system

Abstract

We provide the system. [Solution] A means of collecting asset data from multiple financial institutions, A means of integrating the collected asset data and storing it in a database, A means of analyzing trading data using machine learning algorithms to detect abnormal transactions, A means of sending a notification to the user or related party regarding the detected abnormal transaction, A means of providing users with asset information and details of abnormal transactions through an integrated interface, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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 an aging society, the management of assets held by the elderly in a dispersed manner among multiple financial institutions is complex, and it is particularly difficult for the elderly who are not accustomed to the digital field to grasp. In addition, due to the increase in fraud and illegal transactions, the financial assets of the elderly are at risk. As a result, there is a need for safe and efficient management of assets and rapid detection and notification of fraud, but there is a problem that the current system cannot adequately address this.

Means for Solving the Problems

[0005] This invention provides a system for collecting and integrating asset data from multiple financial institutions. This system stores the collected asset data in a database and uses machine learning algorithms to analyze transaction data, thereby detecting abnormal transactions in real time. Furthermore, it immediately notifies users and their related parties of any detected abnormal transactions. In addition, by providing an intuitive interface that allows users to easily check asset information and details of abnormal transactions, it aims to make asset management for the elderly safer and more efficient.

[0006] "Financial institutions" refer to corporations such as banks, securities companies, and insurance companies that are the main entities responsible for asset management.

[0007] "Asset data" refers to information about an individual's assets obtained from financial institutions, such as deposit balances, transaction history, and investment information.

[0008] A "database" refers to a storage device within a system used to organize and store collected asset data and transaction information.

[0009] A "machine learning algorithm" refers to a computational method that learns patterns based on past data and uses that learning to make predictions and analyses on new data.

[0010] An "abnormal transaction" refers to a financial transaction that is detected as suspicious activity that deviates from normal trading patterns.

[0011] An "interface" refers to the screens and operating systems that allow users to interact with the system, check asset information, and perform operations. [Brief explanation of the drawing]

[0012] [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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface that includes a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] In an embodiment of the present invention, an asset management system is implemented in which a server located on the cloud plays a central role. The server connects to APIs of multiple financial institutions registered by the user and periodically collects asset data using authentication information. This enables the user to centrally manage their asset information.

[0034] The server efficiently integrates the collected asset data and stores it in a database. During this process, it adjusts for data duplication and format inconsistencies, maintaining consistent data. This allows users to instantly view the latest asset status.

[0035] The collected transaction data is analyzed in real time on the server using a machine learning algorithm. This algorithm learns from past transaction data and detects abnormal transactions that deviate from normal transaction patterns. When an abnormal transaction is detected, the server immediately sends a notification to the user and designated related parties. This notification includes details of the transaction in which the abnormality may have occurred, allowing the user to take prompt action.

[0036] On the user's device, collected asset data and details of unusual transactions can be viewed through an intuitive and easy-to-understand interface. The interface is designed to be user-friendly even for the elderly, and the font size and colors can be freely adjusted. In addition, users can enhance security by logging into the system using multi-factor authentication.

[0037] As a concrete example, suppose user X has accounts with banks A and B, and securities company C. The server integrates data from these financial institutions and displays X's asset status on a single screen. One day, if a large transaction exceeding the normal range occurs from X's account, the server detects this in real time and notifies X via email. X can then check the details of this unusual transaction on their smartphone, quickly contact the bank, and verify the transaction.

[0038] Thus, the present invention is provided in a form that combines various system configurations and algorithmic innovations to safely manage the assets of the elderly.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The server connects to the API of the financial institution registered by the user. Using authentication credentials, it securely connects and begins the process of retrieving asset data for each account.

[0042] Step 2:

[0043] The server receives the acquired asset data and integrates it into the database. It checks for duplicates and inconsistencies, ensuring accuracy before storing the data.

[0044] Step 3:

[0045] The server performs analysis on transaction data in the database using machine learning algorithms. It compares patterns of normal and abnormal transactions and identifies transactions that appear abnormal.

[0046] Step 4:

[0047] The server compiles information about detected abnormal transactions and creates notifications for the user and designated stakeholders. Users are immediately notified via email or app push notifications.

[0048] Step 5:

[0049] The terminal retrieves the latest asset information from the server and displays it in an intuitive interface when accessed by the user. It provides an easy-to-understand overview of assets and detailed reports of unusual transactions.

[0050] Step 6:

[0051] The user logs into their device and checks the displayed asset information and information about unusual transactions. If necessary, they contact financial institutions to verify the accuracy of the transactions and take appropriate action.

[0052] (Example 1)

[0053] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0054] Efficiently aggregating financial data from diverse information providers and managing it in a consistent format is a complex and time-consuming task for the average user. Furthermore, systems capable of quickly identifying abnormal transactions and alerting users are not adequately realized with conventional technologies. Additionally, there is a need to provide a user-friendly interface accessible to a diverse range of users, including the elderly.

[0055] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0056] In this invention, the server includes means for acquiring financial data from multiple information providers, means for integrating the acquired financial data and storing it in a storage device, and means for analyzing transaction information using a machine learning model and identifying abnormal transactions. This makes it possible to efficiently integrate financial data and identify and notify abnormal transactions in real time.

[0057] An "information provider" is an organization or entity that provides financial data, such as a financial institution or a data provider.

[0058] "Financial data" refers to information related to the financial status of an individual or organization, including transaction records and account balances.

[0059] "Acquisition" refers to the act of a server collecting financial data from an information provider.

[0060] "Integration" is the process of combining data obtained from multiple sources into a consistent format.

[0061] A "storage device" is a physical or electronic device used to store data.

[0062] A "machine learning model" is a collection of algorithms used to analyze large amounts of data and find patterns and trends.

[0063] "Analysis" is the act of conducting a detailed examination of data to reveal its structure and trends.

[0064] An "unusual transaction" is a transaction that may deviate from normal trading patterns.

[0065] "To identify" means the act of identifying and recognizing an abnormal transaction.

[0066] In embodiments of the present invention, a cloud-based system is provided for integrated management of financial information and for detecting and notifying of abnormal transactions. The server is implemented on a cloud infrastructure and acquires financial data from numerous information providers. The API (Application Programming Interface) of each provider is used to acquire the data. The acquired data is integrated and stored in a database after correcting for duplication and inconsistencies. For this reason, the server typically uses a relational database management system (RDBMS).

[0067] The analysis of transaction data utilizes machine learning models running on the server. These models learn from past transaction data to identify transactions that deviate from established criteria. For example, if a large fund transfer occurs that a user wouldn't normally make, the server will recognize it as an anomaly and immediately notify the user. This notification feature includes alerts via email.

[0068] The terminal provides a user interface designed for smooth operation. Features such as large fonts and adjustable color themes ensure ease of use, even for the elderly. Users can use the terminal to view integrated financial data and details of unusual transactions in real time. Multi-factor authentication is employed for secure access, ensuring the system's security.

[0069] A concrete example is a simulation prompt that states, "When a large transaction occurs, send a real-time notification to the user." Such prompts can provide the generated AI model with a means to more effectively improve the system's operation.

[0070] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0071] Step 1:

[0072] The server accesses APIs from multiple data providers to retrieve financial data. The user's registered authentication information is used as input. The server sends HTTP requests to the API endpoints and receives financial data in JSON format as a response. The output of this step is the retrieved raw JSON data. Specifically, authentication is performed using API keys or OAuth tokens.

[0073] Step 2:

[0074] The server integrates the retrieved JSON data before storing it in the database. The input is the raw data obtained in step 1. The server removes duplicate data and corrects format inconsistencies as needed. This process includes data normalization and mapping. The output is consistent, integrated data, which is then stored in the database. Specifically, it performs INSERT or UPDATE operations on the database using SQL queries.

[0075] Step 3:

[0076] The server analyzes transaction data stored in the database using a machine learning model. The input data is a consistent transaction history. The server inputs this data into the model and analyzes transaction patterns. The output is the result of determining abnormal transactions. Specifically, it supplies data to an already trained model and performs feedforward calculations.

[0077] Step 4:

[0078] The server sends a real-time notification to the user if an abnormal transaction is detected. The input is the abnormal transaction detection result from step 3. The output of the notification is an email or application notification sent to the user. Specifically, it sends an email using the SMTP protocol, and the message includes detailed information about the abnormal transaction.

[0079] Step 5:

[0080] Users view detailed financial data and information about unusual transactions using a user interface displayed on their device. Input consists of financial data and notification content sent from the server. Output is a data display that users can visually confirm on the screen. Specifically, the user can set the font size and color in the interface and filter the data as needed.

[0081] (Application Example 1)

[0082] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0083] Traditionally, there has been a lack of effective means to manage asset information dispersed across multiple financial institutions and to detect fraudulent transactions in real time. In particular, for the elderly, the information provided across multiple platforms was cumbersome, making it difficult to immediately detect and respond to fraud. Furthermore, there were insufficient means to securely access this information and verify its details.

[0084] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0085] In this invention, the server includes a device for collecting financial data from multiple financial institutions, a device for integrating the collected financial data and storing it in a data repository, and a device for analyzing transaction information using machine learning technology and detecting fraudulent transactions. This enables users to centrally manage their asset information and respond quickly to fraudulent transactions.

[0086] A "financial institution" is an organization that provides financial services, such as banks, securities companies, and credit unions.

[0087] "Financial data" refers to information about assets, such as account balances, transaction history, and investment portfolios.

[0088] A "data repository" is a database or storage system used to centrally store and manage collected data.

[0089] "Machine learning technology" is a technique in which computers learn patterns from large amounts of data and perform predictions and classifications.

[0090] An "unfair transaction" is a transaction that is considered abnormal because it deviates from normal trading patterns.

[0091] An "integrated display device" is a device equipped with an interface for displaying asset information and details of fraudulent transactions to the user in an integrated manner.

[0092] Multi-factor authentication is an authentication method that enhances security by requiring users to present multiple pieces of evidence when accessing a system.

[0093] A "generative AI model" is a model that uses artificial intelligence technology to analyze data and generate specific outputs.

[0094] A "prompt sentence" is a sentence used as input to a generative AI model, created with the purpose of prompting specific instructions or outputs.

[0095] To implement this invention, a server located in the cloud plays a crucial role. The server connects to APIs of multiple financial institutions and periodically collects users' financial data. This data is then integrated into a pre-configured format and stored in a data repository. The server can use programming languages ​​and frameworks such as Python and Flask to handle data duplication and format inconsistencies as needed.

[0096] The collected financial data is analyzed using machine learning techniques. Generative AI models built with libraries such as TENSORFLOW® and PyTorch learn past transaction patterns and detect fraudulent transactions. If this analysis detects a transaction that deviates from normal patterns, the server immediately sends a notification to the user. The notification is sent via email, smartphone applications, etc.

[0097] The user terminal utilizes an integrated display device that is easy for elderly users to use. Through this interface, users can view asset information and details of fraudulent transactions. Furthermore, multi-factor authentication provides a high level of security. This allows users to confidently understand their financial situation and take prompt action when necessary.

[0098] For example, if a user has accounts at multiple banks, the server aggregates data from these accounts and issues real-time alerts if there are any fraudulent high-value transactions. Furthermore, because this system automatically detects anomalies based on the user's usual purchasing behavior, it enables a swift and highly accurate response.

[0099] An example of a prompt message given to the generating AI model might be: "Use the user's financial transaction data to build an anomaly detection model that detects fraudulent transactions. Learn the characteristics of normal and abnormal transaction data to achieve highly sensitive anomaly detection."

[0100] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0101] Step 1:

[0102] The server connects to the APIs of multiple financial institutions registered by the user and periodically collects financial data. Inputs include account information and transaction history obtained from each financial institution's API. Outputs are raw financial data that is integrated on the server side.

[0103] Step 2:

[0104] The server stores the collected raw financial data in a data repository. During this process, data processing is performed to eliminate data duplication and format inconsistencies. The input is the collected raw financial data, and the output is the financial data converted and stored in a unified format.

[0105] Step 3:

[0106] The server analyzes financial data using a generative AI model. It utilizes machine learning techniques to detect anomalies from past transaction patterns. The input is financial data in a unified format stored in a data repository, and the output is a list of detected anomaly transactions.

[0107] Step 4:

[0108] The server sends notifications to users about detected abnormal transactions. Alerts are sent in real time via email or a smartphone app. The input is a list of abnormal transactions, and the output is the alert notification to the user.

