies. Based on this analysis, the server can recommend movie-related products to the user (e.g., movie merchandise, the latest movie DVDs, comfortable movie chairs, etc.).

[0248] In this way, it becomes possible to recommend products that reflect the user's personality and values.

[0249] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0250] Step 1:

[0251] The user uses the device to enter profile information, including a self-introduction, hobbies, interests, etc. The entered data is then sent to the server.

[0252] Step 2:

[0253] The server generates a prompt based on the received profile information. Specifically, it creates the following prompt based on the user's input data:

[0254] User profile information: In the self-introduction, users wrote about their favorite movies and their thoughts on watching movies.

[0255] Use this information to analyze your users' personalities and values.

[0256] The generated prompt sentences are sent to a generative AI model.

[0257] Step 3:

[0258] The server uses a generative AI model (text-davinci-003) to analyze the user's personality and values ​​based on the prompt text. The generative AI model takes the prompt text as input and outputs the analysis results on the user's personality and values.

[0259] Step 4:

[0260] The server receives the analysis results from the generative AI model and recommends the most suitable products to the user based on the results. For example, if the analysis shows that the user has a deep passion for movies, it will recommend movie-related products (movie merchandise, the latest movie DVDs, comfortable chairs for watching movies, etc.).

[0261] Step 5:

[0262] The server sends the recommended product information to the terminal, which then displays the received product information to the user. The user can then review the displayed product information and select the product that interests them.

[0263] Step 6:

[0264] When a user selects a product they are interested in, the device sends the selection information to the server, which uses the selection information to record the user's purchase history and interests and use them for future recommendations.

[0265] In this way, product recommendations based on user profile information are realized.

[0266] Example 3

[0267] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0268] Conventional matching and appearance diagnosis systems have difficulty accurately evaluating users' appearances and profile information to ensure appropriate matching. Furthermore, they lack the means to effectively analyze the image data provided by users and generate detailed appearance evaluations. This has led to problems such as low user satisfaction and sluggish system usage.

[0269] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0270] In this invention, the server includes means for users to upload image data, means for the server to receive and preprocess the image data, means for the server to extract feature data from the preprocessed image, means for the server to input the feature data into a generative AI model and generate an evaluation result, means for the server to return the evaluation result to the user, means for supporting matching through AI chat, means for diagnosing a profile using AI, means for adjusting the AI ​​to the marriage consultation association's system to connect with people looking for marriage across the country, means for diagnosing AI conversation skills, and means for diagnosing AI appearance (hair, eyebrows, fashion, skin type).This enables accurate evaluation of a user's appearance and profile information and appropriate matching.

[0271] "Means for users to upload image data" refers to the interface or function that allows users to send their own image data to the system.

[0272] "Means for the server to receive and pre-process image data" refers to the function by which the server receives image data sent by the user and processes it to convert it into a format suitable for analysis.

[0273] "Means for the server to extract feature data from preprocessed images" refers to the function of identifying features such as hairstyle, eyebrow shape, fashion, and skin type from preprocessed image data and extracting them as data.

[0274] "Means for the server to input feature data into the generative AI model and generate an evaluation result" refers to the function of inputting extracted feature data into an AI model and generating an evaluation of appearance.

[0275] "Means by which the server returns the evaluation results to the user" refers to the function for notifying and displaying the generated evaluation results to the user.

[0276] "AI chat matching support means" refers to a function that uses AI to support communication between users and make appropriate matches.

[0277] "AI profile diagnostic means" refers to a function in which AI analyzes a user's profile information and generates diagnostic results.

[0278] "A means of adjusting AI to the marriage consultation association's system to connect with people looking for marriage across the country" refers to a function that uses AI to link with marriage consultation agency systems and connect with people looking for marriage across the country.

[0279] "AI conversational ability diagnostic means" refers to a function in which AI analyzes a user's conversational ability and generates a diagnostic result.

[0280] "AI appearance diagnosis (hair, eyebrows, fashion, skin type) means" refers to a function in which AI analyzes the user's appearance characteristics and generates an evaluation of hairstyle, eyebrow shape, fashion, skin type, etc.

[0281] This invention is a system for allowing users to evaluate their own appearance, and specifically includes a process in which a user uploads image data and a server analyzes the image data to generate an evaluation result.

[0282] Hardware and software used

[0283] Hardware:

[0284] Server: A server with high-performance computing power

[0285] Device: The smartphone or PC used by the user

[0286] software:

[0287] Image processing library: OpenCV (registered trademark)

[0288] Machine learning framework: TENSORFLOW® or PyTorch®

[0289] Data processing and calculation

[0290] Receiving and preprocessing image data:

[0291] When a user uploads a photo from their device, the server receives the image data, which is then pre-processed using OpenCV, including image resizing, noise removal, and facial extraction.

[0292] Feature data extraction:

[0293] From the pre-processed images, the server extracts feature data such as hairstyle, eyebrow shape, fashion, and skin type using face detection algorithms and image analysis techniques.

[0294] Input to generative AI model and generation of evaluation results:

[0295] The extracted feature data is input into a generative AI model built with TensorFlow and PyTorch. The AI ​​model generates an evaluation for each element based on this feature data. For example, for hairstyle, it might generate an evaluation such as "short hair suits you."

[0296] Returning the evaluation result:

[0297] The generated evaluation results are returned from the server to the user's device, where the user can check the evaluation results. The evaluation results are displayed in text and graph format.

[0298] Specific examples

[0299] When a user uploads a photo of themselves from their smartphone, the following happens:

[0300] 1. The user opens the app on their smartphone, selects a photo, and presses the upload button.

[0301] 2. The server receives the image data and stores it in storage.

[0302] 3. The server resizes the image using OpenCV and cuts out the face area.

[0303] 4. The server identifies the hairstyle area and analyzes its shape and color.

[0304] 5. The server inputs the extracted feature data into the TensorFlow model.

[0305] 6. The model generates a rating, such as "short hair looks good."

[0306] 7. The server returns the evaluation results to the user's smartphone, and the results are displayed on the app.

[0307] Prompt Sentence Examples

[0308] "Evaluate hairstyles, eyebrow shapes, fashion, and skin types based on photos uploaded by users."

[0309] In this way, the user can receive a detailed evaluation of his / her appearance. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0310] Step 1:

[0311] Users upload their own photos from their devices.

[0312] Input: An image file selected by the user.

[0313] Output: Image data sent to the server

[0314] Specific operation: The user opens the application on their smartphone or PC, selects a photo, and presses the upload button, which sends the image data to the server.

[0315] Step 2:

[0316] The server receives the image data and stores it in storage.

[0317] Input: Image data sent by the user

[0318] Output: Image data stored in the server storage

[0319] Specific operation: The server receives the HTTP request and temporarily stores the image data in storage. The stored image data is used in the next processing step.

[0320] Step 3:

[0321] The server preprocesses the image data.

[0322] Input: Image data stored in storage

[0323] Output: Preprocessed image data

[0324] How it works: The server uses OpenCV to resize the image, remove noise, and extract the facial region, generating image data in a format suitable for analysis.

[0325] Step 4:

[0326] The server extracts feature data from the preprocessed images.

[0327] Input: Preprocessed image data

[0328] Output: Extracted feature data (hairstyle, eyebrow shape, fashion, skin type, etc.)

[0329] Specific operation: The server uses a face detection algorithm to identify features such as hairstyle, eyebrow shape, fashion, and skin type and extract them as data.

[0330] Step 5:

[0331] The server inputs the feature data into the generative AI model and generates evaluation results.

[0332] Input: extracted feature data

[0333] Output: Generated evaluation results

[0334] How it works: The server inputs the extracted feature data into a generative AI model built with TensorFlow or PyTorch. The AI ​​model generates an evaluation for each element based on this feature data. For example, for hairstyle, it might generate an evaluation such as "short hair suits you."

[0335] Step 6:

[0336] The server returns the evaluation results to the user.

[0337] Input: Generated evaluation results

[0338] Output: The evaluation results displayed on the user's device

[0339] Specific operation: The server returns the generated evaluation results to the user's device. The user can check the evaluation results through the device. The evaluation results are displayed in text and graph format.

[0340] In this way, users can receive a detailed assessment of their appearance.

[0341] (Application example 3)

[0342] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0343] Conventional matching and appearance diagnosis systems are limited to matching support based on users' appearance evaluations and profile information, and lack specific suggestions to improve the shopping experience in physical stores. In addition, users face the challenge of finding the best fashion items for themselves in physical stores and receiving expert advice.

[0344] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0345] In this invention, the server includes a matching support means using AI chat, an AI profile diagnosis means, a means for adjusting the AI ​​to the marriage consultation association's system to connect with people looking for marriage across the country, an AI conversation ability diagnosis means, an AI appearance diagnosis means (hair, eyebrows, fashion, skin type), and a means for analyzing the user's appearance and suggesting the most suitable fashion items from products in a physical store. This allows the user to receive specific suggestions to improve their shopping experience in the physical store.

[0346] "AI chat matching support means" is a means of using artificial intelligence to support chats between users and those seeking a match, and to promote appropriate matching.

[0347] An "AI-based profile diagnostic method" is a method that uses artificial intelligence to analyze a user's profile information and generate diagnostic results based on that information.

[0348] "A means of adjusting AI to the marriage consultation association's system and creating connections with people looking for marriage across the country" means adapting artificial intelligence to the marriage consultation association's system and creating connections with people looking for marriage across the country.

[0349] An "AI conversational ability diagnostic tool" is a tool that uses artificial intelligence to evaluate a user's conversational ability and diagnose the results.

[0350] "AI appearance diagnosis (hair, eyebrows, fashion, skin type) method" is a method that uses artificial intelligence to evaluate a user's appearance (hairstyle, eyebrow shape, fashion, skin type) and diagnose the results.

[0351] "A means for analyzing a user's appearance and suggesting the most suitable fashion item from products available in a physical store" refers to a means for analyzing a user's appearance using artificial intelligence and suggesting the most suitable fashion item from products available in a physical store based on the results of that analysis.

[0352] To implement the present invention, the following system configuration and processing procedures are required.

[0353] System Configuration

[0354] 1. Hardware:

[0355] Smartphone: A device that allows users to upload photos and receive suggestions.

[0356] Server: A computer system for hosting the AI ​​model and performing image analysis and proposal generation.

[0357] 2. Software:

[0358] Python: A programming language used to implement programs.

[0359] OpenCV: Image processing library.

[0360] Keras (registered trademark): A deep learning library.

[0361] Generative AI model: A model that analyzes the user's appearance and suggests the most suitable fashion items.

[0362] Processing steps

[0363] 1. User photo upload:

[0364] Users take photos of themselves using their smartphones and upload them to the application.

[0365] 2. Image preprocessing:

[0366] The server receives the uploaded image and resizes it to the appropriate size using OpenCV.

[0367] 3. Image Analysis:

[0368] The server loads a generative AI model using Keras and inputs the resized image into the model.

[0369] The model analyzes the user's hairstyle, eyebrow shape, fashion, skin type, etc. and generates a rating for each element.

[0370] 4. Fashion item suggestions:

[0371] Based on the generated ratings, the server selects the most suitable fashion items from a product database within the physical store.

[0372] The selected items will be displayed as suggestions on the user's smartphone.

[0373] Specific examples

[0374] While shopping in a physical store, users upload their own photos to the app. The server receives the photos and resizes them using OpenCV. It then loads a generative AI model using Keras and inputs the resized image into the model. The model analyzes the user's appearance and evaluates their hairstyle, eyebrow shape, fashion, skin type, etc. Based on the evaluation results, the server selects the most suitable fashion items from the store's product database and displays them as suggestions on the user's smartphone.

[0375] Prompt Sentence Examples

[0376] "Upload your photo and let AI analyze your hairstyle, eyebrow shape, fashion, and skin type to suggest the best fashion items for you."

[0377] In this way, users can receive specific suggestions to improve their shopping experience in-store.

[0378] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0379] Step 1:

[0380] Users take photos of themselves using their smartphones and upload them to the application.

[0381] Input: User photo

[0382] Output: Uploaded photo data

[0383] Specific operation: A user starts the application, takes a photo using the camera function, and then presses the upload button in the application to send the photo to the server.

[0384] Step 2:

[0385] The server receives the uploaded photo and uses OpenCV to resize the image to the appropriate size.

[0386] Input: Uploaded photo data

[0387] Output: Resized image data

[0388] Specific operation: The server resizes the received photo data to 224x224 pixels using the OpenCV library. The resized image data is passed to the next analysis step.

[0389] Step 3:

[0390] The server loads a generative AI model using Keras and inputs the resized image into the model.

[0391] Input: Resized image data

[0392] Output: Appearance evaluation results

[0393] How it works: The server loads a pre-trained generative AI model using the Keras library into memory, inputs the resized image data into the model, and generates appearance evaluation results such as hairstyle, eyebrow shape, fashion, and skin type.

[0394] Step 4:

[0395] Based on the generated ratings, the server selects the most suitable fashion items from a product database within the physical store.

[0396] Input: Appearance evaluation results

[0397] Output: A list of the best fashion items

[0398] Specific operation: The server analyzes the appearance evaluation results and compares them with the product database in the physical store. Based on the evaluation results, it selects the most suitable fashion items for the user and generates a list of them.

[0399] Step 5:

[0400] The server sends a list of selected fashion items to the user's smartphone and displays them as suggestions.

[0401] Input: A list of the best fashion items

[0402] Output: Suggestions displayed on the user's smartphone

[0403] Specific operation: The server sends a list of selected fashion items to the user's smartphone. The application displays the received list in an easy-to-read format for the user and presents it as specific suggestions.

[0404] In this way, users can receive specific suggestions to enhance their shopping experience in a physical store.

[0405] 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.

[0406] "Example 1"

[0407] One embodiment of the present invention is a system incorporating an emotion engine. This system recognizes a user's emotions and uses those emotions as reference information for matching. Specifically, when a user accesses an application and enters their profile information and preferences, the emotion engine recognizes the user's emotions. For example, the emotion engine recognizes the user's emotions from the keyboard pressure and touch speed when the user enters their profile information, or from the user's facial expressions and tone of voice when using the application. The emotion engine then uses those emotions as reference information for matching.

[0408] "Example 2"

[0409] Furthermore, systems incorporating an emotion engine reflect the user's emotions in the calculation of the matching score. Specifically, when the AI ​​analyzes the information entered by the user and selects match candidates, it reflects the user's emotions recognized by the emotion engine. For example, if the user expresses emotions of joy or excitement, it will increase the scores of match candidates that are deemed to match the user's preferences. Conversely, if the user expresses emotions of displeasure or anger, it will lower the scores of match candidates that are deemed not to match the user's preferences.

[0410] "Example 3"

[0411] Furthermore, in a system that incorporates an emotion engine, the user's emotions are reflected in the generation of an appearance rating. Specifically, image data provided by the user is analyzed, and when generating an appearance rating, the emotion engine reflects the user's emotions recognized by the emotion engine. For example, if the user shows emotions of joy or excitement, the rating of the user's appearance is increased. Conversely, if the user shows emotions of displeasure or anger, the rating of the user's appearance is decreased.

[0412] The processing flow of each embodiment will be described below.

[0413] "Example 1"

[0414] Step 1: The user accesses the application and enters their profile information and preferences.

[0415] Step 2: The emotion engine recognizes the user's emotions, specifically from the pressure and speed of the keyboard when the user enters profile information, or from facial expressions and tone of voice when the user uses the application.

[0416] Step 3: Use the recognized emotions as reference information for matching.

[0417] "Example 2"

[0418] Step 1: The AI ​​analyzes the information entered by the user and selects matching candidates.

[0419] Step 2: The emotion engine reflects the user's emotions in the calculation of the match score. For example, if the user expresses happiness or excitement, the score of the match candidate that is likely to match the user's preferences will be increased. Conversely, if the user expresses displeasure or anger, the score of the match candidate that is likely to not match the user's preferences will be decreased.

[0420] "Example 3"

[0421] Step 1: Analyze the image data provided by the user and generate an appearance rating.

[0422] Step 2: The emotion engine reflects the user's emotions in generating a rating for their appearance. For example, if the user expresses happiness or excitement, the rating for their appearance will be higher. Conversely, if the user expresses displeasure or anger, the rating for their appearance will be lower.

[0423] Example 1

[0424] Next, a description will be given of Example 1 of Form 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."

[0425] Conventional matching systems mainly match users based on their profile information and preferences, but do not take into account their emotional state. This makes it difficult to achieve optimal matching based on the user's emotional state, resulting in problems such as reduced matching accuracy and satisfaction. Furthermore, the system does not adequately assess the user's appearance or conversational ability, resulting in a lack of comprehensive matching support.

[0426] 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.

[0427] In this invention, the server includes a matching support system using AI chat, an AI profile diagnostic system, a system for adjusting the AI ​​to the marriage agency association's system to connect with people looking for marriage across the country, an AI conversational ability diagnostic system, an AI appearance diagnostic system, an emotion recognition system that recognizes the user's emotions and uses those emotions as reference information for matching, and a generative AI model that analyzes the user's input data and selects match candidates. This enables optimal matching that takes the user's emotional state into consideration, improving matching accuracy and satisfaction. Comprehensive matching support can also be achieved by assessing the user's appearance and conversational ability.

[0428] "AI chat matching support means" is a means in which artificial intelligence selects matching candidates based on the profile information and preferences entered by the user.

[0429] "AI profile diagnostic means" is a means of analyzing the profile information provided by the user and diagnosing the user's characteristics and preferences.

[0430] "A means of adjusting AI to the marriage consultation association's system and creating connections with people looking for marriage across the country" means adapting artificial intelligence to the marriage consultation association's system and creating connections with people looking for marriage across the country.

[0431] An "AI conversational ability diagnostic tool" is a tool that uses artificial intelligence to analyze and evaluate a user's conversational ability.

[0432] The "AI appearance diagnosis means" is a means for analyzing image data provided by a user and generating an evaluation of appearance.

[0433] The "emotion recognition means" is a means for recognizing the user's emotions and using the emotions as reference information for matching.

[0434] A "generative AI model" is an artificial intelligence model that analyzes user input data and selects matching candidates.

[0435] MODE FOR CARRYING OUT THE INVENTION

[0436] This invention is a system in which artificial intelligence (AI) selects matching candidates based on the user's profile information and preferences. Furthermore, by combining it with emotion recognition means, matching is realized that takes into account the user's emotional state.

[0437] Hardware and software used

[0438] This system uses the following hardware and software:

[0439] Hardware: Devices (PCs, smartphones, etc.) that receive user input and servers that process the data

[0440] software:

[0441] Generative AI models (e.g., GPT-4)

[0442] Emotion recognition engine (e.g., Affectiva® SDK)

[0443] System operation explanation

[0444] Users access the application using their device and enter their profile information and preferences. The device then sends the entered data to the server, which then sends the received data to the generative AI model to select potential matches.

[0445] Furthermore, the server uses an emotion recognition engine to collect users' emotional data, such as keyboard pressure and speed, facial expressions, and tone of voice, when users enter their profile information. The server then sends the collected emotional data to the generative AI model to adjust the matching results.

[0446] Finally, the server sends the matching results received from the generative AI model to the terminal, which then displays the results to the user.

[0447] Specific examples

[0448] Consider the case where a user accesses an application and types the following:

[0449] Example user input:

[0450] Profile information: "My hobby is watching movies"

[0451] Preference: "I would like a partner who has the same hobbies as me."

[0452] Emotion data collection example:

[0453] Keyboard touch pressure: Strong

[0454] Touch Speed: Fast

[0455] Expression: Smiling

[0456] Tone of voice: bright

[0457] Example prompt sentence:

[0458] User profile information: Hobbies: Watching movies

[0459] User preference: I want a partner with the same hobbies as me

[0460] Emotion data: strong keyboard touch pressure, fast touch speed, smiling face, bright voice tone

[0461] The server sends this data to a generative AI model and an emotion recognition engine, and the generative AI model selects matching candidates whose hobby is watching movies. The emotion recognition engine also analyzes the user's emotions and uses them as reference information for matching. Finally, the server sends the matching results to the device, which displays the results to the user.

[0462] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0463] Step 1:

[0464] The user enters profile information and preferences.

[0465] Input: User profile information (e.g., hobbies, age, gender) and preferences (e.g., partner requirements)

[0466] Output: Input data (profile information and preferences)

[0467] What happens: A user accesses an application and enters their profile information and preferences into an on-screen form.

[0468] Step 2:

[0469] The terminal sends the input data to the server.

[0470] Input: Data entered by the user (profile information and preferences)

[0471] Output: Data sent to the server

[0472] What it does: The device sends the data entered by the user to the server in real time using the HTTPS protocol.

[0473] Step 3:

[0474] The server receives the data and sends it to the generative AI model.

[0475] Input: Data sent from your device (profile information and preferences)

[0476] Output: Data sent to the generative AI model

[0477] Specific operation: The server analyzes the received data, converts it into a format suitable for the generative AI model, and sends it.

[0478] Step 4:

[0479] A generative AI model selects match candidates.

[0480] Input: Data sent from the server (profile information and preferences)

[0481] Output: A list of potential matches

[0482] How it works: Based on a user's profile information and preferences, the generative AI model selects potential matches from other users in its database, using natural language processing techniques.

[0483] Step 5:

[0484] The server collects user emotion data using an emotion recognition engine.

[0485] Input: User input behavior data (keyboard pressure, touch speed, facial expressions, tone of voice, etc.)

