System

Abstract

A system is provided.SOLUTION: A system, comprising: means for analyzing a profile picture of a user by using a generation AI and feeding back specific improvements for elements such as an angle, an expression, and a background; means for analyzing a profile text of the user by using the generation AI and feeding back specific improvements; means for asking a simple question to the user by using the generation AI and generating or suggesting editing of an optimal profile sentence based on an answer to the question; and means for automatically detecting spam accounts and fraudulent accounts by using the generation AI and eliminating suspicious accounts.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 face challenges, such as the difficulty of users making appropriate decisions when creating profiles and selecting images, and the lack of a safe matching environment due to the existence of fraudulent and spam accounts. These issues are particularly pronounced for those new to dating apps, and there is a demand for a safe and secure environment. [Means for solving the problem]

[0005] The present invention provides a means for analyzing a user's profile image and text using a generation AI and providing feedback on specific areas for improvement. It also includes a means for the generation AI to ask the user simple questions and, based on the user's answers, generate an optimal profile statement or suggest edits. It also includes a means for automatically detecting spam and fraudulent accounts using the generation AI and eliminating suspicious accounts. In addition, it also includes a means for recommending compatible individuals based on a personality assessment and a means for providing an interface that makes it easy to improve the profile based on feedback, enabling users to achieve optimal matches with individuals who have an excellent balance between appearance and personality, providing a safe and secure matching environment.

[0006] "Generative AI" refers to artificial intelligence that autonomously analyzes data for a specific purpose and provides appropriate feedback and suggestions.

[0007] "Profile Picture" means a photo or image provided by a User to represent themselves.

[0008] "Profile text" refers to text data in which a user describes their personal information and interests.

[0009] "Feedback" refers to the evaluation and improvements that the generative AI provides to the user based on the input data.

[0010] "Question answering" is the process by which generative AI asks specific questions to users and analyzes their answers.

[0011] "Personality assessment" is the process of analyzing and evaluating a user's personality traits and preferences based on their responses and behavioral data.

[0012] "Recommendation" refers to the process by which generative AI suggests the most suitable people or content to users.

[0013] A "spam account" is a fake account created with the purpose of deceiving others or sending indiscriminate advertisements.

[0014] A "fraudulent account" is a fake account created with the intent of fraudulently obtaining money or personal information from users.

[0015] The "interface" refers to the operating screen and method that allows users to easily improve their profile based on feedback from the generated AI. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] The present invention is a dating matching service system that utilizes generative AI to optimize the balance between a user's appearance and personality and provide a safe matching environment. Specific embodiments of this system are described below.

[0038] 1. Profile image analysis and improvement

[0039] The user uploads their profile picture, which the server receives and sends to the generation AI.

[0040] The server uses generative AI to analyze factors such as the angle, facial expression, and background of the user's profile picture. For example, if the facial expression in the picture is unattractive or the background is cluttered, it will provide specific feedback on how to improve it.

[0041] Based on the feedback provided, the user can modify the image or upload a new image.

[0042] 2. Profile text analysis and improvement

[0043] The user enters their profile text, and the server receives this text and sends it to the generating AI.

[0044] The server uses a generative AI to analyze the profile text and provide specific feedback on areas for improvement. For example, if someone writes, "My hobbies are reading and watching movies," the server might suggest, "You should write more about your personality and work in more detail."

[0045] The user then modifies the text based on the feedback provided.

[0046] 3. Personality diagnosis and profile suggestion through question-and-answering

[0047] The device displays a simple question to the user, who then answers it.

[0048] The server sends the collected response data to the AI, which then performs a personality diagnosis based on the answers. Based on the generated diagnosis results, the AI ​​then suggests the optimal profile text to the user.

[0049] Users can use the suggested profile text as a reference to enrich their own profiles.

[0050] 4. Recommendations of compatible people

[0051] The server then recommends suitable matches to users based on the results of the personality assessment and analyzed profile data, through a process in which generative AI analyzes the data and identifies those with the best compatibility.

[0052] Users can decide whether to contact the recommended people, and this system allows users to meet people who are suitable for them more efficiently.

[0053] 5. Spam and fraudulent account detection

[0054] The server constantly monitors newly registered profiles using generative AI and automatically detects spam accounts and potentially fraudulent accounts.

[0055] The server will then properly remove any spam accounts it detects and maintain a safe matching environment, such as detecting and removing accounts with numerous identical profile statements or accounts that provide excessively incorrect information.

[0056] In this way, the present invention utilizes generative AI to not only optimize users' profile details, but also provide a safe and reliable matching environment, allowing users to achieve the best possible match that balances appearance and personality, and to use the service with peace of mind.

[0057] The processing flow will be explained below.

[0058] Profile image analysis and improvement

[0059] Step 1:

[0060] The user uploads their profile picture to the device.

[0061] The device sends the uploaded image to the server.

[0062] Step 2:

[0063] The server passes the received image to the generation AI.

[0064] Generative AI analyzes the image and evaluates factors such as angle, facial expression, and background.

[0065] Step 3:

[0066] Based on the analysis results from the generated AI, the server provides specific feedback to the user on areas for improvement.

[0067] Based on the feedback, the user modifies the image or uploads a new image.

[0068] Profile text analysis and improvement

[0069] Step 1:

[0070] The user enters his / her profile text into the terminal.

[0071] The terminal sends the entered text to the server.

[0072] Step 2:

[0073] The server passes the received text to the generation AI.

[0074] Generative AI analyzes the text and generates specific improvements as feedback.

[0075] Step 3:

[0076] The server provides feedback from the generated AI to the user.

[0077] The user then corrects the text based on the feedback provided.

[0078] Personality diagnosis and profile suggestions through question-and-answering

[0079] Step 1:

[0080] The terminal displays a simple question to the user.

[0081] The user answers the questions and inputs the answer data into the terminal.

[0082] Step 2:

[0083] The terminal transmits the collected response data to the server.

[0084] The server passes the response data to the generating AI and conducts a personality diagnosis.

[0085] Step 3:

[0086] The generative AI will generate the optimal profile text based on the diagnostic results.

[0087] The server proposes the generated profile statement to the user.

[0088] Step 4:

[0089] Users can use the suggested profile text as a reference to enrich their own profiles.

[0090] Recommendations for compatible people

[0091] Step 1:

[0092] The server passes the personality test results and analyzed profile data to the generation AI.

[0093] Generative AI analyzes the data and identifies the best matches.

[0094] Step 2:

[0095] The server provides the recommendation results to the user.

[0096] The user decides whether to contact the recommended person.

[0097] Spam and fraudulent account detection

[0098] Step 1:

[0099] The server passes the newly registered profile to the generation AI.

[0100] Generative AI analyzes profiles to detect potential spam or fraudulent accounts.

[0101] Step 2:

[0102] The server will now properly remove detected spam accounts.

[0103] Regular monitoring and countermeasures are carried out to ensure the server maintains a safe matching environment.

[0104] Example 1

[0105] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0106] In recent years, with the spread of online dating matching services, it is necessary not only to improve the quality of users' profile images and text, but also to provide a safe and reliable matching environment. However, it requires advanced skills and time for users to properly optimize their profiles, and eliminating spam and fraudulent accounts is also a major challenge. An efficient system to solve these problems is needed.

[0107] 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.

[0108] In this invention, the server includes means for using a generation AI to analyze a user's profile image and provide feedback on specific improvements regarding elements such as angle, facial expression, and background, means for using a generation AI to analyze the user's profile text and provide feedback on specific improvements, means for using a generation AI to ask the user simple questions and generate or suggest edits to an optimal profile statement based on the answers, means for using a generation AI to automatically detect spam accounts and fraudulent accounts and eliminate suspicious accounts, means for using a generation AI to identify and recommend compatible individuals based on the results of a personality assessment and analyzed profile data, and means for suggesting input prompts for the generation AI model. This allows users to achieve optimal matches that balance appearance and inner qualities, enabling them to use a safe and reliable dating matching service.

[0109] "Generative AI" is an artificial intelligence technology that uses machine learning algorithms to generate data and automate specific tasks.

[0110] "Profile Picture" means a photo of a user's face or other image uploaded by a user to express their identity or personality.

[0111] "Profile text" is written information in which a user describes themselves, their hobbies, and interests.

[0112] "Spam Accounts" are automatically generated user accounts registered for irrelevant advertising or fraudulent purposes.

[0113] A "fraudulent account" is a user account that provides false information with the intent to deceive others.

[0114] "Feedback" refers to suggestions and advice for improving images and text that the generative AI provides to the user.

[0115] "Personality assessment" is the process by which the generative AI evaluates a user's personality traits based on the questions the user answers.

[0116] "Recommendation" refers to the generation AI recommending the best match to the user.

[0117] A "prompt" is a guided sentence input to a generative AI model, used to induce a specific response or generation.

[0118] The present invention is a dating matching service system that utilizes generative AI to optimize the balance between a user's appearance and personality and provide a safe matching environment. Specific embodiments of this system are described below.

[0119] 1. Profile image analysis and improvement

[0120] When a user uploads their profile picture, the server receives the image and sends it to the generation AI. The generation AI analyzes elements of the user's profile picture, such as the angle, facial expression, and background. For example, if the facial expression in the image is unattractive or the background is cluttered, it provides specific feedback on how to improve the image. The user can then modify the image or upload a new image based on the feedback provided.

[0121] Examples:

[0122] If the background of an image uploaded by a user is cluttered, the generating AI will provide feedback such as "It would be better to simplify the background."

[0123] Example prompt for a generative AI model:

[0124] "How can we improve this profile picture background?"

[0125] 2. Profile text analysis and improvement

[0126] When a user enters their profile text, the server receives this text and sends it to the generation AI. The generation AI analyzes the profile text and provides feedback on specific areas for improvement. For example, for a profile entry such as "My hobbies are reading and watching movies," the server may suggest "You should write more about your personality and work." The user then edits the text based on the provided feedback.

[0127] Examples:

[0128] For example, if a user writes, "My hobbies are reading and watching movies," the AI ​​generator will provide feedback such as, "You should write in more detail about your personality and work."

[0129] Example prompt for a generative AI model:

[0130] "Please suggest improvements to this profile text."

[0131] 3. Personality diagnosis and profile suggestion through question-and-answering

[0132] The device displays simple questions to the user, who then answers them. The server then sends the collected response data to the AI, which then performs a personality diagnosis based on the answers. Based on the generated diagnosis results, the AI ​​then suggests an optimal profile statement to the user. The user can then use the suggested statement to enhance their profile.

[0133] Examples:

[0134] If a user answers "reading" to the question "How do you spend your holidays?", the AI ​​generator will suggest "the perfect profile sentence for you, a book lover."

[0135] Example prompt for a generative AI model:

[0136] "Suggest the best profile sentence for this user based on their answers."

[0137] 4. Recommendations of compatible people

[0138] The server recommends suitable matches to users based on the results of the personality test and analyzed profile data. This is done through a process in which generative AI analyzes data and identifies people with the best compatibility. Users then decide whether or not to contact the recommended people. This system allows users to meet suitable partners more efficiently.

[0139] Examples:

[0140] If the personality test results and profile data of User A and User B match, the server will use generative AI to recommend the two people.

[0141] Example prompt for a generative AI model:

[0142] "Please suggest the best matches for this user based on their personality test results and profile data."

[0143] 5. Spam and fraudulent account detection

[0144] The server constantly monitors newly registered profiles using generative AI to automatically detect spam accounts and potentially fraudulent accounts. Detected spam accounts are then appropriately removed to maintain a safe matching environment. For example, accounts with numerous identical profile statements or accounts providing extremely incorrect information are detected and removed.

[0145] Examples:

[0146] If there are a large number of newly registered profiles with the same text, the generation AI will identify them as spam accounts and eliminate them.

[0147] Example prompt for a generative AI model:

[0148] "Please determine if this profile is spam or a potential scam."

[0149] In this way, the present invention utilizes generative AI to not only optimize users' profile details, but also provide a safe and reliable matching environment, allowing users to achieve the best possible match that balances appearance and personality, and to use the service with peace of mind.

[0150] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0151] Step 1:

[0152] User uploads a profile picture

[0153] The user selects their profile picture and presses the upload button. The server receives the image data sent by the user.

[0154] input:

[0155] Image data uploaded by users

[0156] Specific behavior:

[0157] Check the image file type and size and save it in the appropriate format.

[0158] output:

[0159] Profile image data stored on the server

[0160] Step 2:

[0161] The server sends the profile image to the AI ​​for analysis.

[0162] The server sends the received profile image to the generation AI for analysis. The generation AI model analyzes the image's angle, facial expression, and background to identify areas for improvement.

[0163] input:

[0164] Profile image data stored on the server

[0165] Specific behavior:

[0166] Generative AI performs facial recognition and evaluates facial expression, background, and angle.

[0167] Data analysis algorithms are used to extract improvements to the images.

[0168] output:

[0169] Specific improvements provided by the generation AI (e.g., stiff facial expressions, cluttered background)

[0170] Step 3:

[0171] User enters profile text

[0172] Users enter text to describe their profile, and the server receives and stores this text data.

[0173] input:

[0174] User-entered profile text

[0175] Specific behavior:

[0176] The text data is stored on the server.

[0177] Check the format of the saved data and make it ready to send to the generating AI.

[0178] output:

[0179] Profile text data stored on the server

[0180] Step 4:

[0181] The server sends the profile text to the AI ​​generator for analysis.

[0182] The server sends the saved profile text to the AI ​​generator for analysis, which analyzes the content of the text and suggests specific improvements.

[0183] input:

[0184] Profile text data stored on the server

[0185] Specific behavior:

[0186] Generative AI performs natural language processing and analyzes the content of the text.

[0187] Extract areas for improvement from the analysis results.

[0188] output:

[0189] Specific improvements provided by the generative AI (e.g., writing more specifically about hobbies)

[0190] Step 5:

[0191] The device asks the user a simple question and collects the answer.

[0192] The terminal displays some simple questions to the user, who answers them. The server receives and stores the answer data.

[0193] input:

[0194] Question data answered by users

[0195] Specific behavior:

[0196] The terminal displays the question and accepts user input.

[0197] The response data is sent to the server.

[0198] output:

[0199] Response data stored on the server

[0200] Step 6:

[0201] The server conducts a personality test and suggests the best profile sentence for you.

[0202] The server sends the collected response data to the AI ​​generator, which then performs a personality diagnosis. Based on the results of the diagnosis, the AI ​​then suggests the most suitable profile text for the user.

[0203] input:

[0204] Response data stored on the server

[0205] Specific behavior:

[0206] The generative AI conducts a personality diagnosis and evaluates the user's personality traits.

[0207] Based on the diagnosis results, the optimal profile statement is generated.

[0208] output:

[0209] The optimal profile text suggested by generative AI

[0210] Step 7:

[0211] The server automatically detects and removes spam and fraudulent accounts.

[0212] The server constantly monitors newly registered profiles using generative AI, automatically detecting spam accounts and potentially fraudulent accounts, and then appropriately removing detected accounts.

[0213] input:

[0214] Newly registered profile data

[0215] Specific behavior:

[0216] Generative AI analyzes registered profile data to identify spam and fraud patterns.

[0217] Remove confirmed spam or fraudulent accounts from the system.

[0218] output:

[0219] Safe User Profile List

[0220] Step 8:

[0221] The server recommends suitable matches to the user.

[0222] The server uses generative AI to recommend the best possible matches to users based on the personality test results and analyzed profile data.

[0223] input:

[0224] Analyzed profile data and personality test results

[0225] Specific behavior:

[0226] Generative AI analyzes the data and identifies people who are compatible.

[0227] The identified matches are displayed to the user.

[0228] output:

[0229] A list of recommended matches for the user

[0230] Thus, through each step of the system, users can optimize their profile and enjoy a safe and effective matching environment.

[0231] (Application example 1)

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

[0233] Conventional matching systems only recommend suitable partners based on the user's profile information and personality assessment, but do not offer services or product suggestions that users can actually experience in physical stores. This makes it difficult for users to choose products and services that suit them. Other issues include insufficient feedback on image and text improvements, and ineffective elimination of fraudulent and spam accounts.

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

[0235] In this invention, the server includes means for analyzing a user's profile image using a generation AI and providing feedback on specific improvements regarding elements such as angle, facial expression, and background, means for analyzing a user's profile text using a generation AI and providing feedback on specific improvements, means for using a generation AI to ask the user simple questions and generate or suggest editing an optimal profile statement based on the answers, means for using a generation AI to automatically detect spam accounts and fraudulent accounts and eliminate suspicious accounts, and means for suggesting optimal products and services based on the user's profile information and personality assessment data. This enables more accurate product and service suggestions based on the user's appearance and inner information, realizing a safe and reliable matching environment.

[0236] "Generative AI" refers to artificial intelligence that generates natural language and images based on user input data.

[0237] A "profile picture" is a photo that a user uses to show their personal information and appearance.

[0238] "Angle" is an element that indicates the relative positions of the subject's face and body in the profile image.

[0239] "Expression" is an element that indicates the facial expression of the user in the profile picture.

[0240] "Background" refers to the environment or scenery that appears behind the subject in a profile picture.

[0241] "Profile text" refers to text information in which a user writes about themselves, their hobbies, etc.

[0242] "Feedback" refers to improvements and advice provided by the generative AI to the user.

[0243] A "question" is a question-and-answer format information gathering tool presented to the user.

[0244] "Personality diagnosis" is the process of evaluating a user's personality traits based on the user's response data, etc.

[0245] "Spam Account" means a user account created for fraudulent purposes.

[0246] A "Fraudulent Account" is a fraudulent user account created with the intent to deceive users.

[0247] "Product" means an object such as a good or service that is provided to a user.

[0248] "Service" means any act or service provided to a User.

[0249] "Profile Information" refers to all data about a user, including images and text.

[0250] "Personality assessment data" refers to data regarding a user's personality characteristics obtained through a personality assessment.

[0251] "Recommendation" means a recommendation of a product or service made to a User.

[0252] An "interface" is a screen or operating means that allows a user to interact with a system.

[0253] The present invention is a system that utilizes generative AI to optimize a user's profile image and text, and safely and effectively suggests optimal products and services to the user. Specific embodiments of this system are described below.

[0254] System Configuration and Operation

[0255] This system consists of three elements: a server, a terminal, and a user. The server operates the generative AI, the terminal provides the user interface, and the user operates the terminal to use the service.

[0256] 1. Profile image analysis and improvement

[0257] The server first receives the profile image uploaded by the user. This image is then sent to the generation AI, which analyzes elements such as the angle, facial expression, and background. For example, the image is analyzed using OpenCV, and if the background is too cluttered, the server provides specific feedback to the user on how to improve it, such as "Please change to a simpler background." This allows users to create more attractive profile images.

[0258] 2. Profile text analysis and improvement

[0259] The profile text entered by the user is also sent to the server. The server uses generative AI to analyze the text and generate specific suggestions for improvement. For example, for the text "My hobbies are reading and watching movies," the server will provide advice such as "You should write more details about your personality and work." This allows users to create a more comprehensive profile text.

[0260] 3. Personality diagnosis and profile suggestion through question-and-answering

[0261] The device displays simple questions to the user, who then answers them. These answers are sent to a server, where a personality diagnosis is performed using a generative AI. Based on the results, the device suggests the profile text that best suits the user's personality. For example, if the answers to the questions are "Q1: A1, Q2: A2, Q3: A3," the device inputs this information into the prompt and generates the appropriate text.

[0262] 4. Product and service recommendations

[0263] Using generative AI, the server will suggest optimal products and services based on the results of a personality test and profile information. These suggestions are based on prompts and are presented to the user in an easy-to-understand format. For example, based on a profile that says, "My hobbies are reading and watching movies," a specific suggestion will be made, such as, "How about a T-shirt from a new movie?"

[0264] Hardware and software used

[0265] Hardware: Storefront customer service robots (e.g., generic company robots), cameras (to capture images of users)

[0266] Software: OpenCV (image analysis), OpenAI API (text generation), Transformers library (implementation of generative AI)

[0267] Examples of concrete examples and prompts

[0268] When implementing this in a brick-and-mortar apparel store, the following specific examples are possible:

[0269] When a user shows the robot an image of themselves, the robot suggests, "The background is too cluttered. Please change it to a simpler background."

[0270] Based on the profile text, the recommendation is, "Since your hobby is watching movies, how about a T-shirt from a new movie?"

[0271] Example prompt sentence:

[0272] 1. Profile text improvement suggestions:

[0273] markdown

[0274] How can we improve the following profile text?

[0275] My hobbies are reading and watching movies

[0276] 2. Personality Test and Suggestions:

[0277] markdown

[0278] Please assess the user's personality based on the answers below and make suggestions:

[0279] Q1: A1, Q2: A2, Q3: A3

[0280] 3. Product Suggestion:

[0281] markdown

[0282] Profile text: My hobbies are reading and watching movies

[0283] Personality Test: Introverted but highly sensitive

[0284] Please suggest suitable products based on these.

[0285] As described above, this system utilizes generative AI to optimize user information and can suggest appropriate products and services in physical stores, providing a safe and reliable matching environment and increasing user satisfaction.

[0286] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0287] Step 1: Upload and analyze your profile image

[0288] The server receives the profile image uploaded by the user via their device. This image is sent to a generative AI model, which analyzes factors such as angle, facial expression, and background. The input is the profile image, and the output is feedback on areas for improvement. Specifically, OpenCV is used to analyze the angle and facial expression of the image, and if the background is cluttered, feedback is generated, such as "Please change to a simpler background."

[0289] Step 2: Enter and parse profile text

[0290] The server receives the profile text entered by the user via their device. This text is sent to the generative AI model, which analyzes the content of the text. Specific improvements are then provided as feedback. The input is the profile text, and the output is feedback on improvements. Specifically, the generative AI model suggests, for example, for text such as "My hobbies are reading and watching movies," that "you should write in more detail about your personality and work."

[0291] Step 3: Personality assessment through questions and answers

[0292] The device displays simple questions to the user. The user answers the questions, and the answer data is sent to the server. The server then sends the collected answer data to a generative AI model, which then performs a personality diagnosis based on that data. The input is the answer data to the questions, and the output is the personality diagnosis result. Specifically, the generative AI model is used to generate prompt sentences, and optimal advice is generated based on those sentences.

[0293] Step 4: Generating and suggesting profile text

[0294] The server generates an optimal profile sentence based on the results of the personality assessment. This generated sentence is then suggested to the user via their device. The input is the personality assessment result and profile information, and the output is the suggested profile sentence. Specifically, the generative AI model generates the optimal text based on prompts such as "Q1: A1, Q2: A2, Q3: A3."

[0295] Step 5: Recommend products and services

[0296] The server uses a generative AI model to suggest optimal products and services based on the user's profile information and personality test results. This information is presented to the user via their device. The input is profile information and personality test results, and the output is suggested products and services. Specifically, based on information such as "My hobbies are reading and watching movies" or "I'm introverted but highly sensitive," the server makes recommendations such as "How about a T-shirt from a new movie?"

[0297] Step 6: Detecting spam and fraudulent accounts

[0298] The server uses a generative AI model to constantly monitor newly registered accounts and automatically detect spam and fraudulent accounts. When a suspicious account is detected, it is appropriately removed. The input is the profile data of the newly registered account, and the output is a list of accounts to be removed. Specifically, the generative AI model is used to identify and remove large numbers of accounts with the same profile text or accounts that provide incorrect information.

[0299] These are the specific processing steps of this system. By executing these steps in an orderly manner, it is possible to provide profile information and product / service suggestions that are optimized for the user.

[0300] 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.

[0301] The present invention is a system that integrates generative AI and an emotion engine to more effectively analyze users' profile images and text, and provide optimal feedback and matching. Specific embodiments of various functions are described below.

