system

Abstract

We provide the system. [Solution] A means of collecting communication data from users and performing anonymization processing, A means for analyzing the aforementioned data using generative artificial intelligence to extract the user's personality traits, A method for identifying and matching compatible candidates based on extracted personality traits, A means of presenting the candidate list generated based on the above to the user, A means of disclosing information based on user choice and mutual consent, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Due to modern diverse lifestyles and changes in work styles, the opportunities for natural face-to-face encounters have decreased. This makes it difficult for many individuals in the appropriate age group to find compatible partners through communication and interaction. Furthermore, concerns regarding the safety and privacy of online dating services remain high, and as a result, users have not fully utilized the opportunities to meet potential partners. It is necessary to solve this problem and provide a method that enables users to find partners with confidence and effectively.

Means for Solving the Problems

[0005] This invention provides a system that analyzes a user's personality traits using communication data and recommends candidates based on those traits. This system utilizes generative artificial intelligence to extract personality traits from communication data and then applies a highly accurate matching algorithm to recommend compatible partners. Within the system, user privacy is strictly protected, and information disclosure is only carried out with the consent of both parties. Furthermore, by providing an option to hide users with whom the user has a specific relationship, the system ensures a safe and secure environment for users to use the platform.

[0006] "Communication data" refers to digital records of messages, texts, audio, or similar information exchanged between users.

[0007] "Anonymization" is a technology that processes data in a way that prevents individuals from being identified, thereby protecting privacy.

[0008] "Generative artificial intelligence" is a type of artificial intelligence that is capable of generating new information and solutions based on large amounts of data.

[0009] "Personality traits" is a concept that refers to the characteristics and tendencies related to an individual's behavior, thoughts, and emotions.

[0010] "Matching" is the process of combining two or more elements according to certain conditions and measuring their degree of agreement.

[0011] "Natural language processing technology" is a technology that enables computers to understand and analyze the language that humans speak naturally.

[0012] "Sentiment analysis" is a technique for identifying and evaluating a speaker's emotions from their writing or speech.

[0013] "Privacy protection" is a general term for operations and technologies used to protect personal information, behavioral history, and other sensitive data from others.

[0014] "Information disclosure" is a process of sharing or making a certain specific information available to others.

[0015] A "non - display option" is a function that allows a user to set specific information or data not to be displayed.

Brief Explanation of Drawings

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

Embodiments for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

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

[0020] In the following embodiments, a RAM (Random Access Memory) with a reference numeral is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, a storage with a reference numeral is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

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

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

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

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

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

[0037] This invention is a system that works in conjunction with a user's everyday communication terminal to recommend a compatible partner based on the user's personality traits. This system is realized by utilizing generative artificial intelligence and combining it with various data analysis technologies.

[0038] First, the user accesses the system through their communication device. With the user's consent, the communication device collects the user's communication data from messaging platforms such as LINE. This data is anonymized to prevent the identification of individuals and is securely transmitted to the server.

[0039] The server uses the received anonymized data to analyze the user's personality traits using generative artificial intelligence. This process includes sentiment analysis and keyword extraction using natural language processing techniques. Personality traits are modeled as quantified evaluations, forming the basis for comparison with data from other users.

[0040] Next, the server compares the personality traits of users in the existing user database with those of other users to identify compatible users. The compatibility assessment uses a similarity-measuring algorithm to determine which users share common hobbies and values.

[0041] The server then sends the matching results to the user's device. The communication device uses this information to present the user with specific candidate profiles. If the user is interested in one of the presented candidates, they send that information through their device, and once the other user agrees, detailed information about both parties is made public via the server.

[0042] As a concrete example, let's say User A starts using the system. Based on messaging data, User A is analyzed as being highly sensitive and cooperative. The server identifies User B, who possesses these characteristics, from its database and determines that they are a good match. User B's profile is displayed on User A's device, and by pressing a button indicating interest, a notification is sent to User B. If User B agrees, both users can view each other's detailed information.

[0043] In this way, the present invention provides users with the opportunity to safely and efficiently meet suitable partners. Privacy protection features have also been enhanced, including an option for users to hide friends they do not want to be personally known to.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] Users access the system using their devices and interact with the messaging platform. The devices collect user communication data via the LINE API, anonymize it, and then send it to the server.

[0047] Step 2:

[0048] The server analyzes the received anonymized data. Using generative artificial intelligence, it performs sentiment analysis and keyword extraction using natural language processing techniques to identify the user's personality traits. Based on these traits, it constructs a quantified evaluation model.

[0049] Step 3:

[0050] The server compares the constructed personality assessment model with models from other users. Using a similarity measurement algorithm, it calculates the compatibility rate between users and lists compatible candidates.

[0051] Step 4:

[0052] The server sends a list of candidates deemed highly compatible to the user's device. Based on this information, the device displays a brief profile of each candidate and an overview of their compatibility to the user.

[0053] Step 5:

[0054] The user indicates interest by pressing the button corresponding to the presented candidate. This information is sent from the terminal to the server, and a notification is sent to the other user.

[0055] Step 6:

[0056] If the server obtains interest and consent from the other user, it will disclose detailed information to both parties. The devices can then display each other's detailed profiles to the users.

[0057] Step 7:

[0058] Users can further provide feedback through the generated AI. The server analyzes this feedback and uses it to improve the accuracy of the system's matching algorithms and personality analysis processes.

[0059] (Example 1)

[0060] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0061] In today's information society, providing optimal interactions for individual users requires flexible recommendations based on their personality traits. However, conventional systems fail to adequately provide highly accurate recommendations based on personality and compatibility, as well as safe and privacy-conscious information exchange. As a result, users may miss opportunities to connect with suitable partners. This challenge needs to be addressed.

[0062] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0063] In this invention, the server includes means for collecting communication information from the user and anonymizing it; means for analyzing the information using a generating AI to extract the user's personality traits; and means for identifying compatible candidates based on the extracted personality traits and matching them. This enables highly accurate partner recommendations based on personality traits in a safe and privacy-protected manner for the user.

[0064] A "user" is an individual or group that uses information systems or digital devices to receive services.

[0065] "Communication information" refers to the content that users exchange using messaging platforms and other means of communication, and includes data such as text, audio, and images.

[0066] "Anonymization" is a data processing technique that protects privacy by removing or transforming information that can identify an individual.

[0067] "Generative AI" refers to artificial intelligence models and algorithms that generate useful information from large amounts of data, and is a technology particularly used in natural language processing and data analysis.

[0068] "Personality traits" are numerical or qualitative representations of an individual's behavioral and thinking characteristics, and are elements obtained as a result of analyzing an individual's attitudes, emotions, hobbies, etc.

[0069] A "candidate" refers to another user who meets specific conditions or criteria selected by the system, and who could potentially be a suitable partner for the user.

[0070] "Mapping" is the process of finding relationships between different datasets or elements and connecting them.

[0071] "Privacy protection" refers to security measures and technologies designed to prevent individuals' private information from being misused or made public.

[0072] To implement this invention, a user's communication terminal, a server, a generative AI model, and necessary data processing technologies are used. The user accesses the system via their own communication terminal. This communication terminal is a device such as a smartphone or a personal computer, and communicates with the server via an internet connection.

[0073] The device first collects user communication data, for example, from a messaging platform. Here, data is retrieved using an API, and with the user's consent, it undergoes anonymization. Anonymization transforms the information into a form that cannot identify individuals before sending it to the server. This protects the user's privacy.

[0074] The server analyzes the received anonymized data using a generating AI model. This analysis uses natural language processing techniques to perform sentiment analysis and keyword extraction, revealing the user's personality traits. Libraries such as TENSORFLOW® and PyTorch may be utilized for this purpose. This process quantifies personality traits, providing a basis for comparison with other users.

[0075] As a concrete example, if a user's message data is examined and determined to be "highly sensitive" through sentiment analysis, the server will compare it with other user information stored in the database. An example of a prompt would be, "Identify highly sensitive and cooperative users and recommend other similar users."

[0076] This system also includes an option to hide specific friends to enhance privacy protection between users. The device notifies the user of recommendations from the server, and if the user shows interest, information is shared with mutual consent. This allows users to safely and effectively interact with compatible people.

[0077] The present invention aims to improve the user experience by combining these technical means to achieve highly accurate recommendations based on the individual personality traits of users.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] Users access the system using a communication terminal. With the user's consent, the terminal collects communication data from the messaging platform. The input is message data, and the output is the collected raw data. Specifically, the terminal retrieves data using an API.

[0081] Step 2:

[0082] The device performs anonymization on the collected data. It removes or transforms personally identifiable information to protect privacy. The input is the collected raw data, and the output is the anonymized data. Specifically, the device replaces usernames and contact information with random IDs before sending the data to the server.

[0083] Step 3:

[0084] The server analyzes the received anonymized data using a generating AI model to extract the user's personality traits. The input is anonymized data, and the output is numerical personality traits. Specifically, the server uses natural language processing techniques to analyze key emotions and keywords and generates a numerical score to evaluate personality.

[0085] Step 4:

[0086] The server compares the extracted personality traits with other user information in the database to perform a compatibility assessment. The input is numerical personality traits, and the output is a list of candidates deemed to be compatible. Specifically, the server ranks the candidates using a similarity algorithm.

[0087] Step 5:

[0088] The server sends the generated candidate list to the device. The device displays the profiles of compatible candidates to the user. The input is the candidate list, and the output is the candidate information displayed in the user interface. Specifically, the device uses push notifications to inform the user that there are new candidates.

[0089] Step 6:

[0090] The user selects candidates they are interested in. The selected information is sent to the server, and once the other candidates consent, detailed information about both parties is made public. The input is the user's selection information, and the output is the shared detailed information. Specifically, the server confirms the consent of the other user and then provides the detailed information to the terminal.

[0091] (Application Example 1)

[0092] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0093] Traditional matching systems are unable to recommend personalized products and services based on users' personality traits, making it difficult to provide information that meets individual needs. Furthermore, there is a need for enhanced privacy protection in information exchange between users.

[0094] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0095] In this invention, the server includes means for collecting and anonymizing communication data, means for analyzing the data using generative artificial intelligence and extracting personality traits, and means for recommending products or services based on the user's personality traits. This enables personalized recommendations of products and services tailored to the user's personality traits. Furthermore, it allows for information exchange that takes privacy protection into consideration.

[0096] "Communication data" is a general term for various types of information generated online, such as messages and activity history collected from users.

[0097] "Anonymization" is a technology that protects data privacy by removing or transforming information that could identify an individual.

[0098] "Generative artificial intelligence" is an artificial intelligence technology that analyzes data and generates new information and patterns.

[0099] "Personality traits" are indicators that show psychological characteristics and tendencies derived from a user's behavior and statements.

[0100] "Candidates" refer to other users or options suggested based on the user's personality traits and needs.

[0101] "Products or services" refers to the general term for goods and support provided to users.

[0102] "Recommendation" is the act of presenting the most suitable options based on the user's interests and characteristics.

[0103] "Information exchange" refers to the process of sending and receiving necessary data and messages between users.

[0104] "Privacy protection" refers to technologies and measures to prevent the leakage of personal information and to protect users' secrets.

[0105] This invention is a system that utilizes communication data collected from users' communication terminals to recommend products and services best suited to each individual user. Here, smartphones are used as the primary communication terminals, and servers located in a cloud environment and generative AI technology are employed.

[0106] The server receives anonymized communication data from the user's communication terminal and performs sentiment analysis using natural language processing techniques. This quantifies the user's personality traits and records them in a database. Machine learning libraries such as TensorFlow and scikit-learn are used in this process. Based on the user's personality traits, a generative AI model identifies other users with similar profiles and recommends them as compatible candidates.

[0107] Furthermore, the server generates a personalized list of products and services based on the user's personality traits and sends it to the communication terminal. For example, a user analyzed as having an active personality might be recommended discounts on new sports equipment. This approach enhances the purchasing experience and enables suggestions that align with the user's interests and values.

[0108] As a concrete example, a prompt to input into a generative AI model might be, "Generate recommended products and services for users with an active and sociable personality." This prompt prompts the generative AI model to generate data to suggest products that match the user's interests. This enables the provision of optimal information tailored to individual users.