[0486] Output: Emotion data

[0487] Specific behavior: The server uses an emotion recognition engine to analyze the user's behavior data during input and collect emotion data.

[0488] Step 6:

[0489] The server sends the emotion data to the generative AI model.

[0490] Input: Emotion data

[0491] Output: Emotion data sent to the generative AI model

[0492] Specific operation: The server sends the collected emotion data to the generative AI model, which uses it to adjust the matching results.

[0493] Step 7:

[0494] The generative AI model uses emotional data to adjust the matching results.

[0495] Input: Emotion data, list of matching candidates

[0496] Output: Adjusted matching results

[0497] How it works: The generative AI model uses emotional data to refine the list of potential matches. For example, if the user is relaxed, it will consider that emotional state when selecting potential matches.

[0498] Step 8:

[0499] The server sends the final matching results to the device.

[0500] Input: Adjusted matching results

[0501] Output: Matching results sent to the terminal

[0502] Specific operation: The server sends the final matching results received from the generative AI model to the terminal.

[0503] Step 9:

[0504] The terminal displays the matching results to the user.

[0505] Input: Matching results sent from the server

[0506] Output: Matching results displayed to the user

[0507] Specific operations: The terminal displays the matching results received from the server to the user. The user can check the profile information of the displayed matching candidates and select the next action.

[0508] (Application example 1)

[0509] Next, a description will be given of Application Example 1 of Embodiment 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."

[0510] Conventional matching support systems selected match candidates based solely on the user's profile information and preferences, making it impossible to match candidates with consideration for the user's emotions or mood at the time. Furthermore, virtual stores did not consider the user's emotions when proposing products, making it difficult to increase user satisfaction. This resulted in issues such as being unable to fully meet the user's needs and reducing the accuracy of matching and product suggestions.

[0511] 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.

[0512] In this invention, the server includes a matching support means using AI chat, an AI profile diagnosis means, a means for adjusting the AI ​​to the marriage consultation association's system to connect with people looking for marriage across the country, an AI conversation ability diagnosis means, an AI appearance diagnosis means (hair, eyebrows, fashion, skin type), a means for analyzing the user's emotions using an emotion recognition engine, and a means for proposing optimal products based on the user's profile information, preferences, and emotions. This makes it possible to perform matching and suggest products that take the user's emotions into consideration.

[0513] "AI chat matching support method" is a method in which artificial intelligence selects the most suitable matching candidate based on the user's profile information and preferences.

[0514] An "AI-based profile diagnostic method" is a method that analyzes the profile information entered by the user and diagnoses the user's characteristics and preferences based on that information.

[0515] "A means of adjusting AI to the marriage consultation association's system and making contact with people looking for marriage across the country" means adapting artificial intelligence to the marriage consultation association's system and making contact with people looking for marriage across the country.

[0516] An "AI conversational ability diagnostic tool" is a method in which artificial intelligence analyzes a user's conversational ability and evaluates them based on the results.

[0517] The "AI appearance diagnosis (hair, eyebrows, fashion, skin type) method" is a method that analyzes image data provided by the user and generates an evaluation of appearance, such as hairstyle, eyebrows, fashion, and skin type.

[0518] "Means for analyzing a user's emotions using an emotion recognition engine" refers to means for using an emotion recognition engine to analyze emotions from a user's facial expressions, tone of voice, keyboard touch pressure and touch speed, etc.

[0519] "Means for suggesting optimal products based on a user's profile information, preferences, and emotions" refers to a means by which artificial intelligence suggests optimal products based on a user's profile information, preferences, and emotional data.

[0520] As an embodiment of the present invention, the following system is constructed.

[0521] System configuration

[0522] 1. Hardware

[0523] Server: A server for hosting the database and AI models.

[0524] Device: Smartphone, smart glasses, head-mounted display for user access.

[0525] 2. Software

[0526] AI Chatbot: An interface for entering user profile information and preferences.

[0527] Emotion recognition engine: Analyzes user emotions using OpenCV (facial expression recognition) and TensorFlow (voice analysis).

[0528] Database: We use MySQL® and Firebase® to store user profile information and sentiment data.

[0529] Generative AI model: GPT-4 is used to suggest the best products based on the user's profile information, preferences, and emotions.

[0530] Program processing

[0531] 1. Enter your profile information and preferences

[0532] Users use their device to enter their profile information (age, gender, hobbies, etc.) and preferences (favorite colors, style, etc.) within the application.

[0533] This information is stored in a database.

[0534] 2. Emotion recognition

[0535] When a user enters information, the emotion recognition engine analyzes the user's emotions in real time based on the pressure and speed of their keyboard touch, facial expressions, tone of voice, and other factors.

[0536] The analyzed emotional data is also stored in the database.

[0537] 3. Product recommendations using AI

[0538] The server uses a generative AI model (GPT-4) to suggest optimal products based on the user's profile information, preferences, and emotional data.

[0539] For example, if a user feels like "I want to relax," the app will suggest relaxing aroma candles or massage chairs.

[0540] Specific examples

[0541] A user accesses the virtual store and enters their profile information (age: 30, gender: female, hobby: yoga) and their preferences (favorite color: blue, style: simple). If the emotion engine recognizes the user's facial expression as indicating a desire to relax, the AI ​​will suggest relaxing yoga mats and aroma candles.

[0542] Prompt Sentence Examples

[0543] User profile information: Age: 30, Gender: Female, Hobbies: Yoga

[0544] User preferences: Favorite color: Blue, Style: Simple

[0545] User sentiment: Relaxation

[0546] Suggested products: Relaxing yoga mats, aroma candles

[0547] In this way, by building a system that allows for matching and product suggestions that take user emotions into consideration, user satisfaction can be increased.

[0548] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0549] Step 1:

[0550] The user uses the device to enter their profile information (age, gender, hobbies, etc.) and preferences (favorite colors, styles, etc.) within the application.

[0551] Input: User profile information and preferences

[0552] Output: User profile information and preferences stored in a database

[0553] Specific operation: The user uses a smartphone or head-mounted display to enter information into the application's input form and press the submit button. The server receives this information and stores it in a database such as MySQL or Firebase.

[0554] Step 2:

[0555] When a user enters information, the emotion recognition engine analyzes the user's emotions in real time based on the pressure and speed of the keyboard touch, facial expressions, tone of voice, and other factors.

[0556] Input: User input behavior (keyboard pressure, touch speed, facial expressions, tone of voice)

[0557] Output: User emotion data stored in a database

[0558] How it works: When a user enters information, the device's sensors and camera capture the keyboard pressure, touch speed, facial expressions, and tone of voice. An emotion recognition engine (OpenCV or TensorFlow) analyzes this data to identify the user's emotions. The analysis results are sent to a server and stored in a database.

[0559] Step 3:

[0560] The server uses a generative AI model (GPT-4) to suggest optimal products based on the user's profile information, preferences, and emotional data.

[0561] Input: User profile information, preferences, and emotional data stored in a database

[0562] Output: A list of optimal products suggested to the user

[0563] How it works: The server retrieves the user's profile information, preferences, and emotional data from the database. This data is input into a generative AI model (GPT-4) to generate a prompt. Based on the generated prompt, the AI ​​selects the most suitable product and suggests it to the user.

[0564] Step 4:

[0565] The user checks the proposed products and, if he / she wishes to purchase them, carries out the purchase procedure.

[0566] Input: A list of products suggested to the user

[0567] Output: Notification of purchase completion

[0568] Specific operation: The user uses the terminal to check the list of suggested products, select the product they wish to purchase, and press the purchase button. The server processes the purchase procedure and sends a completion notification to the user.

[0569] In this way, by building a system that processes and analyzes data based on the user's input and actions at each step and suggests the most suitable products, it is possible to increase user satisfaction.

[0570] Example 2

[0571] Next, a description will be given of Example 2 of Form 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."

[0572] Conventional matching systems have had the problem of low user satisfaction because they do not adequately consider the user's profile information or emotions when matching. In addition, evaluation of appearance is highly subjective, making it difficult to make an objective evaluation.

[0573] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0574] In this invention, the server includes means for a generative AI model to analyze a user's personality and values ​​based on profile information entered by the user, means for analyzing the user's emotions using an emotion engine and adjusting the scores of match candidates, means for comparing the user's profile information with the profile information of match-seeking candidates and calculating a match score, and means for analyzing image data provided by the user and generating an evaluation of appearance, thereby enabling matching that comprehensively takes into account the user's personality, values, emotions, and appearance.

[0575] A "generative AI model" is a type of artificial intelligence that uses natural language processing and machine learning techniques to analyze a user's personality and values ​​based on information entered by the user.

[0576] An "emotion engine" is software that analyzes emotions from information entered by the user and processes data based on those emotions.

[0577] "Profile information" refers to personal information entered by a user, such as a self-introduction, hobbies, interests, etc.

[0578] The "matching score" is a numerical representation of the degree of compatibility with a potential match based on the user's profile information and emotional information.

[0579] "Appearance evaluation" involves analyzing image data provided by the user and evaluating appearance features such as hairstyle, eyebrows, fashion, and skin type.

[0580] MODE FOR CARRYING OUT THE INVENTION

[0581] This invention is a system that uses a generative AI model and an emotion engine to calculate scores for match candidates based on profile information and emotion information entered by the user, and provides the scores to the user. Specific embodiments of this system are described below.

[0582] Hardware and software used

[0583] 1. Terminal

[0584] A device on which a user enters profile information and emotional information. This includes smartphones, tablets, and PCs.

[0585] 2. Server

[0586] A computer system for receiving user-submitted information and analyzing the data using generative AI models and emotion engines. The server includes a database and an analysis engine.

[0587] 3. Generative AI Models

[0588] It is an artificial intelligence model that analyzes a user's personality and values ​​based on their profile information. For example, it uses a natural language processing model such as GPT-4.

[0589] 4. Emotion Engine

[0590] This software analyzes the user's emotional information and recognizes their emotions. For example, it uses the IBM Watson (registered trademark) emotion analysis API.

[0591] Data processing and calculation

[0592] 1. Analysis of profile information

[0593] The profile information entered by the user on their device is sent to a server. The server then sends this information to a generative AI model that analyzes the user's personality and values. For example, if a user writes, "In my self-introduction, I wrote about my favorite movies and my thoughts on watching movies," the generative AI model will determine that the user has a deep passion for movies.

[0594] 2. Emotional Information Analysis

[0595] Emotional information entered by the user into the device is sent to the server. The server then sends this information to the emotion engine, which analyzes the user's emotions. For example, if a user enters, "Today was a really fun day. I was moved by the new movie," the emotion engine will analyze that the user is expressing emotions of joy and excitement.

[0596] 3. Calculating the Matching Score

[0597] The server calculates scores for match candidates based on the analysis results obtained from the generative AI model and emotion engine. For example, if a user expresses emotions such as joy or excitement, the server will increase the scores of match candidates that are deemed to match the user's preferences. Conversely, if a user expresses emotions such as displeasure or anger, the server will decrease the scores of match candidates that are deemed to not match the user's preferences.

[0598] Specific examples

[0599] A specific example of a profile diagnostic system

[0600] User input: "In my self-introduction, I wrote about my favorite movies and my thoughts on watching movies."

[0601] AI analysis results: "Users have a deep passion for movies"

[0602] A specific example of a matching system that combines an emotion engine

[0603] User input: "Today was a really fun day. I was moved by the new movie."

[0604] Emotion engine analysis results: "Shows emotions of joy and excitement"

[0605] Matching score calculation: "Increase the score of match candidates that are likely to match the user's preferences"

[0606] Prompt Sentence Examples

[0607] Prompt for a profile diagnostic system: "Analyze the personality and values ​​of a user based on the profile information they entered. For example, if a user enters, 'In my self-introduction, I wrote about my favorite movies and my thoughts on watching movies,' what kind of personality and values ​​do you think they have?"

[0608] Prompt for a matching system that combines an emotion engine: "Calculate a score for potential matches based on the emotion information entered by the user. For example, if a user enters, 'Today was a great day. I was moved by the new movie,' how would you adjust the score?"

[0609] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0610] Program processing flow

[0611] Profile Diagnostic System

[0612] Step 1:

[0613] The user enters profile information.

[0614] Input: The user enters profile information such as a self-introduction, hobbies, and interests into an input form on the device.

[0615] Specific actions: For example, enter, "In my self-introduction, I wrote about my favorite movies and my thoughts on watching movies."

[0616] Output: The entered profile information is saved on the device.

[0617] Step 2:

[0618] The terminal sends the input information to the server.

[0619] Input: Profile information entered by the user.

[0620] What happens: The device sends the entered profile information to the server using the HTTPS protocol.

[0621] Output: The profile information is sent to the server.

[0622] Step 3:

[0623] The server receives the profile information and sends it to the generative AI model.

[0624] Input: The profile information received by the server.

[0625] Specific operation: The server sends the received profile information to the generative AI model.

[0626] Output: Profile information is sent to the generative AI model.

[0627] Step 4:

[0628] A generative AI model analyzes profile information.

[0629] Input: Profile information received by the generative AI model.

[0630] How it works: The generative AI model analyzes the user's personality and values ​​using, for example, GPT-4. For example, if a user writes about their "feelings about watching movies," the generative AI model determines that the user has a deep passion for movies.

[0631] Output: Analysis results on personality and values ​​are generated.

[0632] Step 5:

[0633] The server returns the analysis results to the user.

[0634] Input: Analysis results obtained from a generative AI model.

[0635] Specific operation: The server returns the analysis results to the user.

[0636] Output: The user can check the analysis results on the terminal.

[0637] A matching system that combines an emotion engine

[0638] Step 1:

[0639] The user inputs information indicating the emotion.

[0640] Input: The user enters information indicating their emotions into an input form on the device.

[0641] Specific actions: For example, entering a diary entry or a review.

[0642] Output: The input emotion information is saved on the device.

[0643] Step 2:

[0644] The device transmits the emotion information to the server.

[0645] Input: Emotion information entered by the user.

[0646] Specific operation: The device uses the HTTPS protocol to send the input emotion information to the server.

[0647] Output: Emotion information is sent to the server.

[0648] Step 3:

[0649] The server receives the emotion information and sends it to the emotion engine.

[0650] Input: Emotion information received by the server.

[0651] Specific operation: The server sends the received emotion information to the emotion engine.

[0652] Output: Emotion information is sent to the emotion engine.

[0653] Step 4:

[0654] The emotion engine analyzes emotions.

[0655] Input: Emotion information received by the emotion engine.

[0656] Specific operation: The emotion engine analyzes the user's emotions using, for example, IBM Watson's emotion analysis API. For example, if the user shows "emotions of joy or excitement," it analyzes that emotion.

[0657] Output: Sentiment analysis results are generated.

[0658] Step 5:

[0659] The server calculates the score of the matching candidate based on the analysis results.

[0660] Input: Analysis results obtained from the emotion engine.

[0661] Specific operation: The server calculates the score of match candidates based on the emotion information obtained from the emotion engine. For example, if the user expresses emotions such as joy or excitement, the server increases the score of match candidates that are deemed to match the user's preferences. Conversely, if the user expresses emotions such as displeasure or anger, the server decreases the score of match candidates that are deemed to not match the user's preferences.

[0662] Output: Scores are generated for match candidates.

[0663] Step 6:

[0664] The server returns the scores of the potential matches to the user.

[0665] Input: Calculated match candidate scores.

[0666] Specific operation: The server returns the calculated scores of the match candidates to the user.

[0667] Output: The user can check the scores of the potential matches on their device.

[0668] (Application example 2)

[0669] Next, a description will be given of Application Example 2 of Form 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."

[0670] Conventional matching systems match users based on their profile information and appearance, but lack the ability to recommend products based on the user's emotions and preferences. This meant that personalized product recommendations based on the user's interests and emotions were not possible, making it difficult to improve the user experience.

[0671] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0672] In this invention, the server includes a matching support means using AI chat, an AI profile diagnosis means, a means for adjusting the AI ​​to the marriage consultation association's system to connect with marriage-seekers nationwide, an AI conversation ability diagnosis means, an AI appearance diagnosis means (hair, eyebrows, fashion, skin type), a means for analyzing the user's profile information and emotions and recommending products that match the user's preferences, and a means for creating prompt sentences using a generative AI model and acquiring recommended products. This enables personalized product recommendations based on the user's emotions and preferences.

[0673] "AI chat matching support means" is a means of using artificial intelligence to support communication between users and make appropriate matches.

[0674] "AI profile diagnostic means" is a method in which artificial intelligence analyzes a user's personality and values ​​based on the profile information entered by the user.

[0675] "A means of adjusting AI to the marriage consultation association's system and creating connections with people looking for marriage across the country" refers to a means of adapting artificial intelligence to the marriage consultation association's system and creating connections with people looking for marriage across the country.

[0676] An "AI conversational ability diagnostic tool" is a tool that uses artificial intelligence to diagnose a user's conversational ability.

[0677] The "AI appearance diagnosis (hair, eyebrows, fashion, skin type) method" is a method in which image data provided by the user is analyzed and an artificial intelligence generates an evaluation regarding hair, eyebrows, fashion, and skin type.

[0678] "Means for analyzing a user's profile information and emotions and recommending products that match the user's preferences" refers to a means in which artificial intelligence analyzes a user's profile information and emotions and recommends products that match the user's preferences.

[0679] "Means for creating a prompt sentence using a generative AI model and obtaining recommended products" refers to means for creating a prompt sentence using a generative AI model and obtaining recommended products based on the prompt sentence.

[0680] A system for implementing the present invention includes a server, a user terminal, and a prompt sentence generation function using a generative AI model. Specific embodiments are described below.

[0681] System Configuration

[0682] The server includes the following means:

[0683] 1. AI chat to support matching

[0684] 2. AI-based profile diagnostics

[0685] 3. A way to adjust AI to the marriage consultation association's system and connect with people looking for marriage across the country

[0686] 4. AI conversation ability assessment tool

[0687] 5. AI Appearance Diagnosis (Hair, Eyebrows, Fashion, Skin Type) Method

[0688] 6. A means of analyzing user profile information and emotions to recommend products that match the user's preferences

[0689] 7. A method to create prompts using a generative AI model and obtain recommended products

[0690] Program processing

[0691] The server receives profile information and emotion data sent from the user's device, including the user's name, age, interests, emotions, etc. The server then uses this information to create prompts using a generative AI model.

[0692] The generated prompt has the following format:

[0693] The user's name is Taro, he is 30 years old, and his interests are movies, music, and reading. The user is currently showing the emotion of joy. Please list some recommended products for this user.

[0694] The server inputs this prompt into a generative AI model to recommend products that match the user's preferences. The recommended products are then sent to the user's device and displayed to the user.

[0695] Hardware and software used

[0696] Hardware: Servers, user devices (smartphones, PCs, etc.)

[0697] Software: Python, OpenAI API, requests library

[0698] Specific examples

[0699] For example, if a user is interested in "movies" and expresses the emotion "joy," the generative AI model will recommend "latest movie DVD sets" or "movie-related merchandise," allowing users to easily find products that match their interests and emotions.

[0700] This system will enable personalized product recommendations based on the user's emotions and preferences, which is expected to improve the user experience.

[0701] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0702] Step 1:

[0703] A user terminal inputs user profile information and emotion data.

[0704] Specifically, the user inputs information such as name, age, interests, and feelings, and sends this to the server.

[0705] Input: Name, Age, Interests, Emotions

[0706] Output: User profile information and emotion data sent to the server

[0707] Step 2:

[0708] The server analyzes the received user profile information and emotion data.

[0709] Specifically, the server analyzes this data and processes it to understand the user's personality and values.

[0710] Input: User profile information and emotion data

[0711] Output: Analyzed user personality and values ​​data

[0712] Step 3:

[0713] The server creates a prompt using a generative AI model.

[0714] Specifically, based on the analyzed user's personality and values ​​data, a prompt sentence is generated to be input into the generative AI model.

[0715] Input: Analyzed user personality and values ​​data

[0716] Output: Generated prompt statement

[0717] Step 4:

[0718] The server inputs a prompt sentence into the generative AI model and obtains recommended products.

[0719] Specifically, the generated prompt sentences are input into a generative AI model to obtain data that will recommend products that match the user's preferences.

[0720] Input: Generated prompt text

[0721] Output: Recommended product data

[0722] Step 5:

[0723] The server transmits the recommended product data to the user terminal.

[0724] Specifically, the acquired recommended product data is sent to the user terminal and displayed to the user.

[0725] Input: Recommended product data

[0726] Output: Recommended product data sent to the user's device

[0727] Step 6:

[0728] The user terminal displays the recommended product data.

[0729] Specifically, the user terminal displays the received recommended product data so that the user can check the products.

[0730] Input: Recommended product data sent from the server

[0731] Output: Recommended product data displayed to the user

[0732] Example 3

[0733] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0734] Conventional matching systems have the problem that the user's appearance is evaluated without taking into account their emotional state, which means that the evaluation is not based on the user's actual emotional state. Furthermore, because there is no way to integrate appearance evaluation and emotional analysis, feedback to users is one-sided, making it difficult to increase user satisfaction.

[0735] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0736] In this invention, the server includes a matching support means using AI chat, an AI profile diagnosis means, a means for adjusting the AI ​​to the marriage consultation association's system to connect with people looking for marriage across the country, an AI conversation ability diagnosis means, an AI appearance diagnosis means (hair, eyebrows, fashion, skin type), and an emotion engine means for analyzing the user's emotions and reflecting them in the appearance evaluation. This makes it possible to evaluate the user's appearance taking into account their emotional state, and provides more accurate feedback with a higher level of satisfaction.