[0109] Step 5:

[0110] The user terminal displays received notifications on its interface, allowing the user to view details. An integrated display device ensures that information is presented in a format easily accessible to the elderly. Input is alert information sent from the server, while output is user-viewable display information.

[0111] Step 6:

[0112] Users access the system through multi-factor authentication to view detailed financial information. Input is the user's authentication information, and output is the financial information and details of unusual transactions accessible to the user.

[0113] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0114] In embodiments of the present invention, an advanced asset management system equipped with an emotion engine is provided to enable more intelligent asset management for users. In addition to conventional asset data collection and integration and abnormal transaction detection, the server uses the emotion engine to analyze the user's emotional state.

[0115] When a user accesses the asset management platform, the server collects the user's voice and facial expressions from sensors and cameras on the device. An emotion engine then analyzes this data to identify the user's emotional state. Based on this information, the server adjusts the interface display to reduce user stress.

[0116] For example, if the emotion engine determines that a user is feeling anxious or stressed, the server can change the design of notifications and interfaces to a calmer tone. Conversely, if a user is experiencing positive emotions, the server can improve the user experience by providing a more proactive interface.

[0117] When a user reviews their asset information and receives a notification, especially regarding unusual transactions, the server can adjust the notification text based on the results of the emotion engine's analysis. For example, if the server determines that the user is experiencing stress, the notification will use simpler and more reassuring language.

[0118] As a concrete example, suppose user Y logs into the system to check their assets. The terminal's camera captures Y's facial expression, and the emotion engine recognizes that Y is experiencing some stress. In this case, the server changes the interface to a softer color scheme, and if an abnormal transaction notification occurs, it tailors the message to reassure Y.

[0119] Thus, by incorporating emotion recognition technology into asset management, the present invention enables flexible responses tailored to the user's psychological state, providing a safer and more comfortable asset management experience.

[0120] The following describes the processing flow.

[0121] Step 1:

[0122] The user logs into the asset management system via their device. Upon login, the device's camera and microphone are activated, recording the user's facial expressions and voice.

[0123] Step 2:

[0124] The device sends the user's recorded facial expression and voice data to the server. The server receives this data and begins analysis using its emotion engine.

[0125] Step 3:

[0126] The server's emotion engine identifies the user's emotional state based on the received data. Specifically, it determines the user's stress level and emotional tendencies from changes in facial expressions and tone of voice.

[0127] Step 4:

[0128] Based on the analysis results of the emotion engine, the server determines an appropriate interface design according to the user's state. The screen's color scheme and layout are changed to reduce stress.

[0129] Step 5:

[0130] The server sends the adjusted interface information to the terminal. The terminal receives this information and displays a visually adjusted asset information screen to the user.

[0131] Step 6:

[0132] The user reviews the displayed asset information and receives a notification if there are any unusual transactions. The server takes the results of the sentiment engine into consideration and automatically adjusts the notification text to best suit the user's emotional state.

[0133] Step 7:

[0134] Users can review notifications and, if necessary, contact financial institutions or share information with family members. Information provided through emotion recognition supports users' decision-making.

[0135] (Example 2)

[0136] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0137] Traditional asset management systems focus on data collection and anomaly detection, but lack consideration for the user's psychological state, resulting in a failure to alleviate user stress and anxiety. Therefore, there is a need to provide a more comfortable and secure asset management experience that takes users' emotional states into account.

[0138] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0139] In this invention, the server includes means for collecting asset data from multiple financial institutions, means for integrating the collected asset data and storing it in an information storage unit, and means for analyzing transaction information using an intelligent computation method and detecting abnormal transactions. This enables asset management that reduces the user's psychological burden and provides a sense of security by analyzing the user's emotional state and dynamically adjusting the interface display content based on the analysis results.

[0140] "Financial institutions" refer to organizations that provide financial services, such as banks and securities companies, and serve as sources for collecting asset data.

[0141] "Asset data" refers to information such as account balances and transaction history provided by financial institutions, and indicates the financial status of users.

[0142] "Information storage unit" refers to a database or storage system used to integrate and manage collected data.

[0143] An "intelligent computing method" refers to an algorithm that uses techniques such as machine learning and data mining to analyze data and detect specific patterns or anomalies.

[0144] "Transaction information" refers to records of data related to economic activities such as buying and selling and money transfers, and this is the subject of analysis.

[0145] An "abnormal transaction" refers to a transaction that deviates from normal trading patterns, and detecting these transactions reduces security risks.

[0146] An "information provision device" refers to a device or software that provides an interface for users to check and manipulate asset information.

[0147] The "emotion analysis unit" refers to a function that analyzes the user's voice and facial expressions to identify their emotional state, thereby optimizing the user experience.

[0148] "Dynamic adjustment" refers to a process that changes functions and display content in real time based on analysis results, responding immediately to user needs.

[0149] In an embodiment of the present invention, a system is constructed to centrally collect and manage asset data from multiple financial institutions and to provide an interface that allows users to manage their assets with greater peace of mind. The server collects asset data such as balance information and transaction history provided by each financial institution. This data is obtained via an API and integrated and stored in the information storage unit within the server. Subsequently, the collected data is analyzed using an intelligent computation method, specifically a machine learning algorithm, to determine whether or not there are any abnormal transactions.

[0150] The analyzed data is displayed on an information provision device accessed through the user's terminal. When the user accesses the information provision device, the terminal uses its camera and microphone to record the user's voice and facial expressions. This emotional data is transferred to a server and analyzed by the emotion analysis unit. The emotion analysis unit utilizes a generative AI model to analyze the user's psychological state in real time. For example, if the server determines that the user is experiencing stress, it immediately changes the interface of the information provision device to a calmer tone to reduce the user's mental burden.

[0151] As a concrete example, if the terminal's camera detects a smile while a user is checking asset information, the server can switch the interface to a brighter, more motivating design. In this way, the system dynamically adjusts the information provider in response to the user's emotions, enabling an improved asset management experience.

[0152] An example of a prompt is, "Please devise a specific method for adjusting the interface of an asset management system to respond immediately to changes in the user's emotions." By inputting this prompt into a generating AI model, it is possible to obtain ideas for interface design that enhance user satisfaction.

[0153] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0154] Step 1:

[0155] The server collects asset data from multiple financial institutions. The received data consists of specific asset information obtained via each financial institution's API. This includes account balances and transaction history. The server integrates this data and stores it in its information storage unit. The input is data from financial institutions' APIs, and the output is an integrated dataset. Through this process, the server builds a comprehensive asset database.

[0156] Step 2:

[0157] The server analyzes the collected asset data using intelligent computation methods. Specifically, it applies machine learning algorithms to detect abnormal transactions. The input is an integrated asset dataset, and the output is a list of detected abnormal transactions. The server identifies abnormalities by picking out deviations from normal transaction patterns and recording the reasons for those deviations.

[0158] Step 3:

[0159] When a user accesses the asset management platform, the device uses its built-in camera and microphone to record the user's facial expressions and voice. The input is the user's visual and auditory data, and the output is this emotional information in digital form. The device then prepares this emotional data for transmission to the server.

[0160] Step 4:

[0161] The server receives visual and audio data transmitted from the terminal and performs analysis in the emotion analysis unit. An intelligent computation method is used to identify the user's emotional state. The input is emotion data from the terminal, and the output is the analysis result indicating the user's emotional state.

[0162] Step 5:

[0163] Based on sentiment analysis, the server dynamically adjusts the interface. It changes the design and notification content in response to the user's emotional state. The input is the result of sentiment analysis, and the output is the adjusted interface settings. Specifically, if the user is feeling stressed, the server softens the notification sound and changes the display colors to calmer tones.

[0164] Step 6:

[0165] When an abnormal transaction is detected, the server sends a notification to the user. The notification text is customized based on the sentiment analysis results. The input is a list of abnormal transactions and the sentiment analysis results, and the output is the adjusted notification message. Users can respond without stress by receiving notifications expressed in concise and reassuring language.

[0166] (Application Example 2)

[0167] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0168] In modern times, asset management systems are essential tools for users to efficiently manage their assets. However, conventional asset management systems fail to consider the user's psychological state and do not adequately provide ways to alleviate user stress and anxiety. Therefore, it is considered necessary to adjust the interface to take the user's emotional state into account.

[0169] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0170] In this invention, the server includes means for collecting asset data from multiple financial institutions, means for integrating the collected asset data and storing it in a data storage unit, means for analyzing transaction information using a learning algorithm and detecting abnormal transactions, means for sending notifications to users or related parties regarding detected abnormal transactions, means for providing users with asset information and details of abnormal transactions through an integrated information display unit, and means for adjusting the display content of the interface using an emotion analysis device that analyzes the emotional state of the user. This enables flexible interface adjustment that takes into account the user's emotions, making it possible to provide a more comfortable and secure asset management experience.

[0171] A "financial institution" is an organization that is responsible for receiving, managing, and investing customers' assets.

[0172] "Asset data" refers to information about financial assets owned by an individual or corporation, including deposit balances, investment details, and transaction history.

[0173] A "data storage unit" is an area or system for organizing and storing collected information.

[0174] A "learning algorithm" is a method or process for analyzing large amounts of data and extracting patterns.

[0175] "Abnormal trading" refers to trading activities that deviate from normal trading patterns and should be closely monitored from a risk management perspective.

[0176] An "information display unit" refers to a screen or interface used to visually convey information to the user.

[0177] An "emotion analysis device" is a device or software that analyzes data such as a user's facial expressions and voice to infer their emotional state.

[0178] As an embodiment of the present invention, an advanced asset management system equipped with an emotion engine is provided. This system provides a safe and comfortable user experience by flexibly adjusting the interface according to the user's emotional state when managing the user's assets.

[0179] The server first collects asset data from multiple financial institutions and integrates it into a data storage unit. This data integration is crucial for centrally managing the user's overall asset status.

[0180] Furthermore, the server uses a learning algorithm to analyze transaction information and detect abnormal transactions. In this process, it analyzes a large amount of transaction data to extract patterns and detect deviations from normal patterns. At the same time, notifications are sent to the user or relevant parties regarding detected abnormal transactions.

[0181] To adjust the interface, an emotion analysis device is used to analyze the user's emotional state. The device collects the user's facial expressions and voice through the camera and microphone, and the emotion analysis device processes this data. Tools such as OpenCV and Google Cloud's emotion analysis API are used for the analysis. Based on the analysis results, the interface's color scheme and displayed content are adjusted. For example, if the user is feeling stressed, the interface's color scheme is changed to a calmer tone, and a message is displayed to provide reassurance.

[0182] As a concrete example, consider a scenario where, while a user is operating an asset management application, the system captures the user's facial expression and detects an emotion of "tension." In this case, the interface changes to a softer color scheme, and a message appears stating, "Don't worry, everything is being handled safely."

[0183] As an example of a prompt statement for a generative AI model,

[0184] "If we capture images showing users experiencing anxiety during electronic payments, what would be the appropriate way to modify the interface?"

[0185] This is one example. In this way, dynamic and personalized responses become possible to improve the user experience.

[0186] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0187] Step 1:

[0188] The server connects to financial institutions and collects user asset data. It receives asset data provided by financial institutions as input, formats and organizes it, and stores it in the data storage unit. The output is integrated asset data. At this point, the data is centralized, allowing for a complete overview of the user's assets.

[0189] Step 2:

[0190] The server analyzes transaction information in the data storage using a learning algorithm. It reads asset data as input and detects abnormal transactions using a machine learning algorithm. The output is a list of abnormal transactions. The data calculation involves learning normal transaction patterns from a large amount of transaction data and detecting deviations.

[0191] Step 3:

[0192] If the server detects an abnormal transaction, it sends a notification to the user or relevant parties. The system receives a list of abnormal transactions obtained in step 2 as input and generates an appropriate message using an AI model. The output is the generated notification message. This step also generates a prompt to clearly convey important information to the user.

[0193] Step 4:

[0194] The device captures the user's facial expressions and voice through its camera and microphone. It collects real-time captured facial images and audio data as input. The output is data representing the user's emotional state. Data processing involves facial recognition using OpenCV and emotion analysis using the Google Cloud Sentiment Analysis API.

[0195] Step 5:

[0196] The server adjusts the interface based on the user's emotional state. It references the emotional data obtained in step 4 as input and modifies the interface design elements. The output is the interface display adjusted according to the user's emotions. Specifically, it dynamically changes the interface's colors and message content to improve the user experience.

[0197] 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.

[0198] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0199] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0200] [Second Embodiment]

[0201] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0202] 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.

