system

Abstract

We provide the system. [Solution] Means for collecting user behavior history information, A means for analyzing the user's personality traits using the said information, A means of evaluating compatibility between multiple users, A means for notifying users of information on users with a high compatibility score based on the evaluation results, A means of mutually disclosing information based on user permission, 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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003] [[ID=2I]]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, in the formation of online human relationships, there is a problem that it is difficult for users to find compatibility unconsciously. In addition, conventional matching services overly rely on appearance and text information, which may cause a deviation from the actual preferences and personalities of users. As a result, there is a problem that sufficient information for users to select appropriate partners is not provided.

Means for Solving the Problems

[0005] This invention provides a system that collects a wide range of behavioral history information, including a user's purchase history, travel history, and social media activity, and uses the generated data to analyze the user's personality traits. Furthermore, it includes a means for evaluating the compatibility between multiple users based on the analysis data and notifying users with high compatibility scores, thereby providing users with the opportunity to meet desirable partners they may not have considered before. In addition, information disclosure is based on the user's permission, and to protect privacy, it includes a setting to hide information of users registered as friends, thus ensuring privacy security.

[0006] A "user" is an individual who utilizes the system, and is both the person who inputs behavioral history information and the recipient of information suggested by the system.

[0007] "Behavioral history information" refers to a collection of data that records a user's behavior, including their purchase history, travel history, and social media activity.

[0008] "Personality traits" refer to characteristics related to a user's unique personality and preferences, which are revealed by analyzing their behavioral history information.

[0009] "Compatibility" is the result of evaluating the matching or complementary relationships of personality traits among multiple users and quantifying their affinity.

[0010] A "notification" is a message or alert that a system sends to a user to inform them of information.

[0011] "Information disclosure" refers to the disclosure of personal profiles and other data through the system, which is carried out with the mutual consent of the users involved.

[0012] "Privacy protection" refers to security settings and protocols designed to prevent users' personal information from being disclosed to third parties without their permission. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This 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] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the numbered processor (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.

[0017] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the numbered storage 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.

[0019] 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).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention is a matching system that uses users' behavioral history information to calculate compatibility between users and propose the most suitable partner. This system consists of three elements: a server, a terminal, and a user.

[0035] First, users register with the system via their device. During registration, users authorize the system to collect data from their purchase history, travel history, or social media activity. This allows the server to collect user behavioral history information from various data sources and store it in a database.

[0036] The server then uses the collected data to analyze the user's personality traits. Using a generative model, it extracts individual personality traits from the user's activity patterns. For example, if a user frequently purchases outdoor equipment, the server will identify that user as someone who "likes the outdoors."

[0037] Next, the server compares the personality traits of multiple users and calculates a compatibility score. The compatibility score is quantified, and the system uses this to create a list of users who are likely to be compatible with each other.

[0038] Subsequently, the device receives a notification from the server informing the user that there are potential partners who are a good match for them. The user can then take action to view the details in response to this notification.

[0039] If a user allows information to be shared, and the other user also agrees, the server will display detailed information about both users on their devices. This allows users to deepen their understanding of each other's profiles, hobbies, and preferences while maintaining their privacy.

[0040] When implementing the system, particular emphasis is placed on privacy protection, and security settings are in place to prevent personal information from being leaked to third parties without permission, such as hiding information if a user is registered as a friend. In this way, by implementing the present invention, users can efficiently meet more suitable partners.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server begins collecting information that the user authorized during registration, including purchase history, travel history, and social media data. This information is retrieved from relevant data sources using APIs and scraping techniques and stored in a database.

[0044] Step 2:

[0045] The server preprocesses the collected data. Specifically, it fills in any incomplete data, removes duplicate data, and corrects outliers, preparing the data for easier analysis.

[0046] Step 3:

[0047] The server utilizes generative models and preprocessed data to analyze the user's personality traits. In this process, it identifies the user's hobbies and daily activities based on behavioral patterns and purchasing tendencies that occur with specific frequencies.

[0048] Step 4:

[0049] The server calculates compatibility based on the similarity or complementarity of personality traits between users. This calculation uses a machine learning algorithm to quantify the compatibility score.

[0050] Step 5:

[0051] The server generates a list of users with a compatibility score above a certain level and sends a notification to each user's device. The notification includes summary information about potential compatible users.

[0052] Step 6:

[0053] The device receives a notification from the server and informs the user that there are potential compatible devices. The user is then given the option to view more detailed information.

[0054] Step 7:

[0055] If a user wishes to view more detailed information about someone they are interested in, they submit a request for permission to disclose that information through the system. This request is then notified to the other user via the server.

[0056] Step 8:

[0057] If both users grant permission to share their information, the server will display each other's detailed profile information on their devices. This allows users to understand each other's personality traits, interests, and activities in detail.

[0058] (Example 1)

[0059] 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."

[0060] In today's world, there is a need for technology that leverages diverse individual behavioral data to evaluate compatibility between users based on a deep understanding, and efficiently presents suitable partners while protecting privacy. However, existing methods suffer from challenges such as fragmented data analysis, the risk of privacy leaks, and poor user experience.

[0061] 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.

[0062] In this invention, the server includes means for collecting user behavior data, means for analyzing the user's personality traits using a generative model based on the data, and means for calculating compatibility between multiple users. This enables the integrated and analytical use of individual behavior data to accurately evaluate compatibility between users and provide appropriate partners while protecting privacy.

[0063] "Behavioral data" refers to a collection of information that includes a user's history of actions in their daily life and online activities.

[0064] A "generative model" is an algorithm used by computers and artificial intelligence to extract and predict unknown data patterns and features based on large datasets.

[0065] "Personality traits" refer to characteristics that indicate behavioral patterns and preference tendencies derived from user behavioral data and habits.

[0066] "Compatibility" refers to the degree of suitability of relationships between multiple users and the extent to which their characteristics match.

[0067] "Privacy" refers to the right or state of protection to prevent an individual's information from being disclosed to third parties without permission or inappropriately.

[0068] A "partner" refers to another user who is rated as a good match for the user, and primarily refers to someone who shares common interests or goals.

[0069] This invention is a system for accurately evaluating the compatibility between users and suggesting suitable partners while protecting privacy. The system mainly consists of a server, terminals, and users.

[0070] First, users register with the system via their device. During registration, users consent to the collection of data such as purchase history, travel history, and social media activity. This allows the server to collect user behavior data from various data sources and store it in a database. The hardware used includes general data servers and cloud storage.

[0071] The server uses a generative AI model to analyze the collected behavioral data. Specifically, the AI ​​model processes a large dataset and extracts the user's personality traits. For example, if a user frequently purchases outdoor equipment and makes many posts about hiking, the server will categorize that user as an "outdoor enthusiast."

[0072] The server then quantifies the compatibility between multiple users based on the calculated personality traits. The system is designed to appropriately match compatible users. As a result, the server sends a notification to the terminal, presenting the user with potential compatible partners.

[0073] Furthermore, if a user allows information to be shared and the other user also agrees, the device will display detailed information about each other to the user. This allows users to deepen their understanding of the other person's profile, hobbies, and preferences while maintaining their privacy.

[0074] For example, if a user frequently purchases self-help books and regularly records their exercise using a jogging app, the server will classify this user as "self-improvement oriented" and "health-conscious." Based on these characteristics, the system will suggest other users with similar orientations as compatible partners.

[0075] An example of a prompt to input into a generative AI model is, "If a user purchases outdoor equipment and frequently uses hiking apps, what personality traits can be estimated to be present?"

[0076] In this way, the present invention realizes efficient and privacy-conscious matching between users.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] Users register with the system via their device. User input includes basic personal information, purchase history, travel history, and permission for data collection regarding social media activity. The device transmits the entered information to the server. This information is stored in the database as the basis for registration.

[0080] Step 2:

[0081] The server stores behavioral data collected from users in a database. Next, the data is input into a generative AI model to analyze personality traits from the user's diverse behavioral patterns. Specific data processing includes the type and frequency of purchases, the location and number of movements in travel history, and the content and frequency of online activities. The output is personality traits, such as labels like "outdoor enthusiast" or "health-conscious."

[0082] Step 3:

[0083] The server calculates compatibility between multiple users based on the provided personality traits. The input is a set of personality trait labels. Similarity measurement and statistical analysis techniques are used in the data calculation, and the compatibility score is output as a numerical value. Based on this result, the system lists users with high compatibility scores and creates partner candidate information.

[0084] Step 4:

[0085] The device notifies the user of potential partner information sent from the server. Specifically, it displays notifications that include the nicknames, shared hobbies, and preferences of users with high compatibility scores. This is to attract the user's attention. At that time, an option to access detailed information is also provided.

[0086] Step 5:

[0087] Users can use their devices to take action to verify detailed information. If both the user and potential partners allow each other to share information, the server sends detailed information, including their profiles and interests, to the device. As a result, users can access the other person's information while maintaining their privacy.

[0088] (Application Example 1)

[0089] 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."

[0090] In modern e-commerce, users often struggle to find suitable products and services from a wide variety of options. Therefore, a system is needed that efficiently utilizes user behavior history information to suggest appropriate products and services based on user characteristics. Furthermore, protecting user privacy while considering compatibility between users is a crucial challenge in providing such information.

[0091] 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.

[0092] In this invention, the server includes means for collecting user behavior history information, means for analyzing the user's personality traits using said information, and means for suggesting products and services based on the user's traits. This makes it possible to automatically suggest products and services that match the user's interests and preferences, thereby improving the shopping experience.

[0093] A "user" is an individual who registers with the system and provides information about their activity history.

[0094] "Behavioral history information" refers to data such as a user's purchase history, travel history, and social activities.

[0095] "Personality traits" are indicators of interests and concerns extracted by analyzing a user's behavioral patterns.

[0096] "Compatibility" is a numerical representation of the degree to which the personality traits of multiple users match.

[0097] "Products and services" refers to products and services that are proposed based on the user's characteristics.

[0098] A "server" is a central control system that collects and analyzes user behavior history information and generates suggestions.

[0099] "Notifications" are pieces of information sent from the server to the user, often concerning users with high compatibility scores or suggested products.

[0100] "Privacy" refers to the protection of a user's personal information and the prevention of its disclosure to third parties without their permission.

[0101] This invention is a system configured to analyze user characteristics based on user behavior history information and propose optimal products and services. The server has the function of collecting and storing behavior history information registered by the user through a terminal. This includes purchase history, travel history, and social activities. To analyze this information, the server uses a generative AI model to extract the user's personality traits.

[0102] The server processes the collected data in real time, extracting individual personality traits from each user's behavioral patterns. For example, if a user frequently purchases outdoor equipment, the server identifies that the user has a preference for the outdoors. Next, it refers to other users with similar traits and suggests relevant products and services based on those personality traits. This suggestion is then notified to the user's device.

[0103] When users receive a notification on their device, they can view details of the suggested products and services. The server places particular emphasis on privacy protection and is configured to only share information with other parties based on the user's permission. This prevents users' personal information from being leaked to third parties without their consent.

[0104] For example, if user A frequently purchases fitness equipment online, the system identifies user A as a "fitness enthusiast." The system then suggests more effective fitness programs and related products to user A.

[0105] An example of a prompt to input into the generative AI model is as follows: "Show me how to analyze a user's behavioral history, estimate their personality traits, and suggest appropriate products and services."

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The server collects behavioral history information from the user's device. The input includes the user's purchase history, travel history, and social activity data, which the server stores in a database.

[0109] Step 2:

[0110] The server uses collected behavioral history information to generate an AI model that analyzes the user's personality traits. The input is behavioral history information in the database, and the model processes and analyzes this data to extract personality traits that indicate the user's interests and preferences. The output is the identified personality traits.

[0111] Step 3:

[0112] The server selects products and services suitable for the user based on the extracted personality traits. The input is the user's personality traits, which the server uses to match them with a product catalog and generate a list of products that match the traits. The output is a list of suggested products.