[0737] "AI chat matching support means" is a means of using artificial intelligence to support chats between users and those seeking a match, and to promote appropriate matching.

[0738] "AI profile diagnostic means" is a means of analyzing a user's profile information using artificial intelligence to diagnose the user's characteristics and aptitude.

[0739] "A means of creating connections with people looking for marriage across the country by adjusting to the system of the Marriage Consultation Association" means a means of creating connections between users and people looking for marriage across the country by adapting to the system of the Marriage Consultation Association.

[0740] An "AI conversational ability diagnostic tool" is a method that uses artificial intelligence to analyze a user's conversational ability and evaluate that ability.

[0741] "AI appearance diagnosis (hair, eyebrows, fashion, skin type) method" is a method that uses artificial intelligence to analyze and evaluate a user's appearance, such as hairstyle, eyebrow shape, fashion, and skin type.

[0742] The "emotion engine means" is a means for analyzing the user's emotions and reflecting the analysis results in the appearance evaluation.

[0743] MODE FOR CARRYING OUT THE INVENTION

[0744] This invention is a system that combines an AI that evaluates appearance based on image data provided by the user with an emotion engine that analyzes the user's emotions. A specific embodiment of this system is described below.

[0745] System configuration

[0746] The system consists of the following main components:

[0747] 1. Server: Receives image data, analyzes it, integrates evaluation results, and provides feedback.

[0748] 2. Terminal: A device (smartphone or PC) through which users upload image data.

[0749] 3. AI Appearance Diagnostic Module: Analyzes the user's appearance using deep learning frameworks such as TensorFlow and PyTorch.

[0750] 4. Emotion Engine: Analyzes user emotions using libraries such as OpenCV and Dlib.

[0751] Program processing

[0752] The server receives image data uploaded by the user from their device. The received image data is first sent to the AI ​​appearance assessment module. This module uses TensorFlow and PyTorch to analyze the user's hairstyle, eyebrow shape, fashion, skin type, etc., and generates an evaluation for each element.

[0753] The server then sends the same image data to the emotion engine, which uses OpenCV and Dlib to analyze the user's facial expressions and recognize emotions such as joy, excitement, displeasure, and anger.

[0754] Finally, the server integrates the evaluation results of the AI ​​appearance diagnosis module with the analysis results of the emotion engine. For example, if the user shows emotions of joy or excitement, the server will set a high appearance rating. Conversely, if the user shows emotions of displeasure or anger, the server will set a low appearance rating. This integrated evaluation result is provided to the user as feedback.

[0755] Specific examples

[0756] Users take photos of themselves with their smartphones and upload them to the system. The server receives the image data and sends it to the AI ​​appearance diagnosis module. The AI ​​appearance diagnosis module uses TensorFlow to evaluate the hairstyle as "short cut," the eyebrow shape as "arched," the fashion style as "casual," and the skin type as "dry." At the same time, the server sends the image data to the emotion engine, which uses OpenCV to analyze the user's facial expressions. The emotion engine recognizes that the user is expressing the emotion of "happiness." The server integrates these evaluation results and provides feedback to the user, saying, "Your appearance is very attractive."

[0757] Prompt Sentence Examples

[0758] "Based on photos uploaded by users, evaluate their hairstyle, eyebrow shape, fashion, and skin type, and generate an appearance rating that reflects the emotions analyzed by the emotion engine. If the user shows an emotion of joy, set the appearance rating high."

[0759] The above is a specific embodiment for carrying out the present invention. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0760] Program processing flow

[0761] Step 1:

[0762] Users upload their own photos.

[0763] Input: A photo taken by a user using a device (smartphone or PC).

[0764] Output: Uploaded image data.

[0765] Specific operation: The user takes a photo of themselves with the smartphone camera and clicks the upload button on the system to send the photo.

[0766] Step 2:

[0767] The server receives the image data.

[0768] Input: Image data uploaded by the user.

[0769] Output: Image data stored on the server.

[0770] Specific operation: The server receives the HTTP request and temporarily stores the image data in storage.

[0771] Step 3:

[0772] The server sends the image data to the AI ​​appearance diagnosis module.

[0773] Input: Image data stored on the server.

[0774] Output: Image data sent to the AI ​​appearance diagnosis module.

[0775] Specific operation: The server uses an API call to send image data to the AI ​​appearance diagnosis module.

[0776] Step 4:

[0777] The AI ​​appearance diagnosis module analyzes the image data and generates an appearance assessment.

[0778] Input: Image data sent to the AI ​​appearance diagnosis module.

[0779] Output: Evaluation results on hairstyle, eyebrow shape, fashion, and skin type.

[0780] How it works: The AI ​​appearance diagnosis module uses TensorFlow and PyTorch to analyze image data and evaluate hairstyle as "short cut," eyebrow shape as "arched," fashion as "casual," and skin type as "dry."

[0781] Step 5:

[0782] The server sends image data to the emotion engine.

[0783] Input: Image data stored on the server.

[0784] Output: Image data sent to the emotion engine.

[0785] Specific operation: The server again uses an API call to send the same image data to the emotion engine.

[0786] Step 6:

[0787] The emotion engine analyzes the image data and recognizes the user's emotions.

[0788] Input: Image data sent to the emotion engine.

[0789] Output: Analysis results about the user's emotions (e.g., happy, excited, displeased, anger).

[0790] Specific operation: The emotion engine uses OpenCV and Dlib to analyze the user's facial expression from image data and recognizes that the user is expressing the emotion of "joy."

[0791] Step 7:

[0792] The server integrates the appearance evaluation and emotion analysis results.

[0793] Input: Evaluation results of the AI ​​appearance diagnosis module and analysis results of the emotion engine.

[0794] Output: Integrated appearance assessment results.

[0795] Specific operation: The server integrates the evaluation results of the AI ​​appearance diagnosis module with the analysis results of the emotion engine, and if the user shows the emotion of "joy," sets a high appearance evaluation.

[0796] Step 8:

[0797] The server provides the integrated evaluation results as feedback to the user.

[0798] Input: The integrated appearance assessment results.

[0799] Output: The feedback message provided to the user.

[0800] Specific operation: Based on the integrated evaluation results, the server generates a feedback message for the user, such as "Your appearance is very attractive," and displays it on the user's device.

[0801] The above are the specific processing steps of the program of this system.

[0802] (Application example 3)

[0803] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0804] Conventional appearance diagnosis systems only evaluated users' appearances uniformly, and were unable to reflect their emotions or individual needs. Furthermore, there was a lack of effective means for suggesting optimal fashion items and styling to customers in physical stores. This made it difficult to improve the customer experience.

[0805] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0806] In this invention, the server includes a matching support means using AI chat, an AI profile diagnosis means, a means for adjusting the AI ​​to the marriage consultation association's system to connect with marriage-seekers nationwide, an AI conversation ability diagnosis means, an AI appearance diagnosis means (hair, eyebrows, fashion, skin type), a means for analyzing the user's emotions using an emotion recognition engine and reflecting them in the appearance evaluation, and a means for generating and displaying optimal suggestions for the user based on the appearance evaluation results. This makes it possible to evaluate the appearance taking the user's emotions into consideration and to suggest optimal fashion items and styling to customers in physical stores.

[0807] "AI chat matching support means" is a means of using artificial intelligence to support matching between users.

[0808] "AI profile diagnosis method" is a method that uses artificial intelligence to analyze a user's profile information and make a diagnosis.

[0809] "A means of adjusting AI to the marriage consultation association's system and creating connections with people looking for marriage across the country" refers to a means of adapting artificial intelligence to the marriage consultation association's system and creating connections with people looking for marriage across the country.

[0810] An "AI conversational ability diagnostic tool" is a tool that uses artificial intelligence to diagnose a user's conversational ability.

[0811] "AI appearance diagnosis (hair, eyebrows, fashion, skin type) method" is a method that uses artificial intelligence to diagnose appearance elements such as a user's hairstyle, eyebrow shape, fashion, and skin type.

[0812] "Means for analyzing a user's emotions using an emotion recognition engine and reflecting the results in an appearance evaluation" refers to means for analyzing a user's emotions using an emotion recognition engine and reflecting the results in an appearance evaluation.

[0813] The "means for generating and displaying an optimal suggestion to the user based on the appearance evaluation results" refers to a means for generating an optimal suggestion to the user based on the appearance evaluation results and displaying it.

[0814] The embodiments of the present invention will be described in detail below.

[0815] System configuration

[0816] This system is configured using the following hardware and software.

[0817] Hardware:

[0818] Smart mirror (with built-in camera, display, and microphone)

[0819] A computer with a high-performance GPU (to run AI models)

[0820] Internet connection

[0821] software:

[0822] Image recognition AI model (e.g. OpenCV, TensorFlow)

[0823] Emotion recognition engine (e.g., Affectiva, Microsoft® Azure® Emotion API)

[0824] Database (for storing customer information and fashion item information)

[0825] Front-end application (for the smart mirror UI)

[0826] Program processing explanation

[0827] 1. Image capture:

[0828] When a user stands in front of the smart mirror, the camera captures image data.

[0829] This image data is sent to an image recognition AI model.

[0830] 2. Image Analysis:

[0831] An image recognition AI model analyzes image data and evaluates appearance factors such as hairstyle, eyebrow shape, fashion, and skin type.

[0832] The evaluation results are temporarily stored in a database.

[0833] 3. Emotion analysis:

[0834] The emotion recognition engine analyzes the user's facial expressions and recognizes their emotions.

[0835] The recognized emotion data is reflected in the appearance evaluation.

[0836] 4. Adjustments:

[0837] Based on the recognized emotion, the appearance rating is adjusted, for example, if the user shows an emotion of happiness, the rating is increased.

[0838] 5. Proposal generation:

[0839] Based on the adjusted evaluation results, the system generates optimal fashion items and styling suggestions for the user from the database.

[0840] The suggestions are displayed on the smart mirror's display.

[0841] Specific examples

[0842] Prompt Sentence Examples

[0843] Analyze the user's image data and evaluate their hairstyle, eyebrow shape, fashion, and skin type. Furthermore, recognize the user's emotions and reflect them in the evaluation. Based on the evaluation results, suggest the most suitable fashion items for the user.

[0844] Real-world usage scenarios

[0845] 1. A user stands in front of the smart mirror and the camera captures an image.

[0846] 2. AI analyzes the image and evaluates hairstyle, eyebrow shape, fashion, and skin type.

[0847] 3. The emotion engine analyzes the user's facial expressions and recognizes the emotion of joy.

[0848] 4. Appearance ratings are adjusted upward based on perceived emotions.

[0849] 5. Based on the adjusted evaluation results, the database will suggest the most suitable fashion items for the user (e.g., a new jacket or accessory).

[0850] 6. The suggestions are displayed on the smart mirror's display, allowing the user to try them on and consider purchasing them on the spot.

[0851] In this way, it becomes possible to evaluate appearance taking into account the user's emotions and to suggest optimal fashion items and styling to customers in physical stores.

[0852] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0853] Step 1:

[0854] When a user stands in front of the smart mirror, the device's (smart mirror's) camera captures image data. The input is the user's image data, and the output is the captured image data. This image data is used in subsequent processing steps.

[0855] Step 2:

[0856] The server sends the captured image data to an image recognition AI model (e.g., OpenCV, TensorFlow). The input is the captured image data, and the output is the analyzed appearance elements (hairstyle, eyebrow shape, fashion, skin type). The image recognition AI model analyzes the image data and evaluates each appearance element.

[0857] Step 3:

[0858] The server temporarily stores the analyzed appearance elements in a database, where the input is the analyzed appearance elements and the output is the evaluation data stored in the database, making the evaluation data available for subsequent processing.

[0859] Step 4:

[0860] The camera on the device (smart mirror) captures the user's facial expression and sends it to the server. The input is the user's facial expression data, and the output is the captured facial expression data. This facial expression data is analyzed by an emotion recognition engine.

[0861] Step 5:

[0862] The server sends the captured facial expression data to an emotion recognition engine (e.g., Affectiva, Microsoft Azure Emotion API). The input is the captured facial expression data, and the output is the recognized emotion data. The emotion recognition engine analyzes the facial expression data and recognizes the user's emotion.

[0863] Step 6:

[0864] The server adjusts the appearance rating based on the recognized emotion data. The input is the recognized emotion data and the analyzed appearance elements, and the output is the adjusted appearance rating. For example, if the user shows an emotion of joy, the appearance rating is increased.

[0865] Step 7:

[0866] The server generates optimal fashion item and styling suggestions for the user from the database based on the adjusted appearance evaluation results. The input is the adjusted appearance evaluation and the fashion item information from the database, and the output is the generated suggestions.

[0867] Step 8:

[0868] The server displays the generated suggestions on the device (smart mirror) display. The input is the generated suggestions, and the output is the suggestions displayed on the display. The user can check the suggested fashion items and styling on the spot, try them on, and consider purchasing.

[0869] 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.

[0870] 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> ) 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.

[0871] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are listed.

[0872] 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.

[0873] [Second embodiment]

[0874] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0875] 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.

[0876] 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).

[0877] 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.

[0878] 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.

[0879] 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).

[0880] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for 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.

[0881] 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.

[0882] 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.

[0883] 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.

[0884] 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.

[0885] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0886] "Example 1"

[0887] In one embodiment of the present invention, AI chat-based matching support involves AI selecting match candidates based on the profile information and preferences entered by the user. Specifically, if a user enters, "My hobby is watching movies, and I would like a partner with the same hobby," the AI ​​will select match candidates whose hobby is watching movies.

[0888] "Example 2"

[0889] In addition, in the AI ​​profile diagnosis, the AI ​​analyzes the user's personality and values ​​based on the profile information they enter. Specifically, if a user enters, "In my self-introduction, I wrote about my favorite movies and my thoughts on watching movies," the AI ​​will determine from that information that the user has a deep passion for movies.

[0890] "Example 3"

[0891] Furthermore, with the AI ​​Appearance Diagnosis, AI evaluates a user's appearance based on image data provided by the user. Specifically, when a user uploads a photo of themselves, the AI ​​analyzes the user's hairstyle, eyebrow shape, fashion, skin type, etc. from the photo and generates an evaluation for each element.

[0892] The processing flow of each embodiment will be described below.

[0893] "Example 1"

[0894] Step 1: The user accesses the application and enters their profile information and preferences.

[0895] Step 2: The AI ​​analyzes the information entered by the user and selects matching candidates.

[0896] Step 3: AI presents the selected match candidates to the user.

[0897] "Example 2"

[0898] Step 1: The user accesses the application and enters their profile information.

[0899] Step 2: The AI ​​analyzes the profile information entered by the user and analyzes the user's personality and values.

[0900] Step 3: The results of the AI ​​analysis are presented to the user and used as reference information for matching.

[0901] "Example 3"

[0902] Step 1: A user accesses the application and uploads a photo of themselves.

[0903] Step 2: The AI ​​analyzes the photo provided by the user and evaluates the user's appearance. Step 3: The AI ​​presents the results of its evaluation to the user, which serves as reference information for matching.

[0904] Example 1

[0905] Next, a description will be given of Example 1 of Form 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."

[0906] Conventional matching systems did not adequately match users based on their profile information and desired conditions, making it difficult to find suitable candidates. Furthermore, they did not adequately evaluate users' appearance or conversational ability, and did not provide comprehensive matching support. This resulted in low user satisfaction and low system utilization.

[0907] 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.

[0908] In this invention, the server includes a means for the user to input profile information using a terminal, a means for the server to receive and store the user's profile information, a means for the server to send prompt messages to the generating AI model, a means for the generating AI model to analyze and select match candidates, and a means for the server to present the match candidates to the user. This enables highly accurate matching based on the user's detailed profile information and desired conditions, thereby improving user satisfaction. In addition, comprehensive matching support can be provided through AI-based appearance and conversation ability assessments.

[0909] "User" refers to an individual who uses the System to enter profile information and search for potential matches.

[0910] "Terminal" means the electronic device used by a User to access the System and enter Profile Information.

[0911] "Server" refers to the central processing unit that stores the profile information received from users, sends prompts to the generative AI model, and selects match candidates.

[0912] "Profile information" refers to personal information such as name, age, hobbies, and desired partner criteria that users enter into the system.

[0913] "Generative AI model" refers to an artificial intelligence model that analyzes the database based on prompt statements received from the server and selects matching candidates.

[0914] A "prompt" refers to an instruction that includes the user's profile information and desired conditions, which the server sends to the generative AI model.

[0915] "Matching candidates" refer to other users who meet the user's desired conditions and are analyzed and selected by the generative AI model.

[0916] "Matching support means" refers to a series of processes for selecting appropriate matching candidates based on the user's profile information and presenting them to the user.

[0917] "Profile diagnostic means" refers to a means for analyzing the profile information entered by a user and finding suitable matching candidates.

[0918] "Conversational ability diagnostic means" refers to a means for evaluating a user's conversational ability and using this information to aid in matching.

[0919] "Appearance assessment means" refers to means for analyzing image data provided by a user and generating an assessment of appearance.

[0920] MODE FOR CARRYING OUT THE INVENTION

[0921] This invention is a system in which a user inputs profile information using a terminal, a server receives and stores the information, and a generative AI model is used to select suitable match candidates and present them to the user. Specific embodiments of this system are described below.

[0922] 1. Enter your profile information

[0923] The user accesses the system's interface using a terminal, which displays information such as name, age, hobbies, and desired partner criteria. The user enters this information and clicks the "Submit" button.

[0924] 2. Saving your profile information

[0925] The server receives the profile information sent by the user and stores it in a database, where each user's profile information is organized and stored.

[0926] 3. Selection of matching candidates

[0927] The server generates a prompt based on the user's profile information and sends it to a generative AI model (e.g., GPT-4). The prompt includes the user's hobbies and desired conditions.

[0928] As an example of a specific prompt, if User A enters "My hobby is watching movies, and I would like a partner with the same hobby," the server will send the following prompt to the generative AI model:

[0929] User A's profile information:

[0930] Hobbies: Watching movies

[0931] Desired criteria: A partner with the same hobbies

[0932] Analyze other users' information in the database and select matching candidates whose hobby is watching movies.

[0933] 4. Analysis and selection of match candidates

[0934] The generative AI model analyzes the database based on the prompt received from the server, and as a result of the analysis, it lists other users whose hobby is watching movies.

[0935] 5. Presentation of matching candidates

[0936] The server presents the user with a list of match candidates received from the generative AI model, which the user can view via their device.

[0937] As a specific example, the generative AI model analyzes user information in the database and selects matching candidates such as "User ID: 002, Hobby: Watching Movies" and "User ID: 003, Hobby: Watching Movies." For User ID: 001, the server displays information such as "Matching Candidates: User ID: 002, User ID: 003" on the device. Users can review this information and contact candidates they are interested in.

[0938] This system enables highly accurate matching based on the user's detailed profile information and desired conditions, improving user satisfaction. It also provides comprehensive matching support through AI-based appearance and conversation ability assessments.

[0939] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0940] Step 1:

[0941] The user uses the device to enter their profile information.

[0942] Input: Profile information such as the user's name, age, hobbies, and partner preferences.

[0943] Specific operations: The user accesses the terminal interface, enters information in each field, and clicks the "Submit" button.

[0944] Output: The entered profile information is sent from the device to the server.

[0945] Step 2:

[0946] The server receives and stores the user's profile information.

[0947] Input: User profile information sent from the device.

[0948] Specific operation: The server stores the received profile information in a database. For example, it stores it as "User ID: 001, Hobby: Watching movies, Desired conditions: Partner with the same hobby."

[0949] Output: User profile information stored in a database.

[0950] Step 3:

[0951] The server sends a prompt to the generative AI model.

[0952] Input: User profile information stored in a database.

[0953] Specific operation: The server generates a prompt based on the user's profile information. For example, a prompt such as "User ID: 001's profile information: Hobbies: Watching movies, Desired conditions: Partner with the same hobby" is sent to the AI ​​model.

[0954] Output: The prompt sent to the generative AI model.

[0955] Step 4:

[0956] A generative AI model analyzes and selects match candidates.

[0957] Input: The prompt text sent by the server.

[0958] Specific operation: The generative AI model analyzes the database based on the prompt sentence and lists other users whose hobby is watching movies. For example, it selects matching candidates such as "User ID: 002, Hobby: Watching Movies" and "User ID: 003, Hobby: Watching Movies."

[0959] Output: A list of selected match candidates.

[0960] Step 5:

[0961] The server presents potential matches to the user.

[0962] Input: A list of match candidates received from a generative AI model.

[0963] Specific operation: The server presents a list of match candidates to the user. For example, information such as "Match candidates: User ID: 002, User ID: 003" is displayed on the terminal.

[0964] Output: A list of potential matches displayed on the user's device.

[0965] The above is the specific processing flow of the program of this system.

[0966] (Application example 1)

[0967] Next, a description will be given of Application Example 1 of Form 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."

[0968] Previous matching support systems were mainly limited to online matching and were unable to support matching between users in physical stores. Furthermore, it was difficult to provide opportunities for users with similar hobbies and preferences to meet in physical stores and enjoy shopping together. This limited the user's shopping experience and potentially reduced satisfaction.

[0969] 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.

