System

Abstract

An object of the system according to the embodiment is to detect an abnormal transaction in a deposit and withdrawal transaction and to quickly take measures.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a detection unit, a warning unit, and a blocking unit. The collection part collects deposit / withdrawal transaction data of the user. The analysis unit analyzes the data collected by the collection unit. The detection unit detects an abnormal transaction based on the data analyzed by the analysis unit. The warning unit issues a warning for the abnormal transaction detected by the detection unit. The block unit blocks the transaction warned by the warning unit as necessary.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have had the problem of making it difficult to quickly and accurately detect fraud in deposit transactions and take countermeasures.

[0005] The system according to the embodiment aims to detect abnormal deposit transactions and take prompt measures. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a detection unit, a warning unit, and a blocking unit. The collection unit collects users' deposit transaction data. The analysis unit analyzes the data collected by the collection unit. The detection unit detects abnormal transactions based on the data analyzed by the analysis unit. The warning unit issues a warning about abnormal transactions detected by the detection unit. The blocking unit blocks transactions warned about by the warning unit as necessary. [Effects of the Invention]

[0007] The system according to the embodiment can detect abnormal deposit transactions and take prompt measures. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[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. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A tool according to an embodiment of the present invention is a system that monitors a user's deposit and savings transactions, detects potentially fraudulent transactions, issues warnings, and blocks the transactions as necessary. This system detects potentially fraudulent transactions by collecting the user's deposit and savings transaction data and analyzing them using AI. For example, when a user withdraws a large amount of money from their bank account, the transaction data is collected and AI detects abnormal transactions that differ from normal transaction patterns. Next, a warning is issued for the detected transaction and, if necessary, the transaction is blocked. For example, a warning is sent to the user via SMS or email to inform the user of the potentially fraudulent transaction, allowing the user to respond quickly. Furthermore, for transactions that are highly likely to be fraudulent, the transaction can be temporarily suspended and the user's confirmation requested. This protects the user's assets. This tool monitors the user's deposit and savings transactions, detects potentially fraudulent transactions, issues warnings, and blocks the transaction as necessary, thereby protecting the user's assets from fraud. For example, this tool is extremely useful for elderly users and users who are unfamiliar with the Internet, allowing them to conduct deposit and savings transactions with peace of mind.

[0029] A fraud detection system according to an embodiment includes a collection unit, an analysis unit, a detection unit, a warning unit, and a blocking unit. The collection unit collects a user's deposit transaction data. The user's deposit transaction data includes detailed data such as the transaction date and time, amount, and customer. The collection unit can acquire data in real time, for example, using a bank's API. The collection unit can also collect data based on the user's transaction history. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, a machine learning algorithm based on past transaction data. Machine learning algorithms include, for example, decision trees, random forests, and neural networks. The detection unit detects abnormal transactions based on the data analyzed by the analysis unit. The detection unit can detect, for example, abnormal transactions that differ from normal transaction patterns. The warning unit issues a warning for abnormal transactions detected by the detection unit. The warning unit can issue a warning via, for example, SMS, email, in-app notification, or phone call. The blocking unit blocks transactions warned about by the warning unit as necessary. The blocking unit can, for example, cooperate with a bank's system to temporarily suspend a transaction. The blocking unit can also request confirmation from the user. As a result, the fraud detection system according to the embodiment can monitor users' deposit and savings transactions, detect potentially fraudulent transactions, issue warnings, and block the transactions as necessary, thereby protecting users' assets from fraud.

[0030] The collection unit can acquire data in real time by using the bank's API. To acquire data in real time, the data needs to be updated, for example, every second or minute. The collection unit can acquire user transaction data in real time by using, for example, the bank's API. For example, when a user withdraws a large amount of money from a bank account, the collection unit can immediately acquire the transaction data. The collection unit can also collect data based on the user's transaction history. In this way, by using the bank's API, transaction data can be collected in real time.

[0031] The analysis unit can analyze the data using a machine learning algorithm based on past transaction data. Examples of machine learning algorithms include decision trees, random forests, and neural networks. The analysis unit can analyze the data using a machine learning algorithm based on past transaction data. For example, a decision tree algorithm can classify transaction data and identify anomalous transactions. A random forest algorithm can combine multiple decision trees to detect anomalous transactions with higher accuracy. A neural network algorithm can learn from large amounts of data and detect complex patterns. As a result, the accuracy of data analysis can be improved by using a machine learning algorithm.

[0032] The warning unit can issue a warning via SMS, email, in-app notification, or telephone call. The warning unit can issue a warning to the user by sending an SMS, for example. For example, if a potentially fraudulent transaction is detected, the warning unit sends a warning to the user by SMS. The warning unit can also issue a warning to the user by sending an email. For example, if a potentially fraudulent transaction is detected, the warning unit sends a warning to the user by email. The warning unit can also issue an in-app notification. For example, if a potentially fraudulent transaction is detected, the warning unit issues a warning to the user by in-app notification. The warning unit can also issue a warning by telephone. For example, if a potentially fraudulent transaction is detected, the warning unit issues a warning to the user by telephone. In this way, by issuing warnings in multiple ways, the user can be notified quickly.

[0033] The block unit can temporarily suspend a transaction in cooperation with the bank's system. The block unit can temporarily suspend a transaction in cooperation with the bank's system, for example. For example, if the block unit detects a transaction that is likely to be fraudulent, the block unit can temporarily suspend the transaction in cooperation with the bank's system. The block unit can also temporarily suspend a transaction until confirmation from the user is obtained. For example, the block unit requests confirmation from the user and temporarily suspends the transaction until that confirmation is obtained. In this way, by cooperating with the bank's system, a transaction can be swiftly suspended.

[0034] The blocking unit can request confirmation from the user. For example, the blocking unit can request confirmation from the user by telephone. For example, if a transaction that is likely to be fraudulent is detected, the blocking unit can request confirmation from the user by telephone. The blocking unit can also request confirmation by email. For example, if a transaction that is likely to be fraudulent is detected, the blocking unit can request confirmation from the user by email. The blocking unit can also request confirmation within the app. For example, if a transaction that is likely to be fraudulent is detected, the blocking unit can request confirmation from the user within the app. In this way, by requesting user confirmation, it is possible to prevent erroneous blocking of transactions.