[0113] Step 4:

[0114] The server notifies the user's terminal of the generated product suggestions. The input is a list of products, and the server sends this information as a notification message to the user's terminal. The user can then review the suggested products upon receiving the notification.

[0115] Step 5:

[0116] Users can view details of suggested products via notifications on their devices and choose to purchase or take other actions based on that information. The input is the notification from the server, and the output is the user's response. Here, users can choose products that interest them while considering the suggestions.

[0117] 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.

[0118] This invention is a matching system that uses user behavior history information and an emotion engine to more precisely evaluate the compatibility between users. This system consists of a server, terminals, and users, and achieves compatibility evaluation that takes emotional factors into particular.

[0119] First, users register with the system using their device. During registration, users authorize the collection of data including purchase history, travel history, social media activity, and sentiment engine data. This data collection allows the server to accumulate rich behavioral history information in its database.

[0120] Next, the server utilizes an emotion engine to analyze the user's emotional state from the collected behavioral history information. This emotion engine uses natural language processing and pattern recognition technologies to identify emotions from the user's messages and posts. For example, it can identify a variety of emotional states, such as joy, sadness, anger, and surprise, from the word choices, expressions, and emojis used in posts.

[0121] The server then calculates compatibility with other users based on the user's emotional state and personality traits. Emotional state is considered an important factor in the compatibility calculation, and the emotions a user is currently experiencing influence the matching result. For example, users in relaxed emotional states may be judged as being compatible with each other. On the other hand, users in contrasting emotional states may be judged as being incompatible.

[0122] Next, a list of users whose compatibility score, taking emotional states into account, exceeds a certain level is sent from the server to each user's device. The device displays this notification, informing the user that there are compatible candidates. If the user wishes to view more details, they can choose the option to allow information to be shared with that candidate.

[0123] Ultimately, if both users grant permission for information disclosure, the server will mutually share detailed profile information. This allows users to understand each other's hobbies, personality traits, and emotional states, enabling them to find emotionally compatible partners. In terms of security, built-in privacy protection features prevent information from being leaked to third parties without permission.

[0124] The following describes the processing flow.

[0125] Step 1:

[0126] Users log in to the system using their devices and authorize the collection of necessary personal information, purchase history, social media activity, and sentiment data. The server then begins collecting the authorized information and stores it in a database.

[0127] Step 2:

[0128] The server analyzes the user's emotional state from the text information and posts collected using an emotion engine. Specifically, the emotion engine utilizes natural language processing to analyze keywords, context, and emojis contained in the text to identify emotions such as joy, anger, and sadness.

[0129] Step 3:

[0130] The server integrates emotional data obtained from the emotion engine with personality traits analyzed based on behavioral history to create a comprehensive user profile. This profile includes the user's personality and current emotional state.

[0131] Step 4:

[0132] The server compares the personality traits and emotional states of multiple users and calculates a compatibility score. The compatibility score is quantified by considering not only similarities in personality but also whether the users are emotionally complementary.

[0133] Step 5:

[0134] Based on compatibility scores, the server creates a list of highly compatible users and generates a candidate window. The candidate window is displayed on the user's terminal and provides additional information about potentially compatible users.

[0135] Step 6:

[0136] The device displays notifications received from the server to the user, showing information about potential compatible users. Users can choose to view more detailed information or to have the information made public.

[0137] Step 7:

[0138] If a user chooses to allow their information to be made public, the server sends a request to the other user to share that information. Only if the other user also agrees to the disclosure will the server display each other's detailed profiles.

[0139] Step 8:

[0140] Ultimately, the device displays profile information of the agreed-upon users on its screen. This information includes personality traits, activity history, and recent emotional states, which users can use to decide on their next action.

[0141] (Example 2)

[0142] 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".

[0143] In recent years, with the advancement of the digital society, user matching systems have diversified. However, conventional systems do not take emotional factors into consideration, making it difficult to build truly comfortable and suitable human relationships. Furthermore, there is a need for matching that reflects not only personality traits derived from users' behavioral history and purchase history, but existing technologies have not been able to adequately achieve this.

[0144] 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.

[0145] In this invention, the server includes means for collecting user behavior history information, means for analyzing the user's personality traits and emotional state using said information and emotion analysis technology, and means for evaluating compatibility between multiple users based on their emotional states. This enables more precise compatibility evaluation that reflects the user's behavior and emotional state, and provides the optimal matching for the user.

[0146] "Behavioral history information" refers to information related to individual user behavior, including data such as user purchasing activities, travel history, and social media activity.

[0147] "Emotion analysis technology" is a technology that uses natural language processing and pattern recognition to identify emotions and psychological states from digital content that users interact with.

[0148] "Personality traits" refer to characteristics that indicate an individual's internal characteristics, such as a user's behavioral patterns, preferences, and values.

[0149] "Compatibility assessment" is an evaluation process that determines the similarity and suitability between multiple users and measures the degree of affinity between them.

[0150] "Emotional state" refers to the psychological mood or feeling of a user at a specific moment, and includes feelings such as joy, sadness, and anger.

[0151] "Privacy protection" refers to the technologies and measures taken to prevent unauthorized access to personal information.

[0152] As an embodiment of the present invention, a system for evaluating the compatibility between users is described below. This system consists of a user terminal, a server, and software equipped with sentiment analysis technology.

[0153] First, the user accesses the system using their own device and registers. The device collects data from the user's purchase history, travel history, and social media activity, and sends this information to the server. This data is stored in a database and forms the user's behavioral history.

[0154] Next, the server uses a dedicated emotion engine to perform sentiment analysis on the accumulated data. This emotion engine utilizes natural language processing and pattern recognition technologies to identify various emotional states from the user's text data. Specifically, it identifies the user's psychological mood from the wording and emojis included in the posts and obtains analysis results.

[0155] The server then evaluates the user's emotional state and personality traits, and calculates their compatibility with other users who have similar emotional states. This compatibility evaluation result is communicated to the user via their device, allowing them to agree to disclose information to those they are interested in.

[0156] Ultimately, if both parties agree, the server will mutually disclose detailed profile information, allowing users to find a more emotionally compatible partner by learning more about each other's hobbies, goals, and emotional states. Furthermore, security features to protect privacy prevent unauthorized access and guarantee the safety of information.

[0157] As a concrete example, a user might input a request to the system such as, "I want friends I can relax with." Based on this prompt, the system searches for other users with similar emotional states and recommends those deemed compatible. This process allows users to build relationships that suit them.

[0158] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0159] Step 1:

[0160] The user initiates the registration process through their device. At this stage, the user authorizes the provision of purchase history, travel history, and social media activity. As input, the device collects the user's basic information and various behavioral history data, and as output, this data is sent to the server.

[0161] Step 2:

[0162] The server stores the received behavioral history information in a database. It receives user data sent from the terminal as input, organizes this data as output, and saves it in a format that can be used for subsequent processing.

[0163] Step 3:

[0164] The server uses an emotion engine to analyze the user's emotional state from behavioral history information stored in the database. It uses purchase history and social media language data as input, and identifies emotions through natural language processing. The output identifies the user's current emotional state.

[0165] Step 4:

[0166] Based on the analyzed emotional state, the server evaluates compatibility with other users, taking into account each user's personality traits and current emotions. It takes the user's emotional state and personality traits as input and generates compatibility scores and a list of compatible users as output.

[0167] Step 5:

[0168] The server notifies the device of a list of users with a compatibility score above a certain level. The device receives this notification and displays the information to the user. The user then reviews this information and decides whether they wish to share their profile details. The input is the notification information from the server, and the output is the information displayed to the user.

[0169] Step 6:

[0170] Users choose whether or not to allow information sharing with someone they are interested in. If both users grant permission, the server will share detailed information with each other. As input, the user's permission selection information is sent to the server, and as output, the profile information will be displayed to the other user.

[0171] Step 7:

[0172] The server implements security features to ensure privacy and safety in all processes. As input, it applies security policies to various data access requests, and as output, it achieves protection of user data and prevention of unauthorized access.

[0173] (Application Example 2)

[0174] 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".

[0175] Conventional matching systems evaluate compatibility based on user personality traits and basic behavioral history, but they fail to consider the user's emotional state, making it difficult to provide individually tailored suggestions. Furthermore, in virtual stores, it is a challenge to realize product suggestions that take into account the user's behavioral history and emotional state.

[0176] 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.

[0177] In this invention, the server includes means for collecting user behavior history information, means for analyzing user personality traits, means for evaluating compatibility between multiple users, and means for analyzing the user's emotional state and making emotionally-based product suggestions. This enables personalized product suggestions that take emotional states into consideration.

[0178] "User behavior history information" refers to records of actions and activities taken by users in the past, and includes data such as purchase history, travel history, and social media activity.

[0179] "Personality traits" refer to characteristics that indicate a user's personality and behavioral patterns, and are the result of analyzing the unique psychological tendencies and behavioral characteristics of each individual user.

[0180] "Methods for evaluating compatibility" refer to methods for evaluating the relationships and emotional compatibility between multiple users using numerical values ​​or indicators.

[0181] A "means of analyzing emotional state" refers to a system that identifies the emotions a user is currently feeling and performs analysis based on that emotional data.

[0182] A "means of providing product recommendations" refers to a system that presents personalized product information to users based on their behavioral history and emotional state.

[0183] The matching system of the present invention consists of a server, a terminal, and a user. In this system, the user registers with the system using a terminal, and at that time, authorizes the collection of behavioral history information and emotional data. The server analyzes the user's emotional state based on this information and utilizes an emotional engine. The emotional engine analyzes the user's social media posts and messages using natural language processing libraries (e.g., NLTK and spaCy) and an emotional analysis API (e.g., IBM Watson® Tone Analyzer). This makes it possible to identify the emotional state based on words and expressions extracted from the user's behavioral history and posted content.

[0184] The server uses this emotional state to provide product suggestions based on the user's emotions. Behavioral history information is managed by a database management system (e.g., MySQL®) and analyzed in combination with the user's purchase history, travel history, etc. Personalized product suggestions are notified to the user from the terminal. This allows the user to select appropriate products that match their emotions and behavior.

[0185] As a concrete example, when a user is experiencing stress at work, the server can analyze the user's emotional state and suggest products such as relaxing aromatherapy candles or healing music. In this case, the following prompt message is applied to the generative AI model:

[0186] "When keywords such as 'business meeting' and 'deadline' are found in the behavioral history, and the emotional state is identified as stress, we use a generative AI model that suggests relaxation products."

[0187] This system aims to provide users with a better shopping experience by offering personalized suggestions.

[0188] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0189] Step 1:

[0190] Users register with the system using a terminal. During this process, users authorize the collection of behavioral history information and emotional data. Inputs include the user's basic information and data collection permission settings, while output is the storage of this information in the system's database. Specifically, user information is sent from the terminal to the server and stored by the database management system.

[0191] Step 2:

[0192] The server collects user activity history information and social media posts. It takes user activity history data and post data as input, and outputs an analyzable dataset. The server collects this data, formats it using natural language processing libraries (such as NLTK and spaCy), and prepares it for analysis.

[0193] Step 3:

[0194] The server uses an emotion analysis API (IBM Watson Tone Analyzer) to analyze behavioral history and posting data to identify the user's emotional state. The input is the dataset from the previous step, and the output is the identified emotional state of the user. The server retrieves the analysis results and saves the emotional state as numerical data.

[0195] Step 4:

[0196] The server generates appropriate product suggestions using a generative AI model based on emotional state and behavioral history information. Emotional state data and behavioral history data are taken as input, and a list of suggested products is generated as output. Specifically, personalized product selection is performed using prompts from the generative AI model.

[0197] Step 5:

[0198] The terminal notifies the user of product suggestions sent from the server. The input is suggestion data from the server, and the output is the display of product suggestions on the terminal screen. The user can then review and select products of interest from this display.

[0199] Step 6:

[0200] When a user decides to purchase an item from the suggested product list, the purchase process proceeds through the terminal. Inputs include the user's purchase intention and payment information, and output is the completion of the purchase process. Purchase information is fed back to the server and reflected in future suggestions.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] [Second Embodiment]

[0205] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0206] 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.

