system

Abstract

The system according to this embodiment aims to effectively detect and counter fraudulent activity by users. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an identification unit, and a blocking unit. The collection unit collects user behavior data. The analysis unit analyzes the data collected by the collection unit. The identification unit identifies fraudulent activity based on the analysis results obtained by the analysis unit. The blocking unit blocks orders based on the fraudulent activity identified by the identification unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to effectively detect and take countermeasures against a user's improper behavior.

[0005] The system according to an embodiment aims to effectively detect a user's improper behavior and take countermeasures.

Means for Solving the Problems

[0006] The system according to an embodiment includes a collection unit, an analysis unit, an identification unit, and a block unit. The collection unit collects a user's behavior data. The analysis unit analyzes the data collected by the collection unit. The identification unit identifies an improper behavior based on the analysis result obtained by the analysis unit. The block unit blocks an order based on the improper behavior identified by the identification unit. [Effects of the Invention]

[0007] The system according to this embodiment can effectively detect and counteract user misconduct. [Brief explanation of the drawing]

[0008] [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. [Modes for carrying out the invention]

[0009] 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.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The fraudulent order detection system according to an embodiment of the present invention is a system that detects fraudulent orders on Demaekan using generative AI. This system is designed to prevent fraudulent activity such as users abusing first-time discounts by using false names. For example, a user can use the first-time discount multiple times by preparing multiple SIM cards and creating multiple accounts. Such fraudulent activity reduces the reliability of the service and increases the cost of fraud investigations. As a countermeasure, fraudulent order data is fed into the generative AI. By analyzing a large amount of fraudulent order data, the generative AI can identify patterns of fraudulent activity. For example, if multiple accounts are created using the same address or phone number, this can be detected as fraudulent activity. Furthermore, by introducing a highly accurate fraud detection system using generative AI, fraudulent orders can be prevented. This system can detect fraudulent activity before the order is confirmed and issue a warning to the user. For example, if the same credit card is used with multiple accounts, the order can be blocked. This mechanism improves the fraud detection rate before the order is confirmed. By detecting fraud before users commit fraudulent activity, the reliability of the service can be maintained and the cost of fraud investigations can be reduced. For example, by preventing fraudulent activity such as abusing first-time discounts, the effectiveness of promotions can be maximized. Furthermore, a fraud detection system using generative AI enables the early identification of predictive information that cannot be covered by AI filtering. This aims to improve the fraud detection rate before order confirmation, such as during account creation. For example, it can analyze user behavior patterns and detect signs of fraudulent activity early. In this way, a fraudulent order detection system utilizing generative AI will greatly contribute to preventing fraudulent activity on Demaekan. By maintaining the reliability of the service and reducing the cost of fraud investigations, a better user experience can be provided. In this way, the fraudulent order detection system can prevent fraudulent orders by collecting and analyzing user behavior data, identifying fraudulent activity, and blocking orders.

[0029] The fraudulent order detection system according to this embodiment comprises a collection unit, an analysis unit, an identification unit, and a blocking unit. The collection unit collects user behavior data. The collection unit can collect, for example, the user's address, telephone number, and credit card information. The collection unit can also collect behavioral data such as website browsing history, purchase history, and click patterns. The collection unit can also collect, for example, the user's IP address and device information. The analysis unit analyzes the data collected by the collection unit. The analysis unit can, for example, use the collected data to identify patterns of fraudulent activity. The analysis unit can, for example, identify abnormal purchase frequencies and abnormal access sources. The analysis unit can also, for example, analyze data correlations and identify signs of fraudulent activity. The identification unit identifies fraudulent activity based on the analysis results obtained by the analysis unit. The identification unit can, for example, identify accounts using the same address or telephone number. The identification unit can also, for example, identify accounts using the same credit card information. The identification unit can also, for example, identify accounts using the same IP address. The blocking unit blocks orders based on the fraudulent activity identified by the identification unit. The blocking unit can, for example, cancel orders or temporarily suspend accounts. The blocking unit can also, for example, issue warnings to users. The blocking unit can also, for example, restrict the use of specific credit cards. Thus, the fraudulent order detection system according to the embodiment can prevent fraudulent orders by collecting and analyzing user behavior data, identifying fraudulent activity, and blocking orders.

[0030] The data collection unit collects user behavior data. For example, it can collect user addresses, phone numbers, and credit card information. Specifically, it collects personal information entered by users when registering on a website and payment information provided at the time of purchase. In addition, the data collection unit can also collect behavioral data such as website browsing history, purchase history, and click patterns. For example, it collects detailed data such as which pages users visited, which products they clicked on, and in what order they viewed pages. Furthermore, the data collection unit can also collect user IP addresses and device information. This includes information such as the type of device the user is using, browser version, and operating system. This data is important for understanding user behavior patterns and device characteristics. The data collection unit can centrally manage and update this diverse data in real time. For example, it can instantly reflect changes in the database when a user accesses from a new device or logs in from a different IP address. This allows the data collection unit to collect user behavior data comprehensively and accurately, improving the overall reliability of the system.

[0031] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can use the collected data to identify patterns of fraudulent activity. Specifically, it uses AI to analyze the data and detect abnormal behavioral patterns and signs of fraud. For example, it can identify abnormal purchase frequencies or abnormal access sources. The AI ​​uses machine learning algorithms to learn from past data and distinguish between normal and abnormal behavioral patterns. Furthermore, the analysis unit can also analyze data correlations to identify signs of fraud. For example, it can detect abnormal correlations such as the same credit card information being used for multiple accounts or multiple accounts accessing from the same IP address. The analysis unit updates these analysis results in real time, always responding to the latest situation. For example, if a new pattern of fraudulent activity is discovered, it is immediately reflected in the system and used for subsequent analyses. The analysis unit can also perform long-term trend analysis based on past data. This allows the analysis unit to contribute not only to real-time fraud detection but also to future risk assessment and countermeasure planning.

