system

Abstract

The system according to this embodiment aims to integrate data from various financial institutions, monitor it in real time, and detect anomalies. [Solution] The system according to the embodiment comprises a collection unit, an integration unit, a monitoring unit, a detection unit, and an alert unit. The collection unit collects data in cooperation with the APIs of each financial institution. The integration unit integrates the data collected by the collection unit. The monitoring unit monitors the data integrated by the integration unit in real time. The detection unit detects anomalies from the data monitored by the monitoring unit. The alert unit notifies the user of the anomalies detected by the detection unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to integrate data of each financial institution, monitor it in real time, and detect abnormalities.

[0005] The system according to the embodiment aims to integrate data of each financial institution, monitor it in real time, and detect abnormalities.

Means for Solving the Problems

[0006] The system according to the embodiment comprises a collection unit, an integration unit, a monitoring unit, a detection unit, and an alert unit. The collection unit collects data in cooperation with the APIs of each financial institution. The integration unit integrates the data collected by the collection unit. The monitoring unit monitors the data integrated by the integration unit in real time. The detection unit detects anomalies from the data monitored by the monitoring unit. The alert unit notifies the user of the anomalies detected by the detection unit. [Effects of the Invention]

[0007] The system according to this embodiment can integrate data from each financial institution, monitor it in real time, and detect anomalies. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

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

[0028] (Example of form 1) The AI ​​tool according to an embodiment of the present invention is a system that securely collects and integrates data by linking with the APIs of various financial institutions. This system can securely manage all income and expenditure data while protecting user privacy. Specifically, it consists of the following steps: First, it links with the APIs of various financial institutions to collect the user's financial data. Next, the AI ​​integrates the collected data and monitors the activity of all financial accounts in real time. The AI ​​agent automatically detects and analyzes abnormal activity and security risks, and if an anomaly occurs, it immediately sends an alert to the user and proposes immediate countermeasures. Furthermore, it is equipped with security features such as enhanced data encryption, multi-factor authentication, anomaly detection and real-time alerts, data backup, and user education. For example, it links with the APIs of various financial institutions to collect the user's financial data. At this time, the data is encrypted and transmitted to protect the user's privacy. For example, if a user has multiple bank accounts, account information can be collected through the API of each bank. Next, the AI ​​integrates the collected data and monitors the activity of all financial accounts in real time. The AI ​​agent analyzes the collected data and automatically detects abnormal activity and security risks. For example, if fraudulent transactions or unusual withdrawals occur, the AI ​​agent will immediately detect them and send an alert to the user. If an anomaly occurs, the AI ​​agent will immediately send an alert to the user and suggest immediate countermeasures. For instance, if fraudulent transactions are detected, the user will be notified and advised to temporarily suspend the transaction. The user can then review the transaction and resume it if there are no issues. Furthermore, it incorporates security features such as enhanced data encryption, multi-factor authentication, anomaly detection and real-time alerts, data backup, and user education. Data encryption ensures that users' financial data is always securely protected. Multi-factor authentication prevents unauthorized access. Anomaly detection and real-time alerts allow for immediate response to unusual activity. Data backup provides protection against potential data loss.User education enables users to understand security risks and take appropriate action. Thus, this invention is an AI tool that securely collects and integrates data by linking with the APIs of various financial institutions, allowing for the safe management of all income and expenditure data while protecting user privacy. The AI ​​agent monitors the activity of all financial accounts in real time, automatically detecting and analyzing abnormal activity and security risks. If an anomaly occurs, it immediately sends an alert to the user and proposes immediate countermeasures. Enhanced security features allow users to manage their financial data with peace of mind. As a result, the AI ​​tool can securely manage users' financial data and quickly detect and respond to abnormal activity and security risks.

[0029] The AI ​​tool according to this embodiment comprises a collection unit, an integration unit, a monitoring unit, a detection unit, and an alert unit. The collection unit collects data in cooperation with the APIs of each financial institution. The collection unit can collect financial data using, for example, REST APIs or SOAP APIs. The collection unit collects users' financial data through the APIs of each financial institution. For example, the collection unit can collect users' bank account information and transaction history. The collection unit can encrypt and transmit the collected data. For example, the collection unit can encrypt the data using AES encryption or RSA encryption. The integration unit integrates the data collected by the collection unit. For example, the integration unit can unify the data format and integrate the data using an integration algorithm. The integration unit integrates the collected data and monitors the activity of all financial accounts in real time. For example, the integration unit can monitor the activity of bank accounts and credit card accounts. The integration unit can eliminate data duplication to maintain data integrity. For example, the integration unit detects data duplication and deletes the duplicate data. The monitoring unit monitors the data integrated by the integration unit in real time. The monitoring unit can, for example, monitor data fluctuations and detect abnormal activity. The monitoring unit analyzes the collected data and automatically detects abnormal activity and security risks. For example, the monitoring unit can detect fraudulent transactions and abnormal access. The monitoring unit can detect anomalies using anomaly detection algorithms. For example, the monitoring unit can detect anomalies using machine learning algorithms and rule-based algorithms. The detection unit detects anomalies from the data monitored by the monitoring unit. The detection unit can detect abnormal transactions and security risks. When an anomaly occurs, the detection unit immediately sends an alert to the user and proposes immediate countermeasures. For example, if a fraudulent transaction is detected, the detection unit will notify the user and propose temporarily suspending the transaction. The alert unit notifies the user of anomalies detected by the detection unit. The alert unit can send notifications to the user using, for example, email or SMS. The alert unit will notify the user and propose temporarily suspending the transaction.For example, the alert unit can prompt the user to confirm the transaction, and if there are no problems, the transaction can be resumed. This allows the AI ​​tool according to the embodiment to securely manage the user's financial data and quickly detect and respond to abnormal activity and security risks.

[0030] The data collection unit collects data by collaborating with the APIs of various financial institutions. For example, the collection unit can collect financial data using REST APIs or SOAP APIs. Specifically, REST APIs use the HTTP protocol to access resources and exchange data in JSON format. SOAP APIs, on the other hand, exchange data in XML format and offer more complex operations and security features. The collection unit collects users' financial data through the APIs of various financial institutions. For example, the collection unit can collect users' bank account information and transaction history. Bank account information includes account number, balance, and transaction history, while transaction history includes details of deposits and withdrawals, transaction dates and times, and information about the transaction partner. The collection unit can encrypt and transmit the collected data. For example, the collection unit can encrypt data using AES encryption or RSA encryption. AES encryption is a symmetric-key cryptography method that can encrypt data quickly and securely. RSA encryption is a public-key cryptography method where the sender and receiver of the data use different keys to encrypt and decrypt the data. This allows the collection unit to securely collect and transmit users' financial data. Furthermore, the data collection unit can adjust the frequency and timing of data collection. For example, when collecting data in real time, increasing the frequency of API requests ensures that the latest data is always available. On the other hand, when collecting data periodically, the frequency of API requests can be set lower to reduce the system load. This allows the data collection unit to collect data efficiently and flexibly, optimizing the overall system performance.

[0031] The Integration Unit integrates the data collected by the Collection Unit. For example, the Integration Unit can standardize data formats and integrate data using integration algorithms. Specifically, because data collected from different financial institutions may differ in format and structure, the Integration Unit converts this data into a common format. For example, it might convert JSON data to XML format or standardize different field names. The Integration Unit integrates the collected data and monitors the activity of all financial accounts in real time. For example, the Integration Unit can monitor the activity of bank accounts and credit card accounts. Bank account activity includes deposit and withdrawal history and balance changes, while credit card account activity includes usage history and changes in credit limits. To maintain data integrity, the Integration Unit can eliminate data duplication. For example, it can detect and remove duplicate data. Unique identifiers or timestamps can be used to detect duplicate data. This allows the Integration Unit to provide accurate and consistent data. Furthermore, the Integration Unit can leverage distributed processing technologies to optimize the data integration process. For example, it can use multiple servers or cloud resources to run data integration processes in parallel, improving processing speed. This allows the integration unit to quickly and efficiently integrate large amounts of data and enable real-time monitoring.

[0032] The monitoring unit monitors the data integrated by the integration unit in real time. For example, the monitoring unit can monitor data fluctuations and detect abnormal activity. Specifically, the monitoring unit analyzes the collected data and automatically detects abnormal activity and security risks. For example, the monitoring unit can detect fraudulent transactions and abnormal access. Examples of fraudulent transactions include large-scale transfers that deviate from normal transaction patterns or multiple transactions occurring in a short period of time. Examples of abnormal access include access from an unusual IP address or multiple login attempts in a short period of time. The monitoring unit can detect anomalies using anomaly detection algorithms. For example, the monitoring unit can detect anomalies using machine learning algorithms or rule-based algorithms. Machine learning algorithms detect abnormal activity by learning from past data and modeling normal transaction and access patterns. Rule-based algorithms detect anomalies based on predefined rules. For example, rules can be set to determine that transactions exceeding a certain amount or access from a specific region are abnormal. This allows the monitoring unit to quickly and accurately analyze the collected data and detect abnormal activity and security risks in real time. Furthermore, the monitoring unit can continuously improve its algorithms to enhance the accuracy of anomaly detection. For example, if a new anomaly pattern is discovered, that pattern can be added to the training data and the algorithm retrained to improve detection accuracy. This allows the monitoring unit to always respond to the latest threats and strengthen the security of the entire system.