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] Communication devices collect communication data with the user's consent. This data includes messages and activity history, and the device anonymizes the data to prevent the identification of individuals. The input is raw communication data, and the output is anonymized communication data.

[0112] Step 2:

[0113] The server receives anonymized data sent from the terminal. The server analyzes the data using natural language processing and quantifies the user's personality traits. Data processing, such as sentiment analysis, is performed during this process. The input is anonymized communication data, and the output is numerical data related to the user's personality traits.

[0114] Step 3:

[0115] The server compares the personality traits extracted using a generative AI model with the profiles of other users in the database. This comparison identifies compatible candidates and generates a list of suggested candidates. The input is the user's personality trait data, and the output is a list of recommended candidates.

[0116] Step 4:

[0117] The server generates a list of recommended products and services based on the user's personality traits. It inputs prompts into a generation AI model based on the user's characteristic data, extracting the most suitable product information. For example, it might use the prompt, "Generate recommended products and services for an active and sociable user." The input consists of the user's personality traits and the prompt text, while the output is a personalized product list.

[0118] Step 5:

[0119] The server sends the generated candidate list and product list to the communication terminal. The terminal displays this information as options to present to the user. When the user selects candidates or products they are interested in, the terminal records that selection. The input is the candidate list and product list, and the output is the user's selection information.

[0120] Step 6:

[0121] The device will disclose various types of information based on the user's choices, while taking privacy protection into consideration. If consent is obtained, the server will exchange detailed information. Input is information selected by the user, and output is information disclosure based on consent.

[0122] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0123] This invention proposes a system that achieves more accurate matching using user communication data and emotion data. This system is implemented by combining generative artificial intelligence and an emotion engine.

[0124] First, users access the system using a communication terminal. The communication terminal collects the user's communication data from the messaging service, anonymizes it appropriately, and then sends it to the server. This includes security protocols to protect user privacy.

[0125] The server uses generative artificial intelligence to analyze the received data. It utilizes natural language processing technology to extract user personality traits from text. Simultaneously, it uses an emotion engine to recognize the user's emotional state in real time and collect emotional data. This emotional data can capture the intensity and trends of the user's emotions during communication.

[0126] Next, the server matches users with other users based on the extracted personality traits and emotional data. In particular, it adjusts the candidate recommendation list based on the emotional state obtained from the emotion engine, enabling flexible recommendations based on the user's temporary emotions. Furthermore, by analyzing the temporal change patterns of emotional data and taking long-term emotional trends into account, it achieves matching that considers more lasting compatibility.

[0127] For example, when user A accesses the system, the terminal retrieves messages from LINE. The server extracts personality traits such as cooperativeness and high sensitivity from user A's text, and measures the intensity of their emotions. If user A expresses very positive emotions on a particular topic, the server takes this into account and prioritizes user B, who has positive emotions on similar topics, as a candidate and includes them in the recommendation list. Also, if user A's emotions tend to fluctuate greatly, user C, who has a flexible communication style that can handle these fluctuations, may also be recommended.

[0128] This system helps users find partners more comprehensively, based not only on short-term feelings but also on long-term personality traits. Furthermore, privacy protection options allow users to prevent their friends and acquaintances from being identified through the system.

[0129] The following describes the processing flow.

[0130] Step 1:

[0131] The user accesses the system via their device and authorizes a connection to the messaging service. The device securely collects the user's communication data using APIs such as the LINE API. This data is anonymized to protect the user's privacy before being sent to the server.

[0132] Step 2:

[0133] The server utilizes generative artificial intelligence to analyze the anonymized data it receives. It analyzes text data through natural language processing techniques to extract user personality traits. This includes evaluating personality tendencies based on specific keywords and language patterns.

[0134] Step 3:

[0135] The server uses an emotion engine to recognize the user's emotional state in real time from the received text data. The emotional state determines what emotions the user is expressing and quantifies their intensity.

[0136] Step 4:

[0137] The server combines extracted personality traits and emotional data, and compares them to other users registered in the database to assess compatibility. By using data from the emotion engine, it also takes temporary emotional states into account and dynamically adjusts the list of recommended candidates.

[0138] Step 5:

[0139] The server sends an optimized recommendation list to the user's device. Based on this list, the device displays brief profiles of candidates deemed highly suitable for the user.

[0140] Step 6:

[0141] When a user expresses interest in a presented candidate, they send that information from their device to the server. The server then communicates this interest to the other user, and only discloses detailed profile information if both parties agree.

[0142] Step 7:

[0143] Users can support improvements to matching results through feedback options provided by the system. The server analyzes this feedback to help improve the accuracy of the algorithm. Additionally, privacy features allow users to hide specific friends.

[0144] (Example 2)

[0145] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0146] In today's information society, finding the optimal communication partner based on a user's individual personality and emotional state is an extremely difficult challenge. Existing systems struggle to provide personalized matching that adequately considers a user's personality and short-term emotions, resulting in a high likelihood of mismatches. Furthermore, consideration for privacy protection is sometimes insufficient. Solving these challenges is essential.

[0147] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0148] In this invention, the server includes means for collecting and anonymizing communication information, means for extracting user personality attributes and emotional information using a generative model, and means for making flexible recommendations based on the extracted information. This enables personalized matching tailored to the user's individual personality and emotions, while also ensuring privacy.

[0149] "Communication information" refers to the content of information exchange between users via messaging services, etc., and includes text messages and other digital content.

[0150] "Anonymization" is a process that makes it impossible to identify a user's personal information, and is carried out to protect data privacy.

[0151] A "generative model" is a form of artificial intelligence technology that analyzes data to derive patterns and generate new data and insights.

[0152] "Personality attributes" refer to the characteristics and tendencies of a person's personality that can be inferred from their behavior and reactions.

[0153] "Emotional information" refers to data that quantifies or categorizes a user's emotional state, representing real-time reactions and trends.

[0154] "Recommendation" refers to the act of presenting suitable options or candidates to a user based on specific criteria.

[0155] "Ensuring privacy" means taking measures to protect users' personal information from being leaked to third parties.

[0156] The system of this invention is designed for efficient user assistance and functions through the cooperation of three parties: the user, the terminal, and the server. The user first connects to the system using a communication device. The terminal collects communication information obtained from the user through a messaging service. The collected information is anonymized to protect the user's personal information and sent to the server in accordance with security protocols.

[0157] The server uses generative models to analyze received communication information. This analysis employs natural language processing techniques to extract user personality attributes and emotional information. In particular, sentiment analysis techniques are used to understand the user's emotional state in real time. Based on this information, the server selects the most suitable other users and generates a recommendation list.

[0158] In this system, a generative AI model functions as the core of the program, accurately processing user information using prompt messages. For example, if a user sends the message "I had a great time today!", the server uses this sentiment to perform appropriate matching.

[0159] As a concrete example of this invention, an example of a prompt message is, "Find and recommend other users who are similar to the user's positive emotions." This prompt encourages information analysis by a generative AI model, enabling personalized information recommendations.

[0160] Thus, this system is designed to meet individual needs through flexible matching based on the user's personality and emotions, while also ensuring privacy protection.

[0161] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0162] Step 1: The user accesses the system using a communication device. The terminal obtains the user's communication information via a messaging service. This information is collected in text format. The input data is user messages, and the output is anonymized data. In particular, processes are performed to remove unique information such as user IDs for privacy protection.

[0163] Step 2: The terminal sends anonymized communication information to the server using a security protocol. The server receives this incoming data for the first time. The input for this step is anonymized communication data, and the output is data securely stored on the server. Encryption technology is used for transmission.

[0164] Step 3: The server begins analyzing the received communication information using a generative model. The input is the stored anonymized communication data, and the output is the user's personality attributes and emotional information obtained through the analysis. Specifically, natural language processing techniques are used to extract attributes such as the user's cooperativeness and sensitivity from the text, and emotional analysis is used to understand the intensity and tendency of emotions.

[0165] Step 4: The server executes a prompt using a generative AI model to calculate the best matching candidates for the user. The input is personality attributes and sentiment information, and the output is a recommendation list. This prompt includes the phrase, "Find and recommend other users who are similar to the user's positive sentiment." The model generates a ranking of candidates based on this.

[0166] Step 5: The server notifies the user of the generated recommendation list. The terminal receives this list and displays it on the user's screen. The input is the recommendation list from the server, and the output is the screen display received by the user. Based on this information, the user can start communicating with people they are interested in.

[0167] (Application Example 2)

[0168] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0169] In today's world, when recommending products best suited to a user's preferences and mood on online platforms such as virtual stores, accurate recommendations that take into account the user's temporary emotions and personality traits are required. However, conventional systems fail to adequately reflect the user's emotional state, and the results are not always satisfactory to the user.

[0170] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0171] In this invention, the server includes means for collecting communication data from the user and anonymizing it; means for analyzing the data using generative artificial intelligence to extract the user's personality traits and emotional state; and means for adjusting a product recommendation list based on the user's emotional state and presenting it to the user. This enables highly accurate product recommendations based on the user's temporary emotional state and long-term personality traits.

[0172] A "user" refers to an individual or group that uses the system and is the entity that provides communication data.

[0173] "Communication data" refers to information including messages and statements exchanged between users and others.

[0174] "Anonymization" is a process that modifies information so that a specific user cannot be identified.

[0175] "Generative artificial intelligence" is an artificial intelligence technology that performs advanced data analysis based on vast datasets.

[0176] "Personality traits" are elements that characterize a user's individuality and behavioral patterns.

[0177] "Emotional state" refers to information that indicates the user's psychological state and the intensity of their emotions.

[0178] "Matching" is the process of determining compatibility and recommending users in order to connect them with each other.

[0179] A "product recommendation list" is a list of products and services presented to a user based on their emotional state and personality traits.

[0180] The system for realizing this invention consists of three main elements: a user, a communication terminal, and a server. The user first accesses the system using the communication terminal, and the communication data they transmit is collected. This communication terminal is equipped with a function to retrieve and anonymize messages from the user, thereby protecting the user's privacy.

[0181] The server analyzes the collected communication data using advanced generative artificial intelligence technology. In this step, NLTK, a Python natural language processing library, is used to extract personality traits from the user's messages. In addition, the Symanto sentiment analysis engine is utilized to analyze emotional states. This engine recognizes the emotions embedded in each message in real time and provides data on the intensity and tendencies of the user's emotions.

[0182] The system then generates a list of product recommendations tailored to the user based on the extracted personality traits and emotional state. This process utilizes a generative artificial intelligence-based recommendation algorithm, which lists products that take into account the user's temporary emotions and long-term personality traits. For example, if the system determines that the user has recently been experiencing stress, products with relaxing effects will be emphasized and recommended.

[0183] An example of a prompt message might be: "What products should be recommended to a user who enjoys fragrances, especially if they had a hobby of enjoying scents last weekend? Also, if recent chat history indicates that the user is prone to stress, how should product recommendations be adjusted?" Based on such prompt messages, the system performs calculations to make the best recommendations.

[0184] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0185] Step 1:

[0186] The terminal collects communication data from the user and performs anonymization processing. The input is the messages sent and received by the user, and the output is anonymized data. The terminal removes specific identifying information and prepares the data for secure transmission to the server.

[0187] Step 2:

[0188] The server receives anonymized communication data. The input is anonymized data sent from the terminal, and the output is clean data ready for analysis. After receiving the data, the server standardizes the format and makes it ready for analysis.

[0189] Step 3:

[0190] The server uses generative artificial intelligence to analyze data and extract user personality traits. The input is clean communication data, and the output is numerical values ​​and indicators that represent the user's personality traits. The server performs natural language processing using the Python NLTK library to analyze behavioral patterns.

[0191] Step 4:

[0192] The server uses the Symanto emotion analysis engine to analyze emotional states in real time. The input is clean communication data, and the output is data showing indicators and trends of emotional states. The server measures the intensity of emotions and calculates their trends.