[0970] In this invention, the server includes a matching support means using AI chat, an AI profile diagnosis means, a means for adjusting the AI ​​to the marriage consultation association's system to connect with people looking for marriage across the country, an AI conversation ability diagnosis means, an AI appearance diagnosis means (hair, eyebrows, fashion, skin type), a means for matching other users with the same preferences in the same store based on the profile information and preferences entered by the user, and a means for matched users to communicate with each other via chat. This enables users with the same hobbies and preferences to meet in a physical store and enjoy shopping together.

[0971] "AI chat matching support means" is a means of using artificial intelligence to select appropriate match candidates based on the profile information and preferences entered by the user, and to support communication between users via chat.

[0972] "AI profile diagnostic means" refers to a means that uses artificial intelligence to analyze the profile information provided by the user and evaluate the user's characteristics and preferences.

[0973] "A means of adjusting AI to the marriage consultation association's system and creating connections with people looking for marriage across the country" means adapting artificial intelligence to the marriage consultation association's system and creating connections with people looking for marriage across the country.

[0974] An "AI conversational ability diagnostic tool" is a tool that uses artificial intelligence to evaluate a user's conversational ability and provide diagnostic results.

[0975] The "AI Appearance Diagnosis (Hair, Eyebrows, Fashion, Skin Type) Method" is a method that uses artificial intelligence to analyze image data provided by users and generate an evaluation of their appearance, such as hair, eyebrows, fashion, and skin type.

[0976] "A means of matching other users with the same preferences within the same store based on the profile information and preferences entered by the user" refers to a means of using artificial intelligence to select and match other users with the same preferences within the same physical store based on the profile information and preferences entered by the user.

[0977] "Means by which matched users can communicate with each other via chat" means means by which matched users can communicate with each other via chat within the application.

[0978] The following system configuration is used as an embodiment of the present invention.

[0979] System Configuration

[0980] The system includes a user's smartphone, a server, and a generative AI model. Users use their smartphone to input their profile information and preferences, and the server selects potential matches based on this information.

[0981] Hardware and software used

[0982] Hardware: Smartphones, servers

[0983] Software: Python, OpenAI API

[0984] Data processing and calculation

[0985] 1. Enter your profile information:

[0986] Users enter profile information such as hobbies, preferences, and favorite brands through a smartphone application.

[0987] 2. Submitting Profile Information:

[0988] The smartphone application sends the entered profile information to the server.

[0989] 3. AI-based selection of match candidates:

[0990] The server sends a prompt to OpenAI's generative AI model (text-davinci-003) based on the received profile information. The prompt has the following format:

[0991] User profile information: Hobbies: Watching movies, Favorite brand: Brand A, Product category: Casual wear

[0992] Find other users with similar interests.

[0993] 4. Acquiring matching candidates:

[0994] The generative AI model selects other users with similar preferences based on the prompt sentence and returns the results to the server.

[0995] 5. Providing chat functionality:

[0996] The server allows matched users to communicate with each other through chat within the application.

[0997] Specific examples

[0998] For example, if User A inputs "My hobby is watching movies, my favorite brand is Brand A, and my product category is casual wear," the server sends this information to the generative AI model. The generative AI model selects User B who has the same preferences in the same store and returns this information to the server. The server then enables User A and User B to communicate via chat within the application.

[0999] In this way, users with the same hobbies and preferences can meet in physical stores and enjoy shopping together.

[1000] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1001] Step 1:

[1002] A user launches a smartphone application and enters profile information and preferences, such as hobbies, favorite brands, and product categories. The entered information is temporarily stored on the smartphone device.

[1003] Step 2:

[1004] The smartphone device sends the entered profile information to the server. The data sent includes the user's hobbies, favorite brands, product categories, etc. The server analyzes the received data and converts it into an appropriate format.

[1005] Step 3:

[1006] The server creates a prompt for the generative AI model based on the received profile information. The prompt has the following format:

[1007] User profile information: Hobbies: Watching movies, Favorite brand: Brand A, Product category: Casual wear

[1008] Find other users with similar interests.

[1009] This prompt is sent to the generative AI model.

[1010] Step 4:

[1011] The generative AI model selects other users with the same preferences based on the prompt. Specifically, the generative AI model analyzes the input prompt and searches a database for users with the same hobbies and preferences. The search results are returned to the server.

[1012] Step 5:

[1013] The server receives the search results returned by the generative AI model and creates a list of matching candidates. The list includes information on users who share the same preferences in the same store. The server then sends this list to the smartphone device.

[1014] Step 6:

[1015] The smartphone device displays the received list of match candidates to the user, who can then select a match candidate from the displayed list and start chatting.

[1016] Step 7:

[1017] The smartphone device starts a chat session with the selected match candidate. During the chat session, users can exchange messages in real time. Chat data is sent and received via the server.

[1018] Through the above steps, users with the same hobbies and preferences can meet in a physical store and enjoy shopping together.

[1019] Example 2

[1020] Next, a description will be given of Example 2 of Form 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."

[1021] Conventional matching and profile diagnostic systems have difficulty accurately analyzing users' personalities and values, and have also had problems with the accuracy and reliability of the analysis results when providing them to users. Furthermore, there has been a lack of methods for effectively utilizing user profile information to perform matching.

[1022] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for collecting profile information input by the user, a means for preprocessing the collected profile information to input it into the generative AI model, a means for analyzing the user's personality and values ​​using the generative AI model, and a means for providing the analysis results to the user. This makes it possible to analyze the user's personality and values ​​with high accuracy and provide the results to the user.

[1023] "User" refers to an individual who uses the System to enter profile information and receive analysis results.

[1024] "Profile information" refers to the self-introduction and information about interests entered by the user.

[1025] "Generative AI model" refers to an artificial intelligence model used to analyze a user's profile information and infer their personality and values.

[1026] "Preprocessing" refers to the process of converting collected profile information into a format suitable for input into a generative AI model.

[1027] "Analysis results" refers to the results of the generative AI model's estimation of the user's personality and values.

[1028] "Matching score" refers to the score calculated by comparing the user's profile information with the profile information of the person seeking a match.

[1029] "Image data" refers to photographs and image files provided by users.

[1030] "Evaluation of appearance" refers to the evaluation results of a user's appearance obtained by analyzing image data.

[1031] MODE FOR CARRYING OUT THE INVENTION

[1032] This invention is a system that uses a generative AI model to analyze a user's personality and values ​​based on profile information entered by the user, and provides the results to the user. Specific embodiments of this system are described below.

[1033] 1. Enter your profile information

[1034] Users use their devices to enter their profile information. The devices provide an interface through a web browser or dedicated application that allows users to enter information about themselves and their interests. For example, a user might enter, "I love movies, and I'm particularly interested in independent films. I always go to the cinema on the weekends to check out new releases."

[1035] 2. Data transmission

[1036] The device sends the profile information entered by the user to the server using the HTTPS protocol to ensure data security, thereby protecting the user's privacy.

[1037] 3. Data preparation for analysis

[1038] The server converts the received profile information into a format suitable for the generative AI model to analyze it. Specifically, it tokenizes the text data and performs preprocessing as needed. For example, it uses the Python NLTK library to tokenize the text.

[1039] 4. Analysis using generative AI models

[1040] The server inputs the preprocessed data into a generative AI model, such as OpenAI's GPT-4, which analyzes the user's personality and values ​​from the input text and generates inferences.

[1041] 5. Obtaining analysis results

[1042] The server receives the analysis results from the generative AI model, which include information about the user's personality and values. For example, it may learn that the user has a strong passion for movies and is particularly interested in independent films.

[1043] 6. Providing Results

[1044] The server provides the analysis results to the user, who can then check them through their device. For example, a message such as "You have a strong passion for film, and are particularly interested in independent films" will appear on the screen of a web browser or dedicated application.

[1045] Examples of concrete examples and prompts

[1046] Specific examples

[1047] Suppose a user enters the following self-description:

[1048] "I love movies, and I'm especially interested in independent films. I always go to the cinema on the weekends to check out new releases."

[1049] In this case, the generative AI model analyzes it as follows:

[1050] Users have a strong passion for movies.

[1051] I'm particularly interested in independent films.

[1052] He places great importance on watching movies as a way to spend his weekends.

[1053] Prompt Sentence Examples

[1054] An example of a prompt to input to the generative AI model is:

[1055] "A user has entered the following self-introduction: 'I love movies, and I'm particularly interested in independent films. I always go to the cinema on the weekends to check out new releases.' Use this information to analyze the user's personality and values."

[1056] Using this prompt, the generative AI model can perform a detailed analysis of the user's personality and values ​​and provide appropriate feedback.

[1057] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1058] Step 1:

[1059] Users enter their profile information using their device. Specifically, they enter a self-introduction and information about their interests through the device's web browser or a dedicated application. The entered information is stored in text format on the device.

[1060] Input: Profile information you enter (e.g., "I love movies, especially independent films.")

[1061] Output: Text data saved on the device

[1062] Step 2:

[1063] The device sends the profile information entered by the user to the server using the HTTPS protocol to ensure data security, and the sent data is stored on the server.

[1064] Input: Text data stored on the device

[1065] Output: Text data sent to the server

[1066] Step 3:

[1067] The server converts the received profile information into a format suitable for the generative AI model to analyze it. Specifically, it tokenizes the text data and performs preprocessing as needed. For example, it uses the Python NLTK library to tokenize the text.

[1068] Input: Text data stored on the server

[1069] Output: Tokenized data

[1070] Step 4:

[1071] The server inputs the preprocessed data into a generative AI model, such as OpenAI's GPT-4. The generative AI model analyzes the user's personality and values ​​from the input text and generates an inference.

[1072] Input: Tokenized data

[1073] Output: Analysis results from the generative AI model

[1074] Step 5:

[1075] The server receives the analysis results from the generative AI model. The analysis results include information about the user's personality and values. For example, information such as "The user has a strong passion for movies" and "They are particularly interested in independent films" may be obtained.

[1076] Input: Analysis results from generative AI model

[1077] Output: Analysis result data

[1078] Step 6:

[1079] The server provides the analysis results to the user, who can then check them through their device. For example, a message such as "You have a strong passion for movies, and are particularly interested in independent films" will appear on the screen of a web browser or dedicated application.

[1080] Input: Analysis result data

[1081] Output: Analysis results provided to the user

[1082] (Application example 2)

[1083] Next, a description will be given of Application Example 2 of Form 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."

[1084] Conventional matching systems and profile diagnostic systems have the problem of being unable to recommend products that fully reflect the user's personality and values. In addition, product recommendations are rarely based on the user's profile information, making it difficult to find the best product for the user.

[1085] The specific processing by the specific 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 a matching support means using AI chat, an AI profile diagnosis means, a means for adjusting the AI ​​to the marriage consultation association's system to connect with marriage-seeking people nationwide, an AI conversation ability diagnosis means, an AI appearance diagnosis means (hair, eyebrows, fashion, skin type), a means for recommending products based on user profile information, a means for analyzing the user's personality and values ​​using a generative AI model, and a means for generating prompt sentences. This enables product recommendations that reflect the user's personality and values.

[1086] "AI chat matching support means" is a means of using artificial intelligence to support communication between users and make appropriate matches.

[1087] "AI profile diagnostic means" is a method in which artificial intelligence analyzes a user's personality and values ​​based on the profile information entered by the user.

[1088] "A means of adjusting AI to the marriage consultation association's system and creating connections with people looking for marriage across the country" refers to a means of adapting artificial intelligence to the marriage consultation association's system and creating connections with people looking for marriage across the country.

[1089] An "AI conversational ability diagnostic tool" is a tool that uses artificial intelligence to diagnose a user's conversational ability.

[1090] "AI appearance diagnosis (hair, eyebrows, fashion, skin type) method" is a method that uses artificial intelligence to diagnose a user's appearance (hair, eyebrows, fashion, skin type).

[1091] "Means for recommending products based on user profile information" refers to means for recommending appropriate products based on the profile information entered by the user.

[1092] "Means for analyzing a user's personality and values ​​using a generative AI model" means means for analyzing a user's personality and values ​​using a generative artificial intelligence model.

[1093] "Means for generating prompt sentences" means means for using artificial intelligence to generate prompt sentences based on the user's profile information and other data.

[1094] A system for carrying out this invention has the following configuration: The server includes a matching support means using AI chat, an AI profile diagnosis means, a means for adjusting the AI ​​to the marriage consultation association's system to connect with people looking for marriage across the country, an AI conversation ability diagnosis means, an AI appearance diagnosis means (hair, eyebrows, fashion, skin type), a means for recommending products based on user profile information, a means for analyzing the user's personality and values ​​using a generative AI model, and a means for generating prompt sentences.

[1095] Hardware and Software

[1096] This system uses the following hardware and software:

[1097] Hardware: Servers, smartphones

[1098] Software: Python, OpenAI API

[1099] Data processing and calculation

[1100] The server receives the profile information entered by the user and analyzes it using a generative AI model. Specifically, it generates the following prompt text and performs the analysis using OpenAI's generative AI model (text-davinci-003).

[1101] Example prompt sentence:

[1102] User profile information: In the self-introduction, users wrote about their favorite movies and their thoughts on watching movies.

[1103] Use this information to analyze your users' personalities and values.

[1104] Based on the generated prompt, the generative AI model analyzes the user's personality and values ​​and returns the results to the server, which then uses the analysis results to recommend the most suitable products for the user.

[1105] Specific examples

[1106] For example, if a user writes, "In my self-introduction, I wrote about my favorite movies and my thoughts on watching movies," the generative AI model can determine from that information that the user has a deep passion for movies. Based on this analysis, the server can recommend movie-related products to the user (e.g., movie merchandise, the latest movie DVDs, comfortable movie chairs, etc.).

[1107] In this way, it becomes possible to recommend products that reflect the user's personality and values.

[1108] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1109] Step 1:

[1110] The user uses the device to enter profile information, including a self-introduction, hobbies, interests, etc. The entered data is then sent to the server.

[1111] Step 2:

[1112] The server generates a prompt based on the received profile information. Specifically, it creates the following prompt based on the user's input data:

[1113] User profile information: In the self-introduction, users wrote about their favorite movies and their thoughts on watching movies.

[1114] Use this information to analyze your users' personalities and values.

[1115] The generated prompt sentences are sent to a generative AI model.

[1116] Step 3:

[1117] The server uses a generative AI model (text-davinci-003) to analyze the user's personality and values ​​based on the prompt text. The generative AI model takes the prompt text as input and outputs the analysis results on the user's personality and values.

[1118] Step 4:

[1119] The server receives the analysis results from the generative AI model and recommends the most suitable products to the user based on the results. For example, if the analysis shows that the user has a deep passion for movies, it will recommend movie-related products (movie merchandise, the latest movie DVDs, comfortable chairs for watching movies, etc.).

[1120] Step 5:

[1121] The server sends the recommended product information to the terminal, which then displays the received product information to the user. The user can then review the displayed product information and select the product that interests them.

[1122] Step 6:

[1123] When a user selects a product they are interested in, the device sends the selection information to the server, which uses the selection information to record the user's purchase history and interests and use them for future recommendations.

[1124] In this way, product recommendations based on user profile information are realized.

[1125] Example 3

[1126] Next, a description will be given of Example 3 of Form Example 3. 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."

[1127] Conventional matching and appearance diagnosis systems have difficulty accurately evaluating users' appearances and profile information to ensure appropriate matching. Furthermore, they lack the means to effectively analyze the image data provided by users and generate detailed appearance evaluations. This has led to problems such as low user satisfaction and sluggish system usage.

[1128] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1129] In this invention, the server includes means for users to upload image data, means for the server to receive and preprocess the image data, means for the server to extract feature data from the preprocessed image, means for the server to input the feature data into a generative AI model and generate an evaluation result, means for the server to return the evaluation result to the user, means for supporting matching through AI chat, means for diagnosing a profile using AI, means for adjusting the AI ​​to the marriage consultation association's system to connect with people looking for marriage across the country, means for diagnosing AI conversation skills, and means for diagnosing AI appearance (hair, eyebrows, fashion, skin type).This enables accurate evaluation of a user's appearance and profile information and appropriate matching.

[1130] "Means for users to upload image data" refers to the interface or function that allows users to send their own image data to the system.

[1131] "Means for the server to receive and pre-process image data" refers to the function by which the server receives image data sent by the user and processes it to convert it into a format suitable for analysis.

[1132] "Means for the server to extract feature data from preprocessed images" refers to the function of identifying features such as hairstyle, eyebrow shape, fashion, and skin type from preprocessed image data and extracting them as data.

[1133] "Means for the server to input feature data into the generative AI model and generate an evaluation result" refers to the function of inputting extracted feature data into an AI model and generating an evaluation of appearance.

[1134] "Means by which the server returns the evaluation results to the user" refers to the function for notifying and displaying the generated evaluation results to the user.

[1135] "AI chat matching support means" refers to a function that uses AI to support communication between users and make appropriate matches.

[1136] "AI profile diagnostic means" refers to a function in which AI analyzes a user's profile information and generates diagnostic results.

[1137] "A means of adjusting AI to the marriage consultation association's system to connect with people looking for marriage across the country" refers to a function that uses AI to link with marriage consultation agency systems and connect with people looking for marriage across the country.

[1138] "AI conversational ability diagnostic means" refers to a function in which AI analyzes a user's conversational ability and generates a diagnostic result.

[1139] "AI appearance diagnosis (hair, eyebrows, fashion, skin type) means" refers to a function in which AI analyzes the user's appearance characteristics and generates an evaluation of hairstyle, eyebrow shape, fashion, skin type, etc.

[1140] This invention is a system for allowing users to evaluate their own appearance, and specifically includes a process in which a user uploads image data and a server analyzes the image data to generate an evaluation result.

[1141] Hardware and software used

[1142] Hardware:

[1143] Server: A server with high-performance computing power

[1144] Device: The smartphone or PC used by the user

[1145] software:

[1146] Image processing library: OpenCV

[1147] Machine learning framework: TensorFlow or PyTorch

[1148] Data processing and calculation

[1149] Receiving and preprocessing image data:

[1150] When a user uploads a photo from their device, the server receives the image data, which is then pre-processed using OpenCV, including image resizing, noise removal, and facial extraction.

[1151] Feature data extraction:

[1152] From the pre-processed images, the server extracts feature data such as hairstyle, eyebrow shape, fashion, and skin type using face detection algorithms and image analysis techniques.

[1153] Input to generative AI model and generation of evaluation results:

[1154] The extracted feature data is input into a generative AI model built with TensorFlow and PyTorch. The AI ​​model generates an evaluation for each element based on this feature data. For example, for hairstyle, it might generate an evaluation such as "short hair suits you."

[1155] Returning the evaluation result:

[1156] The generated evaluation results are returned from the server to the user's device, where the user can check the evaluation results. The evaluation results are displayed in text and graph format.

[1157] Specific examples

[1158] When a user uploads a photo of themselves from their smartphone, the following happens:

[1159] 1. The user opens the app on their smartphone, selects a photo, and presses the upload button.

[1160] 2. The server receives the image data and stores it in storage.

[1161] 3. The server resizes the image using OpenCV and cuts out the face area.

[1162] 4. The server identifies the hairstyle area and analyzes its shape and color.

[1163] 5. The server inputs the extracted feature data into the TensorFlow model.

[1164] 6. The model generates a rating, such as "short hair looks good."

[1165] 7. The server returns the evaluation results to the user's smartphone, and the results are displayed on the app.

[1166] Prompt Sentence Examples

[1167] "Evaluate hairstyles, eyebrow shapes, fashion, and skin types based on photos uploaded by users."

[1168] In this way, the user can receive a detailed evaluation of his / her appearance. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1169] Step 1:

[1170] Users upload their own photos from their devices.

[1171] Input: An image file selected by the user.

[1172] Output: Image data sent to the server

[1173] Specific operation: The user opens the application on their smartphone or PC, selects a photo, and presses the upload button, which sends the image data to the server.

[1174] Step 2:

[1175] The server receives the image data and stores it in storage.

[1176] Input: Image data sent by the user

[1177] Output: Image data stored in the server storage

[1178] Specific operation: The server receives the HTTP request and temporarily stores the image data in storage. The stored image data is used in the next processing step.

[1179] Step 3:

[1180] The server preprocesses the image data.

[1181] Input: Image data stored in storage

[1182] Output: Preprocessed image data

[1183] How it works: The server uses OpenCV to resize the image, remove noise, and extract the facial region, generating image data in a format suitable for analysis.

[1184] Step 4:

[1185] The server extracts feature data from the preprocessed images.

[1186] Input: Preprocessed image data

[1187] Output: Extracted feature data (hairstyle, eyebrow shape, fashion, skin type, etc.)

[1188] Specific operation: The server uses a face detection algorithm to identify features such as hairstyle, eyebrow shape, fashion, and skin type and extract them as data.

[1189] Step 5:

[1190] The server inputs the feature data into the generative AI model and generates evaluation results.

[1191] Input: extracted feature data

[1192] Output: Generated evaluation results

[1193] How it works: The server inputs the extracted feature data into a generative AI model built with TensorFlow or PyTorch. The AI ​​model generates an evaluation for each element based on this feature data. For example, for hairstyle, it might generate an evaluation such as "short hair suits you."

[1194] Step 6:

[1195] The server returns the evaluation results to the user.

[1196] Input: Generated evaluation results

[1197] Output: The evaluation results displayed on the user's device

[1198] Specific operation: The server returns the generated evaluation results to the user's device. The user can check the evaluation results through the device. The evaluation results are displayed in text and graph format.