[0033] The detection unit detects anomalies from data monitored by the monitoring unit. For example, the detection unit can detect abnormal transactions or security risks. Specifically, when an anomaly occurs, the detection unit immediately sends an alert to the user and proposes immediate countermeasures. For example, if a fraudulent transaction is detected, the detection unit will notify the user and suggest temporarily suspending the transaction. Examples of fraudulent transactions include large-scale transfers that deviate from normal transaction patterns, or multiple transactions occurring in a short period. Furthermore, the detection unit can continuously improve its algorithms to enhance the accuracy of anomaly detection. For example, if a new anomaly pattern is discovered, that pattern can be added to the training data, and the algorithm can be retrained to improve detection accuracy. This allows the detection unit to always respond to the latest threats and strengthen the overall system security.

[0034] The alert unit notifies the user of anomalies detected by the detection unit. The alert unit can send notifications to the user using methods such as email or SMS. Specifically, the alert unit sends a notification to the user suggesting that they temporarily suspend trading. For example, the alert unit asks the user to confirm the trading and allows them to resume trading if there are no problems. In the case of email notifications, detailed trading information and the nature of the anomaly are sent to the user's registered email address to encourage a quick response. In the case of SMS notifications, a short message provides an overview of the anomaly, and detailed information can be found on a dedicated webpage or app. This allows the alert unit to quickly and reliably notify the user of anomalies and encourage appropriate action. Furthermore, the alert unit can customize the content and format of notifications. For example, the frequency and level of detail of notifications can be adjusted according to the user's preferences. In addition, multiple notification methods can be combined to ensure that important information is reliably conveyed. This allows the alert unit to provide users with flexible and effective notifications and support a quick response to anomalies. Furthermore, the alert unit can collect user feedback and use it to improve the notification system. For example, it can investigate how users responded and their satisfaction after receiving notifications to identify areas for improvement in notification content and format. This allows the alerting unit to consistently provide optimal notifications tailored to user needs, improving overall system reliability and user satisfaction.

[0035] The data collection unit can collect users' financial data through the APIs of various financial institutions. For example, the data collection unit can use REST APIs or SOAP APIs to collect financial data. The data collection unit can collect users' bank account information and transaction history. For example, the data collection unit can obtain users' bank account information and collect transaction history. The data collection unit can also collect credit card account information. For example, the data collection unit can collect credit card usage history. Furthermore, the data collection unit can also collect investment account information. For example, the data collection unit can collect investment account transaction history. This allows for centralized data management by collecting users' financial data through the APIs of various financial institutions. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input data obtained from the APIs of various financial institutions into a generating AI and have the generating AI perform the data collection.

[0036] The integration unit can consolidate collected data and monitor the activity of all financial accounts in real time. For example, the integration unit can standardize data formats and integrate data using integration algorithms. The integration unit can monitor the activity of bank accounts and credit card accounts. For example, it can monitor the deposit and withdrawal history of bank accounts and the usage history of credit cards. The integration unit can also monitor the activity of investment accounts. For example, it can monitor the transaction history of investment accounts. Furthermore, the integration unit can eliminate data duplication to maintain data integrity. For example, it can detect and delete duplicate data. This allows for constant monitoring of financial account activity by consolidating collected data and monitoring it in real time. Some or all of the above processes in the integration unit may be performed using AI, for example, or not. For example, the integration unit can input collected data into a generating AI and have the generating AI perform the data integration.

[0037] The monitoring unit can analyze collected data and automatically detect abnormal activity and security risks. For example, the monitoring unit can monitor data fluctuations and detect abnormal activity. The monitoring unit can detect fraudulent transactions and abnormal access. For example, the monitoring unit can immediately detect fraudulent transactions. It can also immediately detect abnormal withdrawals. Furthermore, the monitoring unit can detect anomalies using anomaly detection algorithms. For example, the monitoring unit can detect anomalies using machine learning algorithms or rule-based algorithms. This enables rapid response by analyzing collected data and automatically detecting abnormal activity and security risks. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input collected data into a generating AI and have the generating AI perform anomaly detection.

[0038] The detection unit can immediately send an alert to the user and propose immediate countermeasures if an anomaly occurs. For example, if a fraudulent transaction is detected, the detection unit can send a notification to the user and suggest temporarily suspending the transaction. The detection unit can also send a notification to the user and prompt them to review the transaction. For example, the detection unit can have the user review the transaction details and resume the transaction if there are no problems. The detection unit can also propose immediate countermeasures to the user if abnormal activity is detected. For example, the detection unit can suggest to the user that they change their password or strengthen their security settings. This enables a rapid response by immediately sending an alert to the user and proposing immediate countermeasures if an anomaly occurs. Some or all of the above processing in the detection unit may be performed using AI, for example, or not using AI. For example, the detection unit can input the anomaly detection result into a generating AI and have the generating AI execute sending an alert to the user and proposing countermeasures.

[0039] The alert unit can send notifications to users and suggest that they temporarily suspend transactions. The alert unit can send notifications to users, for example, via email or SMS. The alert unit can ask users to review transactions and resume them if there are no problems. For example, the alert unit can have users review transaction details and resume transactions if there are no problems. The alert unit can also suggest that users temporarily suspend transactions. For example, if a fraudulent transaction is detected, the alert unit will suggest that the user temporarily suspend the transaction. This makes it possible to prevent fraudulent transactions by sending notifications to users and suggesting that they temporarily suspend transactions. Some or all of the above processing in the alert unit may be performed using AI, for example, or not using AI. For example, the alert unit can input the results of an anomaly detection into a generating AI and have the generating AI execute notifications to users and suggest suspending transactions.

[0040] The integration unit can incorporate security features such as enhanced data encryption, multi-factor authentication, data backup, and user education. For example, the integration unit can encrypt data using AES or RSA encryption. It can prevent unauthorized access using multi-factor authentication, such as SMS or biometric authentication. Furthermore, the integration unit can perform data backups to prepare for potential data loss, such as regularly backing up and securely storing data. In addition, the integration unit can provide user education to ensure users understand security risks and take appropriate action, such as security training and phishing prevention education. This enhanced security feature ensures the secure protection of users' financial data. Some or all of the above processes in the integration unit may be performed using AI, or not. For example, the integration unit can have a generation AI perform data encryption and authentication processes.

[0041] The data collection unit can analyze the user's past financial data collection history and select the optimal collection method. For example, the data collection unit may prioritize collection methods that the user has frequently used in the past. The data collection unit can also propose the most efficient collection method based on the user's past collection history. Furthermore, the data collection unit can analyze the user's past collection history and optimize the collection frequency. This allows for efficient data collection by selecting the optimal collection method through analysis of the user's past collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past collection history data into a generating AI and have the generating AI select the optimal collection method.

[0042] The data collection unit can filter financial data based on the user's current financial situation and areas of interest. For example, the data collection unit can collect only important data based on the user's current financial situation. The data collection unit can prioritize the collection of relevant data based on the user's areas of interest. The data collection unit can also collect the most suitable data by comprehensively considering the user's financial situation and areas of interest. This allows for the priority collection of important data by filtering based on the user's current financial situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's financial situation data and areas of interest data into a generating AI and have the generating AI perform the filtering.

[0043] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting financial data. For example, if the user is in a specific region, the data collection unit will prioritize the collection of financial data related to that region. The data collection unit can filter highly relevant data based on the user's geographical location. Furthermore, if the user is on the move, the data collection unit can collect the most relevant data based on their current location. This enables efficient data collection by prioritizing the collection of highly relevant data while considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI perform the collection of highly relevant data.

[0044] The data collection unit can analyze a user's social media activity and collect relevant data when collecting financial data. For example, the data collection unit can collect data related to financial products of interest from a user's social media activity. The data collection unit can analyze a user's social media posts and prioritize the collection of relevant financial data. The data collection unit can also collect relevant data by referring to the activities of the user's social media followers and friends. This enables efficient data collection by analyzing the user's social media activity and collecting relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0045] The integration unit can adjust the level of detail of the integration based on the importance of the financial data during data integration. For example, the integration unit can integrate high-importance data in detail and simplify low-importance data. The integration unit can determine the priority of integration according to the importance of the financial data. The integration unit can also prioritize the integration of high-importance data and postpone the integration of low-importance data. This allows for efficient data integration by adjusting the level of detail of the integration based on the importance of the financial data. Some or all of the above processing in the integration unit may be performed using AI, for example, or not using AI. For example, the integration unit can input financial data importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the integration.

