US 10,769,644 B2Granted

Cross-domain identity service

AssigneeAdobe Inc.

InventorsIan Taylor, Eric Kienle, Shaun Klopfenstein

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Abstract

A method is provided, including the following method operations: accessing a first web page associated with a first domain using a browser of a device; in response to the accessing the first web page, accessing an identity server to retrieve an identifier for the browser that accessed the first web page, the identifier being stored to a local storage of the device; accessing a second web page associated with a second domain using the browser; in response to the accessing the second page, accessing the local storage of the device to retrieve the identifier; wherein accessing the first web page and accessing the second web page are associated to the identifier.

Claims20

Dependent claims

A method, comprising:

receiving a first user identifier assigned to a first browser and a first dataset corresponding to the first user identifier indicating a first set of user activities across a first website;
receiving a second user identifier assigned to a second browser and a second dataset corresponding to the second user identifier indicating a second set of user activities across a second web site;
identifying a first user-identifying activity associated with the first browser and a second user-identifying activity associated with the second browser, wherein the first user-identifying activity and the second user-identifying activity both indicate an identity of a single user corresponding to the first user identifier and the second user identifier;
based in part on the first user-identifying activity and the second user-identifying activity both indicating the identity of the single user, generating an account profile for the single user by:
analyzing data corresponding to the first set of user activities across the first website and the second set of user activities across the second website to identify, and including within the account profile, a first set of personal information associated with the single user provided via the first website and a second set of personal information associated with the single user provided via the second website;
generating an interaction score for the single user within the account profile by utilizing a machine-learning model to determine weights for different types of interactions, weighting interactions within the first website by the single user from the first set of user activities across the first website and interactions within the second website by the single user from the second set of user activities across the second website utilizing the determined weights, and utilizing a lead scoring algorithm comprising a decay factor accounting for time to determine a lead score for the single user such that a higher lead score corresponds to a higher interaction score;
determining an amount of visits by the single user to the first website and to the second website;
and
providing an indication within the account profile of the amount of visits by the single user to the first website and to the second website.

6 more elements

A non-transitory computer readable medium storing instructions thereon that, when executed by at least one processor, cause a computing device to:

receive a first user identifier assigned to a first browser and a first dataset corresponding to the first user identifier indicating a first set of user activities across a first website;
receive a second user identifier assigned to a second browser and a second dataset corresponding to the second user identifier indicating a second set of user activities across a second web site;
identify a first user-identifying activity associated with the first browser and a second user-identifying activity associated with the second browser, wherein the first user-identifying activity and the second user-identifying activity both indicate an identity of a single user corresponding to the first user identifier and the second user identifier;
based in part on the first user-identifying activity and the second user-identifying activity both indicating the identity of the single user, generate an account profile for the single user by:
analyzing data corresponding to the first set of user activities across the first website and the second set of user activities across the second website to identify, and including within the account profile, a first set of personal information associated with the single user provided via the first website and a second set of personal information associated with the single user provided via the second website;
generating an interaction score for the single user with in the account profile by utilizing a machine-learning model to determine weights for different types of interactions, weighting interactions within the first website by the single user from the first set of user activities across the first website and interactions within the second website by the single user from the second set of user activities across the second website utilizing the determined weights, and utilizing a lead scoring algorithm comprising a decay factor accounting for time to determine a lead score for the single user such that a higher lead score corresponds to a higher interaction score;
determining an amount of visits by the single user to the first website and to the second website;
and
providing an indication within the account profile of the amount of visits by the single user to the first website and to the second website.

6 more elements

A system comprising:

at least one processor;
at least one non-transitory computer readable medium comprising:
a distribution dataset comprising a set of distribution contacts and corresponding contact characteristics, and instructions that, when executed by the at least one processor, cause the system to:
receive a first user identifier assigned to a first browser and a first dataset corresponding to the first user identifier indicating a first set of user activities across a first website;
receive a second user identifier assigned to a second browser and a second dataset corresponding to the second user identifier indicating a second set of user activities across a second web site;
identify a first user-identifying activity associated with the first browser and a second user-identifying activity associated with the second browser, wherein the first user-identifying activity and the second user-identifying activity both indicate an identity of a single user corresponding to the first user identifier and the second user identifier;
based in part on the first user-identifying activity and the second user-identifying activity both indicating the identity of the single user, generate an account profile for the single user by:
analyzing data corresponding to the first set of user activities across the first website and the second set of user activities across the second website to identify, and including within the account profile, a first set of personal information associated with the single user provided via the first website and a second set of personal information associated with the single user provided via the second website;
generating an interaction score for the single user within the account profile by utilizing a machine-learning model to determine weights for different types of interactions, weighting interactions within the first website by the single user from the first set of user activities across the first website and interactions within the second website by the single user from the second set of user activities across the second website utilizing the determined weights, and utilizing a lead scoring algorithm comprising a decay factor accounting for time to determine a lead score for the single user such that a higher lead score corresponds to a higher interaction score;
determining an amount of visits by the single user to the first website and to the second website;
and
providing an indication within the account profile of the amount of visits by the single user to the first website and to the second website.

9 more elements

Dependencies are inferred from the claim text.

Facts

Application: 14/219,791Filed 19 Mar 2014 · us provisional application
Priority: 11 Mar 2014
Published: US 2015/0262199 A117 Sep 2015
Granted: 8 Sep 2020
Term ends: Not estimatedThe source records don’t support an estimate.

Classification10 codes

G06Q 30/0201
Marketing; Price estimation or determination; Fundraising: market modelling; Market analysis; Collecting market data
G06F 16/9535
Querying, e.g. by the use of web search engines: search customisation based on user profiles and personalisation
G06F 16/9536
Querying, e.g. by the use of web search engines: search customisation based on social or collaborative filtering
G06F 16/954
Retrieval from the web: navigation, e.g. using categorised browsing

Family

Application61/951,473Us provisional application · filed 11 Mar 2014
US 2015/0262199 A1Pre-grant publication · published 17 Sep 2015
US 10,769,644 B2This patent

From the US filing history. Foreign family members aren’t listed.

Timeline

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