[0035] The collection unit can analyze the user's past transaction history and select the optimal collection method. For example, the collection unit can analyze patterns of transactions that the user has frequently made in the past and collect data if similar patterns are observed. The collection unit can also prioritize the collection of transactions made during specific time periods from the user's transaction history. Furthermore, the collection unit can identify times when abnormal transactions are likely to occur based on the user's transaction history and collect data at those times. This allows the optimal collection method to be selected by analyzing the past transaction history.

[0036] When collecting transaction data, the collection unit can filter the data based on the user's current financial situation and areas of interest. For example, the collection unit can prioritize collection of important transaction data taking into account the user's current account balance and asset status. The collection unit can also prioritize collection of transaction data related to financial products and services in which the user is interested. Furthermore, the collection unit can analyze the user's current income and expenditure patterns, identify situations in which abnormal transactions are likely to occur, and collect data. This allows important data to be prioritized by filtering the data based on the user's financial situation and areas of interest.

[0037] When collecting transaction data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user uses voice input, the collection unit can collect transaction data by analyzing the voice data. Also, if the user uses text input, the collection unit can collect transaction data by analyzing the text data. Furthermore, if the user uses image input, the collection unit can collect transaction data by analyzing the image data. This allows efficient data collection by selecting the optimal collection means depending on the user's input method.

[0038] When collecting transaction data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, the collection unit can prioritize collecting transaction data conducted by the user in a specific area. The collection unit can also prioritize collecting highly relevant transaction data based on the user's current location. Furthermore, the collection unit can prioritize collecting transaction data in areas frequently visited by the user. In this way, highly relevant data can be prioritized by taking into account the user's geographical location information.

[0039] When collecting transaction data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit can collect transaction data related to places where the user has checked in on social media. The collection unit can also analyze the content of the user's posts on social media and collect related transaction data. Furthermore, the collection unit can collect related transaction data by referring to the activities of the user's friends on social media. This allows related data to be collected efficiently by analyzing the user's social media activities.

[0040] When collecting transaction data, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit can adjust the collection method based on feedback provided by the user in the past. Furthermore, if the collection unit determines from the user's past feedback that a particular collection method is effective, it can use that method preferentially. Furthermore, the collection unit can analyze the user's feedback and suggest the optimal collection method. This allows the collection method to be optimized by reflecting the user's past feedback.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the transaction. For example, the analysis unit can perform a detailed analysis for transactions with high importance. The analysis unit can also perform a simplified analysis for transactions with low importance. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the amount and frequency of the transaction. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the transaction.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the transaction. For example, the analysis unit can apply a specific financial analysis algorithm to financial transactions. The analysis unit can also apply a consumption pattern analysis algorithm to shopping transactions. Furthermore, the analysis unit can apply a risk analysis algorithm to investment transactions. This allows for highly accurate analysis by applying different analysis algorithms depending on the category of the transaction.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit can adjust the current analysis results based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results and reflect them in the current analysis. Furthermore, the analysis unit can compare the user's past analysis results and identify abnormal transactions. This improves the accuracy of the analysis by referring to the user's past analysis results.

[0044] During analysis, the analysis unit can determine the priority of the analysis based on the time of transaction occurrence. For example, the analysis unit can prioritize analysis of recently occurred transactions. The analysis unit can also prioritize analysis of transactions that occurred during a specific time period. Furthermore, the analysis unit can identify abnormal transactions based on the time of transaction occurrence. This allows for efficient analysis by determining the priority of analysis based on the time of transaction occurrence.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of transactions. For example, the analysis unit can prioritize analysis of highly relevant transactions. The analysis unit can also postpone analysis of less relevant transactions. Furthermore, the analysis unit can optimize the order of analysis based on the relevance of transactions. This allows for efficient analysis by adjusting the order of analysis based on the relevance of transactions.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has expertise in finance, the analysis unit can provide analysis results that use a lot of technical terms. Also, if the user does not have expertise in finance, the analysis unit can provide analysis results that are explained in simple terms. Furthermore, the analysis unit can adjust the way in which the analysis results are expressed according to the user's level of expertise. In this way, by adjusting the use of technical terms according to the user's level of expertise, analysis results that are easy for the user to understand can be provided.

[0047] The detection unit can improve the accuracy of detection by taking into account the interrelationships between transactions during detection. The detection unit can, for example, analyze the interrelationships between multiple transactions and identify abnormal transactions. The detection unit can also improve the accuracy of detection based on the interrelationships between transactions. Furthermore, the detection unit can detect abnormal transactions by taking into account the interrelationships between transactions. As a result, the accuracy of detection is improved by taking into account the interrelationships between transactions.

[0048] The detection unit can perform detection by taking into consideration attribute information of the originator of the transaction. The detection unit can detect abnormal transactions by taking into consideration, for example, the age and gender of the originator of the transaction. The detection unit can also detect abnormal transactions by taking into consideration the past transaction history of the originator of the transaction. Furthermore, the detection unit can identify abnormal transactions based on attribute information of the originator of the transaction. As a result, by taking into consideration attribute information of the originator of the transaction, the accuracy of detecting abnormal transactions is improved.

[0049] The detection unit can weight the detection based on the frequency of transaction occurrence during detection. For example, the detection unit can set a low weight for transactions that occur frequently. The detection unit can also set a high weight for transactions that occur infrequently. Furthermore, the detection unit can identify abnormal transactions based on the frequency of transaction occurrence. Thus, weighting based on the frequency of transaction occurrence improves the accuracy of detecting abnormal transactions.

[0050] The detection unit can perform detection by taking into consideration the geographical distribution of transactions. For example, the detection unit can preferentially detect transactions that occurred in a specific region. The detection unit can also identify geographically abnormal transactions. Furthermore, the detection unit can detect abnormal transactions based on the geographical distribution of transactions. As a result, by taking the geographical distribution of transactions into consideration, the accuracy of detecting abnormal transactions is improved.