[0046] The integration unit can apply different integration algorithms depending on the category of financial data during data integration. For example, the integration unit can integrate income and expenditure data and investment data using different algorithms. The integration unit can select the optimal integration algorithm for each category. Furthermore, the integration unit can adjust the level of detail of the integration depending on the category of financial data. This enables efficient data integration by applying different integration algorithms depending on the category of financial data. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the category data of financial data into a generating AI and have the generating AI execute the application of the integration algorithm.

[0047] The integration unit can determine the integration priority based on the submission date of financial data during data integration. For example, the integration unit may prioritize the integration of the most recent data. It may also postpone the integration of older data. Furthermore, the integration unit can adjust the order of integration based on the submission date. This enables efficient data integration by determining the integration priority based on the submission date of financial data. Some or all of the above processing in the integration unit may be performed using AI, for example, or not using AI. For example, the integration unit can input financial data submission date data into a generating AI and have the generating AI perform the determination of integration priority.

[0048] The integration unit can adjust the integration order based on the relevance of financial data during data integration. For example, the integration unit may prioritize the integration of highly relevant data. It may also postpone the integration of less relevant data. Furthermore, the integration unit can determine the integration order based on the relevance of financial data. This allows for efficient data integration by adjusting the integration order based on the relevance of financial data. Some or all of the above processing in the integration unit may be performed using AI, for example, or not using AI. For example, the integration unit can input relevance data of financial data into a generating AI and have the generating AI perform the adjustment of the integration order.

[0049] The monitoring unit can improve the accuracy of monitoring by considering the interrelationships of financial data during monitoring. For example, the monitoring unit can analyze the interrelationships between income and expenditure data and investment data to improve monitoring accuracy. The monitoring unit can detect anomalies based on the interrelationships of financial data. The monitoring unit can also set monitoring criteria by considering the interrelationships of financial data. As a result, the accuracy of anomaly detection is improved by improving the accuracy of monitoring by considering the interrelationships of financial data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input data on the interrelationships of financial data into a generating AI and have the generating AI perform the improvement of monitoring accuracy.

[0050] The monitoring unit can perform monitoring while considering the attribute information of the financial data submitter. For example, the monitoring unit can set monitoring criteria based on the submitter's attribute information. The monitoring unit can detect anomalies by considering the submitter's attribute information. Furthermore, the monitoring unit can improve the accuracy of monitoring based on the submitter's attribute information. As a result, the accuracy of anomaly detection is improved by performing monitoring while considering the attribute information of the financial data submitter. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input submitter attribute information data into a generating AI and have the generating AI perform the task of improving the accuracy of monitoring.

[0051] The monitoring unit can perform monitoring while considering the geographical distribution of financial data. For example, the monitoring unit will prioritize monitoring when there are geographically unusual movements. The monitoring unit can set monitoring criteria based on geographical distribution. The monitoring unit can also detect anomalies while considering geographical distribution. As a result, the accuracy of anomaly detection is improved by monitoring while considering the geographical distribution of financial data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input geographical distribution data of financial data into a generating AI and have the generating AI perform the task of improving the accuracy of monitoring.

[0052] The monitoring unit can improve the accuracy of its monitoring by referring to relevant literature on financial data during monitoring. For example, the monitoring unit can set monitoring criteria by referring to relevant literature. The monitoring unit can detect anomalies based on the relevant literature. The monitoring unit can also improve the accuracy of its monitoring by referring to relevant literature. As a result, the accuracy of anomaly detection is improved by improving the accuracy of monitoring by referring to relevant literature on financial data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input relevant literature data on financial data into a generating AI and have the generating AI perform the task of improving the accuracy of monitoring.

[0053] The detection unit can predict the current anomaly by referring to past anomaly data when an anomaly is detected. For example, the detection unit predicts the current anomaly based on past anomaly data. The detection unit can analyze past anomaly data and detect similar patterns. The detection unit can also predict the probability of an anomaly occurring by referring to past anomaly data. This improves the accuracy of anomaly detection by predicting the current anomaly by referring to past anomaly data. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI. For example, the detection unit can input past anomaly data into a generating AI and have the generating AI perform a prediction of the current anomaly.

[0054] The detection unit can apply different detection algorithms to each category of financial data when an anomaly is detected. For example, the detection unit can apply different detection algorithms to income and expenditure data and investment data. The detection unit can select the optimal detection algorithm for each category. Furthermore, the detection unit can improve the accuracy of anomaly detection depending on the category of financial data. By applying different detection algorithms to each category of financial data, the accuracy of anomaly detection is improved. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the category data of the financial data into a generating AI and have the generating AI execute the application of the detection algorithm.

[0055] The detection unit can analyze changes in anomalies based on the submission timing of financial data when an anomaly is detected. For example, the detection unit can analyze changes in anomalies based on older data. The detection unit can also analyze changes in anomalies based on newer data. Furthermore, the detection unit can predict changes in anomalies based on the submission timing. This improves the accuracy of anomaly detection by analyzing changes in anomalies based on the submission timing of financial data. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input financial data submission timing data into a generating AI and have the generating AI perform the analysis of changes in anomalies.

[0056] The detection unit can analyze anomalies by referring to relevant market data for financial data when an anomaly is detected. For example, the detection unit analyzes anomalies based on relevant market data. The detection unit can identify the cause of the anomaly by referring to relevant market data. The detection unit can also predict the impact of the anomaly based on relevant market data. As a result, the accuracy of anomaly detection is improved by analyzing anomalies by referring to relevant market data for financial data. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input relevant market data for financial data into a generating AI and have the generating AI perform the anomaly analysis.

[0057] The alert unit can select the optimal display method by referring to the user's past alert history when displaying an alert. For example, the alert unit can select the optimal display method based on the user's past alert history. The alert unit can prioritize the display of high-priority alerts from the user's past alert history. The alert unit can also adjust the display order of alerts by referring to the user's past alert history. This enables efficient alert display by selecting the optimal display method by referring to the user's past alert history. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input the user's past alert history data into a generating AI and have the generating AI select the optimal display method.

[0058] The alert unit can customize the content of alerts based on the user's current financial situation when displaying an alert. For example, the alert unit can customize the content of the alert based on the user's current financial situation. The alert unit can adjust the importance of the alert according to the user's financial situation. The alert unit can also select how to display the alert, taking into account the user's financial situation. This enables efficient alert display by customizing the content of the alert based on the user's current financial situation. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input the user's current financial situation data into a generating AI and have the generating AI perform the customization of the alert content.

[0059] The alert unit can provide the most appropriate alerts by considering the user's geographical location when displaying alerts. For example, the alert unit can display relevant alerts based on the user's current location. The alert unit can provide the most appropriate alerts by considering the user's geographical location. Furthermore, if the user is on the move, the alert unit can also display alerts based on their current location. This enables efficient alert display by providing the most appropriate alerts by considering the user's geographical location. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing the most appropriate alerts.

[0060] The alert unit can analyze the user's social media activity and suggest alert content when displaying an alert. For example, the alert unit can display alerts related to financial products of interest based on the user's social media activity. The alert unit can analyze the user's social media posts and prioritize the display of relevant alerts. The alert unit can also display relevant alerts by referring to the activities of the user's social media followers and friends. This enables efficient alert display by analyzing the user's social media activity and suggesting alert content. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input the user's social media activity data into a generating AI and have the generating AI suggest alert content.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The data collection unit can analyze a user's past financial behavior patterns to determine the optimal timing for collecting their financial data. For example, if a user frequently traded during a specific time period in the past, data can be collected during that time. Similarly, if a user tends to trade on certain days of the week, data can be collected on those days. Furthermore, the collection frequency can be adjusted based on the user's past trading volume. This enables efficient data collection by taking into account the user's past financial behavior patterns.

[0063] The integration unit can prioritize data based on the user's financial goals when integrating collected data. For example, if a user prioritizes saving, savings-related data will be prioritized for integration. Similarly, if a user prioritizes investing, investment-related data can be prioritized for integration. Furthermore, it is possible to adjust the data integration method based on the user's short-term and long-term goals. This enables efficient data integration by prioritizing data based on the user's financial goals.

[0064] The monitoring unit can adjust its monitoring criteria based on the user's financial risk tolerance when analyzing the collected data. For example, if the user is willing to take on risk, the monitoring criteria can be relaxed. Conversely, if the user wishes to avoid risk, the monitoring criteria can be made stricter. Furthermore, it is possible to adjust the frequency of anomaly detection based on the user's risk tolerance. This allows for more efficient monitoring by adjusting the monitoring criteria based on the user's financial risk tolerance.