[0203] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0204] 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.

[0205] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0206] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0207] 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.

[0208] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0209] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0210] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0211] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0212] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0213] In an embodiment of the present invention, an asset management system is implemented in which a server located on the cloud plays a central role. The server connects to APIs of multiple financial institutions registered by the user and periodically collects asset data using authentication information. This enables the user to centrally manage their asset information.

[0214] The server efficiently integrates the collected asset data and stores it in a database. During this process, it adjusts for data duplication and format inconsistencies, maintaining consistent data. This allows users to instantly view the latest asset status.

[0215] The collected transaction data is analyzed in real time on the server using a machine learning algorithm. This algorithm learns from past transaction data and detects abnormal transactions that deviate from normal transaction patterns. When an abnormal transaction is detected, the server immediately sends a notification to the user and designated related parties. This notification includes details of the transaction in which the abnormality may have occurred, allowing the user to take prompt action.

[0216] On the user's device, collected asset data and details of unusual transactions can be viewed through an intuitive and easy-to-understand interface. The interface is designed to be user-friendly even for the elderly, and the font size and colors can be freely adjusted. In addition, users can enhance security by logging into the system using multi-factor authentication.

[0217] As a concrete example, suppose user X has accounts with banks A and B, and securities company C. The server integrates data from these financial institutions and displays X's asset status on a single screen. One day, if a large transaction exceeding the normal range occurs from X's account, the server detects this in real time and notifies X via email. X can then check the details of this unusual transaction on their smartphone, quickly contact the bank, and verify the transaction.

[0218] Thus, the present invention is provided in a form that combines various system configurations and algorithmic innovations to safely manage the assets of the elderly.

[0219] The following describes the processing flow.

[0220] Step 1:

[0221] The server connects to the API of the financial institution registered by the user. Using authentication credentials, it securely connects and begins the process of retrieving asset data for each account.

[0222] Step 2:

[0223] The server receives the acquired asset data and integrates it into the database. It checks for duplicates and inconsistencies, ensuring accuracy before storing the data.

[0224] Step 3:

[0225] The server performs analysis on transaction data in the database using machine learning algorithms. It compares patterns of normal and abnormal transactions and identifies transactions that appear abnormal.

[0226] Step 4:

[0227] The server compiles information about detected abnormal transactions and creates notifications for the user and designated stakeholders. Users are immediately notified via email or app push notifications.

[0228] Step 5:

[0229] The terminal retrieves the latest asset information from the server and displays it in an intuitive interface when accessed by the user. It provides an easy-to-understand overview of assets and detailed reports of unusual transactions.

[0230] Step 6:

[0231] The user logs into their device and checks the displayed asset information and information about unusual transactions. If necessary, they contact financial institutions to verify the accuracy of the transactions and take appropriate action.

[0232] (Example 1)

[0233] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0234] Efficiently aggregating financial data from diverse information providers and managing it in a consistent format is a complex and time-consuming task for the average user. Furthermore, systems capable of quickly identifying abnormal transactions and alerting users are not adequately realized with conventional technologies. Additionally, there is a need to provide a user-friendly interface accessible to a diverse range of users, including the elderly.

[0235] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0236] In this invention, the server includes means for acquiring financial data from multiple information providers, means for integrating the acquired financial data and storing it in a storage device, and means for analyzing transaction information using a machine learning model and identifying abnormal transactions. This makes it possible to efficiently integrate financial data and identify and notify abnormal transactions in real time.

[0237] An "information provider" is an organization or entity that provides financial data, such as a financial institution or a data provider.

[0238] "Financial data" refers to information related to the financial status of an individual or organization, including transaction records and account balances.

[0239] "Acquisition" refers to the act of a server collecting financial data from an information provider.

[0240] "Integration" is the process of combining data obtained from multiple sources into a consistent format.

[0241] A "storage device" is a physical or electronic device used to store data.

[0242] A "machine learning model" is a collection of algorithms used to analyze large amounts of data and find patterns and trends.

[0243] "Analysis" is the act of conducting a detailed examination of data to reveal its structure and trends.

[0244] An "unusual transaction" is a transaction that may deviate from normal trading patterns.

[0245] "To identify" means the act of identifying and recognizing an abnormal transaction.

[0246] In embodiments of the present invention, a cloud-based system is provided for integrated management of financial information and for detecting and notifying of abnormal transactions. The server is implemented on a cloud infrastructure and acquires financial data from numerous information providers. The API (Application Programming Interface) of each provider is used to acquire the data. The acquired data is integrated and stored in a database after correcting for duplication and inconsistencies. For this reason, the server typically uses a relational database management system (RDBMS).

[0247] The analysis of transaction data utilizes machine learning models running on the server. These models learn from past transaction data to identify transactions that deviate from established criteria. For example, if a large fund transfer occurs that a user wouldn't normally make, the server will recognize it as an anomaly and immediately notify the user. This notification feature includes alerts via email.

[0248] The terminal provides a user interface designed for smooth operation. Features such as large fonts and adjustable color themes ensure ease of use, even for the elderly. Users can use the terminal to view integrated financial data and details of unusual transactions in real time. Multi-factor authentication is employed for secure access, ensuring the system's security.

[0249] A concrete example is a simulation prompt that states, "When a large transaction occurs, send a real-time notification to the user." Such prompts can provide the generated AI model with a means to more effectively improve the system's operation.

[0250] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0251] Step 1:

[0252] The server accesses APIs from multiple data providers to retrieve financial data. The user's registered authentication information is used as input. The server sends HTTP requests to the API endpoints and receives financial data in JSON format as a response. The output of this step is the retrieved raw JSON data. Specifically, authentication is performed using API keys or OAuth tokens.

[0253] Step 2:

[0254] The server integrates the retrieved JSON data before storing it in the database. The input is the raw data obtained in step 1. The server removes duplicate data and corrects format inconsistencies as needed. This process includes data normalization and mapping. The output is consistent, integrated data, which is then stored in the database. Specifically, it performs INSERT or UPDATE operations on the database using SQL queries.

[0255] Step 3:

[0256] The server analyzes transaction data stored in the database using a machine learning model. The input data is a consistent transaction history. The server inputs this data into the model and analyzes transaction patterns. The output is the result of determining abnormal transactions. Specifically, it supplies data to an already trained model and performs feedforward calculations.

[0257] Step 4:

[0258] The server sends a real-time notification to the user if an abnormal transaction is detected. The input is the abnormal transaction detection result from step 3. The output of the notification is an email or application notification sent to the user. Specifically, it sends an email using the SMTP protocol, and the message includes detailed information about the abnormal transaction.

[0259] Step 5:

[0260] Users view detailed financial data and information about unusual transactions using a user interface displayed on their device. Input consists of financial data and notification content sent from the server. Output is a data display that users can visually confirm on the screen. Specifically, the user can set the font size and color in the interface and filter the data as needed.

[0261] (Application Example 1)

[0262] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0263] Traditionally, there has been a lack of effective means to manage asset information dispersed across multiple financial institutions and to detect fraudulent transactions in real time. In particular, for the elderly, the information provided across multiple platforms was cumbersome, making it difficult to immediately detect and respond to fraud. Furthermore, there were insufficient means to securely access this information and verify its details.

[0264] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0265] In this invention, the server includes a device for collecting financial data from multiple financial institutions, a device for integrating the collected financial data and storing it in a data repository, and a device for analyzing transaction information using machine learning technology and detecting fraudulent transactions. This enables users to centrally manage their asset information and respond quickly to fraudulent transactions.

[0266] A "financial institution" is an organization that provides financial services, such as banks, securities companies, and credit unions.

[0267] "Financial data" refers to information about assets, such as account balances, transaction history, and investment portfolios.

[0268] A "data repository" is a database or storage system used to centrally store and manage collected data.

[0269] "Machine learning technology" is a technique in which computers learn patterns from large amounts of data and perform predictions and classifications.

[0270] An "unfair transaction" is a transaction that is considered abnormal because it deviates from normal trading patterns.

[0271] An "integrated display device" is a device equipped with an interface for displaying asset information and details of fraudulent transactions to the user in an integrated manner.

[0272] Multi-factor authentication is an authentication method that enhances security by requiring users to present multiple pieces of evidence when accessing a system.

[0273] A "generative AI model" is a model that uses artificial intelligence technology to analyze data and generate specific outputs.

[0274] A "prompt sentence" is a sentence used as input to a generative AI model, created with the purpose of prompting specific instructions or outputs.

[0275] To implement this invention, a server located in the cloud plays a crucial role. The server connects to APIs of multiple financial institutions and periodically collects users' financial data. This data is then integrated into a pre-configured format and stored in a data repository. The server can use programming languages ​​and frameworks such as Python and Flask to handle data duplication and format inconsistencies as needed.

[0276] The collected financial data is analyzed using machine learning techniques. Generative AI models built with libraries such as TensorFlow and PyTorch learn past transaction patterns and detect fraudulent transactions. If this analysis detects a transaction that deviates from normal patterns, the server immediately sends a notification to the user. The notification is sent via email, a smartphone application, etc.

[0277] The user terminal utilizes an integrated display device that is easy for elderly users to use. Through this interface, users can view asset information and details of fraudulent transactions. Furthermore, multi-factor authentication provides a high level of security. This allows users to confidently understand their financial situation and take prompt action when necessary.

[0278] For example, if a user has accounts at multiple banks, the server aggregates data from these accounts and issues real-time alerts if there are any fraudulent high-value transactions. Furthermore, because this system automatically detects anomalies based on the user's usual purchasing behavior, it enables a swift and highly accurate response.

[0279] An example of a prompt message given to the generating AI model might be: "Use the user's financial transaction data to build an anomaly detection model that detects fraudulent transactions. Learn the characteristics of normal and abnormal transaction data to achieve highly sensitive anomaly detection."

[0280] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0281] Step 1:

[0282] The server connects to the APIs of multiple financial institutions registered by the user and periodically collects financial data. The input is the account information and transaction history obtained from the APIs of each financial institution. The output is the raw financial data integrated on the server side.

[0283] Step 2:

[0284] The server stores the collected raw financial data in a database. At this time, data processing is performed to eliminate data duplication and format inconsistencies. The input is the collected raw financial data, and the output is the financial data converted to a unified format and stored.

[0285] Step 3:

[0286] The server analyzes the financial data using a generated AI model. Machine learning techniques are used to detect anomalies from past transaction patterns. The input is the financial data in a unified format stored in the database, and the output is a list of detected abnormal transactions.

[0287] Step 4:

[0288] The server sends a notification to the user about the detected abnormal transactions. Alerts are issued in real time through email or a smartphone app. The input is the list of abnormal transactions, and the output is an alert notification to the user.

[0289] Step 5:

[0290] The user terminal displays received notifications on its interface, allowing the user to view details. An integrated display device ensures that information is presented in a format easily accessible to the elderly. Input is alert information sent from the server, while output is user-viewable display information.

[0291] Step 6:

[0292] Users access the system through multi-factor authentication to view detailed financial information. Input is the user's authentication information, and output is the financial information and details of unusual transactions accessible to the user.

[0293] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0294] In embodiments of the present invention, an advanced asset management system equipped with an emotion engine is provided to enable more intelligent asset management for users. In addition to conventional asset data collection and integration and abnormal transaction detection, the server uses the emotion engine to analyze the user's emotional state.

[0295] When a user accesses the asset management platform, the server collects the user's voice and facial expressions from sensors and cameras on the device. An emotion engine then analyzes this data to identify the user's emotional state. Based on this information, the server adjusts the interface display to reduce user stress.

[0296] For example, if the emotion engine determines that a user is feeling anxious or stressed, the server can change the design of notifications and interfaces to a calmer tone. Conversely, if a user is experiencing positive emotions, the server can improve the user experience by providing a more proactive interface.

[0297] When a user reviews their asset information and receives a notification, especially regarding unusual transactions, the server can adjust the notification text based on the results of the emotion engine's analysis. For example, if the server determines that the user is experiencing stress, the notification will use simpler and more reassuring language.

[0298] As a concrete example, suppose user Y logs into the system to check their assets. The terminal's camera captures Y's facial expression, and the emotion engine recognizes that Y is experiencing some stress. In this case, the server changes the interface to a softer color scheme, and if an abnormal transaction notification occurs, it tailors the message to reassure Y.

[0299] Thus, by incorporating emotion recognition technology into asset management, the present invention enables flexible responses tailored to the user's psychological state, providing a safer and more comfortable asset management experience.