[0207] 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).

[0208] 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.

[0209] 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.

[0210] 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).

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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".

[0217] This invention is a matching system that uses users' behavioral history information to calculate compatibility between users and propose the most suitable partner. This system consists of three elements: a server, a terminal, and a user.

[0218] First, users register with the system via their device. During registration, users authorize the system to collect data from their purchase history, travel history, or social media activity. This allows the server to collect user behavioral history information from various data sources and store it in a database.

[0219] The server then uses the collected data to analyze the user's personality traits. Using a generative model, it extracts individual personality traits from the user's activity patterns. For example, if a user frequently purchases outdoor equipment, the server will identify that user as someone who "likes the outdoors."

[0220] Next, the server compares the personality traits of multiple users and calculates a compatibility score. The compatibility score is quantified, and the system uses this to create a list of users who are likely to be compatible with each other.

[0221] Subsequently, the device receives a notification from the server informing the user that there are potential partners who are a good match for them. The user can then take action to view the details in response to this notification.

[0222] If a user allows information to be shared, and the other user also agrees, the server will display detailed information about both users on their devices. This allows users to deepen their understanding of each other's profiles, hobbies, and preferences while maintaining their privacy.

[0223] When implementing the system, particular emphasis is placed on privacy protection, and security settings are in place to prevent personal information from being leaked to third parties without permission, such as hiding information if a user is registered as a friend. In this way, by implementing the present invention, users can efficiently meet more suitable partners.

[0224] The following describes the processing flow.

[0225] Step 1:

[0226] The server begins collecting information that the user authorized during registration, including purchase history, travel history, and social media data. This information is retrieved from relevant data sources using APIs and scraping techniques and stored in a database.

[0227] Step 2:

[0228] The server preprocesses the collected data. Specifically, it fills in any incomplete data, removes duplicate data, and corrects outliers, preparing the data for easier analysis.

[0229] Step 3:

[0230] The server utilizes generative models and preprocessed data to analyze the user's personality traits. In this process, it identifies the user's hobbies and daily activities based on behavioral patterns and purchasing tendencies that occur with specific frequencies.

[0231] Step 4:

[0232] The server calculates compatibility based on the similarity or complementarity of personality traits between users. This calculation uses a machine learning algorithm to quantify the compatibility score.

[0233] Step 5:

[0234] The server generates a list of users with a compatibility score above a certain level and sends a notification to each user's device. The notification includes summary information about potential compatible users.

[0235] Step 6:

[0236] The device receives a notification from the server and informs the user that there are potential compatible devices. The user is then given the option to view more detailed information.

[0237] Step 7:

[0238] If a user wishes to view more detailed information about someone they are interested in, they submit a request for permission to disclose that information through the system. This request is then notified to the other user via the server.

[0239] Step 8:

[0240] If both users grant permission to share their information, the server will display each other's detailed profile information on their devices. This allows users to understand each other's personality traits, interests, and activities in detail.

[0241] (Example 1)

[0242] 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."

[0243] In today's world, there is a need for technology that leverages diverse individual behavioral data to evaluate compatibility between users based on a deep understanding, and efficiently presents suitable partners while protecting privacy. However, existing methods suffer from challenges such as fragmented data analysis, the risk of privacy leaks, and poor user experience.

[0244] 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.

[0245] In this invention, the server includes means for collecting user behavior data, means for analyzing the user's personality traits using a generative model based on the data, and means for calculating compatibility between multiple users. This enables the integrated and analytical use of individual behavior data to accurately evaluate compatibility between users and provide appropriate partners while protecting privacy.

[0246] "Behavioral data" refers to a collection of information that includes a user's history of actions in their daily life and online activities.

[0247] A "generative model" is an algorithm used by computers and artificial intelligence to extract and predict unknown data patterns and features based on large datasets.

[0248] "Personality traits" refer to characteristics that indicate behavioral patterns and preference tendencies derived from user behavioral data and habits.

[0249] "Compatibility" refers to the degree of suitability of relationships between multiple users and the extent to which their characteristics match.

[0250] "Privacy" refers to the right or state of protection to prevent an individual's information from being disclosed to third parties without permission or inappropriately.

[0251] A "partner" refers to another user who is rated as a good match for the user, and primarily refers to someone who shares common interests or goals.

[0252] This invention is a system for accurately evaluating the compatibility between users and suggesting suitable partners while protecting privacy. The system mainly consists of a server, terminals, and users.

[0253] First, users register with the system via their device. During registration, users consent to the collection of data such as purchase history, travel history, and social media activity. This allows the server to collect user behavior data from various data sources and store it in a database. The hardware used includes general data servers and cloud storage.

[0254] The server uses a generative AI model to analyze the collected behavioral data. Specifically, the AI ​​model processes a large dataset and extracts the user's personality traits. For example, if a user frequently purchases outdoor equipment and makes many posts about hiking, the server will categorize that user as an "outdoor enthusiast."

[0255] The server then quantifies the compatibility between multiple users based on the calculated personality traits. The system is designed to appropriately match compatible users. As a result, the server sends a notification to the terminal, presenting the user with potential compatible partners.

[0256] Furthermore, if a user allows information to be shared and the other user also agrees, the device will display detailed information about each other to the user. This allows users to deepen their understanding of the other person's profile, hobbies, and preferences while maintaining their privacy.

[0257] For example, if a user frequently purchases self-help books and regularly records their exercise using a jogging app, the server will classify this user as "self-improvement oriented" and "health-conscious." Based on these characteristics, the system will suggest other users with similar orientations as compatible partners.

[0258] An example of a prompt to input into a generative AI model is, "If a user purchases outdoor equipment and frequently uses hiking apps, what personality traits can be estimated to be present?"

[0259] In this way, the present invention realizes efficient and privacy-conscious matching between users.

[0260] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0261] Step 1:

[0262] Users register with the system via their device. User input includes basic personal information, purchase history, travel history, and permission for data collection regarding social media activity. The device transmits the entered information to the server. This information is stored in the database as the basis for registration.

[0263] Step 2:

[0264] The server stores behavioral data collected from users in a database. Next, the data is input into a generative AI model to analyze personality traits from the user's diverse behavioral patterns. Specific data processing includes the type and frequency of purchases, the location and number of movements in travel history, and the content and frequency of online activities. The output is personality traits, such as labels like "outdoor enthusiast" or "health-conscious."

[0265] Step 3:

[0266] The server calculates compatibility between multiple users based on the provided personality traits. The input is a set of personality trait labels. Similarity measurement and statistical analysis techniques are used in the data calculation, and the compatibility score is output as a numerical value. Based on this result, the system lists users with high compatibility scores and creates partner candidate information.

[0267] Step 4:

[0268] The device notifies the user of potential partner information sent from the server. Specifically, it displays notifications that include the nicknames, shared hobbies, and preferences of users with high compatibility scores. This is to attract the user's attention. At that time, an option to access detailed information is also provided.

[0269] Step 5:

[0270] Users can use their devices to take action to verify detailed information. If both the user and potential partners allow each other to share information, the server sends detailed information, including their profiles and interests, to the device. As a result, users can access the other person's information while maintaining their privacy.

[0271] (Application Example 1)

[0272] 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."

[0273] In modern e-commerce, users often struggle to find suitable products and services from a wide variety of options. Therefore, a system is needed that efficiently utilizes user behavior history information to suggest appropriate products and services based on user characteristics. Furthermore, protecting user privacy while considering compatibility between users is a crucial challenge in providing such information.

[0274] 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.

[0275] In this invention, the server includes means for collecting user behavior history information, means for analyzing the user's personality traits using said information, and means for suggesting products and services based on the user's traits. This makes it possible to automatically suggest products and services that match the user's interests and preferences, thereby improving the shopping experience.

[0276] A "user" is an individual who registers with the system and provides information about their activity history.

[0277] "Behavioral history information" refers to data such as a user's purchase history, travel history, and social activities.

[0278] "Personality traits" are indicators of interests and concerns extracted by analyzing a user's behavioral patterns.

[0279] "Compatibility" is a numerical representation of the degree to which the personality traits of multiple users match.

[0280] "Products and services" refers to products and services that are proposed based on the user's characteristics.

[0281] The "server" is a central control system that collects, analyzes users' behavioral history information, and generates proposals.

[0282] The "notification" is information sent from the server to the user, regarding highly compatible users and proposed products.

[0283] "Privacy" refers to the state of protecting users' personal information and not disclosing it to third parties without permission.

[0284] This invention is a system configured to analyze users' characteristics based on their behavioral history information and propose optimal products and services. The server has a function of collecting and storing the behavioral history information registered by the user through the terminal. This includes purchase history, movement history, social activities, etc. To analyze this information, the server extracts the user's personality characteristics using a generated AI model.

[0285] The server processes the collected data in real time and extracts individual personality characteristics from each user's behavioral pattern. For example, if there is a user who frequently purchases outdoor supplies, the server identifies that the user has the characteristic of liking the outdoors. Next, it refers to other users with similar characteristics and proposes relevant products and services based on the personality characteristics. This proposal is notified to the user's terminal.

[0286] When the user receives a notification on the terminal, they can check the details of the proposed products and services. The server particularly emphasizes privacy protection and is configured to disclose information to each other only based on the user's permission. This prevents the user's personal information from being leaked to third parties without authorization.

[0287] As a specific example, if user A frequently purchases fitness equipment online, this system identifies user A's characteristic as a "fitness enthusiast". Then, the system proposes more effective fitness programs and related products to user A.

[0288] An example of a prompt to input into the generative AI model is as follows: "Show me how to analyze a user's behavioral history, estimate their personality traits, and suggest appropriate products and services."

[0289] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0290] Step 1:

[0291] The server collects behavioral history information from the user's device. The input includes the user's purchase history, travel history, and social activity data, which the server stores in a database.

[0292] Step 2:

[0293] The server uses collected behavioral history information to generate an AI model that analyzes the user's personality traits. The input is behavioral history information in the database, and the model processes and analyzes this data to extract personality traits that indicate the user's interests and preferences. The output is the identified personality traits.

[0294] Step 3:

[0295] The server selects products and services suitable for the user based on the extracted personality traits. The input is the user's personality traits, which the server uses to match them with a product catalog and generate a list of products that match the traits. The output is a list of suggested products.

[0296] Step 4:

[0297] The server notifies the user's terminal of the generated product suggestions. The input is a list of products, and the server sends this information as a notification message to the user's terminal. The user can then review the suggested products upon receiving the notification.

[0298] Step 5:

[0299] Users can view details of suggested products via notifications on their devices and choose to purchase or take other actions based on that information. The input is the notification from the server, and the output is the user's response. Here, users can choose products that interest them while considering the suggestions.

[0300] 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.

[0301] This invention is a matching system that uses user behavior history information and an emotion engine to more precisely evaluate the compatibility between users. This system consists of a server, terminals, and users, and achieves compatibility evaluation that takes emotional factors into particular.

[0302] First, users register with the system using their device. During registration, users authorize the collection of data including purchase history, travel history, social media activity, and sentiment engine data. This data collection allows the server to accumulate rich behavioral history information in its database.

[0303] Next, the server utilizes an emotion engine to analyze the user's emotional state from the collected behavioral history information. This emotion engine uses natural language processing and pattern recognition technologies to identify emotions from the user's messages and posts. For example, it can identify a variety of emotional states, such as joy, sadness, anger, and surprise, from the word choices, expressions, and emojis used in posts.

[0304] The server then calculates compatibility with other users based on the user's emotional state and personality traits. Emotional state is considered an important factor in the compatibility calculation, and the emotions a user is currently experiencing influence the matching result. For example, users in relaxed emotional states may be judged as being compatible with each other. On the other hand, users in contrasting emotional states may be judged as being incompatible.