[0051] The detection unit can improve the accuracy of detection by referring to literature related to the transaction during detection. The detection unit can, for example, identify abnormal transactions by referring to literature related to the transaction. The detection unit can also improve the accuracy of detection based on the literature related to the transaction. Furthermore, the detection unit can analyze literature related to the transaction and detect abnormal transactions. As a result, by referring to literature related to the transaction, the accuracy of detecting abnormal transactions is improved.

[0052] The detection unit can perform detection by taking into consideration the market value of the transaction. For example, the detection unit can prioritize detection of transactions with high market value. The detection unit can also postpone detection of transactions with low market value. Furthermore, the detection unit can detect abnormal transactions based on the market value of the transaction. In this way, by taking into consideration the market value of the transaction, the accuracy of detecting abnormal transactions is improved.

[0053] When issuing a warning, the warning unit can adjust the level of detail of the warning based on the importance of the transaction. For example, the warning unit can display a detailed warning for transactions with high importance. The warning unit can also display a simplified warning for transactions with low importance. Furthermore, the warning unit can adjust the level of detail of the warning based on the amount and frequency of the transaction. This allows for efficient warnings by adjusting the level of detail of the warning based on the importance of the transaction.

[0054] The warning unit can apply different warning algorithms depending on the category of the transaction when issuing a warning. For example, the warning unit can apply a specific financial warning algorithm to a financial transaction. The warning unit can also apply a spending pattern warning algorithm to a shopping transaction. The warning unit can also apply a risk warning algorithm to an investment transaction. This allows for highly accurate warnings by applying different warning algorithms depending on the category of the transaction.

[0055] When issuing a warning, the warning unit can improve the accuracy of the warning by referring to the user's past warning results. The warning unit can, for example, adjust the current warning based on the user's past warning results. The warning unit can also extract specific patterns from the user's past warning results and reflect them in the current warning. Furthermore, the warning unit can compare the user's past warning results and identify abnormal transactions. This improves the accuracy of the warning by referring to the user's past warning results.

[0056] When issuing a warning, the warning unit can determine the priority of the warning based on the time of transaction occurrence. For example, the warning unit can prioritize warnings for transactions that occurred recently. The warning unit can also prioritize warnings for transactions that occurred during a specific time period. Furthermore, the warning unit can identify abnormal transactions based on the time of transaction occurrence. This allows for efficient warnings by determining the priority of warnings based on the time of transaction occurrence.

[0057] The warning unit can adjust the order of warnings based on the relevance of the transactions when issuing a warning. For example, the warning unit can give priority to warnings about highly relevant transactions. The warning unit can also postpone warnings about less relevant transactions. Furthermore, the warning unit can optimize the order of warnings based on the relevance of the transactions. This allows for efficient warnings by adjusting the order of warnings based on the relevance of the transactions.

[0058] The warning unit may adjust the use of technical terms in the warning depending on the user's level of expertise when issuing the warning. For example, if the user has expertise in finance, the warning unit may display a warning that uses a lot of technical terms. Alternatively, if the user does not have expertise in finance, the warning unit may display a warning that explains the warning in simple terms. Furthermore, the warning unit may adjust the way the warning is expressed depending on the user's level of expertise. This allows the user to be provided with a warning that is easy to understand by adjusting the use of technical terms depending on the user's level of expertise.

[0059] When blocking, the blocker can adjust the level of detail of the block based on the importance of the transaction. For example, the blocker can perform detailed blocking for transactions of high importance. Also, the blocker can perform simplified blocking for transactions of low importance. Furthermore, the blocker can adjust the level of detail of the block based on the amount and frequency of the transaction. This allows for efficient blocking by adjusting the level of detail of the block based on the importance of the transaction.

[0060] When blocking, the blocker can apply different block algorithms depending on the category of the transaction. For example, the blocker can apply a specific financial block algorithm to financial transactions. The blocker can also apply a consumption pattern block algorithm to shopping transactions. Furthermore, the blocker can apply a risk block algorithm to investment transactions. This allows for highly accurate blocking by applying different block algorithms depending on the category of the transaction.

[0061] When blocking, the block unit can improve the accuracy of the block by referring to the user's past block results. For example, the block unit can adjust the current block based on the user's past block results. The block unit can also extract specific patterns from the user's past block results and reflect them in the current block. Furthermore, the block unit can compare the user's past block results and identify abnormal transactions. This improves the accuracy of the block by referring to the user's past block results.

[0062] When blocking, the blocking unit can determine the priority of blocks based on the time of transaction occurrence. For example, the blocking unit can prioritize blocking of recently occurred transactions. The blocking unit can also prioritize blocking of transactions that occurred during a specific time period. Furthermore, the blocking unit can identify abnormal transactions based on the time of transaction occurrence. This allows for efficient blocking by determining the priority of blocks based on the time of transaction occurrence.

[0063] When blocking, the block unit can adjust the order of blocks based on the relevance of transactions. For example, the block unit can prioritize blocking of highly related transactions. Also, the block unit can postpone transactions with low relevance. Furthermore, the block unit can optimize the order of blocks based on the relevance of transactions. This allows for efficient blocking by adjusting the order of blocks based on the relevance of transactions.

[0064] When creating a block, the block unit can adjust the use of technical terms in the block according to the user's level of expertise. For example, if the user has expertise in finance, the block unit can create a block that uses a lot of technical terms. Also, if the user does not have expertise in finance, the block unit can create a block that explains things in simple terms. Furthermore, the block unit can adjust the way the block is expressed according to the user's level of expertise. This allows the use of technical terms to be adjusted according to the user's level of expertise, making it possible to provide blocks that are easy for the user to understand.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] When collecting user transaction data, the collection unit checks the security status of the user's device and can suspend data collection if the security is low. For example, the collection unit can suspend data collection if the user's device has not applied the latest security patch. The collection unit can also suspend data collection if the user's device is infected with a virus. Furthermore, the collection unit can suspend data collection if the user's device has been subject to unauthorized access. This enables safe data collection by checking the security status of the user's device.

