Abstract: A banking processing method according is performed by a processing logic including an application for banking processing implemented on a user terminal and a computer-readable storage medium. The method comprises the steps of: when the application for banking processing is run, searching a hardware security area of the user terminal and confirming the existence of a certificate for confirming an execution history of the application for banking processing; when the existence of the certificate is confirmed, searching the security area and confirming the existence of a token key for identifying whether login information of the user has been set; when the existence of the token key is not confirmed, setting the login information of the user by providing a membership page for setting the login information of the user; and opening an account according to a request of the user whose login information has been set.

Abstract: The present invention discloses a transfer information determination method and device through deep learning, by which a determination is made on whether transfer information is included in a message, and the transfer information is classified. A method for determining transfer information within a message through natural language processing based on deep learning according to the present invention comprises the steps of: pre-processing an acquired message in a user terminal according to a reference word; extracting an embedding vector corresponding to each segmented text from the preprocessed message to determine whether transfer information is included, through weighted calculation of the extracted embedding vector; and classifying the transfer information within the pre-processed message determined to include the transfer information.

Abstract: According to one aspect of the present invention, a group account service providing server capable of converting a private account into a group account in real time through a terminal of the host of a comprises: an account management unit configured to convert a first account into a group account when a group account conversion request for the first account of a host of a group is generated; a transaction ledger management unit configured to record change information in a transaction ledger for the group account when the change information including at least one of a transaction occurrence event and an information occurrence event is generated for the group account; a virtual ledger management unit configured to generate a virtual ledger to be mapped to the transaction ledger when the first account is changed to the group account.

Abstract: According to one aspect of the present invention, a group account service providing server capable of converting a private account into a group account in real time through a terminal of the host of a comprises: an account management unit configured to convert a first account into a group account when a group account conversion request for the first account of a host of a group is generated; a transaction ledger management unit configured to record change information in a transaction ledger for the group account when the change information including at least one of a transaction occurrence event and an information occurrence event is generated for the group account; a virtual ledger management unit configured to generate a virtual ledger to be mapped to the transaction ledger when the first account is changed to the group account.

Abstract: The present invention discloses a transfer information determination method and device through deep learning, by which a determination is made on whether transfer information is included in a message, and the transfer information is classified. A method for determining transfer information within a message through natural language processing based on deep learning according to the present invention comprises the steps of: pre-processing an acquired message in a user terminal according to a reference word; extracting an embedding vector corresponding to each segmented text from the preprocessed message to determine whether transfer information is included, through weighted calculation of the extracted embedding vector; and classifying the transfer information within the pre-processed message determined to include the transfer information.

Abstract: A hologram detection method according to an aspect of the disclosure, includes: inputting a first image, obtained by capturing a detection object on the basis of first flash intensity, to a neural network model to obtain a first detection result value representing the detection or not of a hologram for each of predetermined at least one detection unit regions; and comparing a threshold value with the first detection result value obtained for each detection unit region to determine the detection or not of a hologram in the first image and a first detection unit region where a hologram is detected.

Abstract: Face detection methods using voice are provided. The face detection method comprises the: receiving video data and voice data from a user terminal; deriving a first interval related to a predetermined message based on the received voice data; configuring a second interval based on the derived first interval; extracting a part of the video data corresponding to the second interval; deriving at least one video frame which satisfies a predetermined criterion from the extracted video data; and detecting a facial image included in each of the at least one derived video frame.

Abstract: A distributed learning method of a server managing an ID card recognition model includes releasing an ID card recognition model performing at least one convolution operation on an ID card image captured in a user terminal so that the user terminal uses the ID card recognition model, receiving update information of the ID card recognition model generated according to an ID card recognition result of the released ID card recognition model, and verifying the update information received from the user terminal and updating the ID card recognition model using the verified update information.

Abstract: A method and system for providing secure data communication between two or more user terminals in a communication network is proposed. The method may include receiving, from a first user terminal, a first instruction set configured to request that a private account be converted into a group account, the private account owned by a user of the first user terminal. The method may also include generating the group account jointly owned by at least the user of the first user terminal and a user of a second user terminal. The method may also include controlling a first database to store a second information set relating to the group account, and generating a second virtual database different from the first database. The method may also include copying the second information set stored in the first database and recording the copied second information set in the second virtual database.

Abstract: A user terminal including a graphical user interface (GUI) for providing secure data communication with a server in a communication network is proposed. The user terminal may include a GUI configured to receive one or more user inputs and display data received from the server. The GUI may include a first screen configured to display a first instruction set for converting a private account to a group account and a second screen configured to display a group account information set received from the server. The user terminal may cause the server to generate the group account, control a first database of the server to store a second information set relating to the group account, generate a second virtual database of the server different from the first database, and copy the second information set stored in the first database and record the copied second information set in the second virtual database.

