system

Abstract

We provide the system. [Solution] A means for collecting information on purchasers, A means of identifying purchase patterns by analyzing the collected information, A means to detect and block fraudulent purchases based on the analysis results, A means of notifying legitimate buyers of resale, 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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The problem to be solved by the present invention is that in ticket purchases, due to illegal bulk purchases by resellers, legitimate fans are unable to purchase tickets. Such illegal acts not only deprive fans of the opportunity to enjoy an artist's live performance, but also affect the credibility and brand of ticket sellers.

Means for Solving the Problems

[0005] This invention provides a means for collecting purchaser information, analyzing that data, and identifying purchase patterns. This allows for the detection and real-time blocking of fraudulent purchases based on the analysis results. Furthermore, by providing a means for notifying legitimate purchasers of resale opportunities, it enables fair ticket distribution. As a result, it eliminates scalpers and ensures that tickets are provided to legitimate fans.

[0006] "Means of collecting purchaser information" refers to processes or technological devices for collecting user data related to ticket purchases.

[0007] "Means of analyzing information to identify purchase patterns" refers to the process or technological device of analyzing collected data to find specific patterns or trends from users' purchasing behavior.

[0008] "Means for detecting and blocking fraudulent purchases" refers to a process or technical device that identifies fraudulent ticket acquisition behavior based on identified fraudulent purchase patterns and cancels or invalidates such purchases.

[0009] "Means of notifying legitimate purchasers of resale opportunities" refers to a process or technical device that, after a blocked fraudulent purchase, informs legitimate users of the opportunity to resell the ticket. [Brief explanation of the drawing]

[0010] [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]

[0011] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0012] First, let's explain the terminology used in the following explanation.

[0013] 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.

[0014] 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.

[0015] 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.

[0016] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0017] 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."

[0018] [First Embodiment]

[0019] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0020] 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.

[0021] 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).

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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".

[0031] This invention provides a ticket purchasing system that ensures tickets are delivered to legitimate fans from fraudulent resellers. The following describes its specific embodiments.

[0032] First, the user attempts to purchase tickets online using their device. During this process, the user's input information, such as the number of tickets to purchase, payment information, and user ID, is sent to the system.

[0033] Next, the server receives the transmitted information and collects additional data, such as the user's IP address and device information. This data is then stored in a database and associated with the user's past purchase history.

[0034] The server uses AI or machine learning algorithms to analyze purchase patterns based on the stored data. This analysis compares the data with past normal purchase data to identify any fraudulent patterns.

[0035] If an invalid pattern is detected, the server will immediately block the purchase and invalidate the ticket. The user will also be notified of the result and encouraged to make the purchase through the normal procedure.

[0036] Blocked tickets will be made available again to legitimate fans. The server will notify legitimate users with a past purchase history, as well as high-priority potential buyers, of the resale opportunity. This notification will be sent via email or in-app push notification.

[0037] As a concrete example, if tickets for a particular artist become extremely popular, the server will detect patterns such as large-scale purchases from the same IP address or consecutive purchases in a short period of time by multiple accounts. When these are determined to be fraudulent, they will be immediately blocked, and the opportunity to resell the tickets will be given to legitimate fans, such as users who have a history of attending the artist's concerts multiple times in the past.

[0038] This system provides fans with a secure environment for purchasing tickets, and effectively prevents fraudulent activity by ticket scalpers through various data analysis techniques.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The user accesses the ticket purchase page using their device and enters the number of tickets they wish to purchase and their payment information.

[0042] Step 2:

[0043] The server receives purchase requests from users and collects data such as user ID, number of items purchased, payment information, IP address, and device information.

[0044] Step 3:

[0045] The server stores the collected data in a database. It then compares it with past purchase history and associates it with existing information.

[0046] Step 4:

[0047] The server uses AI models or machine learning algorithms to analyze user behavior based on current purchase data and past safe purchase patterns.

[0048] Step 5:

[0049] Based on the analysis results, the server determines whether a fraudulent purchase pattern exists. For example, it checks if it matches past fraudulent patterns, such as large purchases in a short period or consecutive purchases from the same IP address.

[0050] Step 6:

[0051] The server immediately blocks any purchase request deemed fraudulent and invalidates the ticket purchase. At this time, an error message is generated and sent to the user.

[0052] Step 7:

[0053] The server prepares the fraudulently blocked tickets for resale and makes them available to legitimate buyers.

[0054] Step 8:

[0055] The server identifies users who have previously purchased the same ticket, as well as regular users with high priority based on their interests and preferences, and notifies them of resale opportunities. Notifications are sent via email or in-app push notifications.

[0056] (Example 1)

[0057] 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."

[0058] In ticket purchasing, there is a challenge in preventing fraudulent purchases by unscrupulous resellers and ensuring that tickets are delivered to legitimate buyers.

[0059] 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.

[0060] In this invention, the server includes means for collecting buyer identification information, means for storing acquired additional data in a database, and means for analyzing purchase patterns using artificial intelligence. This makes it possible to detect fraudulent purchases and immediately block them, and to provide legitimate buyers with an opportunity to resell.

[0061] "Purchaser identification information" refers to a unique identifier related to the ticket purchaser, and is composed of information such as user ID and IP address.

[0062] "Additional data" refers to supplementary information obtained based on the purchaser's identification information, including device information and geographical location information.

[0063] A "database" is an advanced storage or memory system used to manage and store collected and acquired identification information and additional data.

[0064] "Artificial intelligence" is a technology in which computers mimic human intelligence to perform data analysis and problem-solving, and it includes machine learning algorithms.

[0065] A "purchase pattern" refers to a characteristic trend or recurring behavior in purchasing activities that is derived from past and present purchase history.

[0066] "Fraudulent purchase" refers to any purchase behavior that the system deems inappropriate or inaccurate, including multiple purchases and purchases made at unusual times.

[0067] A "purchase block" is a process that immediately cancels a transaction and invalidates the purchase when the system detects a fraudulent purchase.

[0068] A "legitimate purchaser" refers to a purchaser with a history of normal purchases and no evidence of fraudulent activity, and is recognized as an individual with legitimate ownership of the ticket.

[0069] A "resale opportunity" is a chance to purchase tickets that have been confiscated due to fraudulent purchases and are offered for resale to legitimate buyers.

[0070] This invention is a system to prevent fraudulent ticket resale, which collects and analyzes the identification information of purchasers and ensures that tickets are delivered to legitimate buyers.

[0071] First, the user purchases tickets online using their device. The information entered by the user on the device, including the number of tickets purchased, payment information, and user ID, is sent to the server.

[0072] Next, the server receives this information and retrieves additional data, such as IP addresses and device information, to fully identify the profile. This additional data is stored in a database.

[0073] Subsequently, the server uses a generative AI model based on the stored information to analyze purchase patterns. The specific software used includes machine learning libraries and data analysis tools based on Python. The data is compared with past purchase history, and any abnormal patterns are identified as fraudulent.

[0074] If a purchase is deemed fraudulent, the server will immediately block the purchase and revert the ticket to a resaleable state. Resale will be notified and offered to users with a legitimate purchase history.

[0075] To give a specific example, in a case where concert tickets for a popular artist are being sold, the server detects a pattern of large-scale purchases made at an unusual time, immediately determines it to be fraudulent, and cancels the purchase. Legitimate fans, such as users who have previously attended events by that artist, are notified of the resale opportunity via email or app notification.

[0076] An example of a prompt to input into the generating AI model would be, "Analyze this user's purchase patterns and determine if any fraudulent activity has occurred."

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

[0078] Step 1:

[0079] The user accesses the ticket purchase page from their device. They provide the number of tickets to purchase, payment information, and user ID as input, and send this information to the server. A specific example of this action would be entering "1 concert ticket".

[0080] Step 2:

[0081] The server receives purchase information submitted by the user. Based on the entered information, it additionally collects the user's IP address and device information and stores it in a database. The database is organized to avoid duplication of information and stored in a format that facilitates later investigation and analysis.

[0082] Step 3:

[0083] The server uses stored purchase information and additional data to analyze purchase patterns using a generative AI model. Specifically, it compares past purchase history with current data to detect fraudulent patterns such as consecutive purchases from the same IP address. Here, the generative AI model uses an anomaly detection algorithm to identify fraud with high accuracy.

[0084] Step 4:

[0085] If a fraudulent purchase pattern is detected, the server immediately blocks the purchase and stops the purchase process. This returns the canceled ticket to the server, ready to be resold. Specifically, the user receives a notification stating, "Your purchase has been canceled."

[0086] Step 5:

[0087] The server notifies legitimate potential buyers to offer them a resale opportunity. Users eligible for resale are often those who have been repeatedly confirmed to have attended events featuring the artist in question. This notification is sent via email or in-app push notifications, encouraging them to complete the purchase process again.

[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 online shopping, fraudulent purchases and forged reviews are serious problems, causing harm to both consumers and sellers. There is a need to prevent such fraudulent activities and provide a trustworthy shopping experience. Therefore, it is necessary to provide a system that accurately tracks buyer behavior and can immediately detect and block fraudulent purchases.

[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 a device for collecting the purchaser's personal information, a device for analyzing the collected personal information to identify purchase behavior patterns, and a device for detecting and blocking fraudulent purchase activities based on the analysis results. This makes it possible to quickly identify fraudulent activities in online shopping and provide a safe and secure purchasing environment for legitimate purchasers.

[0093] A "purchaser" is an individual or legal entity that buys goods or services.

[0094] "Personal information" refers to information that can identify a specific individual, and includes names, addresses, and contact information.

[0095] A "purchase behavior pattern" refers to a series of behavioral tendencies derived from purchase history, frequency of purchases, time of day, and purchased items.

[0096] "Fraudulent purchasing" refers to actions intended to illegally purchase large quantities of goods or manipulate prices.

[0097] "Blocking" refers to a measure taken to detect fraudulent activity and prevent it from continuing.

[0098] A "legitimate buyer" refers to an individual or organization that purchases goods or services through legitimate means.

[0099] A "sales system" is a system that includes a series of processes and devices for providing goods or services to a buyer.

[0100] A description of the embodiment for carrying out the invention will be provided.

[0101] This invention is a system that prevents fraudulent activity in online shopping and provides a safe and secure purchasing environment for legitimate buyers.

[0102] First, the user downloads the shopping app on their smartphone and registers. This app is developed using Swift (iOS) or Kotlin (Android®). It collects user activity information and purchase history and sends it to the server.

[0103] Next, the server is located in the cloud and manages user data using Python and the Django framework. Based on personal information and purchase history submitted by users, the server analyzes purchase behavior patterns using a generative AI model powered by Scikit-learn. This analysis can detect patterns of fraudulent purchases and block the purchase process.

[0104] In particular, the server identifies abnormal activity compared to past normal purchasing behavior and immediately interrupts the purchasing process. This means that, for example, if a large number of product orders are placed in a short period, purchases deemed suspicious will be blocked.

[0105] The server sends push notifications containing special offers to registered buyers. In this way, registered buyers are offered opportunities for priority purchases and special discounts.

[0106] For example, if a user attempts to purchase a large quantity of the same product in a short period of time through the app, this action will be considered fraudulent and the purchase will be immediately blocked. On the other hand, legitimate buyers with a history of normal purchases will be given priority notification of resale opportunities.

[0107] An example of a prompt message for the AI ​​model would be, "Please tell me the procedure for determining if user A's purchase history is fraudulent."

[0108] As a result, this system ensures a safe and fair online shopping environment.

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

[0110] Step 1:

[0111] The user launches a shopping app on their smartphone and begins the purchase process. The app retrieves the entered purchase information (product name, quantity, payment information, etc.) and sends it to the server. This becomes the input data.

[0112] Step 2:

[0113] The server stores purchase information received from the app and user personal information in a database. At this time, it also references the user's past purchase history and device information, and associates them with the database. Input data includes user ID and past purchase history, and this data is used to identify the user.

[0114] Step 3:

[0115] The server analyzes the stored data using a generative AI model powered by Scikit-learn. The AI ​​model has learned normal purchase patterns and compares them to determine whether the current purchase behavior is fraudulent. The data processing here involves pattern recognition of purchase history and execution of anomaly detection algorithms, with the output indicating whether or not fraudulent activity has occurred.

[0116] Step 4:

[0117] If fraudulent activity is detected, the server immediately halts the purchase process and notifies the user's device that a fraudulent purchase has been detected. The user is then prompted to attempt a legitimate purchase again. The output data includes the reason for the interruption and instructions for retrying.