[0305] Subsequently, the list of users whose compatibility degree considering the emotional state is above a certain level is notified from the server to the terminals of each user. The terminal displays this notification to inform the user that there are candidates with good compatibility. If the user wants to check the details, they can select the option to permit information disclosure with that candidate.

[0306] Finally, when permission for information disclosure is obtained from both users, the server discloses the detailed profile information to each other. As a result, the users can understand each other's hobbies, personality traits, and emotional states, and it becomes possible to find a partner who is also emotionally compatible. In terms of security, a privacy protection function is built-in to prevent unauthorized leakage of information to third parties.

[0307] The following explains the processing flow.

[0308] Step 1:

[0309] The user logs in to the system using the terminal and permits the collection of necessary personal information, purchase history, social media activities, and emotional data. The server starts collecting the permitted information and accumulates the information in the database.

[0310] Step 2:

[0311] The server analyzes the user's emotional state from the text information and posted content collected using the emotion engine. Specifically, the emotion engine utilizes natural language processing to analyze keywords, context, and emojis included in the text to identify emotions such as joy, anger, and sadness.

[0312] Step 3:

[0313] The server integrates the emotional data obtained from the emotion engine and the personality traits analyzed based on the action history to create a comprehensive profile of the user. This profile includes the user's personality and current emotional state.

[0314] Step 4:

[0315] The server compares the personality traits and emotional states of multiple users and calculates a compatibility score. The compatibility score is quantified by considering not only similarities in personality but also whether the users are emotionally complementary.

[0316] Step 5:

[0317] Based on compatibility scores, the server creates a list of highly compatible users and generates a candidate window. The candidate window is displayed on the user's terminal and provides additional information about potentially compatible users.

[0318] Step 6:

[0319] The device displays notifications received from the server to the user, showing information about potential compatible users. Users can choose to view more detailed information or to have the information made public.

[0320] Step 7:

[0321] If a user chooses to allow their information to be made public, the server sends a request to the other user to share that information. Only if the other user also agrees to the disclosure will the server display each other's detailed profiles.

[0322] Step 8:

[0323] Ultimately, the device displays profile information of the agreed-upon users on its screen. This information includes personality traits, activity history, and recent emotional states, which users can use to decide on their next action.

[0324] (Example 2)

[0325] 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".

[0326] In recent years, with the advancement of the digital society, user matching systems have diversified. However, conventional systems do not take emotional factors into consideration, making it difficult to build truly comfortable and suitable human relationships. Furthermore, there is a need for matching that reflects not only personality traits derived from users' behavioral history and purchase history, but existing technologies have not been able to adequately achieve this.

[0327] 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.

[0328] In this invention, the server includes means for collecting user behavior history information, means for analyzing the user's personality traits and emotional state using said information and emotion analysis technology, and means for evaluating compatibility between multiple users based on their emotional states. This enables more precise compatibility evaluation that reflects the user's behavior and emotional state, and provides the optimal matching for the user.

[0329] "Behavioral history information" refers to information related to individual user behavior, including data such as user purchasing activities, travel history, and social media activity.

[0330] "Emotion analysis technology" is a technology that uses natural language processing and pattern recognition to identify emotions and psychological states from digital content that users interact with.

[0331] "Personality traits" refer to characteristics that indicate an individual's internal characteristics, such as a user's behavioral patterns, preferences, and values.

[0332] "Compatibility assessment" is an evaluation process that determines the similarity and suitability between multiple users and measures the degree of affinity between them.

[0333] "Emotional state" refers to the psychological mood or feeling of a user at a specific moment, and includes feelings such as joy, sadness, and anger.

[0334] "Privacy protection" refers to the technologies and measures taken to prevent unauthorized access to personal information.

[0335] As an embodiment of the present invention, a system for evaluating the compatibility between users is described below. This system consists of a user terminal, a server, and software equipped with sentiment analysis technology.

[0336] First, the user accesses the system using their own device and registers. The device collects data from the user's purchase history, travel history, and social media activity, and sends this information to the server. This data is stored in a database and forms the user's behavioral history.

[0337] Next, the server uses a dedicated emotion engine to perform sentiment analysis on the accumulated data. This emotion engine utilizes natural language processing and pattern recognition technologies to identify various emotional states from the user's text data. Specifically, it identifies the user's psychological mood from the wording and emojis included in the posts and obtains analysis results.

[0338] The server then evaluates the user's emotional state and personality traits, and calculates their compatibility with other users who have similar emotional states. This compatibility evaluation result is communicated to the user via their device, allowing them to agree to disclose information to those they are interested in.

[0339] Ultimately, if both parties agree, the server will mutually disclose detailed profile information, allowing users to find a more emotionally compatible partner by learning more about each other's hobbies, goals, and emotional states. Furthermore, security features to protect privacy prevent unauthorized access and guarantee the safety of information.

[0340] As a concrete example, a user might input a request to the system such as, "I want friends I can relax with." Based on this prompt, the system searches for other users with similar emotional states and recommends those deemed compatible. This process allows users to build relationships that suit them.

[0341] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0342] Step 1:

[0343] The user initiates the registration process through their device. At this stage, the user authorizes the provision of purchase history, travel history, and social media activity. As input, the device collects the user's basic information and various behavioral history data, and as output, this data is sent to the server.

[0344] Step 2:

[0345] The server stores the received behavioral history information in a database. It receives user data sent from the terminal as input, organizes this data as output, and saves it in a format that can be used for subsequent processing.

[0346] Step 3:

[0347] The server uses an emotion engine to analyze the user's emotional state from behavioral history information stored in the database. It uses purchase history and social media language data as input, and identifies emotions through natural language processing. The output identifies the user's current emotional state.

[0348] Step 4:

[0349] Based on the analyzed emotional state, the server evaluates compatibility with other users, taking into account each user's personality traits and current emotions. It takes the user's emotional state and personality traits as input and generates compatibility scores and a list of compatible users as output.

[0350] Step 5:

[0351] The server notifies the device of a list of users with a compatibility score above a certain level. The device receives this notification and displays the information to the user. The user then reviews this information and decides whether they wish to share their profile details. The input is the notification information from the server, and the output is the information displayed to the user.

[0352] Step 6:

[0353] Users choose whether or not to allow information sharing with someone they are interested in. If both users grant permission, the server will share detailed information with each other. As input, the user's permission selection information is sent to the server, and as output, the profile information will be displayed to the other user.

[0354] Step 7:

[0355] The server implements security features to ensure privacy and safety in all processes. As input, it applies security policies to various data access requests, and as output, it achieves protection of user data and prevention of unauthorized access.

[0356] (Application Example 2)

[0357] 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."

[0358] Conventional matching systems evaluate compatibility based on user personality traits and basic behavioral history, but they fail to consider the user's emotional state, making it difficult to provide individually tailored suggestions. Furthermore, in virtual stores, it is a challenge to realize product suggestions that take into account the user's behavioral history and emotional state.

[0359] 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.

[0360] In this invention, the server includes means for collecting user behavior history information, means for analyzing user personality traits, means for evaluating compatibility between multiple users, and means for analyzing the user's emotional state and making emotionally-based product suggestions. This enables personalized product suggestions that take emotional states into consideration.

[0361] "User behavior history information" refers to records of actions and activities taken by users in the past, and includes data such as purchase history, travel history, and social media activity.

[0362] "Personality traits" refer to characteristics that indicate a user's personality and behavioral patterns, and are the result of analyzing the unique psychological tendencies and behavioral characteristics of each individual user.

[0363] "Methods for evaluating compatibility" refer to methods for evaluating the relationships and emotional compatibility between multiple users using numerical values ​​or indicators.

[0364] A "means of analyzing emotional state" refers to a system that identifies the emotions a user is currently feeling and performs analysis based on that emotional data.

[0365] A "means of providing product recommendations" refers to a system that presents personalized product information to users based on their behavioral history and emotional state.

[0366] The matching system of the present invention consists of a server, a terminal, and a user. In this system, the user registers with the system using a terminal, and at that time, authorizes the collection of behavioral history information and emotional data. The server analyzes the user's emotional state based on this information and utilizes an emotional engine. The emotional engine analyzes the user's social media posts and messages using natural language processing libraries (e.g., NLTK and spaCy) and emotional analysis APIs (e.g., IBM Watson Tone Analyzer). This makes it possible to identify the emotional state based on words and expressions extracted from the user's behavioral history and posted content.

[0367] The server uses this emotional state to provide product recommendations based on the user's emotions. Behavioral history information is managed by a database management system (e.g., MySQL) and analyzed in combination with the user's purchase history, travel history, etc. Personalized product recommendations are notified to the user from the terminal. This allows the user to select appropriate products that match their emotions and behavior.

[0368] As a concrete example, when a user is experiencing stress at work, the server can analyze the user's emotional state and suggest products such as relaxing aromatherapy candles or healing music. In this case, the following prompt message is applied to the generative AI model:

[0369] "When keywords such as 'business meeting' and 'deadline' are found in the behavioral history, and the emotional state is identified as stress, we use a generative AI model that suggests relaxation products."

[0370] This system aims to provide users with a better shopping experience by offering personalized suggestions.

[0371] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0372] Step 1:

[0373] Users register with the system using a terminal. During this process, users authorize the collection of behavioral history information and emotional data. Inputs include the user's basic information and data collection permission settings, while output is the storage of this information in the system's database. Specifically, user information is sent from the terminal to the server and stored by the database management system.

[0374] Step 2:

[0375] The server collects user activity history information and social media posts. It takes user activity history data and post data as input, and outputs an analyzable dataset. The server collects this data, formats it using natural language processing libraries (such as NLTK and spaCy), and prepares it for analysis.

[0376] Step 3:

[0377] The server uses an emotion analysis API (IBM Watson Tone Analyzer) to analyze behavioral history and posting data to identify the user's emotional state. The input is the dataset from the previous step, and the output is the identified emotional state of the user. The server retrieves the analysis results and saves the emotional state as numerical data.

[0378] Step 4:

[0379] The server generates appropriate product suggestions using a generative AI model based on emotional state and behavioral history information. Emotional state data and behavioral history data are taken as input, and a list of suggested products is generated as output. Specifically, personalized product selection is performed using prompts from the generative AI model.

[0380] Step 5:

[0381] The terminal notifies the user of product suggestions sent from the server. The input is suggestion data from the server, and the output is the display of product suggestions on the terminal screen. The user can then review and select products of interest from this display.

[0382] Step 6:

[0383] When a user decides to purchase an item from the suggested product list, the purchase process proceeds through the terminal. Inputs include the user's purchase intention and payment information, and output is the completion of the purchase process. Purchase information is fed back to the server and reflected in future suggestions.

[0384] 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.

[0385] 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.

[0386] 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.

[0387] [Third Embodiment]

[0388] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0389] 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.

[0390] 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).

[0391] 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.

[0392] 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.

[0393] 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).

[0394] 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.

[0395] 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.

[0396] 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.

[0397] 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.

[0398] 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.

[0399] 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".

[0400] This invention is a matching system that uses users' behavioral history information to calculate compatibility between users and propose the most suitable partner. This system consists of three elements: a server, a terminal, and a user.

[0401] First, users register with the system via their device. During registration, users authorize the system to collect data from their purchase history, travel history, or social media activity. This allows the server to collect user behavioral history information from various data sources and store it in a database.

[0402] The server then uses the collected data to analyze the user's personality traits. Using a generative model, it extracts individual personality traits from the user's activity patterns. For example, if a user frequently purchases outdoor equipment, the server will identify that user as someone who "likes the outdoors."

[0403] Next, the server compares the personality traits of multiple users and calculates a compatibility score. The compatibility score is quantified, and the system uses this to create a list of users who are likely to be compatible with each other.

[0404] Subsequently, the device receives a notification from the server informing the user that there are potential partners who are a good match for them. The user can then take action to view the details in response to this notification.

[0405] If a user allows information to be shared, and the other user also agrees, the server will display detailed information about both users on their devices. This allows users to deepen their understanding of each other's profiles, hobbies, and preferences while maintaining their privacy.