[0067] When analyzing a user's transaction data, the analysis unit can refer not only to the user's past transaction data but also to transaction data of other users in the same region. For example, the analysis unit can refer to transaction data of other users in the same region on the same day. The analysis unit can also refer to transaction data of other users in the same region during the same time period. Furthermore, the analysis unit can refer to transaction data of other users in the same region with the same trading partner. This allows for more accurate analysis by referring to the transaction data of other users in the same region.

[0068] When detecting abnormal transactions, the detection unit can take into account not only the user's transaction data but also the credit information of the business partner. For example, if the business partner has a low credit score, the detection unit can determine that the transaction is abnormal. The detection unit can also determine that the transaction is abnormal if the business partner has a history of committing fraud in the past. Furthermore, the detection unit can determine that the transaction is abnormal if the business partner is a new business partner. In this way, by taking the business partner's credit information into account, it is possible to detect abnormal transactions with greater accuracy.

[0069] When issuing a warning to a user, the warning unit can select a warning method taking into consideration the user's current activity status. For example, the warning unit can issue a warning by voice when the user is driving. Also, the warning unit can issue a warning by vibration when the user is in a meeting. Furthermore, the warning unit can issue a warning by an alarm sound when the user is sleeping. In this way, an appropriate warning method can be selected by taking into consideration the user's current activity status.

[0070] When blocking a transaction, the blocking unit can adjust the blocking criteria by taking into account the user's past transaction patterns. For example, if the user has frequently traded with the same trading partner in the past, the blocking unit will not block the transaction. Also, if the user has frequently traded the same amount in the past, the blocking unit may not block the transaction. Furthermore, if the user has traded during the same time period in the past, the blocking unit may not block the transaction. This allows appropriate blocking criteria to be set by taking into account the user's past transaction patterns.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The collection unit collects the user's deposit transaction data. The user's deposit transaction data includes detailed data such as the transaction date and time, amount, and transaction partner. The collection unit can obtain data in real time, for example, by using a bank's API. The collection unit can also collect data based on the user's transaction history. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using machine learning algorithms based on past transaction data. Machine learning algorithms include decision trees, random forests, neural networks, etc. Step 3: The detection unit detects abnormal transactions based on the data analyzed by the analysis unit. The detection unit can detect abnormal transactions that differ from normal transaction patterns. Step 4: The warning unit issues a warning about the abnormal transactions detected by the detection unit. The warning unit can issue warnings via SMS, email, in-app notification, or phone call. Step 5: The Blocking Department blocks the transaction warned by the Warning Department as necessary. The Blocking Department can temporarily suspend the transaction in cooperation with the bank's system. The Blocking Department can also request user confirmation.

[0073] (Example 2) A tool according to an embodiment of the present invention is a system that monitors a user's deposit and savings transactions, detects potentially fraudulent transactions, issues warnings, and blocks the transactions as necessary. This system detects potentially fraudulent transactions by collecting the user's deposit and savings transaction data and analyzing them using AI. For example, when a user withdraws a large amount of money from their bank account, the transaction data is collected and AI detects abnormal transactions that differ from normal transaction patterns. Next, a warning is issued for the detected transaction and, if necessary, the transaction is blocked. For example, a warning is sent to the user via SMS or email to inform the user of the potentially fraudulent transaction, allowing the user to respond quickly. Furthermore, for transactions that are highly likely to be fraudulent, the transaction can be temporarily suspended and the user's confirmation requested. This protects the user's assets. This tool monitors the user's deposit and savings transactions, detects potentially fraudulent transactions, issues warnings, and blocks the transaction as necessary, thereby protecting the user's assets from fraud. For example, this tool is extremely useful for elderly users and users who are unfamiliar with the Internet, allowing them to conduct deposit and savings transactions with peace of mind.

[0074] A fraud detection system according to an embodiment includes a collection unit, an analysis unit, a detection unit, a warning unit, and a blocking unit. The collection unit collects a user's deposit transaction data. The user's deposit transaction data includes detailed data such as the transaction date and time, amount, and customer. The collection unit can acquire data in real time, for example, using a bank's API. The collection unit can also collect data based on the user's transaction history. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, a machine learning algorithm based on past transaction data. Machine learning algorithms include, for example, decision trees, random forests, and neural networks. The detection unit detects abnormal transactions based on the data analyzed by the analysis unit. The detection unit can detect, for example, abnormal transactions that differ from normal transaction patterns. The warning unit issues a warning for abnormal transactions detected by the detection unit. The warning unit can issue a warning via, for example, SMS, email, in-app notification, or phone call. The blocking unit blocks transactions warned about by the warning unit as necessary. The blocking unit can, for example, cooperate with a bank's system to temporarily suspend a transaction. The blocking unit can also request confirmation from the user. As a result, the fraud detection system according to the embodiment can monitor users' deposit and savings transactions, detect potentially fraudulent transactions, issue warnings, and block the transactions as necessary, thereby protecting users' assets from fraud.

[0075] The collection unit can acquire data in real time by using the bank's API. To acquire data in real time, the data needs to be updated, for example, every second or minute. The collection unit can acquire user transaction data in real time by using, for example, the bank's API. For example, when a user withdraws a large amount of money from a bank account, the collection unit can immediately acquire the transaction data. The collection unit can also collect data based on the user's transaction history. In this way, by using the bank's API, transaction data can be collected in real time.

[0076] The analysis unit can analyze the data using a machine learning algorithm based on past transaction data. Examples of machine learning algorithms include decision trees, random forests, and neural networks. The analysis unit can analyze the data using a machine learning algorithm based on past transaction data. For example, a decision tree algorithm can classify transaction data and identify anomalous transactions. A random forest algorithm can combine multiple decision trees to detect anomalous transactions with higher accuracy. A neural network algorithm can learn from large amounts of data and detect complex patterns. As a result, the accuracy of data analysis can be improved by using a machine learning algorithm.

[0077] The warning unit can issue a warning via SMS, email, in-app notification, or telephone call. The warning unit can issue a warning to the user by sending an SMS, for example. For example, if a potentially fraudulent transaction is detected, the warning unit sends a warning to the user by SMS. The warning unit can also issue a warning to the user by sending an email. For example, if a potentially fraudulent transaction is detected, the warning unit sends a warning to the user by email. The warning unit can also issue an in-app notification. For example, if a potentially fraudulent transaction is detected, the warning unit issues a warning to the user by in-app notification. The warning unit can also issue a warning by telephone. For example, if a potentially fraudulent transaction is detected, the warning unit issues a warning to the user by telephone. In this way, by issuing warnings in multiple ways, the user can be notified quickly.