[0118] Step 5:

[0119] If the purchase is deemed legitimate, the server will proceed with the purchase process and provide the legitimate buyer with push notifications offering special offers and priority purchase opportunities. This information is customized based on the user's past purchase history and trustworthiness.

[0120] Step 6:

[0121] The server saves the processed purchase information as order history in the database and also notifies the seller of the purchase. This prompts the seller to begin the shipping process. The input data includes details of the successful purchase, and the output generates order information for the seller.

[0122] In this way, the system enables fair and secure online transactions.

[0123] 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.

[0124] This invention provides a system for analyzing user purchase patterns that combines an emotion engine. This system aims to more accurately detect fraudulent purchases by recognizing the user's emotions at the time of ticket purchase and analyzing the impact of those emotions on their purchasing behavior.

[0125] In this embodiment of the system, when a user attempts to purchase a ticket using a terminal, the emotion engine collects not only the purchase information but also data related to the user's emotions. This emotion data is extracted from the user's voice, facial expressions, or text data.

[0126] Next, the server receives purchase information and sentiment data collected from the user, and integrates and analyzes them. AI or a dedicated machine learning algorithm is used for the analysis, which matches the user's current sentiment state with patterns based on their past purchase history.

[0127] Based on this analysis, the server determines whether the purchase is legitimate or fraudulent. If it is determined to be fraudulent, the process is immediately blocked, and the user is provided with the reason and feedback.

[0128] As a concrete example of the emotion engine, if a specific emotional state (such as excitement, tension, or sadness) is expressed, it may indicate that the purchase motivation is temporary. If this is interpreted as a sign of fraudulent activity, the server can prompt reconfirmation by blocking the purchase.

[0129] Furthermore, the server provides a dashboard for managing discrepancies between detected sentiment and purchase patterns, allowing administrators to take further action based on this information. In this way, leveraging the sentiment engine enables a deeper understanding of user purchasing behavior and allows for highly accurate detection of fraudulent purchases.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] The user accesses the online ticket purchase page using their device and enters the number of tickets they wish to purchase and their payment information. During this process, the emotion engine collects the user's facial expressions and voice data.

[0133] Step 2:

[0134] The server receives purchase information entered by the user and emotional data transmitted from the device. This includes the number of items purchased, payment information, user ID, as well as voice tone and facial expression analysis data.

[0135] Step 3:

[0136] The server stores the received information in a database and begins analyzing purchase patterns and emotional data using an AI algorithm. It compares this data with past purchase history and emotional tendencies to perform pattern analysis.

[0137] Step 4:

[0138] Based on the analysis results, the server determines whether the purchase behavior is fraudulent. For example, if the user's emotional state does not match their purchase motivation, or if there are unusual discrepancies with their past purchase history, a detailed review will be deemed necessary.

[0139] Step 5:

[0140] If a purchase is deemed fraudulent, the server immediately blocks the purchase process and notifies the user of the reason. At the same time, it sends a message requesting reconfirmation or additional information.

[0141] Step 6:

[0142] The server will process purchases that did not present any particular problems and issue tickets. After completion, the AI ​​model will be further tuned based on feedback from emotional data.

[0143] Step 7:

[0144] The server provides administrators with an interface that uses analysis results, purchase patterns, and discrepancies in sentiment data. Administrators then make decisions based on this information and adjust system settings and warning criteria as needed.

[0145] (Example 2)

[0146] 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".

[0147] In user purchasing, there is a challenge in accurately detecting fraudulent purchases using only regular purchase information. Therefore, there is a need to efficiently identify fraudulent purchases that occur without considering the influence of emotions on purchasing behavior, and to prevent fraudulent activity without compromising convenience for legitimate buyers.

[0148] 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.

[0149] In this invention, the server includes means for collecting information about the buyer's emotions, means for integrating and analyzing the collected purchase information and emotion information, and means for detecting and blocking fraudulent purchases based on the analysis results. This improves the accuracy of detecting fraudulent purchases based on emotion data, and by notifying legitimate buyers to reconfirm their purchases, a safe and reliable purchasing environment for users is made possible.

[0150] "Purchaser information" refers to basic data about an individual or organization that intends to purchase goods or services, including name, contact information, payment information, and purchase history.

[0151] "Emotional information" refers to data indicating the emotional state extracted from the buyer's voice, facial expressions, and text data, which allows us to understand the buyer's psychological state.

[0152] "Integrating and analyzing" means combining different types of data and using AI algorithms and machine learning techniques to analyze the correlations and patterns within that data.

[0153] "Fraudulent purchase" refers to purchasing activity that is not considered to be based on legitimate purchase intent, but is carried out using methods or for reasons that differ from normal purchasing behavior.

[0154] "Blocking" refers to the process of stopping a transaction that has been deemed fraudulent within the system, preventing the purchase process from being completed.

[0155] "Providing a reconfirmation notice" means that when suspicious purchasing behavior is detected, the buyer is informed of this and asked to reconfirm the details.

[0156] This invention is a system that allows users to purchase tickets safely while preventing fraudulent activity. Specifically, it includes a mechanism that collects and analyzes information about the user's emotions in addition to their purchase behavior when they attempt to purchase a ticket using a terminal.

[0157] First, the user accesses the online ticket sales platform using a device such as a regular computer or smartphone. Here, the user's purchase information, including their name, payment information, and past purchase history, is collected from the device. Simultaneously, the device uses its built-in microphone and camera to collect emotional data based on the user's voice, facial expressions, and text input. For this process, for example, generic name voice analysis software can be used for speech recognition, and generic name image recognition systems can be used for facial expression recognition.

[0158] Next, the device appropriately encrypts the collected data and sends it to the server using security protocols. The receiving server provides an environment for data integration and analysis, using AI models, such as a commonly known machine learning algorithm platform, to perform pattern analysis by comparing the user's sentiment information with past purchase history. This determines whether the purchase behavior is within the normal range or shows signs of fraudulent purchase.

[0159] For example, if a user attempting to purchase tickets for a sporting event exhibits an unusual level of excitement, the system may interpret this emotional fluctuation as a sign of a temporary impulse. As a result, the server can put the purchase on hold and notify the user to reconfirm. This allows for the prevention of fraudulent activity while providing users with a safe and reliable service.

[0160] An example of a specific prompt for the generating AI model is, "Analyze purchase patterns based on user sentiment data and evaluate the likelihood of fraud." Through this process, the system gains a deeper understanding of user emotions and behavior, enabling more accurate fraud detection.

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

[0162] Step 1:

[0163] The user accesses the ticket purchase site using their device and begins the purchase process. Input includes the user's personal information, payment information, and details of the desired ticket. The device simultaneously collects the user's voice, facial expressions, and text input as emotional data. Speech recognition and facial recognition technologies are used for this process. The output consists of purchase information and emotional data.

[0164] Step 2:

[0165] The device encrypts the purchase information and sentiment data collected in Step 1 and securely transmits it to the server. The input is the encrypted data that is the output of Step 1, and the output is the data packet sent to the server. Specifically, the SSL / TLS protocol is used to maintain data confidentiality.

[0166] Step 3:

[0167] The server receives purchase information and sentiment data transmitted from the terminal. The server takes the received data as input, decodes it, and converts it into an analyzable format. The output is integrated data ready for analysis.

[0168] Step 4:

[0169] The server begins analysis using an AI model based on the integrated data. The input is the integrated data obtained in step 3, and the AI ​​model includes a machine learning algorithm. Specifically, the data is processed by extracting the user's emotional state as a feature and comparing it with past purchase history. The output is the analysis result comparing the emotional state and purchase pattern.

[0170] Step 5:

[0171] The server evaluates whether the purchase is legitimate based on the analysis results. The input is the analysis results from step 4, and the output is a determination of legitimacy. If signs of fraud are detected, a fraud flag is set. Specifically, decisions are made, including temporarily suspending purchases in response to abnormal sentiment fluctuations.

[0172] Step 6:

[0173] If a fraudulent flag is set, the server immediately blocks the purchase process and generates feedback to notify the user to reconfirm. The input is fraudulent flag information, and the output is a feedback message.

[0174] Step 7:

[0175] The server aggregates all data on a dashboard and displays it for administrator review. Inputs include analysis results, fraud flags, and feedback history, while outputs are various statistical information displayed on the dashboard. This includes specific actions to visualize detected patterns and purchasing behavior trends.

[0176] (Application Example 2)

[0177] 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".

[0178] In the context of electronic payments, fraudulent purchases remain a serious problem. Fraudulent purchases not only result in financial losses but also risk damaging consumer and provider trust. However, conventional purchase pattern analysis methods have limitations in their accuracy because they do not take into account the emotional state of users. Therefore, there is a need for more accurate and rapid fraud detection.

[0179] 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.

[0180] In this invention, the server includes means for collecting purchaser information, means for identifying emotional states from audio and visual data, and means for identifying purchase patterns by analyzing the collected information and identified emotional states. This enables highly accurate detection of fraudulent purchases that take emotional states into account.

[0181] "Purchaser information" refers to the personal identification information and purchase history that users provide when making a purchase.

[0182] "Audio and visual data" refers to data used to identify the user's emotional state, including their voice and facial expressions.

[0183] "Emotional state" refers to the user's mental state and psychological responses, and is information analyzed from voice and facial expressions.

[0184] "Methods for analyzing and identifying purchase patterns" refers to techniques that analyze users' past purchasing behavior and current emotional state based on collected data, and extract specific behavioral patterns.

[0185] "Means for detecting and stopping fraudulent purchases" refers to a function that immediately interrupts a transaction when abnormal purchase activity is detected and investigates the relevant activity.

[0186] "Means of notifying legitimate purchasers of verification" refers to a system that requests additional identity verification information from users in order to confirm that the purchase is legitimate.

[0187] The system for implementing this invention uses a smart device equipped with speech recognition and facial expression analysis technology to monitor user purchasing behavior and detect fraudulent purchases in real time. Specifically, it utilizes the camera and microphone of a smartphone or tablet to record the user's voice and facial expressions, and transmits them as predetermined data to a cloud server. The server then analyzes this data using a generative AI model to identify the user's emotional state. This analysis utilizes deep learning algorithms using Python and TENSORFLOW®, as well as real-time facial recognition using OpenCV.

[0188] The server also identifies purchase patterns by referencing the user's past purchase records and comparing them with their current emotional state. This combined analysis identifies suspicious purchase activity and immediately terminates transactions if fraud is suspected. Furthermore, if verification is required, the server sends a notification to the user's terminal for identity verification, facilitating the process of verifying legitimate purchases.

[0189] For example, if a user attempts to make a large purchase while emotionally unstable, the system will recognize this as a deviation from normal purchase patterns and temporarily halt the purchase. The user will receive a notification and can complete the purchase process by confirming again.

[0190] As an example of a prompt, the design requirements for the AI ​​model are presented as follows: "Design a deep learning model to analyze the user's facial expressions and voice and evaluate the impact of their current emotional state (excitement or tension) on their purchasing behavior."

[0191] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0192] Step 1:

[0193] The device uses a camera and microphone to collect user voice and facial expression data in real time to monitor the user's ticket purchase behavior. This input data is stored as facial image frames and audio clips.

[0194] Step 2:

[0195] The device sends the collected audio and image data to a cloud server. This data is compressed and transferred to the server via a communication line. On the server, this data is converted into a format that is easy to handle in the next analysis step.

[0196] Step 3:

[0197] The server analyzes the received data using a generation AI model. Here, to extract emotional nuances from audio data and identify facial expressions from facial images, TensorFlow-based audio processing and OpenCV-based facial recognition technology are applied. This processing allows the user's emotional state to be output as a quantitative indicator.

[0198] Step 4:

[0199] The server references the user's past purchase history database and compares the accumulated purchase patterns with the user's current emotional state. The input consists of historical data on past purchase transactions and emotional states, and the output is a determination of the legitimacy of the user's current purchase behavior.

[0200] Step 5:

[0201] If the server detects any suspicious activity, it will interrupt the process and send a security alert notification to the terminal. This notification will include instructions for the user to reconfirm the transaction.

[0202] Step 6:

[0203] The user receives a notification on their device and follows the instructions to reconfirm their purchase details. This confirmation process involves actions such as re-entering personal information and pressing a confirmation button.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] [Second Embodiment]

[0208] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0209] 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.