[0032] The identification unit identifies fraudulent activity based on the analysis results obtained by the analysis unit. For example, the identification unit can identify accounts using the same address or phone number. Specifically, it can detect multiple accounts using the same personal information based on the data provided by the analysis unit. The identification unit can also identify accounts using the same credit card information. This allows for the early detection and countermeasures against fraudulent credit card use. Furthermore, the identification unit can identify accounts using the same IP address. This allows for the detection and countermeasures against unauthorized access from the same device or network. The identification unit can automate these identification tasks and perform them in real time. For example, if new fraudulent activity is detected, it immediately notifies the system and takes appropriate measures. The identification unit can also perform pattern matching based on past fraudulent activity data to detect similar fraudulent activities early. This allows the identification unit to quickly and accurately identify fraudulent activity and improve the overall security of the system.

[0033] The blocking unit blocks orders based on fraudulent activity identified by the identification unit. The blocking unit can, for example, cancel orders or temporarily suspend accounts. Specifically, if fraudulent activity is identified, it immediately cancels the relevant order and notifies the user. It can also temporarily suspend the account to prevent the user from committing fraud again. Furthermore, the blocking unit can issue warnings to users. For example, if fraudulent activity is suspected, it can send a warning message to the user urging them to change their behavior. It can also restrict the use of specific credit cards. This prevents fraudulent use of credit cards and improves the overall security of the system. The blocking unit can automate these measures and perform them in real time. For example, if new fraudulent activity is detected, it is immediately reflected in the system to prevent future fraudulent activity. The blocking unit can also perform pattern matching based on past fraud data to detect similar fraudulent activities early. This allows the blocking unit to quickly and accurately block fraudulent activity and improve the overall security of the system.

[0034] The data collection unit can collect user addresses, phone numbers, credit card information, etc. For example, the data collection unit can collect user addresses. For example, the data collection unit can also collect user phone numbers. For example, the data collection unit can also collect user credit card information. By collecting user addresses, phone numbers, credit card information, etc., it becomes easier to identify fraudulent activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user addresses, phone numbers, and credit card information into AI, which can analyze this information to identify signs of fraudulent activity.

[0035] The analysis unit can analyze the data collected by the collection unit and identify patterns of fraudulent activity. For example, the analysis unit can use the collected data to identify abnormal purchase frequencies. The analysis unit can also use the collected data to identify abnormal access sources. For example, the analysis unit can analyze the correlation between data and identify signs of fraudulent activity. This improves the accuracy of fraud detection by analyzing the collected data and identifying patterns of fraudulent activity. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into AI, which can then identify patterns of fraudulent activity.

[0036] The identification unit can identify fraudulent activity based on the analysis results obtained by the analysis unit. For example, the identification unit can identify accounts using the same address or phone number. The identification unit can also identify accounts using the same credit card information. The identification unit can also identify accounts using the same IP address. This improves the accuracy of fraud detection by identifying fraudulent activity based on the analysis results. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the analysis results into AI, which can then identify fraudulent activity.

[0037] The blocking unit can block orders based on fraudulent activity identified by the identification unit. The blocking unit can, for example, cancel orders. The blocking unit can, for example, temporarily suspend accounts. The blocking unit can, for example, issue warnings to users. The blocking unit can, for example, restrict the use of specific credit cards. This makes it possible to prevent fraudulent orders by blocking orders based on identified fraudulent activity. Some or all of the above processes in the blocking unit may be performed using AI, for example, or not using AI. For example, the blocking unit can input the identified fraudulent activity into the AI, and the AI ​​can block the order.

[0038] The data collection unit can analyze the user's past behavior history and select the optimal data collection method. For example, the data collection unit can prioritize collecting data from devices the user has frequently used in the past. For example, the data collection unit can determine the optimal data collection timing based on the user's past behavior patterns. For example, the data collection unit can select the necessary data by referring to the types of data the user has provided in the past. This enables efficient data collection by analyzing the user's past behavior history and selecting the optimal data collection method. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavior history into AI, which can then select the optimal data collection method.

[0039] The data collection unit can filter data based on the user's current activities and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to the user's current activities. For example, the data collection unit can collect highly relevant data based on the user's areas of interest. For example, the data collection unit can collect region-related data based on the user's current location information. This allows for the collection of highly relevant data by filtering based on the user's current activities and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current activities and areas of interest into the AI, which can then perform the filtering.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. For example, the data collection unit can collect regional event information based on the user's location information. For example, if the user is on the move, the data collection unit can collect data related to the destination region. This allows for efficient collection of region-related data by prioritizing the collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI, which can then prioritize the collection of highly relevant data.

[0041] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on information shared by the user on social media. For example, the data collection unit can collect relevant data based on the activity of the user's social media followers and friends. For example, the data collection unit can collect data based on topics the user has shown interest in on social media. This allows for the efficient collection of relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into AI, which can then collect relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can perform a simplified analysis on data with low importance. For example, the analysis unit can determine the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into the AI, and the AI ​​can adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a specific financial analysis algorithm to financial data. For example, the analysis unit can apply a text analysis algorithm to social media data. For example, the analysis unit can apply a geographic information system (GIS) analysis algorithm to location data. By applying different analysis algorithms depending on the data category, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into the AI, and the AI ​​can apply an appropriate analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may analyze the most recent data while referring to past data. For example, the analysis unit may adjust the priority of analysis according to the data collection period. This allows for the prioritization of analysis of the most recent data by determining the priority of analysis based on the data collection period. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection period into the AI, and the AI ​​can determine the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. The analysis unit can adjust the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into the AI, and the AI ​​can adjust the order of analysis.

[0046] The identification unit can improve the accuracy of identification by considering the interrelationships of data during the identification process. For example, the identification unit can identify accounts using the same address or phone number. For example, the identification unit can identify accounts using the same credit card information. For example, the identification unit can identify accounts using the same IP address. This improves the accuracy of identifying fraudulent activity by considering the interrelationships of data. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the interrelationships of data into the AI, which can then improve the accuracy of identification.

[0047] The identification unit can perform identification by considering the attribute information of the data submitter. For example, the identification unit can perform identification by considering the submitter's age and gender. For example, the identification unit can perform identification by considering the submitter's past behavioral history. For example, the identification unit can perform identification by considering the submitter's geographical location information. This improves the accuracy of identification by considering the attribute information of the data submitter. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the submitter's attribute information into AI, and the AI ​​can perform the identification.

[0048] The identification unit can perform identification while considering the geographical distribution of the data. For example, the identification unit can identify fraudulent activities that frequently occur in a particular region. For example, the identification unit can identify fraudulent activities that occur in geographically close locations by associating them. For example, the identification unit can identify patterns of fraudulent activities based on geographical distribution. This makes it possible to identify fraudulent activities related to a region by considering the geographical distribution of the data. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the geographical distribution of the data into AI, and the AI ​​can perform the identification.