[0078] The block unit can temporarily suspend a transaction in cooperation with the bank's system. The block unit can temporarily suspend a transaction in cooperation with the bank's system, for example. For example, if the block unit detects a transaction that is likely to be fraudulent, the block unit can temporarily suspend the transaction in cooperation with the bank's system. The block unit can also temporarily suspend a transaction until confirmation from the user is obtained. For example, the block unit requests confirmation from the user and temporarily suspends the transaction until that confirmation is obtained. In this way, by cooperating with the bank's system, a transaction can be swiftly suspended.

[0079] The blocking unit can request confirmation from the user. For example, the blocking unit can request confirmation from the user by telephone. For example, if a transaction that is likely to be fraudulent is detected, the blocking unit can request confirmation from the user by telephone. The blocking unit can also request confirmation by email. For example, if a transaction that is likely to be fraudulent is detected, the blocking unit can request confirmation from the user by email. The blocking unit can also request confirmation within the app. For example, if a transaction that is likely to be fraudulent is detected, the blocking unit can request confirmation from the user within the app. In this way, by requesting user confirmation, it is possible to prevent erroneous blocking of transactions.

[0080] The collection unit can estimate the user's emotions and adjust the timing of collecting transaction data based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can delay the collection timing and collect data when the user is relaxed. Furthermore, if the user is relaxed, the collection unit can immediately collect transaction data and perform rapid analysis. Furthermore, if the user is in a hurry, the collection unit can advance the collection timing and collect data quickly. This allows data to be collected at a more appropriate time by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] The collection unit can analyze the user's past transaction history and select the optimal collection method. For example, the collection unit can analyze patterns of transactions that the user has frequently made in the past and collect data if similar patterns are observed. The collection unit can also prioritize the collection of transactions made during specific time periods from the user's transaction history. Furthermore, the collection unit can identify times when abnormal transactions are likely to occur based on the user's transaction history and collect data at those times. This allows the optimal collection method to be selected by analyzing the past transaction history.

[0082] When collecting transaction data, the collection unit can filter the data based on the user's current financial situation and areas of interest. For example, the collection unit can prioritize collection of important transaction data taking into account the user's current account balance and asset status. The collection unit can also prioritize collection of transaction data related to financial products and services in which the user is interested. Furthermore, the collection unit can analyze the user's current income and expenditure patterns, identify situations in which abnormal transactions are likely to occur, and collect data. This allows important data to be prioritized by filtering the data based on the user's financial situation and areas of interest.

[0083] When collecting transaction data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user uses voice input, the collection unit can collect transaction data by analyzing the voice data. Also, if the user uses text input, the collection unit can collect transaction data by analyzing the text data. Furthermore, if the user uses image input, the collection unit can collect transaction data by analyzing the image data. This allows efficient data collection by selecting the optimal collection means depending on the user's input method.

[0084] The collection unit can estimate the user's emotions and determine the priority of transaction data to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit can prioritize collecting transaction data of high importance. Furthermore, when the user is relaxed, the collection unit can prioritize collecting normal transaction data. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting transaction data that needs to be collected quickly. In this way, by determining the priority of data based on the user's emotions, important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0085] When collecting transaction data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, the collection unit can prioritize collecting transaction data conducted by the user in a specific area. The collection unit can also prioritize collecting highly relevant transaction data based on the user's current location. Furthermore, the collection unit can prioritize collecting transaction data in areas frequently visited by the user. In this way, highly relevant data can be prioritized by taking into account the user's geographical location information.

[0086] When collecting transaction data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit can collect transaction data related to places where the user has checked in on social media. The collection unit can also analyze the content of the user's posts on social media and collect related transaction data. Furthermore, the collection unit can collect related transaction data by referring to the activities of the user's friends on social media. This allows related data to be collected efficiently by analyzing the user's social media activities.

[0087] When collecting transaction data, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit can adjust the collection method based on feedback provided by the user in the past. Furthermore, if the collection unit determines from the user's past feedback that a particular collection method is effective, it can use that method preferentially. Furthermore, the collection unit can analyze the user's feedback and suggest the optimal collection method. This allows the collection method to be optimized by reflecting the user's past feedback.

[0088] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide simple, highly visible analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results that focus on the main points. By adjusting the way the analysis is presented according to the user's emotions, it is possible to provide analysis 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 generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the transaction. For example, the analysis unit can perform a detailed analysis for transactions with high importance. The analysis unit can also perform a simplified analysis for transactions with low importance. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the amount and frequency of the transaction. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the transaction.

[0090] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the transaction. For example, the analysis unit can apply a specific financial analysis algorithm to financial transactions. The analysis unit can also apply a consumption pattern analysis algorithm to shopping transactions. Furthermore, the analysis unit can apply a risk analysis algorithm to investment transactions. This allows for highly accurate analysis by applying different analysis algorithms depending on the category of the transaction.

[0091] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit can adjust the current analysis results based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results and reflect them in the current analysis. Furthermore, the analysis unit can compare the user's past analysis results and identify abnormal transactions. This improves the accuracy of the analysis by referring to the user's past analysis results.

[0092] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a short, to-the-point analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a longer analysis result with detailed explanations. Furthermore, if the user is in a hurry, the analysis unit can provide a short analysis result that can be quickly understood. By adjusting the length of the analysis according to the user's emotions, it is possible to provide an analysis result of an optimal length for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0093] During analysis, the analysis unit can determine the priority of the analysis based on the time of transaction occurrence. For example, the analysis unit can prioritize analysis of recently occurred transactions. The analysis unit can also prioritize analysis of transactions that occurred during a specific time period. Furthermore, the analysis unit can identify abnormal transactions based on the time of transaction occurrence. This allows for efficient analysis by determining the priority of analysis based on the time of transaction occurrence.