[0210] 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).

[0211] 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.

[0212] 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.

[0213] 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).

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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".

[0220] This invention provides a ticket purchasing system that ensures tickets are delivered to legitimate fans from fraudulent resellers. The following describes its specific embodiments.

[0221] First, the user attempts to purchase tickets online using their device. During this process, the user's input information, such as the number of tickets to purchase, payment information, and user ID, is sent to the system.

[0222] Next, the server receives the transmitted information and collects additional data, such as the user's IP address and device information. This data is then stored in a database and associated with the user's past purchase history.

[0223] The server uses AI or machine learning algorithms to analyze purchase patterns based on the stored data. This analysis compares the data with past normal purchase data to identify any fraudulent patterns.

[0224] If an invalid pattern is detected, the server will immediately block the purchase and invalidate the ticket. The user will also be notified of the result and encouraged to make the purchase through the normal procedure.

[0225] Blocked tickets will be made available again to legitimate fans. The server will notify legitimate users with a past purchase history, as well as high-priority potential buyers, of the resale opportunity. This notification will be sent via email or in-app push notification.

[0226] As a concrete example, if tickets for a particular artist become extremely popular, the server will detect patterns such as large-scale purchases from the same IP address or consecutive purchases in a short period of time by multiple accounts. When these are determined to be fraudulent, they will be immediately blocked, and the opportunity to resell the tickets will be given to legitimate fans, such as users who have a history of attending the artist's concerts multiple times in the past.

[0227] This system provides fans with a secure environment for purchasing tickets, and effectively prevents fraudulent activity by ticket scalpers through various data analysis techniques.

[0228] The following describes the processing flow.

[0229] Step 1:

[0230] The user accesses the ticket purchase page using their device and enters the number of tickets they wish to purchase and their payment information.

[0231] Step 2:

[0232] The server receives purchase requests from users and collects data such as user ID, number of items purchased, payment information, IP address, and device information.

[0233] Step 3:

[0234] The server stores the collected data in a database. It then compares it with past purchase history and associates it with existing information.

[0235] Step 4:

[0236] The server uses AI models or machine learning algorithms to analyze user behavior based on current purchase data and past safe purchase patterns.

[0237] Step 5:

[0238] Based on the analysis results, the server determines whether a fraudulent purchase pattern exists. For example, it checks if it matches past fraudulent patterns, such as large purchases in a short period or consecutive purchases from the same IP address.

[0239] Step 6:

[0240] The server immediately blocks any purchase request deemed fraudulent and invalidates the ticket purchase. At this time, an error message is generated and sent to the user.

[0241] Step 7:

[0242] The server prepares the fraudulently blocked tickets for resale and makes them available to legitimate buyers.

[0243] Step 8:

[0244] The server identifies users who have previously purchased the same ticket, as well as regular users with high priority based on their interests and preferences, and notifies them of resale opportunities. Notifications are sent via email or in-app push notifications.

[0245] (Example 1)

[0246] 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."

[0247] In ticket purchasing, there is a challenge in preventing fraudulent purchases by unscrupulous resellers and ensuring that tickets are delivered to legitimate buyers.

[0248] 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.

[0249] In this invention, the server includes means for collecting buyer identification information, means for storing acquired additional data in a database, and means for analyzing purchase patterns using artificial intelligence. This makes it possible to detect fraudulent purchases and immediately block them, and to provide legitimate buyers with an opportunity to resell.

[0250] "Purchaser identification information" refers to a unique identifier related to the ticket purchaser, and is composed of information such as user ID and IP address.

[0251] "Additional data" refers to supplementary information obtained based on the purchaser's identification information, including device information and geographical location information.

[0252] A "database" is an advanced storage or memory system used to manage and store collected and acquired identification information and additional data.

[0253] "Artificial intelligence" is a technology in which computers mimic human intelligence to perform data analysis and problem-solving, and it includes machine learning algorithms.

[0254] A "purchase pattern" refers to a characteristic trend or recurring behavior in purchasing activities that is derived from past and present purchase history.

[0255] "Fraudulent purchase" refers to any purchase behavior that the system deems inappropriate or inaccurate, including multiple purchases and purchases made at unusual times.

[0256] A "purchase block" is a process that immediately cancels a transaction and invalidates the purchase when the system detects a fraudulent purchase.

[0257] A "legitimate purchaser" refers to a purchaser with a history of normal purchases and no evidence of fraudulent activity, and is recognized as an individual with legitimate ownership of the ticket.

[0258] A "resale opportunity" is a chance to purchase tickets that have been confiscated due to fraudulent purchases and are offered for resale to legitimate buyers.

[0259] This invention is a system to prevent fraudulent ticket resale, which collects and analyzes the identification information of purchasers and ensures that tickets are delivered to legitimate buyers.

[0260] First, the user purchases tickets online using their device. The information entered by the user on the device, including the number of tickets purchased, payment information, and user ID, is sent to the server.

[0261] Next, the server receives this information and retrieves additional data, such as IP addresses and device information, to fully identify the profile. This additional data is stored in a database.

[0262] Subsequently, the server uses a generative AI model based on the stored information to analyze purchase patterns. The specific software used includes machine learning libraries and data analysis tools based on Python. The data is compared with past purchase history, and any abnormal patterns are identified as fraudulent.

[0263] If a purchase is deemed fraudulent, the server will immediately block the purchase and revert the ticket to a resaleable state. Resale will be notified and offered to users with a legitimate purchase history.

[0264] To give a specific example, in a case where concert tickets for a popular artist are being sold, the server detects a pattern of large-scale purchases made at an unusual time, immediately determines it to be fraudulent, and cancels the purchase. Legitimate fans, such as users who have previously attended events by that artist, are notified of the resale opportunity via email or app notification.

[0265] An example of a prompt to input into the generating AI model would be, "Analyze this user's purchase patterns and determine if any fraudulent activity has occurred."

[0266] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0267] Step 1:

[0268] The user accesses the ticket purchase page from their device. They provide the number of tickets to purchase, payment information, and user ID as input, and send this information to the server. A specific example of this action would be entering "1 concert ticket".

[0269] Step 2:

[0270] The server receives purchase information submitted by the user. Based on the entered information, it additionally collects the user's IP address and device information and stores it in a database. The database is organized to avoid duplication of information and stored in a format that facilitates later investigation and analysis.

[0271] Step 3:

[0272] The server uses stored purchase information and additional data to analyze purchase patterns using a generative AI model. Specifically, it compares past purchase history with current data to detect fraudulent patterns such as consecutive purchases from the same IP address. Here, the generative AI model uses an anomaly detection algorithm to identify fraud with high accuracy.

[0273] Step 4:

[0274] If a fraudulent purchase pattern is detected, the server immediately blocks the purchase and stops the purchase process. This returns the canceled ticket to the server, ready to be resold. Specifically, the user receives a notification stating, "Your purchase has been canceled."

[0275] Step 5:

[0276] The server notifies legitimate potential buyers to offer them a resale opportunity. Users eligible for resale are often those who have been repeatedly confirmed to have attended events featuring the artist in question. This notification is sent via email or in-app push notifications, encouraging them to complete the purchase process again.

[0277] (Application Example 1)

[0278] 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."

[0279] In online shopping, fraudulent purchasing behavior and fake reviews are problems, which cause disadvantages to both consumers and sellers. It is required to prevent such fraudulent acts and provide a highly reliable purchasing experience. Therefore, it is necessary to accurately grasp the behavior of purchasers and provide a mechanism that can immediately detect and block fraudulent purchases.

[0280] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0281] In this invention, the server includes a device for collecting the personal information of purchasers, a device for analyzing the collected personal information to identify the purchasing behavior pattern, and a device for detecting and blocking fraudulent purchasing behavior based on the analysis result. This makes it possible to quickly identify fraudulent acts in online shopping and provide a safe and reassuring purchasing environment for legitimate purchasers.

[0282] A "purchaser" is an individual or a corporation that purchases goods or services.

[0283] "Personal information" is information that can identify a specific individual and includes name, address, contact information, etc.

[0284] A "purchasing behavior pattern" is a series of behavior trends derived from purchase history, purchase frequency, time zone, purchased goods, etc.

[0285] "Fraudulent purchasing behavior" refers to actions that intend to purchase a large quantity of goods or manipulate prices for improper purposes.

[0286] "Blocking" is a measure to detect fraudulent acts and prevent them from continuing.

[0287] A "legitimate purchaser" refers to a person or group that purchases goods or services in a proper way.

[0288] A "sales system" is a system that includes a series of processes and devices for providing goods or services to a buyer.

[0289] A description of the embodiment for carrying out the invention will be provided.

[0290] This invention is a system that prevents fraudulent activity in online shopping and provides a safe and secure purchasing environment for legitimate buyers.

[0291] First, the user downloads the shopping app on their smartphone and registers. This app is developed using Swift (iOS) or Kotlin (Android). It collects user activity information and purchase history and sends it to the server.

[0292] Next, the server is located in the cloud and manages user data using Python and the Django framework. Based on personal information and purchase history submitted by users, the server analyzes purchase behavior patterns using a generative AI model powered by Scikit-learn. This analysis can detect patterns of fraudulent purchases and block the purchase process.

[0293] In particular, the server identifies abnormal activity compared to past normal purchasing behavior and immediately interrupts the purchasing process. This means that, for example, if a large number of product orders are placed in a short period, purchases deemed suspicious will be blocked.

[0294] The server sends push notifications containing special offers to registered buyers. In this way, registered buyers are offered opportunities for priority purchases and special discounts.

[0295] For example, if a user attempts to purchase a large quantity of the same product in a short period of time through the app, this action will be considered fraudulent and the purchase will be immediately blocked. On the other hand, legitimate buyers with a history of normal purchases will be given priority notification of resale opportunities.

[0296] An example of a prompt message for the AI ​​model would be, "Please tell me the procedure for determining if user A's purchase history is fraudulent."

[0297] As a result, this system ensures a safe and fair online shopping environment.

[0298] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0299] Step 1:

[0300] The user launches a shopping app on their smartphone and begins the purchase process. The app retrieves the entered purchase information (product name, quantity, payment information, etc.) and sends it to the server. This becomes the input data.

[0301] Step 2:

[0302] The server stores purchase information received from the app and user personal information in a database. At this time, it also references the user's past purchase history and device information, and associates them with the database. Input data includes user ID and past purchase history, and this data is used to identify the user.

[0303] Step 3:

[0304] The server analyzes the stored data using a generative AI model powered by Scikit-learn. The AI ​​model has learned normal purchase patterns and compares them to determine whether the current purchase behavior is fraudulent. The data processing here involves pattern recognition of purchase history and execution of anomaly detection algorithms, with the output indicating whether or not fraudulent activity has occurred.

[0305] Step 4:

[0306] If it is determined as improper, the server immediately interrupts the purchase process and notifies the user's terminal that an improper purchase has been detected. As a result, the user is prompted to attempt a normal purchase procedure again. The output data includes the reason for the interruption and guidance for retrial.

[0307] Step 5:

[0308] If it is determined that the purchase is legitimate, the server continues the purchase procedure and provides privilege information and preferential purchase opportunities to the regular purchaser via push notification. This information is customized based on the user's past purchase history and reliability.

[0309] Step 6:

[0310] The server stores the processed purchase information in the database as an order history and also sends a purchase notification to the seller side. As a result, an instruction to start the procedure for shipping the goods is issued. The input data includes the details of the successful purchase, and order information for the seller is generated as output.

[0311] In this way, the system realizes a fair and secure online transaction.

[0312] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion recognition model 59 and perform specific processing using the user's emotion.

[0313] The present invention provides a form in which an emotion engine is combined with a system for analyzing the user's purchase pattern. This system aims to more accurately detect improper purchases by recognizing the user's emotion at the time of ticket purchase and analyzing the influence it has on the purchase behavior.

[0314] In this embodiment of the system, when a user attempts to purchase a ticket using a terminal, the emotion engine collects not only the purchase information but also data related to the user's emotions. This emotion data is extracted from the user's voice, facial expressions, or text data.

[0315] Next, the server receives purchase information and sentiment data collected from the user, and integrates and analyzes them. AI or a dedicated machine learning algorithm is used for the analysis, which matches the user's current sentiment state with patterns based on their past purchase history.