[0193] Step 5:

[0194] The server generates a product recommendation list based on the generated personality traits and emotional state. The input is data on personality traits and emotional state, and the output is a list of products suggested to the user. The server uses a generative artificial intelligence model to identify the optimal set of products for the user.

[0195] Step 6:

[0196] The server sends a generated product recommendation list to the terminal and displays it to the user. The input is the product recommendation list, and the output is the product information that the user views on the terminal. The terminal presents the user with product details and purchase options through its user interface.

[0197] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0198] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0199] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0200] [Second Embodiment]

[0201] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0202] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0203] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0204] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0205] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0206] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0207] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0208] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0209] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0210] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0211] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0212] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0213] This invention is a system that works in conjunction with a user's everyday communication terminal to recommend a compatible partner based on the user's personality traits. This system is realized by utilizing generative artificial intelligence and combining it with various data analysis technologies.

[0214] First, the user accesses the system through their communication device. With the user's consent, the communication device collects the user's communication data from messaging platforms such as LINE. This data is anonymized to prevent the identification of individuals and is securely transmitted to the server.

[0215] The server uses the received anonymized data to analyze the user's personality traits using generative artificial intelligence. This process includes sentiment analysis and keyword extraction using natural language processing techniques. Personality traits are modeled as quantified evaluations, forming the basis for comparison with data from other users.

[0216] Next, the server compares the personality traits of users in the existing user database with those of other users to identify compatible users. The compatibility assessment uses a similarity-measuring algorithm to determine which users share common hobbies and values.

[0217] The server then sends the matching results to the user's device. The communication device uses this information to present the user with specific candidate profiles. If the user is interested in one of the presented candidates, they send that information through their device, and once the other user agrees, detailed information about both parties is made public via the server.

[0218] As a concrete example, let's say User A starts using the system. Based on messaging data, User A is analyzed as being highly sensitive and cooperative. The server identifies User B, who possesses these characteristics, from its database and determines that they are a good match. User B's profile is displayed on User A's device, and by pressing a button indicating interest, a notification is sent to User B. If User B agrees, both users can view each other's detailed information.

[0219] In this way, the present invention provides users with the opportunity to safely and efficiently meet suitable partners. Privacy protection features have also been enhanced, including an option for users to hide friends they do not want to be personally known to.

[0220] The following describes the processing flow.

[0221] Step 1:

[0222] Users access the system using their devices and interact with the messaging platform. The devices collect user communication data via the LINE API, anonymize it, and then send it to the server.

[0223] Step 2:

[0224] The server analyzes the received anonymized data. Using generative artificial intelligence, it performs sentiment analysis and keyword extraction using natural language processing techniques to identify the user's personality traits. Based on these traits, it constructs a quantified evaluation model.

[0225] Step 3:

[0226] The server compares the constructed personality assessment model with models from other users. Using a similarity measurement algorithm, it calculates the compatibility rate between users and lists compatible candidates.

[0227] Step 4:

[0228] The server sends a list of candidates deemed highly compatible to the user's device. Based on this information, the device displays a brief profile of each candidate and an overview of their compatibility to the user.

[0229] Step 5:

[0230] The user indicates interest by pressing the button corresponding to the presented candidate. This information is sent from the terminal to the server, and a notification is sent to the other user.

[0231] Step 6:

[0232] If the server obtains interest and consent from the other user, it will disclose detailed information to both parties. The devices can then display each other's detailed profiles to the users.

[0233] Step 7:

[0234] Users can further provide feedback through the generated AI. The server analyzes this feedback and uses it to improve the accuracy of the system's matching algorithms and personality analysis processes.

[0235] (Example 1)

[0236] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0237] In today's information society, providing optimal interactions for individual users requires flexible recommendations based on their personality traits. However, conventional systems fail to adequately provide highly accurate recommendations based on personality and compatibility, as well as safe and privacy-conscious information exchange. As a result, users may miss opportunities to connect with suitable partners. This challenge needs to be addressed.

[0238] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0239] In this invention, the server includes means for collecting communication information from the user and anonymizing it; means for analyzing the information using a generating AI to extract the user's personality traits; and means for identifying compatible candidates based on the extracted personality traits and matching them. This enables highly accurate partner recommendations based on personality traits in a safe and privacy-protected manner for the user.

[0240] A "user" is an individual or group that uses information systems or digital devices to receive services.

[0241] "Communication information" refers to the content that users exchange using messaging platforms and other means of communication, and includes data such as text, audio, and images.

[0242] "Anonymization" is a data processing technique that protects privacy by removing or transforming information that can identify an individual.

[0243] "Generative AI" refers to artificial intelligence models and algorithms that generate useful information from large amounts of data, and is a technology particularly used in natural language processing and data analysis.

[0244] "Personality traits" are numerical or qualitative representations of an individual's behavioral and thinking characteristics, and are elements obtained as a result of analyzing an individual's attitudes, emotions, hobbies, etc.

[0245] A "candidate" refers to another user who meets specific conditions or criteria selected by the system, and who could potentially be a suitable partner for the user.

[0246] "Mapping" is the process of finding relationships between different datasets or elements and connecting them.

[0247] "Privacy protection" refers to security measures and technologies designed to prevent individuals' private information from being misused or made public.

[0248] To implement this invention, a user's communication terminal, a server, a generative AI model, and necessary data processing technologies are used. The user accesses the system via their own communication terminal. This communication terminal is a device such as a smartphone or a personal computer, and communicates with the server via an internet connection.

[0249] The device first collects user communication data, for example, from a messaging platform. Here, data is retrieved using an API, and with the user's consent, it undergoes anonymization. Anonymization transforms the information into a form that cannot identify individuals before sending it to the server. This protects the user's privacy.

[0250] The server analyzes the received anonymized data using a generating AI model. This analysis uses natural language processing techniques to perform sentiment analysis and keyword extraction, revealing the user's personality traits. Libraries such as TensorFlow and PyTorch may be utilized for this purpose. This process quantifies personality traits, providing a basis for comparison with other users.

[0251] As a concrete example, if a user's message data is examined and determined to be "highly sensitive" through sentiment analysis, the server will compare it with other user information stored in the database. An example of a prompt would be, "Identify highly sensitive and cooperative users and recommend other similar users."

[0252] This system also includes an option to hide specific friends to enhance privacy protection between users. The device notifies the user of recommendations from the server, and if the user shows interest, information is shared with mutual consent. This allows users to safely and effectively interact with compatible people.

[0253] The present invention aims to improve the user experience by combining these technical means to achieve highly accurate recommendations based on the individual personality traits of users.

[0254] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0255] Step 1:

[0256] Users access the system using a communication terminal. With the user's consent, the terminal collects communication data from the messaging platform. The input is message data, and the output is the collected raw data. Specifically, the terminal retrieves data using an API.

[0257] Step 2:

[0258] The device performs anonymization on the collected data. It removes or transforms personally identifiable information to protect privacy. The input is the collected raw data, and the output is the anonymized data. Specifically, the device replaces usernames and contact information with random IDs before sending the data to the server.

[0259] Step 3:

[0260] The server analyzes the received anonymized data using a generating AI model to extract the user's personality traits. The input is anonymized data, and the output is numerical personality traits. Specifically, the server uses natural language processing techniques to analyze key emotions and keywords and generates a numerical score to evaluate personality.

[0261] Step 4:

[0262] The server compares the extracted personality traits with other user information in the database to perform a compatibility assessment. The input is numerical personality traits, and the output is a list of candidates deemed to be compatible. Specifically, the server ranks the candidates using a similarity algorithm.

[0263] Step 5:

[0264] The server sends the generated candidate list to the device. The device displays the profiles of compatible candidates to the user. The input is the candidate list, and the output is the candidate information displayed in the user interface. Specifically, the device uses push notifications to inform the user that there are new candidates.

[0265] Step 6:

[0266] The user selects candidates they are interested in. The selected information is sent to the server, and once the other candidates consent, detailed information about both parties is made public. The input is the user's selection information, and the output is the shared detailed information. Specifically, the server confirms the consent of the other user and then provides the detailed information to the terminal.

[0267] (Application Example 1)

[0268] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0269] Traditional matching systems are unable to recommend personalized products and services based on users' personality traits, making it difficult to provide information that meets individual needs. Furthermore, there is a need for enhanced privacy protection in information exchange between users.

[0270] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0271] In this invention, the server includes means for collecting and anonymizing communication data, means for analyzing the data using generative artificial intelligence and extracting personality traits, and means for recommending products or services based on the user's personality traits. This enables personalized recommendations of products and services tailored to the user's personality traits. Furthermore, it allows for information exchange that takes privacy protection into consideration.

[0272] "Communication data" is a general term for various types of information generated online, such as messages and activity history collected from users.

[0273] "Anonymization" is a technology that protects data privacy by removing or transforming information that could identify an individual.

[0274] "Generative artificial intelligence" is an artificial intelligence technology that analyzes data and generates new information and patterns.

[0275] "Personality traits" are indicators that show psychological characteristics and tendencies derived from a user's behavior and statements.

[0276] "Candidates" refer to other users or options suggested based on the user's personality traits and needs.

[0277] "Products or services" refers to the general term for goods and support provided to users.

[0278] "Recommendation" is the act of presenting the most suitable options based on the user's interests and characteristics.

[0279] "Information exchange" refers to the process of sending and receiving necessary data and messages between users.

[0280] "Privacy protection" refers to technologies and measures to prevent the leakage of personal information and to protect users' secrets.

[0281] This invention is a system that utilizes communication data collected from users' communication terminals to recommend products and services best suited to each individual user. Here, smartphones are used as the primary communication terminals, and servers located in a cloud environment and generative AI technology are employed.

[0282] The server receives anonymized communication data from the user's communication terminal and performs sentiment analysis using natural language processing techniques. This quantifies the user's personality traits and records them in a database. Machine learning libraries such as TensorFlow and scikit-learn are used in this process. Based on the user's personality traits, a generative AI model identifies other users with similar profiles and recommends them as compatible candidates.

[0283] Furthermore, the server generates a list of personalized products and services based on the user's personality traits and sends it to the communication terminal. For example, for a user analyzed to have an active personality, discount information on new sports supplies is recommended. Such an approach improves the purchasing experience and enables proposals in line with the user's interests and values.

[0284] As a specific example, an example of a prompt sentence input into the generation AI model is "Please generate products and services recommended for users with an active and social personality." With this prompt, the generation AI model generates data for proposing products in line with the interests of the corresponding user. This enables optimal information provision tailored to individual users.

[0285] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0286] Step 1:

[0287] The communication terminal collects communication data after obtaining the user's consent. This data includes messages and behavioral history, and the terminal anonymizes the data so that individuals cannot be identified. The input is raw communication data, and the output is anonymized communication data.

[0288] Step 2:

[0289] The server receives the anonymized data sent from the terminal. The server analyzes the data using natural language processing and quantifies the user's personality traits. In this process, data processing such as sentiment analysis is performed. The input is anonymized communication data, and the output is numerical data regarding the user's personality traits.

[0290] Step 3:

[0291] The server compares the personality traits extracted using a generative AI model with the profiles of other users in the database. This comparison identifies compatible candidates and generates a list of suggested candidates. The input is the user's personality trait data, and the output is a list of recommended candidates.

[0292] Step 4:

[0293] The server generates a list of recommended products and services based on the user's personality traits. It inputs prompts into a generation AI model based on the user's characteristic data, extracting the most suitable product information. For example, it might use the prompt, "Generate recommended products and services for an active and sociable user." The input consists of the user's personality traits and the prompt text, while the output is a personalized product list.

[0294] Step 5:

[0295] The server sends the generated candidate list and product list to the communication terminal. The terminal displays this information as options to present to the user. When the user selects candidates or products they are interested in, the terminal records that selection. The input is the candidate list and product list, and the output is the user's selection information.

[0296] Step 6:

[0297] The device will disclose various types of information based on the user's choices, while taking privacy protection into consideration. If consent is obtained, the server will exchange detailed information. Input is information selected by the user, and output is information disclosure based on consent.

[0298] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0299] This invention proposes a system that achieves more accurate matching using user communication data and emotion data. This system is implemented by combining generative artificial intelligence and an emotion engine.