[0302] 1. Profile picture analysis and emotion identification

[0303] The user uploads their profile picture to the device, which then sends the uploaded picture to the server.

[0304] The server sends the received images to the generation AI and emotion engine.

[0305] The generative AI analyzes the image, evaluating factors such as angle, facial expression, and background, while the emotion engine identifies emotions from the user's facial expressions.

[0306] The server provides specific feedback to the user based on the analysis results from the generation AI and the emotion data from the emotion engine. For example, if the user is not smiling, the server will provide advice such as "Use a smile that looks friendlier."

[0307] Based on the feedback provided, the user can modify the image or upload a new image.

[0308] 2. Profile text analysis and sentiment identification

[0309] The user enters their profile text into the terminal, which then sends the entered text to the server.

[0310] The server sends the received text to the generation AI and emotion engine.

[0311] The generative AI analyzes the text and provides specific feedback on improvements, while the emotion engine identifies the user's emotions from the wording of the text.

[0312] The server then uses the analysis results from the generation AI and the emotion data from the emotion engine to suggest specific improvements to the user. For example, if the text seems bland, the server will provide advice such as, "Add more passionate words to better express yourself."

[0313] The user then modifies the text based on the feedback provided.

[0314] 3. Personality diagnosis and profile suggestion through question-and-answering

[0315] The terminal displays simple questions to the user, who answers the questions and inputs the answer data into the terminal.

[0316] The device sends the collected response data to the server, which then sends it to the generation AI and emotion engine.

[0317] The generation AI analyzes the response data and performs a personality diagnosis, and the emotion engine identifies the emotions the user expressed when answering.

[0318] The server uses data from the generative AI and emotion engine to suggest optimal profile sentences to users. For example, if a user is proactive, a profile sentence that reflects this will be provided.

[0319] Users can use the suggested profile text as a reference to enrich their own profiles.

[0320] 4. Recommendations of compatible people

[0321] The server passes the results of the personality test and analyzed profile data to the generation AI and emotion engine.

[0322] Generative AI and an emotion engine analyze data to identify optimal matches. By incorporating emotion data, more accurate matching becomes possible.

[0323] The server provides the recommendation results to the user, who then decides whether to contact the recommended person.

[0324] 5. Spam and fraudulent account detection

[0325] The server sends the newly registered profile to the generation AI and emotion engine.

[0326] Generative AI analyzes profiles to detect potential spam or fraudulent accounts, while a sentiment engine also detects emotional anomalies to identify suspicious accounts.

[0327] The server will then appropriately eliminate spam accounts based on the detection results, maintaining a safe matching environment.

[0328] By integrating these functions, users can achieve the optimal match that balances appearance and personality, providing a matching environment that can be used with peace of mind.

[0329] The processing flow will be explained below.

[0330] Profile picture analysis and emotion identification

[0331] Step 1:

[0332] The user uploads their profile picture to the device.

[0333] The device sends the uploaded image to the server.

[0334] Step 2:

[0335] The server sends the received images to the generation AI and emotion engine.

[0336] Step 3:

[0337] Generative AI analyzes the image and evaluates factors such as angle, facial expression, and background.

[0338] The emotion engine identifies the user's emotions from the facial expressions in the image, obtaining identification results such as "not smiling" or "nervous."

[0339] Step 4:

[0340] The server provides specific feedback to the user on how to improve based on the analysis results from the AI ​​generation and the emotion data from the emotion engine. For example, it provides advice such as "Use a smile that looks friendlier."

[0341] Step 5:

[0342] Modify your profile picture or upload a new one based on the feedback you provide.

[0343] Profile text analysis and sentiment identification

[0344] Step 1:

[0345] The user enters his / her profile text into the terminal.

[0346] The terminal sends the entered text to the server.

[0347] Step 2:

[0348] The server sends the received text to the generation AI and emotion engine.

[0349] Step 3:

[0350] Generative AI analyzes the text and generates specific improvements as feedback.

[0351] The emotion engine identifies the user's emotion from the wording of the text and obtains emotion data such as "negative" or "positive."

[0352] Step 4:

[0353] The server uses the analysis results from the generated AI and the emotional data from the emotion engine to suggest specific improvements to the user, such as advice such as "It would be good to add more passionate words."

[0354] Step 5:

[0355] Modify the profile text based on the feedback provided by the user.

[0356] Personality diagnosis and profile suggestions through question-and-answering

[0357] Step 1:

[0358] The terminal displays a simple question to the user.

[0359] The user answers the questions and inputs the answer data into the terminal.

[0360] Step 2:

[0361] The terminal transmits the collected response data to the server.

[0362] The server sends this to the generation AI and emotion engine.

[0363] Step 3:

[0364] The generating AI analyzes the response data and conducts a personality diagnosis.

[0365] The emotion engine identifies the emotion expressed by the user when answering and obtains emotional data such as "enjoyed" or "indifferent."

[0366] Step 4:

[0367] The server uses data from the generation AI and emotion engine to suggest optimal profile sentences to users, such as sentences that reflect a positive personality.

[0368] Step 5:

[0369] Users can use the suggested profile text as a reference to enrich their own profiles.

[0370] Recommendations for compatible people

[0371] Step 1:

[0372] The server sends the results of the personality test and analyzed profile data to the generation AI and emotion engine.

[0373] Step 2:

[0374] Generative AI and an emotion engine analyze the data to identify optimal matches. For example, by combining the emotion data of users with the same hobbies, it can select a partner with greater accuracy.

[0375] Step 3:

[0376] The server provides the recommendation results to the user.

[0377] The user decides whether to contact the recommended person.

[0378] Spam and fraudulent account detection

[0379] Step 1:

[0380] The server sends the newly registered profile to the generation AI and emotion engine.

[0381] Step 2:

[0382] Generative AI analyzes profiles to detect potential spam or fraudulent accounts.

[0383] The emotion engine detects emotional anomalies, identifying, for example, "unnaturally positive expressions."

[0384] Step 3:

[0385] The server will then appropriately filter out spam accounts based on the detection results.

[0386] Regular monitoring and countermeasures are carried out to ensure the server maintains a safe matching environment.

[0387] Example 2

[0388] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0389] Conventional matching systems lack the accuracy of analyzing user profile images and text, and do not adequately identify emotions, resulting in inadequate feedback and optimal matching. Furthermore, the accuracy of detecting spam and fraudulent accounts is low, meaning a safe matching environment cannot be guaranteed.

[0390] 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.

[0391] In this invention, the server includes: means for analyzing a user's profile image using a generation AI and providing feedback on specific improvements regarding features such as angle, facial expression, and background; means for identifying emotions from the user's profile image using an emotion engine and reflecting the emotion data in the feedback; means for analyzing the user's profile text using a generation AI and providing feedback on specific improvements; means for identifying emotions from the wording of the profile text using an emotion engine and reflecting the emotion data in the feedback; means for using a generation AI to ask the user short questions and, based on the answers, generate an optimal profile statement or suggest editing; and means for automatically detecting spam and fraudulent accounts and eliminating suspicious accounts using the generation AI and the emotion engine. This improves the accuracy of user profile analysis and emotion identification, enabling optimal feedback and matching. It also enables the provision of a safe matching environment.

[0392] "Generative AI" is an artificial intelligence technology that uses machine learning algorithms to analyze data and generate specific feedback and suggestions.

[0393] An "emotion engine" is an analytical technology that identifies human emotions from images and text and reflects the results in applications.

[0394] "Profile Image" means a photo of a user's face or other still image used to represent themselves in the Matching System.

[0395] "Profile text" is text data that allows a user to describe their characteristics, hobbies, interests, etc.

[0396] "Feedback" refers to specific improvements and suggestions provided to users based on the results of analysis by generative AI and emotion engines.

[0397] "Spam accounts" are fraudulent accounts created for advertising or fraudulent purposes, typically sending large volumes of meaningless messages.

[0398] A "fraudulent account" is a fraudulent account created with the intent to deceive others and abuse the user's trust.

[0399] "Simple questions" are short questions that the generative AI asks the user to understand the user's characteristics and personality.

[0400] A "profile sentence" is a sentence that expresses a user's characteristics and appeal, suggested by the AI ​​based on answers to simple questions.

[0401] A "personality diagnosis" is an evaluation method that analyzes the questions answered by the user and the text entered to identify their personality and behavioral characteristics.

[0402] An "interface" refers to the operating screen or input means through which users and systems exchange information, and plays a role in improving usability.

[0403] The present invention is a system that integrates generative AI and an emotion engine to more effectively analyze a user's profile image and text and provide optimal feedback and matching. The system of the present invention has the following configuration and operation.

[0404] Overall structure

[0405] The system mainly consists of a server, a device, and a user. The server is equipped with a generative AI and an emotion engine, and provides feedback and suggestions to the user based on the analysis results. The device accepts user operations and data input and sends it to the server. Users register their own profile image and text via the device and receive feedback and suggestions.

[0406] Hardware and software used

[0407] Hardware:

[0408] Server: A high-performance computer is recommended. If necessary, a GPU can be installed to support high-speed analysis by the generative AI and emotion engine.

[0409] Terminal: A device that provides a user interface, such as a smartphone, tablet, or computer.

[0410] software:

[0411] Generative AI models: Use models trained using deep learning frameworks (e.g., TensorFlow, PyTorch).

[0412] Emotion Engine: Integrates facial recognition algorithms (e.g., OpenCV, dlib) and natural language processing models (e.g., BERT) to perform emotion analysis.

[0413] Server software: Database management systems (e.g., MySQL, PostgreSQL) and web servers (e.g., Apache, Nginx).

[0414] Profile picture analysis and emotion identification

[0415] The user uploads their profile picture to the device. The device sends the uploaded image to the server. The server sends the received image to the generation AI and emotion engine. The generation AI analyzes the image and evaluates features such as angle, facial expression, and background. The emotion engine identifies emotions from the user's facial expression. The server generates feedback based on the analysis results from the generation AI and the emotion data from the emotion engine and provides it to the user. As a specific example, if the user is not smiling, the feedback provided is, "Use a smile that looks more friendly."

[0416] Profile text analysis and sentiment identification

[0417] The user enters profile text into the device. The device sends the entered text to the server. The server sends the received text to the generation AI and emotion engine. The generation AI analyzes the text and provides feedback on specific areas for improvement. The emotion engine identifies emotions from the wording of the text. The server generates suggestions based on the analysis results from the generation AI and the emotion data from the emotion engine, and provides them to the user. For example, if the text seems bland, the server might suggest, "You might want to add more passionate words to better highlight yourself."

[0418] Personality diagnosis and profile suggestions through question-and-answering

[0419] The device displays simple questions to the user. The user answers the questions and enters the answer data into the device. The device sends the collected answer data to the server. The server sends it to the generation AI and emotion engine. The generation AI analyzes the answer data and performs a personality diagnosis, and the emotion engine identifies the emotion the user expressed when answering. The server suggests the most appropriate profile text to the user based on the data from the generation AI and emotion engine. As a specific example, if the user is proactive, a suggested profile text that reflects this will be provided.

[0420] Recommendations for compatible people

[0421] The server passes the personality test results and analyzed profile data to the generation AI and emotion engine. The generation AI and emotion engine analyze the data and identify the most suitable match. By incorporating emotion data, more accurate matching becomes possible. The server provides the recommendation results to the user. The user decides whether to contact the recommended person. For example, if the user's personality test results show that they are proactive and sociable, the server may recommend matching with "someone who is also proactive and sociable."

[0422] Spam and fraudulent account detection

[0423] The server sends newly registered profiles to the generation AI and emotion engine. The generation AI analyzes the profiles and detects possible spam or fraudulent accounts. The emotion engine also detects emotional anomalies and identifies suspicious accounts. The server then appropriately eliminates spam accounts based on the detection results, maintaining a safe matching environment. For example, if a newly registered account sends a large number of messages at once, the generation AI may determine that it is likely to be a spam account, and the server may suspend the account.

[0424] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0425] Profile picture analysis and emotion identification

[0426] Step 1:

[0427] The user uploads a profile picture to the device. The user operates the application on the device, selects an image file, and clicks the upload button.

[0428] Input: The profile image file selected by the user.

[0429] Output: The image file is saved to your device.

[0430] Step 2:

[0431] The device sends the uploaded image to the server. The device sends the image file to the server using an HTTP request.

[0432] Input: The uploaded profile image file.

[0433] Output: An image file is sent to the server.

[0434] Step 3:

[0435] The server sends the received images to the generative AI and emotion engine, and then sends a request to the appropriate API endpoint to forward the image data to the analysis unit.

[0436] Input: The image file received by the server.

[0437] Output: Image data is passed to the generative AI and emotion engine.

[0438] Step 4:

[0439] The generative AI analyzes the image and evaluates features such as angle, facial expression, and background, and then uses image processing algorithms to quantify each feature.

[0440] Input: Image data sent to the analysis unit.

[0441] Output: Quantified feature data such as angle, facial expression, background, etc.

[0442] Step 5:

[0443] The emotion engine identifies emotions from the user's facial expressions. The emotion engine uses image processing algorithms and machine learning models to tag emotions such as "happiness," "anger," and "sadness" from the facial expressions.

[0444] Input: Image data sent to the analysis unit.

[0445] Output: Identified emotion tags.

[0446] Step 6:

[0447] The server generates feedback based on the analysis results from the generation AI and emotion engine and provides it to the user. The server aggregates the analysis results and generates notifications for specific improvements to the user.

[0448] Input: quantified feature data and emotion tags.

[0449] Output: A specific feedback message to the user.

[0450] Profile text analysis and sentiment identification

[0451] Step 1:

[0452] The user enters profile text into the terminal. The user enters text into the text area and clicks the send button.

[0453] Input: The profile text entered by the user.

[0454] Output: Text data is saved to the terminal.

[0455] Step 2:

[0456] The device sends the entered text to the server. The device sends the text data to the server using an HTTP request.

[0457] Input: The profile text entered.

[0458] Output: Text data is sent to the server.

[0459] Step 3:

[0460] The server sends the received text to the generative AI and emotion engine, and then sends a request to the appropriate API endpoint to forward the text data to the analysis unit.

[0461] Input: Text data received by the server.

[0462] Output: Text data is passed to the generative AI and emotion engine.

[0463] Step 4:

[0464] Generative AI analyzes the text and provides specific feedback on improvements. Generative AI uses natural language processing algorithms to analyze the grammar and context of the text and generate specific suggestions.

[0465] Input: Text data sent to the analysis unit.

[0466] Output: Suggested data for grammar correction and content specification.

[0467] Step 5:

[0468] The emotion engine identifies emotions from the wording of the text. The emotion engine uses natural language processing algorithms to identify emotions such as "passion," "calm," or "joy" from the text.

[0469] Input: Text data sent to the analysis unit.

[0470] Output: Identified emotion tags.

[0471] Step 6:

[0472] The server generates suggestions based on the analysis results from the generation AI and emotion engine and provides them to the user. The server aggregates the analysis results and generates notifications for specific improvements and suggestions for the user.

[0473] Input: Grammar correction suggestion data and sentiment tags.

[0474] Output: A specific suggestion message to the user.

[0475] Personality diagnosis and profile suggestions through question-and-answering

[0476] Step 1:

[0477] The terminal displays simple questions to the user, and the terminal application displays the questions in a dialog format.

[0478] Input: Questions created by the generative AI.

[0479] Output: The question dialog that is displayed to the user.

[0480] Step 2:

[0481] The user answers the question and enters the answer data into the terminal. The user enters the answer and clicks the send button.

[0482] Input: The answer data entered by the user.

[0483] Output: The answer data is saved on the device.

[0484] Step 3:

[0485] The device sends the collected response data to the server. The device sends the response data to the server using an HTTP request.

[0486] Input: The entered response data.

[0487] Output: The response data is sent to the server.

[0488] Step 4:

[0489] The server sends this to the generative AI and emotion engine, which then makes a request to the appropriate API endpoint to forward the response data to the analysis unit.

[0490] Input: The response data received by the server.

[0491] Output: The answer data is passed to the generation AI and emotion engine.

[0492] Step 5:

[0493] The generation AI analyzes the response data to conduct a personality diagnosis, and the emotion engine identifies the emotion of the user when answering. The generation AI uses a machine learning model to analyze personality from the response data, and the emotion engine identifies emotion from the text.

[0494] Input: Response data sent to the analysis unit.

[0495] Output: Personality trait data and emotion tags.

[0496] Step 6:

[0497] The server proposes optimal profile sentences to users based on data from the generation AI and emotion engine.The server generates suggested profile sentences for users based on the analysis results.

[0498] Input: personality trait data and emotion tags.

[0499] Output: Suggestion of specific profile text to the user.

[0500] (Application example 2)

[0501] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0502] Conventional systems simply analyze users' profile images and text, but are unable to provide feedback or product recommendations based on the user's emotions and preferences. This makes it difficult to provide more personalized services to users. Furthermore, the accuracy of detecting spam and fraudulent accounts is low, making it impossible to provide a safe environment. Therefore, there is a need for more accurate profile analysis and recommendation systems.

[0503] 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.

[0504] In this invention, the server includes means for analyzing a user's profile image using a generation AI and providing feedback on specific improvements regarding elements such as angle, facial expression, and background, means for analyzing a user's profile text using a generation AI and providing feedback on specific improvements, means for using a generation AI to ask the user simple questions and generate an optimal profile statement or suggest editing based on the answers, means for using a generation AI to automatically detect spam accounts and fraudulent accounts and eliminate suspicious accounts, and means for analyzing a user's profile image and text using a generation AI and an emotion engine and recommending products based on the user's preferences and emotions. This enables more personalized feedback and product recommendations to users and promotes use in a safe environment.

[0505] "Generative AI" refers to artificial intelligence that uses advanced algorithms to analyze data and generate new information and feedback.

[0506] An "emotion engine" is a system that identifies emotions from user input data (e.g., images or text) and provides analysis results based on that.

[0507] A "profile image" is image data uploaded by a user to represent themselves.

[0508] "Profile text" refers to text data entered by a user to describe themselves.

[0509] "Recommendation" means proposing products and services that are individually suited to a user based on their preferences and behavior.

[0510] A "spam account" is an account created for fraudulent purposes, typically used to send random advertising or fraudulent messages.

[0511] A "fraudulent account" is an account created for the purpose of committing fraud, and provides false information with the intent of deceiving others.

[0512] "Analysis" is the process of examining and breaking down data in detail to understand its structure and meaning.

[0513] "Feedback" refers to ratings and suggestions for improvement provided based on user actions and input.

[0514] "Personalization" means providing content that is individualized according to the preferences and characteristics of individual users.

[0515] MODE FOR CARRYING OUT THE INVENTION

[0516] This invention is a system that integrates generative AI and an emotion engine to effectively analyze a user's profile image and text and provide optimal feedback and product recommendations. This system is composed of a server, a terminal, and a user. Specific embodiments of this system are described in detail below.

[0517] Profile picture analysis and emotion identification

[0518] Users upload their profile picture to their device. The device sends the uploaded image to the server. The server sends the received image to the generation AI and emotion engine. The generation AI analyzes the image and evaluates factors such as angle, facial expression, and background. The emotion engine identifies emotions from the user's facial expression. Based on the analysis results from the generation AI and the emotion data from the emotion engine, the server provides specific feedback to the user on areas for improvement. For example, if the user is not smiling, the server will provide advice such as "use a smile that looks more friendly."

[0519] Profile text analysis and sentiment identification

[0520] The user enters their profile text into the device. The device sends the entered text to the server. The server sends the received text to the generation AI and emotion engine. The generation AI analyzes the text and provides feedback on specific areas for improvement. The emotion engine identifies the user's emotions from the wording of the text. The server then suggests specific areas for improvement to the user based on the analysis results from the generation AI and the emotion data from the emotion engine. For example, if the text seems bland, the server will provide advice such as, "Add more passionate words to better highlight yourself."

[0521] Personality diagnosis and profile suggestions through question-and-answering

[0522] The device displays simple questions to the user. The user answers the questions and enters the answer data into the device. The device then sends the collected answer data to the server. The server then sends it to the generation AI and emotion engine. The generation AI analyzes the answer data and performs a personality diagnosis, and the emotion engine identifies the emotion the user felt when answering. The server then uses the data from the generation AI and emotion engine to suggest optimal profile text to the user. For example, if the user is proactive, it will provide suggested profile text that reflects this.

[0523] Recommendations for compatible people

[0524] The server passes the personality test results and analyzed profile data to the generation AI and emotion engine. The generation AI and emotion engine analyze the data and identify the most suitable match. By incorporating emotion data, more accurate matching becomes possible. The server provides the recommendation results to the user. The user decides whether to contact the recommended person.

[0525] Spam and fraudulent account detection

[0526] The server sends newly registered profiles to the generation AI and emotion engine. The generation AI analyzes the profiles and detects possible spam or fraudulent accounts. The emotion engine also detects emotional anomalies and identifies suspicious accounts. The server then appropriately eliminates spam accounts based on the detection results, maintaining a safe matching environment.

[0527] Product recommendations based on user preferences

[0528] Using generative AI and an emotion engine, the system analyzes a user's profile image and text to recommend products based on the user's preferences and emotions. For example, if a user uploads an image of themselves smiling and relaxed, and enters text about casual fashion, the system will recommend relaxed, casual clothing. Specific examples of prompts include:

[0529] We analyzed the user's profile picture to identify their emotions and recognized it as a "relaxed smile." Next, we determined that the user is interested in "casual fashion" from the text "I've been wearing casual clothes a lot lately." Based on this condition, we would like you to list products that are suitable for relaxing casual wear.

[0530] In this way, systems using generative AI models and emotion engines can provide more personalized feedback and product recommendations to users, promoting usage in a safe environment.

[0531] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0532] Step 1:

[0533] The user uploads a profile image to the device, which then sends the image data to the server, which then sends the image data to the generative AI and emotion engine.

[0534] Input: The user's profile picture.

[0535] Data processing: Pre-processing of images, such as transfer and compression.

[0536] Output: Image data for analysis.

[0537] Specific actions: The user selects an image using the app on their smartphone and presses the upload button.

[0538] Step 2:

[0539] The server uses generative AI to analyze the image, evaluating factors such as angle, facial expression, and background, while the emotion engine identifies emotions from the user's facial expressions.

[0540] Input: Profile image.

[0541] Data processing: Extraction of parameters based on image analysis algorithms (angle, facial expression, background, etc.).

[0542] Output: Analysis results and sentiment data.

[0543] Specific operation: The image analysis model on the server extracts image features, and the emotion engine outputs emotion labels.

[0544] Step 3:

[0545] Based on the analysis results from the generative AI and emotion engine, the server provides specific feedback to the user on how to improve their appearance, such as "Use a smile that looks friendlier."

[0546] Input: Analysis results and emotion data.

[0547] Data processing: Creating advice through feedback generation algorithms.

[0548] Output: The feedback message.

[0549] Specific operation: The server generates an advice message and sends it to the terminal.

[0550] Step 4:

[0551] Users enter their profile text into their device, which sends it to the server, which then sends it to the generative AI and emotion engine.

[0552] Input: Profile text.

[0553] Data processing: Text transfer and formatting.

[0554] Output: Text data.

[0555] Specific action: The user enters a profile statement in the text field and presses the submit button.

[0556] Step 5:

[0557] The server analyzes the profile text with generative AI and provides specific feedback on improvements, while the emotion engine identifies emotions from the wording of the text.

[0558] Input: Profile text.

[0559] Data processing: Analysis using text analysis algorithms and generation of sentiment labels.

[0560] Output: Analysis results and sentiment data.

[0561] Specific operation: The analysis model on the server analyzes the text and outputs improvements and emotion labels.

[0562] Step 6:

[0563] The server then uses the analysis results from the generative AI and emotion engine to suggest specific improvements to the user, offering advice such as "Use more passionate words to better express yourself."

