Oracle International Corporation

Qualtrics, LLC

Rovi Technologies Corporation

Vernon W Hui

John Patrick Connelly

Kendra Mariko Chen

Joseph Scott Reese

James Kong Hsu

Stephen John Papa

Jeevan Gheevarghese Joseph

Ashish Gupta

David Abraham Wiener

Edward L Wallace

Anping Wang

Trevor Charles Armstrong

Matthew Perry Abrams

Adison Hana Wongkar

Sherene Wael Hilal

Mayank Ashwin Dedhia

David J Jurenka

David Warren Stevens

Langdon Ward Beeck

Evan Paul Child

Ben Hekster

Hong Bao

Jeanie Michelle DuMont

Michael L Christian

Sanjeev Khodaskar

Todd Gordon Stevens

Adam Lee Potratz

Hyunjung Lee

Padmanabhan Natarajan

This disclosure relates to methods, non-transitory computer readable media, and systems that relay domain-event objects within an enhanced multi-platform data stream to listen for and react to digital events indicated by the domain-event objects that occur across a wide variety of computing platforms. Specifically, the disclosed systems can receive domain-event objects within the multi-platform data stream. From among the domain-even objects transmitted through the multi-platform data stream, the disclosed systems can identify a domain-event object that is relevant to a digital-analytics platform by identifying domain-event objects that include properties satisfying domain-event-listener rules. Based on an entity identifier and an object event from the relevant domain-event object, the disclosed systems can perform a platform action within the digital-analytics platform (e.g., to react to a change in another platform as indicated by the domain-event object).

Domain events for a multi-platform data stream

The present disclosure relates to systems, non-transitory computer-readable media, and methods that provide a graphical user interface for intuitively generating digital action sequences across one or more digital platforms. For example, the disclosed systems generate graphical user interfaces with tools to construct digital action sequences of platform actions performed based on event triggers. To facilitate building such digital action sequences, in some cases, the disclosed systems provide data for digital-action-sequence templates for easy selection of pre-matched event triggers and platform actions or tools to mix-and-match combinations of specific event triggers and platform actions. By providing such intuitive tools in a centralized graphical user interface, the disclosed systems can connect events across multiple platforms or across disconnected components of a single platform. Indeed, in certain implementations, the disclosed systems provide a low-code/no-code graphical user interface for selection of a multitude of different digital action sequences and in myriad different configurations.

Providing a sequence-builder-user interface for generating a digital action sequence

Techniques are provided to quickly process stored objects to generate an accurate estimate of the number of live objects that will be responsive to a client's filtering request defined at least in part based on a filtering parameter. Stored objects generated from user interactions with webpages are modified to simulate live objects and processed by a live object-intake simulator. The simulated results are then filtered using the filtering parameter to form a subset of matching objects. An estimate of the number of live objects produced by the filtering parameter is generated using at least the count of the objects in the subset. A confidence metric of the estimate is computed, in some cases using a statistical model. Under certain conditions, the estimate is delivered, the confidence metric is delivered, and/or live object intake based on the filtering parameter is initiated.

Object count estimation by live object simulation

Techniques are provided for accurately and quickly processing distributed stored objects to provide a timely and accurate prediction of the number of live objects a parameterized file request will produce. Stored objects representing previous user webpage visit interactions are stored in different storage locations in a data store. The stored objects at each storage location are processed in parallel by hashing stored objects with a hash function such that they are spread somewhat uniformly into buckets. Sub-buckets in each bucket are formed that correspond to selected category identifiers. Also in parallel, K-minimum values are computed for each sub-bucket to estimate the count of stored objects in the data store. The K-minimum values for sub-buckets corresponding to the same category ID across all buckets are combined, in some cases harmonically, and used to generate a predicted number of live objects responsive to a parameterized file request.

Object count prediction using distributed processing

The present disclosure generally relates to end-to-end testing of applications using simulated data. More particularly, the present disclosure relates to systems and methods that test applications in a production environment by dynamically generating and tracking the simulated data in real time. In some implementations, an expected number of simulated user profiles (e.g., based on a protocol for generating simulated user profiles) can be compared against an actual number of simulated user profiles stored in a state machine to identify issues within the end-to-end environment of the application being tested.

Systems and methods for end-to-end testing of applications using dynamically simulated data

A server receives incoming data records comprising an ID value and respective user attributes corresponding to a user activity that originates at a user device. Characteristics of the activity and/or characteristics of the user device are considered in order to assign a probabilistic confidence value, which is in turn used to generate links from an incoming data record to other previously-received data records, and in so doing, generates a probabilistic link between one set of user attributes from the incoming data record and another set of user attributes from previously-received data records. A messaging campaign specification that describes target user attributes is used to identify a matching set of target audience member records. The determination of a match or not is based on the probabilistic confidence value and a threshold can be varied to extend audience reach. The identified set of target audience member records are transmitted over a network.

Extending audience reach in messaging campaigns using probabilistic ID linking

Extending audience reach in messaging campaigns using super profiles

Described is a technology by which personalization data is used to select and order video highlight clips for a personalized highlight reel that a user may play to view, such as part of a narrative arranged for that user. The narrative may include introductory content, transition content and/or ending content. The narrative may include advertising content and/or ending content. The narrative may be rearranged in response to an event.

Dynamic personalized program content

Growing community of inventors

Bellevue, WA, United States of America

Vernon W Hui