[1199] In this way, users can receive a detailed assessment of their appearance.

[1200] (Application example 3)

[1201] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1202] Conventional matching and appearance diagnosis systems are limited to matching support based on users' appearance evaluations and profile information, and lack specific suggestions to improve the shopping experience in physical stores. In addition, users face the challenge of finding the best fashion items for themselves in physical stores and receiving expert advice.

[1203] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1204] In this invention, the server includes a matching support means using AI chat, an AI profile diagnosis means, a means for adjusting the AI ​​to the marriage consultation association's system to connect with people looking for marriage across the country, an AI conversation ability diagnosis means, an AI appearance diagnosis means (hair, eyebrows, fashion, skin type), and a means for analyzing the user's appearance and suggesting the most suitable fashion items from products in a physical store. This allows the user to receive specific suggestions to improve their shopping experience in the physical store.

[1205] "AI chat matching support means" is a means of using artificial intelligence to support chats between users and those seeking a match, and to promote appropriate matching.

[1206] An "AI-based profile diagnostic method" is a method that uses artificial intelligence to analyze a user's profile information and generate diagnostic results based on that information.

[1207] "A means of adjusting AI to the marriage consultation association's system and creating connections with people looking for marriage across the country" means adapting artificial intelligence to the marriage consultation association's system and creating connections with people looking for marriage across the country.

[1208] An "AI conversational ability diagnostic tool" is a tool that uses artificial intelligence to evaluate a user's conversational ability and diagnose the results.

[1209] "AI appearance diagnosis (hair, eyebrows, fashion, skin type) method" is a method that uses artificial intelligence to evaluate a user's appearance (hairstyle, eyebrow shape, fashion, skin type) and diagnose the results.

[1210] "A means for analyzing a user's appearance and suggesting the most suitable fashion item from products available in a physical store" refers to a means for analyzing a user's appearance using artificial intelligence and suggesting the most suitable fashion item from products available in a physical store based on the results of that analysis.

[1211] To implement the present invention, the following system configuration and processing procedures are required.

[1212] System Configuration

[1213] 1. Hardware:

[1214] Smartphone: A device that allows users to upload photos and receive suggestions.

[1215] Server: A computer system for hosting the AI ​​model and performing image analysis and proposal generation.

[1216] 2. Software:

[1217] Python: A programming language used to implement programs.

[1218] OpenCV: Image processing library.

[1219] Keras: A deep learning library.

[1220] Generative AI model: A model that analyzes the user's appearance and suggests the most suitable fashion items.

[1221] Processing steps

[1222] 1. User photo upload:

[1223] Users take photos of themselves using their smartphones and upload them to the application.

[1224] 2. Image preprocessing:

[1225] The server receives the uploaded image and resizes it to the appropriate size using OpenCV.

[1226] 3. Image Analysis:

[1227] The server loads a generative AI model using Keras and inputs the resized image into the model.

[1228] The model analyzes the user's hairstyle, eyebrow shape, fashion, skin type, etc. and generates a rating for each element.

[1229] 4. Fashion item suggestions:

[1230] Based on the generated ratings, the server selects the most suitable fashion items from a product database within the physical store.

[1231] The selected items will be displayed as suggestions on the user's smartphone.

[1232] Specific examples

[1233] While shopping in a physical store, users upload their own photos to the app. The server receives the photos and resizes them using OpenCV. It then loads a generative AI model using Keras and inputs the resized image into the model. The model analyzes the user's appearance and evaluates their hairstyle, eyebrow shape, fashion, skin type, etc. Based on the evaluation results, the server selects the most suitable fashion items from the store's product database and displays them as suggestions on the user's smartphone.

[1234] Prompt Sentence Examples

[1235] "Upload your photo and let AI analyze your hairstyle, eyebrow shape, fashion, and skin type to suggest the best fashion items for you."

[1236] In this way, users can receive specific suggestions to improve their shopping experience in-store.

[1237] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1238] Step 1:

[1239] Users take photos of themselves using their smartphones and upload them to the application.

[1240] Input: User photo

[1241] Output: Uploaded photo data

[1242] Specific operation: A user starts the application, takes a photo using the camera function, and then presses the upload button in the application to send the photo to the server.

[1243] Step 2:

[1244] The server receives the uploaded photo and uses OpenCV to resize the image to the appropriate size.

[1245] Input: Uploaded photo data

[1246] Output: Resized image data

[1247] Specific operation: The server resizes the received photo data to 224x224 pixels using the OpenCV library. The resized image data is passed to the next analysis step.

[1248] Step 3:

[1249] The server loads a generative AI model using Keras and inputs the resized image into the model.

[1250] Input: Resized image data

[1251] Output: Appearance evaluation results

[1252] How it works: The server loads a pre-trained generative AI model using the Keras library into memory, inputs the resized image data into the model, and generates appearance evaluation results such as hairstyle, eyebrow shape, fashion, and skin type.

[1253] Step 4:

[1254] Based on the generated ratings, the server selects the most suitable fashion items from a product database within the physical store.

[1255] Input: Appearance evaluation results

[1256] Output: A list of the best fashion items

[1257] Specific operation: The server analyzes the appearance evaluation results and compares them with the product database in the physical store. Based on the evaluation results, it selects the most suitable fashion items for the user and generates a list of them.

[1258] Step 5:

[1259] The server sends a list of selected fashion items to the user's smartphone and displays them as suggestions.

[1260] Input: A list of the best fashion items

[1261] Output: Suggestions displayed on the user's smartphone

[1262] Specific operation: The server sends a list of selected fashion items to the user's smartphone. The application displays the received list in an easy-to-read format for the user and presents it as specific suggestions.

[1263] In this way, users can receive specific suggestions to enhance their shopping experience in a physical store.

[1264] 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.

[1265] "Example 1"

[1266] One embodiment of the present invention is a system incorporating an emotion engine. This system recognizes a user's emotions and uses those emotions as reference information for matching. Specifically, when a user accesses an application and enters their profile information and preferences, the emotion engine recognizes the user's emotions. For example, the emotion engine recognizes the user's emotions from the keyboard pressure and touch speed when the user enters their profile information, or from the user's facial expressions and tone of voice when using the application. The emotion engine then uses those emotions as reference information for matching.

[1267] "Example 2"

[1268] Furthermore, systems incorporating an emotion engine reflect the user's emotions in the calculation of the matching score. Specifically, when the AI ​​analyzes the information entered by the user and selects match candidates, it reflects the user's emotions recognized by the emotion engine. For example, if the user expresses emotions of joy or excitement, it will increase the scores of match candidates that are deemed to match the user's preferences. Conversely, if the user expresses emotions of displeasure or anger, it will lower the scores of match candidates that are deemed not to match the user's preferences.

[1269] "Example 3"

[1270] Furthermore, in a system that incorporates an emotion engine, the user's emotions are reflected in the generation of an appearance rating. Specifically, image data provided by the user is analyzed, and when generating an appearance rating, the emotion engine reflects the user's emotions recognized by the emotion engine. For example, if the user shows emotions of joy or excitement, the rating of the user's appearance is increased. Conversely, if the user shows emotions of displeasure or anger, the rating of the user's appearance is decreased.

[1271] The processing flow of each embodiment will be described below.

[1272] "Example 1"

[1273] Step 1: The user accesses the application and enters their profile information and preferences.

[1274] Step 2: The emotion engine recognizes the user's emotions, specifically from the pressure and speed of the keyboard when the user enters profile information, or from facial expressions and tone of voice when the user uses the application.

[1275] Step 3: Use the recognized emotions as reference information for matching.

[1276] "Example 2"

[1277] Step 1: The AI ​​analyzes the information entered by the user and selects matching candidates.

[1278] Step 2: The emotion engine reflects the user's emotions in the calculation of the match score. For example, if the user expresses happiness or excitement, the score of the match candidate that is likely to match the user's preferences will be increased. Conversely, if the user expresses displeasure or anger, the score of the match candidate that is likely to not match the user's preferences will be decreased.

[1279] "Example 3"

[1280] Step 1: Analyze the image data provided by the user and generate an appearance rating.

[1281] Step 2: The emotion engine reflects the user's emotions in generating a rating for their appearance. For example, if the user expresses happiness or excitement, the rating for their appearance will be higher. Conversely, if the user expresses displeasure or anger, the rating for their appearance will be lower.

[1282] Example 1

[1283] Next, a description will be given of Example 1 of Form 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."

[1284] Conventional matching systems mainly match users based on their profile information and preferences, but do not take into account their emotional state. This makes it difficult to achieve optimal matching based on the user's emotional state, resulting in problems such as reduced matching accuracy and satisfaction. Furthermore, the system does not adequately assess the user's appearance or conversational ability, resulting in a lack of comprehensive matching support.

[1285] 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.

[1286] In this invention, the server includes a matching support system using AI chat, an AI profile diagnostic system, a system for adjusting the AI ​​to the marriage agency association's system to connect with people looking for marriage across the country, an AI conversational ability diagnostic system, an AI appearance diagnostic system, an emotion recognition system that recognizes the user's emotions and uses those emotions as reference information for matching, and a generative AI model that analyzes the user's input data and selects match candidates. This enables optimal matching that takes the user's emotional state into consideration, improving matching accuracy and satisfaction. Comprehensive matching support can also be achieved by assessing the user's appearance and conversational ability.

[1287] "AI chat matching support means" is a means in which artificial intelligence selects matching candidates based on the profile information and preferences entered by the user.

[1288] "AI profile diagnostic means" is a means of analyzing the profile information provided by the user and diagnosing the user's characteristics and preferences.

[1289] "A means of adjusting AI to the marriage consultation association's system and creating connections with people looking for marriage across the country" means adapting artificial intelligence to the marriage consultation association's system and creating connections with people looking for marriage across the country.

[1290] An "AI conversational ability diagnostic tool" is a tool that uses artificial intelligence to analyze and evaluate a user's conversational ability.

[1291] The "AI appearance diagnosis means" is a means for analyzing image data provided by a user and generating an evaluation of appearance.

[1292] The "emotion recognition means" is a means for recognizing the user's emotions and using the emotions as reference information for matching.

[1293] A "generative AI model" is an artificial intelligence model that analyzes user input data and selects matching candidates.

[1294] MODE FOR CARRYING OUT THE INVENTION

[1295] This invention is a system in which artificial intelligence (AI) selects matching candidates based on the user's profile information and preferences. Furthermore, by combining it with emotion recognition means, matching is realized that takes into account the user's emotional state.

[1296] Hardware and software used

[1297] This system uses the following hardware and software:

[1298] Hardware: Devices (PCs, smartphones, etc.) that receive user input and servers that process the data

[1299] software:

[1300] Generative AI models (e.g., GPT-4)

[1301] Emotion recognition engine (e.g. Affectiva SDK)

[1302] System operation explanation

[1303] Users access the application using their device and enter their profile information and preferences. The device then sends the entered data to the server, which then sends the received data to the generative AI model to select potential matches.

[1304] Furthermore, the server uses an emotion recognition engine to collect users' emotional data, such as keyboard pressure and speed, facial expressions, and tone of voice, when users enter their profile information. The server then sends the collected emotional data to the generative AI model to adjust the matching results.

[1305] Finally, the server sends the matching results received from the generative AI model to the terminal, which then displays the results to the user.

[1306] Specific examples

[1307] Consider the case where a user accesses an application and types the following:

[1308] Example user input:

[1309] Profile information: "My hobby is watching movies"

[1310] Preference: "I would like a partner who has the same hobbies as me."

[1311] Emotion data collection example:

[1312] Keyboard touch pressure: Strong

[1313] Touch Speed: Fast

[1314] Expression: Smiling

[1315] Tone of voice: bright

[1316] Example prompt sentence:

[1317] User profile information: Hobbies: Watching movies

[1318] User preference: I want a partner with the same hobbies as me

[1319] Emotion data: strong keyboard touch pressure, fast touch speed, smiling face, bright voice tone

[1320] The server sends this data to a generative AI model and an emotion recognition engine, and the generative AI model selects matching candidates whose hobby is watching movies. The emotion recognition engine also analyzes the user's emotions and uses them as reference information for matching. Finally, the server sends the matching results to the device, which displays the results to the user.

[1321] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1322] Step 1:

[1323] The user enters profile information and preferences.

[1324] Input: User profile information (e.g., hobbies, age, gender) and preferences (e.g., partner requirements)

[1325] Output: Input data (profile information and preferences)

[1326] What happens: A user accesses an application and enters their profile information and preferences into an on-screen form.

[1327] Step 2:

[1328] The terminal sends the input data to the server.

[1329] Input: Data entered by the user (profile information and preferences)

[1330] Output: Data sent to the server

[1331] What it does: The device sends the data entered by the user to the server in real time using the HTTPS protocol.

[1332] Step 3:

[1333] The server receives the data and sends it to the generative AI model.

[1334] Input: Data sent from your device (profile information and preferences)

[1335] Output: Data sent to the generative AI model

[1336] Specific operation: The server analyzes the received data, converts it into a format suitable for the generative AI model, and sends it.

[1337] Step 4:

[1338] A generative AI model selects match candidates.

[1339] Input: Data sent from the server (profile information and preferences)

[1340] Output: A list of potential matches

[1341] How it works: Based on a user's profile information and preferences, the generative AI model selects potential matches from other users in its database, using natural language processing techniques.

[1342] Step 5:

[1343] The server collects user emotion data using an emotion recognition engine.

[1344] Input: User input behavior data (keyboard pressure, touch speed, facial expressions, tone of voice, etc.)

[1345] Output: Emotion data

[1346] Specific behavior: The server uses an emotion recognition engine to analyze the user's behavior data during input and collect emotion data.

[1347] Step 6:

[1348] The server sends the emotion data to the generative AI model.

[1349] Input: Emotion data

[1350] Output: Emotion data sent to the generative AI model

[1351] Specific operation: The server sends the collected emotion data to the generative AI model, which uses it to adjust the matching results.

[1352] Step 7:

[1353] The generative AI model uses emotional data to adjust the matching results.

[1354] Input: Emotion data, list of matching candidates

[1355] Output: Adjusted matching results

[1356] How it works: The generative AI model uses emotional data to refine the list of potential matches. For example, if the user is relaxed, it will consider that emotional state when selecting potential matches.

[1357] Step 8:

[1358] The server sends the final matching results to the device.

[1359] Input: Adjusted matching results

[1360] Output: Matching results sent to the terminal

[1361] Specific operation: The server sends the final matching results received from the generative AI model to the terminal.

[1362] Step 9:

[1363] The terminal displays the matching results to the user.

[1364] Input: Matching results sent from the server

[1365] Output: Matching results displayed to the user

[1366] Specific operations: The terminal displays the matching results received from the server to the user. The user can check the profile information of the displayed matching candidates and select the next action.

[1367] (Application example 1)

[1368] Next, a description will be given of Application Example 1 of Form 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."

[1369] Conventional matching support systems selected match candidates based solely on the user's profile information and preferences, making it impossible to match candidates with consideration for the user's emotions or mood at the time. Furthermore, virtual stores did not consider the user's emotions when proposing products, making it difficult to increase user satisfaction. This resulted in issues such as being unable to fully meet the user's needs and reducing the accuracy of matching and product suggestions.

[1370] 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.

[1371] In this invention, the server includes a matching support means using AI chat, an AI profile diagnosis means, a means for adjusting the AI ​​to the marriage consultation association's system to connect with people looking for marriage across the country, an AI conversation ability diagnosis means, an AI appearance diagnosis means (hair, eyebrows, fashion, skin type), a means for analyzing the user's emotions using an emotion recognition engine, and a means for proposing optimal products based on the user's profile information, preferences, and emotions. This makes it possible to perform matching and suggest products that take the user's emotions into consideration.

[1372] "AI chat matching support method" is a method in which artificial intelligence selects the most suitable matching candidate based on the user's profile information and preferences.

[1373] An "AI-based profile diagnostic method" is a method that analyzes the profile information entered by the user and diagnoses the user's characteristics and preferences based on that information.

[1374] "A means of adjusting AI to the marriage consultation association's system and making contact with people looking for marriage across the country" means adapting artificial intelligence to the marriage consultation association's system and making contact with people looking for marriage across the country.

[1375] An "AI conversational ability diagnostic tool" is a method in which artificial intelligence analyzes a user's conversational ability and evaluates them based on the results.

[1376] The "AI appearance diagnosis (hair, eyebrows, fashion, skin type) method" is a method that analyzes image data provided by the user and generates an evaluation of appearance, such as hairstyle, eyebrows, fashion, and skin type.

[1377] "Means for analyzing a user's emotions using an emotion recognition engine" refers to means for using an emotion recognition engine to analyze emotions from a user's facial expressions, tone of voice, keyboard touch pressure and touch speed, etc.

[1378] "Means for suggesting optimal products based on a user's profile information, preferences, and emotions" refers to a means by which artificial intelligence suggests optimal products based on a user's profile information, preferences, and emotional data.

[1379] As an embodiment of the present invention, the following system is constructed.

[1380] System configuration

[1381] 1. Hardware

[1382] Server: A server for hosting the database and AI models.

[1383] Device: Smartphone, smart glasses, head-mounted display for user access.

[1384] 2. Software

[1385] AI Chatbot: An interface for entering user profile information and preferences.

[1386] Emotion recognition engine: Analyzes user emotions using OpenCV (facial expression recognition) and TensorFlow (voice analysis).

[1387] Database: We use MySQL and Firebase to store user profile information and sentiment data.

[1388] Generative AI model: GPT-4 is used to suggest the best products based on the user's profile information, preferences, and emotions.

[1389] Program processing

[1390] 1. Enter your profile information and preferences

[1391] Users use their device to enter their profile information (age, gender, hobbies, etc.) and preferences (favorite colors, style, etc.) within the application.

[1392] This information is stored in a database.

[1393] 2. Emotion recognition

[1394] When a user enters information, the emotion recognition engine analyzes the user's emotions in real time based on the pressure and speed of their keyboard touch, facial expressions, tone of voice, and other factors.

[1395] The analyzed emotional data is also stored in the database.

[1396] 3. Product recommendations using AI

[1397] The server uses a generative AI model (GPT-4) to suggest optimal products based on the user's profile information, preferences, and emotional data.

[1398] For example, if a user feels like "I want to relax," the app will suggest relaxing aroma candles or massage chairs.

[1399] Specific examples

[1400] A user accesses the virtual store and enters their profile information (age: 30, gender: female, hobby: yoga) and their preferences (favorite color: blue, style: simple). If the emotion engine recognizes the user's facial expression as indicating a desire to relax, the AI ​​will suggest relaxing yoga mats and aroma candles.

[1401] Prompt Sentence Examples

[1402] User profile information: Age: 30, Gender: Female, Hobbies: Yoga

[1403] User preferences: Favorite color: Blue, Style: Simple

[1404] User sentiment: Relaxation

[1405] Suggested products: Relaxing yoga mats, aroma candles

[1406] In this way, by building a system that allows for matching and product suggestions that take user emotions into consideration, user satisfaction can be increased.

[1407] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1408] Step 1:

[1409] The user uses the device to enter their profile information (age, gender, hobbies, etc.) and preferences (favorite colors, styles, etc.) within the application.

[1410] Input: User profile information and preferences

[1411] Output: User profile information and preferences stored in a database

[1412] Specific operation: The user uses a smartphone or head-mounted display to enter information into the application's input form and press the submit button. The server receives this information and stores it in a database such as MySQL or Firebase.

[1413] Step 2:

[1414] When a user enters information, the emotion recognition engine analyzes the user's emotions in real time based on the pressure and speed of the keyboard touch, facial expressions, tone of voice, and other factors.

[1415] Input: User input behavior (keyboard pressure, touch speed, facial expressions, tone of voice)

[1416] Output: User emotion data stored in a database

[1417] How it works: When a user enters information, the device's sensors and camera capture the keyboard pressure, touch speed, facial expressions, and tone of voice. An emotion recognition engine (OpenCV or TensorFlow) analyzes this data to identify the user's emotions. The analysis results are sent to a server and stored in a database.

[1418] Step 3:

[1419] The server uses a generative AI model (GPT-4) to suggest optimal products based on the user's profile information, preferences, and emotional data.

[1420] Input: User profile information, preferences, and emotional data stored in a database

[1421] Output: A list of optimal products suggested to the user

[1422] How it works: The server retrieves the user's profile information, preferences, and emotional data from the database. This data is input into a generative AI model (GPT-4) to generate a prompt. Based on the generated prompt, the AI ​​selects the most suitable product and suggests it to the user.

[1423] Step 4:

[1424] The user checks the proposed products and, if he / she wishes to purchase them, carries out the purchase procedure.

[1425] Input: A list of products suggested to the user

[1426] Output: Notification of purchase completion

[1427] Specific operation: The user uses the terminal to check the list of suggested products, select the product they wish to purchase, and press the purchase button. The server processes the purchase procedure and sends a completion notification to the user.

[1428] In this way, by building a system that processes and analyzes data based on the user's input and actions at each step and suggests the most suitable products, it is possible to increase user satisfaction.

[1429] Example 2

[1430] Next, a description will be given of Example 2 of Form 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."

[1431] Conventional matching systems have had the problem of low user satisfaction because they do not adequately consider the user's profile information or emotions when matching. In addition, evaluation of appearance is highly subjective, making it difficult to make an objective evaluation.