[0564] Input: Analysis results and emotion data.

[0565] Data processing: Creating advice through feedback generation algorithms.

[0566] Output: The feedback message.

[0567] Specific operation: The server generates an advice message and sends it to the terminal.

[0568] Step 7:

[0569] The device displays simple questions to the user, who answers them, and the device sends the answer data to the server.

[0570] Input: Question response data.

[0571] Data processing: Data collection and transfer.

[0572] Output: Question-answer data.

[0573] What happens: The user answers the questions displayed and submits the answers.

[0574] Step 8:

[0575] The server sends the collected response data to the generation AI and emotion engine to conduct a personality diagnosis and identify the emotions the user was feeling when answering.

[0576] Input: Question and answer data.

[0577] Data processing: Analysis using personality diagnosis algorithms and emotion identification algorithms.

[0578] Output: Personality test results and emotional data.

[0579] Specific operation: The model on the server analyzes the data and outputs diagnostic results.

[0580] Step 9:

[0581] The server suggests the most suitable profile sentence for the user based on data from the generative AI and emotion engine.

[0582] Input: Personality test results and emotion data.

[0583] Data processing: Forming suggestions using a profile sentence generation algorithm.

[0584] Output: Profile text suggestions.

[0585] Specific operation: The server generates a proposal message and sends it to the terminal.

[0586] Step 10:

[0587] The server passes the personality test results and analyzed profile data to a generative AI and emotion engine to identify the best matches.

[0588] Input: Personality test results and profile data.

[0589] Data processing: Analysis using matching algorithm.

[0590] Output: A list of matches.

[0591] Specific operation: The server analyzes the data and outputs the matching results.

[0592] Step 11:

[0593] The server analyzes user profile images and text and recommends products based on the user's preferences and emotions.

[0594] Input: Profile image and text, analysis results.

[0595] Data processing: Analysis using product recommendation algorithms.

[0596] Output: A list of products and reasons.

[0597] Specific operation: The server recommends products based on the analysis results and generates a message including the reasons for the recommendation.

[0598] Step 12:

[0599] The server sends newly registered profiles to the generative AI and emotion engine to detect potential spam or fraudulent accounts.

[0600] Input: New profile data.

[0601] Data processing: Analysis using spam detection algorithms and emotion anomaly detection algorithms.

[0602] Output: Detection results.

[0603] Specific operation: The detection model on the server analyzes the profile and outputs the results.

[0604] Step 13:

[0605] The server will then appropriately eliminate spam accounts based on the detection results, maintaining a safe environment.

[0606] Input: The detection result.

[0607] Data processing: Eliminating spam accounts through account management systems.

[0608] Output: A list of secure accounts.

[0609] Specific action: The server will identify spam accounts based on the detection results and remove them appropriately.

[0610] In this way, each step works together to create a system that provides optimal feedback and product recommendations to users and ensures a safe environment.

[0611] 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.

[0612] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0613] 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.

[0614] [Second embodiment]

[0615] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0616] 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.

[0617] 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).

[0618] 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.

[0619] 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.

[0620] 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).

[0621] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0622] 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.

[0623] 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.

[0624] 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.

[0625] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0626] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0627] The present invention is a dating matching service system that utilizes generative AI to optimize the balance between a user's appearance and personality and provide a safe matching environment. Specific embodiments of this system are described below.

[0628] 1. Profile image analysis and improvement

[0629] The user uploads their profile picture, which the server receives and sends to the generation AI.

[0630] The server uses generative AI to analyze factors such as the angle, facial expression, and background of the user's profile picture. For example, if the facial expression in the picture is unattractive or the background is cluttered, it will provide specific feedback on how to improve it.

[0631] Based on the feedback provided, the user can modify the image or upload a new image.

[0632] 2. Profile text analysis and improvement

[0633] The user enters their profile text, and the server receives this text and sends it to the generating AI.

[0634] The server uses a generative AI to analyze the profile text and provide specific feedback on areas for improvement. For example, if someone writes, "My hobbies are reading and watching movies," the server might suggest, "You should write more about your personality and work in more detail."

[0635] The user then modifies the text based on the feedback provided.

[0636] 3. Personality diagnosis and profile suggestion through question-and-answering

[0637] The device displays a simple question to the user, who then answers it.

[0638] The server sends the collected response data to the AI, which then performs a personality diagnosis based on the answers. Based on the generated diagnosis results, the AI ​​then suggests the optimal profile text to the user.

[0639] Users can use the suggested profile text as a reference to enrich their own profiles.

[0640] 4. Recommendations of compatible people

[0641] The server then recommends suitable matches to users based on the results of the personality assessment and analyzed profile data, through a process in which generative AI analyzes the data and identifies those with the best compatibility.

[0642] Users can decide whether to contact the recommended people, and this system allows users to meet people who are suitable for them more efficiently.

[0643] 5. Spam and fraudulent account detection

[0644] The server constantly monitors newly registered profiles using generative AI and automatically detects spam accounts and potentially fraudulent accounts.

[0645] The server will then properly remove any spam accounts it detects and maintain a safe matching environment, such as detecting and removing accounts with numerous identical profile statements or accounts that provide excessively incorrect information.

[0646] In this way, the present invention utilizes generative AI to not only optimize users' profile details, but also provide a safe and reliable matching environment, allowing users to achieve the best possible match that balances appearance and personality, and to use the service with peace of mind.

[0647] The processing flow will be explained below.

[0648] Profile image analysis and improvement

[0649] Step 1:

[0650] The user uploads their profile picture to the device.

[0651] The device sends the uploaded image to the server.

[0652] Step 2:

[0653] The server passes the received image to the generation AI.

[0654] Generative AI analyzes the image and evaluates factors such as angle, facial expression, and background.

[0655] Step 3:

[0656] Based on the analysis results from the generated AI, the server provides specific feedback to the user on areas for improvement.

[0657] Based on the feedback, the user modifies the image or uploads a new image.

[0658] Profile text analysis and improvement

[0659] Step 1:

[0660] The user enters his / her profile text into the terminal.

[0661] The terminal sends the entered text to the server.

[0662] Step 2:

[0663] The server passes the received text to the generation AI.

[0664] Generative AI analyzes the text and generates specific improvements as feedback.

[0665] Step 3:

[0666] The server provides feedback from the generated AI to the user.

[0667] The user then corrects the text based on the feedback provided.

[0668] Personality diagnosis and profile suggestions through question-and-answering

[0669] Step 1:

[0670] The terminal displays a simple question to the user.

[0671] The user answers the questions and inputs the answer data into the terminal.

[0672] Step 2:

[0673] The terminal transmits the collected response data to the server.

[0674] The server passes the response data to the generating AI and conducts a personality diagnosis.

[0675] Step 3:

[0676] The generative AI will generate the optimal profile text based on the diagnostic results.

[0677] The server proposes the generated profile statement to the user.

[0678] Step 4:

[0679] Users can use the suggested profile text as a reference to enrich their own profiles.

[0680] Recommendations for compatible people

[0681] Step 1:

[0682] The server passes the personality test results and analyzed profile data to the generation AI.

[0683] Generative AI analyzes the data and identifies the best matches.

[0684] Step 2:

[0685] The server provides the recommendation results to the user.

[0686] The user decides whether to contact the recommended person.

[0687] Spam and fraudulent account detection

[0688] Step 1:

[0689] The server passes the newly registered profile to the generation AI.

[0690] Generative AI analyzes profiles to detect potential spam or fraudulent accounts.

[0691] Step 2:

[0692] The server will now properly remove detected spam accounts.

[0693] Regular monitoring and countermeasures are carried out to ensure the server maintains a safe matching environment.

[0694] Example 1

[0695] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0696] In recent years, with the spread of online dating matching services, it is necessary not only to improve the quality of users' profile images and text, but also to provide a safe and reliable matching environment. However, it requires advanced skills and time for users to properly optimize their profiles, and eliminating spam and fraudulent accounts is also a major challenge. An efficient system to solve these problems is needed.

[0697] 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.

[0698] In this invention, the server includes means for using a generation AI to analyze a user's profile image and provide feedback on specific improvements regarding elements such as angle, facial expression, and background, means for using a generation AI to analyze the user's profile text and provide feedback on specific improvements, means for using a generation AI to ask the user simple questions and generate or suggest edits to an optimal profile statement based on the answers, means for using a generation AI to automatically detect spam accounts and fraudulent accounts and eliminate suspicious accounts, means for using a generation AI to identify and recommend compatible individuals based on the results of a personality assessment and analyzed profile data, and means for suggesting input prompts for the generation AI model. This allows users to achieve optimal matches that balance appearance and inner qualities, enabling them to use a safe and reliable dating matching service.

[0699] "Generative AI" is an artificial intelligence technology that uses machine learning algorithms to generate data and automate specific tasks.

[0700] "Profile Picture" means a photo of a user's face or other image uploaded by a user to express their identity or personality.

[0701] "Profile text" is written information in which a user describes themselves, their hobbies, and interests.

[0702] "Spam Accounts" are automatically generated user accounts registered for irrelevant advertising or fraudulent purposes.

[0703] A "fraudulent account" is a user account that provides false information with the intent to deceive others.

[0704] "Feedback" refers to suggestions and advice for improving images and text that the generative AI provides to the user.

[0705] "Personality assessment" is the process by which the generative AI evaluates a user's personality traits based on the questions the user answers.

[0706] "Recommendation" refers to the generation AI recommending the best match to the user.

[0707] A "prompt" is a guided sentence input to a generative AI model, used to induce a specific response or generation.

[0708] The present invention is a dating matching service system that utilizes generative AI to optimize the balance between a user's appearance and personality and provide a safe matching environment. Specific embodiments of this system are described below.

[0709] 1. Profile image analysis and improvement

[0710] When a user uploads their profile picture, the server receives the image and sends it to the generation AI. The generation AI analyzes elements of the user's profile picture, such as the angle, facial expression, and background. For example, if the facial expression in the image is unattractive or the background is cluttered, it provides specific feedback on how to improve the image. The user can then modify the image or upload a new image based on the feedback provided.

[0711] Examples:

[0712] If the background of an image uploaded by a user is cluttered, the generating AI will provide feedback such as "It would be better to simplify the background."

[0713] Example prompt for a generative AI model:

[0714] "How can we improve this profile picture background?"

[0715] 2. Profile text analysis and improvement

[0716] When a user enters their profile text, the server receives this text and sends it to the generation AI. The generation AI analyzes the profile text and provides feedback on specific areas for improvement. For example, for a profile entry such as "My hobbies are reading and watching movies," the server may suggest "You should write more about your personality and work." The user then edits the text based on the provided feedback.

[0717] Examples:

[0718] For example, if a user writes, "My hobbies are reading and watching movies," the AI ​​generator will provide feedback such as, "You should write in more detail about your personality and work."

[0719] Example prompt for a generative AI model:

[0720] "Please suggest improvements to this profile text."

[0721] 3. Personality diagnosis and profile suggestion through question-and-answering

[0722] The device displays simple questions to the user, who then answers them. The server then sends the collected response data to the AI, which then performs a personality diagnosis based on the answers. Based on the generated diagnosis results, the AI ​​then suggests an optimal profile statement to the user. The user can then use the suggested statement to enhance their profile.

[0723] Examples:

[0724] If a user answers "reading" to the question "How do you spend your holidays?", the AI ​​generator will suggest "the perfect profile sentence for you, a book lover."

[0725] Example prompt for a generative AI model:

[0726] "Suggest the best profile sentence for this user based on their answers."

[0727] 4. Recommendations of compatible people

[0728] The server recommends suitable matches to users based on the results of the personality test and analyzed profile data. This is done through a process in which generative AI analyzes data and identifies people with the best compatibility. Users then decide whether or not to contact the recommended people. This system allows users to meet suitable partners more efficiently.

[0729] Examples:

[0730] If the personality test results and profile data of User A and User B match, the server will use generative AI to recommend the two people.

[0731] Example prompt for a generative AI model:

[0732] "Please suggest the best matches for this user based on their personality test results and profile data."

[0733] 5. Spam and fraudulent account detection

[0734] The server constantly monitors newly registered profiles using generative AI to automatically detect spam accounts and potentially fraudulent accounts. Detected spam accounts are then appropriately removed to maintain a safe matching environment. For example, accounts with numerous identical profile statements or accounts providing extremely incorrect information are detected and removed.

[0735] Examples:

[0736] If there are a large number of newly registered profiles with the same text, the generation AI will identify them as spam accounts and eliminate them.

[0737] Example prompt for a generative AI model:

[0738] "Please determine if this profile is spam or a potential scam."

[0739] In this way, the present invention utilizes generative AI to not only optimize users' profile details, but also provide a safe and reliable matching environment, allowing users to achieve the best possible match that balances appearance and personality, and to use the service with peace of mind.

[0740] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0741] Step 1:

[0742] User uploads a profile picture

[0743] The user selects their profile picture and presses the upload button. The server receives the image data sent by the user.

[0744] input:

[0745] Image data uploaded by users

[0746] Specific behavior:

[0747] Check the image file type and size and save it in the appropriate format.

[0748] output:

[0749] Profile image data stored on the server

[0750] Step 2:

[0751] The server sends the profile image to the AI ​​for analysis.

[0752] The server sends the received profile image to the generation AI for analysis. The generation AI model analyzes the image's angle, facial expression, and background to identify areas for improvement.

[0753] input:

[0754] Profile image data stored on the server

[0755] Specific behavior:

[0756] Generative AI performs facial recognition and evaluates facial expression, background, and angle.

[0757] Data analysis algorithms are used to extract improvements to the images.

[0758] output:

[0759] Specific improvements provided by the generation AI (e.g., stiff facial expressions, cluttered background)

[0760] Step 3:

[0761] User enters profile text

[0762] Users enter text to describe their profile, and the server receives and stores this text data.

[0763] input:

[0764] User-entered profile text

[0765] Specific behavior:

[0766] The text data is stored on the server.

[0767] Check the format of the saved data and make it ready to send to the generating AI.

[0768] output:

[0769] Profile text data stored on the server

[0770] Step 4:

[0771] The server sends the profile text to the AI ​​generator for analysis.

[0772] The server sends the saved profile text to the AI ​​generator for analysis, which analyzes the content of the text and suggests specific improvements.

[0773] input:

[0774] Profile text data stored on the server

[0775] Specific behavior:

[0776] Generative AI performs natural language processing and analyzes the content of the text.

[0777] Extract areas for improvement from the analysis results.

[0778] output:

[0779] Specific improvements provided by the generative AI (e.g., writing more specifically about hobbies)

[0780] Step 5:

[0781] The device asks the user a simple question and collects the answer.

[0782] The terminal displays some simple questions to the user, who answers them. The server receives and stores the answer data.

[0783] input:

[0784] Question data answered by users

[0785] Specific behavior:

[0786] The terminal displays the question and accepts user input.

[0787] The response data is sent to the server.

[0788] output:

[0789] Response data stored on the server

[0790] Step 6:

[0791] The server conducts a personality test and suggests the best profile sentence for you.

[0792] The server sends the collected response data to the AI ​​generator, which then performs a personality diagnosis. Based on the results of the diagnosis, the AI ​​then suggests the most suitable profile text for the user.

[0793] input:

[0794] Response data stored on the server

[0795] Specific behavior:

[0796] The generative AI conducts a personality diagnosis and evaluates the user's personality traits.

[0797] Based on the diagnosis results, the optimal profile statement is generated.

[0798] output:

[0799] The optimal profile text suggested by generative AI

[0800] Step 7:

[0801] The server automatically detects and removes spam and fraudulent accounts.

[0802] The server constantly monitors newly registered profiles using generative AI, automatically detecting spam accounts and potentially fraudulent accounts, and then appropriately removing detected accounts.

[0803] input:

[0804] Newly registered profile data

[0805] Specific behavior:

[0806] Generative AI analyzes registered profile data to identify spam and fraud patterns.

[0807] Remove confirmed spam or fraudulent accounts from the system.

[0808] output:

[0809] Safe User Profile List

[0810] Step 8:

[0811] The server recommends suitable matches to the user.

[0812] The server uses generative AI to recommend the best possible matches to users based on the personality test results and analyzed profile data.

[0813] input:

[0814] Analyzed profile data and personality test results

[0815] Specific behavior:

[0816] Generative AI analyzes the data and identifies people who are compatible.

[0817] The identified matches are displayed to the user.

[0818] output:

[0819] A list of recommended matches for the user

[0820] Thus, through each step of the system, users can optimize their profile and enjoy a safe and effective matching environment.

[0821] (Application example 1)

[0822] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0823] Conventional matching systems only recommend suitable partners based on the user's profile information and personality assessment, but do not offer services or product suggestions that users can actually experience in physical stores. This makes it difficult for users to choose products and services that suit them. Other issues include insufficient feedback on image and text improvements, and ineffective elimination of fraudulent and spam accounts.

[0824] 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.

[0825] In this invention, the server includes means for analyzing a user's profile image using a generation AI and providing feedback on specific improvements regarding elements such as angle, facial expression, and background, means for analyzing a user's profile text using a generation AI and providing feedback on specific improvements, means for using a generation AI to ask the user simple questions and generate or suggest editing an optimal profile statement based on the answers, means for using a generation AI to automatically detect spam accounts and fraudulent accounts and eliminate suspicious accounts, and means for suggesting optimal products and services based on the user's profile information and personality assessment data. This enables more accurate product and service suggestions based on the user's appearance and inner information, realizing a safe and reliable matching environment.

[0826] "Generative AI" refers to artificial intelligence that generates natural language and images based on user input data.

[0827] A "profile picture" is a photo that a user uses to show their personal information and appearance.

[0828] "Angle" is an element that indicates the relative positions of the subject's face and body in the profile image.

[0829] "Expression" is an element that indicates the facial expression of the user in the profile picture.

[0830] "Background" refers to the environment or scenery that appears behind the subject in a profile picture.

[0831] "Profile text" refers to text information in which a user writes about themselves, their hobbies, etc.

[0832] "Feedback" refers to improvements and advice provided by the generative AI to the user.

[0833] A "question" is a question-and-answer format information gathering tool presented to the user.

[0834] "Personality diagnosis" is the process of evaluating a user's personality traits based on the user's response data, etc.

[0835] "Spam Account" means a user account created for fraudulent purposes.

[0836] A "Fraudulent Account" is a fraudulent user account created with the intent to deceive users.

[0837] "Product" means an object such as a good or service that is provided to a user.

[0838] "Service" means any act or service provided to a User.

[0839] "Profile Information" refers to all data about a user, including images and text.

[0840] "Personality assessment data" refers to data regarding a user's personality characteristics obtained through a personality assessment.

[0841] "Recommendation" means a recommendation of a product or service made to a User.

[0842] An "interface" is a screen or operating means that allows a user to interact with a system.

[0843] The present invention is a system that utilizes generative AI to optimize a user's profile image and text, and safely and effectively suggests optimal products and services to the user. Specific embodiments of this system are described below.

[0844] System Configuration and Operation

[0845] This system consists of three elements: a server, a terminal, and a user. The server operates the generative AI, the terminal provides the user interface, and the user operates the terminal to use the service.

[0846] 1. Profile image analysis and improvement

[0847] The server first receives the profile image uploaded by the user. This image is then sent to the generation AI, which analyzes elements such as the angle, facial expression, and background. For example, the image is analyzed using OpenCV, and if the background is too cluttered, the server provides specific feedback to the user on how to improve it, such as "Please change to a simpler background." This allows users to create more attractive profile images.

[0848] 2. Profile text analysis and improvement

[0849] The profile text entered by the user is also sent to the server. The server uses generative AI to analyze the text and generate specific suggestions for improvement. For example, for the text "My hobbies are reading and watching movies," the server will provide advice such as "You should write more details about your personality and work." This allows users to create a more comprehensive profile text.

[0850] 3. Personality diagnosis and profile suggestion through question-and-answering

[0851] The device displays simple questions to the user, who then answers them. These answers are sent to a server, where a personality diagnosis is performed using a generative AI. Based on the results, the device suggests the profile text that best suits the user's personality. For example, if the answers to the questions are "Q1: A1, Q2: A2, Q3: A3," the device inputs this information into the prompt and generates the appropriate text.

[0852] 4. Product and service recommendations

[0853] Using generative AI, the server will suggest optimal products and services based on the results of a personality test and profile information. These suggestions are based on prompts and are presented to the user in an easy-to-understand format. For example, based on a profile that says, "My hobbies are reading and watching movies," a specific suggestion will be made, such as, "How about a T-shirt from a new movie?"

[0854] Hardware and software used

[0855] Hardware: Storefront customer service robots (e.g., generic company robots), cameras (to capture images of users)

[0856] Software: OpenCV (image analysis), OpenAI API (text generation), Transformers library (implementation of generative AI)

[0857] Examples of concrete examples and prompts

[0858] When implementing this in a brick-and-mortar apparel store, the following specific examples are possible:

[0859] When a user shows the robot an image of themselves, the robot suggests, "The background is too cluttered. Please change it to a simpler background."

[0860] Based on the profile text, the recommendation is, "Since your hobby is watching movies, how about a T-shirt from a new movie?"

[0861] Example prompt sentence:

[0862] 1. Profile text improvement suggestions:

[0863] markdown

[0864] How can we improve the following profile text?

[0865] My hobbies are reading and watching movies

[0866] 2. Personality Test and Suggestions:

[0867] markdown

[0868] Please assess the user's personality based on the answers below and make suggestions:

[0869] Q1: A1, Q2: A2, Q3: A3

[0870] 3. Product Suggestion:

[0871] markdown

[0872] Profile text: My hobbies are reading and watching movies

[0873] Personality Test: Introverted but highly sensitive

[0874] Please suggest suitable products based on these.

[0875] As described above, this system utilizes generative AI to optimize user information and can suggest appropriate products and services in physical stores, providing a safe and reliable matching environment and increasing user satisfaction.

[0876] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0877] Step 1: Upload and analyze your profile image

[0878] The server receives the profile image uploaded by the user via their device. This image is sent to a generative AI model, which analyzes factors such as angle, facial expression, and background. The input is the profile image, and the output is feedback on areas for improvement. Specifically, OpenCV is used to analyze the angle and facial expression of the image, and if the background is cluttered, feedback is generated, such as "Please change to a simpler background."

[0879] Step 2: Enter and parse profile text

[0880] The server receives the profile text entered by the user via their device. This text is sent to the generative AI model, which analyzes the content of the text. Specific improvements are then provided as feedback. The input is the profile text, and the output is feedback on improvements. Specifically, the generative AI model suggests, for example, for text such as "My hobbies are reading and watching movies," that "you should write in more detail about your personality and work."

[0881] Step 3: Personality assessment through questions and answers

[0882] The device displays simple questions to the user. The user answers the questions, and the answer data is sent to the server. The server then sends the collected answer data to a generative AI model, which then performs a personality diagnosis based on that data. The input is the answer data to the questions, and the output is the personality diagnosis result. Specifically, the generative AI model is used to generate prompt sentences, and optimal advice is generated based on those sentences.

[0883] Step 4: Generating and suggesting profile text

[0884] The server generates an optimal profile sentence based on the results of the personality assessment. This generated sentence is then suggested to the user via their device. The input is the personality assessment result and profile information, and the output is the suggested profile sentence. Specifically, the generative AI model generates the optimal text based on prompts such as "Q1: A1, Q2: A2, Q3: A3."

[0885] Step 5: Recommend products and services

[0886] The server uses a generative AI model to suggest optimal products and services based on the user's profile information and personality test results. This information is presented to the user via their device. The input is profile information and personality test results, and the output is suggested products and services. Specifically, based on information such as "My hobbies are reading and watching movies" or "I'm introverted but highly sensitive," the server makes recommendations such as "How about a T-shirt from a new movie?"