[0300] First, users access the system using a communication terminal. The communication terminal collects the user's communication data from the messaging service, anonymizes it appropriately, and then sends it to the server. This includes security protocols to protect user privacy.

[0301] The server uses generative artificial intelligence to analyze the received data. It utilizes natural language processing technology to extract user personality traits from text. Simultaneously, it uses an emotion engine to recognize the user's emotional state in real time and collect emotional data. This emotional data can capture the intensity and trends of the user's emotions during communication.

[0302] Next, the server matches users with other users based on the extracted personality traits and emotional data. In particular, it adjusts the candidate recommendation list based on the emotional state obtained from the emotion engine, enabling flexible recommendations based on the user's temporary emotions. Furthermore, by analyzing the temporal change patterns of emotional data and taking long-term emotional trends into account, it achieves matching that considers more lasting compatibility.

[0303] For example, when user A accesses the system, the terminal retrieves messages from LINE. The server extracts personality traits such as cooperativeness and high sensitivity from user A's text, and measures the intensity of their emotions. If user A expresses very positive emotions on a particular topic, the server takes this into account and prioritizes user B, who has positive emotions on similar topics, as a candidate and includes them in the recommendation list. Also, if user A's emotions tend to fluctuate greatly, user C, who has a flexible communication style that can handle these fluctuations, may also be recommended.

[0304] This system helps users find partners more comprehensively based on not only short-term emotions but also long-term personality traits. Additionally, with privacy protection options, users can prevent their friends and known individuals from being identified in this system.

[0305] The following describes the process flow.

[0306] Step 1:

[0307] The user accesses the system via a terminal and permits connection to the messaging service. The terminal uses, for example, the LINE API to securely collect the user's communication data. This data is anonymized for the user's privacy protection and sent to the server.

[0308] Step 2:

[0309] The server utilizes generative artificial intelligence to analyze the received anonymized data. It analyzes text data through natural language processing technology to extract the user's personality traits. This includes evaluating personality tendencies from specific keywords and language patterns.

[0310] Step 3:

[0311] The server uses an emotion engine to recognize the user's emotional state in real-time from the received text data. The emotional state determines what emotions the user is expressing and quantifies their intensity.

[0312] Step 4:

[0313] The server combines the extracted personality traits and emotion data and conducts a compatibility evaluation by comparing with other users registered in the database. By using data from the emotion engine, temporary emotional states are also considered to dynamically adjust the recommended list of candidates.

[0314] Step 5:

[0315] The server sends an optimized recommendation list to the user's device. Based on this list, the device displays brief profiles of candidates deemed highly suitable for the user.

[0316] Step 6:

[0317] When a user expresses interest in a presented candidate, they send that information from their device to the server. The server then communicates this interest to the other user, and only discloses detailed profile information if both parties agree.

[0318] Step 7:

[0319] Users can support improvements to matching results through feedback options provided by the system. The server analyzes this feedback to help improve the accuracy of the algorithm. Additionally, privacy features allow users to hide specific friends.

[0320] (Example 2)

[0321] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0322] In today's information society, finding the optimal communication partner based on a user's individual personality and emotional state is an extremely difficult challenge. Existing systems struggle to provide personalized matching that adequately considers a user's personality and short-term emotions, resulting in a high likelihood of mismatches. Furthermore, consideration for privacy protection is sometimes insufficient. Solving these challenges is essential.

[0323] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0324] In this invention, the server includes means for collecting and anonymizing communication information, means for extracting user personality attributes and emotional information using a generative model, and means for making flexible recommendations based on the extracted information. This enables personalized matching tailored to the user's individual personality and emotions, while also ensuring privacy.

[0325] "Communication information" refers to the content of information exchange between users via messaging services, etc., and includes text messages and other digital content.

[0326] "Anonymization" is a process that makes it impossible to identify a user's personal information, and is carried out to protect data privacy.

[0327] A "generative model" is a form of artificial intelligence technology that analyzes data to derive patterns and generate new data and insights.

[0328] "Personality attributes" refer to the characteristics and tendencies of a person's personality that can be inferred from their behavior and reactions.

[0329] "Emotional information" refers to data that quantifies or categorizes a user's emotional state, representing real-time reactions and trends.

[0330] "Recommendation" refers to the act of presenting suitable options or candidates to a user based on specific criteria.

[0331] "Ensuring privacy" means taking measures to protect users' personal information from being leaked to third parties.

[0332] The system of this invention is designed for efficient user assistance and functions through the cooperation of three parties: the user, the terminal, and the server. The user first connects to the system using a communication device. The terminal collects communication information obtained from the user through a messaging service. The collected information is anonymized to protect the user's personal information and sent to the server in accordance with security protocols.

[0333] The server uses generative models to analyze received communication information. This analysis employs natural language processing techniques to extract user personality attributes and emotional information. In particular, sentiment analysis techniques are used to understand the user's emotional state in real time. Based on this information, the server selects the most suitable other users and generates a recommendation list.

[0334] In this system, a generative AI model functions as the core of the program, accurately processing user information using prompt messages. For example, if a user sends the message "I had a great time today!", the server uses this sentiment to perform appropriate matching.

[0335] As a concrete example of this invention, an example of a prompt message is, "Find and recommend other users who are similar to the user's positive emotions." This prompt encourages information analysis by a generative AI model, enabling personalized information recommendations.

[0336] Thus, this system is designed to meet individual needs through flexible matching based on the user's personality and emotions, while also ensuring privacy protection.

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

[0338] Step 1: The user accesses the system using a communication device. The terminal obtains the user's communication information via a messaging service. This information is collected in text format. The input data is user messages, and the output is anonymized data. In particular, processes are performed to remove unique information such as user IDs for privacy protection.

[0339] Step 2: The terminal sends anonymized communication information to the server using a security protocol. The server receives this incoming data for the first time. The input for this step is anonymized communication data, and the output is data securely stored on the server. Encryption technology is used for transmission.

[0340] Step 3: The server begins analyzing the received communication information using a generative model. The input is the stored anonymized communication data, and the output is the user's personality attributes and emotional information obtained through the analysis. Specifically, natural language processing techniques are used to extract attributes such as the user's cooperativeness and sensitivity from the text, and emotional analysis is used to understand the intensity and tendency of emotions.

[0341] Step 4: The server executes a prompt using a generative AI model to calculate the best matching candidates for the user. The input is personality attributes and sentiment information, and the output is a recommendation list. This prompt includes the phrase, "Find and recommend other users who are similar to the user's positive sentiment." The model generates a ranking of candidates based on this.

[0342] Step 5: The server notifies the user of the generated recommendation list. The terminal receives this list and displays it on the user's screen. The input is the recommendation list from the server, and the output is the screen display received by the user. Based on this information, the user can start communicating with people they are interested in.

[0343] (Application Example 2)

[0344] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0345] In today's world, when recommending products best suited to a user's preferences and mood on online platforms such as virtual stores, accurate recommendations that take into account the user's temporary emotions and personality traits are required. However, conventional systems fail to adequately reflect the user's emotional state, and the results are not always satisfactory to the user.

[0346] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0347] In this invention, the server includes means for collecting communication data from the user and anonymizing it; means for analyzing the data using generative artificial intelligence to extract the user's personality traits and emotional state; and means for adjusting a product recommendation list based on the user's emotional state and presenting it to the user. This enables highly accurate product recommendations based on the user's temporary emotional state and long-term personality traits.

[0348] A "user" refers to an individual or group that uses the system and is the entity that provides communication data.

[0349] "Communication data" refers to information including messages and statements exchanged between users and others.

[0350] "Anonymization" is a process that modifies information so that a specific user cannot be identified.

[0351] "Generative artificial intelligence" is an artificial intelligence technology that performs advanced data analysis based on vast datasets.

[0352] "Personality traits" are elements that characterize a user's individuality and behavioral patterns.

[0353] "Emotional state" refers to information that indicates the user's psychological state and the intensity of their emotions.

[0354] "Matching" is the process of determining compatibility and recommending users in order to connect them with each other.

[0355] A "product recommendation list" is a list of products and services presented to a user based on their emotional state and personality traits.

[0356] The system for realizing this invention consists of three main elements: a user, a communication terminal, and a server. The user first accesses the system using the communication terminal, and the communication data they transmit is collected. This communication terminal is equipped with a function to retrieve and anonymize messages from the user, thereby protecting the user's privacy.

[0357] The server analyzes the collected communication data using advanced generative artificial intelligence technology. In this step, NLTK, a Python natural language processing library, is used to extract personality traits from the user's messages. In addition, the Symanto sentiment analysis engine is utilized to analyze emotional states. This engine recognizes the emotions embedded in each message in real time and provides data on the intensity and tendencies of the user's emotions.

[0358] The system then generates a list of product recommendations tailored to the user based on the extracted personality traits and emotional state. This process utilizes a generative artificial intelligence-based recommendation algorithm, which lists products that take into account the user's temporary emotions and long-term personality traits. For example, if the system determines that the user has recently been experiencing stress, products with relaxing effects will be emphasized and recommended.

[0359] An example of a prompt message might be: "What products should be recommended to a user who enjoys fragrances, especially if they had a hobby of enjoying scents last weekend? Also, if recent chat history indicates that the user is prone to stress, how should product recommendations be adjusted?" Based on such prompt messages, the system performs calculations to make the best recommendations.

[0360] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0361] Step 1:

[0362] The terminal collects communication data from the user and performs anonymization processing. The input is the messages sent and received by the user, and the output is anonymized data. The terminal removes specific identifying information and prepares the data for secure transmission to the server.

[0363] Step 2:

[0364] The server receives anonymized communication data. The input is anonymized data sent from the terminal, and the output is clean data ready for analysis. After receiving the data, the server standardizes the format and makes it ready for analysis.

[0365] Step 3:

[0366] The server uses generative artificial intelligence to analyze data and extract user personality traits. The input is clean communication data, and the output is numerical values ​​and indicators that represent the user's personality traits. The server performs natural language processing using the Python NLTK library to analyze behavioral patterns.

[0367] Step 4:

[0368] The server uses the Symanto emotion analysis engine to analyze emotional states in real time. The input is clean communication data, and the output is data showing indicators and trends of emotional states. The server measures the intensity of emotions and calculates their trends.

[0369] Step 5:

[0370] The server generates a product recommendation list based on the generated personality traits and emotional state. The input is data on personality traits and emotional state, and the output is a list of products suggested to the user. The server uses a generative artificial intelligence model to identify the optimal set of products for the user.

[0371] Step 6:

[0372] The server sends a generated product recommendation list to the terminal and displays it to the user. The input is the product recommendation list, and the output is the product information that the user views on the terminal. The terminal presents the user with product details and purchase options through its user interface.

[0373] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0374] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0375] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0376] [Third Embodiment]

[0377] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0378] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0379] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0380] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0381] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0382] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0383] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0384] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0385] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0386] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0387] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0388] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0389] This invention is a system that works in conjunction with a user's everyday communication terminal to recommend a compatible partner based on the user's personality traits. This system is realized by utilizing generative artificial intelligence and combining it with various data analysis technologies.

[0390] First, the user accesses the system through their communication device. With the user's consent, the communication device collects the user's communication data from messaging platforms such as LINE. This data is anonymized to prevent the identification of individuals and is securely transmitted to the server.

[0391] The server uses the received anonymized data to analyze the user's personality traits using generative artificial intelligence. This process includes sentiment analysis and keyword extraction using natural language processing techniques. Personality traits are modeled as quantified evaluations, forming the basis for comparison with data from other users.

[0392] Next, the server compares the personality traits of users in the existing user database with those of other users to identify compatible users. The compatibility assessment uses a similarity-measuring algorithm to determine which users share common hobbies and values.

[0393] The server then sends the matching results to the user's device. The communication device uses this information to present the user with specific candidate profiles. If the user is interested in one of the presented candidates, they send that information through their device, and once the other user agrees, detailed information about both parties is made public via the server.