[1432] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1433] In this invention, the server includes means for a generative AI model to analyze a user's personality and values ​​based on profile information entered by the user, means for analyzing the user's emotions using an emotion engine and adjusting the scores of match candidates, means for comparing the user's profile information with the profile information of match-seeking candidates and calculating a match score, and means for analyzing image data provided by the user and generating an evaluation of appearance, thereby enabling matching that comprehensively takes into account the user's personality, values, emotions, and appearance.

[1434] A "generative AI model" is a type of artificial intelligence that uses natural language processing and machine learning techniques to analyze a user's personality and values ​​based on information entered by the user.

[1435] An "emotion engine" is software that analyzes emotions from information entered by the user and processes data based on those emotions.

[1436] "Profile information" refers to personal information entered by a user, such as a self-introduction, hobbies, interests, etc.

[1437] The "matching score" is a numerical representation of the degree of compatibility with a potential match based on the user's profile information and emotional information.

[1438] "Appearance evaluation" involves analyzing image data provided by the user and evaluating appearance features such as hairstyle, eyebrows, fashion, and skin type.

[1439] MODE FOR CARRYING OUT THE INVENTION

[1440] This invention is a system that uses a generative AI model and an emotion engine to calculate scores for match candidates based on profile information and emotion information entered by the user, and provides the scores to the user. Specific embodiments of this system are described below.

[1441] Hardware and software used

[1442] 1. Terminal

[1443] A device on which a user enters profile information and emotional information. This includes smartphones, tablets, and PCs.

[1444] 2. Server

[1445] A computer system for receiving user-submitted information and analyzing the data using generative AI models and emotion engines. The server includes a database and an analysis engine.

[1446] 3. Generative AI Models

[1447] It is an artificial intelligence model that analyzes a user's personality and values ​​based on their profile information. For example, it uses a natural language processing model such as GPT-4.

[1448] 4. Emotion Engine

[1449] This software analyzes the user's emotional information and recognizes their emotions. For example, it uses IBM Watson's emotion analysis API.

[1450] Data processing and calculation

[1451] 1. Analysis of profile information

[1452] The profile information entered by the user on their device is sent to a server. The server then sends this information to a generative AI model that analyzes the user's personality and values. For example, if a user writes, "In my self-introduction, I wrote about my favorite movies and my thoughts on watching movies," the generative AI model will determine that the user has a deep passion for movies.

[1453] 2. Emotional Information Analysis

[1454] Emotional information entered by the user into the device is sent to the server. The server then sends this information to the emotion engine, which analyzes the user's emotions. For example, if a user enters, "Today was a really fun day. I was moved by the new movie," the emotion engine will analyze that the user is expressing emotions of joy and excitement.

[1455] 3. Calculating the Matching Score

[1456] The server calculates scores for match candidates based on the analysis results obtained from the generative AI model and emotion engine. For example, if a user expresses emotions such as joy or excitement, the server will increase the scores of match candidates that are deemed to match the user's preferences. Conversely, if a user expresses emotions such as displeasure or anger, the server will decrease the scores of match candidates that are deemed to not match the user's preferences.

[1457] Specific examples

[1458] A specific example of a profile diagnostic system

[1459] User input: "In my self-introduction, I wrote about my favorite movies and my thoughts on watching movies."

[1460] AI analysis results: "Users have a deep passion for movies"

[1461] A specific example of a matching system that combines an emotion engine

[1462] User input: "Today was a really fun day. I was moved by the new movie."

[1463] Emotion engine analysis results: "Shows emotions of joy and excitement"

[1464] Matching score calculation: "Increase the score of match candidates that are likely to match the user's preferences"

[1465] Prompt Sentence Examples

[1466] Prompt for a profile diagnostic system: "Analyze the personality and values ​​of a user based on the profile information they entered. For example, if a user enters, 'In my self-introduction, I wrote about my favorite movies and my thoughts on watching movies,' what kind of personality and values ​​do you think they have?"

[1467] Prompt for a matching system that combines an emotion engine: "Calculate a score for potential matches based on the emotion information entered by the user. For example, if a user enters, 'Today was a great day. I was moved by the new movie,' how would you adjust the score?"

[1468] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1469] Program processing flow

[1470] Profile Diagnostic System

[1471] Step 1:

[1472] The user enters profile information.

[1473] Input: The user enters profile information such as a self-introduction, hobbies, and interests into an input form on the device.

[1474] Specific actions: For example, enter, "In my self-introduction, I wrote about my favorite movies and my thoughts on watching movies."

[1475] Output: The entered profile information is saved on the device.

[1476] Step 2:

[1477] The terminal sends the input information to the server.

[1478] Input: Profile information entered by the user.

[1479] What happens: The device sends the entered profile information to the server using the HTTPS protocol.

[1480] Output: The profile information is sent to the server.

[1481] Step 3:

[1482] The server receives the profile information and sends it to the generative AI model.

[1483] Input: The profile information received by the server.

[1484] Specific operation: The server sends the received profile information to the generative AI model.

[1485] Output: Profile information is sent to the generative AI model.

[1486] Step 4:

[1487] A generative AI model analyzes profile information.

[1488] Input: Profile information received by the generative AI model.

[1489] How it works: The generative AI model analyzes the user's personality and values ​​using, for example, GPT-4. For example, if a user writes about their "feelings about watching movies," the generative AI model determines that the user has a deep passion for movies.

[1490] Output: Analysis results on personality and values ​​are generated.

[1491] Step 5:

[1492] The server returns the analysis results to the user.

[1493] Input: Analysis results obtained from a generative AI model.

[1494] Specific operation: The server returns the analysis results to the user.

[1495] Output: The user can check the analysis results on the terminal.

[1496] A matching system that combines an emotion engine

[1497] Step 1:

[1498] The user inputs information indicating the emotion.

[1499] Input: The user enters information indicating their emotions into an input form on the device.

[1500] Specific actions: For example, entering a diary entry or a review.

[1501] Output: The input emotion information is saved on the device.

[1502] Step 2:

[1503] The device transmits the emotion information to the server.

[1504] Input: Emotion information entered by the user.

[1505] Specific operation: The device uses the HTTPS protocol to send the input emotion information to the server.

[1506] Output: Emotion information is sent to the server.

[1507] Step 3:

[1508] The server receives the emotion information and sends it to the emotion engine.

[1509] Input: Emotion information received by the server.

[1510] Specific operation: The server sends the received emotion information to the emotion engine.

[1511] Output: Emotion information is sent to the emotion engine.

[1512] Step 4:

[1513] The emotion engine analyzes emotions.

[1514] Input: Emotion information received by the emotion engine.

[1515] Specific operation: The emotion engine analyzes the user's emotions using, for example, IBM Watson's emotion analysis API. For example, if the user shows "emotions of joy or excitement," it analyzes that emotion.

[1516] Output: Sentiment analysis results are generated.

[1517] Step 5:

[1518] The server calculates the score of the matching candidate based on the analysis results.

[1519] Input: Analysis results obtained from the emotion engine.

[1520] Specific operation: The server calculates the score of match candidates based on the emotion information obtained from the emotion engine. For example, if the user expresses emotions such as joy or excitement, the server increases the score of match candidates that are deemed to match the user's preferences. Conversely, if the user expresses emotions such as displeasure or anger, the server decreases the score of match candidates that are deemed to not match the user's preferences.

[1521] Output: Scores are generated for match candidates.

[1522] Step 6:

[1523] The server returns the scores of the potential matches to the user.

[1524] Input: Calculated match candidate scores.

[1525] Specific operation: The server returns the calculated scores of the match candidates to the user.

[1526] Output: The user can check the scores of the potential matches on their device.

[1527] (Application example 2)

[1528] Next, a description will be given of Application Example 2 of Form 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."

[1529] Conventional matching systems match users based on their profile information and appearance, but lack the ability to recommend products based on the user's emotions and preferences. This meant that personalized product recommendations based on the user's interests and emotions were not possible, making it difficult to improve the user experience.

[1530] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1531] In this invention, the server includes a matching support means using AI chat, an AI profile diagnosis means, a means for adjusting the AI ​​to the marriage consultation association's system to connect with marriage-seekers nationwide, an AI conversation ability diagnosis means, an AI appearance diagnosis means (hair, eyebrows, fashion, skin type), a means for analyzing the user's profile information and emotions and recommending products that match the user's preferences, and a means for creating prompt sentences using a generative AI model and acquiring recommended products. This enables personalized product recommendations based on the user's emotions and preferences.

[1532] "AI chat matching support means" is a means of using artificial intelligence to support communication between users and make appropriate matches.

[1533] "AI profile diagnostic means" is a method in which artificial intelligence analyzes a user's personality and values ​​based on the profile information entered by the user.

[1534] "A means of adjusting AI to the marriage consultation association's system and creating connections with people looking for marriage across the country" refers to a means of adapting artificial intelligence to the marriage consultation association's system and creating connections with people looking for marriage across the country.

[1535] An "AI conversational ability diagnostic tool" is a tool that uses artificial intelligence to diagnose a user's conversational ability.

[1536] The "AI appearance diagnosis (hair, eyebrows, fashion, skin type) method" is a method in which image data provided by the user is analyzed and an artificial intelligence generates an evaluation regarding hair, eyebrows, fashion, and skin type.

[1537] "Means for analyzing a user's profile information and emotions and recommending products that match the user's preferences" refers to a means in which artificial intelligence analyzes a user's profile information and emotions and recommends products that match the user's preferences.

[1538] "Means for creating a prompt sentence using a generative AI model and obtaining recommended products" refers to means for creating a prompt sentence using a generative AI model and obtaining recommended products based on the prompt sentence.

[1539] A system for implementing the present invention includes a server, a user terminal, and a prompt sentence generation function using a generative AI model. Specific embodiments are described below.

[1540] System Configuration

[1541] The server includes the following means:

[1542] 1. AI chat to support matching

[1543] 2. AI-based profile diagnostics

[1544] 3. A way to adjust AI to the marriage consultation association's system and connect with people looking for marriage across the country

[1545] 4. AI conversation ability assessment tool

[1546] 5. AI Appearance Diagnosis (Hair, Eyebrows, Fashion, Skin Type) Method

[1547] 6. A means of analyzing user profile information and emotions to recommend products that match the user's preferences

[1548] 7. A method to create prompts using a generative AI model and obtain recommended products

[1549] Program processing

[1550] The server receives profile information and emotion data sent from the user's device, including the user's name, age, interests, emotions, etc. The server then uses this information to create prompts using a generative AI model.

[1551] The generated prompt has the following format:

[1552] The user's name is Taro, he is 30 years old, and his interests are movies, music, and reading. The user is currently showing the emotion of joy. Please list some recommended products for this user.

[1553] The server inputs this prompt into a generative AI model to recommend products that match the user's preferences. The recommended products are then sent to the user's device and displayed to the user.

[1554] Hardware and software used

[1555] Hardware: Servers, user devices (smartphones, PCs, etc.)

[1556] Software: Python, OpenAI API, requests library

[1557] Specific examples

[1558] For example, if a user is interested in "movies" and expresses the emotion "joy," the generative AI model will recommend "latest movie DVD sets" or "movie-related merchandise," allowing users to easily find products that match their interests and emotions.

[1559] This system will enable personalized product recommendations based on the user's emotions and preferences, which is expected to improve the user experience.

[1560] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1561] Step 1:

[1562] A user terminal inputs user profile information and emotion data.

[1563] Specifically, the user inputs information such as name, age, interests, and feelings, and sends this to the server.

[1564] Input: Name, Age, Interests, Emotions

[1565] Output: User profile information and emotion data sent to the server

[1566] Step 2:

[1567] The server analyzes the received user profile information and emotion data.

[1568] Specifically, the server analyzes this data and processes it to understand the user's personality and values.

[1569] Input: User profile information and emotion data

[1570] Output: Analyzed user personality and values ​​data

[1571] Step 3:

[1572] The server creates a prompt using a generative AI model.

[1573] Specifically, based on the analyzed user's personality and values ​​data, a prompt sentence is generated to be input into the generative AI model.

[1574] Input: Analyzed user personality and values ​​data

[1575] Output: Generated prompt statement

[1576] Step 4:

[1577] The server inputs a prompt sentence into the generative AI model and obtains recommended products.

[1578] Specifically, the generated prompt sentences are input into a generative AI model to obtain data that will recommend products that match the user's preferences.

[1579] Input: Generated prompt text

[1580] Output: Recommended product data

[1581] Step 5:

[1582] The server transmits the recommended product data to the user terminal.

[1583] Specifically, the acquired recommended product data is sent to the user terminal and displayed to the user.

[1584] Input: Recommended product data

[1585] Output: Recommended product data sent to the user's device

[1586] Step 6:

[1587] The user terminal displays the recommended product data.

[1588] Specifically, the user terminal displays the received recommended product data so that the user can check the products.

[1589] Input: Recommended product data sent from the server

[1590] Output: Recommended product data displayed to the user

[1591] Example 3

[1592] Next, a description will be given of Example 3 of Form Example 3. 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."

[1593] Conventional matching systems have the problem that the user's appearance is evaluated without taking into account their emotional state, which means that the evaluation is not based on the user's actual emotional state. Furthermore, because there is no way to integrate appearance evaluation and emotional analysis, feedback to users is one-sided, making it difficult to increase user satisfaction.

[1594] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1595] In this invention, the server includes a matching support means using AI chat, an AI profile diagnosis means, a means for adjusting the AI ​​to the marriage consultation association's system to connect with people looking for marriage across the country, an AI conversation ability diagnosis means, an AI appearance diagnosis means (hair, eyebrows, fashion, skin type), and an emotion engine means for analyzing the user's emotions and reflecting them in the appearance evaluation. This makes it possible to evaluate the user's appearance taking into account their emotional state, and provides more accurate feedback with a higher level of satisfaction.

[1596] "AI chat matching support means" is a means of using artificial intelligence to support chats between users and those seeking a match, and to promote appropriate matching.

[1597] "AI profile diagnostic means" is a means of analyzing a user's profile information using artificial intelligence to diagnose the user's characteristics and aptitude.

[1598] "A means of creating connections with people looking for marriage across the country by adjusting to the system of the Marriage Consultation Association" means a means of creating connections between users and people looking for marriage across the country by adapting to the system of the Marriage Consultation Association.

[1599] An "AI conversational ability diagnostic tool" is a method that uses artificial intelligence to analyze a user's conversational ability and evaluate that ability.

[1600] "AI appearance diagnosis (hair, eyebrows, fashion, skin type) method" is a method that uses artificial intelligence to analyze and evaluate a user's appearance, such as hairstyle, eyebrow shape, fashion, and skin type.

[1601] The "emotion engine means" is a means for analyzing the user's emotions and reflecting the analysis results in the appearance evaluation.

[1602] MODE FOR CARRYING OUT THE INVENTION

[1603] This invention is a system that combines an AI that evaluates appearance based on image data provided by the user with an emotion engine that analyzes the user's emotions. A specific embodiment of this system is described below.

[1604] System configuration

[1605] The system consists of the following main components:

[1606] 1. Server: Receives image data, analyzes it, integrates evaluation results, and provides feedback.

[1607] 2. Terminal: A device (smartphone or PC) through which users upload image data.

[1608] 3. AI Appearance Diagnostic Module: Analyzes the user's appearance using deep learning frameworks such as TensorFlow and PyTorch.

[1609] 4. Emotion Engine: Analyzes user emotions using libraries such as OpenCV and Dlib.

[1610] Program processing

[1611] The server receives image data uploaded by the user from their device. The received image data is first sent to the AI ​​appearance assessment module. This module uses TensorFlow and PyTorch to analyze the user's hairstyle, eyebrow shape, fashion, skin type, etc., and generates an evaluation for each element.

[1612] The server then sends the same image data to the emotion engine, which uses OpenCV and Dlib to analyze the user's facial expressions and recognize emotions such as joy, excitement, displeasure, and anger.

[1613] Finally, the server integrates the evaluation results of the AI ​​appearance diagnosis module with the analysis results of the emotion engine. For example, if the user shows emotions of joy or excitement, the server will set a high appearance rating. Conversely, if the user shows emotions of displeasure or anger, the server will set a low appearance rating. This integrated evaluation result is provided to the user as feedback.

[1614] Specific examples

[1615] Users take photos of themselves with their smartphones and upload them to the system. The server receives the image data and sends it to the AI ​​appearance diagnosis module. The AI ​​appearance diagnosis module uses TensorFlow to evaluate the hairstyle as "short cut," the eyebrow shape as "arched," the fashion style as "casual," and the skin type as "dry." At the same time, the server sends the image data to the emotion engine, which uses OpenCV to analyze the user's facial expressions. The emotion engine recognizes that the user is expressing the emotion of "happiness." The server integrates these evaluation results and provides feedback to the user, saying, "Your appearance is very attractive."

[1616] Prompt Sentence Examples

[1617] "Based on photos uploaded by users, evaluate their hairstyle, eyebrow shape, fashion, and skin type, and generate an appearance rating that reflects the emotions analyzed by the emotion engine. If the user shows an emotion of joy, set the appearance rating high."

[1618] The above is a specific embodiment for carrying out the present invention. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1619] Program processing flow

[1620] Step 1:

[1621] Users upload their own photos.

[1622] Input: A photo taken by a user using a device (smartphone or PC).

[1623] Output: Uploaded image data.

[1624] Specific operation: The user takes a photo of themselves with the smartphone camera and clicks the upload button on the system to send the photo.

[1625] Step 2:

[1626] The server receives the image data.

[1627] Input: Image data uploaded by the user.

[1628] Output: Image data stored on the server.

[1629] Specific operation: The server receives the HTTP request and temporarily stores the image data in storage.

[1630] Step 3:

[1631] The server sends the image data to the AI ​​appearance diagnosis module.

[1632] Input: Image data stored on the server.

[1633] Output: Image data sent to the AI ​​appearance diagnosis module.

[1634] Specific operation: The server uses an API call to send image data to the AI ​​appearance diagnosis module.

[1635] Step 4:

[1636] The AI ​​appearance diagnosis module analyzes the image data and generates an appearance assessment.

[1637] Input: Image data sent to the AI ​​appearance diagnosis module.

[1638] Output: Evaluation results on hairstyle, eyebrow shape, fashion, and skin type.

[1639] How it works: The AI ​​appearance diagnosis module uses TensorFlow and PyTorch to analyze image data and evaluate hairstyle as "short cut," eyebrow shape as "arched," fashion as "casual," and skin type as "dry."

[1640] Step 5:

[1641] The server sends image data to the emotion engine.

[1642] Input: Image data stored on the server.

[1643] Output: Image data sent to the emotion engine.

[1644] Specific operation: The server again uses an API call to send the same image data to the emotion engine.

[1645] Step 6:

[1646] The emotion engine analyzes the image data and recognizes the user's emotions.

[1647] Input: Image data sent to the emotion engine.

[1648] Output: Analysis results about the user's emotions (e.g., happy, excited, displeased, anger).

[1649] Specific operation: The emotion engine uses OpenCV and Dlib to analyze the user's facial expression from image data and recognizes that the user is expressing the emotion of "joy."

[1650] Step 7:

[1651] The server integrates the appearance evaluation and emotion analysis results.

[1652] Input: Evaluation results of the AI ​​appearance diagnosis module and analysis results of the emotion engine.

[1653] Output: Integrated appearance assessment results.

[1654] Specific operation: The server integrates the evaluation results of the AI ​​appearance diagnosis module with the analysis results of the emotion engine, and if the user shows the emotion of "joy," sets a high appearance evaluation.

[1655] Step 8:

[1656] The server provides the integrated evaluation results as feedback to the user.

[1657] Input: The integrated appearance assessment results.

[1658] Output: The feedback message provided to the user.

[1659] Specific operation: Based on the integrated evaluation results, the server generates a feedback message for the user, such as "Your appearance is very attractive," and displays it on the user's device.

[1660] The above are the specific processing steps of the program of this system.

[1661] (Application example 3)

[1662] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1663] Conventional appearance diagnosis systems only evaluated users' appearances uniformly, and were unable to reflect their emotions or individual needs. Furthermore, there was a lack of effective means for suggesting optimal fashion items and styling to customers in physical stores. This made it difficult to improve the customer experience.

[1664] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1665] In this invention, the server includes a matching support means using AI chat, an AI profile diagnosis means, a means for adjusting the AI ​​to the marriage consultation association's system to connect with marriage-seekers nationwide, an AI conversation ability diagnosis means, an AI appearance diagnosis means (hair, eyebrows, fashion, skin type), a means for analyzing the user's emotions using an emotion recognition engine and reflecting them in the appearance evaluation, and a means for generating and displaying optimal suggestions for the user based on the appearance evaluation results. This makes it possible to evaluate the appearance taking the user's emotions into consideration and to suggest optimal fashion items and styling to customers in physical stores.

[1666] "AI chat matching support means" is a means of using artificial intelligence to support matching between users.

[1667] "AI profile diagnosis method" is a method that uses artificial intelligence to analyze a user's profile information and make a diagnosis.

[1668] "A means of adjusting AI to the marriage consultation association's system and creating connections with people looking for marriage across the country" refers to a means of adapting artificial intelligence to the marriage consultation association's system and creating connections with people looking for marriage across the country.

[1669] An "AI conversational ability diagnostic tool" is a tool that uses artificial intelligence to diagnose a user's conversational ability.

[1670] "AI appearance diagnosis (hair, eyebrows, fashion, skin type) method" is a method that uses artificial intelligence to diagnose appearance elements such as a user's hairstyle, eyebrow shape, fashion, and skin type.