[0316] Based on this analysis, the server determines whether the purchase is legitimate or fraudulent. If it is determined to be fraudulent, the process is immediately blocked, and the user is provided with the reason and feedback.

[0317] As a concrete example of the emotion engine, if a specific emotional state (such as excitement, tension, or sadness) is expressed, it may indicate that the purchase motivation is temporary. If this is interpreted as a sign of fraudulent activity, the server can prompt reconfirmation by blocking the purchase.

[0318] Furthermore, the server provides a dashboard for managing discrepancies between detected sentiment and purchase patterns, allowing administrators to take further action based on this information. In this way, leveraging the sentiment engine enables a deeper understanding of user purchasing behavior and allows for highly accurate detection of fraudulent purchases.

[0319] The following describes the processing flow.

[0320] Step 1:

[0321] The user accesses the online ticket purchase page using their device and enters the number of tickets they wish to purchase and their payment information. During this process, the emotion engine collects the user's facial expressions and voice data.

[0322] Step 2:

[0323] The server receives purchase information entered by the user and emotional data transmitted from the device. This includes the number of items purchased, payment information, user ID, as well as voice tone and facial expression analysis data.

[0324] Step 3:

[0325] The server stores the received information in a database and begins analyzing purchase patterns and emotional data using an AI algorithm. It compares this data with past purchase history and emotional tendencies to perform pattern analysis.

[0326] Step 4:

[0327] Based on the analysis results, the server determines whether the purchase behavior is fraudulent. For example, if the user's emotional state does not match their purchase motivation, or if there are unusual discrepancies with their past purchase history, a detailed review will be deemed necessary.

[0328] Step 5:

[0329] If a purchase is deemed fraudulent, the server immediately blocks the purchase process and notifies the user of the reason. At the same time, it sends a message requesting reconfirmation or additional information.

[0330] Step 6:

[0331] The server will process purchases that did not present any particular problems and issue tickets. After completion, the AI ​​model will be further tuned based on feedback from emotional data.

[0332] Step 7:

[0333] The server provides administrators with an interface that uses analysis results, purchase patterns, and discrepancies in sentiment data. Administrators then make decisions based on this information and adjust system settings and warning criteria as needed.

[0334] (Example 2)

[0335] 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".

[0336] In user purchasing, there is a challenge in accurately detecting fraudulent purchases using only regular purchase information. Therefore, there is a need to efficiently identify fraudulent purchases that occur without considering the influence of emotions on purchasing behavior, and to prevent fraudulent activity without compromising convenience for legitimate buyers.

[0337] 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.

[0338] In this invention, the server includes means for collecting information about the buyer's emotions, means for integrating and analyzing the collected purchase information and emotion information, and means for detecting and blocking fraudulent purchases based on the analysis results. This improves the accuracy of detecting fraudulent purchases based on emotion data, and by notifying legitimate buyers to reconfirm their purchases, a safe and reliable purchasing environment for users is made possible.

[0339] "Purchaser information" refers to basic data about an individual or organization that intends to purchase goods or services, including name, contact information, payment information, and purchase history.

[0340] "Emotional information" refers to data indicating the emotional state extracted from the buyer's voice, facial expressions, and text data, which allows us to understand the buyer's psychological state.

[0341] "Integrating and analyzing" means combining different types of data and using AI algorithms and machine learning techniques to analyze the correlations and patterns within that data.

[0342] "Fraudulent purchase" refers to purchasing activity that is not considered to be based on legitimate purchase intent, but is carried out using methods or for reasons that differ from normal purchasing behavior.

[0343] "Blocking" refers to the process of stopping a transaction that has been deemed fraudulent within the system, preventing the purchase process from being completed.

[0344] "Providing a reconfirmation notice" means that when suspicious purchasing behavior is detected, the buyer is informed of this and asked to reconfirm the details.

[0345] This invention is a system that allows users to purchase tickets safely while preventing fraudulent activity. Specifically, it includes a mechanism that collects and analyzes information about the user's emotions in addition to their purchase behavior when they attempt to purchase a ticket using a terminal.

[0346] First, the user accesses the online ticket sales platform using a device such as a regular computer or smartphone. Here, the user's purchase information, including their name, payment information, and past purchase history, is collected from the device. Simultaneously, the device uses its built-in microphone and camera to collect emotional data based on the user's voice, facial expressions, and text input. For this process, for example, generic name voice analysis software can be used for speech recognition, and generic name image recognition systems can be used for facial expression recognition.

[0347] Next, the device appropriately encrypts the collected data and sends it to the server using security protocols. The receiving server provides an environment for data integration and analysis, using AI models, such as a commonly known machine learning algorithm platform, to perform pattern analysis by comparing the user's sentiment information with past purchase history. This determines whether the purchase behavior is within the normal range or shows signs of fraudulent purchase.

[0348] For example, if a user attempting to purchase tickets for a sporting event exhibits an unusual level of excitement, the system may interpret this emotional fluctuation as a sign of a temporary impulse. As a result, the server can put the purchase on hold and notify the user to reconfirm. This allows for the prevention of fraudulent activity while providing users with a safe and reliable service.

[0349] An example of a specific prompt for the generating AI model is, "Analyze purchase patterns based on user sentiment data and evaluate the likelihood of fraud." Through this process, the system gains a deeper understanding of user emotions and behavior, enabling more accurate fraud detection.

[0350] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0351] Step 1:

[0352] The user accesses the ticket purchase site using their device and begins the purchase process. Input includes the user's personal information, payment information, and details of the desired ticket. The device simultaneously collects the user's voice, facial expressions, and text input as emotional data. Speech recognition and facial recognition technologies are used for this process. The output consists of purchase information and emotional data.

[0353] Step 2:

[0354] The device encrypts the purchase information and sentiment data collected in Step 1 and securely transmits it to the server. The input is the encrypted data that is the output of Step 1, and the output is the data packet sent to the server. Specifically, the SSL / TLS protocol is used to maintain data confidentiality.

[0355] Step 3:

[0356] The server receives purchase information and sentiment data transmitted from the terminal. The server takes the received data as input, decodes it, and converts it into an analyzable format. The output is integrated data ready for analysis.

[0357] Step 4:

[0358] The server begins analysis using an AI model based on the integrated data. The input is the integrated data obtained in step 3, and the AI ​​model includes a machine learning algorithm. Specifically, the data is processed by extracting the user's emotional state as a feature and comparing it with past purchase history. The output is the analysis result comparing the emotional state and purchase pattern.

[0359] Step 5:

[0360] The server evaluates whether the purchase is legitimate based on the analysis results. The input is the analysis results from step 4, and the output is a determination of legitimacy. If signs of fraud are detected, a fraud flag is set. Specifically, decisions are made, including temporarily suspending purchases in response to abnormal sentiment fluctuations.

[0361] Step 6:

[0362] If a fraudulent flag is set, the server immediately blocks the purchase process and generates feedback to notify the user to reconfirm. The input is fraudulent flag information, and the output is a feedback message.

[0363] Step 7:

[0364] The server aggregates all data on a dashboard and displays it for administrator review. Inputs include analysis results, fraud flags, and feedback history, while outputs are various statistical information displayed on the dashboard. This includes specific actions to visualize detected patterns and purchasing behavior trends.

[0365] (Application Example 2)

[0366] 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."

[0367] In the context of electronic payments, fraudulent purchases remain a serious problem. Fraudulent purchases not only result in financial losses but also risk damaging consumer and provider trust. However, conventional purchase pattern analysis methods have limitations in their accuracy because they do not take into account the emotional state of users. Therefore, there is a need for more accurate and rapid fraud detection.

[0368] 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.

[0369] In this invention, the server includes means for collecting purchaser information, means for identifying emotional states from audio and visual data, and means for identifying purchase patterns by analyzing the collected information and identified emotional states. This enables highly accurate detection of fraudulent purchases that take emotional states into account.

[0370] "Purchaser information" refers to the personal identification information and purchase history that users provide when making a purchase.

[0371] "Audio and visual data" refers to data used to identify the user's emotional state, including their voice and facial expressions.

[0372] "Emotional state" refers to the user's mental state and psychological responses, and is information analyzed from voice and facial expressions.

[0373] "Methods for analyzing and identifying purchase patterns" refers to techniques that analyze users' past purchasing behavior and current emotional state based on collected data, and extract specific behavioral patterns.

[0374] "Means for detecting and stopping fraudulent purchases" refers to a function that immediately interrupts a transaction when abnormal purchase activity is detected and investigates the relevant activity.

[0375] "Means of notifying legitimate purchasers of verification" refers to a system that requests additional identity verification information from users in order to confirm that the purchase is legitimate.

[0376] The system for implementing this invention uses a smart device equipped with speech recognition and facial expression analysis technology to monitor user purchasing behavior and detect fraudulent purchases in real time. Specifically, it utilizes the camera and microphone of a smartphone or tablet to record the user's voice and facial expressions, and transmits them as predetermined data to a cloud server. The server then analyzes this data using a generative AI model to identify the user's emotional state. This analysis utilizes deep learning algorithms using Python and TensorFlow, as well as real-time facial recognition using OpenCV.

[0377] The server also identifies purchase patterns by referencing the user's past purchase records and comparing them with their current emotional state. This combined analysis identifies suspicious purchase activity and immediately terminates transactions if fraud is suspected. Furthermore, if verification is required, the server sends a notification to the user's terminal for identity verification, facilitating the process of verifying legitimate purchases.

[0378] For example, if a user attempts to make a large purchase while emotionally unstable, the system will recognize this as a deviation from normal purchase patterns and temporarily halt the purchase. The user will receive a notification and can complete the purchase process by confirming again.

[0379] As an example of a prompt, the design requirements for the AI ​​model are presented as follows: "Design a deep learning model to analyze the user's facial expressions and voice and evaluate the impact of their current emotional state (excitement or tension) on their purchasing behavior."

[0380] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0381] Step 1:

[0382] The device uses a camera and microphone to collect user voice and facial expression data in real time to monitor the user's ticket purchase behavior. This input data is stored as facial image frames and audio clips.

[0383] Step 2:

[0384] The device sends the collected audio and image data to a cloud server. This data is compressed and transferred to the server via a communication line. On the server, this data is converted into a format that is easy to handle in the next analysis step.

[0385] Step 3:

[0386] The server analyzes the received data using a generation AI model. Here, to extract emotional nuances from audio data and identify facial expressions from facial images, TensorFlow-based audio processing and OpenCV-based facial recognition technology are applied. This processing allows the user's emotional state to be output as a quantitative indicator.

[0387] Step 4:

[0388] The server references the user's past purchase history database and compares the accumulated purchase patterns with the user's current emotional state. The input consists of historical data on past purchase transactions and emotional states, and the output is a determination of the legitimacy of the user's current purchase behavior.

[0389] Step 5:

[0390] If the server detects any suspicious activity, it will interrupt the process and send a security alert notification to the terminal. This notification will include instructions for the user to reconfirm the transaction.

[0391] Step 6:

[0392] The user receives a notification on their device and follows the instructions to reconfirm their purchase details. This confirmation process involves actions such as re-entering personal information and pressing a confirmation button.

[0393] 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.

[0394] 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.

[0395] 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.

[0396] [Third Embodiment]

[0397] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0398] 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.

[0399] 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).

[0400] 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.

[0401] 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.

[0402] 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).

[0403] 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.

[0404] 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.

[0405] 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.

[0406] 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.

[0407] 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.

[0408] 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".

[0409] This invention provides a ticket purchasing system that ensures tickets are delivered to legitimate fans from fraudulent resellers. The following describes its specific embodiments.

[0410] First, the user attempts to purchase tickets online using their device. During this process, the user's input information, such as the number of tickets to purchase, payment information, and user ID, is sent to the system.

[0411] Next, the server receives the transmitted information and collects additional data, such as the user's IP address and device information. This data is then stored in a database and associated with the user's past purchase history.

[0412] The server uses AI or machine learning algorithms to analyze purchase patterns based on the stored data. This analysis compares the data with past normal purchase data to identify any fraudulent patterns.

[0413] If an invalid pattern is detected, the server will immediately block the purchase and invalidate the ticket. The user will also be notified of the result and encouraged to make the purchase through the normal procedure.

[0414] Blocked tickets will be made available again to legitimate fans. The server will notify legitimate users with a past purchase history, as well as high-priority potential buyers, of the resale opportunity. This notification will be sent via email or in-app push notification.