[0065] The detection unit can propose the optimal countermeasure when an anomaly occurs, by referring to the user's past response history. For example, if the user has responded quickly to a particular anomaly in the past, that countermeasure will be prioritized and proposed. Furthermore, if the user has preferred to use a particular countermeasure in the past, that countermeasure can also be proposed. In addition, it is possible to propose new countermeasures based on the user's past response history. This enables a rapid response by proposing the optimal countermeasure based on the user's past response history.

[0066] The alert unit can analyze a user's notification history and select the most suitable notification method when sending notifications to the user. For example, if a user has previously preferred receiving email notifications, email notifications will be prioritized. Similarly, if a user has previously preferred receiving SMS notifications, SMS notifications can be used. Furthermore, it is possible to adjust the timing of notifications based on the user's notification history. This allows for efficient notifications by analyzing the user's notification history and selecting the most suitable notification method.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The data collection unit collects data by integrating with the APIs of each financial institution. For example, it can collect financial data using REST APIs or SOAP APIs, and collect users' bank account information and transaction history. The collected data is encrypted using AES encryption or RSA encryption. Step 2: The Integration Unit integrates the data collected by the Collection Unit. It standardizes data formats, integrates data using integration algorithms, and monitors the activity of all financial accounts in real time. It can also detect and remove duplicate data. Step 3: The monitoring unit monitors the data integrated by the integration unit in real time. It monitors data fluctuations and detects abnormal activity. The monitoring unit can detect anomalies using machine learning algorithms or rule-based algorithms. Step 4: The detection unit detects anomalies from the data monitored by the monitoring unit. It detects abnormal transactions and security risks, and immediately sends an alert to the user when an anomaly occurs, and proposes immediate countermeasures. Step 5: The alert unit notifies the user of any anomalies detected by the detection unit. It sends a notification to the user via email or SMS, suggesting that the transaction be temporarily suspended. The user is asked to confirm the transaction, and if there are no problems, the transaction can be resumed.

[0069] (Example of form 2) The AI ​​tool according to an embodiment of the present invention is a system that securely collects and integrates data by linking with the APIs of various financial institutions. This system can securely manage all income and expenditure data while protecting user privacy. Specifically, it consists of the following steps: First, it links with the APIs of various financial institutions to collect the user's financial data. Next, the AI ​​integrates the collected data and monitors the activity of all financial accounts in real time. The AI ​​agent automatically detects and analyzes abnormal activity and security risks, and if an anomaly occurs, it immediately sends an alert to the user and proposes immediate countermeasures. Furthermore, it is equipped with security features such as enhanced data encryption, multi-factor authentication, anomaly detection and real-time alerts, data backup, and user education. For example, it links with the APIs of various financial institutions to collect the user's financial data. At this time, the data is encrypted and transmitted to protect the user's privacy. For example, if a user has multiple bank accounts, account information can be collected through the API of each bank. Next, the AI ​​integrates the collected data and monitors the activity of all financial accounts in real time. The AI ​​agent analyzes the collected data and automatically detects abnormal activity and security risks. For example, if fraudulent transactions or unusual withdrawals occur, the AI ​​agent will immediately detect them and send an alert to the user. If an anomaly occurs, the AI ​​agent will immediately send an alert to the user and suggest immediate countermeasures. For instance, if fraudulent transactions are detected, the user will be notified and advised to temporarily suspend the transaction. The user can then review the transaction and resume it if there are no issues. Furthermore, it incorporates security features such as enhanced data encryption, multi-factor authentication, anomaly detection and real-time alerts, data backup, and user education. Data encryption ensures that users' financial data is always securely protected. Multi-factor authentication prevents unauthorized access. Anomaly detection and real-time alerts allow for immediate response to unusual activity. Data backup provides protection against potential data loss.User education enables users to understand security risks and take appropriate action. Thus, this invention is an AI tool that securely collects and integrates data by linking with the APIs of various financial institutions, allowing for the safe management of all income and expenditure data while protecting user privacy. The AI ​​agent monitors the activity of all financial accounts in real time, automatically detecting and analyzing abnormal activity and security risks. If an anomaly occurs, it immediately sends an alert to the user and proposes immediate countermeasures. Enhanced security features allow users to manage their financial data with peace of mind. As a result, the AI ​​tool can securely manage users' financial data and quickly detect and respond to abnormal activity and security risks.

[0070] The AI ​​tool according to this embodiment comprises a collection unit, an integration unit, a monitoring unit, a detection unit, and an alert unit. The collection unit collects data in cooperation with the APIs of each financial institution. The collection unit can collect financial data using, for example, REST APIs or SOAP APIs. The collection unit collects users' financial data through the APIs of each financial institution. For example, the collection unit can collect users' bank account information and transaction history. The collection unit can encrypt and transmit the collected data. For example, the collection unit can encrypt the data using AES encryption or RSA encryption. The integration unit integrates the data collected by the collection unit. For example, the integration unit can unify the data format and integrate the data using an integration algorithm. The integration unit integrates the collected data and monitors the activity of all financial accounts in real time. For example, the integration unit can monitor the activity of bank accounts and credit card accounts. The integration unit can eliminate data duplication to maintain data integrity. For example, the integration unit detects data duplication and deletes the duplicate data. The monitoring unit monitors the data integrated by the integration unit in real time. The monitoring unit can, for example, monitor data fluctuations and detect abnormal activity. The monitoring unit analyzes the collected data and automatically detects abnormal activity and security risks. For example, the monitoring unit can detect fraudulent transactions and abnormal access. The monitoring unit can detect anomalies using anomaly detection algorithms. For example, the monitoring unit can detect anomalies using machine learning algorithms and rule-based algorithms. The detection unit detects anomalies from the data monitored by the monitoring unit. The detection unit can detect abnormal transactions and security risks. When an anomaly occurs, the detection unit immediately sends an alert to the user and proposes immediate countermeasures. For example, if a fraudulent transaction is detected, the detection unit will notify the user and propose temporarily suspending the transaction. The alert unit notifies the user of anomalies detected by the detection unit. The alert unit can send notifications to the user using, for example, email or SMS. The alert unit will notify the user and propose temporarily suspending the transaction.For example, the alert unit can prompt the user to confirm the transaction, and if there are no problems, the transaction can be resumed. This allows the AI ​​tool according to the embodiment to securely manage the user's financial data and quickly detect and respond to abnormal activity and security risks.

[0071] The data collection unit collects data by collaborating with the APIs of various financial institutions. For example, the collection unit can collect financial data using REST APIs or SOAP APIs. Specifically, REST APIs use the HTTP protocol to access resources and exchange data in JSON format. SOAP APIs, on the other hand, exchange data in XML format and offer more complex operations and security features. The collection unit collects users' financial data through the APIs of various financial institutions. For example, the collection unit can collect users' bank account information and transaction history. Bank account information includes account number, balance, and transaction history, while transaction history includes details of deposits and withdrawals, transaction dates and times, and information about the transaction partner. The collection unit can encrypt and transmit the collected data. For example, the collection unit can encrypt data using AES encryption or RSA encryption. AES encryption is a symmetric-key cryptography method that can encrypt data quickly and securely. RSA encryption is a public-key cryptography method where the sender and receiver of the data use different keys to encrypt and decrypt the data. This allows the collection unit to securely collect and transmit users' financial data. Furthermore, the data collection unit can adjust the frequency and timing of data collection. For example, when collecting data in real time, increasing the frequency of API requests ensures that the latest data is always available. On the other hand, when collecting data periodically, the frequency of API requests can be set lower to reduce the system load. This allows the data collection unit to collect data efficiently and flexibly, optimizing the overall system performance.

[0072] The Integration Unit integrates the data collected by the Collection Unit. For example, the Integration Unit can standardize data formats and integrate data using integration algorithms. Specifically, because data collected from different financial institutions may differ in format and structure, the Integration Unit converts this data into a common format. For example, it might convert JSON data to XML format or standardize different field names. The Integration Unit integrates the collected data and monitors the activity of all financial accounts in real time. For example, the Integration Unit can monitor the activity of bank accounts and credit card accounts. Bank account activity includes deposit and withdrawal history and balance changes, while credit card account activity includes usage history and changes in credit limits. To maintain data integrity, the Integration Unit can eliminate data duplication. For example, it can detect and remove duplicate data. Unique identifiers or timestamps can be used to detect duplicate data. This allows the Integration Unit to provide accurate and consistent data. Furthermore, the Integration Unit can leverage distributed processing technologies to optimize the data integration process. For example, it can use multiple servers or cloud resources to run data integration processes in parallel, improving processing speed. This allows the integration unit to quickly and efficiently integrate large amounts of data and enable real-time monitoring.