[0394] As a concrete example, let's say User A starts using the system. Based on messaging data, User A is analyzed as being highly sensitive and cooperative. The server identifies User B, who possesses these characteristics, from its database and determines that they are a good match. User B's profile is displayed on User A's device, and by pressing a button indicating interest, a notification is sent to User B. If User B agrees, both users can view each other's detailed information.

[0395] In this way, the present invention provides users with the opportunity to safely and efficiently meet suitable partners. Privacy protection features have also been enhanced, including an option for users to hide friends they do not want to be personally known to.

[0396] The following describes the processing flow.

[0397] Step 1:

[0398] Users access the system using their devices and interact with the messaging platform. The devices collect user communication data via the LINE API, anonymize it, and then send it to the server.

[0399] Step 2:

[0400] The server analyzes the received anonymized data. Using generative artificial intelligence, it performs sentiment analysis and keyword extraction using natural language processing techniques to identify the user's personality traits. Based on these traits, it constructs a quantified evaluation model.

[0401] Step 3:

[0402] The server compares the constructed personality assessment model with models from other users. Using a similarity measurement algorithm, it calculates the compatibility rate between users and lists compatible candidates.

[0403] Step 4:

[0404] The server sends a list of candidates deemed highly compatible to the user's device. Based on this information, the device displays a brief profile of each candidate and an overview of their compatibility to the user.

[0405] Step 5:

[0406] The user indicates interest by pressing the button corresponding to the presented candidate. This information is sent from the terminal to the server, and a notification is sent to the other user.

[0407] Step 6:

[0408] If the server obtains interest and consent from the other user, it will disclose detailed information to both parties. The devices can then display each other's detailed profiles to the users.

[0409] Step 7:

[0410] Users can further provide feedback through the generated AI. The server analyzes this feedback and uses it to improve the accuracy of the system's matching algorithms and personality analysis processes.

[0411] (Example 1)

[0412] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0413] In today's information society, providing optimal interactions for individual users requires flexible recommendations based on their personality traits. However, conventional systems fail to adequately provide highly accurate recommendations based on personality and compatibility, as well as safe and privacy-conscious information exchange. As a result, users may miss opportunities to connect with suitable partners. This challenge needs to be addressed.

[0414] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0415] In this invention, the server includes means for collecting communication information from the user and anonymizing it; means for analyzing the information using a generating AI to extract the user's personality traits; and means for identifying compatible candidates based on the extracted personality traits and matching them. This enables highly accurate partner recommendations based on personality traits in a safe and privacy-protected manner for the user.

[0416] A "user" is an individual or group that uses information systems or digital devices to receive services.

[0417] "Communication information" refers to the content that users exchange using messaging platforms or other means of communication, and includes data such as text, audio, and images.

[0418] "Anonymization" is a data processing technique that protects privacy by removing or transforming information that can identify an individual.

[0419] "Generative AI" refers to artificial intelligence models and algorithms that generate useful information from large amounts of data, and is a technology particularly used in natural language processing and data analysis.

[0420] "Personality traits" are numerical or qualitative representations of an individual's behavioral and thinking characteristics, and are elements obtained as a result of analyzing an individual's attitudes, emotions, hobbies, etc.

[0421] A "candidate" refers to another user who meets specific conditions or criteria selected by the system, and who could potentially be a suitable partner for the user.

[0422] "Mapping" is the process of finding relationships between different datasets or elements and connecting them.

[0423] "Privacy protection" refers to security measures and technologies designed to prevent individuals' private information from being misused or made public.

[0424] To implement this invention, a user's communication terminal, a server, a generative AI model, and necessary data processing technologies are used. The user accesses the system via their own communication terminal. This communication terminal is a device such as a smartphone or a personal computer, and communicates with the server via an internet connection.

[0425] The device first collects user communication data, for example, from a messaging platform. Here, data is retrieved using an API, and with the user's consent, it undergoes anonymization. Anonymization transforms the information into a form that cannot identify individuals before sending it to the server. This protects the user's privacy.

[0426] The server analyzes the received anonymized data using a generating AI model. This analysis uses natural language processing techniques to perform sentiment analysis and keyword extraction, revealing the user's personality traits. Libraries such as TensorFlow and PyTorch may be utilized for this purpose. This process quantifies personality traits, providing a basis for comparison with other users.

[0427] As a concrete example, if a user's message data is examined and determined to be "highly sensitive" through sentiment analysis, the server will compare it with other user information stored in the database. An example of a prompt would be, "Identify highly sensitive and cooperative users and recommend other similar users."

[0428] This system also includes an option to hide specific friends to enhance privacy protection between users. The device notifies the user of recommendations from the server, and if the user shows interest, information is shared with mutual consent. This allows users to safely and effectively interact with compatible people.

[0429] The present invention aims to improve the user experience by combining these technical means to achieve highly accurate recommendations based on the individual personality traits of users.

[0430] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0431] Step 1:

[0432] Users access the system using a communication terminal. With the user's consent, the terminal collects communication data from the messaging platform. The input is message data, and the output is the collected raw data. Specifically, the terminal retrieves data using an API.

[0433] Step 2:

[0434] The device performs anonymization on the collected data. It removes or transforms personally identifiable information to protect privacy. The input is the collected raw data, and the output is the anonymized data. Specifically, the device replaces usernames and contact information with random IDs before sending the data to the server.

[0435] Step 3:

[0436] The server analyzes the received anonymized data using a generating AI model to extract the user's personality traits. The input is anonymized data, and the output is numerical personality traits. Specifically, the server uses natural language processing techniques to analyze key emotions and keywords and generates a numerical score to evaluate personality.

[0437] Step 4:

[0438] The server compares the extracted personality traits with other user information in the database to perform a compatibility assessment. The input is numerical personality traits, and the output is a list of candidates deemed to be compatible. Specifically, the server ranks the candidates using a similarity algorithm.

[0439] Step 5:

[0440] The server sends the generated candidate list to the device. The device displays the profiles of compatible candidates to the user. The input is the candidate list, and the output is the candidate information displayed in the user interface. Specifically, the device uses push notifications to inform the user that there are new candidates.

[0441] Step 6:

[0442] The user selects candidates they are interested in. The selected information is sent to the server, and once the other candidates consent, detailed information about both parties is made public. The input is the user's selection information, and the output is the shared detailed information. Specifically, the server confirms the consent of the other user and then provides the detailed information to the terminal.

[0443] (Application Example 1)

[0444] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0445] Traditional matching systems are unable to recommend personalized products and services based on users' personality traits, making it difficult to provide information that meets individual needs. Furthermore, there is a need for enhanced privacy protection in information exchange between users.

[0446] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0447] In this invention, the server includes means for collecting and anonymizing communication data, means for analyzing the data using generative artificial intelligence and extracting personality traits, and means for recommending products or services based on the user's personality traits. This enables personalized recommendations of products and services tailored to the user's personality traits. Furthermore, it allows for information exchange that takes privacy protection into consideration.

[0448] "Communication data" is a general term for various types of information generated online, such as messages and activity history collected from users.

[0449] "Anonymization" is a technology that protects data privacy by removing or transforming information that could identify an individual.

[0450] "Generative artificial intelligence" is an artificial intelligence technology that analyzes data and generates new information and patterns.

[0451] "Personality traits" are indicators that show psychological characteristics and tendencies derived from a user's behavior and statements.

[0452] "Candidates" refer to other users or options suggested based on the user's personality traits and needs.

[0453] "Products or services" refers to the general term for goods and support provided to users.

[0454] "Recommendation" is the act of presenting the most suitable options based on the user's interests and characteristics.

[0455] "Information exchange" refers to the process of sending and receiving necessary data and messages between users.

[0456] "Privacy protection" refers to technologies and measures to prevent the leakage of personal information and to protect users' secrets.

[0457] This invention is a system that utilizes communication data collected from users' communication terminals to recommend products and services best suited to each individual user. Here, smartphones are used as the primary communication terminals, and servers located in a cloud environment and generative AI technology are employed.

[0458] The server receives anonymized communication data from the user's communication terminal and performs sentiment analysis using natural language processing techniques. This quantifies the user's personality traits and records them in a database. Machine learning libraries such as TensorFlow and scikit-learn are used in this process. Based on the user's personality traits, a generative AI model identifies other users with similar profiles and recommends them as compatible candidates.

[0459] Furthermore, the server generates a personalized list of products and services based on the user's personality traits and sends it to the communication terminal. For example, a user analyzed as having an active personality might be recommended discounts on new sports equipment. This approach enhances the purchasing experience and enables suggestions that align with the user's interests and values.

[0460] As a concrete example, a prompt to input into a generative AI model might be, "Generate recommended products and services for users with an active and sociable personality." This prompt prompts the generative AI model to generate data to suggest products that match the user's interests. This enables the provision of optimal information tailored to individual users.

[0461] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0462] Step 1:

[0463] Communication devices collect communication data with the user's consent. This data includes messages and activity history, and the device anonymizes the data to prevent the identification of individuals. The input is raw communication data, and the output is anonymized communication data.

[0464] Step 2:

[0465] The server receives anonymized data sent from the terminal. The server analyzes the data using natural language processing and quantifies the user's personality traits. Data processing, such as sentiment analysis, is performed during this process. The input is anonymized communication data, and the output is numerical data related to the user's personality traits.

[0466] Step 3:

[0467] The server compares the personality traits extracted using a generative AI model with the profiles of other users in the database. This comparison identifies compatible candidates and generates a list of suggested candidates. The input is the user's personality trait data, and the output is a list of recommended candidates.

[0468] Step 4:

[0469] The server generates a list of recommended products and services based on the user's personality traits. It inputs prompts into a generation AI model based on the user's characteristic data, extracting the most suitable product information. For example, it might use the prompt, "Generate recommended products and services for an active and sociable user." The input consists of the user's personality traits and the prompt text, while the output is a personalized product list.

[0470] Step 5:

[0471] The server sends the generated candidate list and product list to the communication terminal. The terminal displays this information as options to present to the user. When the user selects candidates or products they are interested in, the terminal records that selection. The input is the candidate list and product list, and the output is the user's selection information.

[0472] Step 6:

[0473] The device will disclose various types of information based on the user's choices, while taking privacy protection into consideration. If consent is obtained, the server will exchange detailed information. Input is information selected by the user, and output is information disclosure based on consent.

[0474] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0475] This invention proposes a system that achieves more accurate matching using user communication data and emotion data. This system is implemented by combining generative artificial intelligence and an emotion engine.

[0476] First, users access the system using a communication terminal. The communication terminal collects the user's communication data from the messaging service, anonymizes it appropriately, and then sends it to the server. This includes security protocols to protect user privacy.

[0477] The server uses generative artificial intelligence to analyze the received data. It utilizes natural language processing technology to extract user personality traits from text. Simultaneously, it uses an emotion engine to recognize the user's emotional state in real time and collect emotional data. This emotional data can capture the intensity and trends of the user's emotions during communication.

[0478] Next, the server matches users with other users based on the extracted personality traits and emotional data. In particular, it adjusts the candidate recommendation list based on the emotional state obtained from the emotion engine, enabling flexible recommendations based on the user's temporary emotions. Furthermore, by analyzing the temporal change patterns of emotional data and taking long-term emotional trends into account, it achieves matching that considers more lasting compatibility.

[0479] For example, when user A accesses the system, the terminal retrieves messages from LINE. The server extracts personality traits such as cooperativeness and high sensitivity from user A's text, and measures the intensity of their emotions. If user A expresses very positive emotions on a particular topic, the server takes this into account and prioritizes user B, who has positive emotions on similar topics, as a candidate and includes them in the recommendation list. Also, if user A's emotions tend to fluctuate greatly, user C, who has a flexible communication style that can handle these fluctuations, may also be recommended.

[0480] This system helps users find partners more comprehensively, based not only on short-term feelings but also on long-term personality traits. Furthermore, privacy protection options allow users to prevent their friends and acquaintances from being identified through the system.

[0481] The following describes the processing flow.

[0482] Step 1:

[0483] The user accesses the system via their device and authorizes a connection to the messaging service. The device securely collects the user's communication data using APIs such as the LINE API. This data is anonymized to protect the user's privacy before being sent to the server.