[0049] The identification unit can improve the accuracy of identification by referring to relevant literature on the data at the time of identification. For example, the identification unit identifies patterns of fraudulent activity based on relevant literature. The identification unit can improve the accuracy of identification by referring to relevant literature. For example, the identification unit can discover new patterns of fraudulent activity based on relevant literature. This improves the accuracy of identifying fraudulent activity by referring to relevant literature on the data. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input relevant literature into AI, and the AI ​​can improve the accuracy of identification.

[0050] The blocking unit can analyze the user's past behavior history to select the optimal blocking method when blocking. For example, the blocking unit can select the optimal blocking method based on the user's past fraudulent activity history. For example, the blocking unit can analyze the user's past behavior patterns to select the optimal blocking method. For example, the blocking unit can select the optimal blocking method by referring to the user's past blocking history. This allows for efficient blocking by analyzing the user's past behavior history to select the optimal blocking method. Some or all of the above processing in the blocking unit may be performed using AI, for example, or without AI. For example, the blocking unit can input the user's past behavior history into AI, which can then select the optimal blocking method.

[0051] The blocking unit can customize the blocking method based on the user's current activity status when blocking. For example, the blocking unit can customize the blocking method according to the user's current activity. For example, the blocking unit can customize the blocking method based on the user's current location information. For example, the blocking unit can customize the blocking method based on the user's current device usage status. This makes it possible to perform more effective blocking by customizing the blocking method based on the user's current activity status. Some or all of the above processing in the blocking unit may be performed using AI, for example, or without AI. For example, the blocking unit can input the user's current activity status to the AI, and the AI ​​can customize the blocking method.

[0052] The blocking unit can select the optimal blocking method when blocking, taking into account the user's geographical location information. For example, if the user is in a specific region, the blocking unit can apply a blocking method related to that region. For example, the blocking unit can select a blocking method according to the characteristics of a region based on the user's location information. For example, if the user is on the move, the blocking unit can apply a blocking method related to the destination region. This enables region-related blocking by selecting the optimal blocking method while considering the user's geographical location information. Some or all of the above processing in the blocking unit may be performed using AI, for example, or without AI. For example, the blocking unit can input the user's geographical location information into AI, and the AI ​​can select the optimal blocking method.

[0053] The blocking unit can analyze the user's social media activity and suggest blocking methods when blocking. For example, the blocking unit can suggest relevant blocking methods based on information the user has shared on social media. For example, the blocking unit can suggest blocking methods based on the activities of the user's social media followers and friends. For example, the blocking unit can suggest blocking methods based on topics the user has shown interest in on social media. In this way, relevant blocking methods can be suggested by analyzing the user's social media activity. Some or all of the above processing in the blocking unit may be performed using AI, for example, or without AI. For example, the blocking unit can input the user's social media activity into AI, and the AI ​​can suggest blocking methods.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The fraudulent order detection system comprises a collection unit that collects user behavior data, an analysis unit that analyzes the collected data, an identification unit that identifies fraudulent activity based on the analysis results, and a blocking unit that blocks orders based on the identified fraudulent activity. Furthermore, the system may include a collection unit that analyzes the user's past behavior history and selects the optimal data collection method. For example, data can be preferentially collected from devices that the user has frequently used in the past. Also, the optimal data collection timing can be determined based on the user's past behavior patterns. Furthermore, necessary data can be selected by referring to the types of data the user has provided in the past. This enables efficient data collection by selecting the optimal data collection method through analysis of the user's past behavior history. Some or all of the above-described processes in the collection unit may be performed using AI or not. For example, the collection unit can input the user's past behavior history into AI, which can then select the optimal data collection method.

[0056] The fraudulent order detection system comprises a collection unit that collects user behavior data, an analysis unit that analyzes the collected data, an identification unit that identifies fraudulent activity based on the analysis results, and a blocking unit that blocks orders based on the identified fraudulent activity. Furthermore, the system may include a collection unit that filters data based on the user's current activities and areas of interest during data collection. For example, it can prioritize the collection of data related to the user's current activities. It can also collect highly relevant data based on the user's areas of interest. Furthermore, it can collect region-related data based on the user's current location information. This allows for the collection of highly relevant data by filtering based on the user's current activities and areas of interest. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's current activities and areas of interest into the AI, which can then perform the filtering.

[0057] The fraudulent order detection system comprises a collection unit that collects user behavior data, an analysis unit that analyzes the collected data, an identification unit that identifies fraudulent activity based on the analysis results, and a blocking unit that blocks orders based on the identified fraudulent activity. Furthermore, the system may include an analysis unit that adjusts the level of detail of the analysis based on the importance of the data during the analysis. For example, detailed analysis can be performed on data with high importance, while simplified analysis can be performed on data with low importance. In addition, the system can determine the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the data into the AI, and the AI ​​can adjust the level of detail of the analysis.

[0058] The fraudulent order detection system comprises a collection unit that collects user behavior data, an analysis unit that analyzes the collected data, an identification unit that identifies fraudulent activity based on the analysis results, and a blocking unit that blocks orders based on the identified fraudulent activity. Furthermore, the system may include an analysis unit that applies different analysis algorithms depending on the data category during analysis. For example, a specific financial analysis algorithm can be applied to financial data. A text analysis algorithm can be applied to social media data. Furthermore, a geographic information system (GIS) analysis algorithm can be applied to location data. This improves the accuracy of the analysis by applying different analysis algorithms depending on the data category. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data category into the AI, and the AI ​​can apply an appropriate analysis algorithm.