[0887] Step 6: Detecting spam and fraudulent accounts

[0888] The server uses a generative AI model to constantly monitor newly registered accounts and automatically detect spam and fraudulent accounts. When a suspicious account is detected, it is appropriately removed. The input is the profile data of the newly registered account, and the output is a list of accounts to be removed. Specifically, the generative AI model is used to identify and remove large numbers of accounts with the same profile text or accounts that provide incorrect information.

[0889] These are the specific processing steps of this system. By executing these steps in an orderly manner, it is possible to provide profile information and product / service suggestions that are optimized for the user.

[0890] 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.

[0891] The present invention is a system that integrates generative AI and an emotion engine to more effectively analyze users' profile images and text, and provide optimal feedback and matching. Specific embodiments of various functions are described below.

[0892] 1. Profile picture analysis and emotion identification

[0893] The user uploads their profile picture to the device, which then sends the uploaded picture to the server.

[0894] The server sends the received images to the generation AI and emotion engine.

[0895] The generative AI analyzes the image, evaluating factors such as angle, facial expression, and background, while the emotion engine identifies emotions from the user's facial expressions.

[0896] The server provides specific feedback to the user based on the analysis results from the generation AI and the emotion data from the emotion engine. For example, if the user is not smiling, the server will provide advice such as "Use a smile that looks friendlier."

[0897] Based on the feedback provided, the user can modify the image or upload a new image.

[0898] 2. Profile text analysis and sentiment identification

[0899] The user enters their profile text into the terminal, which then sends the entered text to the server.

[0900] The server sends the received text to the generation AI and emotion engine.

[0901] The generative AI analyzes the text and provides specific feedback on improvements, while the emotion engine identifies the user's emotions from the wording of the text.

[0902] The server then uses the analysis results from the generation AI and the emotion data from the emotion engine to suggest specific improvements to the user. For example, if the text seems bland, the server will provide advice such as, "Add more passionate words to better express yourself."

[0903] The user then modifies the text based on the feedback provided.

[0904] 3. Personality diagnosis and profile suggestion through question-and-answering

[0905] The terminal displays simple questions to the user, who answers the questions and inputs the answer data into the terminal.

[0906] The device sends the collected response data to the server, which then sends it to the generation AI and emotion engine.

[0907] The generation AI analyzes the response data and performs a personality diagnosis, and the emotion engine identifies the emotions the user expressed when answering.

[0908] The server uses data from the generative AI and emotion engine to suggest optimal profile sentences to users. For example, if a user is proactive, a profile sentence that reflects this will be provided.

[0909] Users can use the suggested profile text as a reference to enrich their own profiles.

[0910] 4. Recommendations of compatible people

[0911] The server passes the results of the personality test and analyzed profile data to the generation AI and emotion engine.

[0912] Generative AI and an emotion engine analyze data to identify optimal matches. By incorporating emotion data, more accurate matching becomes possible.

[0913] The server provides the recommendation results to the user, who then decides whether to contact the recommended person.

[0914] 5. Spam and fraudulent account detection

[0915] The server sends the newly registered profile to the generation AI and emotion engine.

[0916] Generative AI analyzes profiles to detect potential spam or fraudulent accounts, while a sentiment engine also detects emotional anomalies to identify suspicious accounts.

[0917] The server will then appropriately eliminate spam accounts based on the detection results, maintaining a safe matching environment.

[0918] By integrating these functions, users can achieve the optimal match that balances appearance and personality, providing a matching environment that can be used with peace of mind.

[0919] The processing flow will be explained below.

[0920] Profile picture analysis and emotion identification

[0921] Step 1:

[0922] The user uploads their profile picture to the device.

[0923] The device sends the uploaded image to the server.

[0924] Step 2:

[0925] The server sends the received images to the generation AI and emotion engine.

[0926] Step 3:

[0927] Generative AI analyzes the image and evaluates factors such as angle, facial expression, and background.

[0928] The emotion engine identifies the user's emotions from the facial expressions in the image, obtaining identification results such as "not smiling" or "nervous."

[0929] Step 4:

[0930] The server provides specific feedback to the user on how to improve based on the analysis results from the AI ​​generation and the emotion data from the emotion engine. For example, it provides advice such as "Use a smile that looks friendlier."

[0931] Step 5:

[0932] Modify your profile picture or upload a new one based on the feedback you provide.

[0933] Profile text analysis and sentiment identification

[0934] Step 1:

[0935] The user enters his / her profile text into the terminal.

[0936] The terminal sends the entered text to the server.

[0937] Step 2:

[0938] The server sends the received text to the generation AI and emotion engine.

[0939] Step 3:

[0940] Generative AI analyzes the text and generates specific improvements as feedback.

[0941] The emotion engine identifies the user's emotion from the wording of the text and obtains emotion data such as "negative" or "positive."

[0942] Step 4:

[0943] The server uses the analysis results from the generated AI and the emotional data from the emotion engine to suggest specific improvements to the user, such as advice such as "It would be good to add more passionate words."

[0944] Step 5:

[0945] Modify the profile text based on the feedback provided by the user.

[0946] Personality diagnosis and profile suggestions through question-and-answering

[0947] Step 1:

[0948] The terminal displays a simple question to the user.

[0949] The user answers the questions and inputs the answer data into the terminal.

[0950] Step 2:

[0951] The terminal transmits the collected response data to the server.

[0952] The server sends this to the generation AI and emotion engine.

[0953] Step 3:

[0954] The generating AI analyzes the response data and conducts a personality diagnosis.

[0955] The emotion engine identifies the emotion expressed by the user when answering and obtains emotional data such as "enjoyed" or "indifferent."

[0956] Step 4:

[0957] The server uses data from the generation AI and emotion engine to suggest optimal profile sentences to users, such as sentences that reflect a positive personality.

[0958] Step 5:

[0959] Users can use the suggested profile text as a reference to enrich their own profiles.

[0960] Recommendations for compatible people

[0961] Step 1:

[0962] The server sends the results of the personality test and analyzed profile data to the generation AI and emotion engine.

[0963] Step 2:

[0964] Generative AI and an emotion engine analyze the data to identify optimal matches. For example, by combining the emotion data of users with the same hobbies, it can select a partner with greater accuracy.

[0965] Step 3:

[0966] The server provides the recommendation results to the user.

[0967] The user decides whether to contact the recommended person.

[0968] Spam and fraudulent account detection

[0969] Step 1:

[0970] The server sends the newly registered profile to the generation AI and emotion engine.

[0971] Step 2:

[0972] Generative AI analyzes profiles to detect potential spam or fraudulent accounts.

[0973] The emotion engine detects emotional anomalies, identifying, for example, "unnaturally positive expressions."

[0974] Step 3:

[0975] The server will then appropriately filter out spam accounts based on the detection results.

[0976] Regular monitoring and countermeasures are carried out to ensure the server maintains a safe matching environment.

[0977] Example 2

[0978] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0979] Conventional matching systems lack the accuracy of analyzing user profile images and text, and do not adequately identify emotions, resulting in inadequate feedback and optimal matching. Furthermore, the accuracy of detecting spam and fraudulent accounts is low, meaning a safe matching environment cannot be guaranteed.

[0980] 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.

[0981] In this invention, the server includes: means for analyzing a user's profile image using a generation AI and providing feedback on specific improvements regarding features such as angle, facial expression, and background; means for identifying emotions from the user's profile image using an emotion engine and reflecting the emotion data in the feedback; means for analyzing the user's profile text using a generation AI and providing feedback on specific improvements; means for identifying emotions from the wording of the profile text using an emotion engine and reflecting the emotion data in the feedback; means for using a generation AI to ask the user short questions and, based on the answers, generate an optimal profile statement or suggest editing; and means for automatically detecting spam and fraudulent accounts and eliminating suspicious accounts using the generation AI and the emotion engine. This improves the accuracy of user profile analysis and emotion identification, enabling optimal feedback and matching. It also enables the provision of a safe matching environment.

[0982] "Generative AI" is an artificial intelligence technology that uses machine learning algorithms to analyze data and generate specific feedback and suggestions.

[0983] An "emotion engine" is an analytical technology that identifies human emotions from images and text and reflects the results in applications.

[0984] "Profile Image" means a photo of a user's face or other still image used to represent themselves in the Matching System.

[0985] "Profile text" is text data that allows a user to describe their characteristics, hobbies, interests, etc.

[0986] "Feedback" refers to specific improvements and suggestions provided to users based on the results of analysis by generative AI and emotion engines.

[0987] "Spam accounts" are fraudulent accounts created for advertising or fraudulent purposes, typically sending large volumes of meaningless messages.

[0988] A "fraudulent account" is a fraudulent account created with the intent to deceive others and abuse the user's trust.

[0989] "Simple questions" are short questions that the generative AI asks the user to understand the user's characteristics and personality.

[0990] A "profile sentence" is a sentence that expresses a user's characteristics and appeal, suggested by the AI ​​based on answers to simple questions.

[0991] A "personality diagnosis" is an evaluation method that analyzes the questions answered by the user and the text entered to identify their personality and behavioral characteristics.

[0992] An "interface" refers to the operating screen or input means through which users and systems exchange information, and plays a role in improving usability.

[0993] The present invention is a system that integrates generative AI and an emotion engine to more effectively analyze a user's profile image and text and provide optimal feedback and matching. The system of the present invention has the following configuration and operation.

[0994] Overall structure

[0995] The system mainly consists of a server, a device, and a user. The server is equipped with a generative AI and an emotion engine, and provides feedback and suggestions to the user based on the analysis results. The device accepts user operations and data input and sends it to the server. Users register their own profile image and text via the device and receive feedback and suggestions.

[0996] Hardware and software used

[0997] Hardware:

[0998] Server: A high-performance computer is recommended. If necessary, a GPU can be installed to support high-speed analysis by the generative AI and emotion engine.

[0999] Terminal: A device that provides a user interface, such as a smartphone, tablet, or computer.

[1000] software:

[1001] Generative AI models: Use models trained using deep learning frameworks (e.g., TensorFlow, PyTorch).

[1002] Emotion Engine: Integrates facial recognition algorithms (e.g., OpenCV, dlib) and natural language processing models (e.g., BERT) to perform emotion analysis.

[1003] Server software: Database management systems (e.g., MySQL, PostgreSQL) and web servers (e.g., Apache, Nginx).

[1004] Profile picture analysis and emotion identification

[1005] The user uploads their profile picture to the device. The device sends the uploaded image to the server. The server sends the received image to the generation AI and emotion engine. The generation AI analyzes the image and evaluates features such as angle, facial expression, and background. The emotion engine identifies emotions from the user's facial expression. The server generates feedback based on the analysis results from the generation AI and the emotion data from the emotion engine and provides it to the user. As a specific example, if the user is not smiling, the feedback provided is, "Use a smile that looks more friendly."

[1006] Profile text analysis and sentiment identification

[1007] The user enters profile text into the device. The device sends the entered text to the server. The server sends the received text to the generation AI and emotion engine. The generation AI analyzes the text and provides feedback on specific areas for improvement. The emotion engine identifies emotions from the wording of the text. The server generates suggestions based on the analysis results from the generation AI and the emotion data from the emotion engine, and provides them to the user. For example, if the text seems bland, the server might suggest, "You might want to add more passionate words to better highlight yourself."

[1008] Personality diagnosis and profile suggestions through question-and-answering

[1009] The device displays simple questions to the user. The user answers the questions and enters the answer data into the device. The device sends the collected answer data to the server. The server sends it to the generation AI and emotion engine. The generation AI analyzes the answer data and performs a personality diagnosis, and the emotion engine identifies the emotion the user expressed when answering. The server suggests the most appropriate profile text to the user based on the data from the generation AI and emotion engine. As a specific example, if the user is proactive, a suggested profile text that reflects this will be provided.

[1010] Recommendations for compatible people

[1011] The server passes the personality test results and analyzed profile data to the generation AI and emotion engine. The generation AI and emotion engine analyze the data and identify the most suitable match. By incorporating emotion data, more accurate matching becomes possible. The server provides the recommendation results to the user. The user decides whether to contact the recommended person. For example, if the user's personality test results show that they are proactive and sociable, the server may recommend matching with "someone who is also proactive and sociable."

[1012] Spam and fraudulent account detection

[1013] The server sends newly registered profiles to the generation AI and emotion engine. The generation AI analyzes the profiles and detects possible spam or fraudulent accounts. The emotion engine also detects emotional anomalies and identifies suspicious accounts. The server then appropriately eliminates spam accounts based on the detection results, maintaining a safe matching environment. For example, if a newly registered account sends a large number of messages at once, the generation AI may determine that it is likely to be a spam account, and the server may suspend the account.

[1014] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1015] Profile picture analysis and emotion identification

[1016] Step 1:

[1017] The user uploads a profile picture to the device. The user operates the application on the device, selects an image file, and clicks the upload button.

[1018] Input: The profile image file selected by the user.

[1019] Output: The image file is saved to your device.

[1020] Step 2:

[1021] The device sends the uploaded image to the server. The device sends the image file to the server using an HTTP request.

[1022] Input: The uploaded profile image file.

[1023] Output: An image file is sent to the server.

[1024] Step 3:

[1025] The server sends the received images to the generative AI and emotion engine, and then sends a request to the appropriate API endpoint to forward the image data to the analysis unit.

[1026] Input: The image file received by the server.

[1027] Output: Image data is passed to the generative AI and emotion engine.

[1028] Step 4:

[1029] The generative AI analyzes the image and evaluates features such as angle, facial expression, and background, and then uses image processing algorithms to quantify each feature.

[1030] Input: Image data sent to the analysis unit.

[1031] Output: Quantified feature data such as angle, facial expression, background, etc.

[1032] Step 5:

[1033] The emotion engine identifies emotions from the user's facial expressions. The emotion engine uses image processing algorithms and machine learning models to tag emotions such as "happiness," "anger," and "sadness" from the facial expressions.

[1034] Input: Image data sent to the analysis unit.

[1035] Output: Identified emotion tags.

[1036] Step 6:

[1037] The server generates feedback based on the analysis results from the generation AI and emotion engine and provides it to the user. The server aggregates the analysis results and generates notifications for specific improvements to the user.

[1038] Input: quantified feature data and emotion tags.

[1039] Output: A specific feedback message to the user.

[1040] Profile text analysis and sentiment identification

[1041] Step 1:

[1042] The user enters profile text into the terminal. The user enters text into the text area and clicks the send button.

[1043] Input: The profile text entered by the user.

[1044] Output: Text data is saved to the terminal.

[1045] Step 2:

[1046] The device sends the entered text to the server. The device sends the text data to the server using an HTTP request.

[1047] Input: The profile text entered.

[1048] Output: Text data is sent to the server.

[1049] Step 3:

[1050] The server sends the received text to the generative AI and emotion engine, and then sends a request to the appropriate API endpoint to forward the text data to the analysis unit.

[1051] Input: Text data received by the server.

[1052] Output: Text data is passed to the generative AI and emotion engine.

[1053] Step 4:

[1054] Generative AI analyzes the text and provides specific feedback on improvements. Generative AI uses natural language processing algorithms to analyze the grammar and context of the text and generate specific suggestions.

[1055] Input: Text data sent to the analysis unit.

[1056] Output: Suggested data for grammar correction and content specification.

[1057] Step 5:

[1058] The emotion engine identifies emotions from the wording of the text. The emotion engine uses natural language processing algorithms to identify emotions such as "passion," "calm," or "joy" from the text.

[1059] Input: Text data sent to the analysis unit.

[1060] Output: Identified emotion tags.

[1061] Step 6:

[1062] The server generates suggestions based on the analysis results from the generation AI and emotion engine and provides them to the user. The server aggregates the analysis results and generates notifications for specific improvements and suggestions for the user.

[1063] Input: Grammar correction suggestion data and sentiment tags.

[1064] Output: A specific suggestion message to the user.

[1065] Personality diagnosis and profile suggestions through question-and-answering

[1066] Step 1:

[1067] The terminal displays simple questions to the user, and the terminal application displays the questions in a dialog format.

[1068] Input: Questions created by the generative AI.

[1069] Output: The question dialog that is displayed to the user.

[1070] Step 2:

[1071] The user answers the question and enters the answer data into the terminal. The user enters the answer and clicks the send button.

[1072] Input: The answer data entered by the user.

[1073] Output: The answer data is saved on the device.

[1074] Step 3:

[1075] The device sends the collected response data to the server. The device sends the response data to the server using an HTTP request.

[1076] Input: The entered response data.

[1077] Output: The response data is sent to the server.

[1078] Step 4:

[1079] The server sends this to the generative AI and emotion engine, which then makes a request to the appropriate API endpoint to forward the response data to the analysis unit.

[1080] Input: The response data received by the server.

[1081] Output: The answer data is passed to the generation AI and emotion engine.

[1082] Step 5:

[1083] The generation AI analyzes the response data to conduct a personality diagnosis, and the emotion engine identifies the emotion of the user when answering. The generation AI uses a machine learning model to analyze personality from the response data, and the emotion engine identifies emotion from the text.

[1084] Input: Response data sent to the analysis unit.

[1085] Output: Personality trait data and emotion tags.

[1086] Step 6:

[1087] The server proposes optimal profile sentences to users based on data from the generation AI and emotion engine.The server generates suggested profile sentences for users based on the analysis results.

[1088] Input: personality trait data and emotion tags.

[1089] Output: Suggestion of specific profile text to the user.

[1090] (Application example 2)

[1091] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1092] Conventional systems simply analyze users' profile images and text, but are unable to provide feedback or product recommendations based on the user's emotions and preferences. This makes it difficult to provide more personalized services to users. Furthermore, the accuracy of detecting spam and fraudulent accounts is low, making it impossible to provide a safe environment. Therefore, there is a need for more accurate profile analysis and recommendation systems.

[1093] 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.

[1094] In this invention, the server includes means for analyzing a user's profile image using a generation AI and providing feedback on specific improvements regarding elements such as angle, facial expression, and background, means for analyzing a user's profile text using a generation AI and providing feedback on specific improvements, means for using a generation AI to ask the user simple questions and generate an optimal profile statement or suggest editing based on the answers, means for using a generation AI to automatically detect spam accounts and fraudulent accounts and eliminate suspicious accounts, and means for analyzing a user's profile image and text using a generation AI and an emotion engine and recommending products based on the user's preferences and emotions. This enables more personalized feedback and product recommendations to users and promotes use in a safe environment.

[1095] "Generative AI" refers to artificial intelligence that uses advanced algorithms to analyze data and generate new information and feedback.

[1096] An "emotion engine" is a system that identifies emotions from user input data (e.g., images or text) and provides analysis results based on that.

[1097] A "profile image" is image data uploaded by a user to represent themselves.

[1098] "Profile text" refers to text data entered by a user to describe themselves.

[1099] "Recommendation" means proposing products and services that are individually suited to a user based on their preferences and behavior.

[1100] A "spam account" is an account created for fraudulent purposes, typically used to send random advertising or fraudulent messages.

[1101] A "fraudulent account" is an account created for the purpose of committing fraud, and provides false information with the intent of deceiving others.

[1102] "Analysis" is the process of examining and breaking down data in detail to understand its structure and meaning.

[1103] "Feedback" refers to ratings and suggestions for improvement provided based on user actions and input.

[1104] "Personalization" means providing content that is individualized according to the preferences and characteristics of individual users.

[1105] MODE FOR CARRYING OUT THE INVENTION

[1106] This invention is a system that integrates generative AI and an emotion engine to effectively analyze a user's profile image and text and provide optimal feedback and product recommendations. This system is composed of a server, a terminal, and a user. Specific embodiments of this system are described in detail below.

[1107] Profile picture analysis and emotion identification

[1108] Users upload their profile picture to their device. The device sends the uploaded image to the server. The server sends the received image to the generation AI and emotion engine. The generation AI analyzes the image and evaluates factors such as angle, facial expression, and background. The emotion engine identifies emotions from the user's facial expression. Based on the analysis results from the generation AI and the emotion data from the emotion engine, the server provides specific feedback to the user on areas for improvement. For example, if the user is not smiling, the server will provide advice such as "use a smile that looks more friendly."

[1109] Profile text analysis and sentiment identification

[1110] The user enters their profile text into the device. The device sends the entered text to the server. The server sends the received text to the generation AI and emotion engine. The generation AI analyzes the text and provides feedback on specific areas for improvement. The emotion engine identifies the user's emotions from the wording of the text. The server then suggests specific areas for improvement to the user based on the analysis results from the generation AI and the emotion data from the emotion engine. For example, if the text seems bland, the server will provide advice such as, "Add more passionate words to better highlight yourself."

[1111] Personality diagnosis and profile suggestions through question-and-answering

[1112] The device displays simple questions to the user. The user answers the questions and enters the answer data into the device. The device then sends the collected answer data to the server. The server then sends it to the generation AI and emotion engine. The generation AI analyzes the answer data and performs a personality diagnosis, and the emotion engine identifies the emotion the user felt when answering. The server then uses the data from the generation AI and emotion engine to suggest optimal profile text to the user. For example, if the user is proactive, it will provide suggested profile text that reflects this.

[1113] Recommendations for compatible people

[1114] The server passes the personality test results and analyzed profile data to the generation AI and emotion engine. The generation AI and emotion engine analyze the data and identify the most suitable match. By incorporating emotion data, more accurate matching becomes possible. The server provides the recommendation results to the user. The user decides whether to contact the recommended person.

[1115] Spam and fraudulent account detection

[1116] The server sends newly registered profiles to the generation AI and emotion engine. The generation AI analyzes the profiles and detects possible spam or fraudulent accounts. The emotion engine also detects emotional anomalies and identifies suspicious accounts. The server then appropriately eliminates spam accounts based on the detection results, maintaining a safe matching environment.

[1117] Product recommendations based on user preferences

[1118] Using generative AI and an emotion engine, the system analyzes a user's profile image and text to recommend products based on the user's preferences and emotions. For example, if a user uploads an image of themselves smiling and relaxed, and enters text about casual fashion, the system will recommend relaxed, casual clothing. Specific examples of prompts include:

[1119] We analyzed the user's profile picture to identify their emotions and recognized it as a "relaxed smile." Next, we determined that the user is interested in "casual fashion" from the text "I've been wearing casual clothes a lot lately." Based on this condition, we would like you to list products that are suitable for relaxing casual wear.

[1120] In this way, systems using generative AI models and emotion engines can provide more personalized feedback and product recommendations to users, promoting usage in a safe environment.

[1121] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1122] Step 1:

[1123] The user uploads a profile image to the device, which then sends the image data to the server, which then sends the image data to the generative AI and emotion engine.

[1124] Input: The user's profile picture.

[1125] Data processing: Pre-processing of images, such as transfer and compression.

[1126] Output: Image data for analysis.

[1127] Specific actions: The user selects an image using the app on their smartphone and presses the upload button.

[1128] Step 2:

[1129] The server uses generative AI to analyze the image, evaluating factors such as angle, facial expression, and background, while the emotion engine identifies emotions from the user's facial expressions.

[1130] Input: Profile image.

[1131] Data processing: Extraction of parameters based on image analysis algorithms (angle, facial expression, background, etc.).

[1132] Output: Analysis results and sentiment data.

[1133] Specific operation: The image analysis model on the server extracts image features, and the emotion engine outputs emotion labels.

[1134] Step 3:

[1135] Based on the analysis results from the generative AI and emotion engine, the server provides specific feedback to the user on how to improve their appearance, such as "Use a smile that looks friendlier."

[1136] Input: Analysis results and emotion data.

[1137] Data processing: Creating advice through feedback generation algorithms.

[1138] Output: The feedback message.

[1139] Specific operation: The server generates an advice message and sends it to the terminal.

[1140] Step 4:

[1141] Users enter their profile text into their device, which sends it to the server, which then sends it to the generative AI and emotion engine.

