Capital One Services, LLC

Reza Farivar

Austin Grant Walters

Jeremy Edward Goodsitt

Vincent Pham

Anh Truong

Fardin Abdi Taghi Abad

Mark Watson

Kenneth Taylor

Galen Rafferty

Kate Key

Mark Louis Watson

Joseph Ford, Iii

Rittika Adhikari

Ahn Truong

Ernest Kwak

Noriaki Tatsumi

Michael Kubiske

Michael Walters

Cody Stancil

Fardin Abdi Tagh Abad

Fardi Abdi Taghi Abad

Anh Troung

Various embodiments are directed to a system that utilizes regular expression (regex) to recognize at least portions of characters, words, text, numbers, etc. in a structured or unstructured dataset, any patterns associated therewith, and/or similarities between the determined patterns. In examples, a regex-based pattern recognition platform may receive a dataset and determine whether at least a first regex pattern and a second regex pattern can be identified. The occurrences of the first and second regex patterns and the frequency of those occurrences may reveal something about the dataset itself or any patterns contained therein.

Utilizing regular expression embeddings for named entity recognition systems

Disclosed herein are system, method, and computer program product embodiments for process corruption prevention. An embodiment operates by determining security vulnerabilities for an entity and correlation values for the security vulnerabilities by applying completed processed records of the entity to a machine learning model. Each of the correlation values quantifies a relationship strength between a security vulnerability and fraudulent activity. The embodiment further operates by generating a security vulnerability score for the entity using the correlation values and identifying one or more patches for at least one of the security vulnerabilities. The one or more patches may be ranked and the ranking may be revised using a feedback mechanism after the one or more patches are implemented by the entity.

Patching security vulnerabilities using machine learning

Techniques for providing a localized blockchain that uses a mesh network to facilitate transactions for a localized event are provided. An electronic device can receive data from a localized mesh network to establish the device as a node on the localized mesh network. The established node can generate a cryptocurrency wallet that can be loaded with an initial amount of localized cryptocurrency tokens from a total amount of localized cryptocurrency tokens. The total amount of localized cryptocurrency tokens can be previously mined by a central authority of the localized mesh network. The established node can conduct transactions within the localized mesh network utilizing the localized cryptocurrency tokens. The established node can receive a portion of a cryptocurrency token for transmitting transactional data to a next nearest node on the localized mesh network to help facilitate a transaction involving other nodes on the network.

Localized blockchain utilizing mesh networks for localized events

In the disclosed transaction processing system, members of an authorized network of consumers and merchants manage account information using blockchain ledgers. Because both consumers and merchants maintain copies of the blockchain, for any consumer/merchant transaction, both entities can quickly validate the transaction because both are aware, via their blockchain entries, of the current status of the account sourcing the transaction, allowing fast and accurate transaction validation without the need to incur the processing charges inherent in traditional fiat currency credit transactions.

System and method for authorizing transactions in an authorized member network

Systems and methods for censoring text-based data are provided. In some embodiments a censoring system may include at least one processor and at least one non-transitory memory storing application programming interface instructions. The censoring system may be configured to perform operations comprising storing a target pattern type and a computer-based model for identifying a target data pattern corresponding to a target pattern type within text based data. The censoring system may also be configured to receive text-based data by a server, and to retrieve the stored target pattern type to be censored in the text-based data. The censoring system may be configured to identify within the received text-based data, a target data pattern corresponding to the retrieved target pattern type. The censoring system may be configured to censor target characters within the identified target data pattern, and transmit the censored text-based data to a receiving party.

Systems and methods for censoring text inline

Systems and methods for generating synthetic data are disclosed. For example, a system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving a dataset including time-series data. The operations may include generating a plurality of data segments based on the dataset, determining respective segment parameters of the data segments, and determining respective distribution measures of the data segments. The operations may include training a parameter model to generate synthetic segment parameters. Training the parameter model may be based on the segment parameters. The operations may include training a distribution model to generate synthetic data segments. Training the distribution model may be based on the distribution measures and the segment parameters. The operations may include generating a synthetic dataset using the parameter model and the distribution model and storing the synthetic dataset.

Systems and methods for synthetic data generation for time-series data using data segments

Systems and methods for providing encryption and decryption involving texture warping, comprising: obtaining a visual input; obtaining a private key; generating an encrypted visual representation (visual representation A) based on the private key and the visual input; determining at least one 3D object configured so that the private key is derivable when the visual representation A is mapped to a digital model of the at least one 3D object; transmitting the visual representation A to a second computing device associated with a second user; transmitting a representation of the digital model of the at least one 3D model to the second computing device; and instructing the second computing device so that the second computing device is configured to map the visual representation A to the digital model generated based on the representation of the digital model of the at least one 3D model to extract the private key.