[0300] The following describes the processing flow.

[0301] Step 1:

[0302] The user logs into the asset management system via their device. Upon login, the device's camera and microphone are activated, recording the user's facial expressions and voice.

[0303] Step 2:

[0304] The device sends the user's recorded facial expression and voice data to the server. The server receives this data and begins analysis using its emotion engine.

[0305] Step 3:

[0306] The server's emotion engine identifies the user's emotional state based on the received data. Specifically, it determines the user's stress level and emotional tendencies from changes in facial expressions and tone of voice.

[0307] Step 4:

[0308] Based on the analysis results of the emotion engine, the server determines an appropriate interface design according to the user's state. To reduce stress, the screen color scheme and layout are changed.

[0309] Step 5:

[0310] The server sends the adjusted interface information to the terminal. The terminal receives this and displays a visually adjusted asset information screen to the user.

[0311] Step 6:

[0312] The user checks the displayed asset information and receives a notification if there is an abnormal transaction. The server automatically adjusts the notification text to the content most suitable for the user's emotional state in consideration of the results of the emotion engine.

[0313] Step 7:

[0314] The user checks the notification and contacts the financial institution or shares information with family if necessary. The information provided through emotion recognition supports the user's decision-making.

[0315] (Example 2)

[0316] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0317] The conventional asset management system specializes in data collection and anomaly detection, but lacks consideration for the psychological state of the user, resulting in the problem that it cannot reduce the stress and anxiety of the user. Therefore, there is a need to provide a more comfortable and secure asset management experience considering the user's emotional state.

[0318] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0319] In this invention, the server includes means for collecting asset data from multiple financial institutions, means for integrating the collected asset data and storing it in an information storage unit, and means for analyzing transaction information using an intelligent computation method and detecting abnormal transactions. This enables asset management that reduces the user's psychological burden and provides a sense of security by analyzing the user's emotional state and dynamically adjusting the interface display content based on the analysis results.

[0320] "Financial institutions" refer to organizations that provide financial services, such as banks and securities companies, and serve as sources for collecting asset data.

[0321] "Asset data" refers to information such as account balances and transaction history provided by financial institutions, and indicates the financial status of users.

[0322] "Information storage unit" refers to a database or storage system used to integrate and manage collected data.

[0323] An "intelligent computing method" refers to an algorithm that uses techniques such as machine learning and data mining to analyze data and detect specific patterns or anomalies.

[0324] "Transaction information" refers to records of data related to economic activities such as buying and selling and money transfers, and this is the subject of analysis.

[0325] An "abnormal transaction" refers to a transaction that deviates from normal trading patterns, and detecting these transactions reduces security risks.

[0326] An "information provision device" refers to a device or software that provides an interface for users to check and manipulate asset information.

[0327] The "emotion analysis unit" refers to a function that analyzes the user's voice and facial expressions to identify their emotional state, thereby optimizing the user experience.

[0328] "Dynamic adjustment" refers to a process that changes functions and display content in real time based on analysis results, responding immediately to user needs.

[0329] In an embodiment of the present invention, a system is constructed to centrally collect and manage asset data from multiple financial institutions and to provide an interface that allows users to manage their assets with greater peace of mind. The server collects asset data such as balance information and transaction history provided by each financial institution. This data is obtained via an API and integrated and stored in the information storage unit within the server. Subsequently, the collected data is analyzed using an intelligent computation method, specifically a machine learning algorithm, to determine whether or not there are any abnormal transactions.

[0330] The analyzed data is displayed on an information provision device accessed through the user's terminal. When the user accesses the information provision device, the terminal uses its camera and microphone to record the user's voice and facial expressions. This emotional data is transferred to a server and analyzed by the emotion analysis unit. The emotion analysis unit utilizes a generative AI model to analyze the user's psychological state in real time. For example, if the server determines that the user is experiencing stress, it immediately changes the interface of the information provision device to a calmer tone to reduce the user's mental burden.

[0331] As a concrete example, if the terminal's camera detects a smile while a user is checking asset information, the server can switch the interface to a brighter, more motivating design. In this way, the system dynamically adjusts the information provider in response to the user's emotions, enabling an improved asset management experience.

[0332] An example of a prompt is, "Please devise a specific method for adjusting the interface of an asset management system to respond immediately to changes in the user's emotions." By inputting this prompt into a generating AI model, it is possible to obtain ideas for interface design that enhance user satisfaction.

[0333] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0334] Step 1:

[0335] The server collects asset data from multiple financial institutions. The received data consists of specific asset information obtained via each financial institution's API. This includes account balances and transaction history. The server integrates this data and stores it in its information storage unit. The input is data from financial institutions' APIs, and the output is an integrated dataset. Through this process, the server builds a comprehensive asset database.

[0336] Step 2:

[0337] The server analyzes the collected asset data using intelligent computation methods. Specifically, it applies machine learning algorithms to detect abnormal transactions. The input is an integrated asset dataset, and the output is a list of detected abnormal transactions. The server identifies abnormalities by picking out deviations from normal transaction patterns and recording the reasons for those deviations.

[0338] Step 3:

[0339] When a user accesses the asset management platform, the device uses its built-in camera and microphone to record the user's facial expressions and voice. The input is the user's visual and auditory data, and the output is this emotional information in digital form. The device then prepares this emotional data for transmission to the server.

[0340] Step 4:

[0341] The server receives visual and audio data transmitted from the terminal and performs analysis in the emotion analysis unit. An intelligent computation method is used to identify the user's emotional state. The input is emotion data from the terminal, and the output is the analysis result indicating the user's emotional state.

[0342] Step 5:

[0343] Based on sentiment analysis, the server dynamically adjusts the interface. It changes the design and notification content in response to the user's emotional state. The input is the result of sentiment analysis, and the output is the adjusted interface settings. Specifically, if the user is feeling stressed, the server softens the notification sound and changes the display colors to calmer tones.

[0344] Step 6:

[0345] When an abnormal transaction is detected, the server sends a notification to the user. The notification text is customized based on the sentiment analysis results. The input is a list of abnormal transactions and the sentiment analysis results, and the output is the adjusted notification message. Users can respond without stress by receiving notifications expressed in concise and reassuring language.

[0346] (Application Example 2)

[0347] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0348] In modern times, asset management systems are essential tools for users to efficiently manage their assets. However, conventional asset management systems fail to consider the user's psychological state and do not adequately provide ways to alleviate user stress and anxiety. Therefore, it is considered necessary to adjust the interface to take the user's emotional state into account.

[0349] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0350] In this invention, the server includes means for collecting asset data from multiple financial institutions, means for integrating the collected asset data and storing it in a data storage unit, means for analyzing transaction information using a learning algorithm and detecting abnormal transactions, means for sending notifications to users or related parties regarding detected abnormal transactions, means for providing users with asset information and details of abnormal transactions through an integrated information display unit, and means for adjusting the display content of the interface using an emotion analysis device that analyzes the emotional state of the user. This enables flexible interface adjustment that takes into account the user's emotions, making it possible to provide a more comfortable and secure asset management experience.

[0351] A "financial institution" is an organization that is responsible for receiving, managing, and investing customers' assets.

[0352] "Asset data" refers to information about financial assets owned by an individual or corporation, including deposit balances, investment details, and transaction history.

[0353] A "data storage unit" is an area or system for organizing and storing collected information.

[0354] A "learning algorithm" is a method or process for analyzing large amounts of data and extracting patterns.

[0355] "Abnormal trading" refers to trading activities that deviate from normal trading patterns and should be closely monitored from a risk management perspective.

[0356] An "information display unit" refers to a screen or interface used to visually convey information to the user.

[0357] An "emotion analysis device" is a device or software that analyzes data such as a user's facial expressions and voice to infer their emotional state.

[0358] As an embodiment of the present invention, an advanced asset management system equipped with an emotion engine is provided. This system provides a safe and comfortable user experience by flexibly adjusting the interface according to the user's emotional state when managing the user's assets.

[0359] The server first collects asset data from multiple financial institutions and integrates it into a data storage unit. This data integration is crucial for centrally managing the user's overall asset status.

[0360] Furthermore, the server uses a learning algorithm to analyze transaction information and detect abnormal transactions. In this process, it analyzes a large amount of transaction data to extract patterns and detect deviations from normal patterns. At the same time, notifications are sent to the user or relevant parties regarding detected abnormal transactions.

[0361] To adjust the interface, an emotion analysis device is used to analyze the user's emotional state. The device collects the user's facial expressions and voice through the camera and microphone, and the emotion analysis device processes this data. Tools such as OpenCV and Google Cloud's emotion analysis API are used for the analysis. Based on the analysis results, the interface's color scheme and displayed content are adjusted. For example, if the user is feeling stressed, the interface's color scheme is changed to a calmer tone, and a message is displayed to provide reassurance.

[0362] As a concrete example, consider a scenario where, while a user is operating an asset management application, the system captures the user's facial expression and detects an emotion of "tension." In this case, the interface changes to a softer color scheme, and a message appears stating, "Don't worry, everything is being handled safely."

[0363] As an example of a prompt statement for a generative AI model,

[0364] "If we capture images showing users experiencing anxiety during electronic payments, what would be the appropriate way to modify the interface?"

[0365] This is one example. In this way, dynamic and personalized responses become possible to improve the user experience.

[0366] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0367] Step 1:

[0368] The server connects to financial institutions and collects user asset data. It receives asset data provided by financial institutions as input, formats and organizes it, and stores it in the data storage unit. The output is integrated asset data. At this point, the data is centralized, allowing for a complete overview of the user's assets.

[0369] Step 2:

[0370] The server analyzes transaction information in the data storage using a learning algorithm. It reads asset data as input and detects abnormal transactions using a machine learning algorithm. The output is a list of abnormal transactions. The data calculation involves learning normal transaction patterns from a large amount of transaction data and detecting deviations.

[0371] Step 3:

[0372] If the server detects an abnormal transaction, it sends a notification to the user or relevant parties. The system receives a list of abnormal transactions obtained in step 2 as input and generates an appropriate message using an AI model. The output is the generated notification message. This step also generates a prompt to clearly convey important information to the user.

[0373] Step 4:

[0374] The device captures the user's facial expressions and voice through its camera and microphone. It collects real-time captured facial images and audio data as input. The output is data representing the user's emotional state. Data processing involves facial recognition using OpenCV and emotion analysis using the Google Cloud Sentiment Analysis API.

[0375] Step 5:

[0376] The server adjusts the interface based on the user's emotional state. It references the emotional data obtained in step 4 as input and modifies the interface design elements. The output is the interface display adjusted according to the user's emotions. Specifically, it dynamically changes the interface's colors and message content to improve the user experience.

[0377] 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.

[0378] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0379] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0380] [Third Embodiment]

[0381] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0382] 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.

[0383] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0384] 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.

[0385] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0386] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0387] 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.

[0388] 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.

[0389] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0390] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0391] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0392] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0393] In an embodiment of the present invention, an asset management system is implemented in which a server located on the cloud plays a central role. The server connects to APIs of multiple financial institutions registered by the user and periodically collects asset data using authentication information. This enables the user to centrally manage their asset information.

[0394] The server efficiently integrates the collected asset data and stores it in a database. During this process, it adjusts for data duplication and format inconsistencies, maintaining consistent data. This allows users to instantly view the latest asset status.

[0395] The collected transaction data is analyzed in real time on the server using a machine learning algorithm. This algorithm learns from past transaction data and detects abnormal transactions that deviate from normal transaction patterns. When an abnormal transaction is detected, the server immediately sends a notification to the user and designated related parties. This notification includes details of the transaction in which the abnormality may have occurred, allowing the user to take prompt action.

[0396] On the user's device, collected asset data and details of unusual transactions can be viewed through an intuitive and easy-to-understand interface. The interface is designed to be user-friendly even for the elderly, and the font size and colors can be freely adjusted. In addition, users can enhance security by logging into the system using multi-factor authentication.

[0397] As a concrete example, suppose user X has accounts with banks A and B, and securities company C. The server integrates data from these financial institutions and displays X's asset status on a single screen. One day, if a large transaction exceeding the normal range occurs from X's account, the server detects this in real time and notifies X via email. X can then check the details of this unusual transaction on their smartphone, quickly contact the bank, and verify the transaction.

[0398] Thus, the present invention is provided in a form that combines various system configurations and algorithmic innovations to safely manage the assets of the elderly.

[0399] The following describes the processing flow.