[1142] Input: Profile text.

[1143] Data processing: Text transfer and formatting.

[1144] Output: Text data.

[1145] Specific action: The user enters a profile statement in the text field and presses the submit button.

[1146] Step 5:

[1147] The server analyzes the profile text with generative AI and provides specific feedback on improvements, while the emotion engine identifies emotions from the wording of the text.

[1148] Input: Profile text.

[1149] Data processing: Analysis using text analysis algorithms and generation of sentiment labels.

[1150] Output: Analysis results and sentiment data.

[1151] Specific operation: The analysis model on the server analyzes the text and outputs improvements and emotion labels.

[1152] Step 6:

[1153] The server then uses the analysis results from the generative AI and emotion engine to suggest specific improvements to the user, offering advice such as "Use more passionate words to better express yourself."

[1154] Input: Analysis results and emotion data.

[1155] Data processing: Creating advice through feedback generation algorithms.

[1156] Output: The feedback message.

[1157] Specific operation: The server generates an advice message and sends it to the terminal.

[1158] Step 7:

[1159] The device displays simple questions to the user, who answers them, and the device sends the answer data to the server.

[1160] Input: Question response data.

[1161] Data processing: Data collection and transfer.

[1162] Output: Question-answer data.

[1163] What happens: The user answers the questions displayed and submits the answers.

[1164] Step 8:

[1165] The server sends the collected response data to the generation AI and emotion engine to conduct a personality diagnosis and identify the emotions the user was feeling when answering.

[1166] Input: Question and answer data.

[1167] Data processing: Analysis using personality diagnosis algorithms and emotion identification algorithms.

[1168] Output: Personality test results and emotional data.

[1169] Specific operation: The model on the server analyzes the data and outputs diagnostic results.

[1170] Step 9:

[1171] The server suggests the most suitable profile sentence for the user based on data from the generative AI and emotion engine.

[1172] Input: Personality test results and emotion data.

[1173] Data processing: Forming suggestions using a profile sentence generation algorithm.

[1174] Output: Profile text suggestions.

[1175] Specific operation: The server generates a proposal message and sends it to the terminal.

[1176] Step 10:

[1177] The server passes the personality test results and analyzed profile data to a generative AI and emotion engine to identify the best matches.

[1178] Input: Personality test results and profile data.

[1179] Data processing: Analysis using matching algorithm.

[1180] Output: A list of matches.

[1181] Specific operation: The server analyzes the data and outputs the matching results.

[1182] Step 11:

[1183] The server analyzes user profile images and text and recommends products based on the user's preferences and emotions.

[1184] Input: Profile image and text, analysis results.

[1185] Data processing: Analysis using product recommendation algorithms.

[1186] Output: A list of products and reasons.

[1187] Specific operation: The server recommends products based on the analysis results and generates a message including the reasons for the recommendation.

[1188] Step 12:

[1189] The server sends newly registered profiles to the generative AI and emotion engine to detect potential spam or fraudulent accounts.

[1190] Input: New profile data.

[1191] Data processing: Analysis using spam detection algorithms and emotion anomaly detection algorithms.

[1192] Output: Detection results.

[1193] Specific operation: The detection model on the server analyzes the profile and outputs the results.

[1194] Step 13:

[1195] The server will then appropriately eliminate spam accounts based on the detection results, maintaining a safe environment.

[1196] Input: The detection result.

[1197] Data processing: Eliminating spam accounts through account management systems.

[1198] Output: A list of secure accounts.

[1199] Specific action: The server will identify spam accounts based on the detection results and remove them appropriately.

[1200] In this way, each step works together to create a system that provides optimal feedback and product recommendations to users and ensures a safe environment.

[1201] 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.

[1202] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1203] 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.

[1204] [Third embodiment]

[1205] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1206] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1207] 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).

[1208] 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.

[1209] 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.

[1210] 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).

[1211] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1212] 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.

[1213] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1214] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1215] In the 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.

[1216] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1217] The present invention is a dating matching service system that utilizes generative AI to optimize the balance between a user's appearance and personality and provide a safe matching environment. Specific embodiments of this system are described below.

[1218] 1. Profile image analysis and improvement

[1219] The user uploads their profile picture, which the server receives and sends to the generation AI.

[1220] The server uses generative AI to analyze factors such as the angle, facial expression, and background of the user's profile picture. For example, if the facial expression in the picture is unattractive or the background is cluttered, it will provide specific feedback on how to improve it.

[1221] Based on the feedback provided, the user can modify the image or upload a new image.

[1222] 2. Profile text analysis and improvement

[1223] The user enters their profile text, and the server receives this text and sends it to the generating AI.

[1224] The server uses a generative AI to analyze the profile text and provide specific feedback on areas for improvement. For example, if someone writes, "My hobbies are reading and watching movies," the server might suggest, "You should write more about your personality and work in more detail."

[1225] The user then modifies the text based on the feedback provided.

[1226] 3. Personality diagnosis and profile suggestion through question-and-answering

[1227] The device displays a simple question to the user, who then answers it.

[1228] The server sends the collected response data to the AI, which then performs a personality diagnosis based on the answers. Based on the generated diagnosis results, the AI ​​then suggests the optimal profile text to the user.

[1229] Users can use the suggested profile text as a reference to enrich their own profiles.

[1230] 4. Recommendations of compatible people

[1231] The server then recommends suitable matches to users based on the results of the personality assessment and analyzed profile data, through a process in which generative AI analyzes the data and identifies those with the best compatibility.

[1232] Users can decide whether to contact the recommended people, and this system allows users to meet people who are suitable for them more efficiently.

[1233] 5. Spam and fraudulent account detection

[1234] The server constantly monitors newly registered profiles using generative AI and automatically detects spam accounts and potentially fraudulent accounts.

[1235] The server will then properly remove any spam accounts it detects and maintain a safe matching environment, such as detecting and removing accounts with numerous identical profile statements or accounts that provide excessively incorrect information.

[1236] In this way, the present invention utilizes generative AI to not only optimize users' profile details, but also provide a safe and reliable matching environment, allowing users to achieve the best possible match that balances appearance and personality, and to use the service with peace of mind.

[1237] The processing flow will be explained below.

[1238] Profile image analysis and improvement

[1239] Step 1:

[1240] The user uploads their profile picture to the device.

[1241] The device sends the uploaded image to the server.

[1242] Step 2:

[1243] The server passes the received image to the generation AI.

[1244] Generative AI analyzes the image and evaluates factors such as angle, facial expression, and background.

[1245] Step 3:

[1246] Based on the analysis results from the generated AI, the server provides specific feedback to the user on areas for improvement.

[1247] Based on the feedback, the user modifies the image or uploads a new image.

[1248] Profile text analysis and improvement

[1249] Step 1:

[1250] The user enters his / her profile text into the terminal.

[1251] The terminal sends the entered text to the server.

[1252] Step 2:

[1253] The server passes the received text to the generation AI.

[1254] Generative AI analyzes the text and generates specific improvements as feedback.

[1255] Step 3:

[1256] The server provides feedback from the generated AI to the user.

[1257] The user then corrects the text based on the feedback provided.

[1258] Personality diagnosis and profile suggestions through question-and-answering

[1259] Step 1:

[1260] The terminal displays a simple question to the user.

[1261] The user answers the questions and inputs the answer data into the terminal.

[1262] Step 2:

[1263] The terminal transmits the collected response data to the server.

[1264] The server passes the response data to the generating AI and conducts a personality diagnosis.

[1265] Step 3:

[1266] The generative AI will generate the optimal profile text based on the diagnostic results.

[1267] The server proposes the generated profile statement to the user.

[1268] Step 4:

[1269] Users can use the suggested profile text as a reference to enrich their own profiles.

[1270] Recommendations for compatible people

[1271] Step 1:

[1272] The server passes the personality test results and analyzed profile data to the generation AI.

[1273] Generative AI analyzes the data and identifies the best matches.

[1274] Step 2:

[1275] The server provides the recommendation results to the user.

[1276] The user decides whether to contact the recommended person.

[1277] Spam and fraudulent account detection

[1278] Step 1:

[1279] The server passes the newly registered profile to the generation AI.

[1280] Generative AI analyzes profiles to detect potential spam or fraudulent accounts.

[1281] Step 2:

[1282] The server will now properly remove detected spam accounts.

[1283] Regular monitoring and countermeasures are carried out to ensure the server maintains a safe matching environment.

[1284] Example 1

[1285] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1286] In recent years, with the spread of online dating matching services, it is necessary not only to improve the quality of users' profile images and text, but also to provide a safe and reliable matching environment. However, it requires advanced skills and time for users to properly optimize their profiles, and eliminating spam and fraudulent accounts is also a major challenge. An efficient system to solve these problems is needed.

[1287] 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.

[1288] In this invention, the server includes means for using a generation AI to analyze a user's profile image and provide feedback on specific improvements regarding elements such as angle, facial expression, and background, means for using a generation AI to analyze the user's profile text and provide feedback on specific improvements, means for using a generation AI to ask the user simple questions and generate or suggest edits to an optimal profile statement based on the answers, means for using a generation AI to automatically detect spam accounts and fraudulent accounts and eliminate suspicious accounts, means for using a generation AI to identify and recommend compatible individuals based on the results of a personality assessment and analyzed profile data, and means for suggesting input prompts for the generation AI model. This allows users to achieve optimal matches that balance appearance and inner qualities, enabling them to use a safe and reliable dating matching service.

[1289] "Generative AI" is an artificial intelligence technology that uses machine learning algorithms to generate data and automate specific tasks.

[1290] "Profile Picture" means a photo of a user's face or other image uploaded by a user to express their identity or personality.

[1291] "Profile text" is written information in which a user describes themselves, their hobbies, and interests.

[1292] "Spam Accounts" are automatically generated user accounts registered for irrelevant advertising or fraudulent purposes.

[1293] A "fraudulent account" is a user account that provides false information with the intent to deceive others.

[1294] "Feedback" refers to suggestions and advice for improving images and text that the generative AI provides to the user.

[1295] "Personality assessment" is the process by which the generative AI evaluates a user's personality traits based on the questions the user answers.

[1296] "Recommendation" refers to the generation AI recommending the best match to the user.

[1297] A "prompt" is a guided sentence input to a generative AI model, used to induce a specific response or generation.

[1298] The present invention is a dating matching service system that utilizes generative AI to optimize the balance between a user's appearance and personality and provide a safe matching environment. Specific embodiments of this system are described below.

[1299] 1. Profile image analysis and improvement

[1300] When a user uploads their profile picture, the server receives the image and sends it to the generation AI. The generation AI analyzes elements of the user's profile picture, such as the angle, facial expression, and background. For example, if the facial expression in the image is unattractive or the background is cluttered, it provides specific feedback on how to improve the image. The user can then modify the image or upload a new image based on the feedback provided.

[1301] Examples:

[1302] If the background of an image uploaded by a user is cluttered, the generating AI will provide feedback such as "It would be better to simplify the background."

[1303] Example prompt for a generative AI model:

[1304] "How can we improve this profile picture background?"

[1305] 2. Profile text analysis and improvement

[1306] When a user enters their profile text, the server receives this text and sends it to the generation AI. The generation AI analyzes the profile text and provides feedback on specific areas for improvement. For example, for a profile entry such as "My hobbies are reading and watching movies," the server may suggest "You should write more about your personality and work." The user then edits the text based on the provided feedback.

[1307] Examples:

[1308] For example, if a user writes, "My hobbies are reading and watching movies," the AI ​​generator will provide feedback such as, "You should write in more detail about your personality and work."

[1309] Example prompt for a generative AI model:

[1310] "Please suggest improvements to this profile text."

[1311] 3. Personality diagnosis and profile suggestion through question-and-answering

[1312] The device displays simple questions to the user, who then answers them. The server then sends the collected response data to the AI, which then performs a personality diagnosis based on the answers. Based on the generated diagnosis results, the AI ​​then suggests an optimal profile statement to the user. The user can then use the suggested statement to enhance their profile.

[1313] Examples:

[1314] If a user answers "reading" to the question "How do you spend your holidays?", the AI ​​generator will suggest "the perfect profile sentence for you, a book lover."

[1315] Example prompt for a generative AI model:

[1316] "Suggest the best profile sentence for this user based on their answers."

[1317] 4. Recommendations of compatible people

[1318] The server recommends suitable matches to users based on the results of the personality test and analyzed profile data. This is done through a process in which generative AI analyzes data and identifies people with the best compatibility. Users then decide whether or not to contact the recommended people. This system allows users to meet suitable partners more efficiently.

[1319] Examples:

[1320] If the personality test results and profile data of User A and User B match, the server will use generative AI to recommend the two people.

[1321] Example prompt for a generative AI model:

[1322] "Please suggest the best matches for this user based on their personality test results and profile data."

[1323] 5. Spam and fraudulent account detection

[1324] The server constantly monitors newly registered profiles using generative AI to automatically detect spam accounts and potentially fraudulent accounts. Detected spam accounts are then appropriately removed to maintain a safe matching environment. For example, accounts with numerous identical profile statements or accounts providing extremely incorrect information are detected and removed.

[1325] Examples:

[1326] If there are a large number of newly registered profiles with the same text, the generation AI will identify them as spam accounts and eliminate them.

[1327] Example prompt for a generative AI model:

[1328] "Please determine if this profile is spam or a potential scam."

[1329] In this way, the present invention utilizes generative AI to not only optimize users' profile details, but also provide a safe and reliable matching environment, allowing users to achieve the best possible match that balances appearance and personality, and to use the service with peace of mind.

[1330] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1331] Step 1:

[1332] User uploads a profile picture

[1333] The user selects their profile picture and presses the upload button. The server receives the image data sent by the user.

[1334] input:

[1335] Image data uploaded by users

[1336] Specific behavior:

[1337] Check the image file type and size and save it in the appropriate format.

[1338] output:

[1339] Profile image data stored on the server

[1340] Step 2:

[1341] The server sends the profile image to the AI ​​for analysis.

[1342] The server sends the received profile image to the generation AI for analysis. The generation AI model analyzes the image's angle, facial expression, and background to identify areas for improvement.

[1343] input:

[1344] Profile image data stored on the server

[1345] Specific behavior:

[1346] Generative AI performs facial recognition and evaluates facial expression, background, and angle.

[1347] Data analysis algorithms are used to extract improvements to the images.

[1348] output:

[1349] Specific improvements provided by the generation AI (e.g., stiff facial expressions, cluttered background)

[1350] Step 3:

[1351] User enters profile text

[1352] Users enter text to describe their profile, and the server receives and stores this text data.

[1353] input:

[1354] User-entered profile text

[1355] Specific behavior:

[1356] The text data is stored on the server.

[1357] Check the format of the saved data and make it ready to send to the generating AI.

[1358] output:

[1359] Profile text data stored on the server

[1360] Step 4:

[1361] The server sends the profile text to the AI ​​generator for analysis.

[1362] The server sends the saved profile text to the AI ​​generator for analysis, which analyzes the content of the text and suggests specific improvements.

[1363] input:

[1364] Profile text data stored on the server

[1365] Specific behavior:

[1366] Generative AI performs natural language processing and analyzes the content of the text.

[1367] Extract areas for improvement from the analysis results.

[1368] output:

[1369] Specific improvements provided by the generative AI (e.g., writing more specifically about hobbies)

[1370] Step 5:

[1371] The device asks the user a simple question and collects the answer.

[1372] The terminal displays some simple questions to the user, who answers them. The server receives and stores the answer data.

[1373] input:

[1374] Question data answered by users

[1375] Specific behavior:

[1376] The terminal displays the question and accepts user input.

[1377] The response data is sent to the server.

[1378] output:

[1379] Response data stored on the server

[1380] Step 6:

[1381] The server conducts a personality test and suggests the best profile sentence for you.

[1382] The server sends the collected response data to the AI ​​generator, which then performs a personality diagnosis. Based on the results of the diagnosis, the AI ​​then suggests the most suitable profile text for the user.

[1383] input:

[1384] Response data stored on the server

[1385] Specific behavior:

[1386] The generative AI conducts a personality diagnosis and evaluates the user's personality traits.

[1387] Based on the diagnosis results, the optimal profile statement is generated.

[1388] output:

[1389] The optimal profile text suggested by generative AI

[1390] Step 7:

[1391] The server automatically detects and removes spam and fraudulent accounts.

[1392] The server constantly monitors newly registered profiles using generative AI, automatically detecting spam accounts and potentially fraudulent accounts, and then appropriately removing detected accounts.

[1393] input:

[1394] Newly registered profile data

[1395] Specific behavior:

[1396] Generative AI analyzes registered profile data to identify spam and fraud patterns.

[1397] Remove confirmed spam or fraudulent accounts from the system.

[1398] output:

[1399] Safe User Profile List

[1400] Step 8:

[1401] The server recommends suitable matches to the user.

[1402] The server uses generative AI to recommend the best possible matches to users based on the personality test results and analyzed profile data.

[1403] input:

[1404] Analyzed profile data and personality test results

[1405] Specific behavior:

[1406] Generative AI analyzes the data and identifies people who are compatible.

[1407] The identified matches are displayed to the user.

[1408] output:

[1409] A list of recommended matches for the user

[1410] Thus, through each step of the system, users can optimize their profile and enjoy a safe and effective matching environment.

[1411] (Application example 1)

[1412] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1413] Conventional matching systems only recommend suitable partners based on the user's profile information and personality assessment, but do not offer services or product suggestions that users can actually experience in physical stores. This makes it difficult for users to choose products and services that suit them. Other issues include insufficient feedback on image and text improvements, and ineffective elimination of fraudulent and spam accounts.

[1414] 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.

[1415] In this invention, the server includes means for analyzing a user's profile image using a generation AI and providing feedback on specific improvements regarding elements such as angle, facial expression, and background, means for analyzing a user's profile text using a generation AI and providing feedback on specific improvements, means for using a generation AI to ask the user simple questions and generate or suggest editing an optimal profile statement based on the answers, means for using a generation AI to automatically detect spam accounts and fraudulent accounts and eliminate suspicious accounts, and means for suggesting optimal products and services based on the user's profile information and personality assessment data. This enables more accurate product and service suggestions based on the user's appearance and inner information, realizing a safe and reliable matching environment.

[1416] "Generative AI" refers to artificial intelligence that generates natural language and images based on user input data.

[1417] A "profile picture" is a photo that a user uses to show their personal information and appearance.

[1418] "Angle" is an element that indicates the relative positions of the subject's face and body in the profile image.

[1419] "Expression" is an element that indicates the facial expression of the user in the profile picture.

[1420] "Background" refers to the environment or scenery that appears behind the subject in a profile picture.

[1421] "Profile text" refers to text information in which a user writes about themselves, their hobbies, etc.

[1422] "Feedback" refers to improvements and advice provided by the generative AI to the user.

[1423] A "question" is a question-and-answer format information gathering tool presented to the user.

[1424] "Personality diagnosis" is the process of evaluating a user's personality traits based on the user's response data, etc.

[1425] "Spam Account" means a user account created for fraudulent purposes.

[1426] A "Fraudulent Account" is a fraudulent user account created with the intent to deceive users.

[1427] "Product" means an object such as a good or service that is provided to a user.

[1428] "Service" means any act or service provided to a User.

[1429] "Profile Information" refers to all data about a user, including images and text.

[1430] "Personality assessment data" refers to data regarding a user's personality characteristics obtained through a personality assessment.

[1431] "Recommendation" means a recommendation of a product or service made to a User.

[1432] An "interface" is a screen or operating means that allows a user to interact with a system.

[1433] The present invention is a system that utilizes generative AI to optimize a user's profile image and text, and safely and effectively suggests optimal products and services to the user. Specific embodiments of this system are described below.

[1434] System Configuration and Operation

[1435] This system consists of three elements: a server, a terminal, and a user. The server operates the generative AI, the terminal provides the user interface, and the user operates the terminal to use the service.

[1436] 1. Profile image analysis and improvement

[1437] The server first receives the profile image uploaded by the user. This image is then sent to the generation AI, which analyzes elements such as the angle, facial expression, and background. For example, the image is analyzed using OpenCV, and if the background is too cluttered, the server provides specific feedback to the user on how to improve it, such as "Please change to a simpler background." This allows users to create more attractive profile images.

[1438] 2. Profile text analysis and improvement

[1439] The profile text entered by the user is also sent to the server. The server uses generative AI to analyze the text and generate specific suggestions for improvement. For example, for the text "My hobbies are reading and watching movies," the server will provide advice such as "You should write more details about your personality and work." This allows users to create a more comprehensive profile text.

[1440] 3. Personality diagnosis and profile suggestion through question-and-answering

[1441] The device displays simple questions to the user, who then answers them. These answers are sent to a server, where a personality diagnosis is performed using a generative AI. Based on the results, the device suggests the profile text that best suits the user's personality. For example, if the answers to the questions are "Q1: A1, Q2: A2, Q3: A3," the device inputs this information into the prompt and generates the appropriate text.

[1442] 4. Product and service recommendations

[1443] Using generative AI, the server will suggest optimal products and services based on the results of a personality test and profile information. These suggestions are based on prompts and are presented to the user in an easy-to-understand format. For example, based on a profile that says, "My hobbies are reading and watching movies," a specific suggestion will be made, such as, "How about a T-shirt from a new movie?"

[1444] Hardware and software used

[1445] Hardware: Storefront customer service robots (e.g., generic company robots), cameras (to capture images of users)

[1446] Software: OpenCV (image analysis), OpenAI API (text generation), Transformers library (implementation of generative AI)

[1447] Examples of concrete examples and prompts

[1448] When implementing this in a brick-and-mortar apparel store, the following specific examples are possible:

[1449] When a user shows the robot an image of themselves, the robot suggests, "The background is too cluttered. Please change it to a simpler background."

[1450] Based on the profile text, the recommendation is, "Since your hobby is watching movies, how about a T-shirt from a new movie?"

[1451] Example prompt sentence:

[1452] 1. Profile text improvement suggestions:

[1453] markdown

[1454] How can we improve the following profile text?

[1455] My hobbies are reading and watching movies

[1456] 2. Personality Test and Suggestions:

[1457] markdown

[1458] Please assess the user's personality based on the answers below and make suggestions:

[1459] Q1: A1, Q2: A2, Q3: A3

[1460] 3. Product Suggestion:

[1461] markdown

[1462] Profile text: My hobbies are reading and watching movies

[1463] Personality Test: Introverted but highly sensitive

[1464] Please suggest suitable products based on these.

[1465] As described above, this system utilizes generative AI to optimize user information and can suggest appropriate products and services in physical stores, providing a safe and reliable matching environment and increasing user satisfaction.

[1466] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1467] Step 1: Upload and analyze your profile image

[1468] The server receives the profile image uploaded by the user via their device. This image is sent to a generative AI model, which analyzes factors such as angle, facial expression, and background. The input is the profile image, and the output is feedback on areas for improvement. Specifically, OpenCV is used to analyze the angle and facial expression of the image, and if the background is cluttered, feedback is generated, such as "Please change to a simpler background."

[1469] Step 2: Enter and parse profile text

[1470] The server receives the profile text entered by the user via their device. This text is sent to the generative AI model, which analyzes the content of the text. Specific improvements are then provided as feedback. The input is the profile text, and the output is feedback on improvements. Specifically, the generative AI model suggests, for example, for text such as "My hobbies are reading and watching movies," that "you should write in more detail about your personality and work."

[1471] Step 3: Personality assessment through questions and answers

[1472] The device displays simple questions to the user. The user answers the questions, and the answer data is sent to the server. The server then sends the collected answer data to a generative AI model, which then performs a personality diagnosis based on that data. The input is the answer data to the questions, and the output is the personality diagnosis result. Specifically, the generative AI model is used to generate prompt sentences, and optimal advice is generated based on those sentences.