Abstract: The disclosure relates to a method for providing a scraping-based service. The method for providing a scraping-based service includes: requesting and receiving at least one of scraping rule information defining a series of tasks for obtaining external data; collecting the external data by executing tasks according to the received rule information; and providing service information to a user using the collected external data. According to the disclosure, by providing a scraping service through a client terminal and a remote server in a distributed manner, it is possible to increase efficiency of a scraping task and actively cope with changes in a scraping target.

Abstract: A distributed learning method of a server managing an ID card recognition model includes releasing an ID card recognition model performing at least one convolution operation on an ID card image captured in a user terminal so that the user terminal uses the ID card recognition model, receiving update information of the ID card recognition model generated according to an ID card recognition result of the released ID card recognition model, and verifying the update information received from the user terminal and updating the ID card recognition model using the verified update information.

Abstract: This application relates to a method for adjusting a user interface displayed on a user device. In one aspect, the method for providing a service of hiding account information, performed in a user device linked with a financial institution server, including displaying a plurality of accounts included in account information received from the financial institution server on a home screen of the user device, receiving a screen edit request of a user through selection of an icon displayed on the home screen, and displaying the plurality of accounts in a first area of an edit screen, moving locations of some of the plurality of accounts from the first area to a second area different from the first area based on a user input, and displaying on the home screen only accounts included in the first area of the edit screen.

Abstract: A hologram detection method according to an aspect of the disclosure, includes: inputting a first image, obtained by capturing a detection object on the basis of first flash intensity, to a neural network model to obtain a first detection result value representing the detection or not of a hologram for each of predetermined at least one detection unit regions; and comparing a threshold value with the first detection result value obtained for each detection unit region to determine the detection or not of a hologram in the first image and a first detection unit region where a hologram is detected.

Abstract: The present invention relates to a neural network training method. The neural network training method using a de-identified image according to the present invention comprises the steps of: encoding a first image represented by a n-th dimensional vector into a predetermined p-th dimensional second image; decoding the second image into a q-th dimensional third image; inputting the third image to a neural network and extracting object information included in the third image; and training at least one parameter information used for computation in the neural network by using the extracted object information. According to the present invention, de-identified images are used for neural network training such that neural network training is made possible without using personal information included in images.

Abstract: The disclosure relates to a method for providing a scraping-based service. The method for providing a scraping-based service includes: requesting and receiving at least one of scraping rule information defining a series of tasks for obtaining external data; collecting the external data by executing tasks according to the received rule information; and providing service information to a user using the collected external data. According to the disclosure, by providing a scraping service through a client terminal and a remote server in a distributed manner, it is possible to increase efficiency of a scraping task and actively cope with changes in a scraping target.

Abstract: An identification card authenticity determining method based on deep learning according to the disclosure for automatically checking authenticity of an identification card includes: inputting identification card data to a feature information extraction model to extract pieces of feature information, expressing an indicator for checking authenticity of the identification card, from the identification card data; inputting the extracted pieces of feature information to a classification model to determine authenticity of the identification card; and when it is determined that the identification card is falsified, extracting a class activation map, where a falsification region of the identification card data is activated, from the pieces of feature information.

Abstract: A method for recognizing an identification (ID) card of a user terminal using deep learning includes extracting an outline region of the ID card included in an input image using a first neural network model, modifying the ID card image of the image to a reference form using at least a partial value of the extracted outline region, and determining whether the modified ID card image is valid and recognizing text information in the valid ID card image. A recognition rate of the ID card may be increased by modifying an ID card image into a reference form using a trained neural network model.

Abstract: An identification card authenticity determining method based on deep learning according to the disclosure for automatically checking authenticity of an identification card includes: inputting identification card data to a feature information extraction model to extract pieces of feature information, expressing an indicator for checking authenticity of the identification card, from the identification card data; inputting the extracted pieces of feature information to a classification model to determine authenticity of the identification card; and when it is determined that the identification card is falsified, extracting a class activation map, where a falsification region of the identification card data is activated, from the pieces of feature information.

Abstract: A method for recognizing an identification (ID) card of a user terminal using deep learning includes extracting an outline region of the ID card included in an input image using a first neural network model, modifying the ID card image of the image to a reference form using at least a partial value of the extracted outline region, and determining whether the modified ID card image is valid and recognizing text information in the valid ID card image. A recognition rate of the ID card may be increased by modifying an ID card image into a reference form using a trained neural network model.