[0073] The monitoring unit monitors the data integrated by the integration unit in real time. For example, the monitoring unit can monitor data fluctuations and detect abnormal activity. Specifically, the monitoring unit analyzes the collected data and automatically detects abnormal activity and security risks. For example, the monitoring unit can detect fraudulent transactions and abnormal access. Examples of fraudulent transactions include large-scale transfers that deviate from normal transaction patterns or multiple transactions occurring in a short period of time. Examples of abnormal access include access from an unusual IP address or multiple login attempts in a short period of time. The monitoring unit can detect anomalies using anomaly detection algorithms. For example, the monitoring unit can detect anomalies using machine learning algorithms or rule-based algorithms. Machine learning algorithms detect abnormal activity by learning from past data and modeling normal transaction and access patterns. Rule-based algorithms detect anomalies based on predefined rules. For example, rules can be set to determine that transactions exceeding a certain amount or access from a specific region are abnormal. This allows the monitoring unit to quickly and accurately analyze the collected data and detect abnormal activity and security risks in real time. Furthermore, the monitoring unit can continuously improve its algorithms to enhance the accuracy of anomaly detection. For example, if a new anomaly pattern is discovered, that pattern can be added to the training data and the algorithm retrained to improve detection accuracy. This allows the monitoring unit to always respond to the latest threats and strengthen the security of the entire system.

[0074] The detection unit detects anomalies from data monitored by the monitoring unit. For example, the detection unit can detect abnormal transactions or security risks. Specifically, when an anomaly occurs, the detection unit immediately sends an alert to the user and proposes immediate countermeasures. For example, if a fraudulent transaction is detected, the detection unit will notify the user and suggest temporarily suspending the transaction. Examples of fraudulent transactions include large-scale transfers that deviate from normal transaction patterns, or multiple transactions occurring in a short period. Furthermore, the detection unit can continuously improve its algorithms to enhance the accuracy of anomaly detection. For example, if a new anomaly pattern is discovered, that pattern can be added to the training data, and the algorithm can be retrained to improve detection accuracy. This allows the detection unit to always respond to the latest threats and strengthen the overall system security.

[0075] The alert unit notifies the user of anomalies detected by the detection unit. The alert unit can send notifications to the user using methods such as email or SMS. Specifically, the alert unit sends a notification to the user suggesting that they temporarily suspend trading. For example, the alert unit asks the user to confirm the trading and allows them to resume trading if there are no problems. In the case of email notifications, detailed trading information and the nature of the anomaly are sent to the user's registered email address to encourage a quick response. In the case of SMS notifications, a short message provides an overview of the anomaly, and detailed information can be found on a dedicated webpage or app. This allows the alert unit to quickly and reliably notify the user of anomalies and encourage appropriate action. Furthermore, the alert unit can customize the content and format of notifications. For example, the frequency and level of detail of notifications can be adjusted according to the user's preferences. In addition, multiple notification methods can be combined to ensure that important information is reliably conveyed. This allows the alert unit to provide users with flexible and effective notifications and support a quick response to anomalies. Furthermore, the alert unit can collect user feedback and use it to improve the notification system. For example, it can investigate how users responded and their satisfaction after receiving notifications to identify areas for improvement in notification content and format. This allows the alerting unit to consistently provide optimal notifications tailored to user needs, improving overall system reliability and user satisfaction.

[0076] The data collection unit can collect users' financial data through the APIs of various financial institutions. For example, the data collection unit can use REST APIs or SOAP APIs to collect financial data. The data collection unit can collect users' bank account information and transaction history. For example, the data collection unit can obtain users' bank account information and collect transaction history. The data collection unit can also collect credit card account information. For example, the data collection unit can collect credit card usage history. Furthermore, the data collection unit can also collect investment account information. For example, the data collection unit can collect investment account transaction history. This allows for centralized data management by collecting users' financial data through the APIs of various financial institutions. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input data obtained from the APIs of various financial institutions into a generating AI and have the generating AI perform the data collection.

[0077] The integration unit can consolidate collected data and monitor the activity of all financial accounts in real time. For example, the integration unit can standardize data formats and integrate data using integration algorithms. The integration unit can monitor the activity of bank accounts and credit card accounts. For example, it can monitor the deposit and withdrawal history of bank accounts and the usage history of credit cards. The integration unit can also monitor the activity of investment accounts. For example, it can monitor the transaction history of investment accounts. Furthermore, the integration unit can eliminate data duplication to maintain data integrity. For example, it can detect and delete duplicate data. This allows for constant monitoring of financial account activity by consolidating collected data and monitoring it in real time. Some or all of the above processes in the integration unit may be performed using AI, for example, or not. For example, the integration unit can input collected data into a generating AI and have the generating AI perform the data integration.

[0078] The monitoring unit can analyze collected data and automatically detect abnormal activity and security risks. For example, the monitoring unit can monitor data fluctuations and detect abnormal activity. The monitoring unit can detect fraudulent transactions and abnormal access. For example, the monitoring unit can immediately detect fraudulent transactions. It can also immediately detect abnormal withdrawals. Furthermore, the monitoring unit can detect anomalies using anomaly detection algorithms. For example, the monitoring unit can detect anomalies using machine learning algorithms or rule-based algorithms. This enables rapid response by analyzing collected data and automatically detecting abnormal activity and security risks. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input collected data into a generating AI and have the generating AI perform anomaly detection.

[0079] The detection unit can immediately send an alert to the user and propose immediate countermeasures if an anomaly occurs. For example, if a fraudulent transaction is detected, the detection unit can send a notification to the user and suggest temporarily suspending the transaction. The detection unit can also send a notification to the user and prompt them to review the transaction. For example, the detection unit can have the user review the transaction details and resume the transaction if there are no problems. The detection unit can also propose immediate countermeasures to the user if abnormal activity is detected. For example, the detection unit can suggest to the user that they change their password or strengthen their security settings. This enables a rapid response by immediately sending an alert to the user and proposing immediate countermeasures if an anomaly occurs. Some or all of the above processing in the detection unit may be performed using AI, for example, or not using AI. For example, the detection unit can input the anomaly detection result into a generating AI and have the generating AI execute sending an alert to the user and proposing countermeasures.

[0080] The alert unit can send notifications to users and suggest that they temporarily suspend transactions. The alert unit can send notifications to users, for example, via email or SMS. The alert unit can ask users to review transactions and resume them if there are no problems. For example, the alert unit can have users review transaction details and resume transactions if there are no problems. The alert unit can also suggest that users temporarily suspend transactions. For example, if a fraudulent transaction is detected, the alert unit will suggest that the user temporarily suspend the transaction. This makes it possible to prevent fraudulent transactions by sending notifications to users and suggesting that they temporarily suspend transactions. Some or all of the above processing in the alert unit may be performed using AI, for example, or not using AI. For example, the alert unit can input the results of an anomaly detection into a generating AI and have the generating AI execute notifications to users and suggest suspending transactions.

[0081] The integration unit can incorporate security features such as enhanced data encryption, multi-factor authentication, data backup, and user education. For example, the integration unit can encrypt data using AES or RSA encryption. It can prevent unauthorized access using multi-factor authentication, such as SMS or biometric authentication. Furthermore, the integration unit can perform data backups to prepare for potential data loss, such as regularly backing up and securely storing data. In addition, the integration unit can provide user education to ensure users understand security risks and take appropriate action, such as security training and phishing prevention education. This enhanced security feature ensures the secure protection of users' financial data. Some or all of the above processes in the integration unit may be performed using AI, or not. For example, the integration unit can have a generation AI perform data encryption and authentication processes.

[0082] The data collection unit estimates the user's emotions and adjusts the timing of financial data collection based on the estimated emotions. The data collection unit can estimate the user's emotions and adjust the timing of financial data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing and collect data when the user is relaxed. If the user is in a hurry, the data collection unit can advance the collection timing to collect data quickly. Alternatively, if the user is relaxed, the data collection unit can collect data at the normal timing. This allows for efficient data collection by adjusting the collection timing based on the user's emotions, thereby reducing user stress. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the collection timing.

[0083] The data collection unit can analyze the user's past financial data collection history and select the optimal collection method. For example, the data collection unit may prioritize collection methods that the user has frequently used in the past. The data collection unit can also propose the most efficient collection method based on the user's past collection history. Furthermore, the data collection unit can analyze the user's past collection history and optimize the collection frequency. This allows for efficient data collection by selecting the optimal collection method through analysis of the user's past collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past collection history data into a generating AI and have the generating AI select the optimal collection method.