[0415] As a concrete example, if tickets for a particular artist become extremely popular, the server will detect patterns such as large-scale purchases from the same IP address or consecutive purchases in a short period of time by multiple accounts. When these are determined to be fraudulent, they will be immediately blocked, and the opportunity to resell the tickets will be given to legitimate fans, such as users who have a history of attending the artist's concerts multiple times in the past.

[0416] This system provides fans with a secure environment for purchasing tickets, and effectively prevents fraudulent activity by ticket scalpers through various data analysis techniques.

[0417] The following describes the processing flow.

[0418] Step 1:

[0419] The user accesses the ticket purchase page using their device and enters the number of tickets they wish to purchase and their payment information.

[0420] Step 2:

[0421] The server receives purchase requests from users and collects data such as user ID, number of items purchased, payment information, IP address, and device information.

[0422] Step 3:

[0423] The server stores the collected data in a database. It then compares it with past purchase history and associates it with existing information.

[0424] Step 4:

[0425] The server uses AI models or machine learning algorithms to analyze user behavior based on current purchase data and past safe purchase patterns.

[0426] Step 5:

[0427] Based on the analysis results, the server determines whether a fraudulent purchase pattern exists. For example, it checks if it matches past fraudulent patterns, such as large purchases in a short period or consecutive purchases from the same IP address.

[0428] Step 6:

[0429] The server immediately blocks any purchase request deemed fraudulent and invalidates the ticket purchase. At this time, an error message is generated and sent to the user.

[0430] Step 7:

[0431] The server prepares the fraudulently blocked tickets for resale and makes them available to legitimate buyers.

[0432] Step 8:

[0433] The server identifies users who have previously purchased the same ticket, as well as regular users with high priority based on their interests and preferences, and notifies them of resale opportunities. Notifications are sent via email or in-app push notifications.

[0434] (Example 1)

[0435] 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."

[0436] In ticket purchasing, there is a challenge in preventing fraudulent purchases by unscrupulous resellers and ensuring that tickets are delivered to legitimate buyers.

[0437] 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.

[0438] In this invention, the server includes means for collecting buyer identification information, means for storing acquired additional data in a database, and means for analyzing purchase patterns using artificial intelligence. This makes it possible to detect fraudulent purchases and immediately block them, and to provide legitimate buyers with an opportunity to resell.

[0439] "Purchaser identification information" refers to a unique identifier related to the ticket purchaser, and is composed of information such as user ID and IP address.

[0440] "Additional data" refers to supplementary information obtained based on the purchaser's identification information, including device information and geographical location information.

[0441] A "database" is an advanced storage or memory system used to manage and store collected and acquired identification information and additional data.

[0442] "Artificial intelligence" is a technology in which computers mimic human intelligence to perform data analysis and problem-solving, and it includes machine learning algorithms.

[0443] A "purchase pattern" refers to a characteristic trend or recurring behavior in purchasing activities that is derived from past and present purchase history.

[0444] "Fraudulent purchase" refers to any purchase behavior that the system deems inappropriate or inaccurate, including multiple purchases and purchases made at unusual times.

[0445] A "purchase block" is a process that immediately cancels a transaction and invalidates the purchase when the system detects a fraudulent purchase.

[0446] A "legitimate purchaser" refers to a purchaser with a history of normal purchases and no evidence of fraudulent activity, and is recognized as an individual with legitimate ownership of the ticket.

[0447] A "resale opportunity" is a chance to purchase tickets that have been confiscated due to fraudulent purchases and are offered for resale to legitimate buyers.

[0448] This invention is a system to prevent fraudulent ticket resale, which collects and analyzes the identification information of purchasers and ensures that tickets are delivered to legitimate buyers.

[0449] First, the user purchases tickets online using their device. The information entered by the user on the device, including the number of tickets purchased, payment information, and user ID, is sent to the server.

[0450] Next, the server receives this information and retrieves additional data, such as IP addresses and device information, to fully identify the profile. This additional data is stored in a database.

[0451] Subsequently, the server uses a generative AI model based on the stored information to analyze purchase patterns. The specific software used includes machine learning libraries and data analysis tools based on Python. The data is compared with past purchase history, and any abnormal patterns are identified as fraudulent.

[0452] If a purchase is deemed fraudulent, the server will immediately block the purchase and revert the ticket to a resaleable state. Resale will be notified and offered to users with a legitimate purchase history.

[0453] To give a specific example, in a case where concert tickets for a popular artist are being sold, the server detects a pattern of large-scale purchases made at an unusual time, immediately determines it to be fraudulent, and cancels the purchase. Legitimate fans, such as users who have previously attended events by that artist, are notified of the resale opportunity via email or app notification.

[0454] An example of a prompt to input into the generating AI model would be, "Analyze this user's purchase patterns and determine if any fraudulent activity has occurred."

[0455] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0456] Step 1:

[0457] The user accesses the ticket purchase page from their device. They provide the number of tickets to purchase, payment information, and user ID as input, and send this information to the server. A specific example of this action would be entering "1 concert ticket".

[0458] Step 2:

[0459] The server receives purchase information submitted by the user. Based on the entered information, it additionally collects the user's IP address and device information and stores it in a database. The database is organized to avoid duplication of information and stored in a format that facilitates later investigation and analysis.

[0460] Step 3:

[0461] The server uses stored purchase information and additional data to analyze purchase patterns using a generative AI model. Specifically, it compares past purchase history with current data to detect fraudulent patterns such as consecutive purchases from the same IP address. Here, the generative AI model uses an anomaly detection algorithm to identify fraud with high accuracy.

[0462] Step 4:

[0463] If a fraudulent purchase pattern is detected, the server immediately blocks the purchase and stops the purchase process. This returns the canceled ticket to the server, ready to be resold. Specifically, the user receives a notification stating, "Your purchase has been canceled."

[0464] Step 5:

[0465] The server notifies legitimate potential buyers to offer them a resale opportunity. Users eligible for resale are often those who have been repeatedly confirmed to have attended events featuring the artist in question. This notification is sent via email or in-app push notifications, encouraging them to complete the purchase process again.

[0466] (Application Example 1)

[0467] 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."

[0468] In online shopping, fraudulent purchases and forged reviews are serious problems, causing harm to both consumers and sellers. There is a need to prevent such fraudulent activities and provide a trustworthy shopping experience. Therefore, it is necessary to provide a system that accurately tracks buyer behavior and can immediately detect and block fraudulent purchases.

[0469] 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.

[0470] In this invention, the server includes a device for collecting the purchaser's personal information, a device for analyzing the collected personal information to identify purchase behavior patterns, and a device for detecting and blocking fraudulent purchase activities based on the analysis results. This makes it possible to quickly identify fraudulent activities in online shopping and provide a safe and secure purchasing environment for legitimate purchasers.

[0471] A "purchaser" is an individual or legal entity that buys goods or services.

[0472] "Personal information" refers to information that can identify a specific individual, and includes names, addresses, and contact information.

[0473] A "purchase behavior pattern" refers to a series of behavioral tendencies derived from purchase history, frequency of purchases, time of day, and purchased items.

[0474] "Fraudulent purchasing" refers to actions intended to illegally purchase large quantities of goods or manipulate prices.

[0475] "Blocking" refers to a measure taken to detect fraudulent activity and prevent it from continuing.

[0476] A "legitimate buyer" refers to an individual or organization that purchases goods or services through legitimate means.

[0477] A "sales system" is a system that includes a series of processes and devices for providing goods or services to a buyer.

[0478] A description of the embodiment for carrying out the invention will be provided.

[0479] This invention is a system that prevents fraudulent activity in online shopping and provides a safe and secure purchasing environment for legitimate buyers.

[0480] First, the user downloads the shopping app on their smartphone and registers. This app is developed using Swift (iOS) or Kotlin (Android). It collects user activity information and purchase history and sends it to the server.

[0481] Next, the server is located in the cloud and manages user data using Python and the Django framework. Based on personal information and purchase history submitted by users, the server analyzes purchase behavior patterns using a generative AI model powered by Scikit-learn. This analysis can detect patterns of fraudulent purchases and block the purchase process.

[0482] In particular, the server identifies abnormal activity compared to past normal purchasing behavior and immediately interrupts the purchasing process. This means that, for example, if a large number of product orders are placed in a short period, purchases deemed suspicious will be blocked.

[0483] The server sends push notifications containing special offers to registered buyers. In this way, registered buyers are offered opportunities for priority purchases and special discounts.

[0484] For example, if a user attempts to purchase a large quantity of the same product in a short period of time through the app, this action will be considered fraudulent and the purchase will be immediately blocked. On the other hand, legitimate buyers with a history of normal purchases will be given priority notification of resale opportunities.

[0485] An example of a prompt message for the AI ​​model would be, "Please tell me the procedure for determining if user A's purchase history is fraudulent."

[0486] As a result, this system ensures a safe and fair online shopping environment.

[0487] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0488] Step 1:

[0489] The user launches a shopping app on their smartphone and begins the purchase process. The app retrieves the entered purchase information (product name, quantity, payment information, etc.) and sends it to the server. This becomes the input data.

[0490] Step 2:

[0491] The server stores purchase information received from the app and user personal information in a database. At this time, it also references the user's past purchase history and device information, and associates them with the database. Input data includes user ID and past purchase history, and this data is used to identify the user.

[0492] Step 3:

[0493] The server analyzes the stored data using a generative AI model powered by Scikit-learn. The AI ​​model has learned normal purchase patterns and compares them to determine whether the current purchase behavior is fraudulent. The data processing here involves pattern recognition of purchase history and execution of anomaly detection algorithms, with the output indicating whether or not fraudulent activity has occurred.

[0494] Step 4:

[0495] If fraudulent activity is detected, the server immediately halts the purchase process and notifies the user's device that a fraudulent purchase has been detected. The user is then prompted to attempt a legitimate purchase again. The output data includes the reason for the interruption and instructions for retrying.

[0496] Step 5:

[0497] If the purchase is deemed legitimate, the server will proceed with the purchase process and provide the legitimate buyer with push notifications offering special offers and priority purchase opportunities. This information is customized based on the user's past purchase history and trustworthiness.

[0498] Step 6:

[0499] The server saves the processed purchase information as order history in the database and also notifies the seller of the purchase. This prompts the seller to begin the shipping process. The input data includes details of the successful purchase, and the output generates order information for the seller.

[0500] In this way, the system enables fair and secure online transactions.

[0501] 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.

[0502] This invention provides a system for analyzing user purchase patterns that combines an emotion engine. This system aims to more accurately detect fraudulent purchases by recognizing the user's emotions at the time of ticket purchase and analyzing the impact of those emotions on their purchasing behavior.

[0503] In this embodiment of the system, when a user attempts to purchase a ticket using a terminal, the emotion engine collects not only the purchase information but also data related to the user's emotions. This emotion data is extracted from the user's voice, facial expressions, or text data.

[0504] Next, the server receives purchase information and sentiment data collected from the user, and integrates and analyzes them. AI or a dedicated machine learning algorithm is used for the analysis, which matches the user's current sentiment state with patterns based on their past purchase history.

[0505] Based on this analysis, the server determines whether the purchase is legitimate or fraudulent. If it is determined to be fraudulent, the process is immediately blocked, and the user is provided with the reason and feedback.

[0506] As a concrete example of the emotion engine, if a specific emotional state (such as excitement, tension, or sadness) is expressed, it may indicate that the purchase motivation is temporary. If this is interpreted as a sign of fraudulent activity, the server can prompt reconfirmation by blocking the purchase.

[0507] Furthermore, the server provides a dashboard for managing discrepancies between detected sentiment and purchase patterns, allowing administrators to take further action based on this information. In this way, leveraging the sentiment engine enables a deeper understanding of user purchasing behavior and allows for highly accurate detection of fraudulent purchases.

[0508] The following describes the processing flow.

[0509] Step 1:

[0510] The user accesses the online ticket purchase page using their device and enters the number of tickets they wish to purchase and their payment information. During this process, the emotion engine collects the user's facial expressions and voice data.

[0511] Step 2:

[0512] The server receives purchase information entered by the user and emotional data transmitted from the device. This includes the number of items purchased, payment information, user ID, as well as voice tone and facial expression analysis data.

[0513] Step 3:

[0514] The server stores the received information in a database and begins analyzing purchase patterns and emotional data using an AI algorithm. It compares this data with past purchase history and emotional tendencies to perform pattern analysis.