[0406] When implementing the system, particular emphasis is placed on privacy protection, and security settings are in place to prevent personal information from being leaked to third parties without permission, such as hiding information if a user is registered as a friend. In this way, by implementing the present invention, users can efficiently meet more suitable partners.

[0407] The following describes the processing flow.

[0408] Step 1:

[0409] The server begins collecting information that the user authorized during registration, including purchase history, travel history, and social media data. This information is retrieved from relevant data sources using APIs and scraping techniques and stored in a database.

[0410] Step 2:

[0411] The server preprocesses the collected data. Specifically, it fills in any incomplete data, removes duplicate data, and corrects outliers, preparing the data for easier analysis.

[0412] Step 3:

[0413] The server utilizes generative models and preprocessed data to analyze the user's personality traits. In this process, it identifies the user's hobbies and daily activities based on behavioral patterns and purchasing tendencies that occur with specific frequencies.

[0414] Step 4:

[0415] The server calculates compatibility based on the similarity or complementarity of personality traits between users. This calculation uses a machine learning algorithm to quantify the compatibility score.

[0416] Step 5:

[0417] The server generates a list of users with a compatibility score above a certain level and sends a notification to each user's device. The notification includes summary information about potential compatible users.

[0418] Step 6:

[0419] The device receives a notification from the server and informs the user that there are potential compatible devices. The user is then given the option to view more detailed information.

[0420] Step 7:

[0421] If a user wishes to view more detailed information about someone they are interested in, they submit a request for permission to disclose that information through the system. This request is then notified to the other user via the server.

[0422] Step 8:

[0423] If both users grant permission to share their information, the server will display each other's detailed profile information on their devices. This allows users to understand each other's personality traits, interests, and activities in detail.

[0424] (Example 1)

[0425] 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."

[0426] In today's world, there is a need for technology that leverages diverse individual behavioral data to evaluate compatibility between users based on a deep understanding, and efficiently presents suitable partners while protecting privacy. However, existing methods suffer from challenges such as fragmented data analysis, the risk of privacy leaks, and poor user experience.

[0427] 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.

[0428] In this invention, the server includes means for collecting user behavior data, means for analyzing the user's personality traits using a generative model based on the data, and means for calculating compatibility between multiple users. This enables the integrated and analytical use of individual behavior data to accurately evaluate compatibility between users and provide appropriate partners while protecting privacy.

[0429] "Behavioral data" refers to a collection of information that includes a user's history of actions in their daily life and online activities.

[0430] A "generative model" is an algorithm used by computers and artificial intelligence to extract and predict unknown data patterns and features based on large datasets.

[0431] "Personality traits" refer to characteristics that indicate behavioral patterns and preference tendencies derived from user behavioral data and habits.

[0432] "Compatibility" refers to the degree of suitability of relationships between multiple users and the extent to which their characteristics match.

[0433] "Privacy" refers to the right or state of protection to prevent an individual's information from being disclosed to third parties without permission or inappropriately.

[0434] A "partner" refers to another user who is rated as a good match for the user, and primarily refers to someone who shares common interests or goals.

[0435] This invention is a system for accurately evaluating the compatibility between users and suggesting suitable partners while protecting privacy. The system mainly consists of a server, terminals, and users.

[0436] First, users register with the system via their device. During registration, users consent to the collection of data such as purchase history, travel history, and social media activity. This allows the server to collect user behavior data from various data sources and store it in a database. The hardware used includes general data servers and cloud storage.

[0437] The server uses a generative AI model to analyze the collected behavioral data. Specifically, the AI ​​model processes a large dataset and extracts the user's personality traits. For example, if a user frequently purchases outdoor equipment and makes many posts about hiking, the server will categorize that user as an "outdoor enthusiast."

[0438] The server then quantifies the compatibility between multiple users based on the calculated personality traits. The system is designed to appropriately match compatible users. As a result, the server sends a notification to the terminal, presenting the user with potential compatible partners.

[0439] Furthermore, if a user allows information to be shared and the other user also agrees, the device will display detailed information about each other to the user. This allows users to deepen their understanding of the other person's profile, hobbies, and preferences while maintaining their privacy.

[0440] For example, if a user frequently purchases self-help books and regularly records their exercise using a jogging app, the server will classify this user as "self-improvement oriented" and "health-conscious." Based on these characteristics, the system will suggest other users with similar orientations as compatible partners.

[0441] An example of a prompt to input into a generative AI model is, "If a user purchases outdoor equipment and frequently uses hiking apps, what personality traits can be estimated to be present?"

[0442] In this way, the present invention realizes efficient and privacy-conscious matching between users.

[0443] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0444] Step 1:

[0445] Users register with the system via their device. User input includes basic personal information, purchase history, travel history, and permission for data collection regarding social media activity. The device transmits the entered information to the server. This information is stored in the database as the basis for registration.

[0446] Step 2:

[0447] The server stores behavioral data collected from users in a database. Next, the data is input into a generative AI model to analyze personality traits from the user's diverse behavioral patterns. Specific data processing includes the type and frequency of purchases, the location and number of movements in travel history, and the content and frequency of online activities. The output is personality traits, such as labels like "outdoor enthusiast" or "health-conscious."

[0448] Step 3:

[0449] The server calculates compatibility between multiple users based on the provided personality traits. The input is a set of personality trait labels. Similarity measurement and statistical analysis techniques are used in the data calculation, and the compatibility score is output as a numerical value. Based on this result, the system lists users with high compatibility scores and creates partner candidate information.

[0450] Step 4:

[0451] The device notifies the user of potential partner information sent from the server. Specifically, it displays notifications that include the nicknames, shared hobbies, and preferences of users with high compatibility scores. This is to attract the user's attention. At that time, an option to access detailed information is also provided.

[0452] Step 5:

[0453] Users can use their devices to take action to verify detailed information. If both the user and potential partners allow each other to share information, the server sends detailed information, including their profiles and interests, to the device. As a result, users can access the other person's information while maintaining their privacy.

[0454] (Application Example 1)

[0455] 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."

[0456] In modern e-commerce, users often struggle to find suitable products and services from a wide variety of options. Therefore, a system is needed that efficiently utilizes user behavior history information to suggest appropriate products and services based on user characteristics. Furthermore, protecting user privacy while considering compatibility between users is a crucial challenge in providing such information.

[0457] 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.

[0458] In this invention, the server includes means for collecting user behavior history information, means for analyzing the user's personality traits using said information, and means for suggesting products and services based on the user's traits. This makes it possible to automatically suggest products and services that match the user's interests and preferences, thereby improving the shopping experience.

[0459] A "user" is an individual who registers with the system and provides information about their activity history.

[0460] "Behavioral history information" refers to data such as a user's purchase history, travel history, and social activities.

[0461] "Personality traits" are indicators of interests and concerns extracted by analyzing a user's behavioral patterns.

[0462] "Compatibility" is a numerical representation of the degree to which the personality traits of multiple users match.

[0463] "Products and services" refers to products and services that are proposed based on the user's characteristics.

[0464] A "server" is a central control system that collects and analyzes user behavior history information and generates suggestions.

[0465] "Notifications" are pieces of information sent from the server to the user, often concerning users with high compatibility scores or suggested products.

[0466] "Privacy" refers to the protection of a user's personal information and the prevention of its disclosure to third parties without their permission.

[0467] This invention is a system configured to analyze user characteristics based on user behavior history information and propose optimal products and services. The server has the function of collecting and storing behavior history information registered by the user through a terminal. This includes purchase history, travel history, and social activities. To analyze this information, the server uses a generative AI model to extract the user's personality traits.

[0468] The server processes the collected data in real time, extracting individual personality traits from each user's behavioral patterns. For example, if a user frequently purchases outdoor equipment, the server identifies that the user has a preference for the outdoors. Next, it refers to other users with similar traits and suggests relevant products and services based on those personality traits. This suggestion is then notified to the user's device.

[0469] When users receive a notification on their device, they can view details of the suggested products and services. The server places particular emphasis on privacy protection and is configured to only share information with other parties based on the user's permission. This prevents users' personal information from being leaked to third parties without their consent.

[0470] For example, if user A frequently purchases fitness equipment online, the system identifies user A as a "fitness enthusiast." The system then suggests more effective fitness programs and related products to user A.

[0471] An example of a prompt to input into the generative AI model is as follows: "Show me how to analyze a user's behavioral history, estimate their personality traits, and suggest appropriate products and services."

[0472] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0473] Step 1:

[0474] The server collects behavioral history information from the user's device. The input includes the user's purchase history, travel history, and social activity data, which the server stores in a database.

[0475] Step 2:

[0476] The server uses collected behavioral history information to generate an AI model that analyzes the user's personality traits. The input is behavioral history information in the database, and the model processes and analyzes this data to extract personality traits that indicate the user's interests and preferences. The output is the identified personality traits.

[0477] Step 3:

[0478] The server selects products and services suitable for the user based on the extracted personality traits. The input is the user's personality traits, which the server uses to match them with a product catalog and generate a list of products that match the traits. The output is a list of suggested products.

[0479] Step 4:

[0480] The server notifies the user's terminal of the generated product suggestions. The input is a list of products, and the server sends this information as a notification message to the user's terminal. The user can then review the suggested products upon receiving the notification.

[0481] Step 5:

[0482] Users can view details of suggested products via notifications on their devices and choose to purchase or take other actions based on that information. The input is the notification from the server, and the output is the user's response. Here, users can choose products that interest them while considering the suggestions.

[0483] 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.

[0484] This invention is a matching system that uses user behavior history information and an emotion engine to more precisely evaluate the compatibility between users. This system consists of a server, terminals, and users, and achieves compatibility evaluation that takes emotional factors into particular.

[0485] First, users register with the system using their device. During registration, users authorize the collection of data including purchase history, travel history, social media activity, and sentiment engine data. This data collection allows the server to accumulate rich behavioral history information in its database.

[0486] Next, the server utilizes an emotion engine to analyze the user's emotional state from the collected behavioral history information. This emotion engine uses natural language processing and pattern recognition technologies to identify emotions from the user's messages and posts. For example, it can identify a variety of emotional states, such as joy, sadness, anger, and surprise, from the word choices, expressions, and emojis used in posts.

[0487] The server then calculates compatibility with other users based on the user's emotional state and personality traits. Emotional state is considered an important factor in the compatibility calculation, and the emotions a user is currently experiencing influence the matching result. For example, users in relaxed emotional states may be judged as being compatible with each other. On the other hand, users in contrasting emotional states may be judged as being incompatible.

[0488] Next, a list of users whose compatibility score, taking emotional states into account, exceeds a certain level is sent from the server to each user's device. The device displays this notification, informing the user that there are compatible candidates. If the user wishes to view more details, they can choose the option to allow information to be shared with that candidate.

[0489] Ultimately, if both users grant permission for information disclosure, the server will mutually share detailed profile information. This allows users to understand each other's hobbies, personality traits, and emotional states, enabling them to find emotionally compatible partners. In terms of security, built-in privacy protection features prevent information from being leaked to third parties without permission.

[0490] The following describes the processing flow.

[0491] Step 1:

[0492] Users log in to the system using their devices and authorize the collection of necessary personal information, purchase history, social media activity, and sentiment data. The server then begins collecting the authorized information and stores it in a database.

[0493] Step 2:

[0494] The server analyzes the user's emotional state from the text information and posts collected using an emotion engine. Specifically, the emotion engine utilizes natural language processing to analyze keywords, context, and emojis contained in the text to identify emotions such as joy, anger, and sadness.

[0495] Step 3:

[0496] The server integrates emotional data obtained from the emotion engine with personality traits analyzed based on behavioral history to create a comprehensive user profile. This profile includes the user's personality and current emotional state.

[0497] Step 4:

[0498] The server compares the personality traits and emotional states of multiple users and calculates a compatibility score. The compatibility score is quantified by considering not only similarities in personality but also whether the users are emotionally complementary.