[0400] Step 1:

[0401] The server connects to the API of the financial institution registered by the user. Using authentication credentials, it securely connects and begins the process of retrieving asset data for each account.

[0402] Step 2:

[0403] The server receives the acquired asset data and integrates it into the database. It checks for duplicates and inconsistencies, ensuring accuracy before storing the data.

[0404] Step 3:

[0405] The server performs analysis on transaction data in the database using machine learning algorithms. It compares patterns of normal and abnormal transactions and identifies transactions that appear abnormal.

[0406] Step 4:

[0407] The server compiles information about detected abnormal transactions and creates notifications for the user and designated stakeholders. Users are immediately notified via email or app push notifications.

[0408] Step 5:

[0409] The terminal retrieves the latest asset information from the server and displays it in an intuitive interface when accessed by the user. It provides an easy-to-understand overview of assets and detailed reports of unusual transactions.

[0410] Step 6:

[0411] The user logs into their device and checks the displayed asset information and information about unusual transactions. If necessary, they contact financial institutions to verify the accuracy of the transactions and take appropriate action.

[0412] (Example 1)

[0413] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0414] Efficiently aggregating financial data from diverse information providers and managing it in a consistent format is a complex and time-consuming task for the average user. Furthermore, systems capable of quickly identifying abnormal transactions and alerting users are not adequately realized with conventional technologies. Additionally, there is a need to provide a user-friendly interface accessible to a diverse range of users, including the elderly.

[0415] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0416] In this invention, the server includes means for acquiring financial data from multiple information providers, means for integrating the acquired financial data and storing it in a storage device, and means for analyzing transaction information using a machine learning model and identifying abnormal transactions. This makes it possible to efficiently integrate financial data and identify and notify abnormal transactions in real time.

[0417] An "information provider" is an organization or entity that provides financial data, such as a financial institution or a data provider.

[0418] "Financial data" refers to information related to the financial status of an individual or organization, including transaction records and account balances.

[0419] "Acquisition" refers to the act of a server collecting financial data from an information provider.

[0420] "Integration" is the process of combining data obtained from multiple sources into a consistent format.

[0421] A "storage device" is a physical or electronic device used to store data.

[0422] A "machine learning model" is a collection of algorithms used to analyze large amounts of data and find patterns and trends.

[0423] "Analysis" is the act of conducting a detailed examination of data to reveal its structure and trends.

[0424] An "unusual transaction" is a transaction that may deviate from normal trading patterns.

[0425] "To identify" means the act of identifying and recognizing an abnormal transaction.

[0426] In embodiments of the present invention, a cloud-based system is provided for integrated management of financial information and for detecting and notifying of abnormal transactions. The server is implemented on a cloud infrastructure and acquires financial data from numerous information providers. The API (Application Programming Interface) of each provider is used to acquire the data. The acquired data is integrated and stored in a database after correcting for duplication and inconsistencies. For this reason, the server typically uses a relational database management system (RDBMS).

[0427] The analysis of transaction data utilizes machine learning models running on the server. These models learn from past transaction data to identify transactions that deviate from established criteria. For example, if a large fund transfer occurs that a user wouldn't normally make, the server will recognize it as an anomaly and immediately notify the user. This notification feature includes alerts via email.

[0428] The terminal provides a user interface designed for smooth operation. Features such as large fonts and adjustable color themes ensure ease of use, even for the elderly. Users can use the terminal to view integrated financial data and details of unusual transactions in real time. Multi-factor authentication is employed for secure access, ensuring the system's security.

[0429] A concrete example is a simulation prompt that states, "When a large transaction occurs, send a real-time notification to the user." Such prompts can provide the generated AI model with a means to more effectively improve the system's operation.

[0430] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0431] Step 1:

[0432] The server accesses APIs from multiple data providers to retrieve financial data. The user's registered authentication information is used as input. The server sends HTTP requests to the API endpoints and receives financial data in JSON format as a response. The output of this step is the retrieved raw JSON data. Specifically, authentication is performed using API keys or OAuth tokens.

[0433] Step 2:

[0434] The server integrates the retrieved JSON data before storing it in the database. The input is the raw data obtained in step 1. The server removes duplicate data and corrects format inconsistencies as needed. This process includes data normalization and mapping. The output is consistent, integrated data, which is then stored in the database. Specifically, it performs INSERT or UPDATE operations on the database using SQL queries.

[0435] Step 3:

[0436] The server analyzes transaction data stored in the database using a machine learning model. The input data is a consistent transaction history. The server inputs this data into the model and analyzes transaction patterns. The output is the result of determining abnormal transactions. Specifically, it supplies data to an already trained model and performs feedforward calculations.

[0437] Step 4:

[0438] The server sends a real-time notification to the user if an abnormal transaction is detected. The input is the abnormal transaction detection result from step 3. The output of the notification is an email or application notification sent to the user. Specifically, it sends an email using the SMTP protocol, and the message includes detailed information about the abnormal transaction.

[0439] Step 5:

[0440] Users view detailed financial data and information about unusual transactions using a user interface displayed on their device. Input consists of financial data and notification content sent from the server. Output is a data display that users can visually confirm on the screen. Specifically, the user can set the font size and color in the interface and filter the data as needed.

[0441] (Application Example 1)

[0442] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0443] Traditionally, there has been a lack of effective means to manage asset information dispersed across multiple financial institutions and to detect fraudulent transactions in real time. In particular, for the elderly, the information provided across multiple platforms was cumbersome, making it difficult to immediately detect and respond to fraud. Furthermore, there were insufficient means to securely access this information and verify its details.

[0444] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0445] In this invention, the server includes a device for collecting financial data from multiple financial institutions, a device for integrating the collected financial data and storing it in a data repository, and a device for analyzing transaction information using machine learning technology and detecting fraudulent transactions. This enables users to centrally manage their asset information and respond quickly to fraudulent transactions.

[0446] A "financial institution" is an organization that provides financial services, such as banks, securities companies, and credit unions.

[0447] "Financial data" refers to information about assets, such as account balances, transaction history, and investment portfolios.

[0448] A "data repository" is a database or storage system used to centrally store and manage collected data.

[0449] "Machine learning technology" is a technique in which computers learn patterns from large amounts of data and perform predictions and classifications.

[0450] An "unfair transaction" is a transaction that is considered abnormal because it deviates from normal trading patterns.

[0451] An "integrated display device" is a device equipped with an interface for displaying asset information and details of fraudulent transactions to the user in an integrated manner.

[0452] Multi-factor authentication is an authentication method that enhances security by requiring users to present multiple pieces of evidence when accessing a system.

[0453] A "generative AI model" is a model that uses artificial intelligence technology to analyze data and generate specific outputs.

[0454] A "prompt sentence" is a sentence used as input to a generative AI model, created with the purpose of prompting specific instructions or outputs.

[0455] To implement this invention, a server located in the cloud plays a crucial role. The server connects to APIs of multiple financial institutions and periodically collects users' financial data. This data is then integrated into a pre-configured format and stored in a data repository. The server can use programming languages ​​and frameworks such as Python and Flask to handle data duplication and format inconsistencies as needed.

[0456] The collected financial data is analyzed using machine learning techniques. Generative AI models built with libraries such as TensorFlow and PyTorch learn past transaction patterns and detect fraudulent transactions. If this analysis detects a transaction that deviates from normal patterns, the server immediately sends a notification to the user. The notification is sent via email, a smartphone application, etc.

[0457] The user terminal utilizes an integrated display device that is easy for elderly users to use. Through this interface, users can view asset information and details of fraudulent transactions. Furthermore, multi-factor authentication provides a high level of security. This allows users to confidently understand their financial situation and take prompt action when necessary.

[0458] For example, if a user has accounts at multiple banks, the server aggregates data from these accounts and issues real-time alerts if there are any fraudulent high-value transactions. Furthermore, because this system automatically detects anomalies based on the user's usual purchasing behavior, it enables a swift and highly accurate response.

[0459] An example of a prompt message given to the generating AI model might be: "Use the user's financial transaction data to build an anomaly detection model that detects fraudulent transactions. Learn the characteristics of normal and abnormal transaction data to achieve highly sensitive anomaly detection."

[0460] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0461] Step 1:

[0462] The server connects to the APIs of multiple financial institutions registered by the user and periodically collects financial data. Inputs include account information and transaction history obtained from each financial institution's API. Outputs are raw financial data that is integrated on the server side.

[0463] Step 2:

[0464] The server stores the collected raw financial data in a data repository. During this process, data processing is performed to eliminate data duplication and format inconsistencies. The input is the collected raw financial data, and the output is the financial data converted and stored in a unified format.

[0465] Step 3:

[0466] The server analyzes financial data using a generative AI model. It utilizes machine learning techniques to detect anomalies from past transaction patterns. The input is financial data in a unified format stored in a data repository, and the output is a list of detected anomaly transactions.

[0467] Step 4:

[0468] The server sends notifications to users about detected abnormal transactions. Alerts are sent in real time via email or a smartphone app. The input is a list of abnormal transactions, and the output is the alert notification to the user.

[0469] Step 5:

[0470] The user terminal displays received notifications on its interface, allowing the user to view details. An integrated display device ensures that information is presented in a format easily accessible to the elderly. Input is alert information sent from the server, while output is user-viewable display information.

[0471] Step 6:

[0472] Users access the system through multi-factor authentication to view detailed financial information. Input is the user's authentication information, and output is the financial information and details of unusual transactions accessible to the user.

[0473] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0474] In embodiments of the present invention, an advanced asset management system equipped with an emotion engine is provided to enable more intelligent asset management for users. In addition to conventional asset data collection and integration and abnormal transaction detection, the server uses the emotion engine to analyze the user's emotional state.

[0475] When a user accesses the asset management platform, the server collects the user's voice and facial expressions from sensors and cameras on the device. An emotion engine then analyzes this data to identify the user's emotional state. Based on this information, the server adjusts the interface display to reduce user stress.

[0476] For example, if the emotion engine determines that a user is feeling anxious or stressed, the server can change the design of notifications and interfaces to a calmer tone. Conversely, if a user is experiencing positive emotions, the server can improve the user experience by providing a more proactive interface.

[0477] When a user reviews their asset information and receives a notification, especially regarding unusual transactions, the server can adjust the notification text based on the results of the emotion engine's analysis. For example, if the server determines that the user is experiencing stress, the notification will use simpler and more reassuring language.

[0478] As a concrete example, suppose user Y logs into the system to check their assets. The terminal's camera captures Y's facial expression, and the emotion engine recognizes that Y is experiencing some stress. In this case, the server changes the interface to a softer color scheme, and if an abnormal transaction notification occurs, it tailors the message to reassure Y.

[0479] Thus, by incorporating emotion recognition technology into asset management, the present invention enables flexible responses tailored to the user's psychological state, providing a safer and more comfortable asset management experience.

[0480] The following describes the processing flow.

[0481] Step 1:

[0482] The user logs into the asset management system via their device. Upon login, the device's camera and microphone are activated, recording the user's facial expressions and voice.

[0483] Step 2:

[0484] The device sends the user's recorded facial expression and voice data to the server. The server receives this data and begins analysis using its emotion engine.

[0485] Step 3:

[0486] The server's emotion engine identifies the user's emotional state based on the received data. Specifically, it determines the user's stress level and emotional tendencies from changes in facial expressions and tone of voice.

[0487] Step 4:

[0488] Based on the analysis results of the emotion engine, the server determines an appropriate interface design according to the user's state. The screen's color scheme and layout are changed to reduce stress.

[0489] Step 5:

[0490] The server sends the adjusted interface information to the terminal. The terminal receives this information and displays a visually adjusted asset information screen to the user.

[0491] Step 6:

[0492] The user reviews the displayed asset information and receives a notification if there are any unusual transactions. The server takes the results of the sentiment engine into consideration and automatically adjusts the notification text to best suit the user's emotional state.

[0493] Step 7:

[0494] Users can review notifications and, if necessary, contact financial institutions or share information with family members. Information provided through emotion recognition supports users' decision-making.

[0495] (Example 2)

[0496] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0497] Traditional asset management systems focus on data collection and anomaly detection, but lack consideration for the user's psychological state, resulting in a failure to alleviate user stress and anxiety. Therefore, there is a need to provide a more comfortable and secure asset management experience that takes users' emotional states into account.

[0498] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0499] In this invention, the server includes means for collecting asset data from multiple financial institutions, means for integrating the collected asset data and storing it in an information storage unit, and means for analyzing transaction information using an intelligent computation method and detecting abnormal transactions. This enables asset management that reduces the user's psychological burden and provides a sense of security by analyzing the user's emotional state and dynamically adjusting the interface display content based on the analysis results.