[0515] Step 4:

[0516] Based on the analysis results, the server determines whether the purchase behavior is fraudulent. For example, if the user's emotional state does not match their purchase motivation, or if there are unusual discrepancies with their past purchase history, a detailed review will be deemed necessary.

[0517] Step 5:

[0518] If a purchase is deemed fraudulent, the server immediately blocks the purchase process and notifies the user of the reason. At the same time, it sends a message requesting reconfirmation or additional information.

[0519] Step 6:

[0520] The server will process purchases that did not present any particular problems and issue tickets. After completion, the AI ​​model will be further tuned based on feedback from emotional data.

[0521] Step 7:

[0522] The server provides administrators with an interface that uses analysis results, purchase patterns, and discrepancies in sentiment data. Administrators then make decisions based on this information and adjust system settings and warning criteria as needed.

[0523] (Example 2)

[0524] 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."

[0525] In user purchasing, there is a challenge in accurately detecting fraudulent purchases using only regular purchase information. Therefore, there is a need to efficiently identify fraudulent purchases that occur without considering the influence of emotions on purchasing behavior, and to prevent fraudulent activity without compromising convenience for legitimate buyers.

[0526] 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.

[0527] In this invention, the server includes means for collecting information about the buyer's emotions, means for integrating and analyzing the collected purchase information and emotion information, and means for detecting and blocking fraudulent purchases based on the analysis results. This improves the accuracy of detecting fraudulent purchases based on emotion data, and by notifying legitimate buyers to reconfirm their purchases, a safe and reliable purchasing environment for users is made possible.

[0528] "Purchaser information" refers to basic data about an individual or organization that intends to purchase goods or services, including name, contact information, payment information, and purchase history.

[0529] "Emotional information" refers to data indicating the emotional state extracted from the buyer's voice, facial expressions, and text data, which allows us to understand the buyer's psychological state.

[0530] "Integrating and analyzing" means combining different types of data and using AI algorithms and machine learning techniques to analyze the correlations and patterns within that data.

[0531] "Fraudulent purchase" refers to purchasing activity that is not considered to be based on legitimate purchase intent, but is carried out using methods or for reasons that differ from normal purchasing behavior.

[0532] "Blocking" refers to the process of stopping a transaction that has been deemed fraudulent within the system, preventing the purchase process from being completed.

[0533] "Providing a reconfirmation notice" means that when suspicious purchasing behavior is detected, the buyer is informed of this and asked to reconfirm the details.

[0534] This invention is a system that allows users to purchase tickets safely while preventing fraudulent activity. Specifically, it includes a mechanism that collects and analyzes information about the user's emotions in addition to their purchase behavior when they attempt to purchase a ticket using a terminal.

[0535] First, the user accesses the online ticket sales platform using a device such as a regular computer or smartphone. Here, the user's purchase information, including their name, payment information, and past purchase history, is collected from the device. Simultaneously, the device uses its built-in microphone and camera to collect emotional data based on the user's voice, facial expressions, and text input. For this process, for example, generic name voice analysis software can be used for speech recognition, and generic name image recognition systems can be used for facial expression recognition.

[0536] Next, the device appropriately encrypts the collected data and sends it to the server using security protocols. The receiving server provides an environment for data integration and analysis, using AI models, such as a commonly known machine learning algorithm platform, to perform pattern analysis by comparing the user's sentiment information with past purchase history. This determines whether the purchase behavior is within the normal range or shows signs of fraudulent purchase.

[0537] For example, if a user attempting to purchase tickets for a sporting event exhibits an unusual level of excitement, the system may interpret this emotional fluctuation as a sign of a temporary impulse. As a result, the server can put the purchase on hold and notify the user to reconfirm. This allows for the prevention of fraudulent activity while providing users with a safe and reliable service.

[0538] An example of a specific prompt for the generating AI model is, "Analyze purchase patterns based on user sentiment data and evaluate the likelihood of fraud." Through this process, the system gains a deeper understanding of user emotions and behavior, enabling more accurate fraud detection.

[0539] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0540] Step 1:

[0541] The user accesses the ticket purchase site using their device and begins the purchase process. Input includes the user's personal information, payment information, and details of the desired ticket. The device simultaneously collects the user's voice, facial expressions, and text input as emotional data. Speech recognition and facial recognition technologies are used for this process. The output consists of purchase information and emotional data.

[0542] Step 2:

[0543] The device encrypts the purchase information and sentiment data collected in Step 1 and securely transmits it to the server. The input is the encrypted data that is the output of Step 1, and the output is the data packet sent to the server. Specifically, the SSL / TLS protocol is used to maintain data confidentiality.

[0544] Step 3:

[0545] The server receives purchase information and sentiment data transmitted from the terminal. The server takes the received data as input, decodes it, and converts it into an analyzable format. The output is integrated data ready for analysis.

[0546] Step 4:

[0547] The server begins analysis using an AI model based on the integrated data. The input is the integrated data obtained in step 3, and the AI ​​model includes a machine learning algorithm. Specifically, the data is processed by extracting the user's emotional state as a feature and comparing it with past purchase history. The output is the analysis result comparing the emotional state and purchase pattern.

[0548] Step 5:

[0549] The server evaluates whether the purchase is legitimate based on the analysis results. The input is the analysis results from step 4, and the output is a determination of legitimacy. If signs of fraud are detected, a fraud flag is set. Specifically, decisions are made, including temporarily suspending purchases in response to abnormal sentiment fluctuations.

[0550] Step 6:

[0551] If a fraudulent flag is set, the server immediately blocks the purchase process and generates feedback to notify the user to reconfirm. The input is fraudulent flag information, and the output is a feedback message.

[0552] Step 7:

[0553] The server aggregates all data on a dashboard and displays it for administrator review. Inputs include analysis results, fraud flags, and feedback history, while outputs are various statistical information displayed on the dashboard. This includes specific actions to visualize detected patterns and purchasing behavior trends.

[0554] (Application Example 2)

[0555] 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."

[0556] In the context of electronic payments, fraudulent purchases remain a serious problem. Fraudulent purchases not only result in financial losses but also risk damaging consumer and provider trust. However, conventional purchase pattern analysis methods have limitations in their accuracy because they do not take into account the emotional state of users. Therefore, there is a need for more accurate and rapid fraud detection.

[0557] 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.

[0558] In this invention, the server includes means for collecting purchaser information, means for identifying emotional states from audio and visual data, and means for identifying purchase patterns by analyzing the collected information and identified emotional states. This enables highly accurate detection of fraudulent purchases that take emotional states into account.

[0559] "Purchaser information" refers to the personal identification information and purchase history that users provide when making a purchase.

[0560] "Audio and visual data" refers to data used to identify the user's emotional state, including their voice and facial expressions.

[0561] "Emotional state" refers to the user's mental state and psychological responses, and is information analyzed from voice and facial expressions.

[0562] "Methods for analyzing and identifying purchase patterns" refers to techniques that analyze users' past purchasing behavior and current emotional state based on collected data, and extract specific behavioral patterns.

[0563] "Means for detecting and stopping fraudulent purchases" refers to a function that immediately interrupts a transaction when abnormal purchase activity is detected and investigates the relevant activity.

[0564] "Means of notifying legitimate purchasers of verification" refers to a system that requests additional identity verification information from users in order to confirm that the purchase is legitimate.

[0565] The system for implementing this invention uses a smart device equipped with speech recognition and facial expression analysis technology to monitor user purchasing behavior and detect fraudulent purchases in real time. Specifically, it utilizes the camera and microphone of a smartphone or tablet to record the user's voice and facial expressions, and transmits them as predetermined data to a cloud server. The server then analyzes this data using a generative AI model to identify the user's emotional state. This analysis utilizes deep learning algorithms using Python and TensorFlow, as well as real-time facial recognition using OpenCV.

[0566] The server also identifies purchase patterns by referencing the user's past purchase records and comparing them with their current emotional state. This combined analysis identifies suspicious purchase activity and immediately terminates transactions if fraud is suspected. Furthermore, if verification is required, the server sends a notification to the user's terminal for identity verification, facilitating the process of verifying legitimate purchases.

[0567] For example, if a user attempts to make a large purchase while emotionally unstable, the system will recognize this as a deviation from normal purchase patterns and temporarily halt the purchase. The user will receive a notification and can complete the purchase process by confirming again.

[0568] As an example of a prompt, the design requirements for the AI ​​model are presented as follows: "Design a deep learning model to analyze the user's facial expressions and voice and evaluate the impact of their current emotional state (excitement or tension) on their purchasing behavior."

[0569] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0570] Step 1:

[0571] The device uses a camera and microphone to collect user voice and facial expression data in real time to monitor the user's ticket purchase behavior. This input data is stored as facial image frames and audio clips.

[0572] Step 2:

[0573] The device sends the collected audio and image data to a cloud server. This data is compressed and transferred to the server via a communication line. On the server, this data is converted into a format that is easy to handle in the next analysis step.

[0574] Step 3:

[0575] The server analyzes the received data using a generation AI model. Here, to extract emotional nuances from audio data and identify facial expressions from facial images, TensorFlow-based audio processing and OpenCV-based facial recognition technology are applied. This processing allows the user's emotional state to be output as a quantitative indicator.

[0576] Step 4:

[0577] The server references the user's past purchase history database and compares the accumulated purchase patterns with the user's current emotional state. The input consists of historical data on past purchase transactions and emotional states, and the output is a determination of the legitimacy of the user's current purchase behavior.

[0578] Step 5:

[0579] If the server detects any suspicious activity, it will interrupt the process and send a security alert notification to the terminal. This notification will include instructions for the user to reconfirm the transaction.

[0580] Step 6:

[0581] The user receives a notification on their device and follows the instructions to reconfirm their purchase details. This confirmation process involves actions such as re-entering personal information and pressing a confirmation button.

[0582] 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.

[0583] 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.

[0584] 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.

[0585] [Fourth Embodiment]

[0586] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0587] 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.

[0588] 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).

[0589] 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.

[0590] 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.

[0591] 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).

[0592] 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.

[0593] 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.

[0594] 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.

[0595] 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.

[0596] 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.

[0597] 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.

[0598] 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".

[0599] This invention provides a ticket purchasing system that ensures tickets are delivered to legitimate fans from fraudulent resellers. The following describes its specific embodiments.

[0600] First, the user attempts to purchase tickets online using their device. During this process, the user's input information, such as the number of tickets to purchase, payment information, and user ID, is sent to the system.

[0601] Next, the server receives the transmitted information and collects additional data, such as the user's IP address and device information. This data is then stored in a database and associated with the user's past purchase history.

[0602] The server uses AI or machine learning algorithms to analyze purchase patterns based on the stored data. This analysis compares the data with past normal purchase data to identify any fraudulent patterns.

[0603] If an invalid pattern is detected, the server will immediately block the purchase and invalidate the ticket. The user will also be notified of the result and encouraged to make the purchase through the normal procedure.

[0604] Blocked tickets will be made available again to legitimate fans. The server will notify legitimate users with a past purchase history, as well as high-priority potential buyers, of the resale opportunity. This notification will be sent via email or in-app push notification.

[0605] As a concrete example, if tickets for a particular artist become extremely popular, the server will detect patterns such as large-scale purchases from the same IP address or consecutive purchases in a short period of time by multiple accounts. When these are determined to be fraudulent, they will be immediately blocked, and the opportunity to resell the tickets will be given to legitimate fans, such as users who have a history of attending the artist's concerts multiple times in the past.

[0606] This system provides fans with a secure environment for purchasing tickets, and effectively prevents fraudulent activity by ticket scalpers through various data analysis techniques.

[0607] The following describes the processing flow.

[0608] Step 1:

[0609] The user accesses the ticket purchase page using their device and enters the number of tickets they wish to purchase and their payment information.

[0610] Step 2:

[0611] The server receives purchase requests from users and collects data such as user ID, number of items purchased, payment information, IP address, and device information.

[0612] Step 3:

[0613] The server stores the collected data in a database. It then compares it with past purchase history and associates it with existing information.

[0614] Step 4:

[0615] The server uses AI models or machine learning algorithms to analyze user behavior based on current purchase data and past safe purchase patterns.

[0616] Step 5:

[0617] Based on the analysis results, the server determines whether a fraudulent purchase pattern exists. For example, it checks if it matches past fraudulent patterns, such as large purchases in a short period or consecutive purchases from the same IP address.

