Capital One Services, LLC

Other

Austin Grant Walters

Jeremy Edward Goodsitt

Vincent Pham

Anh Truong

Fardin Abdi Taghi Abad

Reza Farivar

Mark Watson

Galen Rafferty

Kenneth Taylor

Kate Key

Grant Eden

Samuel Sharpe

Mark Louis Watson

Ernest Kwak

Brian Chandler Barr

Alvin Hua

Christopher Wallace

Joseph Ford, Iii

Rittika Adhikari

Kenny Bean

Noriaki Tatsumi

Ahn Truong

Paul Hurlocker

Michael Walters

Robert P Seaton

Anh Troung

Daniele Rosa

Fardin Abdi Tagh Abad

Fardi Abdi Taghi Abad

Fardin Abdi Taghi

Fardin Abdi Abad

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

Methods, systems, and computing devices for visualizing natural language processing algorithm processes are described herein. A plurality of categories may be determined. Each color of a plurality of colors may correspond to the categories. Text content may be processed using a natural language processing algorithm. Confidence values indicating, for each of a plurality of portions of the text content, a degree of confidence corresponding to one or more of the plurality of categories may be determined. Display colors may be determined based on the confidence values. A user interface comprising a visualization of the text content may be displayed, and the user interface may be configured to show each portion of the text content using a display color such that the user interface indicates changes in confidence across the plurality of characters.

Representing confidence in natural language processing

An exemplary system, method, and computer-accessible medium can include, for example, establishing a unique rule-identifier in one-to-one correspondence with at least one set of unknown time-variable rules against which data is to be made compliant, obtaining at least one set of data marked compliant against the one or more set of rules, obtaining meta-data from the compliant data, obtaining at least one set of data marked non-compliant against the set of unknown time-variable rules, extracting meta-data from the non-compliant data, joining the set of compliant and non-compliant metadata to generate a set of estimated rules corresponding to the rule-identifier based at least one of (i) the meta-data of the joined set and (ii) machine learning algorithms.

System, method, and computer-accessible medium to verify data compliance by iterative learning

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

A computer-implemented method includes tracking, by a computing device, user browsing activity of a first page having known elements; mapping, by the computing device, the user browsing activity to the known elements; storing, by the computing device, mapping information that maps the user browsing activity to the known elements; tracking, by the computing device, user browsing activity of a second page having unknown elements; identifying, by the computing device, the unknown elements based on the mapping information and the user browsing activity of the second page; and executing, by the computing device, one or more computer-based instructions based on the determining the unknown elements that were identified.

Identifying web elements based on user browsing activity and machine learning

A cloud computing system can be configured to generate a synthetic data stream that tracks a reference data stream. A model optimizer of the cloud computing system can receive, from an interface of the cloud computing system, a synthetic data stream request indicating a reference data stream. A dataset generator of the cloud computing system can generate a synthetic data stream that tracks the reference data stream by repeatedly swapping data models of the reference data stream. One such repeat can include retrieving, by the dataset generator from a model storage, a current data model of the reference data stream and generating a new data model of the reference data stream. The model optimizer can store the new data model in the model storage. The dataset generator can generate a synthetic data stream using the current data model of the reference data stream.

Systems and methods for data stream simulation

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

Techniques for providing a secure mesh network based on a cryptocurrency blockchain are provided. An electronic device can receive data from a mesh network to establish the device as a node on the mesh network. The established node can generate a cryptocurrency wallet that can be loaded with an amount of cryptocurrency. The established node can receive a payment for facilitating transfers on the mesh network including routing transactional or communication data. Public and/or private keys associated with the cryptocurrency wallets can be used to encrypt communications, thereby providing a secure mesh network. Further, the blockchain, which can be managed by one or more authorized nodes of the mesh network, can provide a payment system within the mesh network. The distributed nature of the mesh network allows it to operate securely without a central authority. In turn, the mesh network is more adaptable and flexible.

System and method for creating a secure mesh network utilizing the blockchain

Exemplary embodiments may provide an approach to converting multidimensional color data for an image encoded in a first color space into an intermediate form that is a single dimensional value. The exemplary embodiments may then decode the intermediate form value to produce an encoding of the color data that is encoded in a second color space that differs from the first color space. In this manner, the data for the image may be efficiently converted from an encoding in the first color space into an encoding in the second color space.

Color conversion between color spaces using reduced dimension embeddings

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

An apparatus includes a display, a memory, a light sensor array and a light source array. The light source array emits light to display an image on the display. A controller is configured to receive a sensor output from each light sensor in the light sensor array. An ambient-light illuminance difference between a first illuminance of a first ambient light externally directed onto a first region of the displayed image of the display and a second illuminance of a second ambient light externally directed respectively onto a second region of the displayed image of the display is computed. Light source controls of light sources of the light source array are varied to change a luminous emittance of the light source array within the at least one second region of the displayed image so as to reduce a luminance difference between the first region and the second region of the displayed image.

Systems and methods for correcting ambient-light illuminance differences of ambient light directed onto regions of a display

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

Systems and methods for authorizing a transfer

A system for returning synthetic database query results. The system may include a memory unit for storing instructions, and a processor configured to execute the instructions to perform operations comprising: receiving a query input by a user at a user interface; determining, based on natural language processing, a type of the query input; determining, based on the received query input and a database language interpreter, an output data format; returning, based on a generation model and the output data format, a result of the query input; providing, to a plurality of training models and based on the determined query type, the query input and the result; and training the training models, based on the query input and the result.

Systems and methods for synthetic database query generation

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.

Growing community of inventors

Savoy, IL, United States of America

Austin Grant Walters