[0484] Step 2:

[0485] The server utilizes generative artificial intelligence to analyze the anonymized data it receives. It analyzes text data through natural language processing techniques to extract user personality traits. This includes evaluating personality tendencies based on specific keywords and language patterns.

[0486] Step 3:

[0487] The server uses an emotion engine to recognize the user's emotional state in real time from the received text data. The emotional state determines what emotions the user is expressing and quantifies their intensity.

[0488] Step 4:

[0489] The server combines extracted personality traits and emotional data, and compares them to other users registered in the database to assess compatibility. By using data from the emotion engine, it also takes temporary emotional states into account and dynamically adjusts the list of recommended candidates.

[0490] Step 5:

[0491] The server sends an optimized recommendation list to the user's device. Based on this list, the device displays brief profiles of candidates deemed highly suitable for the user.

[0492] Step 6:

[0493] When a user expresses interest in a presented candidate, they send that information from their device to the server. The server then communicates this interest to the other user, and only discloses detailed profile information if both parties agree.

[0494] Step 7:

[0495] Users can support improvements to matching results through feedback options provided by the system. The server analyzes this feedback to help improve the accuracy of the algorithm. Additionally, privacy features allow users to hide specific friends.

[0496] (Example 2)

[0497] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0498] In today's information society, finding the optimal communication partner based on a user's individual personality and emotional state is an extremely difficult challenge. Existing systems struggle to provide personalized matching that adequately considers a user's personality and short-term emotions, resulting in a high likelihood of mismatches. Furthermore, consideration for privacy protection is sometimes insufficient. Solving these challenges is essential.

[0499] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0500] In this invention, the server includes means for collecting and anonymizing communication information, means for extracting user personality attributes and emotional information using a generative model, and means for making flexible recommendations based on the extracted information. This enables personalized matching tailored to the user's individual personality and emotions, while also ensuring privacy.

[0501] "Communication information" refers to the content of information exchange between users via messaging services, etc., and includes text messages and other digital content.

[0502] "Anonymization" is a process that makes it impossible to identify a user's personal information, and is carried out to protect data privacy.

[0503] A "generative model" is a form of artificial intelligence technology that analyzes data to derive patterns and generate new data and insights.

[0504] "Personality attributes" refer to the characteristics and tendencies of a person's personality that can be inferred from their behavior and reactions.

[0505] "Emotional information" refers to data that quantifies or categorizes a user's emotional state, representing real-time reactions and trends.

[0506] "Recommendation" refers to the act of presenting suitable options or candidates to a user based on specific criteria.

[0507] "Ensuring privacy" means taking measures to protect users' personal information from being leaked to third parties.

[0508] The system of this invention is designed for efficient user assistance and functions through the cooperation of three parties: the user, the terminal, and the server. The user first connects to the system using a communication device. The terminal collects communication information obtained from the user through a messaging service. The collected information is anonymized to protect the user's personal information and sent to the server in accordance with security protocols.

[0509] The server uses generative models to analyze received communication information. This analysis employs natural language processing techniques to extract user personality attributes and emotional information. In particular, sentiment analysis techniques are used to understand the user's emotional state in real time. Based on this information, the server selects the most suitable other users and generates a recommendation list.

[0510] In this system, a generative AI model functions as the core of the program, accurately processing user information using prompt messages. For example, if a user sends the message "I had a great time today!", the server uses this sentiment to perform appropriate matching.

[0511] As a concrete example of this invention, an example of a prompt message is, "Find and recommend other users who are similar to the user's positive emotions." This prompt encourages information analysis by a generative AI model, enabling personalized information recommendations.

[0512] Thus, this system is designed to meet individual needs through flexible matching based on the user's personality and emotions, while also ensuring privacy protection.

[0513] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0514] Step 1: The user accesses the system using a communication device. The terminal obtains the user's communication information via a messaging service. This information is collected in text format. The input data is user messages, and the output is anonymized data. In particular, processes are performed to remove unique information such as user IDs for privacy protection.

[0515] Step 2: The terminal sends anonymized communication information to the server using a security protocol. The server receives this incoming data for the first time. The input for this step is anonymized communication data, and the output is data securely stored on the server. Encryption technology is used for transmission.

[0516] Step 3: The server begins analyzing the received communication information using a generative model. The input is the stored anonymized communication data, and the output is the user's personality attributes and emotional information obtained through the analysis. Specifically, natural language processing techniques are used to extract attributes such as the user's cooperativeness and sensitivity from the text, and emotional analysis is used to understand the intensity and tendency of emotions.

[0517] Step 4: The server executes a prompt using a generative AI model to calculate the best matching candidates for the user. The input is personality attributes and sentiment information, and the output is a recommendation list. This prompt includes the phrase, "Find and recommend other users who are similar to the user's positive sentiment." The model generates a ranking of candidates based on this.

[0518] Step 5: The server notifies the user of the generated recommendation list. The terminal receives this list and displays it on the user's screen. The input is the recommendation list from the server, and the output is the screen display received by the user. Based on this information, the user can start communicating with people they are interested in.

[0519] (Application Example 2)

[0520] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0521] In today's world, when recommending products best suited to a user's preferences and mood on online platforms such as virtual stores, accurate recommendations that take into account the user's temporary emotions and personality traits are required. However, conventional systems fail to adequately reflect the user's emotional state, and the results are not always satisfactory to the user.

[0522] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0523] In this invention, the server includes means for collecting communication data from the user and anonymizing it; means for analyzing the data using generative artificial intelligence to extract the user's personality traits and emotional state; and means for adjusting a product recommendation list based on the user's emotional state and presenting it to the user. This enables highly accurate product recommendations based on the user's temporary emotional state and long-term personality traits.

[0524] A "user" refers to an individual or group that uses the system and is the entity that provides communication data.

[0525] "Communication data" refers to information including messages and statements exchanged between users and others.

[0526] "Anonymization" is a process that modifies information so that a specific user cannot be identified.

[0527] "Generative artificial intelligence" is an artificial intelligence technology that performs advanced data analysis based on vast datasets.

[0528] "Personality traits" are elements that characterize a user's individuality and behavioral patterns.

[0529] "Emotional state" refers to information that indicates the user's psychological state and the intensity of their emotions.

[0530] "Matching" is the process of determining compatibility and recommending users in order to connect them with each other.

[0531] A "product recommendation list" is a list of products and services presented to a user based on their emotional state and personality traits.

[0532] The system for realizing this invention consists of three main elements: a user, a communication terminal, and a server. The user first accesses the system using the communication terminal, and the communication data they transmit is collected. This communication terminal is equipped with a function to retrieve and anonymize messages from the user, thereby protecting the user's privacy.

[0533] The server analyzes the collected communication data using advanced generative artificial intelligence technology. In this step, NLTK, a Python natural language processing library, is used to extract personality traits from the user's messages. In addition, the Symanto sentiment analysis engine is utilized to analyze emotional states. This engine recognizes the emotions embedded in each message in real time and provides data on the intensity and tendencies of the user's emotions.

[0534] The system then generates a list of product recommendations tailored to the user based on the extracted personality traits and emotional state. This process utilizes a generative artificial intelligence-based recommendation algorithm, which lists products that take into account the user's temporary emotions and long-term personality traits. For example, if the system determines that the user has recently been experiencing stress, products with relaxing effects will be emphasized and recommended.

[0535] An example of a prompt message might be: "What products should be recommended to a user who enjoys fragrances, especially if they had a hobby of enjoying scents last weekend? Also, if recent chat history indicates that the user is prone to stress, how should product recommendations be adjusted?" Based on such prompt messages, the system performs calculations to make the best recommendations.

[0536] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0537] Step 1:

[0538] The terminal collects communication data from the user and performs anonymization processing. The input is the messages sent and received by the user, and the output is anonymized data. The terminal removes specific identifying information and prepares the data for secure transmission to the server.

[0539] Step 2:

[0540] The server receives anonymized communication data. The input is anonymized data sent from the terminal, and the output is clean data ready for analysis. After receiving the data, the server standardizes the format and makes it ready for analysis.

[0541] Step 3:

[0542] The server uses generative artificial intelligence to analyze data and extract user personality traits. The input is clean communication data, and the output is numerical values ​​and indicators that represent the user's personality traits. The server performs natural language processing using the Python NLTK library to analyze behavioral patterns.

[0543] Step 4:

[0544] The server uses the Symanto emotion analysis engine to analyze emotional states in real time. The input is clean communication data, and the output is data showing indicators and trends of emotional states. The server measures the intensity of emotions and calculates their trends.

[0545] Step 5:

[0546] The server generates a product recommendation list based on the generated personality traits and emotional state. The input is data on personality traits and emotional state, and the output is a list of products suggested to the user. The server uses a generative artificial intelligence model to identify the optimal set of products for the user.

[0547] Step 6:

[0548] The server sends a generated product recommendation list to the terminal and displays it to the user. The input is the product recommendation list, and the output is the product information that the user views on the terminal. The terminal presents the user with product details and purchase options through its user interface.

[0549] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0550] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0551] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0552] [Fourth Embodiment]

[0553] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0554] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0555] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0556] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0557] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0558] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0559] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0560] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0561] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0562] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0563] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0564] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0565] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0566] This invention is a system that works in conjunction with a user's everyday communication terminal to recommend a compatible partner based on the user's personality traits. This system is realized by utilizing generative artificial intelligence and combining it with various data analysis technologies.

[0567] First, the user accesses the system through their communication device. With the user's consent, the communication device collects the user's communication data from messaging platforms such as LINE. This data is anonymized to prevent the identification of individuals and is securely transmitted to the server.

[0568] The server uses the received anonymized data to analyze the user's personality traits using generative artificial intelligence. This process includes sentiment analysis and keyword extraction using natural language processing techniques. Personality traits are modeled as quantified evaluations, forming the basis for comparison with data from other users.

[0569] Next, the server compares the personality traits of users in the existing user database with those of other users to identify compatible users. The compatibility assessment uses a similarity-measuring algorithm to determine which users share common hobbies and values.

[0570] The server then sends the matching results to the user's device. The communication device uses this information to present the user with specific candidate profiles. If the user is interested in one of the presented candidates, they send that information through their device, and once the other user agrees, detailed information about both parties is made public via the server.

[0571] As a concrete example, let's say User A starts using the system. Based on messaging data, User A is analyzed as being highly sensitive and cooperative. The server identifies User B, who possesses these characteristics, from its database and determines that they are a good match. User B's profile is displayed on User A's device, and by pressing a button indicating interest, a notification is sent to User B. If User B agrees, both users can view each other's detailed information.

[0572] In this way, the present invention provides users with the opportunity to safely and efficiently meet suitable partners. Privacy protection features have also been enhanced, including an option for users to hide friends they do not want to be personally known to.

[0573] The following describes the processing flow.

[0574] Step 1:

[0575] Users access the system using their devices and interact with the messaging platform. The devices collect user communication data via the LINE API, anonymize it, and then send it to the server.

[0576] Step 2:

[0577] The server analyzes the received anonymized data. Using generative artificial intelligence, it performs sentiment analysis and keyword extraction using natural language processing techniques to identify the user's personality traits. Based on these traits, it constructs a quantified evaluation model.

[0578] Step 3:

[0579] The server compares the constructed personality assessment model with models from other users. Using a similarity measurement algorithm, it calculates the compatibility rate between users and lists compatible candidates.

[0580] Step 4:

[0581] The server sends a list of candidates deemed highly compatible to the user's device. Based on this information, the device displays a brief profile of each candidate and an overview of their compatibility to the user.

[0582] Step 5:

[0583] The user indicates interest by pressing the button corresponding to the presented candidate. This information is sent from the terminal to the server, and a notification is sent to the other user.

[0584] Step 6:

[0585] If the server obtains interest and consent from the other user, it will disclose detailed information to both parties. The devices can then display each other's detailed profiles to the users.

[0586] Step 7:

[0587] Users can further provide feedback through the generated AI. The server analyzes this feedback and uses it to improve the accuracy of the system's matching algorithms and personality analysis processes.

