Capital One Services, LLC

Galen Rafferty

Austin Grant Walters

Jeremy Edward Goodsitt

Anh Truong

Vincent Pham

Reza Farivar

Mark Watson

Samuel Sharpe

Grant Eden

Kate Key

Brian Chandler Barr

Ernest Kwak

Alvin Hua

Christopher Wallace

Michael Davis

Taylor Turner

Mark Louis Watson

Kenny Bean

Justin Au-yeung

Owen Reinert

Tyler Farnan

Daniele Rosa

In some implementations, a verification device may receive a request for an electronic exchange of information. The verification device may receive geographic location information associated with the request for the electronic exchange of information. The verification device may determine, using a machine learning model and based on the geographic location information, a validity of the request for the electronic exchange of information. The verification device may determine whether to execute the electronic exchange of information based on the determination of the validity of the request for the electronic exchange of information. The verification device may perform one of executing the electronic exchange of information or rejecting the request for the electronic exchange of information based on the determination of whether to execute the electronic exchange of information.

Dynamic verification of an electronic exchange of information

Methods and systems described herein relate to the creation of a digital repository of artificial intelligence models that allows users to determine their individual fairness metric. More specifically, the methods and systems provide this digital repository by storing it on a blockchain network and tracking any changes made to the model and/or its fairness metric.

Systems and methods for a digital register of models monitoring changes in accuracy of artificial intelligence models

Systems and methods are disclosed herein for generating updated descriptions of items based on analyzing candidate embeddings of semantic representations of item descriptions. The system may obtain a text file describing an item. The system may provide the text file to a generative language model to generate semantic representations of the text file. The system may generate, based on the text file, candidate embeddings in an embedding space. The system may obtain embeddings associated with existing items. The system may determine subsets of the embeddings within a threshold distance. The system may compare the subsets. The system may determine attributes associated with a candidate embedding based on the comparison. The system may generate an updated text file based on the attributes.

Systems and methods for updating textual item descriptions using an embedding space

Disclosed embodiments may include systems and methods for utilizing gesture-based transaction cards. Examples include: first, using a mobile device to preauthorize transaction cards to enter an unlocked state before making a purchase by making a gesture with the transaction card toward the mobile device. Second, using a first transaction card to change an authorization condition of a second transaction card associated with the same account by making a gesture from the first transaction card to the second transaction card. Third, using a first transaction card to distribute a predebited virtual card to the user device of a second user by making a gesture from the first transaction card to the user device of the second user. Additionally, the primary user may be able to upload new authorization conditions to the transaction card and control the system through a related mobile application on a connected mobile device.

Systems and methods for sharing a virtual card

In some implementations, a device may obtain data indicating reparations issued by an entity that uses artificial intelligence to provide artificial intelligence outputs in connection with users, the reparations being issued for one or more of the artificial intelligence outputs being erroneous. The device may determine, using a machine learning model, an artificial intelligence reparation characterization for the entity. The artificial intelligence reparation characterization determined using the machine learning model may be indicative of an amount of reparations predicted for the entity in connection with uses of artificial intelligence by the entity. The machine learning model may be trained to determine the artificial intelligence reparation characterization based on the data. The device may transmit information indicating the artificial intelligence reparation characterization.

Characterization for erroneous artificial intelligence outputs

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

Systems and methods for authorizing a transfer

A device may receive a preauthorization associated with missed transaction prevention for a transaction account of a user, wherein the missed transaction prevention involves preventing a missed transaction associated with merchant accounts of the user. The device may monitor a transaction log of the transaction account and identify a transaction pattern associated with a merchant account based on a plurality of historical transactions identified in the transaction log related to the merchant account. The device may determine that a historical transaction of the plurality of historical transactions is not designated for automatic execution and that an execution of an upcoming transaction corresponding to the plurality of historical transactions is not scheduled. The device may cause an account transaction associated with the upcoming transaction to be automatically executed before a transaction period expiration, associated with the merchant account, passes.

Automatic transaction execution based on transaction log analysis

In some aspects, a computing system obtain a first dataset including a set of original data samples. The computing system may generate a key that indicates a location within the first dataset where spurious data should be stored. The computing system may determine a modified value associated with a first data sample of the set of original data samples, where the modified value causes a machine learning model to generate output that does not match a label associated with the first data sample. Based on the first data sample, the computer system may generate a spurious data sample comprising the modified value. Based on the key, the computer system may add the spurious data sample to the first dataset. In some aspects, based on a request for the first dataset, the computing system may remove the spurious data sample from the first dataset.

Generation of effective spurious data for model degradation

In some aspects, a computing system may iterate between adding spurious data to the dataset and training a model on the dataset. If the model's performance has not dropped by more than a threshold amount, then additional spurious data may be added to the dataset until the desired amount of performance decrease has been achieved. the computing system may determine the amount of impact each feature has on a model's output. The computing system may generate a spurious data sample by modifying values of features that are more impactful than other features. The computing system may repeatedly modify the spurious data that is stored in a dataset. If a cybersecurity incident occurs (e.g., the dataset is stolen or leaked), the system may identify when the cybersecurity incident took place based on the spurious data that is stored in the dataset.

Layered cybersecurity using spurious data samples

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

Disclosed embodiments may include a system for executing programmable macros based on external device interactions. The system may include one or more processors, and a memory in communication with the one or more processors and storing instructions that, when executed by the one or more processors, are configured to cause the system to execute programmable macros based on external device interactions. The system may receive a device identification of an external device. The system may receive a rule associated with the external device, wherein the rule comprises a triggering event and transfer instructions. The system may receive, from the external device, device data. Responsive to determining, from the device data, that the triggering event has occurred, the system may execute the transfer instructions.

Systems and methods for executing programmable macros based on external device interactions

Systems and methods of procuring real data items based on user affinity gauged via synthetic data items are disclosed. In one embodiment, an exemplary computer-implemented method may comprise: utilizing a trained machine learning model to generate a synthetic data item based on real user data; presenting the synthetic data item to those users; obtaining indications identifying user responses to the synthetic data item; obtaining user-defined control parameters from the users; configuring a user-defined control mechanism to share a portion of the real user data based thereon; obtaining a subset of the real user data based the user-defined control parameters; providing to a particular third-party data source at least one of: data regarding the synthetic data item, the at least one portion of the real user data, and the indications of the users; and then receiving a second real data item from the particular third-party data source.

Computer-based systems configured for procuring real content items based on user affinity gauged via synthetic content items and methods of use thereof

Methods and systems described herein relate to authenticating users-based on generating images that have modified features. More specifically, the methods and systems generate these images by processing existing training data to identify a common feature in existing photos that may be modified, and then modifying that feature with the use of generative adversarial networks.

Growing community of inventors

Mahomet, IL, United States of America

Galen Rafferty