Adobe, Inc.

David Meier Weinstein

Edward Schuchardt

Margarita Savova

Harleen S Sahni

Charles Menguy

Matvey Kapilevich

Matthew Donofrio

Vinay Goel

Rafaat Hossain

Vishwa Vinay

Ritwik Sinha

Asa Whillock

Virgil-Artimon Palanciuc

Sunny Dhamnani

David Allen Jared

Ravi Prakash Singh

Catalin Costache

Roshan Singh

Mihai Daniel Fecioru

Lilly Kumari

Sudhakar Pandey

Nicholas M Jordon

Ankush Sharma

Paul Mackles

Nicholas M Jordan

Systems and techniques for delivery of contextual interest from interaction information are described that process user interactions with digital content to generate user interest scores for various topics. A contextual user interest system uses user interaction data to identify and contextualize content, and assigns propensity scores to the contextualized content. By dynamically contextualizing pages of content, the contextual user interest system may adapt to changes in the content and provide more accurate and robust information over time, which is not possible using conventional techniques. The contextualized pages of content are used to assign user interest scores across a number of topics to users who have visited the pages of content, and the user interest scores are normalized in a manner that allows a user's degree of interest in a topic to be compared to that of another user.

Delivery of contextual interest from interaction information

Methods, systems, and non-transitory computer readable storage media are disclosed for detecting robotic activity while monitoring Internet traffic across a plurality of domains. For example, the disclosed system identifies network session data for each domain of a plurality of domains, the network session data including network sessions comprising features that indicate human activity. In one or more embodiments, the disclosed system generates a classifier to output a probability that a network session at a domain includes human activity. In one or more embodiments, the disclosed system also generates a classifier to output a probability that a network session includes good robotic activity. Additionally, the disclosed system generates a domain-agnostic machine-learning model by combining models from a plurality of domains with network sessions including human activity.

Detecting robotic internet activity across domains utilizing one-class and domain adaptation machine-learning models

Selection of a trait may be received. A complex segment rule may be created that is usable to evaluate one or more qualification events. For example, the segment rule may be usable to evaluate a combined recency and frequency of the one or more qualification events. The qualification events may be based on collected network data associated with the plurality of visitors with each qualification event corresponding to a separate qualification of visitor according to the trait. The qualification events may be evaluated together according to the segment rule. For example, the combined recency and frequency of the one or more qualification events may be evaluated according to the segment rule. Evaluating the segment rule may include estimating a segment population size in real-time.

Audience size estimation and complex segment logic

Systems and methods disclosed herein generate a segment of tracked entities from a collection of profiles merged from different sources. Individuals and other entities use multiple electronic device and online accounts. Options for merging authentication-based profiles and device-based profiles of tracked entities are presented on a user interface accessible to a tracking entity. Systems and methods receive a selection of the options, the selection specifying criteria for merging authentication-based profiles with device-based profiles. Upon generating a collection of profiles by merging the authentication-based and device-based profiles based on the received options, a targeted segment of tracked entities is generated by applying defined segment rules to the collection of profiles.

Systems and techniques for configuring profile merge rules prior to determining segments of recipients of electronic communication

The disclosure describes systems and methods of limiting access to data that is commonly held, such as by a data cooperative. Certain embodiments involve providing a filter system that receives a record with user device identifiers, and uses the received record to create or modify a filter that is associated with a participant system. The filter system creates a filter key based on a device identifier from the record and assigns the filter key to the associated filter. The filter is applied to a data source including one or more stored device identifiers, and a determination is made whether the stored device identifiers are linked to the filter key. In some embodiments, determining a link is based on characteristics of the stored device identifiers, or on user indications (e.g., 'opt-out' preferences), or both. The linked device identifiers are provided to the participant system.

Device-keyed filtering for data cooperative access

Tunable algorithmic segment techniques are described. In one or more implementations, a target audience definition is obtained that is input to initiate creation of a look-alike model. The target audience definition indicates traits associated with a baseline group of consumers who have interacted with online resources in a designated manner, such as by buying a product, visiting a website, using a service, and so forth. Tuning parameters designated for the look-alike model are ascertained and the look-alike model is built based on the target audience definition and the tuning parameters. The tuning parameters may include at least a setting selectable to control reach versus accuracy for the look-alike model. Segment data indicative of market segments generated according to the look-alike model may then be exposed for manipulation by a client. The manipulation may include selectable control over the tuning parameters to generate different look-alike groups from the segment data.

Tunable algorithmic segments

This disclosure relates to enforcing restrictions on data collected from a first set of systems and disseminated to a second set of systems. For example, enforcing a set of restrictions includes receiving a first trait and a second trait that include data describing a user that has interacted with an online service. The first trait is labelled with a first usage restriction and the second trait is labelled with a second usage restriction different from the first usage restriction. The first trait and the second trait are combined into a segment. The segment preserves labelling of the first trait with the first usage restriction and the second trait with the second usage restriction. Use of the segment is controlled based on the first usage restriction and the second usage restriction.

Enforcing usage policies on combinations of collected data to be disseminated to destination systems

Systems and methods are provided for identifying stable attributes in clusters of devices and naming the clusters of devices using the stable attributes. The stable attribute is selected from data associated with user/household behavior and data associated with each device. Because the stable attribute is associated with the device in the cluster determined to be the most stable, it is a persistent identifier that can be used to identify the user/household over time. Because the stable attribute is determined each time the clusters are determined, resources that would be required to track the user/household over time are saved and the system of tracking users/households is more efficient. Attributes evaluated to determine the stable attribute include IP address, the location of the device, hardware characteristics of the device, the date a CRM profile was created, timestamp of the first or last user authentication, and the number of authentications on that device.

Techniques for associating variable versions of a cluster of devices identified at different times with the same user by assigning cluster identifiers

This disclosure relates to enforcing restrictions on data collected from a first set of systems and disseminated to a second set of systems. For example, a method for enforcing a set of restrictions includes receiving a first trait and a second trait that include data describing a user that has interacted with an online service. The first trait is labelled with a first usage restriction and the second trait is labelled with a second usage restriction different from the first usage restriction. The method further includes combining the first trait and the second trait into a segment. The segment preserves labelling of the first trait with the first usage restriction and the second trait with the second usage restriction. The method further includes controlling use of the segment based on the first usage restriction and the second usage restriction.

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Rockville Centre, NY, United States of America

David Meier Weinstein