[0588] (Example 1)

[0589] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0590] In today's information society, providing optimal interactions for individual users requires flexible recommendations based on their personality traits. However, conventional systems fail to adequately provide highly accurate recommendations based on personality and compatibility, as well as safe and privacy-conscious information exchange. As a result, users may miss opportunities to connect with suitable partners. This challenge needs to be addressed.

[0591] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0592] In this invention, the server includes means for collecting communication information from the user and anonymizing it; means for analyzing the information using a generating AI to extract the user's personality traits; and means for identifying compatible candidates based on the extracted personality traits and matching them. This enables highly accurate partner recommendations based on personality traits in a safe and privacy-protected manner for the user.

[0593] A "user" is an individual or group that uses information systems or digital devices to receive services.

[0594] "Communication information" refers to the content that users exchange using messaging platforms or other means of communication, and includes data such as text, audio, and images.

[0595] "Anonymization" is a data processing technique that protects privacy by removing or transforming information that can identify an individual.

[0596] "Generative AI" refers to artificial intelligence models and algorithms that generate useful information from large amounts of data, and is a technology particularly used in natural language processing and data analysis.

[0597] "Personality traits" are numerical or qualitative representations of an individual's behavioral and thinking characteristics, and are elements obtained as a result of analyzing an individual's attitudes, emotions, hobbies, etc.

[0598] A "candidate" refers to another user who meets specific conditions or criteria selected by the system, and who could potentially be a suitable partner for the user.

[0599] "Mapping" is the process of finding relationships between different datasets or elements and connecting them.

[0600] "Privacy protection" refers to security measures and technologies designed to prevent individuals' private information from being misused or made public.

[0601] To implement this invention, a user's communication terminal, a server, a generative AI model, and necessary data processing technologies are used. The user accesses the system via their own communication terminal. This communication terminal is a device such as a smartphone or a personal computer, and communicates with the server via an internet connection.

[0602] The device first collects user communication data, for example, from a messaging platform. Here, data is retrieved using an API, and with the user's consent, it undergoes anonymization. Anonymization transforms the information into a form that cannot identify individuals before sending it to the server. This protects the user's privacy.

[0603] The server analyzes the received anonymized data using a generating AI model. This analysis uses natural language processing techniques to perform sentiment analysis and keyword extraction, revealing the user's personality traits. Libraries such as TensorFlow and PyTorch may be utilized for this purpose. This process quantifies personality traits, providing a basis for comparison with other users.

[0604] As a concrete example, if a user's message data is examined and determined to be "highly sensitive" through sentiment analysis, the server will compare it with other user information stored in the database. An example of a prompt would be, "Identify highly sensitive and cooperative users and recommend other similar users."

[0605] This system also includes an option to hide specific friends to enhance privacy protection between users. The device notifies the user of recommendations from the server, and if the user shows interest, information is shared with mutual consent. This allows users to safely and effectively interact with compatible people.

[0606] The present invention aims to improve the user experience by combining these technical means to achieve highly accurate recommendations based on the individual personality traits of users.

[0607] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0608] Step 1:

[0609] Users access the system using a communication terminal. With the user's consent, the terminal collects communication data from the messaging platform. The input is message data, and the output is the collected raw data. Specifically, the terminal retrieves data using an API.

[0610] Step 2:

[0611] The device performs anonymization on the collected data. It removes or transforms personally identifiable information to protect privacy. The input is the collected raw data, and the output is the anonymized data. Specifically, the device replaces usernames and contact information with random IDs before sending the data to the server.

[0612] Step 3:

[0613] The server analyzes the received anonymized data using a generating AI model to extract the user's personality traits. The input is anonymized data, and the output is numerical personality traits. Specifically, the server uses natural language processing techniques to analyze key emotions and keywords and generates a numerical score to evaluate personality.

[0614] Step 4:

[0615] The server compares the extracted personality traits with other user information in the database to perform a compatibility assessment. The input is numerical personality traits, and the output is a list of candidates deemed to be compatible. Specifically, the server ranks the candidates using a similarity algorithm.

[0616] Step 5:

[0617] The server sends the generated candidate list to the device. The device displays the profiles of compatible candidates to the user. The input is the candidate list, and the output is the candidate information displayed in the user interface. Specifically, the device uses push notifications to inform the user that there are new candidates.

[0618] Step 6:

[0619] The user selects candidates they are interested in. The selected information is sent to the server, and once the other candidates consent, detailed information about both parties is made public. The input is the user's selection information, and the output is the shared detailed information. Specifically, the server confirms the consent of the other user and then provides the detailed information to the terminal.

[0620] (Application Example 1)

[0621] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0622] Traditional matching systems are unable to recommend personalized products and services based on users' personality traits, making it difficult to provide information that meets individual needs. Furthermore, there is a need for enhanced privacy protection in information exchange between users.

[0623] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0624] In this invention, the server includes means for collecting and anonymizing communication data, means for analyzing the data using generative artificial intelligence and extracting personality traits, and means for recommending products or services based on the user's personality traits. This enables personalized recommendations of products and services tailored to the user's personality traits. Furthermore, it allows for information exchange that takes privacy protection into consideration.

[0625] "Communication data" is a general term for various types of information generated online, such as messages and activity history collected from users.

[0626] "Anonymization" is a technology that protects data privacy by removing or transforming information that could identify an individual.

[0627] "Generative artificial intelligence" is an artificial intelligence technology that analyzes data and generates new information and patterns.

[0628] "Personality traits" are indicators that show psychological characteristics and tendencies derived from a user's behavior and statements.

[0629] "Candidates" refer to other users or options suggested based on the user's personality traits and needs.

[0630] "Products or services" refers to the general term for goods and support provided to users.

[0631] "Recommendation" is the act of presenting the most suitable options based on the user's interests and characteristics.

[0632] "Information exchange" refers to the process of sending and receiving necessary data and messages between users.

[0633] "Privacy protection" refers to technologies and measures to prevent the leakage of personal information and to protect users' secrets.

[0634] This invention is a system that utilizes communication data collected from users' communication terminals to recommend products and services best suited to each individual user. Here, smartphones are used as the primary communication terminals, and servers located in a cloud environment and generative AI technology are employed.

[0635] The server receives anonymized communication data from the user's communication terminal and performs sentiment analysis using natural language processing techniques. This quantifies the user's personality traits and records them in a database. Machine learning libraries such as TensorFlow and scikit-learn are used in this process. Based on the user's personality traits, a generative AI model identifies other users with similar profiles and recommends them as compatible candidates.

[0636] Furthermore, the server generates a personalized list of products and services based on the user's personality traits and sends it to the communication terminal. For example, a user analyzed as having an active personality might be recommended discounts on new sports equipment. This approach enhances the purchasing experience and enables suggestions that align with the user's interests and values.

[0637] As a concrete example, a prompt to input into a generative AI model might be, "Generate recommended products and services for users with an active and sociable personality." This prompt prompts the generative AI model to generate data to suggest products that match the user's interests. This enables the provision of optimal information tailored to individual users.

[0638] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0639] Step 1:

[0640] Communication devices collect communication data with the user's consent. This data includes messages and activity history, and the device anonymizes the data to prevent the identification of individuals. The input is raw communication data, and the output is anonymized communication data.

[0641] Step 2:

[0642] The server receives anonymized data sent from the terminal. The server analyzes the data using natural language processing and quantifies the user's personality traits. Data processing, such as sentiment analysis, is performed during this process. The input is anonymized communication data, and the output is numerical data related to the user's personality traits.

[0643] Step 3:

[0644] The server compares the personality traits extracted using a generative AI model with the profiles of other users in the database. This comparison identifies compatible candidates and generates a list of suggested candidates. The input is the user's personality trait data, and the output is a list of recommended candidates.

[0645] Step 4:

[0646] The server generates a list of recommended products and services based on the user's personality traits. It inputs prompts into a generation AI model based on the user's characteristic data, extracting the most suitable product information. For example, it might use the prompt, "Generate recommended products and services for an active and sociable user." The input consists of the user's personality traits and the prompt text, while the output is a personalized product list.

[0647] Step 5:

[0648] The server sends the generated candidate list and product list to the communication terminal. The terminal displays this information as options to present to the user. When the user selects candidates or products they are interested in, the terminal records that selection. The input is the candidate list and product list, and the output is the user's selection information.

[0649] Step 6:

[0650] The device will disclose various information based on the user's choices, while taking privacy protection into consideration. If consent is obtained, the server will exchange detailed information. Input is user-selected information, and output is information disclosure based on consent.

[0651] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0652] This invention proposes a system that achieves more accurate matching using user communication data and emotion data. This system is implemented by combining generative artificial intelligence and an emotion engine.

[0653] First, users access the system using a communication terminal. The communication terminal collects the user's communication data from the messaging service, anonymizes it appropriately, and then sends it to the server. This includes security protocols to protect user privacy.

[0654] The server uses generative artificial intelligence to analyze the received data. It utilizes natural language processing technology to extract user personality traits from text. Simultaneously, it uses an emotion engine to recognize the user's emotional state in real time and collect emotional data. This emotional data can capture the intensity and trends of the user's emotions during communication.

[0655] Next, the server matches users with other users based on the extracted personality traits and emotional data. In particular, it adjusts the candidate recommendation list based on the emotional state obtained from the emotion engine, enabling flexible recommendations based on the user's temporary emotions. Furthermore, by analyzing the temporal change patterns of emotional data and taking long-term emotional trends into account, it achieves matching that considers more lasting compatibility.

[0656] For example, when user A accesses the system, the terminal retrieves messages from LINE. The server extracts personality traits such as cooperativeness and high sensitivity from user A's text, and measures the intensity of their emotions. If user A expresses very positive emotions on a particular topic, the server takes this into account and prioritizes user B, who has positive emotions on similar topics, as a candidate and includes them in the recommendation list. Also, if user A's emotions tend to fluctuate greatly, user C, who has a flexible communication style that can handle these fluctuations, may also be recommended.

[0657] This system helps users find partners more comprehensively, based not only on short-term feelings but also on long-term personality traits. Furthermore, privacy protection options allow users to prevent their friends and acquaintances from being identified through the system.

[0658] The following describes the processing flow.

[0659] Step 1:

[0660] The user accesses the system via their device and authorizes a connection to the messaging service. The device securely collects the user's communication data using APIs such as the LINE API. This data is anonymized to protect the user's privacy before being sent to the server.

[0661] Step 2:

[0662] The server utilizes generative artificial intelligence to analyze the anonymized data it receives. It analyzes text data through natural language processing techniques to extract user personality traits. This includes evaluating personality tendencies based on specific keywords and language patterns.

[0663] Step 3:

[0664] The server uses an emotion engine to recognize the user's emotional state in real time from the received text data. The emotional state determines what emotions the user is expressing and quantifies their intensity.

[0665] Step 4:

[0666] The server combines extracted personality traits and emotional data, and compares them to other users registered in the database to assess compatibility. By using data from the emotion engine, it also takes temporary emotional states into account and dynamically adjusts the list of recommended candidates.

[0667] Step 5:

[0668] The server sends an optimized recommendation list to the user's device. Based on this list, the device displays brief profiles of candidates deemed highly suitable for the user.

[0669] Step 6:

[0670] When a user expresses interest in a presented candidate, they send that information from their device to the server. The server then communicates this interest to the other user, and only discloses detailed profile information if both parties agree.

[0671] Step 7:

[0672] Users can support improvements to matching results through feedback options provided by the system. The server analyzes this feedback to help improve the accuracy of the algorithm. Additionally, privacy features allow users to hide specific friends.

[0673] (Example 2)

[0674] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0675] In today's information society, finding the optimal communication partner based on a user's individual personality and emotional state is an extremely difficult challenge. Existing systems struggle to provide personalized matching that adequately considers a user's personality and short-term emotions, resulting in a high likelihood of mismatches. Furthermore, consideration for privacy protection is sometimes insufficient. Solving these challenges is essential.

[0676] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0677] In this invention, the server includes means for collecting and anonymizing communication information, means for extracting user personality attributes and emotional information using a generative model, and means for making flexible recommendations based on the extracted information. This enables personalized matching tailored to the user's individual personality and emotions, while also ensuring privacy.