[1671] "Means for analyzing a user's emotions using an emotion recognition engine and reflecting the results in an appearance evaluation" refers to means for analyzing a user's emotions using an emotion recognition engine and reflecting the results in an appearance evaluation.

[1672] The "means for generating and displaying an optimal suggestion to the user based on the appearance evaluation results" refers to a means for generating an optimal suggestion to the user based on the appearance evaluation results and displaying it.

[1673] The embodiments of the present invention will be described in detail below.

[1674] System configuration

[1675] This system is configured using the following hardware and software.

[1676] Hardware:

[1677] Smart mirror (with built-in camera, display, and microphone)

[1678] A computer with a high-performance GPU (to run AI models)

[1679] Internet connection

[1680] software:

[1681] Image recognition AI model (e.g. OpenCV, TensorFlow)

[1682] Emotion recognition engine (e.g., Affectiva, Microsoft Azure Emotion API)

[1683] Database (for storing customer information and fashion item information)

[1684] Front-end application (for the smart mirror UI)

[1685] Program processing explanation

[1686] 1. Image capture:

[1687] When a user stands in front of the smart mirror, the camera captures image data.

[1688] This image data is sent to an image recognition AI model.

[1689] 2. Image Analysis:

[1690] An image recognition AI model analyzes image data and evaluates appearance factors such as hairstyle, eyebrow shape, fashion, and skin type.

[1691] The evaluation results are temporarily stored in a database.

[1692] 3. Emotion analysis:

[1693] The emotion recognition engine analyzes the user's facial expressions and recognizes their emotions.

[1694] The recognized emotion data is reflected in the appearance evaluation.

[1695] 4. Adjustments:

[1696] Based on the recognized emotion, the appearance rating is adjusted, for example, if the user shows an emotion of happiness, the rating is increased.

[1697] 5. Proposal generation:

[1698] Based on the adjusted evaluation results, the system generates optimal fashion items and styling suggestions for the user from the database.

[1699] The suggestions are displayed on the smart mirror's display.

[1700] Specific examples

[1701] Prompt Sentence Examples

[1702] Analyze the user's image data and evaluate their hairstyle, eyebrow shape, fashion, and skin type. Furthermore, recognize the user's emotions and reflect them in the evaluation. Based on the evaluation results, suggest the most suitable fashion items for the user.

[1703] Real-world usage scenarios

[1704] 1. A user stands in front of the smart mirror and the camera captures an image.

[1705] 2. AI analyzes the image and evaluates hairstyle, eyebrow shape, fashion, and skin type.

[1706] 3. The emotion engine analyzes the user's facial expressions and recognizes the emotion of joy.

[1707] 4. Appearance ratings are adjusted upward based on perceived emotions.

[1708] 5. Based on the adjusted evaluation results, the database will suggest the most suitable fashion items for the user (e.g., a new jacket or accessory).

[1709] 6. The suggestions are displayed on the smart mirror's display, allowing the user to try them on and consider purchasing them on the spot.

[1710] In this way, it becomes possible to evaluate appearance taking into account the user's emotions and to suggest optimal fashion items and styling to customers in physical stores.

[1711] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1712] Step 1:

[1713] When a user stands in front of the smart mirror, the device's (smart mirror's) camera captures image data. The input is the user's image data, and the output is the captured image data. This image data is used in subsequent processing steps.

[1714] Step 2:

[1715] The server sends the captured image data to an image recognition AI model (e.g., OpenCV, TensorFlow). The input is the captured image data, and the output is the analyzed appearance elements (hairstyle, eyebrow shape, fashion, skin type). The image recognition AI model analyzes the image data and evaluates each appearance element.

[1716] Step 3:

[1717] The server temporarily stores the analyzed appearance elements in a database, where the input is the analyzed appearance elements and the output is the evaluation data stored in the database, making the evaluation data available for subsequent processing.

[1718] Step 4:

[1719] The camera on the device (smart mirror) captures the user's facial expression and sends it to the server. The input is the user's facial expression data, and the output is the captured facial expression data. This facial expression data is analyzed by an emotion recognition engine.

[1720] Step 5:

[1721] The server sends the captured facial expression data to an emotion recognition engine (e.g., Affectiva, Microsoft Azure Emotion API). The input is the captured facial expression data, and the output is the recognized emotion data. The emotion recognition engine analyzes the facial expression data and recognizes the user's emotion.

[1722] Step 6:

[1723] The server adjusts the appearance rating based on the recognized emotion data. The input is the recognized emotion data and the analyzed appearance elements, and the output is the adjusted appearance rating. For example, if the user shows an emotion of joy, the appearance rating is increased.

[1724] Step 7:

[1725] The server generates optimal fashion item and styling suggestions for the user from the database based on the adjusted appearance evaluation results. The input is the adjusted appearance evaluation and the fashion item information from the database, and the output is the generated suggestions.

[1726] Step 8:

[1727] The server displays the generated suggestions on the device (smart mirror) display. The input is the generated suggestions, and the output is the suggestions displayed on the display. The user can check the suggested fashion items and styling on the spot, try them on, and consider purchasing.

[1728] 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.

[1729] 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> ) 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.

[1730] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are listed.

[1731] 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.

[1732] [Third embodiment]

[1733] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1734] 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.

[1735] 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).

[1736] 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.

[1737] 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.

[1738] 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).

[1739] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for 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.

[1740] 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.

[1741] 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.

[1742] 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.

[1743] 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.

[1744] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1745] "Example 1"

[1746] In one embodiment of the present invention, AI chat-based matching support involves AI selecting match candidates based on the profile information and preferences entered by the user. Specifically, if a user enters, "My hobby is watching movies, and I would like a partner with the same hobby," the AI ​​will select match candidates whose hobby is watching movies.

[1747] "Example 2"

[1748] In addition, in the AI ​​profile diagnosis, the AI ​​analyzes the user's personality and values ​​based on the profile information they enter. Specifically, if a user enters, "In my self-introduction, I wrote about my favorite movies and my thoughts on watching movies," the AI ​​will determine from that information that the user has a deep passion for movies.

[1749] "Example 3"

[1750] Furthermore, with the AI ​​Appearance Diagnosis, AI evaluates a user's appearance based on image data provided by the user. Specifically, when a user uploads a photo of themselves, the AI ​​analyzes the user's hairstyle, eyebrow shape, fashion, skin type, etc. from the photo and generates an evaluation for each element.

[1751] The processing flow of each embodiment will be described below.

[1752] "Example 1"

[1753] Step 1: The user accesses the application and enters their profile information and preferences.

[1754] Step 2: The AI ​​analyzes the information entered by the user and selects matching candidates.

[1755] Step 3: AI presents the selected match candidates to the user.

[1756] "Example 2"

[1757] Step 1: The user accesses the application and enters their profile information.

[1758] Step 2: The AI ​​analyzes the profile information entered by the user and analyzes the user's personality and values.

[1759] Step 3: The results of the AI ​​analysis are presented to the user and used as reference information for matching.

[1760] "Example 3"

[1761] Step 1: A user accesses the application and uploads a photo of themselves.

[1762] Step 2: The AI ​​analyzes the photo provided by the user and evaluates the user's appearance. Step 3: The AI ​​presents the results of its evaluation to the user, which serves as reference information for matching.

[1763] Example 1

[1764] Next, a description will be given of Example 1 of Form 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."

[1765] Conventional matching systems did not adequately match users based on their profile information and desired conditions, making it difficult to find suitable candidates. Furthermore, they did not adequately evaluate users' appearance or conversational ability, and did not provide comprehensive matching support. This resulted in low user satisfaction and low system utilization.

[1766] 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.

[1767] In this invention, the server includes a means for the user to input profile information using a terminal, a means for the server to receive and store the user's profile information, a means for the server to send prompt messages to the generating AI model, a means for the generating AI model to analyze and select match candidates, and a means for the server to present the match candidates to the user. This enables highly accurate matching based on the user's detailed profile information and desired conditions, thereby improving user satisfaction. In addition, comprehensive matching support can be provided through AI-based appearance and conversation ability assessments.

[1768] "User" refers to an individual who uses the System to enter profile information and search for potential matches.

[1769] "Terminal" means the electronic device used by a User to access the System and enter Profile Information.

[1770] "Server" refers to the central processing unit that stores the profile information received from users, sends prompts to the generative AI model, and selects match candidates.

[1771] "Profile information" refers to personal information such as name, age, hobbies, and desired partner criteria that users enter into the system.

[1772] "Generative AI model" refers to an artificial intelligence model that analyzes the database based on prompt statements received from the server and selects matching candidates.

[1773] A "prompt" refers to an instruction that includes the user's profile information and desired conditions, which the server sends to the generative AI model.

[1774] "Matching candidates" refer to other users who meet the user's desired conditions and are analyzed and selected by the generative AI model.

[1775] "Matching support means" refers to a series of processes for selecting appropriate matching candidates based on the user's profile information and presenting them to the user.

[1776] "Profile diagnostic means" refers to a means for analyzing the profile information entered by a user and finding suitable matching candidates.

[1777] "Conversational ability diagnostic means" refers to a means for evaluating a user's conversational ability and using this information to aid in matching.

[1778] "Appearance assessment means" refers to means for analyzing image data provided by a user and generating an assessment of appearance.

[1779] MODE FOR CARRYING OUT THE INVENTION

[1780] This invention is a system in which a user inputs profile information using a terminal, a server receives and stores the information, and a generative AI model is used to select suitable match candidates and present them to the user. Specific embodiments of this system are described below.

[1781] 1. Enter your profile information

[1782] The user accesses the system's interface using a terminal, which displays information such as name, age, hobbies, and desired partner criteria. The user enters this information and clicks the "Submit" button.

[1783] 2. Saving your profile information

[1784] The server receives the profile information sent by the user and stores it in a database, where each user's profile information is organized and stored.

[1785] 3. Selection of matching candidates

[1786] The server generates a prompt based on the user's profile information and sends it to a generative AI model (e.g., GPT-4). The prompt includes the user's hobbies and desired conditions.

[1787] As an example of a specific prompt, if User A enters "My hobby is watching movies, and I would like a partner with the same hobby," the server will send the following prompt to the generative AI model:

[1788] User A's profile information:

[1789] Hobbies: Watching movies

[1790] Desired criteria: A partner with the same hobbies

[1791] Analyze other users' information in the database and select matching candidates whose hobby is watching movies.

[1792] 4. Analysis and selection of match candidates

[1793] The generative AI model analyzes the database based on the prompt received from the server, and as a result of the analysis, it lists other users whose hobby is watching movies.

[1794] 5. Presentation of matching candidates

[1795] The server presents the user with a list of match candidates received from the generative AI model, which the user can view via their device.

[1796] As a specific example, the generative AI model analyzes user information in the database and selects matching candidates such as "User ID: 002, Hobby: Watching Movies" and "User ID: 003, Hobby: Watching Movies." For User ID: 001, the server displays information such as "Matching Candidates: User ID: 002, User ID: 003" on the device. Users can review this information and contact candidates they are interested in.

[1797] This system enables highly accurate matching based on the user's detailed profile information and desired conditions, improving user satisfaction. It also provides comprehensive matching support through AI-based appearance and conversation ability assessments.

[1798] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1799] Step 1:

[1800] The user uses the device to enter their profile information.

[1801] Input: Profile information such as the user's name, age, hobbies, and partner preferences.

[1802] Specific operations: The user accesses the terminal interface, enters information in each field, and clicks the "Submit" button.

[1803] Output: The entered profile information is sent from the device to the server.

[1804] Step 2:

[1805] The server receives and stores the user's profile information.

[1806] Input: User profile information sent from the device.

[1807] Specific operation: The server stores the received profile information in a database. For example, it stores it as "User ID: 001, Hobby: Watching movies, Desired conditions: Partner with the same hobby."

[1808] Output: User profile information stored in a database.

[1809] Step 3:

[1810] The server sends a prompt to the generative AI model.

[1811] Input: User profile information stored in a database.

[1812] Specific operation: The server generates a prompt based on the user's profile information. For example, a prompt such as "User ID: 001's profile information: Hobbies: Watching movies, Desired conditions: Partner with the same hobby" is sent to the AI ​​model.

[1813] Output: The prompt sent to the generative AI model.

[1814] Step 4:

[1815] A generative AI model analyzes and selects match candidates.

[1816] Input: The prompt text sent by the server.

[1817] Specific operation: The generative AI model analyzes the database based on the prompt sentence and lists other users whose hobby is watching movies. For example, it selects matching candidates such as "User ID: 002, Hobby: Watching Movies" and "User ID: 003, Hobby: Watching Movies."

[1818] Output: A list of selected match candidates.

[1819] Step 5:

[1820] The server presents potential matches to the user.

[1821] Input: A list of match candidates received from a generative AI model.

[1822] Specific operation: The server presents a list of match candidates to the user. For example, information such as "Match candidates: User ID: 002, User ID: 003" is displayed on the terminal.

[1823] Output: A list of potential matches displayed on the user's device.

[1824] The above is the specific processing flow of the program of this system.

[1825] (Application example 1)

[1826] Next, a description will be given of Application Example 1 of Form 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."

[1827] Previous matching support systems were mainly limited to online matching and were unable to support matching between users in physical stores. Furthermore, it was difficult to provide opportunities for users with similar hobbies and preferences to meet in physical stores and enjoy shopping together. This limited the user's shopping experience and potentially reduced satisfaction.

[1828] 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.

[1829] In this invention, the server includes a matching support means using AI chat, an AI profile diagnosis means, a means for adjusting the AI ​​to the marriage consultation association's system to connect with people looking for marriage across the country, an AI conversation ability diagnosis means, an AI appearance diagnosis means (hair, eyebrows, fashion, skin type), a means for matching other users with the same preferences in the same store based on the profile information and preferences entered by the user, and a means for matched users to communicate with each other via chat. This enables users with the same hobbies and preferences to meet in a physical store and enjoy shopping together.

[1830] "AI chat matching support means" is a means of using artificial intelligence to select appropriate match candidates based on the profile information and preferences entered by the user, and to support communication between users via chat.

[1831] "AI profile diagnostic means" refers to a means that uses artificial intelligence to analyze the profile information provided by the user and evaluate the user's characteristics and preferences.

[1832] "A means of adjusting AI to the marriage consultation association's system and creating connections with people looking for marriage across the country" means adapting artificial intelligence to the marriage consultation association's system and creating connections with people looking for marriage across the country.

[1833] An "AI conversational ability diagnostic tool" is a tool that uses artificial intelligence to evaluate a user's conversational ability and provide diagnostic results.

[1834] The "AI Appearance Diagnosis (Hair, Eyebrows, Fashion, Skin Type) Method" is a method that uses artificial intelligence to analyze image data provided by users and generate an evaluation of their appearance, such as hair, eyebrows, fashion, and skin type.

[1835] "A means of matching other users with the same preferences within the same store based on the profile information and preferences entered by the user" refers to a means of using artificial intelligence to select and match other users with the same preferences within the same physical store based on the profile information and preferences entered by the user.

[1836] "Means by which matched users can communicate with each other via chat" means means by which matched users can communicate with each other via chat within the application.

[1837] The following system configuration is used as an embodiment of the present invention.

[1838] System Configuration

[1839] The system includes a user's smartphone, a server, and a generative AI model. Users use their smartphone to input their profile information and preferences, and the server selects potential matches based on this information.

[1840] Hardware and software used

[1841] Hardware: Smartphones, servers

[1842] Software: Python, OpenAI API

[1843] Data processing and calculation

[1844] 1. Enter your profile information:

[1845] Users enter profile information such as hobbies, preferences, and favorite brands through a smartphone application.

[1846] 2. Submitting Profile Information:

[1847] The smartphone application sends the entered profile information to the server.

[1848] 3. AI-based selection of match candidates:

[1849] The server sends a prompt to OpenAI's generative AI model (text-davinci-003) based on the received profile information. The prompt has the following format:

[1850] User profile information: Hobbies: Watching movies, Favorite brand: Brand A, Product category: Casual wear

[1851] Find other users with similar interests.

[1852] 4. Acquiring matching candidates:

[1853] The generative AI model selects other users with similar preferences based on the prompt sentence and returns the results to the server.

[1854] 5. Providing chat functionality:

[1855] The server allows matched users to communicate with each other through chat within the application.

[1856] Specific examples

[1857] For example, if User A inputs "My hobby is watching movies, my favorite brand is Brand A, and my product category is casual wear," the server sends this information to the generative AI model. The generative AI model selects User B who has the same preferences in the same store and returns this information to the server. The server then enables User A and User B to communicate via chat within the application.

[1858] In this way, users with the same hobbies and preferences can meet in physical stores and enjoy shopping together.

[1859] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1860] Step 1:

[1861] A user launches a smartphone application and enters profile information and preferences, such as hobbies, favorite brands, and product categories. The entered information is temporarily stored on the smartphone device.

[1862] Step 2:

[1863] The smartphone device sends the entered profile information to the server. The data sent includes the user's hobbies, favorite brands, product categories, etc. The server analyzes the received data and converts it into an appropriate format.

[1864] Step 3:

[1865] The server creates a prompt for the generative AI model based on the received profile information. The prompt has the following format:

[1866] User profile information: Hobbies: Watching movies, Favorite brand: Brand A, Product category: Casual wear

[1867] Find other users with similar interests.

[1868] This prompt is sent to the generative AI model.

[1869] Step 4:

[1870] The generative AI model selects other users with the same preferences based on the prompt. Specifically, the generative AI model analyzes the input prompt and searches a database for users with the same hobbies and preferences. The search results are returned to the server.

[1871] Step 5:

[1872] The server receives the search results returned by the generative AI model and creates a list of matching candidates. The list includes information on users who share the same preferences in the same store. The server then sends this list to the smartphone device.

[1873] Step 6:

[1874] The smartphone device displays the received list of match candidates to the user, who can then select a match candidate from the displayed list and start chatting.

[1875] Step 7:

[1876] The smartphone device starts a chat session with the selected match candidate. During the chat session, users can exchange messages in real time. Chat data is sent and received via the server.

[1877] Through the above steps, users with the same hobbies and preferences can meet in a physical store and enjoy shopping together.

[1878] Example 2

[1879] Next, a description will be given of Example 2 of Form 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."

[1880] Conventional matching and profile diagnostic systems have difficulty accurately analyzing users' personalities and values, and have also had problems with the accuracy and reliability of the analysis results when providing them to users. Furthermore, there has been a lack of methods for effectively utilizing user profile information to perform matching.

[1881] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for collecting profile information input by the user, a means for preprocessing the collected profile information to input it into the generative AI model, a means for analyzing the user's personality and values ​​using the generative AI model, and a means for providing the analysis results to the user. This makes it possible to analyze the user's personality and values ​​with high accuracy and provide the results to the user.

[1882] "User" refers to an individual who uses the System to enter profile information and receive analysis results.

[1883] "Profile information" refers to the self-introduction and information about interests entered by the user.

[1884] "Generative AI model" refers to an artificial intelligence model used to analyze a user's profile information and infer their personality and values.

[1885] "Preprocessing" refers to the process of converting collected profile information into a format suitable for input into a generative AI model.

[1886] "Analysis results" refers to the results of the generative AI model's estimation of the user's personality and values.

[1887] "Matching score" refers to the score calculated by comparing the user's profile information with the profile information of the person seeking a match.

[1888] "Image data" refers to photographs and image files provided by users.

[1889] "Evaluation of appearance" refers to the evaluation results of a user's appearance obtained by analyzing image data.

[1890] MODE FOR CARRYING OUT THE INVENTION

[1891] This invention is a system that uses a generative AI model to analyze a user's personality and values ​​based on profile information entered by the user, and provides the results to the user. Specific embodiments of this system are described below.

[1892] 1. Enter your profile information

[1893] Users use their devices to enter their profile information. The devices provide an interface through a web browser or dedicated application that allows users to enter information about themselves and their interests. For example, a user might enter, "I love movies, and I'm particularly interested in independent films. I always go to the cinema on the weekends to check out new releases."

[1894] 2. Data transmission

[1895] The device sends the profile information entered by the user to the server using the HTTPS protocol to ensure data security, thereby protecting the user's privacy.

[1896] 3. Data preparation for analysis

[1897] The server converts the received profile information into a format suitable for the generative AI model to analyze it. Specifically, it tokenizes the text data and performs preprocessing as needed. For example, it uses the Python NLTK library to tokenize the text.

[1898] 4. Analysis using generative AI models

[1899] The server inputs the preprocessed data into a generative AI model, such as OpenAI's GPT-4, which analyzes the user's personality and values ​​from the input text and generates inferences.

[1900] 5. Obtaining analysis results

[1901] The server receives the analysis results from the generative AI model, which include information about the user's personality and values. For example, it may learn that the user has a strong passion for movies and is particularly interested in independent films.

[1902] 6. Providing Results

[1903] The server provides the analysis results to the user, who can then check them through their device. For example, a message such as "You have a strong passion for film, and are particularly interested in independent films" will appear on the screen of a web browser or dedicated application.

[1904] Examples of concrete examples and prompts

[1905] Specific examples

[1906] Suppose a user enters the following self-description:

[1907] "I love movies, and I'm especially interested in independent films. I always go to the cinema on the weekends to check out new releases."

[1908] In this case, the generative AI model analyzes it as follows:

[1909] Users have a strong passion for movies.

[1910] I'm particularly interested in independent films.

[1911] He places great importance on watching movies as a way to spend his weekends.