[0059] The fraudulent order detection system comprises a collection unit that collects user behavior data, an analysis unit that analyzes the collected data, an identification unit that identifies fraudulent activity based on the analysis results, and a blocking unit that blocks orders based on the identified fraudulent activity. Furthermore, the system may include a blocking unit that, when blocking an order, analyzes the user's past behavior history to select the optimal blocking method. For example, the optimal blocking method can be selected based on the user's past fraudulent activity history. Alternatively, the optimal blocking method can be selected by analyzing the user's past behavior patterns. Furthermore, the optimal blocking method can be selected by referring to the user's past blocking history. This allows for efficient blocking by selecting the optimal blocking method through analysis of the user's past behavior history. Some or all of the above-described processes in the blocking unit may be performed using AI or not. For example, the blocking unit can input the user's past behavior history into the AI, which can then select the optimal blocking method.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The data collection unit collects user behavior data. For example, the data collection unit can collect the user's address, phone number, credit card information, website browsing history, purchase history, click patterns, IP address, and device information. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can use the collected data to identify patterns of fraudulent activity, identify abnormal purchase frequencies and abnormal access sources, and analyze data correlations to identify signs of fraudulent activity. Step 3: The identification unit identifies fraudulent activity based on the analysis results obtained by the analysis unit. For example, the identification unit can identify accounts using the same address, phone number, credit card information, or IP address. Step 4: The blocking unit blocks the order based on the fraudulent activity identified by the identification unit. The blocking unit can, for example, cancel the order, suspend the account, warn the user, or restrict the use of specific credit cards.

[0062] (Example of form 2) The fraudulent order detection system according to an embodiment of the present invention is a system that detects fraudulent orders on Demaekan using generative AI. This system is designed to prevent fraudulent activity such as users abusing first-time discounts by using false names. For example, a user can use the first-time discount multiple times by preparing multiple SIM cards and creating multiple accounts. Such fraudulent activity reduces the reliability of the service and increases the cost of fraud investigations. As a countermeasure, fraudulent order data is fed into the generative AI. By analyzing a large amount of fraudulent order data, the generative AI can identify patterns of fraudulent activity. For example, if multiple accounts are created using the same address or phone number, this can be detected as fraudulent activity. Furthermore, by introducing a highly accurate fraud detection system using generative AI, fraudulent orders can be prevented. This system can detect fraudulent activity before the order is confirmed and issue a warning to the user. For example, if the same credit card is used with multiple accounts, the order can be blocked. This mechanism improves the fraud detection rate before the order is confirmed. By detecting fraud before users commit fraudulent activity, the reliability of the service can be maintained and the cost of fraud investigations can be reduced. For example, by preventing fraudulent activity such as abusing first-time discounts, the effectiveness of promotions can be maximized. Furthermore, a fraud detection system using generative AI enables the early identification of predictive information that cannot be covered by AI filtering. This aims to improve the fraud detection rate before order confirmation, such as during account creation. For example, it can analyze user behavior patterns and detect signs of fraudulent activity early. In this way, a fraudulent order detection system utilizing generative AI will greatly contribute to preventing fraudulent activity on Demaekan. By maintaining the reliability of the service and reducing the cost of fraud investigations, a better user experience can be provided. In this way, the fraudulent order detection system can prevent fraudulent orders by collecting and analyzing user behavior data, identifying fraudulent activity, and blocking orders.

[0063] The fraudulent order detection system according to this embodiment comprises a collection unit, an analysis unit, an identification unit, and a blocking unit. The collection unit collects user behavior data. The collection unit can collect, for example, the user's address, telephone number, and credit card information. The collection unit can also collect behavioral data such as website browsing history, purchase history, and click patterns. The collection unit can also collect, for example, the user's IP address and device information. The analysis unit analyzes the data collected by the collection unit. The analysis unit can, for example, use the collected data to identify patterns of fraudulent activity. The analysis unit can, for example, identify abnormal purchase frequencies and abnormal access sources. The analysis unit can also, for example, analyze data correlations and identify signs of fraudulent activity. The identification unit identifies fraudulent activity based on the analysis results obtained by the analysis unit. The identification unit can, for example, identify accounts using the same address or telephone number. The identification unit can also, for example, identify accounts using the same credit card information. The identification unit can also, for example, identify accounts using the same IP address. The blocking unit blocks orders based on the fraudulent activity identified by the identification unit. The blocking unit can, for example, cancel orders or temporarily suspend accounts. The blocking unit can also, for example, issue warnings to users. The blocking unit can also, for example, restrict the use of specific credit cards. Thus, the fraudulent order detection system according to the embodiment can prevent fraudulent orders by collecting and analyzing user behavior data, identifying fraudulent activity, and blocking orders.

[0064] The data collection unit collects user behavior data. For example, it can collect user addresses, phone numbers, and credit card information. Specifically, it collects personal information entered by users when registering on a website and payment information provided at the time of purchase. In addition, the data collection unit can also collect behavioral data such as website browsing history, purchase history, and click patterns. For example, it collects detailed data such as which pages users visited, which products they clicked on, and in what order they viewed pages. Furthermore, the data collection unit can also collect user IP addresses and device information. This includes information such as the type of device the user is using, browser version, and operating system. This data is important for understanding user behavior patterns and device characteristics. The data collection unit can centrally manage and update this diverse data in real time. For example, it can instantly reflect changes in the database when a user accesses from a new device or logs in from a different IP address. This allows the data collection unit to collect user behavior data comprehensively and accurately, improving the overall reliability of the system.

[0065] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can use the collected data to identify patterns of fraudulent activity. Specifically, it uses AI to analyze the data and detect abnormal behavioral patterns and signs of fraud. For example, it can identify abnormal purchase frequencies or abnormal access sources. The AI ​​uses machine learning algorithms to learn from past data and distinguish between normal and abnormal behavioral patterns. Furthermore, the analysis unit can also analyze data correlations to identify signs of fraud. For example, it can detect abnormal correlations such as the same credit card information being used for multiple accounts or multiple accounts accessing from the same IP address. The analysis unit updates these analysis results in real time, always responding to the latest situation. For example, if a new pattern of fraudulent activity is discovered, it is immediately reflected in the system and used for subsequent analyses. The analysis unit can also perform long-term trend analysis based on past data. This allows the analysis unit to contribute not only to real-time fraud detection but also to future risk assessment and countermeasure planning.

[0066] The identification unit identifies fraudulent activity based on the analysis results obtained by the analysis unit. For example, the identification unit can identify accounts using the same address or phone number. Specifically, it can detect multiple accounts using the same personal information based on the data provided by the analysis unit. The identification unit can also identify accounts using the same credit card information. This allows for the early detection and countermeasures against fraudulent credit card use. Furthermore, the identification unit can identify accounts using the same IP address. This allows for the detection and countermeasures against unauthorized access from the same device or network. The identification unit can automate these identification tasks and perform them in real time. For example, if new fraudulent activity is detected, it immediately notifies the system and takes appropriate measures. The identification unit can also perform pattern matching based on past fraudulent activity data to detect similar fraudulent activities early. This allows the identification unit to quickly and accurately identify fraudulent activity and improve the overall security of the system.