[1473] Step 4: Generating and suggesting profile text

[1474] The server generates an optimal profile sentence based on the results of the personality assessment. This generated sentence is then suggested to the user via their device. The input is the personality assessment result and profile information, and the output is the suggested profile sentence. Specifically, the generative AI model generates the optimal text based on prompts such as "Q1: A1, Q2: A2, Q3: A3."

[1475] Step 5: Recommend products and services

[1476] The server uses a generative AI model to suggest optimal products and services based on the user's profile information and personality test results. This information is presented to the user via their device. The input is profile information and personality test results, and the output is suggested products and services. Specifically, based on information such as "My hobbies are reading and watching movies" or "I'm introverted but highly sensitive," the server makes recommendations such as "How about a T-shirt from a new movie?"

[1477] Step 6: Detecting spam and fraudulent accounts

[1478] The server uses a generative AI model to constantly monitor newly registered accounts and automatically detect spam and fraudulent accounts. When a suspicious account is detected, it is appropriately removed. The input is the profile data of the newly registered account, and the output is a list of accounts to be removed. Specifically, the generative AI model is used to identify and remove large numbers of accounts with the same profile text or accounts that provide incorrect information.

[1479] These are the specific processing steps of this system. By executing these steps in an orderly manner, it is possible to provide profile information and product / service suggestions that are optimized for the user.

[1480] 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.

[1481] The present invention is a system that integrates generative AI and an emotion engine to more effectively analyze users' profile images and text, and provide optimal feedback and matching. Specific embodiments of various functions are described below.

[1482] 1. Profile picture analysis and emotion identification

[1483] The user uploads their profile picture to the device, which then sends the uploaded picture to the server.

[1484] The server sends the received images to the generation AI and emotion engine.

[1485] The generative AI analyzes the image, evaluating factors such as angle, facial expression, and background, while the emotion engine identifies emotions from the user's facial expressions.

[1486] The server provides specific feedback to the user based on the analysis results from the generation AI and the emotion data from the emotion engine. For example, if the user is not smiling, the server will provide advice such as "Use a smile that looks friendlier."

[1487] Based on the feedback provided, the user can modify the image or upload a new image.

[1488] 2. Profile text analysis and sentiment identification

[1489] The user enters their profile text into the terminal, which then sends the entered text to the server.

[1490] The server sends the received text to the generation AI and emotion engine.

[1491] The generative AI analyzes the text and provides specific feedback on improvements, while the emotion engine identifies the user's emotions from the wording of the text.

[1492] The server then uses the analysis results from the generation AI and the emotion data from the emotion engine to suggest specific improvements to the user. For example, if the text seems bland, the server will provide advice such as, "Add more passionate words to better express yourself."

[1493] The user then modifies the text based on the feedback provided.

[1494] 3. Personality diagnosis and profile suggestion through question-and-answering

[1495] The terminal displays simple questions to the user, who answers the questions and inputs the answer data into the terminal.

[1496] The device sends the collected response data to the server, which then sends it to the generation AI and emotion engine.

[1497] The generation AI analyzes the response data and performs a personality diagnosis, and the emotion engine identifies the emotions the user expressed when answering.

[1498] The server uses data from the generative AI and emotion engine to suggest optimal profile sentences to users. For example, if a user is proactive, a profile sentence that reflects this will be provided.

[1499] Users can use the suggested profile text as a reference to enrich their own profiles.

[1500] 4. Recommendations of compatible people

[1501] The server passes the results of the personality test and analyzed profile data to the generation AI and emotion engine.

[1502] Generative AI and an emotion engine analyze data to identify optimal matches. By incorporating emotion data, more accurate matching becomes possible.

[1503] The server provides the recommendation results to the user, who then decides whether to contact the recommended person.

[1504] 5. Spam and fraudulent account detection

[1505] The server sends the newly registered profile to the generation AI and emotion engine.

[1506] Generative AI analyzes profiles to detect potential spam or fraudulent accounts, while a sentiment engine also detects emotional anomalies to identify suspicious accounts.

[1507] The server will then appropriately eliminate spam accounts based on the detection results, maintaining a safe matching environment.

[1508] By integrating these functions, users can achieve the optimal match that balances appearance and personality, providing a matching environment that can be used with peace of mind.

[1509] The processing flow will be explained below.

[1510] Profile picture analysis and emotion identification

[1511] Step 1:

[1512] The user uploads their profile picture to the device.

[1513] The device sends the uploaded image to the server.

[1514] Step 2:

[1515] The server sends the received images to the generation AI and emotion engine.

[1516] Step 3:

[1517] Generative AI analyzes the image and evaluates factors such as angle, facial expression, and background.

[1518] The emotion engine identifies the user's emotions from the facial expressions in the image, obtaining identification results such as "not smiling" or "nervous."

[1519] Step 4:

[1520] The server provides specific feedback to the user on how to improve based on the analysis results from the AI ​​generation and the emotion data from the emotion engine. For example, it provides advice such as "Use a smile that looks friendlier."

[1521] Step 5:

[1522] Modify your profile picture or upload a new one based on the feedback you provide.

[1523] Profile text analysis and sentiment identification

[1524] Step 1:

[1525] The user enters his / her profile text into the terminal.

[1526] The terminal sends the entered text to the server.

[1527] Step 2:

[1528] The server sends the received text to the generation AI and emotion engine.

[1529] Step 3:

[1530] Generative AI analyzes the text and generates specific improvements as feedback.

[1531] The emotion engine identifies the user's emotion from the wording of the text and obtains emotion data such as "negative" or "positive."

[1532] Step 4:

[1533] The server uses the analysis results from the generated AI and the emotional data from the emotion engine to suggest specific improvements to the user, such as advice such as "It would be good to add more passionate words."

[1534] Step 5:

[1535] Modify the profile text based on the feedback provided by the user.

[1536] Personality diagnosis and profile suggestions through question-and-answering

[1537] Step 1:

[1538] The terminal displays a simple question to the user.

[1539] The user answers the questions and inputs the answer data into the terminal.

[1540] Step 2:

[1541] The terminal transmits the collected response data to the server.

[1542] The server sends this to the generation AI and emotion engine.

[1543] Step 3:

[1544] The generating AI analyzes the response data and conducts a personality diagnosis.

[1545] The emotion engine identifies the emotion expressed by the user when answering and obtains emotional data such as "enjoyed" or "indifferent."

[1546] Step 4:

[1547] The server uses data from the generation AI and emotion engine to suggest optimal profile sentences to users, such as sentences that reflect a positive personality.

[1548] Step 5:

[1549] Users can use the suggested profile text as a reference to enrich their own profiles.

[1550] Recommendations for compatible people

[1551] Step 1:

[1552] The server sends the results of the personality test and analyzed profile data to the generation AI and emotion engine.

[1553] Step 2:

[1554] Generative AI and an emotion engine analyze the data to identify optimal matches. For example, by combining the emotion data of users with the same hobbies, it can select a partner with greater accuracy.

[1555] Step 3:

[1556] The server provides the recommendation results to the user.

[1557] The user decides whether to contact the recommended person.

[1558] Spam and fraudulent account detection

[1559] Step 1:

[1560] The server sends the newly registered profile to the generation AI and emotion engine.

[1561] Step 2:

[1562] Generative AI analyzes profiles to detect potential spam or fraudulent accounts.

[1563] The emotion engine detects emotional anomalies, identifying, for example, "unnaturally positive expressions."

[1564] Step 3:

[1565] The server will then appropriately filter out spam accounts based on the detection results.

[1566] Regular monitoring and countermeasures are carried out to ensure the server maintains a safe matching environment.

[1567] Example 2

[1568] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1569] Conventional matching systems lack the accuracy of analyzing user profile images and text, and do not adequately identify emotions, resulting in inadequate feedback and optimal matching. Furthermore, the accuracy of detecting spam and fraudulent accounts is low, meaning a safe matching environment cannot be guaranteed.

[1570] 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.

[1571] In this invention, the server includes: means for analyzing a user's profile image using a generation AI and providing feedback on specific improvements regarding features such as angle, facial expression, and background; means for identifying emotions from the user's profile image using an emotion engine and reflecting the emotion data in the feedback; means for analyzing the user's profile text using a generation AI and providing feedback on specific improvements; means for identifying emotions from the wording of the profile text using an emotion engine and reflecting the emotion data in the feedback; means for using a generation AI to ask the user short questions and, based on the answers, generate an optimal profile statement or suggest editing; and means for automatically detecting spam and fraudulent accounts and eliminating suspicious accounts using the generation AI and the emotion engine. This improves the accuracy of user profile analysis and emotion identification, enabling optimal feedback and matching. It also enables the provision of a safe matching environment.

[1572] "Generative AI" is an artificial intelligence technology that uses machine learning algorithms to analyze data and generate specific feedback and suggestions.

[1573] An "emotion engine" is an analytical technology that identifies human emotions from images and text and reflects the results in applications.

[1574] "Profile Image" means a photo of a user's face or other still image used to represent themselves in the Matching System.

[1575] "Profile text" is text data that allows a user to describe their characteristics, hobbies, interests, etc.

[1576] "Feedback" refers to specific improvements and suggestions provided to users based on the results of analysis by generative AI and emotion engines.

[1577] "Spam accounts" are fraudulent accounts created for advertising or fraudulent purposes, typically sending large volumes of meaningless messages.

[1578] A "fraudulent account" is a fraudulent account created with the intent to deceive others and abuse the user's trust.

[1579] "Simple questions" are short questions that the generative AI asks the user to understand the user's characteristics and personality.

[1580] A "profile sentence" is a sentence that expresses a user's characteristics and appeal, suggested by the AI ​​based on answers to simple questions.

[1581] A "personality diagnosis" is an evaluation method that analyzes the questions answered by the user and the text entered to identify their personality and behavioral characteristics.

[1582] An "interface" refers to the operating screen or input means through which users and systems exchange information, and plays a role in improving usability.

[1583] The present invention is a system that integrates generative AI and an emotion engine to more effectively analyze a user's profile image and text and provide optimal feedback and matching. The system of the present invention has the following configuration and operation.

[1584] Overall structure

[1585] The system mainly consists of a server, a device, and a user. The server is equipped with a generative AI and an emotion engine, and provides feedback and suggestions to the user based on the analysis results. The device accepts user operations and data input and sends it to the server. Users register their own profile image and text via the device and receive feedback and suggestions.

[1586] Hardware and software used

[1587] Hardware:

[1588] Server: A high-performance computer is recommended. If necessary, a GPU can be installed to support high-speed analysis by the generative AI and emotion engine.

[1589] Terminal: A device that provides a user interface, such as a smartphone, tablet, or computer.

[1590] software:

[1591] Generative AI models: Use models trained using deep learning frameworks (e.g., TensorFlow, PyTorch).

[1592] Emotion Engine: Integrates facial recognition algorithms (e.g., OpenCV, dlib) and natural language processing models (e.g., BERT) to perform emotion analysis.

[1593] Server software: Database management systems (e.g., MySQL, PostgreSQL) and web servers (e.g., Apache, Nginx).

[1594] Profile picture analysis and emotion identification

[1595] The user uploads their profile picture to the device. The device sends the uploaded image to the server. The server sends the received image to the generation AI and emotion engine. The generation AI analyzes the image and evaluates features such as angle, facial expression, and background. The emotion engine identifies emotions from the user's facial expression. The server generates feedback based on the analysis results from the generation AI and the emotion data from the emotion engine and provides it to the user. As a specific example, if the user is not smiling, the feedback provided is, "Use a smile that looks more friendly."

[1596] Profile text analysis and sentiment identification

[1597] The user enters profile text into the device. The device sends the entered text to the server. The server sends the received text to the generation AI and emotion engine. The generation AI analyzes the text and provides feedback on specific areas for improvement. The emotion engine identifies emotions from the wording of the text. The server generates suggestions based on the analysis results from the generation AI and the emotion data from the emotion engine, and provides them to the user. For example, if the text seems bland, the server might suggest, "You might want to add more passionate words to better highlight yourself."

[1598] Personality diagnosis and profile suggestions through question-and-answering

[1599] The device displays simple questions to the user. The user answers the questions and enters the answer data into the device. The device sends the collected answer data to the server. The server sends it to the generation AI and emotion engine. The generation AI analyzes the answer data and performs a personality diagnosis, and the emotion engine identifies the emotion the user expressed when answering. The server suggests the most appropriate profile text to the user based on the data from the generation AI and emotion engine. As a specific example, if the user is proactive, a suggested profile text that reflects this will be provided.

[1600] Recommendations for compatible people

[1601] The server passes the personality test results and analyzed profile data to the generation AI and emotion engine. The generation AI and emotion engine analyze the data and identify the most suitable match. By incorporating emotion data, more accurate matching becomes possible. The server provides the recommendation results to the user. The user decides whether to contact the recommended person. For example, if the user's personality test results show that they are proactive and sociable, the server may recommend matching with "someone who is also proactive and sociable."

[1602] Spam and fraudulent account detection

[1603] The server sends newly registered profiles to the generation AI and emotion engine. The generation AI analyzes the profiles and detects possible spam or fraudulent accounts. The emotion engine also detects emotional anomalies and identifies suspicious accounts. The server then appropriately eliminates spam accounts based on the detection results, maintaining a safe matching environment. For example, if a newly registered account sends a large number of messages at once, the generation AI may determine that it is likely to be a spam account, and the server may suspend the account.

[1604] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1605] Profile picture analysis and emotion identification

[1606] Step 1:

[1607] The user uploads a profile picture to the device. The user operates the application on the device, selects an image file, and clicks the upload button.

[1608] Input: The profile image file selected by the user.

[1609] Output: The image file is saved to your device.

[1610] Step 2:

[1611] The device sends the uploaded image to the server. The device sends the image file to the server using an HTTP request.

[1612] Input: The uploaded profile image file.

[1613] Output: An image file is sent to the server.

[1614] Step 3:

[1615] The server sends the received images to the generative AI and emotion engine, and then sends a request to the appropriate API endpoint to forward the image data to the analysis unit.

[1616] Input: The image file received by the server.

[1617] Output: Image data is passed to the generative AI and emotion engine.

[1618] Step 4:

[1619] The generative AI analyzes the image and evaluates features such as angle, facial expression, and background, and then uses image processing algorithms to quantify each feature.

[1620] Input: Image data sent to the analysis unit.

[1621] Output: Quantified feature data such as angle, facial expression, background, etc.

[1622] Step 5:

[1623] The emotion engine identifies emotions from the user's facial expressions. The emotion engine uses image processing algorithms and machine learning models to tag emotions such as "happiness," "anger," and "sadness" from the facial expressions.

[1624] Input: Image data sent to the analysis unit.

[1625] Output: Identified emotion tags.

[1626] Step 6:

[1627] The server generates feedback based on the analysis results from the generation AI and emotion engine and provides it to the user. The server aggregates the analysis results and generates notifications for specific improvements to the user.

[1628] Input: quantified feature data and emotion tags.

[1629] Output: A specific feedback message to the user.

[1630] Profile text analysis and sentiment identification

[1631] Step 1:

[1632] The user enters profile text into the terminal. The user enters text into the text area and clicks the send button.

[1633] Input: The profile text entered by the user.

[1634] Output: Text data is saved to the terminal.

[1635] Step 2:

[1636] The device sends the entered text to the server. The device sends the text data to the server using an HTTP request.

[1637] Input: The profile text entered.

[1638] Output: Text data is sent to the server.

[1639] Step 3:

[1640] The server sends the received text to the generative AI and emotion engine, and then sends a request to the appropriate API endpoint to forward the text data to the analysis unit.

[1641] Input: Text data received by the server.

[1642] Output: Text data is passed to the generative AI and emotion engine.

[1643] Step 4:

[1644] Generative AI analyzes the text and provides specific feedback on improvements. Generative AI uses natural language processing algorithms to analyze the grammar and context of the text and generate specific suggestions.

[1645] Input: Text data sent to the analysis unit.

[1646] Output: Suggested data for grammar correction and content specification.

[1647] Step 5:

[1648] The emotion engine identifies emotions from the wording of the text. The emotion engine uses natural language processing algorithms to identify emotions such as "passion," "calm," or "joy" from the text.

[1649] Input: Text data sent to the analysis unit.

[1650] Output: Identified emotion tags.

[1651] Step 6:

[1652] The server generates suggestions based on the analysis results from the generation AI and emotion engine and provides them to the user. The server aggregates the analysis results and generates notifications for specific improvements and suggestions for the user.

[1653] Input: Grammar correction suggestion data and sentiment tags.

[1654] Output: A specific suggestion message to the user.

[1655] Personality diagnosis and profile suggestions through question-and-answering

[1656] Step 1:

[1657] The terminal displays simple questions to the user, and the terminal application displays the questions in a dialog format.

[1658] Input: Questions created by the generative AI.

[1659] Output: The question dialog that is displayed to the user.

[1660] Step 2:

[1661] The user answers the question and enters the answer data into the terminal. The user enters the answer and clicks the send button.

[1662] Input: The answer data entered by the user.

[1663] Output: The answer data is saved on the device.

[1664] Step 3:

[1665] The device sends the collected response data to the server. The device sends the response data to the server using an HTTP request.

[1666] Input: The entered response data.

[1667] Output: The response data is sent to the server.

[1668] Step 4:

[1669] The server sends this to the generative AI and emotion engine, which then makes a request to the appropriate API endpoint to forward the response data to the analysis unit.

[1670] Input: The response data received by the server.

[1671] Output: The answer data is passed to the generation AI and emotion engine.

[1672] Step 5:

[1673] The generation AI analyzes the response data to conduct a personality diagnosis, and the emotion engine identifies the emotion of the user when answering. The generation AI uses a machine learning model to analyze personality from the response data, and the emotion engine identifies emotion from the text.

[1674] Input: Response data sent to the analysis unit.

[1675] Output: Personality trait data and emotion tags.

[1676] Step 6:

[1677] The server proposes optimal profile sentences to users based on data from the generation AI and emotion engine.The server generates suggested profile sentences for users based on the analysis results.

[1678] Input: personality trait data and emotion tags.

[1679] Output: Suggestion of specific profile text to the user.

[1680] (Application example 2)

[1681] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1682] Conventional systems simply analyze users' profile images and text, but are unable to provide feedback or product recommendations based on the user's emotions and preferences. This makes it difficult to provide more personalized services to users. Furthermore, the accuracy of detecting spam and fraudulent accounts is low, making it impossible to provide a safe environment. Therefore, there is a need for more accurate profile analysis and recommendation systems.

[1683] 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.

[1684] In this invention, the server includes means for analyzing a user's profile image using a generation AI and providing feedback on specific improvements regarding elements such as angle, facial expression, and background, means for analyzing a user's profile text using a generation AI and providing feedback on specific improvements, means for using a generation AI to ask the user simple questions and generate an optimal profile statement or suggest editing based on the answers, means for using a generation AI to automatically detect spam accounts and fraudulent accounts and eliminate suspicious accounts, and means for analyzing a user's profile image and text using a generation AI and an emotion engine and recommending products based on the user's preferences and emotions. This enables more personalized feedback and product recommendations to users and promotes use in a safe environment.

[1685] "Generative AI" refers to artificial intelligence that uses advanced algorithms to analyze data and generate new information and feedback.

[1686] An "emotion engine" is a system that identifies emotions from user input data (e.g., images or text) and provides analysis results based on that.

[1687] A "profile image" is image data uploaded by a user to represent themselves.

[1688] "Profile text" refers to text data entered by a user to describe themselves.

[1689] "Recommendation" means proposing products and services that are individually suited to a user based on their preferences and behavior.

[1690] A "spam account" is an account created for fraudulent purposes, typically used to send random advertising or fraudulent messages.

[1691] A "fraudulent account" is an account created for the purpose of committing fraud, and provides false information with the intent of deceiving others.

[1692] "Analysis" is the process of examining and breaking down data in detail to understand its structure and meaning.

[1693] "Feedback" refers to ratings and suggestions for improvement provided based on user actions and input.

[1694] "Personalization" means providing content that is individualized according to the preferences and characteristics of individual users.

[1695] MODE FOR CARRYING OUT THE INVENTION

[1696] This invention is a system that integrates generative AI and an emotion engine to effectively analyze a user's profile image and text and provide optimal feedback and product recommendations. This system is composed of a server, a terminal, and a user. Specific embodiments of this system are described in detail below.

[1697] Profile picture analysis and emotion identification

[1698] Users upload their profile picture to their device. The device sends the uploaded image to the server. The server sends the received image to the generation AI and emotion engine. The generation AI analyzes the image and evaluates factors such as angle, facial expression, and background. The emotion engine identifies emotions from the user's facial expression. Based on the analysis results from the generation AI and the emotion data from the emotion engine, the server provides specific feedback to the user on areas for improvement. For example, if the user is not smiling, the server will provide advice such as "use a smile that looks more friendly."

[1699] Profile text analysis and sentiment identification

[1700] The user enters their profile text into the device. The device sends the entered text to the server. The server sends the received text to the generation AI and emotion engine. The generation AI analyzes the text and provides feedback on specific areas for improvement. The emotion engine identifies the user's emotions from the wording of the text. The server then suggests specific areas for improvement to the user based on the analysis results from the generation AI and the emotion data from the emotion engine. For example, if the text seems bland, the server will provide advice such as, "Add more passionate words to better highlight yourself."

[1701] Personality diagnosis and profile suggestions through question-and-answering

[1702] The device displays simple questions to the user. The user answers the questions and enters the answer data into the device. The device then sends the collected answer data to the server. The server then sends it to the generation AI and emotion engine. The generation AI analyzes the answer data and performs a personality diagnosis, and the emotion engine identifies the emotion the user felt when answering. The server then uses the data from the generation AI and emotion engine to suggest optimal profile text to the user. For example, if the user is proactive, it will provide suggested profile text that reflects this.

[1703] Recommendations for compatible people

[1704] The server passes the personality test results and analyzed profile data to the generation AI and emotion engine. The generation AI and emotion engine analyze the data and identify the most suitable match. By incorporating emotion data, more accurate matching becomes possible. The server provides the recommendation results to the user. The user decides whether to contact the recommended person.

[1705] Spam and fraudulent account detection

[1706] The server sends newly registered profiles to the generation AI and emotion engine. The generation AI analyzes the profiles and detects possible spam or fraudulent accounts. The emotion engine also detects emotional anomalies and identifies suspicious accounts. The server then appropriately eliminates spam accounts based on the detection results, maintaining a safe matching environment.

[1707] Product recommendations based on user preferences

[1708] Using generative AI and an emotion engine, the system analyzes a user's profile image and text to recommend products based on the user's preferences and emotions. For example, if a user uploads an image of themselves smiling and relaxed, and enters text about casual fashion, the system will recommend relaxed, casual clothing. Specific examples of prompts include:

[1709] We analyzed the user's profile picture to identify their emotions and recognized it as a "relaxed smile." Next, we determined that the user is interested in "casual fashion" from the text "I've been wearing casual clothes a lot lately." Based on this condition, we would like you to list products that are suitable for relaxing casual wear.

[1710] In this way, systems using generative AI models and emotion engines can provide more personalized feedback and product recommendations to users, promoting usage in a safe environment.

[1711] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1712] Step 1:

[1713] The user uploads a profile image to the device, which then sends the image data to the server, which then sends the image data to the generative AI and emotion engine.

[1714] Input: The user's profile picture.

[1715] Data processing: Pre-processing of images, such as transfer and compression.

[1716] Output: Image data for analysis.

[1717] Specific actions: The user selects an image using the app on their smartphone and presses the upload button.

[1718] Step 2:

[1719] The server uses generative AI to analyze the image, evaluating factors such as angle, facial expression, and background, while the emotion engine identifies emotions from the user's facial expressions.

[1720] Input: Profile image.