[0500] "Financial institutions" refer to organizations that provide financial services, such as banks and securities companies, and serve as sources for collecting asset data.

[0501] "Asset data" refers to information such as account balances and transaction history provided by financial institutions, and indicates the financial status of users.

[0502] "Information storage unit" refers to a database or storage system used to integrate and manage collected data.

[0503] An "intelligent computing method" refers to an algorithm that uses techniques such as machine learning and data mining to analyze data and detect specific patterns or anomalies.

[0504] "Transaction information" refers to records of data related to economic activities such as buying and selling and money transfers, and this is the subject of analysis.

[0505] An "abnormal transaction" refers to a transaction that deviates from normal trading patterns, and detecting these transactions reduces security risks.

[0506] An "information provision device" refers to a device or software that provides an interface for users to check and manipulate asset information.

[0507] The "emotion analysis unit" refers to a function that analyzes the user's voice and facial expressions to identify their emotional state, thereby optimizing the user experience.

[0508] "Dynamic adjustment" refers to a process that changes functions and display content in real time based on analysis results, responding immediately to user needs.

[0509] In an embodiment of the present invention, a system is constructed to centrally collect and manage asset data from multiple financial institutions and to provide an interface that allows users to manage their assets with greater peace of mind. The server collects asset data such as balance information and transaction history provided by each financial institution. This data is obtained via an API and integrated and stored in the information storage unit within the server. Subsequently, the collected data is analyzed using an intelligent computation method, specifically a machine learning algorithm, to determine whether or not there are any abnormal transactions.

[0510] The analyzed data is displayed on an information provision device accessed through the user's terminal. When the user accesses the information provision device, the terminal uses its camera and microphone to record the user's voice and facial expressions. This emotional data is transferred to a server and analyzed by the emotion analysis unit. The emotion analysis unit utilizes a generative AI model to analyze the user's psychological state in real time. For example, if the server determines that the user is experiencing stress, it immediately changes the interface of the information provision device to a calmer tone to reduce the user's mental burden.

[0511] As a concrete example, if the terminal's camera detects a smile while a user is checking asset information, the server can switch the interface to a brighter, more motivating design. In this way, the system dynamically adjusts the information provider in response to the user's emotions, enabling an improved asset management experience.

[0512] An example of a prompt is, "Please devise a specific method for adjusting the interface of an asset management system to respond immediately to changes in the user's emotions." By inputting this prompt into a generating AI model, it is possible to obtain ideas for interface design that enhance user satisfaction.

[0513] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0514] Step 1:

[0515] The server collects asset data from multiple financial institutions. The received data consists of specific asset information obtained via each financial institution's API. This includes account balances and transaction history. The server integrates this data and stores it in its information storage unit. The input is data from financial institutions' APIs, and the output is an integrated dataset. Through this process, the server builds a comprehensive asset database.

[0516] Step 2:

[0517] The server analyzes the collected asset data using intelligent computation methods. Specifically, it applies machine learning algorithms to detect abnormal transactions. The input is an integrated asset dataset, and the output is a list of detected abnormal transactions. The server identifies abnormalities by picking out deviations from normal transaction patterns and recording the reasons for those deviations.

[0518] Step 3:

[0519] When a user accesses the asset management platform, the device uses its built-in camera and microphone to record the user's facial expressions and voice. The input is the user's visual and auditory data, and the output is this emotional information in digital form. The device then prepares this emotional data for transmission to the server.

[0520] Step 4:

[0521] The server receives visual and audio data transmitted from the terminal and performs analysis in the emotion analysis unit. An intelligent computation method is used to identify the user's emotional state. The input is emotion data from the terminal, and the output is the analysis result indicating the user's emotional state.

[0522] Step 5:

[0523] Based on sentiment analysis, the server dynamically adjusts the interface. It changes the design and notification content in response to the user's emotional state. The input is the result of sentiment analysis, and the output is the adjusted interface settings. Specifically, if the user is feeling stressed, the server softens the notification sound and changes the display colors to calmer tones.

[0524] Step 6:

[0525] When an abnormal transaction is detected, the server sends a notification to the user. The notification text is customized based on the sentiment analysis results. The input is a list of abnormal transactions and the sentiment analysis results, and the output is the adjusted notification message. Users can respond without stress by receiving notifications expressed in concise and reassuring language.

[0526] (Application Example 2)

[0527] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0528] In modern times, asset management systems are essential tools for users to efficiently manage their assets. However, conventional asset management systems fail to consider the user's psychological state and do not adequately provide ways to alleviate user stress and anxiety. Therefore, it is considered necessary to adjust the interface to take the user's emotional state into account.

[0529] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0530] In this invention, the server includes means for collecting asset data from multiple financial institutions, means for integrating the collected asset data and storing it in a data storage unit, means for analyzing transaction information using a learning algorithm and detecting abnormal transactions, means for sending notifications to users or related parties regarding detected abnormal transactions, means for providing users with asset information and details of abnormal transactions through an integrated information display unit, and means for adjusting the display content of the interface using an emotion analysis device that analyzes the emotional state of the user. This enables flexible interface adjustment that takes into account the user's emotions, making it possible to provide a more comfortable and secure asset management experience.

[0531] A "financial institution" is an organization that is responsible for receiving, managing, and investing customers' assets.

[0532] "Asset data" refers to information about financial assets owned by an individual or corporation, including deposit balances, investment details, and transaction history.

[0533] A "data storage unit" is an area or system for organizing and storing collected information.

[0534] A "learning algorithm" is a method or process for analyzing large amounts of data and extracting patterns.

[0535] "Abnormal trading" refers to trading activities that deviate from normal trading patterns and should be closely monitored from a risk management perspective.

[0536] An "information display unit" refers to a screen or interface used to visually convey information to the user.

[0537] An "emotion analysis device" is a device or software that analyzes data such as a user's facial expressions and voice to infer their emotional state.

[0538] As an embodiment of the present invention, an advanced asset management system equipped with an emotion engine is provided. This system provides a safe and comfortable user experience by flexibly adjusting the interface according to the user's emotional state when managing the user's assets.

[0539] The server first collects asset data from multiple financial institutions and integrates it into a data storage unit. This data integration is crucial for centrally managing the user's overall asset status.

[0540] Furthermore, the server uses a learning algorithm to analyze transaction information and detect abnormal transactions. In this process, it analyzes a large amount of transaction data to extract patterns and detect deviations from normal patterns. At the same time, notifications are sent to the user or relevant parties regarding detected abnormal transactions.

[0541] To adjust the interface, an emotion analysis device is used to analyze the user's emotional state. The device collects the user's facial expressions and voice through the camera and microphone, and the emotion analysis device processes this data. Tools such as OpenCV and Google Cloud's emotion analysis API are used for the analysis. Based on the analysis results, the interface's color scheme and displayed content are adjusted. For example, if the user is feeling stressed, the interface's color scheme is changed to a calmer tone, and a message is displayed to provide reassurance.

[0542] As a concrete example, consider a scenario where, while a user is operating an asset management application, the system captures the user's facial expression and detects an emotion of "tension." In this case, the interface changes to a softer color scheme, and a message appears stating, "Don't worry, everything is being handled safely."

[0543] As an example of a prompt statement for a generative AI model,

[0544] "If we capture images showing users experiencing anxiety during electronic payments, what would be the appropriate way to modify the interface?"

[0545] This is one example. In this way, dynamic and personalized responses become possible to improve the user experience.

[0546] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0547] Step 1:

[0548] The server connects to financial institutions and collects user asset data. It receives asset data provided by financial institutions as input, formats and organizes it, and stores it in the data storage unit. The output is integrated asset data. At this point, the data is centralized, allowing for a complete overview of the user's assets.

[0549] Step 2:

[0550] The server analyzes transaction information in the data storage using a learning algorithm. It reads asset data as input and detects abnormal transactions using a machine learning algorithm. The output is a list of abnormal transactions. The data calculation involves learning normal transaction patterns from a large amount of transaction data and detecting deviations.

[0551] Step 3:

[0552] If the server detects an abnormal transaction, it sends a notification to the user or relevant parties. The system receives a list of abnormal transactions obtained in step 2 as input and generates an appropriate message using an AI model. The output is the generated notification message. This step also generates a prompt to clearly convey important information to the user.

[0553] Step 4:

[0554] The device captures the user's facial expressions and voice through its camera and microphone. It collects real-time captured facial images and audio data as input. The output is data representing the user's emotional state. Data processing involves facial recognition using OpenCV and emotion analysis using the Google Cloud Sentiment Analysis API.

[0555] Step 5:

[0556] The server adjusts the interface based on the user's emotional state. It references the emotional data obtained in step 4 as input and modifies the interface design elements. The output is the interface display adjusted according to the user's emotions. Specifically, it dynamically changes the interface's colors and message content to improve the user experience.

[0557] 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.

[0558] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0559] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0560] [Fourth Embodiment]

[0561] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0562] 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.

[0563] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0564] 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.

[0565] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0566] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0567] 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.

[0568] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0569] 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.

[0570] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0571] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0572] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0573] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0574] In an embodiment of the present invention, an asset management system is implemented in which a server located on the cloud plays a central role. The server connects to APIs of multiple financial institutions registered by the user and periodically collects asset data using authentication information. This enables the user to centrally manage their asset information.

[0575] The server efficiently integrates the collected asset data and stores it in a database. During this process, it adjusts for data duplication and format inconsistencies, maintaining consistent data. This allows users to instantly view the latest asset status.

[0576] The collected transaction data is analyzed in real time on the server using a machine learning algorithm. This algorithm learns from past transaction data and detects abnormal transactions that deviate from normal transaction patterns. When an abnormal transaction is detected, the server immediately sends a notification to the user and designated related parties. This notification includes details of the transaction in which the abnormality may have occurred, allowing the user to take prompt action.

[0577] On the user's device, collected asset data and details of unusual transactions can be viewed through an intuitive and easy-to-understand interface. The interface is designed to be user-friendly even for the elderly, and the font size and colors can be freely adjusted. In addition, users can enhance security by logging into the system using multi-factor authentication.

[0578] As a concrete example, suppose user X has accounts with banks A and B, and securities company C. The server integrates data from these financial institutions and displays X's asset status on a single screen. One day, if a large transaction exceeding the normal range occurs from X's account, the server detects this in real time and notifies X via email. X can then check the details of this unusual transaction on their smartphone, quickly contact the bank, and verify the transaction.

[0579] Thus, the present invention is provided in a form that combines various system configurations and algorithmic innovations to safely manage the assets of the elderly.

[0580] The following describes the processing flow.

[0581] Step 1:

[0582] The server connects to the API of the financial institution registered by the user. Using authentication credentials, it securely connects and begins the process of retrieving asset data for each account.

[0583] Step 2:

[0584] The server receives the acquired asset data and integrates it into the database. It checks for duplicates and inconsistencies, ensuring accuracy before storing the data.

[0585] Step 3:

[0586] The server performs analysis on transaction data in the database using machine learning algorithms. It compares patterns of normal and abnormal transactions and identifies transactions that appear abnormal.

[0587] Step 4:

[0588] The server compiles information about detected abnormal transactions and creates notifications for the user and designated stakeholders. Users are immediately notified via email or app push notifications.

[0589] Step 5:

[0590] The terminal retrieves the latest asset information from the server and displays it in an intuitive interface when accessed by the user. It provides an easy-to-understand overview of assets and detailed reports of unusual transactions.

[0591] Step 6:

[0592] The user logs into their device and checks the displayed asset information and information about unusual transactions. If necessary, they contact financial institutions to verify the accuracy of the transactions and take appropriate action.

[0593] (Example 1)

[0594] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0595] Efficiently aggregating financial data from diverse information providers and managing it in a consistent format is a complex and time-consuming task for the average user. Furthermore, systems capable of quickly identifying abnormal transactions and alerting users are not adequately realized with conventional technologies. Additionally, there is a need to provide a user-friendly interface accessible to a diverse range of users, including the elderly.

[0596] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0597] In this invention, the server includes means for acquiring financial data from multiple information providers, means for integrating the acquired financial data and storing it in a storage device, and means for analyzing transaction information using a machine learning model and identifying abnormal transactions. This makes it possible to efficiently integrate financial data and identify and notify abnormal transactions in real time.