[0499] Step 5:

[0500] Based on compatibility scores, the server creates a list of highly compatible users and generates a candidate window. The candidate window is displayed on the user's terminal and provides additional information about potentially compatible users.

[0501] Step 6:

[0502] The device displays notifications received from the server to the user, showing information about potential compatible users. Users can choose to view more detailed information or to have the information made public.

[0503] Step 7:

[0504] If a user chooses to allow their information to be made public, the server sends a request to the other user to share that information. Only if the other user also agrees to the disclosure will the server display each other's detailed profiles.

[0505] Step 8:

[0506] Ultimately, the device displays profile information of the agreed-upon users on its screen. This information includes personality traits, activity history, and recent emotional states, which users can use to decide on their next action.

[0507] (Example 2)

[0508] 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."

[0509] In recent years, with the advancement of the digital society, user matching systems have diversified. However, conventional systems do not take emotional factors into consideration, making it difficult to build truly comfortable and suitable human relationships. Furthermore, there is a need for matching that reflects not only personality traits derived from users' behavioral history and purchase history, but existing technologies have not been able to adequately achieve this.

[0510] 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.

[0511] In this invention, the server includes means for collecting user behavior history information, means for analyzing the user's personality traits and emotional state using said information and emotion analysis technology, and means for evaluating compatibility between multiple users based on their emotional states. This enables more precise compatibility evaluation that reflects the user's behavior and emotional state, and provides the optimal matching for the user.

[0512] "Behavioral history information" refers to information related to individual user behavior, including data such as user purchasing activities, travel history, and social media activity.

[0513] "Emotion analysis technology" is a technology that uses natural language processing and pattern recognition to identify emotions and psychological states from digital content that users interact with.

[0514] "Personality traits" refer to characteristics that indicate an individual's internal characteristics, such as a user's behavioral patterns, preferences, and values.

[0515] "Compatibility assessment" is an evaluation process that determines the similarity and suitability between multiple users and measures the degree of affinity between them.

[0516] "Emotional state" refers to the psychological mood or feeling of a user at a specific moment, and includes feelings such as joy, sadness, and anger.

[0517] "Privacy protection" refers to the technologies and measures taken to prevent unauthorized access to personal information.

[0518] As an embodiment of the present invention, a system for evaluating the compatibility between users is described below. This system consists of a user terminal, a server, and software equipped with sentiment analysis technology.

[0519] First, the user accesses the system using their own device and registers. The device collects data from the user's purchase history, travel history, and social media activity, and sends this information to the server. This data is stored in a database and forms the user's behavioral history.

[0520] Next, the server uses a dedicated emotion engine to perform sentiment analysis on the accumulated data. This emotion engine utilizes natural language processing and pattern recognition technologies to identify various emotional states from the user's text data. Specifically, it identifies the user's psychological mood from the wording and emojis included in the posts and obtains analysis results.

[0521] The server then evaluates the user's emotional state and personality traits, and calculates their compatibility with other users who have similar emotional states. This compatibility evaluation result is communicated to the user via their device, allowing them to agree to disclose information to those they are interested in.

[0522] Ultimately, if both parties agree, the server will mutually disclose detailed profile information, allowing users to find a more emotionally compatible partner by learning more about each other's hobbies, goals, and emotional states. Furthermore, security features to protect privacy prevent unauthorized access and guarantee the safety of information.

[0523] As a concrete example, a user might input a request to the system such as, "I want friends I can relax with." Based on this prompt, the system searches for other users with similar emotional states and recommends those deemed compatible. This process allows users to build relationships that suit them.

[0524] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0525] Step 1:

[0526] The user initiates the registration process through their device. At this stage, the user authorizes the provision of purchase history, travel history, and social media activity. As input, the device collects the user's basic information and various behavioral history data, and as output, this data is sent to the server.

[0527] Step 2:

[0528] The server stores the received behavioral history information in a database. It receives user data sent from the terminal as input, organizes this data as output, and saves it in a format that can be used for subsequent processing.

[0529] Step 3:

[0530] The server uses an emotion engine to analyze the user's emotional state from behavioral history information stored in the database. It uses purchase history and social media language data as input, and identifies emotions through natural language processing. The output identifies the user's current emotional state.

[0531] Step 4:

[0532] Based on the analyzed emotional state, the server evaluates compatibility with other users, taking into account each user's personality traits and current emotions. It takes the user's emotional state and personality traits as input and generates compatibility scores and a list of compatible users as output.

[0533] Step 5:

[0534] The server notifies the device of a list of users with a compatibility score above a certain level. The device receives this notification and displays the information to the user. The user then reviews this information and decides whether they wish to share their profile details. The input is the notification information from the server, and the output is the information displayed to the user.

[0535] Step 6:

[0536] Users choose whether or not to allow information sharing with someone they are interested in. If both users grant permission, the server will share detailed information with each other. As input, the user's permission selection information is sent to the server, and as output, the profile information will be displayed to the other user.

[0537] Step 7:

[0538] The server implements security features to ensure privacy and safety in all processes. As input, it applies security policies to various data access requests, and as output, it achieves protection of user data and prevention of unauthorized access.

[0539] (Application Example 2)

[0540] 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."

[0541] Conventional matching systems evaluate compatibility based on user personality traits and basic behavioral history, but they fail to consider the user's emotional state, making it difficult to provide individually tailored suggestions. Furthermore, in virtual stores, it is a challenge to realize product suggestions that take into account the user's behavioral history and emotional state.

[0542] 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.

[0543] In this invention, the server includes means for collecting user behavior history information, means for analyzing user personality traits, means for evaluating compatibility between multiple users, and means for analyzing the user's emotional state and making emotionally-based product suggestions. This enables personalized product suggestions that take emotional states into consideration.

[0544] "User behavior history information" refers to records of actions and activities taken by users in the past, and includes data such as purchase history, travel history, and social media activity.

[0545] "Personality traits" refer to characteristics that indicate a user's personality and behavioral patterns, and are the result of analyzing the unique psychological tendencies and behavioral characteristics of each individual user.

[0546] "Methods for evaluating compatibility" refer to methods for evaluating the relationships and emotional compatibility between multiple users using numerical values ​​or indicators.

[0547] A "means of analyzing emotional state" refers to a system that identifies the emotions a user is currently feeling and performs analysis based on that emotional data.

[0548] A "means of providing product recommendations" refers to a system that presents personalized product information to users based on their behavioral history and emotional state.

[0549] The matching system of the present invention consists of a server, a terminal, and a user. In this system, the user registers with the system using a terminal, and at that time, authorizes the collection of behavioral history information and emotional data. The server analyzes the user's emotional state based on this information and utilizes an emotional engine. The emotional engine analyzes the user's social media posts and messages using natural language processing libraries (e.g., NLTK and spaCy) and emotional analysis APIs (e.g., IBM Watson Tone Analyzer). This makes it possible to identify the emotional state based on words and expressions extracted from the user's behavioral history and posted content.

[0550] The server uses this emotional state to provide product recommendations based on the user's emotions. Behavioral history information is managed by a database management system (e.g., MySQL) and analyzed in combination with the user's purchase history, travel history, etc. Personalized product recommendations are notified to the user from the terminal. This allows the user to select appropriate products that match their emotions and behavior.

[0551] As a concrete example, when a user is experiencing stress at work, the server can analyze the user's emotional state and suggest products such as relaxing aromatherapy candles or healing music. In this case, the following prompt message is applied to the generative AI model:

[0552] "When keywords such as 'business meeting' and 'deadline' are found in the behavioral history, and the emotional state is identified as stress, we use a generative AI model that suggests relaxation products."

[0553] This system aims to provide users with a better shopping experience by offering personalized suggestions.

[0554] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0555] Step 1:

[0556] Users register with the system using a terminal. During this process, users authorize the collection of behavioral history information and emotional data. Inputs include the user's basic information and data collection permission settings, while output is the storage of this information in the system's database. Specifically, user information is sent from the terminal to the server and stored by the database management system.

[0557] Step 2:

[0558] The server collects user activity history information and social media posts. It takes user activity history data and post data as input, and outputs an analyzable dataset. The server collects this data, formats it using natural language processing libraries (such as NLTK and spaCy), and prepares it for analysis.

[0559] Step 3:

[0560] The server uses an emotion analysis API (IBM Watson Tone Analyzer) to analyze behavioral history and posting data to identify the user's emotional state. The input is the dataset from the previous step, and the output is the identified emotional state of the user. The server retrieves the analysis results and saves the emotional state as numerical data.

[0561] Step 4:

[0562] The server generates appropriate product suggestions using a generative AI model based on emotional state and behavioral history information. Emotional state data and behavioral history data are taken as input, and a list of suggested products is generated as output. Specifically, personalized product selection is performed using prompts from the generative AI model.

[0563] Step 5:

[0564] The terminal notifies the user of product suggestions sent from the server. The input is suggestion data from the server, and the output is the display of product suggestions on the terminal screen. The user can then review and select products of interest from this display.

[0565] Step 6:

[0566] When a user decides to purchase an item from the suggested product list, the purchase process proceeds through the terminal. Inputs include the user's purchase intention and payment information, and output is the completion of the purchase process. Purchase information is fed back to the server and reflected in future suggestions.

[0567] 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.

[0568] 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.

[0569] 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.

[0570] [Fourth Embodiment]

[0571] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0572] 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.

[0573] 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).

[0574] 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.

[0575] 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.

[0576] 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).

[0577] 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.

[0578] 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.

[0579] 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.

[0580] 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.

[0581] 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.

[0582] 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.

[0583] 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".

[0584] This invention is a matching system that uses users' behavioral history information to calculate compatibility between users and propose the most suitable partner. This system consists of three elements: a server, a terminal, and a user.

[0585] First, users register with the system via their device. During registration, users authorize the system to collect data from their purchase history, travel history, or social media activity. This allows the server to collect user behavioral history information from various data sources and store it in a database.

[0586] The server then uses the collected data to analyze the user's personality traits. Using a generative model, it extracts individual personality traits from the user's activity patterns. For example, if a user frequently purchases outdoor equipment, the server will identify that user as someone who "likes the outdoors."

[0587] Next, the server compares the personality traits of multiple users and calculates a compatibility score. The compatibility score is quantified, and the system uses this to create a list of users who are likely to be compatible with each other.

[0588] Subsequently, the device receives a notification from the server informing the user that there are potential partners who are a good match for them. The user can then take action to view the details in response to this notification.

[0589] If a user allows information to be shared, and the other user also agrees, the server will display detailed information about both users on their devices. This allows users to deepen their understanding of each other's profiles, hobbies, and preferences while maintaining their privacy.

[0590] When implementing the system, particular emphasis is placed on privacy protection, and security settings are in place to prevent personal information from being leaked to third parties without permission, such as hiding information if a user is registered as a friend. In this way, by implementing the present invention, users can efficiently meet more suitable partners.

[0591] The following describes the processing flow.

[0592] Step 1:

[0593] The server begins collecting information that the user authorized during registration, including purchase history, travel history, and social media data. This information is retrieved from relevant data sources using APIs and scraping techniques and stored in a database.

[0594] Step 2:

[0595] The server preprocesses the collected data. Specifically, it fills in any incomplete data, removes duplicate data, and corrects outliers, preparing the data for easier analysis.

[0596] Step 3:

[0597] The server utilizes generative models and preprocessed data to analyze the user's personality traits. In this process, it identifies the user's hobbies and daily activities based on behavioral patterns and purchasing tendencies that occur with specific frequencies.

[0598] Step 4:

[0599] The server calculates compatibility based on the similarity or complementarity of personality traits between users. This calculation uses a machine learning algorithm to quantify the compatibility score.

[0600] Step 5:

[0601] The server generates a list of users with a compatibility score above a certain level and sends a notification to each user's device. The notification includes summary information about potential compatible users.

[0602] Step 6:

[0603] The device receives a notification from the server and informs the user that there are potential compatible devices. The user is then given the option to view more detailed information.