[0084] The data collection unit can filter financial data based on the user's current financial situation and areas of interest. For example, the data collection unit can collect only important data based on the user's current financial situation. The data collection unit can prioritize the collection of relevant data based on the user's areas of interest. The data collection unit can also collect the most suitable data by comprehensively considering the user's financial situation and areas of interest. This allows for the priority collection of important data by filtering based on the user's current financial situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's financial situation data and areas of interest data into a generating AI and have the generating AI perform the filtering.

[0085] The data collection unit estimates the user's emotions and determines the priority of financial data to collect based on the estimated emotions. The data collection unit can estimate the user's emotions and determine the priority of financial data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will postpone the collection of less important data. If the user is relaxed, the data collection unit can collect all data equally. Furthermore, if the user is in a hurry, the data collection unit can prioritize the collection of highly important data. This enables efficient data collection by prioritizing financial data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of the data to collect.

[0086] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting financial data. For example, if the user is in a specific region, the data collection unit will prioritize the collection of financial data related to that region. The data collection unit can filter highly relevant data based on the user's geographical location. Furthermore, if the user is on the move, the data collection unit can collect the most relevant data based on their current location. This enables efficient data collection by prioritizing the collection of highly relevant data while considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI perform the collection of highly relevant data.

[0087] The data collection unit can analyze a user's social media activity and collect relevant data when collecting financial data. For example, the data collection unit can collect data related to financial products of interest from a user's social media activity. The data collection unit can analyze a user's social media posts and prioritize the collection of relevant financial data. The data collection unit can also collect relevant data by referring to the activities of the user's social media followers and friends. This enables efficient data collection by analyzing the user's social media activity and collecting relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0088] The integration unit estimates the user's emotions and adjusts the data integration method based on the estimated emotions. The integration unit can estimate the user's emotions and adjust the data integration method based on the estimated emotions. For example, if the user is stressed, the integration unit can select a simple integration method. If the user is relaxed, the integration unit can select a more detailed integration method. Furthermore, if the user is in a hurry, the integration unit can select a method that allows for rapid integration. This enables efficient data integration by adjusting the data integration method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the integration unit may be performed using AI, or not. For example, the integration unit can input user emotion data into a generative AI and have the generative AI adjust the data integration method.

[0089] The integration unit can adjust the level of detail of the integration based on the importance of the financial data during data integration. For example, the integration unit can integrate high-importance data in detail and simplify low-importance data. The integration unit can determine the priority of integration according to the importance of the financial data. The integration unit can also prioritize the integration of high-importance data and postpone the integration of low-importance data. This allows for efficient data integration by adjusting the level of detail of the integration based on the importance of the financial data. Some or all of the above processing in the integration unit may be performed using AI, for example, or not using AI. For example, the integration unit can input financial data importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the integration.

[0090] The integration unit can apply different integration algorithms depending on the category of financial data during data integration. For example, the integration unit can integrate income and expenditure data and investment data using different algorithms. The integration unit can select the optimal integration algorithm for each category. Furthermore, the integration unit can adjust the level of detail of the integration depending on the category of financial data. This enables efficient data integration by applying different integration algorithms depending on the category of financial data. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the category data of financial data into a generating AI and have the generating AI execute the application of the integration algorithm.

[0091] The integration unit estimates the user's emotions and adjusts the display method of the integrated data based on the estimated user emotions. The integration unit can estimate the user's emotions and adjust the display method of the integrated data based on the estimated user emotions. For example, if the user is stressed, the integration unit can provide a simple and highly visible display method. If the user is relaxed, the integration unit can provide a display method that includes detailed information. Also, if the user is in a hurry, the integration unit can provide a concise display method. This allows for efficient data display by adjusting the display method of the integrated data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the integration unit may be performed using AI, for example, or not using AI. For example, the integration unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method.

[0092] The integration unit can determine the integration priority based on the submission date of financial data during data integration. For example, the integration unit may prioritize the integration of the most recent data. It may also postpone the integration of older data. Furthermore, the integration unit can adjust the order of integration based on the submission date. This enables efficient data integration by determining the integration priority based on the submission date of financial data. Some or all of the above processing in the integration unit may be performed using AI, for example, or not using AI. For example, the integration unit can input financial data submission date data into a generating AI and have the generating AI perform the determination of integration priority.

[0093] The integration unit can adjust the integration order based on the relevance of financial data during data integration. For example, the integration unit may prioritize the integration of highly relevant data. It may also postpone the integration of less relevant data. Furthermore, the integration unit can determine the integration order based on the relevance of financial data. This allows for efficient data integration by adjusting the integration order based on the relevance of financial data. Some or all of the above processing in the integration unit may be performed using AI, for example, or not using AI. For example, the integration unit can input relevance data of financial data into a generating AI and have the generating AI perform the adjustment of the integration order.

[0094] The monitoring unit estimates the user's emotions and adjusts the monitoring criteria based on the estimated emotions. The monitoring unit can estimate the user's emotions and adjust the monitoring criteria based on the estimated emotions. For example, if the user is stressed, the monitoring unit can relax the monitoring criteria. If the user is relaxed, the monitoring unit can tighten the monitoring criteria. The monitoring unit can also set criteria that allow for quick monitoring if the user is in a hurry. This enables efficient monitoring by adjusting the monitoring criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, or not. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI adjust the monitoring criteria.

[0095] The monitoring unit can improve the accuracy of monitoring by considering the interrelationships of financial data during monitoring. For example, the monitoring unit can analyze the interrelationships between income and expenditure data and investment data to improve monitoring accuracy. The monitoring unit can detect anomalies based on the interrelationships of financial data. The monitoring unit can also set monitoring criteria by considering the interrelationships of financial data. As a result, the accuracy of anomaly detection is improved by improving the accuracy of monitoring by considering the interrelationships of financial data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input data on the interrelationships of financial data into a generating AI and have the generating AI perform the improvement of monitoring accuracy.

[0096] The monitoring unit can perform monitoring while considering the attribute information of the financial data submitter. For example, the monitoring unit can set monitoring criteria based on the submitter's attribute information. The monitoring unit can detect anomalies by considering the submitter's attribute information. Furthermore, the monitoring unit can improve the accuracy of monitoring based on the submitter's attribute information. As a result, the accuracy of anomaly detection is improved by performing monitoring while considering the attribute information of the financial data submitter. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input submitter attribute information data into a generating AI and have the generating AI perform the task of improving the accuracy of monitoring.

[0097] The monitoring unit estimates the user's emotions and adjusts the display order of monitoring results based on the estimated emotions. The monitoring unit can estimate the user's emotions and adjust the display order of monitoring results based on the estimated emotions. For example, if the user is stressed, the monitoring unit can prioritize displaying high-priority monitoring results. If the user is relaxed, the monitoring unit can display all monitoring results equally. Furthermore, if the user is in a hurry, the monitoring unit can display monitoring results in a way that allows for quick review. This enables efficient display of monitoring results by adjusting the display order based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, or not. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI adjust the display order.

[0098] The monitoring unit can perform monitoring while considering the geographical distribution of financial data. For example, the monitoring unit will prioritize monitoring when there are geographically unusual movements. The monitoring unit can set monitoring criteria based on geographical distribution. The monitoring unit can also detect anomalies while considering geographical distribution. As a result, the accuracy of anomaly detection is improved by monitoring while considering the geographical distribution of financial data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input geographical distribution data of financial data into a generating AI and have the generating AI perform the task of improving the accuracy of monitoring.

[0099] The monitoring unit can improve the accuracy of its monitoring by referring to relevant literature on financial data during monitoring. For example, the monitoring unit can set monitoring criteria by referring to relevant literature. The monitoring unit can detect anomalies based on the relevant literature. The monitoring unit can also improve the accuracy of its monitoring by referring to relevant literature. As a result, the accuracy of anomaly detection is improved by improving the accuracy of monitoring by referring to relevant literature on financial data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input relevant literature data on financial data into a generating AI and have the generating AI perform the task of improving the accuracy of monitoring.

[0100] The detection unit estimates the user's emotions and adjusts the anomaly detection criteria based on the estimated user emotions. The detection unit can estimate the user's emotions and adjust the anomaly detection criteria based on the estimated user emotions. For example, if the user is stressed, the detection unit can relax the anomaly detection criteria. If the user is relaxed, the detection unit can tighten the anomaly detection criteria. The detection unit can also set criteria that allow for rapid anomaly detection if the user is in a hurry. This enables efficient anomaly detection by adjusting the anomaly 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the anomaly detection criteria.

[0101] The detection unit can predict the current anomaly by referring to past anomaly data when an anomaly is detected. For example, the detection unit predicts the current anomaly based on past anomaly data. The detection unit can analyze past anomaly data and detect similar patterns. The detection unit can also predict the probability of an anomaly occurring by referring to past anomaly data. This improves the accuracy of anomaly detection by predicting the current anomaly by referring to past anomaly data. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI. For example, the detection unit can input past anomaly data into a generating AI and have the generating AI perform a prediction of the current anomaly.