[0598] An "information provider" is an organization or entity that provides financial data, such as a financial institution or a data provider.

[0599] "Financial data" refers to information related to the financial status of an individual or organization, including transaction records and account balances.

[0600] "Acquisition" refers to the act of a server collecting financial data from an information provider.

[0601] "Integration" is the process of combining data obtained from multiple sources into a consistent format.

[0602] A "storage device" is a physical or electronic device used to store data.

[0603] A "machine learning model" is a collection of algorithms used to analyze large amounts of data and find patterns and trends.

[0604] "Analysis" is the act of conducting a detailed examination of data to reveal its structure and trends.

[0605] An "unusual transaction" is a transaction that may deviate from normal trading patterns.

[0606] "To identify" means the act of identifying and recognizing an abnormal transaction.

[0607] In embodiments of the present invention, a cloud-based system is provided for integrated management of financial information and for detecting and notifying of abnormal transactions. The server is implemented on a cloud infrastructure and acquires financial data from numerous information providers. The API (Application Programming Interface) of each provider is used to acquire the data. The acquired data is integrated and stored in a database after correcting for duplication and inconsistencies. For this reason, the server typically uses a relational database management system (RDBMS).

[0608] The analysis of transaction data utilizes machine learning models running on the server. These models learn from past transaction data to identify transactions that deviate from established criteria. For example, if a large fund transfer occurs that a user wouldn't normally make, the server will recognize it as an anomaly and immediately notify the user. This notification feature includes alerts via email.

[0609] The terminal provides a user interface designed for smooth operation. Features such as large fonts and adjustable color themes ensure ease of use, even for the elderly. Users can use the terminal to view integrated financial data and details of unusual transactions in real time. Multi-factor authentication is employed for secure access, ensuring the system's security.

[0610] A concrete example is a simulation prompt that states, "When a large transaction occurs, send a real-time notification to the user." Such prompts can provide the generated AI model with a means to more effectively improve the system's operation.

[0611] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0612] Step 1:

[0613] The server accesses APIs from multiple data providers to retrieve financial data. The user's registered authentication information is used as input. The server sends HTTP requests to the API endpoints and receives financial data in JSON format as a response. The output of this step is the retrieved raw JSON data. Specifically, authentication is performed using API keys or OAuth tokens.

[0614] Step 2:

[0615] The server integrates the retrieved JSON data before storing it in the database. The input is the raw data obtained in step 1. The server removes duplicate data and corrects format inconsistencies as needed. This process includes data normalization and mapping. The output is consistent, integrated data, which is then stored in the database. Specifically, it performs INSERT or UPDATE operations on the database using SQL queries.

[0616] Step 3:

[0617] The server analyzes transaction data stored in the database using a machine learning model. The input data is a consistent transaction history. The server inputs this data into the model and analyzes transaction patterns. The output is the result of determining abnormal transactions. Specifically, it supplies data to an already trained model and performs feedforward calculations.

[0618] Step 4:

[0619] The server sends a real-time notification to the user if an abnormal transaction is detected. The input is the abnormal transaction detection result from step 3. The output of the notification is an email or application notification sent to the user. Specifically, it sends an email using the SMTP protocol, and the message includes detailed information about the abnormal transaction.

[0620] Step 5:

[0621] Users view detailed financial data and information about unusual transactions using a user interface displayed on their device. Input consists of financial data and notification content sent from the server. Output is a data display that users can visually confirm on the screen. Specifically, the user can set the font size and color in the interface and filter the data as needed.

[0622] (Application Example 1)

[0623] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0624] Traditionally, there has been a lack of effective means to manage asset information dispersed across multiple financial institutions and to detect fraudulent transactions in real time. In particular, for the elderly, the information provided across multiple platforms was cumbersome, making it difficult to immediately detect and respond to fraud. Furthermore, there were insufficient means to securely access this information and verify its details.

[0625] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0626] In this invention, the server includes a device for collecting financial data from multiple financial institutions, a device for integrating the collected financial data and storing it in a data repository, and a device for analyzing transaction information using machine learning technology and detecting fraudulent transactions. This enables users to centrally manage their asset information and respond quickly to fraudulent transactions.

[0627] A "financial institution" is an organization that provides financial services, such as banks, securities companies, and credit unions.

[0628] "Financial data" refers to information about assets, such as account balances, transaction history, and investment portfolios.

[0629] A "data repository" is a database or storage system used to centrally store and manage collected data.

[0630] "Machine learning technology" is a technique in which computers learn patterns from large amounts of data and perform predictions and classifications.

[0631] An "unfair transaction" is a transaction that is considered abnormal because it deviates from normal trading patterns.

[0632] An "integrated display device" is a device equipped with an interface for displaying asset information and details of fraudulent transactions to the user in an integrated manner.

[0633] Multi-factor authentication is an authentication method that enhances security by requiring users to present multiple pieces of evidence when accessing a system.

[0634] A "generative AI model" is a model that uses artificial intelligence technology to analyze data and generate specific outputs.

[0635] A "prompt sentence" is a sentence used as input to a generative AI model, created with the purpose of prompting specific instructions or outputs.

[0636] To implement this invention, a server located in the cloud plays a crucial role. The server connects to APIs of multiple financial institutions and periodically collects users' financial data. This data is then integrated into a pre-configured format and stored in a data repository. The server can use programming languages ​​and frameworks such as Python and Flask to handle data duplication and format inconsistencies as needed.

[0637] The collected financial data is analyzed using machine learning techniques. Generative AI models built with libraries such as TensorFlow and PyTorch learn past transaction patterns and detect fraudulent transactions. If this analysis detects a transaction that deviates from normal patterns, the server immediately sends a notification to the user. The notification is sent via email, a smartphone application, etc.

[0638] The user terminal utilizes an integrated display device that is easy for elderly users to use. Through this interface, users can view asset information and details of fraudulent transactions. Furthermore, multi-factor authentication provides a high level of security. This allows users to confidently understand their financial situation and take prompt action when necessary.

[0639] For example, if a user has accounts at multiple banks, the server aggregates data from these accounts and issues real-time alerts if there are any fraudulent high-value transactions. Furthermore, because this system automatically detects anomalies based on the user's usual purchasing behavior, it enables a swift and highly accurate response.

[0640] An example of a prompt message given to the generating AI model might be: "Use the user's financial transaction data to build an anomaly detection model that detects fraudulent transactions. Learn the characteristics of normal and abnormal transaction data to achieve highly sensitive anomaly detection."

[0641] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0642] Step 1:

[0643] The server connects to the APIs of multiple financial institutions registered by the user and periodically collects financial data. Inputs include account information and transaction history obtained from each financial institution's API. Outputs are raw financial data that is integrated on the server side.

[0644] Step 2:

[0645] The server stores the collected raw financial data in a data repository. During this process, data processing is performed to eliminate data duplication and format inconsistencies. The input is the collected raw financial data, and the output is the financial data converted and stored in a unified format.

[0646] Step 3:

[0647] The server analyzes financial data using a generative AI model. It utilizes machine learning techniques to detect anomalies from past transaction patterns. The input is financial data in a unified format stored in a data repository, and the output is a list of detected anomaly transactions.

[0648] Step 4:

[0649] The server sends notifications to users about detected abnormal transactions. Alerts are sent in real time via email or a smartphone app. The input is a list of abnormal transactions, and the output is the alert notification to the user.

[0650] Step 5:

[0651] The user terminal displays received notifications on its interface, allowing the user to view details. An integrated display device ensures that information is presented in a format easily accessible to the elderly. Input is alert information sent from the server, while output is user-viewable display information.

[0652] Step 6:

[0653] Users access the system through multi-factor authentication to view detailed financial information. Input is the user's authentication information, and output is the financial information and details of unusual transactions accessible to the user.

[0654] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0655] In embodiments of the present invention, an advanced asset management system equipped with an emotion engine is provided to enable more intelligent asset management for users. In addition to conventional asset data collection and integration and abnormal transaction detection, the server uses the emotion engine to analyze the user's emotional state.

[0656] When a user accesses the asset management platform, the server collects the user's voice and facial expressions from sensors and cameras on the device. An emotion engine then analyzes this data to identify the user's emotional state. Based on this information, the server adjusts the interface display to reduce user stress.

[0657] For example, if the emotion engine determines that a user is feeling anxious or stressed, the server can change the design of notifications and interfaces to a calmer tone. Conversely, if a user is experiencing positive emotions, the server can improve the user experience by providing a more proactive interface.

[0658] When a user reviews their asset information and receives a notification, especially regarding unusual transactions, the server can adjust the notification text based on the results of the emotion engine's analysis. For example, if the server determines that the user is experiencing stress, the notification will use simpler and more reassuring language.

[0659] As a concrete example, suppose user Y logs into the system to check their assets. The terminal's camera captures Y's facial expression, and the emotion engine recognizes that Y is experiencing some stress. In this case, the server changes the interface to a softer color scheme, and if an abnormal transaction notification occurs, it tailors the message to reassure Y.

[0660] Thus, by incorporating emotion recognition technology into asset management, the present invention enables flexible responses tailored to the user's psychological state, providing a safer and more comfortable asset management experience.

[0661] The following describes the processing flow.

[0662] Step 1:

[0663] The user logs into the asset management system via their device. Upon login, the device's camera and microphone are activated, recording the user's facial expressions and voice.

[0664] Step 2:

[0665] The device sends the user's recorded facial expression and voice data to the server. The server receives this data and begins analysis using its emotion engine.

[0666] Step 3:

[0667] The server's emotion engine identifies the user's emotional state based on the received data. Specifically, it determines the user's stress level and emotional tendencies from changes in facial expressions and tone of voice.

[0668] Step 4:

[0669] Based on the analysis results of the emotion engine, the server determines an appropriate interface design according to the user's state. The screen's color scheme and layout are changed to reduce stress.

[0670] Step 5:

[0671] The server sends the adjusted interface information to the terminal. The terminal receives this information and displays a visually adjusted asset information screen to the user.

[0672] Step 6:

[0673] The user reviews the displayed asset information and receives a notification if there are any unusual transactions. The server takes the results of the sentiment engine into consideration and automatically adjusts the notification text to best suit the user's emotional state.

[0674] Step 7:

[0675] Users can review notifications and, if necessary, contact financial institutions or share information with family members. Information provided through emotion recognition supports users' decision-making.

[0676] (Example 2)

[0677] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0678] Traditional asset management systems focus on data collection and anomaly detection, but lack consideration for the user's psychological state, resulting in a failure to alleviate user stress and anxiety. Therefore, there is a need to provide a more comfortable and secure asset management experience that takes users' emotional states into account.

[0679] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0680] In this invention, the server includes means for collecting asset data from multiple financial institutions, means for integrating the collected asset data and storing it in an information storage unit, and means for analyzing transaction information using an intelligent computation method and detecting abnormal transactions. This enables asset management that reduces the user's psychological burden and provides a sense of security by analyzing the user's emotional state and dynamically adjusting the interface display content based on the analysis results.

[0681] "Financial institutions" refer to organizations that provide financial services, such as banks and securities companies, and serve as sources for collecting asset data.

[0682] "Asset data" refers to information such as account balances and transaction history provided by financial institutions, and indicates the financial status of users.

[0683] "Information storage unit" refers to a database or storage system used to integrate and manage collected data.

[0684] An "intelligent computing method" refers to an algorithm that uses techniques such as machine learning and data mining to analyze data and detect specific patterns or anomalies.

[0685] "Transaction information" refers to records of data related to economic activities such as buying and selling and money transfers, and this is the subject of analysis.

[0686] An "abnormal transaction" refers to a transaction that deviates from normal trading patterns, and detecting these transactions reduces security risks.

[0687] An "information provision device" refers to a device or software that provides an interface for users to check and manipulate asset information.

[0688] The "emotion analysis unit" refers to a function that analyzes the user's voice and facial expressions to identify their emotional state, thereby optimizing the user experience.

[0689] "Dynamic adjustment" refers to a process that changes functions and display content in real time based on analysis results, responding immediately to user needs.

[0690] In an embodiment of the present invention, a system is constructed to centrally collect and manage asset data from multiple financial institutions and to provide an interface that allows users to manage their assets with greater peace of mind. The server collects asset data such as balance information and transaction history provided by each financial institution. This data is obtained via an API and integrated and stored in the information storage unit within the server. Subsequently, the collected data is analyzed using an intelligent computation method, specifically a machine learning algorithm, to determine whether or not there are any abnormal transactions.