Computer-based systems configured for texture warping-based encryption and methods of use thereof

Aspects described herein may allow for deploying third-party callback to be performed on a mobile device. For example, the mobile device may configure a third-party callback to be performed after a triggering event is detected by a remote device. The triggering event may relate to a remote device associated with the mobile device. A determination may be made as to whether the mobile device is within a pre-determined proximity of the remote device. If the two devices are in proximity and if the mobile device receives a message that indicates the triggering event has been detected, the mobile device may perform the third-party callback. In this way, a user's need to request services conveniently and accurately is facilitated.

Systems and methods for integrated third-party callbacks

Systems as described herein may label data to preserve privacy. An annotation server may receive a document comprising a collection of text representing a plurality of confidential data from a first computing device. The annotation server may convert the document to a plurality of text embeddings. The annotation server may input the text embeddings into a machine learning model to generate a plurality of synthetic images, and receive a label for each of the plurality of synthetic images from a third-party labeler. Accordingly, the annotation server may send the confidential data and the corresponding labels to a second computing device.

Privacy preserving data labeling

In some implementations, a training platform may receive data for generating synthetic models of a body part, such as a hand. The data may include information relating to a plurality of potential poses of the hand. The training platform may generate a set of synthetic models of the hand based on the information, where each synthetic model, in the set of synthetic models, representing a respective pose of the plurality of potential poses. The training platform may derive an additional set of synthetic models based on the set of synthetic models by performing one or more processing operations with respect to at least one synthetic model in the set of synthetic models, and causing the set of synthetic models and the additional set of synthetic models to be provided to a deep learning network to train the deep learning network to perform image segmentation, object recognition, or motion recognition.

Generating synthetic models or virtual objects for training a deep learning network

Systems and methods for managing indexing and tagging datasets using a plurality of nodes are disclosed. For example, the system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving a dataset comprising a plurality of columns and applying a series of nodes to the dataset. Applying the series of nodes may compose applying a first node comprising a machine learning model to generate a first probability, appending a first tag based on the first probability, and selecting second nodes subsequent in the series based on the first probability. Applying the series may include iteratively applying the selected second nodes to generate second probabilities and second tags. The operations may include generating a data structure comprising the first and second probabilities and first and second tags. The operations may include outputting metadata.

Systems and methods for tagging datasets using models arranged in a series of nodes

A system for determining candidate answers during an interview. The system may include a memory storing executable instructions, and at least one processor configured to execute the instructions to perform operations. The operations may include capturing, by an image sensor, a plurality of images comprising a plurality of candidate answers; detecting, based on the captured images, a first bounding box comprising the plurality of the candidate answers; detecting, based on the captured images, a second bounding box comprising a plurality of alphanumeric characters, a first one of the alphanumeric characters being located on a line of software code; calculating a distance from a side of a first bounding box to a side of a second bounding box; and determining, based on the calculated distance, a source code alignment.

Systems and methods for facilitating candidate interviews

Systems and methods for censoring text characters in text-based data are provided. In some embodiments, an artificial intelligence system may be configured to receive text-based data and store the text-based data in a database. The artificial intelligence system may be configured to receive a list of target pattern types identifying sensitive data and receive censorship rules for the target pattern types determining target pattern types requiring censorship. The artificial intelligence system may be configured to assemble a computer-based model related to a received target pattern type in the list of target pattern types. The artificial intelligence system may be configured to use a computer-based model to identify a target data pattern corresponding to the received target pattern type within the text-based data, identify target characters within the target data pattern, and to assign an identification token to the target characters.

Systems and methods for removing identifiable information

Exemplary embodiments may use word embeddings to enhance scanning of programming code scripts for sensitive subject matter, such as confidential subject matter. The scanning may be performed by a neural network in some exemplary embodiments. The neural network initially may be trained on a corpus of programming code scripts to identify keywords relating to sensitive subject matter, such as passwords, tokens or credentials. The neural network may not only identify instances of the keywords but also may identify related terms as well. The output of the scan may be a ranked list of terms in the programming code script that may relate to sensitive subject matter.

Use of word embeddings to locate sensitive text in computer programming scripts

Various embodiments are generally directed to techniques utilizing computers to process service billing data, generate one or more groups of customers based on one or more attributes of the data, and train a recurrent neural network (RNN) to predict future bill amounts for customers. Embodiments further include techniques to predict the future bill amounts of the customers and providing an indication of the bill amounts to the customers.

Growing community of inventors

Champaign, IL, United States of America

Reza Farivar