Maplebear Inc. Granted Patent on Content Eligibility Using Machine Learning
Summary
The USPTO granted Patent US12608725B2 to Maplebear Inc. on April 21, 2026, covering an online system that uses machine-learning models to predict user metrics and determine content-item presentation eligibility. The system generates optimal values for users based on predicted metrics, stated objectives, and constraints, then sends content to client devices only when a user qualifies. The patent contains 18 claims and lists Tilman Drerup, Levi Boxell, and Rishikesh Yardi as inventors.
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GovPing monitors USPTO Patent Grants - Business Methods (G06Q) for new banking & finance regulatory changes. Every update since tracking began is archived, classified, and available as free RSS or email alerts — 25 changes logged to date.
What changed
The USPTO issued Patent US12608725B2 to Maplebear Inc. on April 21, 2026, for a system that determines user eligibility for content-item presentation using machine-learning models. The system predicts user metrics, applies objectives and constraints, generates optimal eligibility values for each user, and sends content items only to qualifying users. The patent was filed on January 19, 2024, as Application No. 18418103 and contains 18 claims under CPC classifications G06Q 30/0244 and G06Q 30/0277.
Technology companies developing content-recommendation, targeted-advertising, or personalization systems should review this patent to assess potential freedom-to-operate implications. The patent's broad claim scope covering machine-learning-based eligibility determination may be relevant to firms deploying similar algorithmic content-filtering or user-segmentation approaches. Investors and IP professionals may use this grant to update patent portfolio landscapes for Maplebear Inc.
Archived snapshot
Apr 22, 2026GovPing captured this document from the original source. If the source has since changed or been removed, this is the text as it existed at that time.
Determining user eligibility for content item presentation based on multiple objective-based metrics
Grant US12608725B2 Kind: B2 Apr 21, 2026
Assignee
Maplebear Inc.
Inventors
Tilman Drerup, Levi Boxell, Rishikesh Yardi
Abstract
An online system sends content items for display to client devices associated with users and detects actions associated with the content items performed by the users. The system accesses and applies machine-learning models to predict metrics for a set of users and generates a set of optimal values for the set of users based on the metrics, one or more objectives, and a set of constraints, in which each optimal value indicates whether a user is eligible to be presented with a content item. Responsive to identifying an opportunity to present the content item to a user of the set of users, the system determines whether the user is eligible to be presented with the content item based on an optimal value determined for the user and sends the content item for display to a client device associated with the user if the user is eligible.
CPC Classifications
G06Q 30/0244 G06Q 30/0277
Filing Date
2024-01-19
Application No.
18418103
Claims
18
Parties
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