[0618] Step 6:

[0619] The server immediately blocks any purchase request deemed fraudulent and invalidates the ticket purchase. At this time, an error message is generated and sent to the user.

[0620] Step 7:

[0621] The server prepares the fraudulently blocked tickets for resale and makes them available to legitimate buyers.

[0622] Step 8:

[0623] The server identifies users who have previously purchased the same ticket, as well as regular users with high priority based on their interests and preferences, and notifies them of resale opportunities. Notifications are sent via email or in-app push notifications.

[0624] (Example 1)

[0625] 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".

[0626] In ticket purchasing, there is a challenge in preventing fraudulent purchases by unscrupulous resellers and ensuring that tickets are delivered to legitimate buyers.

[0627] 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.

[0628] In this invention, the server includes means for collecting buyer identification information, means for storing acquired additional data in a database, and means for analyzing purchase patterns using artificial intelligence. This makes it possible to detect fraudulent purchases and immediately block them, and to provide legitimate buyers with an opportunity to resell.

[0629] "Purchaser identification information" refers to a unique identifier related to the ticket purchaser, and is composed of information such as user ID and IP address.

[0630] "Additional data" refers to supplementary information obtained based on the purchaser's identification information, including device information and geographical location information.

[0631] A "database" is an advanced storage or memory system used to manage and store collected and acquired identification information and additional data.

[0632] "Artificial intelligence" is a technology in which computers mimic human intelligence to perform data analysis and problem-solving, and it includes machine learning algorithms.

[0633] A "purchase pattern" refers to a characteristic trend or recurring behavior in purchasing activities that is derived from past and present purchase history.

[0634] "Fraudulent purchase" refers to any purchase behavior that the system deems inappropriate or inaccurate, including multiple purchases and purchases made at unusual times.

[0635] A "purchase block" is a process that immediately cancels a transaction and invalidates the purchase when the system detects a fraudulent purchase.

[0636] A "legitimate purchaser" refers to a purchaser with a history of normal purchases and no evidence of fraudulent activity, and is recognized as an individual with legitimate ownership of the ticket.

[0637] A "resale opportunity" is a chance to purchase tickets that have been confiscated due to fraudulent purchases and are offered for resale to legitimate buyers.

[0638] This invention is a system to prevent fraudulent ticket resale, which collects and analyzes the identification information of purchasers and ensures that tickets are delivered to legitimate buyers.

[0639] First, the user purchases tickets online using their device. The information entered by the user on the device, including the number of tickets purchased, payment information, and user ID, is sent to the server.

[0640] Next, the server receives this information and retrieves additional data, such as IP addresses and device information, to fully identify the profile. This additional data is stored in a database.

[0641] Subsequently, the server uses a generative AI model based on the stored information to analyze purchase patterns. The specific software used includes machine learning libraries and data analysis tools based on Python. The data is compared with past purchase history, and any abnormal patterns are identified as fraudulent.

[0642] If a purchase is deemed fraudulent, the server will immediately block the purchase and revert the ticket to a resaleable state. Resale will be notified and offered to users with a legitimate purchase history.

[0643] To give a specific example, in a case where concert tickets for a popular artist are being sold, the server detects a pattern of large-scale purchases made at an unusual time, immediately determines it to be fraudulent, and cancels the purchase. Legitimate fans, such as users who have previously attended events by that artist, are notified of the resale opportunity via email or app notification.

[0644] An example of a prompt to input into the generating AI model would be, "Analyze this user's purchase patterns and determine if any fraudulent activity has occurred."

[0645] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0646] Step 1:

[0647] The user accesses the ticket purchase page from their device. They provide the number of tickets to purchase, payment information, and user ID as input, and send this information to the server. A specific example of this action would be entering "1 concert ticket".

[0648] Step 2:

[0649] The server receives purchase information submitted by the user. Based on the entered information, it additionally collects the user's IP address and device information and stores it in a database. The database is organized to avoid duplication of information and stored in a format that facilitates later investigation and analysis.

[0650] Step 3:

[0651] The server uses stored purchase information and additional data to analyze purchase patterns using a generative AI model. Specifically, it compares past purchase history with current data to detect fraudulent patterns such as consecutive purchases from the same IP address. Here, the generative AI model uses an anomaly detection algorithm to identify fraud with high accuracy.

[0652] Step 4:

[0653] If a fraudulent purchase pattern is detected, the server immediately blocks the purchase and stops the purchase process. This returns the canceled ticket to the server, ready to be resold. Specifically, the user receives a notification stating, "Your purchase has been canceled."

[0654] Step 5:

[0655] The server notifies legitimate potential buyers to offer them a resale opportunity. Users eligible for resale are often those who have been repeatedly confirmed to have attended events featuring the artist in question. This notification is sent via email or in-app push notifications, encouraging them to complete the purchase process again.

[0656] (Application Example 1)

[0657] 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".

[0658] In online shopping, fraudulent purchases and forged reviews are serious problems, causing harm to both consumers and sellers. There is a need to prevent such fraudulent activities and provide a trustworthy shopping experience. Therefore, it is necessary to provide a system that accurately tracks buyer behavior and can immediately detect and block fraudulent purchases.

[0659] 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.

[0660] In this invention, the server includes a device for collecting the purchaser's personal information, a device for analyzing the collected personal information to identify purchase behavior patterns, and a device for detecting and blocking fraudulent purchase activities based on the analysis results. This makes it possible to quickly identify fraudulent activities in online shopping and provide a safe and secure purchasing environment for legitimate purchasers.

[0661] A "purchaser" is an individual or legal entity that buys goods or services.

[0662] "Personal information" refers to information that can identify a specific individual, and includes names, addresses, and contact information.

[0663] A "purchase behavior pattern" refers to a series of behavioral tendencies derived from purchase history, frequency of purchases, time of day, and purchased items.

[0664] "Fraudulent purchasing" refers to actions intended to illegally purchase large quantities of goods or manipulate prices.

[0665] "Blocking" refers to a measure taken to detect fraudulent activity and prevent it from continuing.

[0666] A "legitimate buyer" refers to an individual or organization that purchases goods or services through legitimate means.

[0667] A "sales system" is a system that includes a series of processes and devices for providing goods or services to a buyer.

[0668] A description of the embodiment for carrying out the invention will be provided.

[0669] This invention is a system that prevents fraudulent activity in online shopping and provides a safe and secure purchasing environment for legitimate buyers.

[0670] First, the user downloads the shopping app on their smartphone and registers. This app is developed using Swift (iOS) or Kotlin (Android). It collects user activity information and purchase history and sends it to the server.

[0671] Next, the server is located in the cloud and manages user data using Python and the Django framework. Based on personal information and purchase history submitted by users, the server analyzes purchase behavior patterns using a generative AI model powered by Scikit-learn. This analysis can detect patterns of fraudulent purchases and block the purchase process.

[0672] In particular, the server identifies abnormal activity compared to past normal purchasing behavior and immediately interrupts the purchasing process. This means that, for example, if a large number of product orders are placed in a short period, purchases deemed suspicious will be blocked.

[0673] The server sends push notifications containing special offers to registered buyers. In this way, registered buyers are offered opportunities for priority purchases and special discounts.

[0674] For example, if a user attempts to purchase a large quantity of the same product in a short period of time through the app, this action will be considered fraudulent and the purchase will be immediately blocked. On the other hand, legitimate buyers with a history of normal purchases will be given priority notification of resale opportunities.

[0675] An example of a prompt message for the AI ​​model would be, "Please tell me the procedure for determining if user A's purchase history is fraudulent."

[0676] As a result, this system ensures a safe and fair online shopping environment.

[0677] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0678] Step 1:

[0679] The user launches a shopping app on their smartphone and begins the purchase process. The app retrieves the entered purchase information (product name, quantity, payment information, etc.) and sends it to the server. This becomes the input data.

[0680] Step 2:

[0681] The server stores purchase information received from the app and user personal information in a database. At this time, it also references the user's past purchase history and device information, and associates them with the database. Input data includes user ID and past purchase history, and this data is used to identify the user.

[0682] Step 3:

[0683] The server analyzes the stored data using a generative AI model powered by Scikit-learn. The AI ​​model has learned normal purchase patterns and compares them to determine whether the current purchase behavior is fraudulent. The data processing here involves pattern recognition of purchase history and execution of anomaly detection algorithms, with the output indicating whether or not fraudulent activity has occurred.

[0684] Step 4:

[0685] If fraudulent activity is detected, the server immediately halts the purchase process and notifies the user's device that a fraudulent purchase has been detected. The user is then prompted to attempt a legitimate purchase again. The output data includes the reason for the interruption and instructions for retrying.

[0686] Step 5:

[0687] If the purchase is deemed legitimate, the server will proceed with the purchase process and provide the legitimate buyer with push notifications offering special offers and priority purchase opportunities. This information is customized based on the user's past purchase history and trustworthiness.

[0688] Step 6:

[0689] The server saves the processed purchase information as order history in the database and also notifies the seller of the purchase. This prompts the seller to begin the shipping process. The input data includes details of the successful purchase, and the output generates order information for the seller.

[0690] In this way, the system enables fair and secure online transactions.

[0691] 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.

[0692] This invention provides a system for analyzing user purchase patterns that combines an emotion engine. This system aims to more accurately detect fraudulent purchases by recognizing the user's emotions at the time of ticket purchase and analyzing the impact of those emotions on their purchasing behavior.

[0693] In this embodiment of the system, when a user attempts to purchase a ticket using a terminal, the emotion engine collects not only the purchase information but also data related to the user's emotions. This emotion data is extracted from the user's voice, facial expressions, or text data.

[0694] Next, the server receives purchase information and sentiment data collected from the user, and integrates and analyzes them. AI or a dedicated machine learning algorithm is used for the analysis, which matches the user's current sentiment state with patterns based on their past purchase history.

[0695] Based on this analysis, the server determines whether the purchase is legitimate or fraudulent. If it is determined to be fraudulent, the process is immediately blocked, and the user is provided with the reason and feedback.

[0696] As a concrete example of the emotion engine, if a specific emotional state (such as excitement, tension, or sadness) is expressed, it may indicate that the purchase motivation is temporary. If this is interpreted as a sign of fraudulent activity, the server can prompt reconfirmation by blocking the purchase.

[0697] Furthermore, the server provides a dashboard for managing discrepancies between detected sentiment and purchase patterns, allowing administrators to take further action based on this information. In this way, leveraging the sentiment engine enables a deeper understanding of user purchasing behavior and allows for highly accurate detection of fraudulent purchases.

[0698] The following describes the processing flow.

[0699] Step 1:

[0700] The user accesses the online ticket purchase page using their device and enters the number of tickets they wish to purchase and their payment information. During this process, the emotion engine collects the user's facial expressions and voice data.

[0701] Step 2:

[0702] The server receives purchase information entered by the user and emotional data transmitted from the device. This includes the number of items purchased, payment information, user ID, as well as voice tone and facial expression analysis data.

[0703] Step 3:

[0704] The server stores the received information in a database and begins analyzing purchase patterns and emotional data using an AI algorithm. It compares this data with past purchase history and emotional tendencies to perform pattern analysis.

[0705] Step 4:

[0706] Based on the analysis results, the server determines whether the purchase behavior is fraudulent. For example, if the user's emotional state does not match their purchase motivation, or if there are unusual discrepancies with their past purchase history, a detailed review will be deemed necessary.

[0707] Step 5:

[0708] If a purchase is deemed fraudulent, the server immediately blocks the purchase process and notifies the user of the reason. At the same time, it sends a message requesting reconfirmation or additional information.

[0709] Step 6:

[0710] The server will process purchases that did not present any particular problems and issue tickets. After completion, the AI ​​model will be further tuned based on feedback from emotional data.

[0711] Step 7:

[0712] The server provides administrators with an interface that uses analysis results, purchase patterns, and discrepancies in sentiment data. Administrators then make decisions based on this information and adjust system settings and warning criteria as needed.

[0713] (Example 2)

[0714] 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".

[0715] In user purchasing, there is a challenge in accurately detecting fraudulent purchases using only regular purchase information. Therefore, there is a need to efficiently identify fraudulent purchases that occur without considering the influence of emotions on purchasing behavior, and to prevent fraudulent activity without compromising convenience for legitimate buyers.

[0716] 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.