[0102] The detection unit can apply different detection algorithms to each category of financial data when an anomaly is detected. For example, the detection unit can apply different detection algorithms to income and expenditure data and investment data. The detection unit can select the optimal detection algorithm for each category. Furthermore, the detection unit can improve the accuracy of anomaly detection depending on the category of financial data. By applying different detection algorithms to each category of financial data, the accuracy of anomaly detection is improved. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the category data of the financial data into a generating AI and have the generating AI execute the application of the detection algorithm.

[0103] The detection unit estimates the user's emotions and adjusts the importance of anomaly detection based on the estimated emotions. The detection unit can estimate the user's emotions and adjust the importance of anomaly detection based on the estimated emotions. For example, if the user is stressed, the detection unit will prioritize detecting less important anomalies. If the user is relaxed, the detection unit can detect all anomalies equally. Furthermore, if the user is in a hurry, the detection unit can prioritize detecting more important anomalies. This allows for efficient anomaly detection by adjusting the importance of anomaly detection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the detection unit may be performed using AI, or not. For example, the detection unit can input user emotion data into a generative AI and have the generative AI adjust the importance of anomaly detection.

[0104] The detection unit can analyze changes in anomalies based on the submission timing of financial data when an anomaly is detected. For example, the detection unit can analyze changes in anomalies based on older data. The detection unit can also analyze changes in anomalies based on newer data. Furthermore, the detection unit can predict changes in anomalies based on the submission timing. This improves the accuracy of anomaly detection by analyzing changes in anomalies based on the submission timing of financial data. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input financial data submission timing data into a generating AI and have the generating AI perform the analysis of changes in anomalies.

[0105] The detection unit can analyze anomalies by referring to relevant market data for financial data when an anomaly is detected. For example, the detection unit analyzes anomalies based on relevant market data. The detection unit can identify the cause of the anomaly by referring to relevant market data. The detection unit can also predict the impact of the anomaly based on relevant market data. As a result, the accuracy of anomaly detection is improved by analyzing anomalies by referring to relevant market data for financial data. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input relevant market data for financial data into a generating AI and have the generating AI perform the anomaly analysis.

[0106] The alert unit estimates the user's emotions and adjusts how alerts are displayed based on the estimated emotions. The alert unit can estimate the user's emotions and adjust how alerts are displayed based on the estimated emotions. For example, if the user is stressed, the alert unit can display a simple, highly visible alert. If the user is relaxed, the alert unit can display an alert with more detailed information. Furthermore, if the user is in a hurry, the alert unit can display a concise alert. This allows for efficient alert display by adjusting how alerts are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the alert unit may be performed using AI, or not. For example, the alert unit can input user emotion data into a generative AI and have the generative AI adjust the display method.

[0107] The alert unit can select the optimal display method by referring to the user's past alert history when displaying an alert. For example, the alert unit can select the optimal display method based on the user's past alert history. The alert unit can prioritize the display of high-priority alerts from the user's past alert history. The alert unit can also adjust the display order of alerts by referring to the user's past alert history. This enables efficient alert display by selecting the optimal display method by referring to the user's past alert history. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input the user's past alert history data into a generating AI and have the generating AI select the optimal display method.

[0108] The alert unit can customize the content of alerts based on the user's current financial situation when displaying an alert. For example, the alert unit can customize the content of the alert based on the user's current financial situation. The alert unit can adjust the importance of the alert according to the user's financial situation. The alert unit can also select how to display the alert, taking into account the user's financial situation. This enables efficient alert display by customizing the content of the alert based on the user's current financial situation. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input the user's current financial situation data into a generating AI and have the generating AI perform the customization of the alert content.

[0109] The alert unit estimates the user's emotions and determines the priority of alerts based on the estimated emotions. For example, if the user is stressed, the alert unit may postpone less important alerts. If the user is relaxed, the alert unit can display all alerts equally. Furthermore, if the user is in a hurry, the alert unit can prioritize displaying more important alerts. This allows for efficient alert display by prioritizing alerts based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the alert unit may be performed using AI, or not. For example, the alert unit can input user emotion data into a generative AI and have the generative AI determine the priority of alerts.

[0110] The alert unit can provide the most appropriate alerts by considering the user's geographical location when displaying alerts. For example, the alert unit can display relevant alerts based on the user's current location. The alert unit can provide the most appropriate alerts by considering the user's geographical location. Furthermore, if the user is on the move, the alert unit can also display alerts based on their current location. This enables efficient alert display by providing the most appropriate alerts by considering the user's geographical location. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing the most appropriate alerts.

[0111] The alert unit can analyze the user's social media activity and suggest alert content when displaying an alert. For example, the alert unit can display alerts related to financial products of interest based on the user's social media activity. The alert unit can analyze the user's social media posts and prioritize the display of relevant alerts. The alert unit can also display relevant alerts by referring to the activities of the user's social media followers and friends. This enables efficient alert display by analyzing the user's social media activity and suggesting alert content. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input the user's social media activity data into a generating AI and have the generating AI suggest alert content.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The data collection unit can analyze a user's past financial behavior patterns to determine the optimal timing for collecting their financial data. For example, if a user frequently traded during a specific time period in the past, data can be collected during that time. Similarly, if a user tends to trade on certain days of the week, data can be collected on those days. Furthermore, the collection frequency can be adjusted based on the user's past trading volume. This enables efficient data collection by taking into account the user's past financial behavior patterns.

[0114] The integration unit can prioritize data based on the user's financial goals when integrating collected data. For example, if a user prioritizes saving, savings-related data will be prioritized for integration. Similarly, if a user prioritizes investing, investment-related data can be prioritized for integration. Furthermore, it is possible to adjust the data integration method based on the user's short-term and long-term goals. This enables efficient data integration by prioritizing data based on the user's financial goals.

[0115] The monitoring unit can adjust its monitoring criteria based on the user's financial risk tolerance when analyzing the collected data. For example, if the user is willing to take on risk, the monitoring criteria can be relaxed. Conversely, if the user wishes to avoid risk, the monitoring criteria can be made stricter. Furthermore, it is possible to adjust the frequency of anomaly detection based on the user's risk tolerance. This allows for more efficient monitoring by adjusting the monitoring criteria based on the user's financial risk tolerance.

[0116] The detection unit can propose the optimal countermeasure when an anomaly occurs, by referring to the user's past response history. For example, if the user has responded quickly to a particular anomaly in the past, that countermeasure will be prioritized and proposed. Furthermore, if the user has preferred to use a particular countermeasure in the past, that countermeasure can also be proposed. In addition, it is possible to propose new countermeasures based on the user's past response history. This enables a rapid response by proposing the optimal countermeasure based on the user's past response history.

[0117] The alert unit can analyze a user's notification history and select the most suitable notification method when sending notifications to the user. For example, if a user has previously preferred receiving email notifications, email notifications will be prioritized. Similarly, if a user has previously preferred receiving SMS notifications, SMS notifications can be used. Furthermore, it is possible to adjust the timing of notifications based on the user's notification history. This allows for efficient notifications by analyzing the user's notification history and selecting the most suitable notification method.

[0118] The data collection unit can estimate the user's emotions and adjust the types of financial data collected based on those emotions. For example, if the user is stressed, only high-priority data will be collected. If the user is relaxed, all data can be collected equally. If the user is in a hurry, data that can be collected quickly can be prioritized. This allows for efficient data collection by adjusting the types of financial data collected based on the user's emotions.

[0119] The integration unit can estimate the user's emotions and adjust the frequency of data integration based on those emotions. For example, if the user is stressed, the integration frequency can be reduced. If the user is relaxed, the integration frequency can be increased. Furthermore, if the user is in a hurry, a frequency that allows for rapid integration can be set. This enables efficient data integration by adjusting the integration frequency based on the user's emotions.

[0120] The monitoring unit can estimate the user's emotions and adjust the level of detail based on that estimation. For example, if the user is stressed, the level of detail can be lowered. If the user is relaxed, the level of detail can be increased. Furthermore, if the user is in a hurry, a level of detail that allows for quick monitoring can be set. This allows for efficient monitoring by adjusting the level of detail based on the user's emotions.

[0121] The detection unit can estimate the user's emotions and adjust the frequency of anomaly detection based on the estimated emotions. For example, if the user is stressed, the frequency of anomaly detection can be reduced. If the user is relaxed, the frequency of anomaly detection can be increased. Also, if the user is in a hurry, a frequency that allows for rapid anomaly detection can be set. In this way, by adjusting the frequency of anomaly detection based on the user's emotions, efficient anomaly detection becomes possible.

