Capital One Services, LLC

Netflix Inc.

Qualcomm Incorporated

Digital Fountain, Inc.

Neustar, Inc.

Aqseptence Group Gmbh

Johnson Screens, Inc.

Mark Watson

Austin Grant Walters

Jeremy Edward Goodsitt

Anh Truong

Vincent Pham

Reza Farivar

Fardin Abdi Taghi Abad

Galen Rafferty

Kenneth Taylor

Kate Key

Michael G Luby

Bin Wang

Lorenzo Vicisano

Payam Pakzad

Wei Wei

Thomas Stockhammer

Ying Hai Chen

Ioannis Katsavounidis

John Funge

M Amin Shokrollahi

Anthony Neal Park

Aaron Hipple

Will Wolcott

Ahn Truong

Brian R Knopf

Aditya Mavlankar

Mitch Zollinger

Noriaki Tatsumi

Kevin Pei-Wen Lee

Carla Christine Fisher

Yves Raimond

Maxine Cheung

Saumitra Mohan Das

Ramin Rezaiifar

David Chen

Michael Richard Ekholm

Richard Charles Maxson

Steve (Aihua) Chen

Michael Kubiske

Fabrice Jean Alphonse Courageot

Thomas Kunz

Bill Seed

Rajarshi Gupta

Behrooz Khorashadi

Neil D Hunt

Jaber Mohammad Borran

Mohammad Amin Shokrollahi

Michael Walters

Fardin Abdi Tagh Abad

Fardi Abdi Taghi Abad

Flavio Ribeiro Nogueira Barbosa

Shuzhen Chen

Jourdan J Clish

Lorenz Minder

Loannis Katsavounidis

David Clardy

Tracy L Hayford

Mike E Ciholas

Fardin Abdi Abad

Ying Chen

Timothy M Debaillie

Justin E Bennett

Christopher Elpers

Evan M Buchanan

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

A method includes receiving, with a computing system, data representing a video item into a buffer. The method further includes outputting the video item from the buffer to a display system. The method further includes determining that utilization of the buffer falls below a predetermined threshold. The method further includes, in response to determining that the utilization of the buffer falls below the predetermined threshold, determining that there is a specified rebuffering point within a predetermined time frame. The method further includes pausing with the computing system, the video item at the specified rebuffering point in response to determining that there is the specified rebuffering point within the predetermined time frame.

Temporal placement of a rebuffering event

One embodiment of the invention sets forth a mechanism for encoding video streams associated with the same digital content such that switch points staggered across two video streams occur at every offset temporal distance. The offset temporal distance is less than the distance between two consecutive key frames in a given video stream. This enables a content player to switch to a video stream having a playback quality up or down one level from a current video stream at the offset temporal distance from the most recently played key frame. In effect, the content player does not wait the entire key frame temporal distance before switching.

Staggered key frame video encoding

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

Computer systems and methods for verifying a route taken by a communication are disclosed. In one implementation, a device for verifying a route taken by a communication may include one or more processors configured to obtain a communication transmitted by a source entity. The communication may include data and digital signatures, and each of the digital signatures may be generated based on at least the data. Further, the digital signatures may include a digital signature associated with the source entity, and a set of digital signatures associated with at least a subset of intermediate entities on a route taken by the communication. The one or more processors may be further configured to verify the digital signatures included in the communication, verify whether the entities associated with the digital signatures form an expected route for the communication, and process the data.

Systems and methods for verifying a route taken by a communication

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

Methods and systems disclosed herein describe using machine learning to lock and unlock a device. Machine learning may be trained to recognize one or more features. Once the device has been trained to recognize one or more features, a user may define an unlock condition for the device using the one or more trained features. After defining the unlock condition, the device may be locked by verifying the one or more features that the user defined as the unlock condition using machine learning. When verification is successful, the device may be unlocked and the user allowed to access the device.

Active locking mechanism using machine learning

A playback application is configured to analyze audio frames associated with transitions between segments within a media title to identify one or more portions of extraneous audio. The playback application is configured to analyze the one or more portions of extraneous audio and then determine which of the one or more corresponding audio frames should be dropped. In doing so, the playback application can analyze a topology associated with the media title to determine whether any specific portions of extraneous audio are to be played outside of a logical ordering of audio samples set forth in the topology. These specific portions of extraneous audio are preferentially removed.

Audio transitions when streaming audiovisual media titles

Systems and methods for determining neural network brittleness 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 modeling request comprising a preliminary model and a dataset. The operations may include determining a preliminary brittleness score of the preliminary model. The operations may include identifying a reference model and determining a reference brittleness score of the reference model. The operations may include comparing the preliminary brittleness score to the reference brittleness score and generating a preferred model based on the comparison. The operations may include providing the preferred model.

Systems and methods to identify neural network brittleness based on sample data and seed generation

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

The disclosed computer-implemented methods and systems can splice targeted content such as advertisements into a live stream of a real-time event. For example, the methods and systems discussed herein determine targeted content items for splicing into a live stream by generating a computing model of targeted content recommendations. In one or more examples, the computing model generates targeted content recommendations that are specific to a length of a targeted content break, the viewer of the live stream, and the player where the live stream is being viewed. The systems and methods discussed herein further determine the placement and duration of targeted content breaks based on signals and markers that are inserted into the live stream. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for splicing targeted content into live broadcast streams with targeted content breaks of unknown placement and duration

A playback application is configured to dynamically generate topology for an interactive media title. The playback application obtains an initial topology and also collects various data associated with a user interacting with the feature. The playback application then modifies the initial topology, based on the collected data, to generate a dynamic topology tailored to the user. The dynamic topology describes the set of choices available to the user during playback as well as which options can be selected by the user when making a given choice. In addition, the playback application also selectively buffers different portions of the interactive media title, based on the collected data, in anticipation of the user selecting particular options for available choices.

Dynamic topology generation for branching narratives

Methods and systems for the dynamically re-clustering of nodes in clusters to provide optimal performance and/or the most efficient use of resources through the use of machine learning models. Specifically, the methods and systems may determine a cluster that optimally performs and/or has the most efficient use of resources based on a first machine learning model. The methods and system may then retrieve available substitute nodes from other domains and/or networks that may lie outside the cluster, but may nonetheless be available to, or accessed by the cluster. The methods and systems may then generate an additional plurality of clusters using one or more of the original nodes of the originally selected clusters and/or one or more of the available substitute nodes.

Methods and systems for dynamic re-clustering of nodes in computer networks using machine learning models

Systems and methods for searching datasets and classifying datasets 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 test dataset from a client device and generating a test data model output using a data model, based on the test dataset. The operations may include processing test data model output by implementing an encoding method, a factorizing method, and/or a vectorizing method. The operations may include retrieving a reference data model output from a dataset index, based on a reference dataset. The operations may include generating a similarity metric based on the reference data model output and the test data model output. The operations may include classifying the test dataset based on the similarity metric and transmitting, to the client device, information comprising the classification.

Systems and methods for quickly searching datasets by indexing synthetic data generating models

Growing community of inventors

Urbana, IL, United States of America

Mark Watson