[1721] Data processing: Extraction of parameters based on image analysis algorithms (angle, facial expression, background, etc.).

[1722] Output: Analysis results and sentiment data.

[1723] Specific operation: The image analysis model on the server extracts image features, and the emotion engine outputs emotion labels.

[1724] Step 3:

[1725] Based on the analysis results from the generative AI and emotion engine, the server provides specific feedback to the user on how to improve their appearance, such as "Use a smile that looks friendlier."

[1726] Input: Analysis results and emotion data.

[1727] Data processing: Creating advice through feedback generation algorithms.

[1728] Output: The feedback message.

[1729] Specific operation: The server generates an advice message and sends it to the terminal.

[1730] Step 4:

[1731] Users enter their profile text into their device, which sends it to the server, which then sends it to the generative AI and emotion engine.

[1732] Input: Profile text.

[1733] Data processing: Text transfer and formatting.

[1734] Output: Text data.

[1735] Specific action: The user enters a profile statement in the text field and presses the submit button.

[1736] Step 5:

[1737] The server analyzes the profile text with generative AI and provides specific feedback on improvements, while the emotion engine identifies emotions from the wording of the text.

[1738] Input: Profile text.

[1739] Data processing: Analysis using text analysis algorithms and generation of sentiment labels.

[1740] Output: Analysis results and sentiment data.

[1741] Specific operation: The analysis model on the server analyzes the text and outputs improvements and emotion labels.

[1742] Step 6:

[1743] The server then uses the analysis results from the generative AI and emotion engine to suggest specific improvements to the user, offering advice such as "Use more passionate words to better express yourself."

[1744] Input: Analysis results and emotion data.

[1745] Data processing: Creating advice through feedback generation algorithms.

[1746] Output: The feedback message.

[1747] Specific operation: The server generates an advice message and sends it to the terminal.

[1748] Step 7:

[1749] The device displays simple questions to the user, who answers them, and the device sends the answer data to the server.

[1750] Input: Question response data.

[1751] Data processing: Data collection and transfer.

[1752] Output: Question-answer data.

[1753] What happens: The user answers the questions displayed and submits the answers.

[1754] Step 8:

[1755] The server sends the collected response data to the generation AI and emotion engine to conduct a personality diagnosis and identify the emotions the user was feeling when answering.

[1756] Input: Question and answer data.

[1757] Data processing: Analysis using personality diagnosis algorithms and emotion identification algorithms.

[1758] Output: Personality test results and emotional data.

[1759] Specific operation: The model on the server analyzes the data and outputs diagnostic results.

[1760] Step 9:

[1761] The server suggests the most suitable profile sentence for the user based on data from the generative AI and emotion engine.

[1762] Input: Personality test results and emotion data.

[1763] Data processing: Forming suggestions using a profile sentence generation algorithm.

[1764] Output: Profile text suggestions.

[1765] Specific operation: The server generates a proposal message and sends it to the terminal.

[1766] Step 10:

[1767] The server passes the personality test results and analyzed profile data to a generative AI and emotion engine to identify the best matches.

[1768] Input: Personality test results and profile data.

[1769] Data processing: Analysis using matching algorithm.

[1770] Output: A list of matches.

[1771] Specific operation: The server analyzes the data and outputs the matching results.

[1772] Step 11:

[1773] The server analyzes user profile images and text and recommends products based on the user's preferences and emotions.

[1774] Input: Profile image and text, analysis results.

[1775] Data processing: Analysis using product recommendation algorithms.

[1776] Output: A list of products and reasons.

[1777] Specific operation: The server recommends products based on the analysis results and generates a message including the reasons for the recommendation.

[1778] Step 12:

[1779] The server sends newly registered profiles to the generative AI and emotion engine to detect potential spam or fraudulent accounts.

[1780] Input: New profile data.

[1781] Data processing: Analysis using spam detection algorithms and emotion anomaly detection algorithms.

[1782] Output: Detection results.

[1783] Specific operation: The detection model on the server analyzes the profile and outputs the results.

[1784] Step 13:

[1785] The server will then appropriately eliminate spam accounts based on the detection results, maintaining a safe environment.

[1786] Input: The detection result.

[1787] Data processing: Eliminating spam accounts through account management systems.

[1788] Output: A list of secure accounts.

[1789] Specific action: The server will identify spam accounts based on the detection results and remove them appropriately.

[1790] In this way, each step works together to create a system that provides optimal feedback and product recommendations to users and ensures a safe environment.

[1791] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1792] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1793] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1794] [Fourth embodiment]

[1795] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1796] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1797] 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).

[1798] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1799] 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.

[1800] 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).

[1801] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1802] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1803] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1804] 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.

[1805] 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.

[1806] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1807] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1808] The present invention is a dating matching service system that utilizes generative AI to optimize the balance between a user's appearance and personality and provide a safe matching environment. Specific embodiments of this system are described below.

[1809] 1. Profile image analysis and improvement

[1810] The user uploads their profile picture, which the server receives and sends to the generation AI.

[1811] The server uses generative AI to analyze factors such as the angle, facial expression, and background of the user's profile picture. For example, if the facial expression in the picture is unattractive or the background is cluttered, it will provide specific feedback on how to improve it.

[1812] Based on the feedback provided, the user can modify the image or upload a new image.

[1813] 2. Profile text analysis and improvement

[1814] The user enters their profile text, and the server receives this text and sends it to the generating AI.

[1815] The server uses a generative AI to analyze the profile text and provide specific feedback on areas for improvement. For example, if someone writes, "My hobbies are reading and watching movies," the server might suggest, "You should write more about your personality and work in more detail."

[1816] The user then modifies the text based on the feedback provided.

[1817] 3. Personality diagnosis and profile suggestion through question-and-answering

[1818] The device displays a simple question to the user, who then answers it.

[1819] The server sends the collected response data to the AI, which then performs a personality diagnosis based on the answers. Based on the generated diagnosis results, the AI ​​then suggests the optimal profile text to the user.

[1820] Users can use the suggested profile text as a reference to enrich their own profiles.

[1821] 4. Recommendations of compatible people

[1822] The server then recommends suitable matches to users based on the results of the personality assessment and analyzed profile data, through a process in which generative AI analyzes the data and identifies those with the best compatibility.

[1823] Users can decide whether to contact the recommended people, and this system allows users to meet people who are suitable for them more efficiently.

[1824] 5. Spam and fraudulent account detection

[1825] The server constantly monitors newly registered profiles using generative AI and automatically detects spam accounts and potentially fraudulent accounts.

[1826] The server will then properly remove any spam accounts it detects and maintain a safe matching environment, such as detecting and removing accounts with numerous identical profile statements or accounts that provide excessively incorrect information.

[1827] In this way, the present invention utilizes generative AI to not only optimize users' profile details, but also provide a safe and reliable matching environment, allowing users to achieve the best possible match that balances appearance and personality, and to use the service with peace of mind.

[1828] The processing flow will be explained below.

[1829] Profile image analysis and improvement

[1830] Step 1:

[1831] The user uploads their profile picture to the device.

[1832] The device sends the uploaded image to the server.

[1833] Step 2:

[1834] The server passes the received image to the generation AI.

[1835] Generative AI analyzes the image and evaluates factors such as angle, facial expression, and background.

[1836] Step 3:

[1837] Based on the analysis results from the generated AI, the server provides specific feedback to the user on areas for improvement.

[1838] Based on the feedback, the user modifies the image or uploads a new image.

[1839] Profile text analysis and improvement

[1840] Step 1:

[1841] The user enters his / her profile text into the terminal.

[1842] The terminal sends the entered text to the server.

[1843] Step 2:

[1844] The server passes the received text to the generation AI.

[1845] Generative AI analyzes the text and generates specific improvements as feedback.

[1846] Step 3:

[1847] The server provides feedback from the generated AI to the user.

[1848] The user then corrects the text based on the feedback provided.

[1849] Personality diagnosis and profile suggestions through question-and-answering

[1850] Step 1:

[1851] The terminal displays a simple question to the user.

[1852] The user answers the questions and inputs the answer data into the terminal.

[1853] Step 2:

[1854] The terminal transmits the collected response data to the server.

[1855] The server passes the response data to the generating AI and conducts a personality diagnosis.

[1856] Step 3:

[1857] The generative AI will generate the optimal profile text based on the diagnostic results.

[1858] The server proposes the generated profile statement to the user.

[1859] Step 4:

[1860] Users can use the suggested profile text as a reference to enrich their own profiles.

[1861] Recommendations for compatible people

[1862] Step 1:

[1863] The server passes the personality test results and analyzed profile data to the generation AI.

[1864] Generative AI analyzes the data and identifies the best matches.

[1865] Step 2:

[1866] The server provides the recommendation results to the user.

[1867] The user decides whether to contact the recommended person.

[1868] Spam and fraudulent account detection

[1869] Step 1:

[1870] The server passes the newly registered profile to the generation AI.

[1871] Generative AI analyzes profiles to detect potential spam or fraudulent accounts.

[1872] Step 2:

[1873] The server will now properly remove detected spam accounts.

[1874] Regular monitoring and countermeasures are carried out to ensure the server maintains a safe matching environment.

[1875] Example 1

[1876] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1877] In recent years, with the spread of online dating matching services, it is necessary not only to improve the quality of users' profile images and text, but also to provide a safe and reliable matching environment. However, it requires advanced skills and time for users to properly optimize their profiles, and eliminating spam and fraudulent accounts is also a major challenge. An efficient system to solve these problems is needed.

[1878] 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.

[1879] In this invention, the server includes means for using a generation AI to analyze a user's profile image and provide feedback on specific improvements regarding elements such as angle, facial expression, and background, means for using a generation AI to analyze the user's profile text and provide feedback on specific improvements, means for using a generation AI to ask the user simple questions and generate or suggest edits to an optimal profile statement based on the answers, means for using a generation AI to automatically detect spam accounts and fraudulent accounts and eliminate suspicious accounts, means for using a generation AI to identify and recommend compatible individuals based on the results of a personality assessment and analyzed profile data, and means for suggesting input prompts for the generation AI model. This allows users to achieve optimal matches that balance appearance and inner qualities, enabling them to use a safe and reliable dating matching service.

[1880] "Generative AI" is an artificial intelligence technology that uses machine learning algorithms to generate data and automate specific tasks.

[1881] "Profile Picture" means a photo of a user's face or other image uploaded by a user to express their identity or personality.

[1882] "Profile text" is written information in which a user describes themselves, their hobbies, and interests.

[1883] "Spam Accounts" are automatically generated user accounts registered for irrelevant advertising or fraudulent purposes.

[1884] A "fraudulent account" is a user account that provides false information with the intent to deceive others.

[1885] "Feedback" refers to suggestions and advice for improving images and text that the generative AI provides to the user.

[1886] "Personality assessment" is the process by which the generative AI evaluates a user's personality traits based on the questions the user answers.

[1887] "Recommendation" refers to the generation AI recommending the best match to the user.

[1888] A "prompt" is a guided sentence input to a generative AI model, used to induce a specific response or generation.

[1889] The present invention is a dating matching service system that utilizes generative AI to optimize the balance between a user's appearance and personality and provide a safe matching environment. Specific embodiments of this system are described below.

[1890] 1. Profile image analysis and improvement

[1891] When a user uploads their profile picture, the server receives the image and sends it to the generation AI. The generation AI analyzes elements of the user's profile picture, such as the angle, facial expression, and background. For example, if the facial expression in the image is unattractive or the background is cluttered, it provides specific feedback on how to improve the image. The user can then modify the image or upload a new image based on the feedback provided.

[1892] Examples:

[1893] If the background of an image uploaded by a user is cluttered, the generating AI will provide feedback such as "It would be better to simplify the background."

[1894] Example prompt for a generative AI model:

[1895] "How can we improve this profile picture background?"

[1896] 2. Profile text analysis and improvement

[1897] When a user enters their profile text, the server receives this text and sends it to the generation AI. The generation AI analyzes the profile text and provides feedback on specific areas for improvement. For example, for a profile entry such as "My hobbies are reading and watching movies," the server may suggest "You should write more about your personality and work." The user then edits the text based on the provided feedback.

[1898] Examples:

[1899] For example, if a user writes, "My hobbies are reading and watching movies," the AI ​​generator will provide feedback such as, "You should write in more detail about your personality and work."

[1900] Example prompt for a generative AI model:

[1901] "Please suggest improvements to this profile text."

[1902] 3. Personality diagnosis and profile suggestion through question-and-answering

[1903] The device displays simple questions to the user, who then answers them. The server then sends the collected response data to the AI, which then performs a personality diagnosis based on the answers. Based on the generated diagnosis results, the AI ​​then suggests an optimal profile statement to the user. The user can then use the suggested statement to enhance their profile.

[1904] Examples:

[1905] If a user answers "reading" to the question "How do you spend your holidays?", the AI ​​generator will suggest "the perfect profile sentence for you, a book lover."

[1906] Example prompt for a generative AI model:

[1907] "Suggest the best profile sentence for this user based on their answers."

[1908] 4. Recommendations of compatible people

[1909] The server recommends suitable matches to users based on the results of the personality test and analyzed profile data. This is done through a process in which generative AI analyzes data and identifies people with the best compatibility. Users then decide whether or not to contact the recommended people. This system allows users to meet suitable partners more efficiently.

[1910] Examples:

[1911] If the personality test results and profile data of User A and User B match, the server will use generative AI to recommend the two people.

[1912] Example prompt for a generative AI model:

[1913] "Please suggest the best matches for this user based on their personality test results and profile data."

[1914] 5. Spam and fraudulent account detection

[1915] The server constantly monitors newly registered profiles using generative AI to automatically detect spam accounts and potentially fraudulent accounts. Detected spam accounts are then appropriately removed to maintain a safe matching environment. For example, accounts with numerous identical profile statements or accounts providing extremely incorrect information are detected and removed.

[1916] Examples:

[1917] If there are a large number of newly registered profiles with the same text, the generation AI will identify them as spam accounts and eliminate them.

[1918] Example prompt for a generative AI model:

[1919] "Please determine if this profile is spam or a potential scam."

[1920] In this way, the present invention utilizes generative AI to not only optimize users' profile details, but also provide a safe and reliable matching environment, allowing users to achieve the best possible match that balances appearance and personality, and to use the service with peace of mind.

[1921] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1922] Step 1:

[1923] User uploads a profile picture

[1924] The user selects their profile picture and presses the upload button. The server receives the image data sent by the user.

[1925] input:

[1926] Image data uploaded by users

[1927] Specific behavior:

[1928] Check the image file type and size and save it in the appropriate format.

[1929] output:

[1930] Profile image data stored on the server

[1931] Step 2:

[1932] The server sends the profile image to the AI ​​for analysis.

[1933] The server sends the received profile image to the generation AI for analysis. The generation AI model analyzes the image's angle, facial expression, and background to identify areas for improvement.

[1934] input:

[1935] Profile image data stored on the server

[1936] Specific behavior:

[1937] Generative AI performs facial recognition and evaluates facial expression, background, and angle.

[1938] Data analysis algorithms are used to extract improvements to the images.

[1939] output:

[1940] Specific improvements provided by the generation AI (e.g., stiff facial expressions, cluttered background)

[1941] Step 3:

[1942] User enters profile text

[1943] Users enter text to describe their profile, and the server receives and stores this text data.

[1944] input:

[1945] User-entered profile text

[1946] Specific behavior:

[1947] The text data is stored on the server.

[1948] Check the format of the saved data and make it ready to send to the generating AI.

[1949] output:

[1950] Profile text data stored on the server

[1951] Step 4:

[1952] The server sends the profile text to the AI ​​generator for analysis.

[1953] The server sends the saved profile text to the AI ​​generator for analysis, which analyzes the content of the text and suggests specific improvements.

[1954] input:

[1955] Profile text data stored on the server

[1956] Specific behavior:

[1957] Generative AI performs natural language processing and analyzes the content of the text.

[1958] Extract areas for improvement from the analysis results.

[1959] output:

[1960] Specific improvements provided by the generative AI (e.g., writing more specifically about hobbies)

[1961] Step 5:

[1962] The device asks the user a simple question and collects the answer.

[1963] The terminal displays some simple questions to the user, who answers them. The server receives and stores the answer data.

[1964] input:

[1965] Question data answered by users

[1966] Specific behavior:

[1967] The terminal displays the question and accepts user input.

[1968] The response data is sent to the server.

[1969] output:

[1970] Response data stored on the server

[1971] Step 6:

[1972] The server conducts a personality test and suggests the best profile sentence for you.

[1973] The server sends the collected response data to the AI ​​generator, which then performs a personality diagnosis. Based on the results of the diagnosis, the AI ​​then suggests the most suitable profile text for the user.

[1974] input:

[1975] Response data stored on the server

[1976] Specific behavior:

[1977] The generative AI conducts a personality diagnosis and evaluates the user's personality traits.

[1978] Based on the diagnosis results, the optimal profile statement is generated.

[1979] output:

[1980] The optimal profile text suggested by generative AI

[1981] Step 7:

[1982] The server automatically detects and removes spam and fraudulent accounts.

[1983] The server constantly monitors newly registered profiles using generative AI, automatically detecting spam accounts and potentially fraudulent accounts, and then appropriately removing detected accounts.

[1984] input:

[1985] Newly registered profile data

[1986] Specific behavior:

[1987] Generative AI analyzes registered profile data to identify spam and fraud patterns.

[1988] Remove confirmed spam or fraudulent accounts from the system.

[1989] output:

[1990] Safe User Profile List

[1991] Step 8:

[1992] The server recommends suitable matches to the user.

[1993] The server uses generative AI to recommend the best possible matches to users based on the personality test results and analyzed profile data.

[1994] input:

[1995] Analyzed profile data and personality test results

[1996] Specific behavior:

[1997] Generative AI analyzes the data and identifies people who are compatible.

[1998] The identified matches are displayed to the user.

[1999] output:

[2000] A list of recommended matches for the user

[2001] Thus, through each step of the system, users can optimize their profile and enjoy a safe and effective matching environment.

[2002] (Application example 1)

[2003] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2004] Conventional matching systems only recommend suitable partners based on the user's profile information and personality assessment, but do not offer services or product suggestions that users can actually experience in physical stores. This makes it difficult for users to choose products and services that suit them. Other issues include insufficient feedback on image and text improvements, and ineffective elimination of fraudulent and spam accounts.

[2005] 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.

[2006] In this invention, the server includes means for analyzing a user's profile image using a generation AI and providing feedback on specific improvements regarding elements such as angle, facial expression, and background, means for analyzing a user's profile text using a generation AI and providing feedback on specific improvements, means for using a generation AI to ask the user simple questions and generate or suggest editing an optimal profile statement based on the answers, means for using a generation AI to automatically detect spam accounts and fraudulent accounts and eliminate suspicious accounts, and means for suggesting optimal products and services based on the user's profile information and personality assessment data. This enables more accurate product and service suggestions based on the user's appearance and inner information, realizing a safe and reliable matching environment.

[2007] "Generative AI" refers to artificial intelligence that generates natural language and images based on user input data.

[2008] A "profile picture" is a photo that a user uses to show their personal information and appearance.

[2009] "Angle" is an element that indicates the relative positions of the subject's face and body in the profile image.

[2010] "Expression" is an element that indicates the facial expression of the user in the profile picture.

[2011] "Background" refers to the environment or scenery that appears behind the subject in a profile picture.

[2012] "Profile text" refers to text information in which a user writes about themselves, their hobbies, etc.

[2013] "Feedback" refers to improvements and advice provided by the generative AI to the user.

[2014] A "question" is a question-and-answer format information gathering tool presented to the user.

[2015] "Personality diagnosis" is the process of evaluating a user's personality traits based on the user's response data, etc.

[2016] "Spam Account" means a user account created for fraudulent purposes.

[2017] A "Fraudulent Account" is a fraudulent user account created with the intent to deceive users.

[2018] "Product" means an object such as a good or service that is provided to a user.

[2019] "Service" means any act or service provided to a User.

[2020] "Profile Information" refers to all data about a user, including images and text.

[2021] "Personality assessment data" refers to data regarding a user's personality characteristics obtained through a personality assessment.

[2022] "Recommendation" means a recommendation of a product or service made to a User.

[2023] An "interface" is a screen or operating means that allows a user to interact with a system.

[2024] The present invention is a system that utilizes generative AI to optimize a user's profile image and text, and safely and effectively suggests optimal products and services to the user. Specific embodiments of this system are described below.

[2025] System Configuration and Operation

[2026] This system consists of three elements: a server, a terminal, and a user. The server operates the generative AI, the terminal provides the user interface, and the user operates the terminal to use the service.

[2027] 1. Profile image analysis and improvement

[2028] The server first receives the profile image uploaded by the user. This image is then sent to the generation AI, which analyzes elements such as the angle, facial expression, and background. For example, the image is analyzed using OpenCV, and if the background is too cluttered, the server provides specific feedback to the user on how to improve it, such as "Please change to a simpler background." This allows users to create more attractive profile images.

[2029] 2. Profile text analysis and improvement

[2030] The profile text entered by the user is also sent to the server. The server uses generative AI to analyze the text and generate specific suggestions for improvement. For example, for the text "My hobbies are reading and watching movies," the server will provide advice such as "You should write more details about your personality and work." This allows users to create a more comprehensive profile text.

[2031] 3. Personality diagnosis and profile suggestion through question-and-answering

[2032] The device displays simple questions to the user, who then answers them. These answers are sent to a server, where a personality diagnosis is performed using a generative AI. Based on the results, the device suggests the profile text that best suits the user's personality. For example, if the answers to the questions are "Q1: A1, Q2: A2, Q3: A3," the device inputs this information into the prompt and generates the appropriate text.

[2033] 4. Product and service recommendations

[2034] Using generative AI, the server will suggest optimal products and services based on the results of a personality test and profile information. These suggestions are based on prompts and are presented to the user in an easy-to-understand format. For example, based on a profile that says, "My hobbies are reading and watching movies," a specific suggestion will be made, such as, "How about a T-shirt from a new movie?"

[2035] Hardware and software used

[2036] Hardware: Storefront customer service robots (e.g., generic company robots), cameras (to capture images of users)

[2037] Software: OpenCV (image analysis), OpenAI API (text generation), Transformers library (implementation of generative AI)

[2038] Examples of concrete examples and prompts

[2039] When implementing this in a brick-and-mortar apparel store, the following specific examples are possible:

[2040] When a user shows the robot an image of themselves, the robot suggests, "The background is too cluttered. Please change it to a simpler background."

[2041] Based on the profile text, the recommendation is, "Since your hobby is watching movies, how about a T-shirt from a new movie?"

[2042] Example prompt sentence:

[2043] 1. Profile text improvement suggestions:

[2044] markdown

[2045] How can we improve the following profile text?

[2046] My hobbies are reading and watching movies

[2047] 2. Personality Test and Suggestions:

[2048] markdown

[2049] Please assess the user's personality based on the answers below and make suggestions:

[2050] Q1: A1, Q2: A2, Q3: A3

[2051] 3. Product Suggestion:

[2052] markdown

[2053] Profile text: My hobbies are reading and watching movies

[2054] Personality Test: Introverted but highly sensitive

[2055] Please suggest suitable products based on these.

[2056] As described above, this system utilizes generative AI to optimize user information and can suggest appropriate products and services in physical stores, providing a safe and reliable matching environment and increasing user satisfaction.

[2057] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2058] Step 1: Upload and analyze your profile image

[2059] The server receives the profile image uploaded by the user via their device. This image is sent to a generative AI model, which analyzes factors such as angle, facial expression, and background. The input is the profile image, and the output is feedback on areas for improvement. Specifically, OpenCV is used to analyze the angle and facial expression of the image, and if the background is cluttered, feedback is generated, such as "Please change to a simpler background."