[0094] During analysis, the analysis unit can adjust the order of analysis based on the relevance of transactions. For example, the analysis unit can prioritize analysis of highly relevant transactions. The analysis unit can also postpone analysis of less relevant transactions. Furthermore, the analysis unit can optimize the order of analysis based on the relevance of transactions. This allows for efficient analysis by adjusting the order of analysis based on the relevance of transactions.

[0095] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has expertise in finance, the analysis unit can provide analysis results that use a lot of technical terms. Also, if the user does not have expertise in finance, the analysis unit can provide analysis results that are explained in simple terms. Furthermore, the analysis unit can adjust the way in which the analysis results are expressed according to the user's level of expertise. In this way, by adjusting the use of technical terms according to the user's level of expertise, analysis results that are easy for the user to understand can be provided.

[0096] The detection unit can estimate the user's emotions and adjust the detection criteria for abnormal transactions based on the estimated user emotions. For example, the detection unit can apply strict detection criteria when the user is nervous. Furthermore, the detection unit can apply normal detection criteria when the user is relaxed. Furthermore, the detection unit can apply criteria that enable quick detection when the user is in a hurry. This allows for more appropriate detection of abnormal transactions by adjusting the detection criteria 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 can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0097] The detection unit can improve the accuracy of detection by taking into account the interrelationships between transactions during detection. The detection unit can, for example, analyze the interrelationships between multiple transactions and identify abnormal transactions. The detection unit can also improve the accuracy of detection based on the interrelationships between transactions. Furthermore, the detection unit can detect abnormal transactions by taking into account the interrelationships between transactions. As a result, the accuracy of detection is improved by taking into account the interrelationships between transactions.

[0098] The detection unit can perform detection by taking into consideration attribute information of the originator of the transaction. The detection unit can detect abnormal transactions by taking into consideration, for example, the age and gender of the originator of the transaction. The detection unit can also detect abnormal transactions by taking into consideration the past transaction history of the originator of the transaction. Furthermore, the detection unit can identify abnormal transactions based on attribute information of the originator of the transaction. As a result, by taking into consideration attribute information of the originator of the transaction, the accuracy of detecting abnormal transactions is improved.

[0099] The detection unit can weight the detection based on the frequency of transaction occurrence during detection. For example, the detection unit can set a low weight for transactions that occur frequently. The detection unit can also set a high weight for transactions that occur infrequently. Furthermore, the detection unit can identify abnormal transactions based on the frequency of transaction occurrence. Thus, weighting based on the frequency of transaction occurrence improves the accuracy of detecting abnormal transactions.

[0100] The detection unit can estimate the user's emotion and adjust the display order of the detection results based on the estimated user's emotion. For example, when the user is nervous, the detection unit can prioritize displaying important detection results. Furthermore, when the user is relaxed, the detection unit can display detection results in a normal order. Furthermore, when the user is in a hurry, the detection unit can prioritize displaying detection results that require a quick response. In this way, by adjusting the display order based on the user's emotion, important detection results can be prioritized and displayed. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0101] The detection unit can perform detection by taking into consideration the geographical distribution of transactions. For example, the detection unit can preferentially detect transactions that occurred in a specific region. The detection unit can also identify geographically abnormal transactions. Furthermore, the detection unit can detect abnormal transactions based on the geographical distribution of transactions. As a result, by taking the geographical distribution of transactions into consideration, the accuracy of detecting abnormal transactions is improved.

[0102] The detection unit can improve the accuracy of detection by referring to literature related to the transaction during detection. The detection unit can, for example, identify abnormal transactions by referring to literature related to the transaction. The detection unit can also improve the accuracy of detection based on the literature related to the transaction. Furthermore, the detection unit can analyze literature related to the transaction and detect abnormal transactions. As a result, by referring to literature related to the transaction, the accuracy of detecting abnormal transactions is improved.

[0103] The detection unit can perform detection by taking into consideration the market value of the transaction. For example, the detection unit can prioritize detection of transactions with high market value. The detection unit can also postpone detection of transactions with low market value. Furthermore, the detection unit can detect abnormal transactions based on the market value of the transaction. In this way, by taking into consideration the market value of the transaction, the accuracy of detecting abnormal transactions is improved.

[0104] The warning unit can estimate the user's emotions and adjust the display method of the warning based on the estimated user emotions. For example, if the user is nervous, the warning unit can display a simple, highly visible warning. If the user is relaxed, the warning unit can display a detailed warning. If the user is in a hurry, the warning unit can display a concise warning that focuses on the main points. By adjusting the display method of the warning based on the user's emotions, it is possible to provide a warning that is easy for the user to understand. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0105] When issuing a warning, the warning unit can adjust the level of detail of the warning based on the importance of the transaction. For example, the warning unit can display a detailed warning for transactions with high importance. The warning unit can also display a simplified warning for transactions with low importance. Furthermore, the warning unit can adjust the level of detail of the warning based on the amount and frequency of the transaction. This allows for efficient warnings by adjusting the level of detail of the warning based on the importance of the transaction.

[0106] The warning unit can apply different warning algorithms depending on the category of the transaction when issuing a warning. For example, the warning unit can apply a specific financial warning algorithm to a financial transaction. The warning unit can also apply a spending pattern warning algorithm to a shopping transaction. The warning unit can also apply a risk warning algorithm to an investment transaction. This allows for highly accurate warnings by applying different warning algorithms depending on the category of the transaction.

[0107] When issuing a warning, the warning unit can improve the accuracy of the warning by referring to the user's past warning results. The warning unit can, for example, adjust the current warning based on the user's past warning results. The warning unit can also extract specific patterns from the user's past warning results and reflect them in the current warning. Furthermore, the warning unit can compare the user's past warning results and identify abnormal transactions. This improves the accuracy of the warning by referring to the user's past warning results.