[0604] Step 7:

[0605] If a user wishes to view more detailed information about someone they are interested in, they submit a request for permission to disclose that information through the system. This request is then notified to the other user via the server.

[0606] Step 8:

[0607] If both users grant permission to share their information, the server will display each other's detailed profile information on their devices. This allows users to understand each other's personality traits, interests, and activities in detail.

[0608] (Example 1)

[0609] 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".

[0610] In today's world, there is a need for technology that leverages diverse individual behavioral data to evaluate compatibility between users based on a deep understanding, and efficiently presents suitable partners while protecting privacy. However, existing methods suffer from challenges such as fragmented data analysis, the risk of privacy leaks, and poor user experience.

[0611] 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.

[0612] In this invention, the server includes means for collecting user behavior data, means for analyzing the user's personality traits using a generative model based on the data, and means for calculating compatibility between multiple users. This enables the integrated and analytical use of individual behavior data to accurately evaluate compatibility between users and provide appropriate partners while protecting privacy.

[0613] "Behavioral data" refers to a collection of information that includes a user's history of actions in their daily life and online activities.

[0614] A "generative model" is an algorithm used by computers and artificial intelligence to extract and predict unknown data patterns and features based on large datasets.

[0615] "Personality traits" refer to characteristics that indicate behavioral patterns and preference tendencies derived from user behavioral data and habits.

[0616] "Compatibility" refers to the degree of suitability of relationships between multiple users and the extent to which their characteristics match.

[0617] "Privacy" refers to the right or state of protection to prevent an individual's information from being disclosed to third parties without permission or inappropriately.

[0618] A "partner" refers to another user who is rated as a good match for the user, and primarily refers to someone who shares common interests or goals.

[0619] This invention is a system for accurately evaluating the compatibility between users and suggesting suitable partners while protecting privacy. The system mainly consists of a server, terminals, and users.

[0620] First, users register with the system via their device. During registration, users consent to the collection of data such as purchase history, travel history, and social media activity. This allows the server to collect user behavior data from various data sources and store it in a database. The hardware used includes general data servers and cloud storage.

[0621] The server uses a generative AI model to analyze the collected behavioral data. Specifically, the AI ​​model processes a large dataset and extracts the user's personality traits. For example, if a user frequently purchases outdoor equipment and makes many posts about hiking, the server will categorize that user as an "outdoor enthusiast."

[0622] The server then quantifies the compatibility between multiple users based on the calculated personality traits. The system is designed to appropriately match compatible users. As a result, the server sends a notification to the terminal, presenting the user with potential compatible partners.

[0623] Furthermore, if a user allows information to be shared and the other user also agrees, the device will display detailed information about each other to the user. This allows users to deepen their understanding of the other person's profile, hobbies, and preferences while maintaining their privacy.

[0624] For example, if a user frequently purchases self-help books and regularly records their exercise using a jogging app, the server will classify this user as "self-improvement oriented" and "health-conscious." Based on these characteristics, the system will suggest other users with similar orientations as compatible partners.

[0625] An example of a prompt to input into a generative AI model is, "If a user purchases outdoor equipment and frequently uses hiking apps, what personality traits can be estimated to be present?"

[0626] In this way, the present invention realizes efficient and privacy-conscious matching between users.

[0627] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0628] Step 1:

[0629] Users register with the system via their device. User input includes basic personal information, purchase history, travel history, and permission for data collection regarding social media activity. The device transmits the entered information to the server. This information is stored in the database as the basis for registration.

[0630] Step 2:

[0631] The server stores behavioral data collected from users in a database. Next, the data is input into a generative AI model to analyze personality traits from the user's diverse behavioral patterns. Specific data processing includes the type and frequency of purchases, the location and number of movements in travel history, and the content and frequency of online activities. The output is personality traits, such as labels like "outdoor enthusiast" or "health-conscious."

[0632] Step 3:

[0633] The server calculates compatibility between multiple users based on the provided personality traits. The input is a set of personality trait labels. Similarity measurement and statistical analysis techniques are used in the data calculation, and the compatibility score is output as a numerical value. Based on this result, the system lists users with high compatibility scores and creates partner candidate information.

[0634] Step 4:

[0635] The device notifies the user of potential partner information sent from the server. Specifically, it displays notifications that include the nicknames, shared hobbies, and preferences of users with high compatibility scores. This is to attract the user's attention. At that time, an option to access detailed information is also provided.

[0636] Step 5:

[0637] Users can use their devices to take action to verify detailed information. If both the user and potential partners allow each other to share information, the server sends detailed information, including their profiles and interests, to the device. As a result, users can access the other person's information while maintaining their privacy.

[0638] (Application Example 1)

[0639] 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".

[0640] In modern e-commerce, users often struggle to find suitable products and services from a wide variety of options. Therefore, a system is needed that efficiently utilizes user behavior history information to suggest appropriate products and services based on user characteristics. Furthermore, protecting user privacy while considering compatibility between users is a crucial challenge in providing such information.

[0641] 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.

[0642] In this invention, the server includes means for collecting user behavior history information, means for analyzing the user's personality traits using said information, and means for suggesting products and services based on the user's traits. This makes it possible to automatically suggest products and services that match the user's interests and preferences, thereby improving the shopping experience.

[0643] A "user" is an individual who registers with the system and provides information about their activity history.

[0644] "Behavioral history information" refers to data such as a user's purchase history, travel history, and social activities.

[0645] "Personality traits" are indicators of interests and concerns extracted by analyzing a user's behavioral patterns.

[0646] "Compatibility" is a numerical representation of the degree to which the personality traits of multiple users match.

[0647] "Products and services" refers to products and services that are proposed based on the user's characteristics.

[0648] A "server" is a central control system that collects and analyzes user behavior history information and generates suggestions.

[0649] "Notifications" are pieces of information sent from the server to the user, often concerning users with high compatibility scores or suggested products.

[0650] "Privacy" refers to the protection of a user's personal information and the prevention of its disclosure to third parties without their permission.

[0651] This invention is a system configured to analyze user characteristics based on user behavior history information and propose optimal products and services. The server has the function of collecting and storing behavior history information registered by the user through a terminal. This includes purchase history, travel history, and social activities. To analyze this information, the server uses a generative AI model to extract the user's personality traits.

[0652] The server processes the collected data in real time, extracting individual personality traits from each user's behavioral patterns. For example, if a user frequently purchases outdoor equipment, the server identifies that the user has a preference for the outdoors. Next, it refers to other users with similar traits and suggests relevant products and services based on those personality traits. This suggestion is then notified to the user's device.

[0653] When users receive a notification on their device, they can view details of the suggested products and services. The server places particular emphasis on privacy protection and is configured to only share information with other parties based on the user's permission. This prevents users' personal information from being leaked to third parties without their consent.

[0654] For example, if user A frequently purchases fitness equipment online, the system identifies user A as a "fitness enthusiast." The system then suggests more effective fitness programs and related products to user A.

[0655] An example of a prompt to input into the generative AI model is as follows: "Show me how to analyze a user's behavioral history, estimate their personality traits, and suggest appropriate products and services."

[0656] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0657] Step 1:

[0658] The server collects behavioral history information from the user's device. The input includes the user's purchase history, travel history, and social activity data, which the server stores in a database.

[0659] Step 2:

[0660] The server uses collected behavioral history information to generate an AI model that analyzes the user's personality traits. The input is behavioral history information in the database, and the model processes and analyzes this data to extract personality traits that indicate the user's interests and preferences. The output is the identified personality traits.

[0661] Step 3:

[0662] The server selects products and services suitable for the user based on the extracted personality traits. The input is the user's personality traits, which the server uses to match them with a product catalog and generate a list of products that match the traits. The output is a list of suggested products.

[0663] Step 4:

[0664] The server notifies the user's terminal of the generated product suggestions. The input is a list of products, and the server sends this information as a notification message to the user's terminal. The user can then review the suggested products upon receiving the notification.

[0665] Step 5:

[0666] Users can view details of suggested products via notifications on their devices and choose to purchase or take other actions based on that information. The input is the notification from the server, and the output is the user's response. Here, users can choose products that interest them while considering the suggestions.

[0667] 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.

[0668] This invention is a matching system that uses user behavior history information and an emotion engine to more precisely evaluate the compatibility between users. This system consists of a server, terminals, and users, and achieves compatibility evaluation that takes emotional factors into particular.

[0669] First, users register with the system using their device. During registration, users authorize the collection of data including purchase history, travel history, social media activity, and sentiment engine data. This data collection allows the server to accumulate rich behavioral history information in its database.

[0670] Next, the server utilizes an emotion engine to analyze the user's emotional state from the collected behavioral history information. This emotion engine uses natural language processing and pattern recognition technologies to identify emotions from the user's messages and posts. For example, it can identify a variety of emotional states, such as joy, sadness, anger, and surprise, from the word choices, expressions, and emojis used in posts.

[0671] The server then calculates compatibility with other users based on the user's emotional state and personality traits. Emotional state is considered an important factor in the compatibility calculation, and the emotions a user is currently experiencing influence the matching result. For example, users in relaxed emotional states may be judged as being compatible with each other. On the other hand, users in contrasting emotional states may be judged as being incompatible.

[0672] Next, a list of users whose compatibility score, taking emotional states into account, exceeds a certain level is sent from the server to each user's device. The device displays this notification, informing the user that there are compatible candidates. If the user wishes to view more details, they can choose the option to allow information to be shared with that candidate.

[0673] Ultimately, if both users grant permission for information disclosure, the server will mutually share detailed profile information. This allows users to understand each other's hobbies, personality traits, and emotional states, enabling them to find emotionally compatible partners. In terms of security, built-in privacy protection features prevent information from being leaked to third parties without permission.

[0674] The following describes the processing flow.

[0675] Step 1:

[0676] Users log in to the system using their devices and authorize the collection of necessary personal information, purchase history, social media activity, and sentiment data. The server then begins collecting the authorized information and stores it in a database.

[0677] Step 2:

[0678] The server analyzes the user's emotional state from the text information and posts collected using an emotion engine. Specifically, the emotion engine utilizes natural language processing to analyze keywords, context, and emojis contained in the text to identify emotions such as joy, anger, and sadness.

[0679] Step 3:

[0680] The server integrates emotional data obtained from the emotion engine with personality traits analyzed based on behavioral history to create a comprehensive user profile. This profile includes the user's personality and current emotional state.

[0681] Step 4:

[0682] The server compares the personality traits and emotional states of multiple users and calculates a compatibility score. The compatibility score is quantified by considering not only similarities in personality but also whether the users are emotionally complementary.

[0683] Step 5:

[0684] Based on compatibility scores, the server creates a list of highly compatible users and generates a candidate window. The candidate window is displayed on the user's terminal and provides additional information about potentially compatible users.

[0685] Step 6:

[0686] The device displays notifications received from the server to the user, showing information about potential compatible users. Users can choose to view more detailed information or to have the information made public.

[0687] Step 7:

[0688] If a user chooses to allow their information to be made public, the server sends a request to the other user to share that information. Only if the other user also agrees to the disclosure will the server display each other's detailed profiles.

[0689] Step 8:

[0690] Ultimately, the device displays profile information of the agreed-upon users on its screen. This information includes personality traits, activity history, and recent emotional states, which users can use to decide on their next action.

[0691] (Example 2)

[0692] 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".

[0693] In recent years, with the advancement of the digital society, user matching systems have diversified. However, conventional systems do not take emotional factors into consideration, making it difficult to build truly comfortable and suitable human relationships. Furthermore, there is a need for matching that reflects not only personality traits derived from users' behavioral history and purchase history, but existing technologies have not been able to adequately achieve this.

[0694] 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.

[0695] In this invention, the server includes means for collecting user behavior history information, means for analyzing the user's personality traits and emotional state using said information and emotion analysis technology, and means for evaluating compatibility between multiple users based on their emotional states. This enables more precise compatibility evaluation that reflects the user's behavior and emotional state, and provides the optimal matching for the user.