[0067] The blocking unit blocks orders based on fraudulent activity identified by the identification unit. The blocking unit can, for example, cancel orders or temporarily suspend accounts. Specifically, if fraudulent activity is identified, it immediately cancels the relevant order and notifies the user. It can also temporarily suspend the account to prevent the user from committing fraud again. Furthermore, the blocking unit can issue warnings to users. For example, if fraudulent activity is suspected, it can send a warning message to the user urging them to change their behavior. It can also restrict the use of specific credit cards. This prevents fraudulent use of credit cards and improves the overall security of the system. The blocking unit can automate these measures and perform them in real time. For example, if new fraudulent activity is detected, it is immediately reflected in the system to prevent future fraudulent activity. The blocking unit can also perform pattern matching based on past fraud data to detect similar fraudulent activities early. This allows the blocking unit to quickly and accurately block fraudulent activity and improve the overall security of the system.

[0068] The data collection unit can collect user addresses, phone numbers, credit card information, etc. For example, the data collection unit can collect user addresses. For example, the data collection unit can also collect user phone numbers. For example, the data collection unit can also collect user credit card information. By collecting user addresses, phone numbers, credit card information, etc., it becomes easier to identify fraudulent activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user addresses, phone numbers, and credit card information into AI, which can analyze this information to identify signs of fraudulent activity.

[0069] The analysis unit can analyze the data collected by the collection unit and identify patterns of fraudulent activity. For example, the analysis unit can use the collected data to identify abnormal purchase frequencies. The analysis unit can also use the collected data to identify abnormal access sources. For example, the analysis unit can analyze the correlation between data and identify signs of fraudulent activity. This improves the accuracy of fraud detection by analyzing the collected data and identifying patterns of fraudulent activity. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into AI, which can then identify patterns of fraudulent activity.

[0070] The identification unit can identify fraudulent activity based on the analysis results obtained by the analysis unit. For example, the identification unit can identify accounts using the same address or phone number. The identification unit can also identify accounts using the same credit card information. The identification unit can also identify accounts using the same IP address. This improves the accuracy of fraud detection by identifying fraudulent activity based on the analysis results. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the analysis results into AI, which can then identify fraudulent activity.

[0071] The blocking unit can block orders based on fraudulent activity identified by the identification unit. The blocking unit can, for example, cancel orders. The blocking unit can, for example, temporarily suspend accounts. The blocking unit can, for example, issue warnings to users. The blocking unit can, for example, restrict the use of specific credit cards. This makes it possible to prevent fraudulent orders by blocking orders based on identified fraudulent activity. Some or all of the above processes in the blocking unit may be performed using AI, for example, or not using AI. For example, the blocking unit can input the identified fraudulent activity into the AI, and the AI ​​can block the order.

[0072] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to lessen the user's burden. For example, if the user is relaxed, the data collection unit can collect detailed data and perform more accurate analysis. For example, if the user is in a hurry, the data collection unit can quickly collect only the minimum necessary data. This reduces the user's burden and enables more accurate data collection by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's emotion data into the generative AI, which can then adjust the timing of data collection.

[0073] The data collection unit can analyze the user's past behavior history and select the optimal data collection method. For example, the data collection unit can prioritize collecting data from devices the user has frequently used in the past. For example, the data collection unit can determine the optimal data collection timing based on the user's past behavior patterns. For example, the data collection unit can select the necessary data by referring to the types of data the user has provided in the past. This enables efficient data collection by analyzing the user's past behavior history and selecting the optimal data collection method. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavior history into AI, which can then select the optimal data collection method.

[0074] The data collection unit can filter data based on the user's current activities and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to the user's current activities. For example, the data collection unit can collect highly relevant data based on the user's areas of interest. For example, the data collection unit can collect region-related data based on the user's current location information. This allows for the collection of highly relevant data by filtering based on the user's current activities and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current activities and areas of interest into the AI, which can then perform the filtering.

[0075] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit can prioritize collecting high-priority data. For example, if the user is relaxed, the data collection unit can collect detailed data to improve the accuracy of the analysis. For example, if the user is in a hurry, the data collection unit can prioritize data that can be collected quickly. This allows for the priority collection of important data by determining the priority of data to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and determine the priority of data to be collected by the generative AI.

[0076] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. For example, the data collection unit can collect regional event information based on the user's location information. For example, if the user is on the move, the data collection unit can collect data related to the destination region. This allows for efficient collection of region-related data by prioritizing the collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI, which can then prioritize the collection of highly relevant data.

[0077] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on information shared by the user on social media. For example, the data collection unit can collect relevant data based on the activity of the user's social media followers and friends. For example, the data collection unit can collect data based on topics the user has shown interest in on social media. This allows for the efficient collection of relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into AI, which can then collect relevant data.

[0078] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. In this way, by adjusting the presentation of the analysis based on the user's emotions, the analysis results can be provided that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI, and the generative AI can adjust the presentation of the analysis.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can perform a simplified analysis on data with low importance. For example, the analysis unit can determine the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into the AI, and the AI ​​can adjust the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a specific financial analysis algorithm to financial data. For example, the analysis unit can apply a text analysis algorithm to social media data. For example, the analysis unit can apply a geographic information system (GIS) analysis algorithm to location data. By applying different analysis algorithms depending on the data category, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into the AI, and the AI ​​can apply an appropriate analysis algorithm.

[0081] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, if the user is excited, the analysis unit can provide a visually stimulating analysis result. By adjusting the length of the analysis based on the user's emotions, the analysis unit can provide an analysis result of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into the generative AI, which can then adjust the length of the analysis.

[0082] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may analyze the most recent data while referring to past data. For example, the analysis unit may adjust the priority of analysis according to the data collection period. This allows for the prioritization of analysis of the most recent data by determining the priority of analysis based on the data collection period. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection period into the AI, and the AI ​​can determine the priority of analysis.

[0083] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. The analysis unit can adjust the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into the AI, and the AI ​​can adjust the order of analysis.