[0108] The warning unit can estimate the user's emotions and adjust the length of the warning based on the estimated user emotions. For example, if the user is nervous, the warning unit can display a short and to-the-point warning. If the user is relaxed, the warning unit can display a longer warning with detailed explanations. If the user is in a hurry, the warning unit can display a short warning that can be quickly understood. This allows the length of the warning to be adjusted according to the user's emotions, thereby providing a warning of an optimal length for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0109] When issuing a warning, the warning unit can determine the priority of the warning based on the time of transaction occurrence. For example, the warning unit can prioritize warnings for transactions that occurred recently. The warning unit can also prioritize warnings for transactions that occurred during a specific time period. Furthermore, the warning unit can identify abnormal transactions based on the time of transaction occurrence. This allows for efficient warnings by determining the priority of warnings based on the time of transaction occurrence.

[0110] The warning unit can adjust the order of warnings based on the relevance of the transactions when issuing a warning. For example, the warning unit can give priority to warnings about highly relevant transactions. The warning unit can also postpone warnings about less relevant transactions. Furthermore, the warning unit can optimize the order of warnings based on the relevance of the transactions. This allows for efficient warnings by adjusting the order of warnings based on the relevance of the transactions.

[0111] The warning unit may adjust the use of technical terms in the warning depending on the user's level of expertise when issuing the warning. For example, if the user has expertise in finance, the warning unit may display a warning that uses a lot of technical terms. Alternatively, if the user does not have expertise in finance, the warning unit may display a warning that explains the warning in simple terms. Furthermore, the warning unit may adjust the way the warning is expressed depending on the user's level of expertise. This allows the user to be provided with a warning that is easy to understand by adjusting the use of technical terms depending on the user's level of expertise.

[0112] The blocking unit can estimate the user's emotions and adjust the blocking method based on the estimated user's emotions. For example, the blocking unit can quickly block when the user is nervous. Furthermore, the blocking unit can apply a normal blocking method when the user is relaxed. Furthermore, the blocking unit can apply a blocking method that allows for quick response when the user is in a hurry. 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 generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0113] When blocking, the blocker can adjust the level of detail of the block based on the importance of the transaction. For example, the blocker can perform detailed blocking for transactions of high importance. Also, the blocker can perform simplified blocking for transactions of low importance. Furthermore, the blocker can adjust the level of detail of the block based on the amount and frequency of the transaction. This allows for efficient blocking by adjusting the level of detail of the block based on the importance of the transaction.

[0114] When blocking, the blocker can apply different block algorithms depending on the category of the transaction. For example, the blocker can apply a specific financial block algorithm to financial transactions. The blocker can also apply a consumption pattern block algorithm to shopping transactions. Furthermore, the blocker can apply a risk block algorithm to investment transactions. This allows for highly accurate blocking by applying different block algorithms depending on the category of the transaction.

[0115] When blocking, the block unit can improve the accuracy of the block by referring to the user's past block results. For example, the block unit can adjust the current block based on the user's past block results. The block unit can also extract specific patterns from the user's past block results and reflect them in the current block. Furthermore, the block unit can compare the user's past block results and identify abnormal transactions. This improves the accuracy of the block by referring to the user's past block results.

[0116] The block unit can estimate the user's emotions and adjust the length of the block based on the estimated user emotions. For example, if the user is nervous, the block unit can provide a short, to-the-point block. If the user is relaxed, the block unit can provide a longer block with detailed explanations. If the user is in a hurry, the block unit can provide a short block that can be quickly understood. This allows the block length to be adjusted according to the user's emotions, thereby providing a block of an optimal length for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0117] When blocking, the blocking unit can determine the priority of blocks based on the time of transaction occurrence. For example, the blocking unit can prioritize blocking of recently occurred transactions. The blocking unit can also prioritize blocking of transactions that occurred during a specific time period. Furthermore, the blocking unit can identify abnormal transactions based on the time of transaction occurrence. This allows for efficient blocking by determining the priority of blocks based on the time of transaction occurrence.

[0118] When blocking, the block unit can adjust the order of blocks based on the relevance of transactions. For example, the block unit can prioritize blocking of highly related transactions. Also, the block unit can postpone transactions with low relevance. Furthermore, the block unit can optimize the order of blocks based on the relevance of transactions. This allows for efficient blocking by adjusting the order of blocks based on the relevance of transactions.

[0119] When creating a block, the block unit can adjust the use of technical terms in the block according to the user's level of expertise. For example, if the user has expertise in finance, the block unit can create a block that uses a lot of technical terms. Also, if the user does not have expertise in finance, the block unit can create a block that explains things in simple terms. Furthermore, the block unit can adjust the way the block is expressed according to the user's level of expertise. This allows the use of technical terms to be adjusted according to the user's level of expertise, making it possible to provide blocks that are easy for the user to understand. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, detection unit, warning unit, and blocking unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can acquire data from a bank's API via the communication I / F 44 of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes data using a machine learning algorithm. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and detects abnormal transactions. The warning unit is realized, for example, by the control unit 46A of the smart device 14 and issues a warning via SMS or email. The blocking unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and temporarily suspends transactions. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, detection unit, warning unit, and blocking unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can acquire data from a bank's API via the communication I / F 44 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes data using a machine learning algorithm. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and detects abnormal transactions. The warning unit is realized, for example, by the control unit 46A of the smart glasses 214 and issues a warning via SMS or email. The blocking unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and temporarily suspends transactions. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, detection unit, warning unit, and blocking unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit can acquire data from a bank's API via the communication I / F 44 of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes data using a machine learning algorithm. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and detects abnormal transactions. The warning unit is realized, for example, by the control unit 46A of the headset type terminal 314 and issues a warning via SMS or email. The blocking unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and temporarily suspends transactions. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, detection unit, warning unit, and blocking unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can acquire data from a bank's API via the communication I / F 44 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the data using a machine learning algorithm. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and detects abnormal transactions. The warning unit is realized, for example, by the control unit 46A of the robot 414 and issues a warning via SMS or email. The blocking unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and temporarily suspends transactions.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] When collecting user transaction data, the collection unit checks the security status of the user's device and can suspend data collection if the security is low. For example, the collection unit can suspend data collection if the user's device has not applied the latest security patch. The collection unit can also suspend data collection if the user's device is infected with a virus. Furthermore, the collection unit can suspend data collection if the user's device has been subject to unauthorized access. This enables safe data collection by checking the security status of the user's device.