[0122] The alert function can estimate the user's emotions and adjust the timing of alert transmission based on those emotions. For example, if the user is stressed, the alert transmission timing can be delayed. If the user is relaxed, the alert can be sent at the normal time. Furthermore, if the user is in a hurry, the alert can be sent quickly. This allows for efficient alert transmission by adjusting the timing of alert transmission based on the user's emotions.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The data collection unit collects data by integrating with the APIs of each financial institution. For example, it can collect financial data using REST APIs or SOAP APIs, and collect users' bank account information and transaction history. The collected data is encrypted using AES encryption or RSA encryption. Step 2: The Integration Unit integrates the data collected by the Collection Unit. It standardizes data formats, integrates data using integration algorithms, and monitors the activity of all financial accounts in real time. It can also detect and remove duplicate data. Step 3: The monitoring unit monitors the data integrated by the integration unit in real time. It monitors data fluctuations and detects abnormal activity. The monitoring unit can detect anomalies using machine learning algorithms or rule-based algorithms. Step 4: The detection unit detects anomalies from the data monitored by the monitoring unit. It detects abnormal transactions and security risks, and immediately sends an alert to the user when an anomaly occurs, and proposes immediate countermeasures. Step 5: The alert unit notifies the user of any anomalies detected by the detection unit. It sends a notification to the user via email or SMS, suggesting that the transaction be temporarily suspended. The user is asked to confirm the transaction, and if there are no problems, the transaction can be resumed.

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0127] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the collection unit, integration unit, monitoring unit, detection unit, and alert unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects data in cooperation with the APIs of each financial institution. The integration unit is implemented by the specific processing unit 290 of the data processing unit 12 and integrates the collected data. The monitoring unit is implemented by the control unit 46A of the smart device 14 and monitors the integrated data in real time. The detection unit is implemented by the specific processing unit 290 of the data processing unit 12 and detects anomalies. The alert unit is implemented by the control unit 46A of the smart device 14 and sends notifications to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0141] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0143] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the collection unit, integration unit, monitoring unit, detection unit, and alert unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects data in cooperation with the APIs of each financial institution. The integration unit is implemented by the specific processing unit 290 of the data processing unit 12 and integrates the collected data. The monitoring unit is implemented by the control unit 46A of the smart glasses 214 and monitors the integrated data in real time. The detection unit is implemented by the specific processing unit 290 of the data processing unit 12 and detects anomalies. The alert unit is implemented by the control unit 46A of the smart glasses 214 and sends notifications to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0153] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0154] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0155] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0156] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0157] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0159] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0160] Each of the multiple elements described above, including the collection unit, integration unit, monitoring unit, detection unit, and alert unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects data in cooperation with the APIs of each financial institution. The integration unit is implemented by the specific processing unit 290 of the data processing unit 12 and integrates the collected data. The monitoring unit is implemented by the control unit 46A of the headset terminal 314 and monitors the integrated data in real time. The detection unit is implemented by the specific processing unit 290 of the data processing unit 12 and detects anomalies. The alert unit is implemented by the control unit 46A of the headset terminal 314 and sends notifications to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0163] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0164] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0165] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0166] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0167] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0168] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0169] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0170] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0171] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0172] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0173] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0174] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0175] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0176] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0177] Each of the multiple elements described above, including the collection unit, integration unit, monitoring unit, detection unit, and alert unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects data in cooperation with the APIs of each financial institution. The integration unit is implemented by the specific processing unit 290 of the data processing unit 12 and integrates the collected data. The monitoring unit is implemented by the control unit 46A of the robot 414 and monitors the integrated data in real time. The detection unit is implemented by the specific processing unit 290 of the data processing unit 12 and detects anomalies. The alert unit is implemented by the control unit 46A of the robot 414 and sends notifications to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0178] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0179] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0180] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0181] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0182] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0183] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0184] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0185] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0186] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0187] 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.

[0188] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0189] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0190] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0191] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0192] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0193] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0194] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0195] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0196] (Note 1) The data collection unit collects data by linking with the APIs of each financial institution, An integration unit that integrates the data collected by the aforementioned collection unit, A monitoring unit monitors the data integrated by the aforementioned integration unit in real time, A detection unit that detects anomalies from the data monitored by the aforementioned monitoring unit, The system includes an alert unit that notifies the user of any abnormalities detected by the detection unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect users' financial data through APIs provided by various financial institutions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned integration unit is The collected data is integrated, and the activity of all financial accounts is monitored in real time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned monitoring unit, The collected data is analyzed to automatically detect abnormal activity and security risks. The system described in Appendix 1, characterized by the features described herein. (Note 5) The detection unit is If an anomaly occurs, an alert will be sent to the user immediately, and a corrective action will be proposed. The system described in Appendix 1, characterized by the features described herein. (Note 6) The alert unit is, Send a notification to the user and suggest they temporarily suspend the transaction. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned integration unit is It features enhanced data encryption, multi-factor authentication, data backup, and user training as security features. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate user sentiment and adjust the timing of financial data collection based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the user's past financial data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting financial data, filtering is performed based on the user's current financial situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates user sentiment and prioritizes the financial data to collect based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting financial data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting financial data, we analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned integration unit is We estimate user sentiment and adjust the data integration method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned integration unit is When integrating data, adjust the level of detail of the integration based on the importance of the financial data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned integration unit is During data integration, different integration algorithms are applied depending on the category of financial data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned integration unit is It estimates the user's emotions and adjusts how the integrated data is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned integration unit is When integrating data, prioritize integration based on when the financial data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned integration unit is When integrating data, adjust the integration order based on the relevance of the financial data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned monitoring unit, We estimate user sentiment and adjust monitoring criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned monitoring unit, During monitoring, consider the interrelationships of financial data to improve the accuracy of monitoring. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned monitoring unit, During monitoring, the data is monitored while taking into account the attribute information of the financial data submitter. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned monitoring unit, It estimates the user's emotions and adjusts the display order of monitoring results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned monitoring unit, When monitoring, the geographical distribution of financial data should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned monitoring unit, During monitoring, we improve the accuracy of monitoring by referring to relevant literature on financial data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The detection unit is The system estimates the user's emotions and adjusts the anomaly detection criteria based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The detection unit is When an anomaly is detected, past anomaly data is referenced to predict the current anomaly. The system described in Appendix 1, characterized by the features described herein. (Note 28) The detection unit is When an anomaly is detected, different detection algorithms are applied for each category of financial data. The system described in Appendix 1, characterized by the features described herein. (Note 29) The detection unit is It estimates the user's emotions and adjusts the importance of anomaly detection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The detection unit is When an anomaly is detected, the change in the anomaly is analyzed based on the timing of financial data submission. The system described in Appendix 1, characterized by the features described herein. (Note 31) The detection unit is When an anomaly is detected, the financial data is analyzed by referring to related market data. The system described in Appendix 1, characterized by the features described herein. (Note 32) The alert unit is, It estimates the user's emotions and adjusts how alerts are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The alert unit is, When displaying an alert, the system will refer to the user's past alert history to select the most suitable display method. The system described in Appendix 1, characterized by the features described herein. (Note 34) The alert unit is, When an alert is displayed, the content of the alert will be customized based on the user's current financial situation. The system described in Appendix 1, characterized by the features described herein. (Note 35) The alert unit is, It estimates the user's emotions and determines the priority of alerts based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The alert unit is, When displaying alerts, the system takes the user's geographical location into consideration to provide the most appropriate alerts. The system described in Appendix 1, characterized by the features described herein. (Note 37) The alert unit is, When displaying an alert, the system analyzes the user's social media activity to suggest content for the alert. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The data collection unit collects data by linking with the APIs of each financial institution, An integration unit that integrates the data collected by the aforementioned collection unit, A monitoring unit monitors the data integrated by the aforementioned integration unit in real time, A detection unit that detects anomalies from the data monitored by the aforementioned monitoring unit, The system includes an alert unit that notifies the user of any abnormalities detected by the detection unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect users' financial data through APIs provided by various financial institutions. The system according to feature 1.

3. The aforementioned integration unit is The collected data is integrated, and the activity of all financial accounts is monitored in real time. The system according to feature 1.

4. The aforementioned monitoring unit, The collected data is analyzed to automatically detect abnormal activity and security risks. The system according to feature 1.

5. The detection unit, If an anomaly occurs, an alert will be sent to the user immediately, and a corrective action will be proposed. The system according to feature 1.

6. The alert unit is, Send a notification to the user and suggest they temporarily suspend the transaction. The system according to feature 1.

7. The aforementioned integration unit is It features enhanced data encryption, multi-factor authentication, data backup, and user training as security features. The system according to feature 1.

8. The aforementioned collection unit is We estimate user sentiment and adjust the timing of financial data collection based on the estimated user sentiment. The system according to feature 1.

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