[2060] Step 2: Enter and parse profile text

[2061] The server receives the profile text entered by the user via their device. This text is sent to the generative AI model, which analyzes the content of the text. Specific improvements are then provided as feedback. The input is the profile text, and the output is feedback on improvements. Specifically, the generative AI model suggests, for example, for text such as "My hobbies are reading and watching movies," that "you should write in more detail about your personality and work."

[2062] Step 3: Personality assessment through questions and answers

[2063] The device displays simple questions to the user. The user answers the questions, and the answer data is sent to the server. The server then sends the collected answer data to a generative AI model, which then performs a personality diagnosis based on that data. The input is the answer data to the questions, and the output is the personality diagnosis result. Specifically, the generative AI model is used to generate prompt sentences, and optimal advice is generated based on those sentences.

[2064] Step 4: Generating and suggesting profile text

[2065] The server generates an optimal profile sentence based on the results of the personality assessment. This generated sentence is then suggested to the user via their device. The input is the personality assessment result and profile information, and the output is the suggested profile sentence. Specifically, the generative AI model generates the optimal text based on prompts such as "Q1: A1, Q2: A2, Q3: A3."

[2066] Step 5: Recommend products and services

[2067] The server uses a generative AI model to suggest optimal products and services based on the user's profile information and personality test results. This information is presented to the user via their device. The input is profile information and personality test results, and the output is suggested products and services. Specifically, based on information such as "My hobbies are reading and watching movies" or "I'm introverted but highly sensitive," the server makes recommendations such as "How about a T-shirt from a new movie?"

[2068] Step 6: Detecting spam and fraudulent accounts

[2069] The server uses a generative AI model to constantly monitor newly registered accounts and automatically detect spam and fraudulent accounts. When a suspicious account is detected, it is appropriately removed. The input is the profile data of the newly registered account, and the output is a list of accounts to be removed. Specifically, the generative AI model is used to identify and remove large numbers of accounts with the same profile text or accounts that provide incorrect information.

[2070] These are the specific processing steps of this system. By executing these steps in an orderly manner, it is possible to provide profile information and product / service suggestions that are optimized for the user.

[2071] 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.

[2072] The present invention is a system that integrates generative AI and an emotion engine to more effectively analyze users' profile images and text, and provide optimal feedback and matching. Specific embodiments of various functions are described below.

[2073] 1. Profile picture analysis and emotion identification

[2074] The user uploads their profile picture to the device, which then sends the uploaded picture to the server.

[2075] The server sends the received images to the generation AI and emotion engine.

[2076] The generative AI analyzes the image, evaluating factors such as angle, facial expression, and background, while the emotion engine identifies emotions from the user's facial expressions.

[2077] The server provides specific feedback to the user based on the analysis results from the generation AI and the emotion data from the emotion engine. For example, if the user is not smiling, the server will provide advice such as "Use a smile that looks friendlier."

[2078] Based on the feedback provided, the user can modify the image or upload a new image.

[2079] 2. Profile text analysis and sentiment identification

[2080] The user enters their profile text into the terminal, which then sends the entered text to the server.

[2081] The server sends the received text to the generation AI and emotion engine.

[2082] The generative AI analyzes the text and provides specific feedback on improvements, while the emotion engine identifies the user's emotions from the wording of the text.

[2083] The server then uses the analysis results from the generation AI and the emotion data from the emotion engine to suggest specific improvements to the user. For example, if the text seems bland, the server will provide advice such as, "Add more passionate words to better express yourself."

[2084] The user then modifies the text based on the feedback provided.

[2085] 3. Personality diagnosis and profile suggestion through question-and-answering

[2086] The terminal displays simple questions to the user, who answers the questions and inputs the answer data into the terminal.

[2087] The device sends the collected response data to the server, which then sends it to the generation AI and emotion engine.

[2088] The generation AI analyzes the response data and performs a personality diagnosis, and the emotion engine identifies the emotions the user expressed when answering.

[2089] The server uses data from the generative AI and emotion engine to suggest optimal profile sentences to users. For example, if a user is proactive, a profile sentence that reflects this will be provided.

[2090] Users can use the suggested profile text as a reference to enrich their own profiles.

[2091] 4. Recommendations of compatible people

[2092] The server passes the results of the personality test and analyzed profile data to the generation AI and emotion engine.

[2093] Generative AI and an emotion engine analyze data to identify optimal matches. By incorporating emotion data, more accurate matching becomes possible.

[2094] The server provides the recommendation results to the user, who then decides whether to contact the recommended person.

[2095] 5. Spam and fraudulent account detection

[2096] The server sends the newly registered profile to the generation AI and emotion engine.

[2097] Generative AI analyzes profiles to detect potential spam or fraudulent accounts, while a sentiment engine also detects emotional anomalies to identify suspicious accounts.

[2098] The server will then appropriately eliminate spam accounts based on the detection results, maintaining a safe matching environment.

[2099] By integrating these functions, users can achieve the optimal match that balances appearance and personality, providing a matching environment that can be used with peace of mind.

[2100] The processing flow will be explained below.

[2101] Profile picture analysis and emotion identification

[2102] Step 1:

[2103] The user uploads their profile picture to the device.

[2104] The device sends the uploaded image to the server.

[2105] Step 2:

[2106] The server sends the received images to the generation AI and emotion engine.

[2107] Step 3:

[2108] Generative AI analyzes the image and evaluates factors such as angle, facial expression, and background.

[2109] The emotion engine identifies the user's emotions from the facial expressions in the image, obtaining identification results such as "not smiling" or "nervous."

[2110] Step 4:

[2111] The server provides specific feedback to the user on how to improve based on the analysis results from the AI ​​generation and the emotion data from the emotion engine. For example, it provides advice such as "Use a smile that looks friendlier."

[2112] Step 5:

[2113] Modify your profile picture or upload a new one based on the feedback you provide.

[2114] Profile text analysis and sentiment identification

[2115] Step 1:

[2116] The user enters his / her profile text into the terminal.

[2117] The terminal sends the entered text to the server.

[2118] Step 2:

[2119] The server sends the received text to the generation AI and emotion engine.

[2120] Step 3:

[2121] Generative AI analyzes the text and generates specific improvements as feedback.

[2122] The emotion engine identifies the user's emotion from the wording of the text and obtains emotion data such as "negative" or "positive."

[2123] Step 4:

[2124] The server uses the analysis results from the generated AI and the emotional data from the emotion engine to suggest specific improvements to the user, such as advice such as "It would be good to add more passionate words."

[2125] Step 5:

[2126] Modify the profile text based on the feedback provided by the user.

[2127] Personality diagnosis and profile suggestions through question-and-answering

[2128] Step 1:

[2129] The terminal displays a simple question to the user.

[2130] The user answers the questions and inputs the answer data into the terminal.

[2131] Step 2:

[2132] The terminal transmits the collected response data to the server.

[2133] The server sends this to the generation AI and emotion engine.

[2134] Step 3:

[2135] The generating AI analyzes the response data and conducts a personality diagnosis.

[2136] The emotion engine identifies the emotion expressed by the user when answering and obtains emotional data such as "enjoyed" or "indifferent."

[2137] Step 4:

[2138] The server uses data from the generation AI and emotion engine to suggest optimal profile sentences to users, such as sentences that reflect a positive personality.

[2139] Step 5:

[2140] Users can use the suggested profile text as a reference to enrich their own profiles.

[2141] Recommendations for compatible people

[2142] Step 1:

[2143] The server sends the results of the personality test and analyzed profile data to the generation AI and emotion engine.

[2144] Step 2:

[2145] Generative AI and an emotion engine analyze the data to identify optimal matches. For example, by combining the emotion data of users with the same hobbies, it can select a partner with greater accuracy.

[2146] Step 3:

[2147] The server provides the recommendation results to the user.

[2148] The user decides whether to contact the recommended person.

[2149] Spam and fraudulent account detection

[2150] Step 1:

[2151] The server sends the newly registered profile to the generation AI and emotion engine.

[2152] Step 2:

[2153] Generative AI analyzes profiles to detect potential spam or fraudulent accounts.

[2154] The emotion engine detects emotional anomalies, identifying, for example, "unnaturally positive expressions."

[2155] Step 3:

[2156] The server will then appropriately filter out spam accounts based on the detection results.

[2157] Regular monitoring and countermeasures are carried out to ensure the server maintains a safe matching environment.

[2158] Example 2

[2159] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2160] Conventional matching systems lack the accuracy of analyzing user profile images and text, and do not adequately identify emotions, resulting in inadequate feedback and optimal matching. Furthermore, the accuracy of detecting spam and fraudulent accounts is low, meaning a safe matching environment cannot be guaranteed.

[2161] 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.

[2162] In this invention, the server includes: means for analyzing a user's profile image using a generation AI and providing feedback on specific improvements regarding features such as angle, facial expression, and background; means for identifying emotions from the user's profile image using an emotion engine and reflecting the emotion data in the feedback; means for analyzing the user's profile text using a generation AI and providing feedback on specific improvements; means for identifying emotions from the wording of the profile text using an emotion engine and reflecting the emotion data in the feedback; means for using a generation AI to ask the user short questions and, based on the answers, generate an optimal profile statement or suggest editing; and means for automatically detecting spam and fraudulent accounts and eliminating suspicious accounts using the generation AI and the emotion engine. This improves the accuracy of user profile analysis and emotion identification, enabling optimal feedback and matching. It also enables the provision of a safe matching environment.

[2163] "Generative AI" is an artificial intelligence technology that uses machine learning algorithms to analyze data and generate specific feedback and suggestions.

[2164] An "emotion engine" is an analytical technology that identifies human emotions from images and text and reflects the results in applications.

[2165] "Profile Image" means a photo of a user's face or other still image used to represent themselves in the Matching System.

[2166] "Profile text" is text data that allows a user to describe their characteristics, hobbies, interests, etc.

[2167] "Feedback" refers to specific improvements and suggestions provided to users based on the results of analysis by generative AI and emotion engines.

[2168] "Spam accounts" are fraudulent accounts created for advertising or fraudulent purposes, typically sending large volumes of meaningless messages.

[2169] A "fraudulent account" is a fraudulent account created with the intent to deceive others and abuse the user's trust.

[2170] "Simple questions" are short questions that the generative AI asks the user to understand the user's characteristics and personality.

[2171] A "profile sentence" is a sentence that expresses a user's characteristics and appeal, suggested by the AI ​​based on answers to simple questions.

[2172] A "personality diagnosis" is an evaluation method that analyzes the questions answered by the user and the text entered to identify their personality and behavioral characteristics.

[2173] An "interface" refers to the operating screen or input means through which users and systems exchange information, and plays a role in improving usability.

[2174] The present invention is a system that integrates generative AI and an emotion engine to more effectively analyze a user's profile image and text and provide optimal feedback and matching. The system of the present invention has the following configuration and operation.

[2175] Overall structure

[2176] The system mainly consists of a server, a device, and a user. The server is equipped with a generative AI and an emotion engine, and provides feedback and suggestions to the user based on the analysis results. The device accepts user operations and data input and sends it to the server. Users register their own profile image and text via the device and receive feedback and suggestions.

[2177] Hardware and software used

[2178] Hardware:

[2179] Server: A high-performance computer is recommended. If necessary, a GPU can be installed to support high-speed analysis by the generative AI and emotion engine.

[2180] Terminal: A device that provides a user interface, such as a smartphone, tablet, or computer.

[2181] software:

[2182] Generative AI models: Use models trained using deep learning frameworks (e.g., TensorFlow, PyTorch).

[2183] Emotion Engine: Integrates facial recognition algorithms (e.g., OpenCV, dlib) and natural language processing models (e.g., BERT) to perform emotion analysis.

[2184] Server software: Database management systems (e.g., MySQL, PostgreSQL) and web servers (e.g., Apache, Nginx).

[2185] Profile picture analysis and emotion identification

[2186] The user uploads their profile picture to the device. The device sends the uploaded image to the server. The server sends the received image to the generation AI and emotion engine. The generation AI analyzes the image and evaluates features such as angle, facial expression, and background. The emotion engine identifies emotions from the user's facial expression. The server generates feedback based on the analysis results from the generation AI and the emotion data from the emotion engine and provides it to the user. As a specific example, if the user is not smiling, the feedback provided is, "Use a smile that looks more friendly."

[2187] Profile text analysis and sentiment identification

[2188] The user enters profile text into the device. The device sends the entered text to the server. The server sends the received text to the generation AI and emotion engine. The generation AI analyzes the text and provides feedback on specific areas for improvement. The emotion engine identifies emotions from the wording of the text. The server generates suggestions based on the analysis results from the generation AI and the emotion data from the emotion engine, and provides them to the user. For example, if the text seems bland, the server might suggest, "You might want to add more passionate words to better highlight yourself."

[2189] Personality diagnosis and profile suggestions through question-and-answering

[2190] The device displays simple questions to the user. The user answers the questions and enters the answer data into the device. The device sends the collected answer data to the server. The server sends it to the generation AI and emotion engine. The generation AI analyzes the answer data and performs a personality diagnosis, and the emotion engine identifies the emotion the user expressed when answering. The server suggests the most appropriate profile text to the user based on the data from the generation AI and emotion engine. As a specific example, if the user is proactive, a suggested profile text that reflects this will be provided.

[2191] Recommendations for compatible people

[2192] The server passes the personality test results and analyzed profile data to the generation AI and emotion engine. The generation AI and emotion engine analyze the data and identify the most suitable match. By incorporating emotion data, more accurate matching becomes possible. The server provides the recommendation results to the user. The user decides whether to contact the recommended person. For example, if the user's personality test results show that they are proactive and sociable, the server may recommend matching with "someone who is also proactive and sociable."

[2193] Spam and fraudulent account detection

[2194] The server sends newly registered profiles to the generation AI and emotion engine. The generation AI analyzes the profiles and detects possible spam or fraudulent accounts. The emotion engine also detects emotional anomalies and identifies suspicious accounts. The server then appropriately eliminates spam accounts based on the detection results, maintaining a safe matching environment. For example, if a newly registered account sends a large number of messages at once, the generation AI may determine that it is likely to be a spam account, and the server may suspend the account.

[2195] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2196] Profile picture analysis and emotion identification

[2197] Step 1:

[2198] The user uploads a profile picture to the device. The user operates the application on the device, selects an image file, and clicks the upload button.

[2199] Input: The profile image file selected by the user.

[2200] Output: The image file is saved to your device.

[2201] Step 2:

[2202] The device sends the uploaded image to the server. The device sends the image file to the server using an HTTP request.

[2203] Input: The uploaded profile image file.

[2204] Output: An image file is sent to the server.

[2205] Step 3:

[2206] The server sends the received images to the generative AI and emotion engine, and then sends a request to the appropriate API endpoint to forward the image data to the analysis unit.

[2207] Input: The image file received by the server.

[2208] Output: Image data is passed to the generative AI and emotion engine.

[2209] Step 4:

[2210] The generative AI analyzes the image and evaluates features such as angle, facial expression, and background, and then uses image processing algorithms to quantify each feature.

[2211] Input: Image data sent to the analysis unit.

[2212] Output: Quantified feature data such as angle, facial expression, background, etc.

[2213] Step 5:

[2214] The emotion engine identifies emotions from the user's facial expressions. The emotion engine uses image processing algorithms and machine learning models to tag emotions such as "happiness," "anger," and "sadness" from the facial expressions.

[2215] Input: Image data sent to the analysis unit.

[2216] Output: Identified emotion tags.

[2217] Step 6:

[2218] The server generates feedback based on the analysis results from the generation AI and emotion engine and provides it to the user. The server aggregates the analysis results and generates notifications for specific improvements to the user.

[2219] Input: quantified feature data and emotion tags.

[2220] Output: A specific feedback message to the user.

[2221] Profile text analysis and sentiment identification

[2222] Step 1:

[2223] The user enters profile text into the terminal. The user enters text into the text area and clicks the send button.

[2224] Input: The profile text entered by the user.

[2225] Output: Text data is saved to the terminal.

[2226] Step 2:

[2227] The device sends the entered text to the server. The device sends the text data to the server using an HTTP request.

[2228] Input: The profile text entered.

[2229] Output: Text data is sent to the server.

[2230] Step 3:

[2231] The server sends the received text to the generative AI and emotion engine, and then sends a request to the appropriate API endpoint to forward the text data to the analysis unit.

[2232] Input: Text data received by the server.

[2233] Output: Text data is passed to the generative AI and emotion engine.

[2234] Step 4:

[2235] Generative AI analyzes the text and provides specific feedback on improvements. Generative AI uses natural language processing algorithms to analyze the grammar and context of the text and generate specific suggestions.

[2236] Input: Text data sent to the analysis unit.

[2237] Output: Suggested data for grammar correction and content specification.

[2238] Step 5:

[2239] The emotion engine identifies emotions from the wording of the text. The emotion engine uses natural language processing algorithms to identify emotions such as "passion," "calm," or "joy" from the text.

[2240] Input: Text data sent to the analysis unit.

[2241] Output: Identified emotion tags.

[2242] Step 6:

[2243] The server generates suggestions based on the analysis results from the generation AI and emotion engine and provides them to the user. The server aggregates the analysis results and generates notifications for specific improvements and suggestions for the user.

[2244] Input: Grammar correction suggestion data and sentiment tags.

[2245] Output: A specific suggestion message to the user.

[2246] Personality diagnosis and profile suggestions through question-and-answering

[2247] Step 1:

[2248] The terminal displays simple questions to the user, and the terminal application displays the questions in a dialog format.

[2249] Input: Questions created by the generative AI.

[2250] Output: The question dialog that is displayed to the user.

[2251] Step 2:

[2252] The user answers the question and enters the answer data into the terminal. The user enters the answer and clicks the send button.

[2253] Input: The answer data entered by the user.

[2254] Output: The answer data is saved on the device.

[2255] Step 3:

[2256] The device sends the collected response data to the server. The device sends the response data to the server using an HTTP request.

[2257] Input: The entered response data.

[2258] Output: The response data is sent to the server.

[2259] Step 4:

[2260] The server sends this to the generative AI and emotion engine, which then makes a request to the appropriate API endpoint to forward the response data to the analysis unit.

[2261] Input: The response data received by the server.

[2262] Output: The answer data is passed to the generation AI and emotion engine.

[2263] Step 5:

[2264] The generation AI analyzes the response data to conduct a personality diagnosis, and the emotion engine identifies the emotion of the user when answering. The generation AI uses a machine learning model to analyze personality from the response data, and the emotion engine identifies emotion from the text.

[2265] Input: Response data sent to the analysis unit.

[2266] Output: Personality trait data and emotion tags.

[2267] Step 6:

[2268] The server proposes optimal profile sentences to users based on data from the generation AI and emotion engine.The server generates suggested profile sentences for users based on the analysis results.

[2269] Input: personality trait data and emotion tags.

[2270] Output: Suggestion of specific profile text to the user.

[2271] (Application example 2)

[2272] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2273] Conventional systems simply analyze users' profile images and text, but are unable to provide feedback or product recommendations based on the user's emotions and preferences. This makes it difficult to provide more personalized services to users. Furthermore, the accuracy of detecting spam and fraudulent accounts is low, making it impossible to provide a safe environment. Therefore, there is a need for more accurate profile analysis and recommendation systems.

[2274] 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.

[2275] In this invention, the server includes means for analyzing a user's profile image using a generation AI and providing feedback on specific improvements regarding elements such as angle, facial expression, and background, means for analyzing a user's profile text using a generation AI and providing feedback on specific improvements, means for using a generation AI to ask the user simple questions and generate an optimal profile statement or suggest editing based on the answers, means for using a generation AI to automatically detect spam accounts and fraudulent accounts and eliminate suspicious accounts, and means for analyzing a user's profile image and text using a generation AI and an emotion engine and recommending products based on the user's preferences and emotions. This enables more personalized feedback and product recommendations to users and promotes use in a safe environment.

[2276] "Generative AI" refers to artificial intelligence that uses advanced algorithms to analyze data and generate new information and feedback.

[2277] An "emotion engine" is a system that identifies emotions from user input data (e.g., images or text) and provides analysis results based on that.

[2278] A "profile image" is image data uploaded by a user to represent themselves.

[2279] "Profile text" refers to text data entered by a user to describe themselves.

[2280] "Recommendation" means proposing products and services that are individually suited to a user based on their preferences and behavior.

[2281] A "spam account" is an account created for fraudulent purposes, typically used to send random advertising or fraudulent messages.

[2282] A "fraudulent account" is an account created for the purpose of committing fraud, and provides false information with the intent of deceiving others.

[2283] "Analysis" is the process of examining and breaking down data in detail to understand its structure and meaning.

[2284] "Feedback" refers to ratings and suggestions for improvement provided based on user actions and input.

[2285] "Personalization" means providing content that is individualized according to the preferences and characteristics of individual users.

[2286] MODE FOR CARRYING OUT THE INVENTION

[2287] This invention is a system that integrates generative AI and an emotion engine to effectively analyze a user's profile image and text and provide optimal feedback and product recommendations. This system is composed of a server, a terminal, and a user. Specific embodiments of this system are described in detail below.

[2288] Profile picture analysis and emotion identification

[2289] Users upload their profile picture to their device. The device sends the uploaded image to the server. The server sends the received image to the generation AI and emotion engine. The generation AI analyzes the image and evaluates factors such as angle, facial expression, and background. The emotion engine identifies emotions from the user's facial expression. Based on the analysis results from the generation AI and the emotion data from the emotion engine, the server provides specific feedback to the user on areas for improvement. For example, if the user is not smiling, the server will provide advice such as "use a smile that looks more friendly."

[2290] Profile text analysis and sentiment identification

[2291] The user enters their profile text into the device. The device sends the entered text to the server. The server sends the received text to the generation AI and emotion engine. The generation AI analyzes the text and provides feedback on specific areas for improvement. The emotion engine identifies the user's emotions from the wording of the text. The server then suggests specific areas for improvement to the user based on the analysis results from the generation AI and the emotion data from the emotion engine. For example, if the text seems bland, the server will provide advice such as, "Add more passionate words to better highlight yourself."

[2292] Personality diagnosis and profile suggestions through question-and-answering

[2293] The device displays simple questions to the user. The user answers the questions and enters the answer data into the device. The device then sends the collected answer data to the server. The server then sends it to the generation AI and emotion engine. The generation AI analyzes the answer data and performs a personality diagnosis, and the emotion engine identifies the emotion the user felt when answering. The server then uses the data from the generation AI and emotion engine to suggest optimal profile text to the user. For example, if the user is proactive, it will provide suggested profile text that reflects this.

[2294] Recommendations for compatible people

[2295] The server passes the personality test results and analyzed profile data to the generation AI and emotion engine. The generation AI and emotion engine analyze the data and identify the most suitable match. By incorporating emotion data, more accurate matching becomes possible. The server provides the recommendation results to the user. The user decides whether to contact the recommended person.

[2296] Spam and fraudulent account detection

[2297] The server sends newly registered profiles to the generation AI and emotion engine. The generation AI analyzes the profiles and detects possible spam or fraudulent accounts. The emotion engine also detects emotional anomalies and identifies suspicious accounts. The server then appropriately eliminates spam accounts based on th...

Claims

1. Using generative AI to analyze users' profile images and provide feedback on specific improvements to elements such as angle, facial expression, and background. A method to analyze user profile text using generative AI and provide specific feedback on areas for improvement. A method that uses generative AI to ask users simple questions and generate or suggest edits to the optimal profile text based on the answers. Using generative AI to automatically detect spam and fraudulent accounts and eliminate suspicious accounts; A system including:

2. The system according to claim 1 , further comprising means for recommending compatible people based on the personality assessment.

3. The system of claim 1 , further comprising: means for providing a user with an easy-to-use interface for improving the profile image and text based on feedback provided by the generation AI.

Images