Capital One Services, LLC

General Electric Company

Brian Chandler Barr

Samuel Sharpe

Galen Rafferty

Jeremy Edward Goodsitt

Austin Grant Walters

Grant Eden

Christopher Wallace

Anh Truong

Kenny Bean

Taylor Turner

Michael Davis

Justin Au-yeung

Owen Reinert

Brian Keith

Gregory John Kajfasz

Craig Allen Bielek

Kevin Richard Kirtley

Gunnar Leif Siden

Marc Thomas Edgar

Anurag Gupta

Bradley Taylor Boyer

Christopher Bayan Bruss

Thomas William Vandeputte

Prem Venugopal

Johan Michael Reimann

Tyler Farnan

Anna Tam

Peter A Galie

Daniele Rosa

Roberto Claretti

Peter A Gailie

Brian David Keith

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

Methods and systems are described herein for generating updated sets of attributes that would turn a negative decision of an automated system into a positive decision. A received set of attributes associated with a negative decision of an automated system may be used to generate a latent representation of that set of attributes. A machine learning model may then be used to output a change value (an alpha value). The alpha value may represent a minimum change needed to be made to the set of attributes to change the negative decision to a positive decision. The alpha value may then be applied to the latent representation to generate an updated latent representation, which may then be decoded into an updated set of attributes that would generate a positive decision from the automated system.

Automatic generation of attribute sets for counterfactual explanations

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

A method and related system operations for summarizing information includes determining a plurality of local explainability parameters for a set of inputs using a local explainability model for a prediction model. The method further includes generating a plurality of feature effect indicators for a target prediction and generating a plurality of phrases based on the plurality of feature effect indicators by using a text generation model. The method further includes generating a summarization using a large language model based on the plurality of phrases.

Large language model summarization

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

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 for generating real-time location-based notifications are described. In some aspects, the system processes, using a first machine learning model, a real-time video stream to determine a measure of significance of an event depicted in the real-time video stream. The system determines whether the user was present at the event. Based on determining that the user was present at the event, the system processes a user profile for the user and the measure of significance of the event to determine whether to generate for the user a cryptographic token associated with the event. Based on determining to generate for the user the cryptographic token associated with the event, the system generates a notification to the user including information for accessing the cryptographic token for the user.

Systems and methods for generating real-time location-based notifications

Disclosed embodiments may include a method for authentication using partitioned authentication tokens. The system can receive an indication of a first and second user device associated with a user. The indication can include a priority order of the first and second user device. The system can then receive an authentication request associated with the user from an application. The system can generate an authentication token to authenticate the user and partition the authentication token to create a first token portion and a second token portion. The system can determine which device of the first and second user device has a higher priority based on the priority order and can transmit the first token portion and the second token portion to the devices in order of priority. The system can receive a receipt of the token portions and transmit instructions to the application to authenticate the user.

Systems and methods for authentication using partitioned authentication tokens

In some aspects, a computing system obtain a first dataset including a set of original data samples and a first set of spurious data samples. Based on a time period expiring, the computing system may replace the first set of spurious data samples in the first dataset with a second set of spurious data samples. The computing system may obtain an indication that a second dataset is available via a third-party computing device. Based on a determination that a subset of samples of the second dataset correspond to the first set of spurious data samples, the computing system may determine a time window in which an incident occurred. As an example, the time window may be determined to correspond to a time before the first set of spurious data samples were replaced with the second set of spurious data samples.

Spurious-data-based detection related to malicious activity

Disclosed embodiments include systems and methods for providing a secure physical storage system. The system may receive a request to register a first physical storage box from an entity including location information and security information. The request can be validated by comparing the security information to a predetermined security threshold. The location information associated with the first physical storage box can be recorded to a blockchain. The first registration information can be received from a first user device. The first physical storage box can be assigned to the first user device and a first identity token associated with the first physical storage box. The assignment information associated with the first physical storage box can be recorded to the blockchain. A first identity token can be received from the first user device. Instructions can be transmitted to the first physical storage box to transition from a locked to an unlocked state.

Systems and methods for providing a secure physical storage system

Systems as described herein may include detecting live browser information that a user navigates to a first website displayed in a first open browser tab and a second website displayed in a second open browser tab. A data sharing server may provide the live browser information to a machine learning model as input. Based on feedback from the machine learning model, one or more similar products displayed in the first website and the second website may be determined. The data sharing server may detect an update on the one or more similar products, and cause a user device to display an alert indicating the update on the similar products.

Growing community of inventors

Niskayuna, NY, United States of America

Brian Chandler Barr