[0084] The identification unit can estimate the user's emotions and adjust the criteria for identifying fraudulent activity based on the estimated emotions. For example, if the user is tense, the identification unit can apply strict criteria. For example, if the user is relaxed, the identification unit can apply flexible criteria. For example, if the user is in a hurry, the identification unit can apply criteria that allow for quick identification. This allows for more appropriate identification of fraudulent activity by adjusting the criteria for identifying fraudulent activity based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the identification unit may be performed using AI or not using AI. For example, the identification unit can input user emotion data into a generative AI, which can then adjust the criteria for identifying fraudulent activity.

[0085] The identification unit can improve the accuracy of identification by considering the interrelationships of data during the identification process. For example, the identification unit can identify accounts using the same address or phone number. For example, the identification unit can identify accounts using the same credit card information. For example, the identification unit can identify accounts using the same IP address. This improves the accuracy of identifying fraudulent activity by considering the interrelationships of data. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the interrelationships of data into the AI, which can then improve the accuracy of identification.

[0086] The identification unit can perform identification by considering the attribute information of the data submitter. For example, the identification unit can perform identification by considering the submitter's age and gender. For example, the identification unit can perform identification by considering the submitter's past behavioral history. For example, the identification unit can perform identification by considering the submitter's geographical location information. This improves the accuracy of identification by considering the attribute information of the data submitter. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the submitter's attribute information into AI, and the AI ​​can perform the identification.

[0087] The identification unit can estimate the user's emotions and adjust the display order of the identification results based on the estimated user emotions. For example, if the user is tense, the identification unit can prioritize displaying important identification results. For example, if the user is relaxed, the identification unit can display detailed identification results. For example, if the user is in a hurry, the identification unit can display concise identification results. By adjusting the display order of the identification results based on the user's emotions, the system can provide identification results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the identification unit may be performed using AI, for example, or not using AI. For example, the identification unit can input user emotion data into the generative AI, and the generative AI can adjust the display order of the identification results.

[0088] The identification unit can perform identification while considering the geographical distribution of the data. For example, the identification unit can identify fraudulent activities that frequently occur in a particular region. For example, the identification unit can identify fraudulent activities that occur in geographically close locations by associating them. For example, the identification unit can identify patterns of fraudulent activities based on geographical distribution. This makes it possible to identify fraudulent activities related to a region by considering the geographical distribution of the data. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the geographical distribution of the data into AI, and the AI ​​can perform the identification.

[0089] The identification unit can improve the accuracy of identification by referring to relevant literature on the data at the time of identification. For example, the identification unit identifies patterns of fraudulent activity based on relevant literature. The identification unit can improve the accuracy of identification by referring to relevant literature. For example, the identification unit can discover new patterns of fraudulent activity based on relevant literature. This improves the accuracy of identifying fraudulent activity by referring to relevant literature on the data. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input relevant literature into AI, and the AI ​​can improve the accuracy of identification.

[0090] The blocking unit can estimate the user's emotions and adjust the blocking method based on the estimated emotions. For example, if the user is tense, the blocking unit can apply a flexible blocking method. For example, if the user is relaxed, the blocking unit can apply a strict blocking method. For example, if the user is in a hurry, the blocking unit can apply a method that allows for quick blocking. This allows for more appropriate blocking by adjusting the blocking method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the blocking unit may be performed using AI, for example, or not using AI. For example, the blocking unit can input user emotion data into the generative AI, which can then adjust the blocking method.

[0091] The blocking unit can analyze the user's past behavior history to select the optimal blocking method when blocking. For example, the blocking unit can select the optimal blocking method based on the user's past fraudulent activity history. For example, the blocking unit can analyze the user's past behavior patterns to select the optimal blocking method. For example, the blocking unit can select the optimal blocking method by referring to the user's past blocking history. This allows for efficient blocking by analyzing the user's past behavior history to select the optimal blocking method. Some or all of the above processing in the blocking unit may be performed using AI, for example, or without AI. For example, the blocking unit can input the user's past behavior history into AI, which can then select the optimal blocking method.

[0092] The blocking unit can customize the blocking method based on the user's current activity status when blocking. For example, the blocking unit can customize the blocking method according to the user's current activity. For example, the blocking unit can customize the blocking method based on the user's current location information. For example, the blocking unit can customize the blocking method based on the user's current device usage status. This makes it possible to perform more effective blocking by customizing the blocking method based on the user's current activity status. Some or all of the above processing in the blocking unit may be performed using AI, for example, or without AI. For example, the blocking unit can input the user's current activity status to the AI, and the AI ​​can customize the blocking method.

[0093] The blocking unit can estimate the user's emotions and determine the priority of blocks based on the estimated emotions. For example, if the user is tense, the blocking unit will prioritize important blocks. If the user is relaxed, the blocking unit can perform detailed blocks. If the user is in a hurry, the blocking unit can perform blocks quickly. This allows for prioritizing important blocks by determining the priority of blocks based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the blocking unit may be performed using AI or not. For example, the blocking unit can input user emotion data into a generative AI, which can then determine the priority of blocks.

[0094] The blocking unit can select the optimal blocking method when blocking, taking into account the user's geographical location information. For example, if the user is in a specific region, the blocking unit can apply a blocking method related to that region. For example, the blocking unit can select a blocking method according to the characteristics of a region based on the user's location information. For example, if the user is on the move, the blocking unit can apply a blocking method related to the destination region. This enables region-related blocking by selecting the optimal blocking method while considering the user's geographical location information. Some or all of the above processing in the blocking unit may be performed using AI, for example, or without AI. For example, the blocking unit can input the user's geographical location information into AI, and the AI ​​can select the optimal blocking method.