[0678] "Communication information" refers to the content of information exchange between users via messaging services, etc., and includes text messages and other digital content.

[0679] "Anonymization" is a process that makes it impossible to identify a user's personal information, and is carried out to protect data privacy.

[0680] A "generative model" is a form of artificial intelligence technology that analyzes data to derive patterns and generate new data and insights.

[0681] "Personality attributes" refer to the characteristics and tendencies of a person's personality that can be inferred from their behavior and reactions.

[0682] "Emotional information" refers to data that quantifies or categorizes a user's emotional state, representing real-time reactions and trends.

[0683] "Recommendation" refers to the act of presenting suitable options or candidates to a user based on specific criteria.

[0684] "Ensuring privacy" means taking measures to protect users' personal information from being leaked to third parties.

[0685] The system of this invention is designed for efficient user assistance and functions through the cooperation of three parties: the user, the terminal, and the server. The user first connects to the system using a communication device. The terminal collects communication information obtained from the user through a messaging service. The collected information is anonymized to protect the user's personal information and sent to the server in accordance with security protocols.

[0686] The server uses generative models to analyze received communication information. This analysis employs natural language processing techniques to extract user personality attributes and emotional information. In particular, sentiment analysis techniques are used to understand the user's emotional state in real time. Based on this information, the server selects the most suitable other users and generates a recommendation list.

[0687] In this system, a generative AI model functions as the core of the program, accurately processing user information using prompt messages. For example, if a user sends the message "I had a great time today!", the server uses this sentiment to perform appropriate matching.

[0688] As a concrete example of this invention, an example of a prompt message is, "Find and recommend other users who are similar to the user's positive emotions." This prompt encourages information analysis by a generative AI model, enabling personalized information recommendations.

[0689] Thus, this system is designed to meet individual needs through flexible matching based on the user's personality and emotions, while also ensuring privacy protection.

[0690] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0691] Step 1: The user accesses the system using a communication device. The terminal obtains the user's communication information via a messaging service. This information is collected in text format. The input data is user messages, and the output is anonymized data. In particular, processes are performed to remove unique information such as user IDs for privacy protection.

[0692] Step 2: The terminal sends anonymized communication information to the server using a security protocol. The server receives this incoming data for the first time. The input for this step is anonymized communication data, and the output is data securely stored on the server. Encryption technology is used for transmission.

[0693] Step 3: The server begins analyzing the received communication information using a generative model. The input is the stored anonymized communication data, and the output is the user's personality attributes and emotional information obtained through the analysis. Specifically, natural language processing techniques are used to extract attributes such as the user's cooperativeness and sensitivity from the text, and emotional analysis is used to understand the intensity and tendency of emotions.

[0694] Step 4: The server executes a prompt using a generative AI model to calculate the best matching candidates for the user. The input is personality attributes and sentiment information, and the output is a recommendation list. This prompt includes the phrase, "Find and recommend other users who are similar to the user's positive sentiment." The model generates a ranking of candidates based on this.

[0695] Step 5: The server notifies the user of the generated recommendation list. The terminal receives this list and displays it on the user's screen. The input is the recommendation list from the server, and the output is the screen display received by the user. Based on this information, the user can start communicating with people they are interested in.

[0696] (Application Example 2)

[0697] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0698] In today's world, when recommending products best suited to a user's preferences and mood on online platforms such as virtual stores, accurate recommendations that take into account the user's temporary emotions and personality traits are required. However, conventional systems fail to adequately reflect the user's emotional state, and the results are not always satisfactory to the user.

[0699] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0700] In this invention, the server includes means for collecting communication data from the user and anonymizing it; means for analyzing the data using generative artificial intelligence to extract the user's personality traits and emotional state; and means for adjusting a product recommendation list based on the user's emotional state and presenting it to the user. This enables highly accurate product recommendations based on the user's temporary emotional state and long-term personality traits.

[0701] A "user" refers to an individual or group that uses the system and is the entity that provides communication data.

[0702] "Communication data" refers to information including messages and statements exchanged between users and others.

[0703] "Anonymization" is a process that modifies information so that a specific user cannot be identified.

[0704] "Generative artificial intelligence" is an artificial intelligence technology that performs advanced data analysis based on vast datasets.

[0705] "Personality traits" are elements that characterize a user's individuality and behavioral patterns.

[0706] "Emotional state" refers to information that indicates the user's psychological state and the intensity of their emotions.

[0707] "Matching" is the process of determining compatibility and recommending users in order to connect them with each other.

[0708] A "product recommendation list" is a list of products and services presented to a user based on their emotional state and personality traits.

[0709] The system for realizing this invention consists of three main elements: a user, a communication terminal, and a server. The user first accesses the system using the communication terminal, and the communication data they transmit is collected. This communication terminal is equipped with a function to retrieve and anonymize messages from the user, thereby protecting the user's privacy.

[0710] The server analyzes the collected communication data using advanced generative artificial intelligence technology. In this step, NLTK, a Python natural language processing library, is used to extract personality traits from the user's messages. In addition, the Symanto sentiment analysis engine is utilized to analyze emotional states. This engine recognizes the emotions embedded in each message in real time and provides data on the intensity and tendencies of the user's emotions.

[0711] The system then generates a list of product recommendations tailored to the user based on the extracted personality traits and emotional state. This process utilizes a generative artificial intelligence-based recommendation algorithm, which lists products that take into account the user's temporary emotions and long-term personality traits. For example, if the system determines that the user has recently been experiencing stress, products with relaxing effects will be emphasized and recommended.

[0712] An example of a prompt message might be: "What products should be recommended to a user who enjoys fragrances, especially if they had a hobby of enjoying scents last weekend? Also, if recent chat history indicates that the user is prone to stress, how should product recommendations be adjusted?" Based on such prompt messages, the system performs calculations to make the best recommendations.

[0713] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0714] Step 1:

[0715] The terminal collects communication data from the user and performs anonymization processing. The input is the messages sent and received by the user, and the output is anonymized data. The terminal removes specific identifying information and prepares the data for secure transmission to the server.

[0716] Step 2:

[0717] The server receives anonymized communication data. The input is anonymized data sent from the terminal, and the output is clean data ready for analysis. After receiving the data, the server standardizes the format and makes it ready for analysis.

[0718] Step 3:

[0719] The server uses generative artificial intelligence to analyze data and extract user personality traits. The input is clean communication data, and the output is numerical values ​​and indicators that represent the user's personality traits. The server performs natural language processing using the Python NLTK library to analyze behavioral patterns.

[0720] Step 4:

[0721] The server uses the Symanto emotion analysis engine to analyze emotional states in real time. The input is clean communication data, and the output is data showing indicators and trends of emotional states. The server measures the intensity of emotions and calculates their trends.

[0722] Step 5:

[0723] The server generates a product recommendation list based on the generated personality traits and emotional state. The input is data on personality traits and emotional state, and the output is a list of products suggested to the user. The server uses a generative artificial intelligence model to identify the optimal set of products for the user.

[0724] Step 6:

[0725] The server sends a generated product recommendation list to the terminal and displays it to the user. The input is the product recommendation list, and the output is the product information that the user views on the terminal. The terminal presents the user with product details and purchase options through its user interface.

[0726] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0727] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0728] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0729] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0730] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0731] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0732] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0733] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0734] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0735] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0736] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0737] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0738] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0739] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0740] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0741] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0742] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0743] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0744] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0745] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0746] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0747] The following is further disclosed regarding the embodiments described above.

[0748] (Claim 1)

[0749] A means of collecting communication data from users and performing anonymization processing,

[0750] A means for analyzing the aforementioned data using generative artificial intelligence to extract the user's personality traits,

[0751] A method for identifying and matching compatible candidates based on extracted personality traits,

[0752] A means of presenting the candidate list generated based on the above to the user,

[0753] A means of disclosing information based on user choice and mutual consent,

[0754] A system that includes this.

[0755] (Claim 2)

[0756] The system according to claim 1, which performs sentiment analysis using natural language processing technology in extracting personality traits based on communication data.

[0757] (Claim 3)

[0758] The system according to claim 1, which, in consent confirmation, provides an option to hide friends for privacy protection when users disclose information to each other.

[0759] "Example 1"

[0760] (Claim 1)

[0761] A means of collecting communication information from users and performing anonymization processing,

[0762] A means for analyzing the aforementioned information using a generative AI to extract the user's personality traits,

[0763] A method for identifying and matching compatible candidates based on extracted personality traits,

[0764] A means of presenting the candidate list generated based on the above to the user,

[0765] A means of disclosing information based on user choice and mutual consent,

[0766] A means to provide an option to hide specific individuals for privacy protection,

[0767] A system that includes this.

[0768] (Claim 2)

[0769] The system according to claim 1, which performs sentiment analysis using natural language processing technology in extracting personality traits based on communication information.

[0770] (Claim 3)

[0771] The system according to claim 1, which protects information using encryption technology when users disclose information to each other during consent confirmation.

[0772] "Application Example 1"

[0773] (Claim 1)

[0774] A means of collecting communication data from users and performing anonymization processing,

[0775] A means for analyzing the aforementioned data using generative artificial intelligence to extract the user's personality traits,

[0776] A method for identifying and proposing compatible candidates based on extracted personality traits,

[0777] A means of recommending products or services based on the user's personality traits,

[0778] A means of presenting the user with a list of candidates or products generated based on the above,

[0779] A means of disclosing information based on user choice and mutual consent,

[0780] A system that includes this.

[0781] (Claim 2)

[0782] The system according to claim 1, which performs emotion analysis using natural language processing technology in extracting personality traits based on communication data.

[0783] (Claim 3)

[0784] The system according to claim 1, which, in consent confirmation, provides an option to hide acquaintances for privacy protection when users disclose information to each other.

[0785] "Example 2 of combining an emotion engine"

[0786] (Claim 1)

[0787] A means of collecting communication information from users and performing anonymization processing,

[0788] A means for analyzing the aforementioned information using a generative model to extract the user's personality attributes,

[0789] A means of providing flexible recommendations by evaluating the degree of similarity with other users based on extracted personality attributes and emotional information,

[0790] A means of presenting the generated recommendation list to the user,

[0791] A means of securely sharing information based on user choices,

[0792] A system that includes this.

[0793] (Claim 2)

[0794] The system according to claim 1, which uses emotion analysis technology in extracting personality attributes based on communication information.

[0795] (Claim 3)

[0796] The system according to claim 1, which, in sharing confirmation, provides an option to hide close associates in order to protect personal information when sharing information among users.

[0797] "Application example 2 when combining with an emotional engine"

[0798] (Claim 1)

[0799] A means of collecting communication data from users and performing anonymization processing,

[0800] A means for analyzing the aforementioned data using generative artificial intelligence to extract the user's personality traits,

[0801] A means of identifying and matching compatible candidates based on extracted personality traits and emotional states,

[0802] A means of adjusting and presenting product recommendation lists to users based on their emotional state,

[0803] A means of presenting the candidate list generated based on the above to the user,

[0804] A means of disclosing information based on user choice and mutual consent,

[0805] A system that includes this.

[0806] (Claim 2)

[0807] The system according to claim 1, which performs sentiment analysis using natural language processing technology in extracting personality traits and recognizing emotional states based on communication data.

[0808] (Claim 3)

[0809] The system according to claim 1, which, in consent confirmation, provides an option to hide friends for privacy protection when users disclose information to each other. [Explanation of Symbols]

[0810] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting communication data from users and performing anonymization processing, A means for analyzing the aforementioned data using generative artificial intelligence to extract the user's personality traits, A method for identifying and matching compatible candidates based on extracted personality traits, A means of presenting the candidate list generated based on the above to the user, A means of disclosing information based on user choice and mutual consent, A system that includes this.

2. The system according to claim 1, which performs sentiment analysis using natural language processing technology in extracting personality traits based on communication data.

3. The system according to claim 1, which, in consent confirmation, provides an option to hide friends for privacy protection when users disclose information to each other.

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