[0696] "Behavioral history information" refers to information related to individual user behavior, including data such as user purchasing activities, travel history, and social media activity.

[0697] "Emotion analysis technology" is a technology that uses natural language processing and pattern recognition to identify emotions and psychological states from digital content that users interact with.

[0698] "Personality traits" refer to characteristics that indicate an individual's internal characteristics, such as a user's behavioral patterns, preferences, and values.

[0699] "Compatibility assessment" is an evaluation process that determines the similarity and suitability between multiple users and measures the degree of affinity between them.

[0700] "Emotional state" refers to the psychological mood or feeling of a user at a specific moment, and includes feelings such as joy, sadness, and anger.

[0701] "Privacy protection" refers to the technologies and measures taken to prevent unauthorized access to personal information.

[0702] As an embodiment of the present invention, a system for evaluating the compatibility between users is described below. This system consists of a user terminal, a server, and software equipped with sentiment analysis technology.

[0703] First, the user accesses the system using their own device and registers. The device collects data from the user's purchase history, travel history, and social media activity, and sends this information to the server. This data is stored in a database and forms the user's behavioral history.

[0704] Next, the server uses a dedicated emotion engine to perform sentiment analysis on the accumulated data. This emotion engine utilizes natural language processing and pattern recognition technologies to identify various emotional states from the user's text data. Specifically, it identifies the user's psychological mood from the wording and emojis included in the posts and obtains analysis results.

[0705] The server then evaluates the user's emotional state and personality traits, and calculates their compatibility with other users who have similar emotional states. This compatibility evaluation result is communicated to the user via their device, allowing them to agree to disclose information to those they are interested in.

[0706] Ultimately, if both parties agree, the server will mutually disclose detailed profile information, allowing users to find a more emotionally compatible partner by learning more about each other's hobbies, goals, and emotional states. Furthermore, security features to protect privacy prevent unauthorized access and guarantee the safety of information.

[0707] As a concrete example, a user might input a request to the system such as, "I want friends I can relax with." Based on this prompt, the system searches for other users with similar emotional states and recommends those deemed compatible. This process allows users to build relationships that suit them.

[0708] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0709] Step 1:

[0710] The user initiates the registration process through their device. At this stage, the user authorizes the provision of purchase history, travel history, and social media activity. As input, the device collects the user's basic information and various behavioral history data, and as output, this data is sent to the server.

[0711] Step 2:

[0712] The server stores the received behavioral history information in a database. It receives user data sent from the terminal as input, organizes this data as output, and saves it in a format that can be used for subsequent processing.

[0713] Step 3:

[0714] The server uses an emotion engine to analyze the user's emotional state from behavioral history information stored in the database. It uses purchase history and social media language data as input, and identifies emotions through natural language processing. The output identifies the user's current emotional state.

[0715] Step 4:

[0716] Based on the analyzed emotional state, the server evaluates compatibility with other users, taking into account each user's personality traits and current emotions. It takes the user's emotional state and personality traits as input and generates compatibility scores and a list of compatible users as output.

[0717] Step 5:

[0718] The server notifies the device of a list of users with a compatibility score above a certain level. The device receives this notification and displays the information to the user. The user then reviews this information and decides whether they wish to share their profile details. The input is the notification information from the server, and the output is the information displayed to the user.

[0719] Step 6:

[0720] Users choose whether or not to allow information sharing with someone they are interested in. If both users grant permission, the server will share detailed information with each other. As input, the user's permission selection information is sent to the server, and as output, the profile information will be displayed to the other user.

[0721] Step 7:

[0722] The server implements security features to ensure privacy and safety in all processes. As input, it applies security policies to various data access requests, and as output, it achieves protection of user data and prevention of unauthorized access.

[0723] (Application Example 2)

[0724] 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".

[0725] Conventional matching systems evaluate compatibility based on user personality traits and basic behavioral history, but they fail to consider the user's emotional state, making it difficult to provide individually tailored suggestions. Furthermore, in virtual stores, it is a challenge to realize product suggestions that take into account the user's behavioral history and emotional state.

[0726] 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.

[0727] In this invention, the server includes means for collecting user behavior history information, means for analyzing user personality traits, means for evaluating compatibility between multiple users, and means for analyzing the user's emotional state and making emotionally-based product suggestions. This enables personalized product suggestions that take emotional states into consideration.

[0728] "User behavior history information" refers to records of actions and activities taken by users in the past, and includes data such as purchase history, travel history, and social media activity.

[0729] "Personality traits" refer to characteristics that indicate a user's personality and behavioral patterns, and are the result of analyzing the unique psychological tendencies and behavioral characteristics of each individual user.

[0730] "Methods for evaluating compatibility" refer to methods for evaluating the relationships and emotional compatibility between multiple users using numerical values ​​or indicators.

[0731] A "means of analyzing emotional state" refers to a system that identifies the emotions a user is currently feeling and performs analysis based on that emotional data.

[0732] A "means of providing product recommendations" refers to a system that presents personalized product information to users based on their behavioral history and emotional state.

[0733] The matching system of the present invention consists of a server, a terminal, and a user. In this system, the user registers with the system using a terminal, and at that time, authorizes the collection of behavioral history information and emotional data. The server analyzes the user's emotional state based on this information and utilizes an emotional engine. The emotional engine analyzes the user's social media posts and messages using natural language processing libraries (e.g., NLTK and spaCy) and emotional analysis APIs (e.g., IBM Watson Tone Analyzer). This makes it possible to identify the emotional state based on words and expressions extracted from the user's behavioral history and posted content.

[0734] The server uses this emotional state to provide product recommendations based on the user's emotions. Behavioral history information is managed by a database management system (e.g., MySQL) and analyzed in combination with the user's purchase history, travel history, etc. Personalized product recommendations are notified to the user from the terminal. This allows the user to select appropriate products that match their emotions and behavior.

[0735] As a concrete example, when a user is experiencing stress at work, the server can analyze the user's emotional state and suggest products such as relaxing aromatherapy candles or healing music. In this case, the following prompt message is applied to the generative AI model:

[0736] "When keywords such as 'business meeting' and 'deadline' are found in the behavioral history, and the emotional state is identified as stress, we use a generative AI model that suggests relaxation products."

[0737] This system aims to provide users with a better shopping experience by offering personalized suggestions.

[0738] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0739] Step 1:

[0740] Users register with the system using a terminal. During this process, users authorize the collection of behavioral history information and emotional data. Inputs include the user's basic information and data collection permission settings, while output is the storage of this information in the system's database. Specifically, user information is sent from the terminal to the server and stored by the database management system.

[0741] Step 2:

[0742] The server collects user activity history information and social media posts. It takes user activity history data and post data as input, and outputs an analyzable dataset. The server collects this data, formats it using natural language processing libraries (such as NLTK and spaCy), and prepares it for analysis.

[0743] Step 3:

[0744] The server uses an emotion analysis API (IBM Watson Tone Analyzer) to analyze behavioral history and posting data to identify the user's emotional state. The input is the dataset from the previous step, and the output is the identified emotional state of the user. The server retrieves the analysis results and saves the emotional state as numerical data.

[0745] Step 4:

[0746] The server generates appropriate product suggestions using a generative AI model based on emotional state and behavioral history information. Emotional state data and behavioral history data are taken as input, and a list of suggested products is generated as output. Specifically, personalized product selection is performed using prompts from the generative AI model.

[0747] Step 5:

[0748] The terminal notifies the user of product suggestions sent from the server. The input is suggestion data from the server, and the output is the display of product suggestions on the terminal screen. The user can then review and select products of interest from this display.

[0749] Step 6:

[0750] When a user decides to purchase an item from the suggested product list, the purchase process proceeds through the terminal. Inputs include the user's purchase intention and payment information, and output is the completion of the purchase process. Purchase information is fed back to the server and reflected in future suggestions.

[0751] 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.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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."

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] The following is further disclosed regarding the embodiments described above.

[0773] (Claim 1)

[0774] Means for collecting user behavior history information,

[0775] A means for analyzing the user's personality traits using the said information,

[0776] A means of evaluating compatibility between multiple users,

[0777] A means for notifying users of information on users with a high compatibility score based on the evaluation results,

[0778] A means of mutually disclosing information based on user permission,

[0779] A system that includes this.

[0780] (Claim 2)

[0781] The system according to claim 1, which includes means for hiding information of users registered as friends in order to protect user privacy.

[0782] (Claim 3)

[0783] The system according to claim 1, comprising means for comprehensively analyzing a user's purchase history, travel history, and social media activity.

[0784] "Example 1"

[0785] (Claim 1)

[0786] Means for collecting user behavior data,

[0787] A means for analyzing the user's personality traits using a generative model with the said data,

[0788] A means of calculating compatibility between multiple users,

[0789] A means for notifying information about people with a high compatibility score based on the calculation results,

[0790] A means of disclosing detailed information based on the consent of the users,

[0791] Methods using digital terminals and data servers,

[0792] A system that includes this.

[0793] (Claim 2)

[0794] The system according to claim 1, which includes a configuration for hiding information of specific registered individuals in order to protect their privacy.

[0795] (Claim 3)

[0796] The system according to claim 1, comprising a configuration for integrally processing a user's purchasing behavior, travel routes, and online activities.

[0797] "Application Example 1"

[0798] (Claim 1)

[0799] Means for collecting user behavior history information,

[0800] A means for analyzing the user's personality traits using the said information,

[0801] A means of evaluating compatibility between multiple users,

[0802] A means of proposing products and services based on user characteristics,

[0803] A means for notifying users of information on users with a high compatibility score based on the evaluation results,

[0804] A means of mutually disclosing information based on user permission,

[0805] A system that includes this.

[0806] (Claim 2)

[0807] The system according to claim 1, which includes means for hiding information of users registered as friends in order to protect user privacy.

[0808] (Claim 3)

[0809] The system according to claim 1, comprising means for comprehensively analyzing a user's purchase history, travel history, and social activities.

[0810] "Example 2 of combining an emotion engine"

[0811] (Claim 1)

[0812] Means for collecting user behavior history information,

[0813] A means for analyzing the user's personality traits and emotional state using the said information and emotion analysis technology,

[0814] A means of evaluating compatibility between multiple users based on their emotional states,

[0815] A means for notifying users of information on users with a high compatibility score based on the evaluation results,

[0816] A means of mutually disclosing detailed information based on user permission,

[0817] Means of providing security features to protect privacy,

[0818] A system that includes this.

[0819] (Claim 2)

[0820] The system according to claim 1, comprising means for identifying a user's diverse emotions from behavioral history information using an emotion engine.

[0821] (Claim 3)

[0822] The system according to claim 1, comprising means for comprehensively analyzing a user's purchase history, travel history, and social media activity, and for evaluating their emotional state.

[0823] "Application example 2 when combining with an emotional engine"

[0824] (Claim 1)

[0825] Means for collecting user behavior history information,

[0826] A means for analyzing the user's personality traits using the said information,

[0827] A means of evaluating compatibility between multiple users,

[0828] A means for notifying users of information on users with a high compatibility score based on the evaluation results,

[0829] A means of mutually disclosing information based on user permission,

[0830] A means of analyzing the user's emotional state and making product recommendations based on those emotions,

[0831] A system that includes this.

[0832] (Claim 2)

[0833] The system according to claim 1, which includes means for hiding information of users registered as friends in order to protect user privacy.

[0834] (Claim 3)

[0835] The system according to claim 1, comprising means for comprehensively analyzing a user's purchase history, travel history, and social media activity. [Explanation of Symbols]

[0836] 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. Means for collecting user behavior history information, A means for analyzing the user's personality traits using the said information, A means of evaluating compatibility between multiple users, A means for notifying users of information on users with a high compatibility score based on the evaluation results, A means of mutually disclosing information based on user permission, A system that includes this.

2. The system according to claim 1, which includes means for hiding information of users registered as friends in order to protect user privacy.

3. The system according to claim 1, comprising means for comprehensively analyzing a user's purchase history, travel history, and social media activity.

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