[0095] The blocking unit can analyze the user's social media activity and suggest blocking methods when blocking. For example, the blocking unit can suggest relevant blocking methods based on information the user has shared on social media. For example, the blocking unit can suggest blocking methods based on the activities of the user's social media followers and friends. For example, the blocking unit can suggest blocking methods based on topics the user has shown interest in on social media. In this way, relevant blocking methods can be suggested by analyzing the user's social media activity. Some or all of the above processing in the blocking unit may be performed using AI, for example, or without AI. For example, the blocking unit can input the user's social media activity into AI, and the AI ​​can suggest blocking methods.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The fraudulent order detection system comprises a collection unit that collects user behavior data, an analysis unit that analyzes the collected data, an identification unit that identifies fraudulent activity based on the analysis results, and a blocking unit that blocks orders based on the identified fraudulent activity. Furthermore, the system may include a collection unit that estimates the user's emotions and adjusts the timing of data collection based on the estimated emotions. For example, if the user is stressed, the frequency of data collection can be reduced to lessen the user's burden. If the user is relaxed, more detailed data can be collected for more accurate analysis. Furthermore, if the user is in a hurry, only the minimum necessary data can be collected quickly. By adjusting the timing of data collection based on the user's emotions, the system can reduce the user's burden and enable more accurate data collection. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using AI or not. For example, the collection unit can input user emotion data into the generative AI, which can then adjust the timing of data collection.

[0098] The fraudulent order detection system comprises a collection unit that collects user behavior data, an analysis unit that analyzes the collected data, an identification unit that identifies fraudulent activity based on the analysis results, and a blocking unit that blocks orders based on the identified fraudulent activity. Furthermore, the system may include a collection unit that analyzes the user's past behavior history and selects the optimal data collection method. For example, data can be preferentially collected from devices that the user has frequently used in the past. Also, the optimal data collection timing can be determined based on the user's past behavior patterns. Furthermore, necessary data can be selected by referring to the types of data the user has provided in the past. This enables efficient data collection by selecting the optimal data collection method through analysis of the user's past behavior history. Some or all of the above-described processes in the collection unit may be performed using AI or not. For example, the collection unit can input the user's past behavior history into AI, which can then select the optimal data collection method.

[0099] The fraudulent order detection system comprises a collection unit that collects user behavior data, an analysis unit that analyzes the collected data, an identification unit that identifies fraudulent activity based on the analysis results, and a blocking unit that blocks orders based on the identified fraudulent activity. Furthermore, the system may include a collection unit that estimates the user's emotions and determines the priority of data to collect based on the estimated emotions. For example, if the user is stressed, high-priority data can be collected preferentially. If the user is relaxed, detailed data can be collected to improve the accuracy of the analysis. Furthermore, if the user is in a hurry, data that can be collected quickly can be prioritized. In this way, important data can be collected preferentially by determining the priority of data to collect based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input user emotion data into a generative AI and determine the priority of data to be collected by the generative AI.

[0100] The fraudulent order detection system comprises a collection unit that collects user behavior data, an analysis unit that analyzes the collected data, an identification unit that identifies fraudulent activity based on the analysis results, and a blocking unit that blocks orders based on the identified fraudulent activity. Furthermore, the system may include a collection unit that filters data based on the user's current activities and areas of interest during data collection. For example, it can prioritize the collection of data related to the user's current activities. It can also collect highly relevant data based on the user's areas of interest. Furthermore, it can collect region-related data based on the user's current location information. This allows for the collection of highly relevant data by filtering based on the user's current activities and areas of interest. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's current activities and areas of interest into the AI, which can then perform the filtering.

[0101] The fraudulent order detection system comprises a collection unit that collects user behavior data, an analysis unit that analyzes the collected data, an identification unit that identifies fraudulent activity based on the analysis results, and a blocking unit that blocks orders based on the identified fraudulent activity. Furthermore, the system may include an analysis unit that estimates the user's emotions and adjusts the presentation of the analysis based on the estimated emotions. For example, if the user is tense, a simple and highly visual analysis result can be provided. If the user is relaxed, a detailed analysis result can be provided. Furthermore, if the user is in a hurry, a concise analysis result that gets straight to the point can be provided. In this way, by adjusting the presentation of the analysis based on the user's emotions, the system can provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into the generative AI, and the generative AI can adjust the presentation of the analysis.

[0102] The fraudulent order detection system comprises a collection unit that collects user behavior data, an analysis unit that analyzes the collected data, an identification unit that identifies fraudulent activity based on the analysis results, and a blocking unit that blocks orders based on the identified fraudulent activity. Furthermore, the system may include an analysis unit that adjusts the level of detail of the analysis based on the importance of the data during the analysis. For example, detailed analysis can be performed on data with high importance, while simplified analysis can be performed on data with low importance. In addition, the system can determine the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the data into the AI, and the AI ​​can adjust the level of detail of the analysis.

[0103] The fraudulent order detection system comprises a collection unit that collects user behavior data, an analysis unit that analyzes the collected data, an identification unit that identifies fraudulent activity based on the analysis results, and a blocking unit that blocks orders based on the identified fraudulent activity. Furthermore, the system may include an analysis unit that estimates the user's emotions and adjusts the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, a short and concise analysis result can be provided. If the user is relaxed, a detailed analysis result can be provided. Furthermore, if the user is excited, a visually stimulating analysis result can be provided. In this way, by adjusting the length of the analysis based on the user's emotions, an analysis result of an appropriate length for the user can be provided. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into the generative AI, and the generative AI can adjust the length of the analysis.

[0104] The fraudulent order detection system comprises a collection unit that collects user behavior data, an analysis unit that analyzes the collected data, an identification unit that identifies fraudulent activity based on the analysis results, and a blocking unit that blocks orders based on the identified fraudulent activity. Furthermore, the system may include an analysis unit that applies different analysis algorithms depending on the data category during analysis. For example, a specific financial analysis algorithm can be applied to financial data. A text analysis algorithm can be applied to social media data. Furthermore, a geographic information system (GIS) analysis algorithm can be applied to location data. This improves the accuracy of the analysis by applying different analysis algorithms depending on the data category. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data category into the AI, and the AI ​​can apply an appropriate analysis algorithm.

[0105] The fraudulent order detection system comprises a collection unit that collects user behavior data, an analysis unit that analyzes the collected data, an identification unit that identifies fraudulent activity based on the analysis results, and a blocking unit that blocks orders based on the identified fraudulent activity. Furthermore, the system may include an identification unit that estimates the user's emotions and adjusts the criteria for identifying fraudulent activity based on the estimated emotions. For example, if the user is stressed, strict identification criteria can be applied. If the user is relaxed, flexible identification criteria can be applied. Furthermore, if the user is in a hurry, criteria that allow for quick identification can be applied. This allows for more appropriate identification of fraudulent activity by adjusting the criteria for identifying fraudulent activity based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above-described processing in the identification unit may be performed using AI or not. For example, the identification unit can input user emotion data into the generative AI, which can then adjust the criteria for identifying fraudulent activity.