[0691] The analyzed data is displayed on an information provision device accessed through the user's terminal. When the user accesses the information provision device, the terminal uses its camera and microphone to record the user's voice and facial expressions. This emotional data is transferred to a server and analyzed by the emotion analysis unit. The emotion analysis unit utilizes a generative AI model to analyze the user's psychological state in real time. For example, if the server determines that the user is experiencing stress, it immediately changes the interface of the information provision device to a calmer tone to reduce the user's mental burden.

[0692] As a concrete example, if the terminal's camera detects a smile while a user is checking asset information, the server can switch the interface to a brighter, more motivating design. In this way, the system dynamically adjusts the information provider in response to the user's emotions, enabling an improved asset management experience.

[0693] An example of a prompt is, "Please devise a specific method for adjusting the interface of an asset management system to respond immediately to changes in the user's emotions." By inputting this prompt into a generating AI model, it is possible to obtain ideas for interface design that enhance user satisfaction.

[0694] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0695] Step 1:

[0696] The server collects asset data from multiple financial institutions. The received data consists of specific asset information obtained via each financial institution's API. This includes account balances and transaction history. The server integrates this data and stores it in its information storage unit. The input is data from financial institutions' APIs, and the output is an integrated dataset. Through this process, the server builds a comprehensive asset database.

[0697] Step 2:

[0698] The server analyzes the collected asset data using intelligent computation methods. Specifically, it applies machine learning algorithms to detect abnormal transactions. The input is an integrated asset dataset, and the output is a list of detected abnormal transactions. The server identifies abnormalities by picking out deviations from normal transaction patterns and recording the reasons for those deviations.

[0699] Step 3:

[0700] When a user accesses the asset management platform, the device uses its built-in camera and microphone to record the user's facial expressions and voice. The input is the user's visual and auditory data, and the output is this emotional information in digital form. The device then prepares this emotional data for transmission to the server.

[0701] Step 4:

[0702] The server receives visual and audio data transmitted from the terminal and performs analysis in the emotion analysis unit. An intelligent computation method is used to identify the user's emotional state. The input is emotion data from the terminal, and the output is the analysis result indicating the user's emotional state.

[0703] Step 5:

[0704] Based on sentiment analysis, the server dynamically adjusts the interface. It changes the design and notification content in response to the user's emotional state. The input is the result of sentiment analysis, and the output is the adjusted interface settings. Specifically, if the user is feeling stressed, the server softens the notification sound and changes the display colors to calmer tones.

[0705] Step 6:

[0706] When an abnormal transaction is detected, the server sends a notification to the user. The notification text is customized based on the sentiment analysis results. The input is a list of abnormal transactions and the sentiment analysis results, and the output is the adjusted notification message. Users can respond without stress by receiving notifications expressed in concise and reassuring language.

[0707] (Application Example 2)

[0708] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0709] In modern times, asset management systems are essential tools for users to efficiently manage their assets. However, conventional asset management systems fail to consider the user's psychological state and do not adequately provide ways to alleviate user stress and anxiety. Therefore, it is considered necessary to adjust the interface to take the user's emotional state into account.

[0710] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0711] In this invention, the server includes means for collecting asset data from multiple financial institutions, means for integrating the collected asset data and storing it in a data storage unit, means for analyzing transaction information using a learning algorithm and detecting abnormal transactions, means for sending notifications to users or related parties regarding detected abnormal transactions, means for providing users with asset information and details of abnormal transactions through an integrated information display unit, and means for adjusting the display content of the interface using an emotion analysis device that analyzes the emotional state of the user. This enables flexible interface adjustment that takes into account the user's emotions, making it possible to provide a more comfortable and secure asset management experience.

[0712] A "financial institution" is an organization that is responsible for receiving, managing, and investing customers' assets.

[0713] "Asset data" refers to information about financial assets owned by an individual or corporation, including deposit balances, investment details, and transaction history.

[0714] A "data storage unit" is an area or system for organizing and storing collected information.

[0715] A "learning algorithm" is a method or process for analyzing large amounts of data and extracting patterns.

[0716] "Abnormal trading" refers to trading activities that deviate from normal trading patterns and should be closely monitored from a risk management perspective.

[0717] An "information display unit" refers to a screen or interface used to visually convey information to the user.

[0718] An "emotion analysis device" is a device or software that analyzes data such as a user's facial expressions and voice to infer their emotional state.

[0719] As an embodiment of the present invention, an advanced asset management system equipped with an emotion engine is provided. This system provides a safe and comfortable user experience by flexibly adjusting the interface according to the user's emotional state when managing the user's assets.

[0720] The server first collects asset data from multiple financial institutions and integrates it into a data storage unit. This data integration is crucial for centrally managing the user's overall asset status.

[0721] Furthermore, the server uses a learning algorithm to analyze transaction information and detect abnormal transactions. In this process, it analyzes a large amount of transaction data to extract patterns and detect deviations from normal patterns. At the same time, notifications are sent to the user or relevant parties regarding detected abnormal transactions.

[0722] To adjust the interface, an emotion analysis device is used to analyze the user's emotional state. The device collects the user's facial expressions and voice through the camera and microphone, and the emotion analysis device processes this data. Tools such as OpenCV and Google Cloud's emotion analysis API are used for the analysis. Based on the analysis results, the interface's color scheme and displayed content are adjusted. For example, if the user is feeling stressed, the interface's color scheme is changed to a calmer tone, and a message is displayed to provide reassurance.

[0723] As a concrete example, consider a scenario where, while a user is operating an asset management application, the system captures the user's facial expression and detects an emotion of "tension." In this case, the interface changes to a softer color scheme, and a message appears stating, "Don't worry, everything is being handled safely."

[0724] As an example of a prompt statement for a generative AI model,

[0725] "If we capture images showing users experiencing anxiety during electronic payments, what would be the appropriate way to modify the interface?"

[0726] This is one example. In this way, dynamic and personalized responses become possible to improve the user experience.

[0727] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0728] Step 1:

[0729] The server connects to financial institutions and collects user asset data. It receives asset data provided by financial institutions as input, formats and organizes it, and stores it in the data storage unit. The output is integrated asset data. At this point, the data is centralized, allowing for a complete overview of the user's assets.

[0730] Step 2:

[0731] The server analyzes transaction information in the data storage using a learning algorithm. It reads asset data as input and detects abnormal transactions using a machine learning algorithm. The output is a list of abnormal transactions. The data calculation involves learning normal transaction patterns from a large amount of transaction data and detecting deviations.

[0732] Step 3:

[0733] If the server detects an abnormal transaction, it sends a notification to the user or relevant parties. The system receives a list of abnormal transactions obtained in step 2 as input and generates an appropriate message using an AI model. The output is the generated notification message. This step also generates a prompt to clearly convey important information to the user.

[0734] Step 4:

[0735] The device captures the user's facial expressions and voice through its camera and microphone. It collects real-time captured facial images and audio data as input. The output is data representing the user's emotional state. Data processing involves facial recognition using OpenCV and emotion analysis using the Google Cloud Sentiment Analysis API.

[0736] Step 5:

[0737] The server adjusts the interface based on the user's emotional state. It references the emotional data obtained in step 4 as input and modifies the interface design elements. The output is the interface display adjusted according to the user's emotions. Specifically, it dynamically changes the interface's colors and message content to improve the user experience.

[0738] 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.

[0739] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0740] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0741] 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.

[0742] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0743] 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.

[0744] 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.

[0745] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0746] 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."

[0747] 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.

[0748] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0749] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0758] 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 as being incorporated by reference.

[0759] The following is further disclosed regarding the embodiments described above.

[0760] (Claim 1)

[0761] A means of collecting asset data from multiple financial institutions,

[0762] A means of integrating the collected asset data and storing it in a database,

[0763] A means of analyzing trading data using machine learning algorithms to detect abnormal transactions,

[0764] A means of sending a notification to the user or related party regarding the detected abnormal transaction,

[0765] A means of providing users with asset information and details of abnormal transactions through an integrated interface,

[0766] A system that includes this.

[0767] (Claim 2)

[0768] The system according to claim 1, further comprising means for recording the details of an abnormal transaction as a reason for deviating from a normal transaction pattern.

[0769] (Claim 3)

[0770] The system according to claim 1, further comprising means for a user to verify detailed asset information using the interface after undergoing an authentication procedure.

[0771] "Example 1"

[0772] (Claim 1)

[0773] Means of obtaining financial data from multiple information providers,

[0774] A means of integrating acquired financial data and storing it in a storage device,

[0775] A method for analyzing trading information using machine learning models to identify abnormal transactions,

[0776] A means of sending a notification to the user or related party regarding an identified unusual transaction,

[0777] A means of providing users with financial information and details of abnormal transactions through an integrated operation screen,

[0778] A system that includes this.

[0779] (Claim 2)

[0780] The system according to claim 1, comprising means for recording the reason that an abnormal transaction deviates from normal trading standards when identifying such transactions.

[0781] (Claim 3)

[0782] The system according to claim 1, further comprising means for a user to use authentication means to check detailed financial information through the operation screen.

[0783] "Application Example 1"

[0784] (Claim 1)

[0785] A device that collects financial data from multiple financial institutions,

[0786] A device that integrates collected financial data and stores it in a data repository,

[0787] A device that uses machine learning technology to analyze transaction information and detect fraudulent transactions,

[0788] A device that sends a notification to the user or related party regarding detected fraudulent transactions,

[0789] A device that provides users with financial information and details of fraudulent transactions through an integrated display device suitable for the elderly,

[0790] A device that allows users to verify asset details using a display device through multi-factor authentication,

[0791] A safety management system that includes this.

[0792] (Claim 2)

[0793] The security management system according to claim 1, comprising a device for recording deviations from normal transaction patterns when detecting fraudulent transactions.

[0794] (Claim 3)

[0795] The security management system according to claim 1, comprising a device that generates prompt sentences based on the analysis of transaction information using a generative AI model.

[0796] "Example 2 of combining an emotion engine"

[0797] (Claim 1)

[0798] A means of collecting asset data from multiple financial institutions,

[0799] A means for integrating the collected asset data and storing it in an information storage unit,

[0800] A means for analyzing transaction information using an intelligent computation method and detecting abnormal transactions,

[0801] A means of sending a notification to the user or related party regarding the detected abnormal transaction,

[0802] A means of providing users with asset information and details of abnormal transactions through an integrated information provision device,

[0803] A means equipped with an emotion analysis unit that analyzes the emotional state of the user,

[0804] A means for dynamically adjusting the display content of the information provider based on the analyzed emotional state,

[0805] A system that includes this.

[0806] (Claim 2)

[0807] The system according to claim 1, further comprising means for detecting abnormal transactions and recording the reasons for the deviation from normal transaction patterns, and means for adjusting the wording of notifications according to the emotional state of the user.

[0808] (Claim 3)

[0809] The system according to claim 1, wherein a user can confirm detailed information about an asset using the information providing device after going through an authentication process, and further comprises means for optimizing the displayed content based on the user's emotional state at that time.

[0810] "Application example 2 of combining emotional engines"

[0811] (Claim 1)

[0812] A means of collecting asset data from multiple financial institutions,

[0813] A means for integrating the collected asset data and storing it in a data storage unit,

[0814] A means for analyzing transaction information using a learning algorithm and detecting abnormal transactions,

[0815] A means of sending a notification to the user or related party regarding the detected abnormal transaction,

[0816] A means of providing users with asset information and details of abnormal transactions through an integrated information display unit,

[0817] A means for adjusting the display content of the interface using an emotion analysis device that analyzes the emotional state of the user,

[0818] A system that includes this.

[0819] (Claim 2)

[0820] The system according to claim 1, further comprising means for recording the details of an abnormal transaction as a reason for deviating from a normal transaction pattern.

[0821] (Claim 3)

[0822] The system according to claim 1, further comprising means for a user to confirm detailed asset information using the information display unit after going through an authentication procedure. [Explanation of Symbols]

[0823] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting asset data from multiple financial institutions, A means of integrating the collected asset data and storing it in a database, A means of analyzing trading data using machine learning algorithms to detect abnormal transactions, A means of sending a notification to the user or related party regarding the detected abnormal transaction, A means of providing users with asset information and details of abnormal transactions through an integrated interface, A system that includes this.

2. The system according to claim 1, further comprising means for recording the details of an abnormal transaction as a reason for deviating from a normal transaction pattern.

3. The system according to claim 1, further comprising means for a user to verify detailed asset information using the interface after going through an authentication procedure.

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