[1912] Prompt Sentence Examples

[1913] An example of a prompt to input to the generative AI model is:

[1914] "A user has entered the following self-introduction: 'I love movies, and I'm particularly interested in independent films. I always go to the cinema on the weekends to check out new releases.' Use this information to analyze the user's personality and values."

[1915] Using this prompt, the generative AI model can perform a detailed analysis of the user's personality and values ​​and provide appropriate feedback.

[1916] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1917] Step 1:

[1918] Users enter their profile information using their device. Specifically, they enter a self-introduction and information about their interests through the device's web browser or a dedicated application. The entered information is stored in text format on the device.

[1919] Input: Profile information you enter (e.g., "I love movies, especially independent films.")

[1920] Output: Text data saved on the device

[1921] Step 2:

[1922] The device sends the profile information entered by the user to the server using the HTTPS protocol to ensure data security, and the sent data is stored on the server.

[1923] Input: Text data stored on the device

[1924] Output: Text data sent to the server

[1925] Step 3:

[1926] The server converts the received profile information into a format suitable for the generative AI model to analyze it. Specifically, it tokenizes the text data and performs preprocessing as needed. For example, it uses the Python NLTK library to tokenize the text.

[1927] Input: Text data stored on the server

[1928] Output: Tokenized data

[1929] Step 4:

[1930] The server inputs the preprocessed data into a generative AI model, such as OpenAI's GPT-4. The generative AI model analyzes the user's personality and values ​​from the input text and generates an inference.

[1931] Input: Tokenized data

[1932] Output: Analysis results from the generative AI model

[1933] Step 5:

[1934] The server receives the analysis results from the generative AI model. The analysis results include information about the user's personality and values. For example, information such as "The user has a strong passion for movies" and "They are particularly interested in independent films" may be obtained.

[1935] Input: Analysis results from generative AI model

[1936] Output: Analysis result data

[1937] Step 6:

[1938] The server provides the analysis results to the user, who can then check them through their device. For example, a message such as "You have a strong passion for movies, and are particularly interested in independent films" will appear on the screen of a web browser or dedicated application.

[1939] Input: Analysis result data

[1940] Output: Analysis results provided to the user

[1941] (Application example 2)

[1942] Next, a description will be given of Application Example 2 of Form 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."

[1943] Conventional matching systems and profile diagnostic systems have the problem of being unable to recommend products that fully reflect the user's personality and values. In addition, product recommendations are rarely based on the user's profile information, making it difficult to find the best product for the user.

[1944] The specific processing by the specific 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 a matching support means using AI chat, an AI profile diagnosis means, a means for adjusting the AI ​​to the marriage consultation association's system to connect with marriage-seeking people nationwide, an AI conversation ability diagnosis means, an AI appearance diagnosis means (hair, eyebrows, fashion, skin type), a means for recommending products based on user profile information, a means for analyzing the user's personality and values ​​using a generative AI model, and a means for generating prompt sentences. This enables product recommendations that reflect the user's personality and values.

[1945] "AI chat matching support means" is a means of using artificial intelligence to support communication between users and make appropriate matches.

[1946] "AI profile diagnostic means" is a method in which artificial intelligence analyzes a user's personality and values ​​based on the profile information entered by the user.

[1947] "A means of adjusting AI to the marriage consultation association's system and creating connections with people looking for marriage across the country" refers to a means of adapting artificial intelligence to the marriage consultation association's system and creating connections with people looking for marriage across the country.

[1948] An "AI conversational ability diagnostic tool" is a tool that uses artificial intelligence to diagnose a user's conversational ability.

[1949] "AI appearance diagnosis (hair, eyebrows, fashion, skin type) method" is a method that uses artificial intelligence to diagnose a user's appearance (hair, eyebrows, fashion, skin type).

[1950] "Means for recommending products based on user profile information" refers to means for recommending appropriate products based on the profile information entered by the user.

[1951] "Means for analyzing a user's personality and values ​​using a generative AI model" means means for analyzing a user's personality and values ​​using a generative artificial intelligence model.

[1952] "Means for generating prompt sentences" means means for using artificial intelligence to generate prompt sentences based on the user's profile information and other data.

[1953] A system for carrying out this invention has the following configuration: The server includes a matching support means using AI chat, an AI profile diagnosis means, a means for adjusting the AI ​​to the marriage consultation association's system to connect with people looking for marriage across the country, an AI conversation ability diagnosis means, an AI appearance diagnosis means (hair, eyebrows, fashion, skin type), a means for recommending products based on user profile information, a means for analyzing the user's personality and values ​​using a generative AI model, and a means for generating prompt sentences.

[1954] Hardware and Software

[1955] This system uses the following hardware and software:

[1956] Hardware: Servers, smartphones

[1957] Software: Python, OpenAI API

[1958] Data processing and calculation

[1959] The server receives the profile information entered by the user and analyzes it using a generative AI model. Specifically, it generates the following prompt text and performs the analysis using OpenAI's generative AI model (text-davinci-003).

[1960] Example prompt sentence:

[1961] User profile information: In the self-introduction, users wrote about their favorite movies and their thoughts on watching movies.

[1962] Use this information to analyze your users' personalities and values.

[1963] Based on the generated prompt, the generative AI model analyzes the user's personality and values ​​and returns the results to the server, which then uses the analysis results to recommend the most suitable products for the user.

[1964] Specific examples

[1965] For example, if a user writes, "In my self-introduction, I wrote about my favorite movies and my thoughts on watching movies," the generative AI model can determine from that information that the user has a deep passion for movies. Based on this analysis, the server can recommend movie-related products to the user (e.g., movie merchandise, the latest movie DVDs, comfortable movie chairs, etc.).

[1966] In this way, it becomes possible to recommend products that reflect the user's personality and values.

[1967] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1968] Step 1:

[1969] The user uses the device to enter profile information, including a self-introduction, hobbies, interests, etc. The entered data is then sent to the server.

[1970] Step 2:

[1971] The server generates a prompt based on the received profile information. Specifically, it creates the following prompt based on the user's input data:

[1972] User profile information: In the self-introduction, users wrote about their favorite movies and their thoughts on watching movies.

[1973] Use this information to analyze your users' personalities and values.

[1974] The generated prompt sentences are sent to a generative AI model.

[1975] Step 3:

[1976] The server uses a generative AI model (text-davinci-003) to analyze the user's personality and values ​​based on the prompt text. The generative AI model takes the prompt text as input and outputs the analysis results on the user's personality and values.

[1977] Step 4:

[1978] The server receives the analysis results from the generative AI model and recommends the most suitable products to the user based on the results. For example, if the analysis shows that the user has a deep passion for movies, it will recommend movie-related products (movie merchandise, the latest movie DVDs, comfortable chairs for watching movies, etc.).

[1979] Step 5:

[1980] The server sends the recommended product information to the terminal, which then displays the received product information to the user. The user can then review the displayed product information and select the product that interests them.

[1981] Step 6:

[1982] When a user selects a product they are interested in, the device sends the selection information to the server, which uses the selection information to record the user's purchase history and interests and use them for future recommendations.

[1983] In this way, product recommendations based on user profile information are realized.

[1984] Example 3

[1985] Next, a third embodiment of the third embodiment will be described. 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."

[1986] Conventional matching and appearance diagnosis systems have difficulty accurately evaluating users' appearances and profile information to ensure appropriate matching. Furthermore, they lack the means to effectively analyze the image data provided by users and generate detailed appearance evaluations. This has led to problems such as low user satisfaction and sluggish system usage.

[1987] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1988] In this invention, the server includes means for users to upload image data, means for the server to receive and preprocess the image data, means for the server to extract feature data from the preprocessed image, means for the server to input the feature data into a generative AI model and generate an evaluation result, means for the server to return the evaluation result to the user, means for supporting matching through AI chat, means for diagnosing a profile using AI, means for adjusting the AI ​​to the marriage consultation association's system to connect with people looking for marriage across the country, means for diagnosing AI conversation skills, and means for diagnosing AI appearance (hair, eyebrows, fashion, skin type).This enables accurate evaluation of a user's appearance and profile information and appropriate matching.

[1989] "Means for users to upload image data" refers to the interface or function that allows users to send their own image data to the system.

[1990] "Means for the server to receive and pre-process image data" refers to the function by which the server receives image data sent by the user and processes it to convert it into a format suitable for analysis.

[1991] "Means for the server to extract feature data from preprocessed images" refers to the function of identifying features such as hairstyle, eyebrow shape, fashion, and skin type from preprocessed image data and extracting them as data.

[1992] "Means for the server to input feature data into the generative AI model and generate an evaluation result" refers to the function of inputting extracted feature data into an AI model and generating an evaluation of appearance.

[1993] "Means by which the server returns the evaluation results to the user" refers to the function for notifying and displaying the generated evaluation results to the user.

[1994] "AI chat matching support means" refers to a function that uses AI to support communication between users and make appropriate matches.

[1995] "AI profile diagnostic means" refers to a function in which AI analyzes a user's profile information and generates diagnostic results.

[1996] "A means of adjusting AI to the marriage consultation association's system to connect with people looking for marriage across the country" refers to a function that uses AI to link with marriage consultation agency systems and connect with people looking for marriage across the country.

[1997] "AI conversational ability diagnostic means" refers to a function in which AI analyzes a user's conversational ability and generates a diagnostic result.

[1998] "AI appearance diagnosis (hair, eyebrows, fashion, skin type) means" refers to a function in which AI analyzes the user's appearance characteristics and generates an evaluation of hairstyle, eyebrow shape, fashion, skin type, etc.

[1999] This invention is a system for allowing users to evaluate their own appearance, and specifically includes a process in which a user uploads image data and a server analyzes the image data to generate an evaluation result.

[2000] Hardware and software used

[2001] Hardware:

[2002] Server: A server with high-performance computing power

[2003] Device: The smartphone or PC used by the user

[2004] software:

[2005] Image processing library: OpenCV

[2006] Machine learning framework: TensorFlow or PyTorch

[2007] Data processing and calculation

[2008] Receiving and preprocessing image data:

[2009] When a user uploads a photo from their device, the server receives the image data, which is then pre-processed using OpenCV, including image resizing, noise removal, and facial extraction.

[2010] Feature data extraction:

[2011] From the pre-processed images, the server extracts feature data such as hairstyle, eyebrow shape, fashion, and skin type using face detection algorithms and image analysis techniques.

[2012] Input to generative AI model and generation of evaluation results:

[2013] The extracted feature data is input into a generative AI model built with TensorFlow and PyTorch. The AI ​​model generates an evaluation for each element based on this feature data. For example, for hairstyle, it might generate an evaluation such as "short hair suits you."

[2014] Returning the evaluation result:

[2015] The generated evaluation results are returned from the server to the user's device, where the user can check the evaluation results. The evaluation results are displayed in text and graph format.

[2016] Specific examples

[2017] When a user uploads a photo of themselves from their smartphone, the following happens:

[2018] 1. The user opens the app on their smartphone, selects a photo, and presses the upload button.

[2019] 2. The server receives the image data and stores it in storage.

[2020] 3. The server resizes the image using OpenCV and cuts out the face area.

[2021] 4. The server identifies the hairstyle area and analyzes its shape and color.

[2022] 5. The server inputs the extracted feature data into the TensorFlow model.

[2023] 6. The model generates a rating, such as "short hair looks good."

[2024] 7. The server returns the evaluation results to the user's smartphone, and the results are displayed on the app.

[2025] Prompt Sentence Examples

[2026] "Evaluate hairstyles, eyebrow shapes, fashion, and skin types based on photos uploaded by users."

[2027] In this way, the user can receive a detailed evaluation of his / her appearance. The flow of the identification process in the third embodiment will be described with reference to FIG.

[2028] Step 1:

[2029] Users upload their own photos from their devices.

[2030] Input: An image file selected by the user.

[2031] Output: Image data sent to the server

[2032] Specific operation: The user opens the application on their smartphone or PC, selects a photo, and presses the upload button, which sends the image data to the server.

[2033] Step 2:

[2034] The server receives the image data and stores it in storage.

[2035] Input: Image data sent by the user

[2036] Output: Image data stored in the server storage

[2037] Specific operation: The server receives the HTTP request and temporarily stores the image data in storage. The stored image data is used in the next processing step.

[2038] Step 3:

[2039] The server preprocesses the image data.

[2040] Input: Image data stored in storage

[2041] Output: Preprocessed image data

[2042] How it works: The server uses OpenCV to resize the image, remove noise, and extract the facial region, generating image data in a format suitable for analysis.

[2043] Step 4:

[2044] The server extracts feature data from the preprocessed images.

[2045] Input: Preprocessed image data

[2046] Output: Extracted feature data (hairstyle, eyebrow shape, fashion, skin type, etc.)

[2047] Specific operation: The server uses a face detection algorithm to identify features such as hairstyle, eyebrow shape, fashion, and skin type and extract them as data.

[2048] Step 5:

[2049] The server inputs the feature data into the generative AI model and generates evaluation results.

[2050] Input: extracted feature data

[2051] Output: Generated evaluation results

[2052] How it works: The server inputs the extracted feature data into a generative AI model built with TensorFlow or PyTorch. The AI ​​model generates an evaluation for each element based on this feature data. For example, for hairstyle, it might generate an evaluation such as "short hair suits you."

[2053] Step 6:

[2054] The server returns the evaluation results to the user.

[2055] Input: Generated evaluation results

[2056] Output: The evaluation results displayed on the user's device

[2057] Specific operation: The server returns the generated evaluation results to the user's device. The user can check the evaluation results through the device. The evaluation results are displayed in text and graph format.

[2058] In this way, users can receive a detailed assessment of their appearance.

[2059] (Application example 3)

[2060] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[2061] Conventional matching and appearance diagnosis systems are limited to matching support based on users' appearance evaluations and profile information, and lack specific suggestions to improve the shopping experience in physical stores. In addition, users face the challenge of finding the best fashion items for themselves in physical stores and receiving expert advice.

[2062] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[2063] In this invention, the server includes a matching support means using AI chat, an AI profile diagnosis means, a means for adjusting the AI ​​to the marriage consultation association's system to connect with people looking for marriage across the country, an AI conversation ability diagnosis means, an AI appearance diagnosis means (hair, eyebrows, fashion, skin type), and a means for analyzing the user's appearance and suggesting the most suitable fashion items from products in a physical store. This allows the user to receive specific suggestions to improve their shopping experience in the physical store.

[2064] "AI chat matching support means" is a means of using artificial intelligence to support chats between users and those seeking a match, and to promote appropriate matching.

[2065] An "AI-based profile diagnostic method" is a method that uses artificial intelligence to analyze a user's profile information and generate diagnostic results based on that information.

[2066] "A means of adjusting AI to the marriage consultation association's system and creating connections with people looking for marriage across the country" means adapting artificial intelligence to the marriage consultation association's system and creating connections with people looking for marriage across the country.

[2067] An "AI conversational ability diagnostic tool" is a tool that uses artificial intelligence to evaluate a user's conversational ability and diagnose the results.

[2068] "AI appearance diagnosis (hair, eyebrows, fashion, skin type) method" is a method that uses artificial intelligence to evaluate a user's appearance (hairstyle, eyebrow shape, fashion, skin type) and diagnose the results.

[2069] "A means for analyzing a user's appearance and suggesting the most suitable fashion item from products available in a physical store" refers to a means for analyzing a user's appearance using artificial intelligence and suggesting the most suitable fashion item from products available in a physical store based on the results of that analysis.

[2070] To implement the present invention, the following system configuration and processing procedures are required.

[2071] System Configuration

[2072] 1. Hardware:

[2073] Smartphone: A device that allows users to upload photos and receive suggestions.

[2074] Server: A computer system for hosting the AI ​​model and performing image analysis and proposal generation.

[2075] 2. Software:

[2076] Python: A programming language used to implement programs.

[2077] OpenCV: Image processing library.

[2078] Keras: A deep learning library.

[2079] Generative AI model: A model that analyzes the user's appearance and suggests the most suitable fashion items.

[2080] Processing steps

[2081] 1. User photo upload:

[2082] Users take photos of themselves using their smartphones and upload them to the application.

[2083] 2. Image preprocessing:

[2084] The server receives the uploaded image and resizes it to the appropriate size using OpenCV.

[2085] 3. Image Analysis:

[2086] The server loads a generative AI model using Keras and inputs the resized image into the model.

[2087] The model analyzes the user's hairstyle, eyebrow shape, fashion, skin type, etc. and generates a rating for each element.

[2088] 4. Fashion item suggestions:

[2089] Based on the generated ratings, the server selects the most suitable fashion items from a product database within the physical store.

[2090] The selected items will be displayed as suggestions on the user's smartphone.

[2091] Specific examples

[2092] While shopping in a physical store, users upload their own photos to the app. The server receives the photos and resizes them using OpenCV. It then loads a generative AI model using Keras and inputs the resized image into the model. The model analyzes the user's appearance and evaluates their hairstyle, eyebrow shape, fashion, skin type, etc. Based on the evaluation results, the server selects the most suitable fashion items from the store's product database and displays them as suggestions on the user's smartphone.

[2093] Prompt Sentence Examples

[2094] "Upload your photo and let AI analyze your hairstyle, eyebrow shape, fashion, and skin type to suggest the best fashion items for you."

[2095] In this way, users can receive specific suggestions to improve their shopping experience in-store.

[2096] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[2097] Step 1:

[2098] Users take photos of themselves using their smartphones and upload them to the application.

[2099] Input: User photo

[2100] Output: Uploaded photo data

[2101] Specific operation: A user starts the application, takes a photo using the camera function, and then presses the upload button in the application to send the photo to the server.

[2102] Step 2:

[2103] The server receives the uploaded photo and uses OpenCV to resize the image to the appropriate size.

[2104] Input: Uploaded photo data

[2105] Output: Resized image data

[2106] Specific operation: The server resizes the received photo data to 224x224 pixels using the OpenCV library. The resized image data is passed to the next analysis step.

[2107] Step 3:

[2108] The server loads a generative AI model using Keras and inputs the resized image into the model.

[2109] Input: Resized image data

[2110] Output: Appearance evaluation results

[2111] How it works: The server loads a pre-trained generative AI model using the Keras library into memory, inputs the resized image data into the model, and generates appearance evaluation results such as hairstyle, eyebrow shape, fashion, and skin type.

[2112] Step 4:

[2113] Based on the generated ratings, the server selects the most suitable fashion items from a product database within the physical store.

[2114] Input: Appearance evaluation results

[2115] Output: A list of the best fashion items

[2116] Specific operation: The server analyzes the appearance evaluation results and compares them with the product database in the physical store. Based on the evaluation results, it selects the most suitable fashion items for the user and generates a list of them.

[2117] Step 5:

[2118] The server sends a list of selected fashion items to the user's smartphone and displays them as suggestions.

[2119] Input: A list of the best fashion items

[2120] Output: Suggestions displayed on the user's smartphone

[2121] Specific operation: The server sends a list of selected fashion items to the user's smartphone. The application displays the received list in an easy-to-read format for the user and presents it as specific suggestions.

[2122] In this way, users can receive specific suggestions to enhance their shopping experience in a physical store.

[2123] 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.

[2124] "Example 1"

[2125] One embodiment of the present invention is a system incorporating an emotion engine. This system recognizes a user's emotions and uses those emotions as reference information for matching. Specifically, when a user accesses an application and enters their profile information and preferences, the emotion engine recognizes the user's emotions. For example, the emotion engine recognizes the user's emotions from the keyboard pressure and touch speed when the user enters their profile information, or from the user's facial expressions and tone of voice when using the application. The emotion engine then uses those emotions as reference information for matching.

[2126] "Example 2"

[2127] Furthermore, systems incorporating an emotion engine reflect the user's emotions in the calculation of the matching score. Specifically, when the AI ​​analyzes the information entered by the user and selects match candidates, it reflects the user's emotions recognized by the emotion engine. For example, if the user expresses emotions of joy or excitement, it will increase the scores of match candidates that are deemed to match the user's preferences. Conversely, if the user expresses emotions of displeasure or anger, it will lower the scores of match candidates that are deemed not to match the user's preferences.

[2128] "Example 3"

[2129] Furthermore, in a system that incorporates an emotion engine, the user's emotions are reflected in the generation of an appearance rating. Specifically, image data provided by the user is analyzed, and when generating an appearance rating, the emotion engine reflects the user's emotions recognized by the emotion engine. For example, if the user shows emotions of joy or excitement, the rating of the user's appearance is increased. Conversely, if the user shows emotions of displeasure or anger, the rating of the user's appearance is decreased.

[2130] The processing flow of each embodiment will be described below.

[2131] "Example 1"

[2132] ...

Claims

1. A first means for accepting input of profile information by a user, the profile information including name, age, hobbies, and desired partner requirements; A second means for generating a prompt sentence for selecting a matching candidate that meets the user's desired conditions based on the profile information; A third means for inputting the generated prompt sentence into a generative AI model to select the matching candidate from within a database; A fourth means for presenting the selected matching candidates to the user; A system including:

2. A fifth means for evaluating the user's conversational ability using the generative AI model and providing the evaluation result to the user. The system of claim 1 .

3. A sixth means for analyzing the emotion of the user from information inputted into the terminal by the user using an emotion engine; and a seventh means for adjusting a score of the selected matching candidate based on an analysis result of the user's emotion, The fourth means presents the scores of the matching candidates to the user.

3. A system according to claim 1 or 2.

Images