[0106] The fraudulent order detection system comprises a collection unit that collects user behavior data, an analysis unit that analyzes the collected data, an identification unit that identifies fraudulent activity based on the analysis results, and a blocking unit that blocks orders based on the identified fraudulent activity. Furthermore, the system may include a blocking unit that, when blocking an order, analyzes the user's past behavior history to select the optimal blocking method. For example, the optimal blocking method can be selected based on the user's past fraudulent activity history. Alternatively, the optimal blocking method can be selected by analyzing the user's past behavior patterns. Furthermore, the optimal blocking method can be selected by referring to the user's past blocking history. This allows for efficient blocking by selecting the optimal blocking method through analysis of the user's past behavior history. Some or all of the above-described processes in the blocking unit may be performed using AI or not. For example, the blocking unit can input the user's past behavior history into the AI, which can then select the optimal blocking method.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The data collection unit collects user behavior data. For example, the data collection unit can collect the user's address, phone number, credit card information, website browsing history, purchase history, click patterns, IP address, and device information. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can use the collected data to identify patterns of fraudulent activity, identify abnormal purchase frequencies and abnormal access sources, and analyze data correlations to identify signs of fraudulent activity. Step 3: The identification unit identifies fraudulent activity based on the analysis results obtained by the analysis unit. For example, the identification unit can identify accounts using the same address, phone number, credit card information, or IP address. Step 4: The blocking unit blocks the order based on the fraudulent activity identified by the identification unit. The blocking unit can, for example, cancel the order, suspend the account, warn the user, or restrict the use of specific credit cards.

[0109] 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.

[0110] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements described above, including the collection unit, analysis unit, identification unit, and blocking unit, is implemented in, for example, at least one of the smart device 14 and the data processing unit 12. For example, the collection unit can collect user behavior data by the control unit 46A of the smart device 14. The analysis unit can analyze the collected data by, for example, the identification processing unit 290 of the data processing unit 12. The identification unit can identify fraudulent activity by, for example, the identification processing unit 290 of the data processing unit 12. The blocking unit can block orders by, for example, the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0116] 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.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0118] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] 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.

[0120] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] 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.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the collection unit, analysis unit, identification unit, and blocking unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit can collect user behavior data by the control unit 46A of the smart glasses 214. The analysis unit can analyze the collected data by the identification processing unit 290 of the data processing unit 12. The identification unit can identify fraudulent activity by the identification processing unit 290 of the data processing unit 12. The blocking unit can block orders by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0132] 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.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0134] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] 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.

[0136] 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.

[0137] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] 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.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the collection unit, analysis unit, identification unit, and blocking unit, is implemented in, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit can collect user behavior data by the control unit 46A of the headset terminal 314. The analysis unit can analyze the collected data by, for example, the identification unit 290 of the data processing unit 12. The identification unit can identify fraudulent activity by, for example, the identification unit 290 of the data processing unit 12. The blocking unit can block orders by, for example, the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0148] 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.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0150] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] 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.

[0152] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] 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.

[0154] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] 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.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the collection unit, analysis unit, identification unit, and blocking unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit can collect user behavior data by the control unit 46A of the robot 414. The analysis unit can analyze the collected data by the identification unit 290 of the data processing unit 12. The identification unit can identify fraudulent activity by the identification unit 290 of the data processing unit 12. The blocking unit can block orders by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0162] 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.

[0163] Figure 9 shows the 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.

[0164] 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.

[0165] 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.

[0166] 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, and motorcycles, 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 based, for example, 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.

[0167] 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."

[0168] 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.

[0169] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] 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 other things 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.

[0179] 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.

[0180] (Note 1) A data collection unit that collects user behavior data, An analysis unit analyzes the data collected by the aforementioned collection unit, An identification unit that identifies fraudulent activity based on the analysis results obtained by the aforementioned analysis unit, The system includes a blocking unit that blocks orders based on fraudulent activity identified by the aforementioned identification unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collects user addresses, phone numbers, credit card information, etc. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The data collected by the aforementioned collection unit is analyzed to identify patterns of fraudulent activity. The system described in Appendix 1, characterized by the features described herein. (Note 4) The specified part is, The analysis unit identifies fraudulent activity based on the analysis results obtained. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned block section is The order is blocked based on the fraudulent activity identified by the aforementioned specific unit. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the user's past behavior history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current activities and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The specified part is, We estimate user sentiment and adjust the criteria for identifying fraudulent activity based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The specified part is, At specific times, improve specific accuracy by considering the interrelationships between data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The specified part is, When identifying data, the attribute information of the data submitter is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 21) The specified part is, It estimates the user's emotions and adjusts the display order of specific results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The specified part is, When identifying data, the geographical distribution of the data is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The specified part is, At specific times, we refer to relevant literature for data to improve specific accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned block section is It estimates the user's emotions and adjusts the blocking method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned block section is When blocking a user, the system analyzes the user's past behavior history to select the most appropriate blocking method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned block section is When blocking a user, customize the blocking method based on the user's current activity level. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned block section is It estimates the user's emotions and determines the priority of blocking based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned block section is When blocking, the system selects the optimal blocking method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned block section is When blocking a user, the system analyzes their social media activity and suggests blocking methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects user behavior data, An analysis unit analyzes the data collected by the aforementioned collection unit, An identification unit that identifies fraudulent activity based on the analysis results obtained by the aforementioned analysis unit, The system includes a blocking unit that blocks orders based on fraudulent activity identified by the aforementioned identification unit. A system characterized by the following features.

2. The aforementioned collection unit is Collects user addresses, phone numbers, credit card information, etc. The system according to feature 1.

3. The aforementioned analysis unit, The data collected by the aforementioned collection unit is analyzed to identify patterns of fraudulent activity. The system according to feature 1.

4. The specified part is, The analysis unit identifies fraudulent activity based on the analysis results obtained. The system according to feature 1.

5. The aforementioned block section is The order is blocked based on the fraudulent activity identified by the aforementioned specific unit. The system according to feature 1.

6. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze the user's past behavior history and select the optimal data collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting data, filtering is performed based on the user's current activities and areas of interest. The system according to feature 1.

9. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

10. The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system according to feature 1.

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