[0122] When analyzing a user's transaction data, the analysis unit can refer not only to the user's past transaction data but also to transaction data of other users in the same region. For example, the analysis unit can refer to transaction data of other users in the same region on the same day. The analysis unit can also refer to transaction data of other users in the same region during the same time period. Furthermore, the analysis unit can refer to transaction data of other users in the same region with the same trading partner. This allows for more accurate analysis by referring to the transaction data of other users in the same region.

[0123] When detecting abnormal transactions, the detection unit can take into account not only the user's transaction data but also the credit information of the business partner. For example, if the business partner has a low credit score, the detection unit can determine that the transaction is abnormal. The detection unit can also determine that the transaction is abnormal if the business partner has a history of committing fraud in the past. Furthermore, the detection unit can determine that the transaction is abnormal if the business partner is a new business partner. In this way, by taking the business partner's credit information into account, it is possible to detect abnormal transactions with greater accuracy.

[0124] When issuing a warning to a user, the warning unit can select a warning method taking into consideration the user's current activity status. For example, the warning unit can issue a warning by voice when the user is driving. Also, the warning unit can issue a warning by vibration when the user is in a meeting. Furthermore, the warning unit can issue a warning by an alarm sound when the user is sleeping. In this way, an appropriate warning method can be selected by taking into consideration the user's current activity status.

[0125] When blocking a transaction, the blocking unit can adjust the blocking criteria by taking into account the user's past transaction patterns. For example, if the user has frequently traded with the same trading partner in the past, the blocking unit will not block the transaction. Also, if the user has frequently traded the same amount in the past, the blocking unit may not block the transaction. Furthermore, if the user has traded during the same time period in the past, the blocking unit may not block the transaction. This allows appropriate blocking criteria to be set by taking into account the user's past transaction patterns.

[0126] The collection unit can estimate the user's emotions and adjust the type of data to be collected based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit collects only transaction data of high importance. Alternatively, if the user is relaxed, the collection unit can collect all transaction data. Furthermore, if the user is in a hurry, the collection unit can prioritize collection of transaction data that needs to be collected quickly. This allows for efficient data collection by adjusting the type of data to be collected based on the user's emotions.

[0127] The analysis unit can estimate the user's emotions and adjust the timing of the analysis based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit delays the analysis and performs the analysis when the user is relaxed. Also, if the user is relaxed, the analysis unit can perform the analysis immediately. Furthermore, if the user is in a hurry, the analysis unit can perform the analysis quickly. Thus, by adjusting the timing of the analysis based on the user's emotions, the analysis can be performed at a more appropriate time.

[0128] The detection unit can estimate the user's emotions and adjust the abnormal transaction detection method based on the estimated user's emotions. For example, the detection unit can apply a strict detection method when the user is stressed. Alternatively, the detection unit can apply a normal detection method when the user is relaxed. Furthermore, the detection unit can apply a method that allows for quick detection when the user is in a hurry. This allows for more appropriate detection of abnormal transactions by adjusting the detection method based on the user's emotions.

[0129] The warning unit can estimate the user's emotions and adjust the content of the warning based on the estimated user's emotions. For example, if the user is feeling stressed, the warning unit can issue a concise and to-the-point warning. If the user is relaxed, the warning unit can issue a detailed warning. Furthermore, if the user is in a hurry, the warning unit can issue a quickly understandable warning. In this way, by adjusting the content of the warning based on the user's emotions, it is possible to provide a warning that is easy for the user to understand.

[0130] The blocking unit can estimate the user's emotions and adjust the unblocking method based on the estimated user's emotions. For example, if the user is feeling stressed, the blocking unit can unblock the user with a simple procedure. If the user is relaxed, the blocking unit can unblock the user with a normal procedure. Furthermore, if the user is in a hurry, the blocking unit can apply a procedure to quickly unblock the user. In this way, by adjusting the unblocking method based on the user's emotions, it is possible to provide an appropriate unblocking method for the user.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The collection unit collects the user's deposit transaction data. The user's deposit transaction data includes detailed data such as the transaction date and time, amount, and transaction partner. The collection unit can obtain data in real time, for example, by using a bank's API. The collection unit can also collect data based on the user's transaction history. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using machine learning algorithms based on past transaction data. Machine learning algorithms include decision trees, random forests, neural networks, etc. Step 3: The detection unit detects abnormal transactions based on the data analyzed by the analysis unit. The detection unit can detect abnormal transactions that differ from normal transaction patterns. Step 4: The warning unit issues a warning about the abnormal transactions detected by the detection unit. The warning unit can issue warnings via SMS, email, in-app notification, or phone call. Step 5: The Blocking Department blocks the transaction warned by the Warning Department as necessary. The Blocking Department can temporarily suspend the transaction in cooperation with the bank's system. The Blocking Department can also request user confirmation.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 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.

[0139] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0155] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0161] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0162] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0163] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0164] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0165] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0167] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0171] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0172] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0173] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0175] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0176] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0177] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0178] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0179] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0180] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0181] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0182] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0183] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0184] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0187] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0188] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0189] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0190] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0191] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0192] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0193] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0194] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0195] 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.

[0196] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0197] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0198] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0199] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0200] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0201] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0202] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0203] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0204] [Explanation of symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects user deposit transaction data; an analysis unit that analyzes the data collected by the collection unit; a detection unit that detects abnormal transactions based on the data analyzed by the analysis unit; a warning unit that issues a warning regarding an abnormal transaction detected by the detection unit; a blocking unit that blocks the transaction warned by the warning unit as necessary. A system characterized by:

2. The collecting unit Use the bank's API to obtain data in real time 2. The system of claim 1.

3. The analysis unit Analyze data using machine learning algorithms based on historical transaction data 2. The system of claim 1.

4. The warning unit Alerts via SMS, email, in-app notifications and phone calls 2. The system of claim 1.

5. The block portion is Temporarily suspend transactions in cooperation with bank systems 2. The system of claim 1.

6. The block portion is Ask for user confirmation 2. The system of claim 1.

7. The collecting unit Estimate user emotions and adjust the timing of collecting transaction data based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit Analyze the user's past transaction history and select the optimal collection method 2. The system of claim 1.

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