[0717] In this invention, the server includes means for collecting information about the buyer's emotions, means for integrating and analyzing the collected purchase information and emotion information, and means for detecting and blocking fraudulent purchases based on the analysis results. This improves the accuracy of detecting fraudulent purchases based on emotion data, and by notifying legitimate buyers to reconfirm their purchases, a safe and reliable purchasing environment for users is made possible.

[0718] "Purchaser information" refers to basic data about an individual or organization that intends to purchase goods or services, including name, contact information, payment information, and purchase history.

[0719] "Emotional information" refers to data indicating the emotional state extracted from the buyer's voice, facial expressions, and text data, which allows us to understand the buyer's psychological state.

[0720] "Integrating and analyzing" means combining different types of data and using AI algorithms and machine learning techniques to analyze the correlations and patterns within that data.

[0721] "Fraudulent purchase" refers to purchasing activity that is not considered to be based on legitimate purchase intent, but is carried out using methods or for reasons that differ from normal purchasing behavior.

[0722] "Blocking" refers to the process of stopping a transaction that has been deemed fraudulent within the system, preventing the purchase process from being completed.

[0723] "Providing a reconfirmation notice" means that when suspicious purchasing behavior is detected, the buyer is informed of this and asked to reconfirm the details.

[0724] This invention is a system that allows users to purchase tickets safely while preventing fraudulent activity. Specifically, it includes a mechanism that collects and analyzes information about the user's emotions in addition to their purchase behavior when they attempt to purchase a ticket using a terminal.

[0725] First, the user accesses the online ticket sales platform using a device such as a regular computer or smartphone. Here, the user's purchase information, including their name, payment information, and past purchase history, is collected from the device. Simultaneously, the device uses its built-in microphone and camera to collect emotional data based on the user's voice, facial expressions, and text input. For this process, for example, generic name voice analysis software can be used for speech recognition, and generic name image recognition systems can be used for facial expression recognition.

[0726] Next, the device appropriately encrypts the collected data and sends it to the server using security protocols. The receiving server provides an environment for data integration and analysis, using AI models, such as a commonly known machine learning algorithm platform, to perform pattern analysis by comparing the user's sentiment information with past purchase history. This determines whether the purchase behavior is within the normal range or shows signs of fraudulent purchase.

[0727] For example, if a user attempting to purchase tickets for a sporting event exhibits an unusual level of excitement, the system may interpret this emotional fluctuation as a sign of a temporary impulse. As a result, the server can put the purchase on hold and notify the user to reconfirm. This allows for the prevention of fraudulent activity while providing users with a safe and reliable service.

[0728] An example of a specific prompt for the generating AI model is, "Analyze purchase patterns based on user sentiment data and evaluate the likelihood of fraud." Through this process, the system gains a deeper understanding of user emotions and behavior, enabling more accurate fraud detection.

[0729] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0730] Step 1:

[0731] The user accesses the ticket purchase site using their device and begins the purchase process. Input includes the user's personal information, payment information, and details of the desired ticket. The device simultaneously collects the user's voice, facial expressions, and text input as emotional data. Speech recognition and facial recognition technologies are used for this process. The output consists of purchase information and emotional data.

[0732] Step 2:

[0733] The device encrypts the purchase information and sentiment data collected in Step 1 and securely transmits it to the server. The input is the encrypted data that is the output of Step 1, and the output is the data packet sent to the server. Specifically, the SSL / TLS protocol is used to maintain data confidentiality.

[0734] Step 3:

[0735] The server receives purchase information and sentiment data transmitted from the terminal. The server takes the received data as input, decodes it, and converts it into an analyzable format. The output is integrated data ready for analysis.

[0736] Step 4:

[0737] The server begins analysis using an AI model based on the integrated data. The input is the integrated data obtained in step 3, and the AI ​​model includes a machine learning algorithm. Specifically, the data is processed by extracting the user's emotional state as a feature and comparing it with past purchase history. The output is the analysis result comparing the emotional state and purchase pattern.

[0738] Step 5:

[0739] The server evaluates whether the purchase is legitimate based on the analysis results. The input is the analysis results from step 4, and the output is a determination of legitimacy. If signs of fraud are detected, a fraud flag is set. Specifically, decisions are made, including temporarily suspending purchases in response to abnormal sentiment fluctuations.

[0740] Step 6:

[0741] If a fraudulent flag is set, the server immediately blocks the purchase process and generates feedback to notify the user to reconfirm. The input is fraudulent flag information, and the output is a feedback message.

[0742] Step 7:

[0743] The server aggregates all data on a dashboard and displays it for administrator review. Inputs include analysis results, fraud flags, and feedback history, while outputs are various statistical information displayed on the dashboard. This includes specific actions to visualize detected patterns and purchasing behavior trends.

[0744] (Application Example 2)

[0745] 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".

[0746] In the context of electronic payments, fraudulent purchases remain a serious problem. Fraudulent purchases not only result in financial losses but also risk damaging consumer and provider trust. However, conventional purchase pattern analysis methods have limitations in their accuracy because they do not take into account the emotional state of users. Therefore, there is a need for more accurate and rapid fraud detection.

[0747] 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.

[0748] In this invention, the server includes means for collecting purchaser information, means for identifying emotional states from audio and visual data, and means for identifying purchase patterns by analyzing the collected information and identified emotional states. This enables highly accurate detection of fraudulent purchases that take emotional states into account.

[0749] "Purchaser information" refers to the personal identification information and purchase history that users provide when making a purchase.

[0750] "Audio and visual data" refers to data used to identify the user's emotional state, including their voice and facial expressions.

[0751] "Emotional state" refers to the user's mental state and psychological responses, and is information analyzed from voice and facial expressions.

[0752] "Methods for analyzing and identifying purchase patterns" refers to techniques that analyze users' past purchasing behavior and current emotional state based on collected data, and extract specific behavioral patterns.

[0753] "Means for detecting and stopping fraudulent purchases" refers to a function that immediately interrupts a transaction when abnormal purchase activity is detected and investigates the relevant activity.

[0754] "Means of notifying legitimate purchasers of verification" refers to a system that requests additional identity verification information from users in order to confirm that the purchase is legitimate.

[0755] The system for implementing this invention uses a smart device equipped with speech recognition and facial expression analysis technology to monitor user purchasing behavior and detect fraudulent purchases in real time. Specifically, it utilizes the camera and microphone of a smartphone or tablet to record the user's voice and facial expressions, and transmits them as predetermined data to a cloud server. The server then analyzes this data using a generative AI model to identify the user's emotional state. This analysis utilizes deep learning algorithms using Python and TensorFlow, as well as real-time facial recognition using OpenCV.

[0756] The server also identifies purchase patterns by referencing the user's past purchase records and comparing them with their current emotional state. This combined analysis identifies suspicious purchase activity and immediately terminates transactions if fraud is suspected. Furthermore, if verification is required, the server sends a notification to the user's terminal for identity verification, facilitating the process of verifying legitimate purchases.

[0757] For example, if a user attempts to make a large purchase while emotionally unstable, the system will recognize this as a deviation from normal purchase patterns and temporarily halt the purchase. The user will receive a notification and can complete the purchase process by confirming again.

[0758] As an example of a prompt, the design requirements for the AI ​​model are presented as follows: "Design a deep learning model to analyze the user's facial expressions and voice and evaluate the impact of their current emotional state (excitement or tension) on their purchasing behavior."

[0759] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0760] Step 1:

[0761] The device uses a camera and microphone to collect user voice and facial expression data in real time to monitor the user's ticket purchase behavior. This input data is stored as facial image frames and audio clips.

[0762] Step 2:

[0763] The device sends the collected audio and image data to a cloud server. This data is compressed and transferred to the server via a communication line. On the server, this data is converted into a format that is easy to handle in the next analysis step.

[0764] Step 3:

[0765] The server analyzes the received data using a generation AI model. Here, to extract emotional nuances from audio data and identify facial expressions from facial images, TensorFlow-based audio processing and OpenCV-based facial recognition technology are applied. This processing allows the user's emotional state to be output as a quantitative indicator.

[0766] Step 4:

[0767] The server references the user's past purchase history database and compares the accumulated purchase patterns with the user's current emotional state. The input consists of historical data on past purchase transactions and emotional states, and the output is a determination of the legitimacy of the user's current purchase behavior.

[0768] Step 5:

[0769] If the server detects any suspicious activity, it will interrupt the process and send a security alert notification to the terminal. This notification will include instructions for the user to reconfirm the transaction.

[0770] Step 6:

[0771] The user receives a notification on their device and follows the instructions to reconfirm their purchase details. This confirmation process involves actions such as re-entering personal information and pressing a confirmation button.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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."

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] The following is further disclosed regarding the embodiments described above.

[0794] (Claim 1)

[0795] Means of collecting buyer information,

[0796] A means of identifying purchase patterns by analyzing the collected information,

[0797] A means to detect and block fraudulent purchases based on the analysis results,

[0798] A means of notifying legitimate buyers of resale,

[0799] A system that includes this.

[0800] (Claim 2)

[0801] The system according to claim 1, which includes means for performing analysis by comparing it with the purchaser's past history.

[0802] (Claim 3)

[0803] The system according to claim 1, including means for stopping the purchase process in real time if it is determined to be a fraudulent purchase.

[0804] "Example 1"

[0805] (Claim 1)

[0806] Means for collecting the buyer's identification information,

[0807] Means for obtaining additional data based on collected identification information,

[0808] A means of storing the acquired additional data in a database,

[0809] A method for analyzing purchase patterns using artificial intelligence based on stored data,

[0810] A means to identify and block fraudulent purchases based on the results of the analyzed purchase patterns,

[0811] A means of notifying candidates with legitimate purchase rights of the opportunity to resell,

[0812] A system that includes this.

[0813] (Claim 2)

[0814] The system according to claim 1, which performs analysis using past transaction history.

[0815] (Claim 3)

[0816] The system according to claim 1, which immediately terminates a transaction if it is determined to be a fraudulent transaction.

[0817] "Application Example 1"

[0818] (Claim 1)

[0819] A device that collects the personal information of purchasers,

[0820] A device that analyzes collected personal information to identify purchasing behavior patterns,

[0821] A device that detects and blocks fraudulent purchase activities based on the analysis results,

[0822] A device that transmits bonus information to legitimate purchasers,

[0823] A sales system that includes this.

[0824] (Claim 2)

[0825] The sales system according to claim 1, comprising a device that performs analysis by comparing it with the past behavioral history of the buyer.

[0826] (Claim 3)

[0827] The sales system according to claim 1, which includes a device that immediately suspends the purchase procedure when it is determined to be an illegal purchase.

[0828] "Example 2 of combining an emotion engine"

[0829] (Claim 1)

[0830] Means of collecting buyer information,

[0831] Means of collecting information about the buyer's emotions,

[0832] A means of integrating and analyzing collected purchase information and emotional information,

[0833] A means to detect and block fraudulent purchases based on the analysis results,

[0834] A means of notifying legitimate buyers to reconfirm,

[0835] A system that includes this.

[0836] (Claim 2)

[0837] The system according to claim 1, which includes means for analyzing a purchaser's past history and emotional information by comparing them.

[0838] (Claim 3)

[0839] The system according to claim 1, which includes means for stopping the purchase process in real time if it is determined to be a fraudulent purchase and providing feedback to the user.

[0840] "Application example 2 of combining emotional engines"

[0841] (Claim 1)

[0842] Means of collecting buyer information,

[0843] A means of identifying emotional states from audio and visual data,

[0844] A means of identifying purchase patterns by analyzing collected information and identified emotional states,

[0845] A means to detect fraudulent purchases based on the analysis results and stop the process,

[0846] A means of notifying legitimate buyers of confirmation,

[0847] A system that includes this.

[0848] (Claim 2)

[0849] The system according to claim 1, comprising means for comparing and analyzing the purchaser's past records and emotional state.

[0850] (Claim 3)

[0851] The system according to claim 1, including means for immediately suspending the purchase process if it is determined to be a fraudulent purchase. [Explanation of symbols]

[0852] 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 of collecting buyer information, A means of identifying purchase patterns by analyzing the collected information, A means to detect and block fraudulent purchases based on the analysis results, A means of notifying legitimate buyers of resale, A system that includes this.

2. The system according to claim 1, which includes means for performing analysis by comparing it with the purchaser's past history.

3. The system according to claim 1, including means for stopping the purchase process in real time if it is determined to be a fraudulent purchase.

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