Counterfactual evaluation of policies for categories of items using machine learning prediction of outcomes

US12361361B2 · kind B2 · utility

Assignee

• Maplebear Inc. · US

Inventors

• Cameron Nicholas Taylor · Walnut Creek, US
• Robert W. Fletcher · Louisville, US
• Pedro Tanure Veloso · San Francisco, US
• Tilman Drerup · Palo Alto, US
• Rob Donnelly · San Francisco, US
• Ben Lowenstein · San Francisco, US
• Matthew Wean · San Francisco, US

Key dates

• Filing date: Dec 22, 2022
• Grant date: Jul 15, 2025
• Expiry date: Sep 21, 2043

Classification

• Technology area (CPC G): Physics
• CPC primary: G06Q10/087
• WIPO field: IT methods for management
• WIPO sector: Electrical engineering

Abstract

An online concierge system fulfills orders for items offered by retailers and may increase the price of an item offered by a retailer in some instances. The online concierge system applies a markup to an item by applying a pricing policy to a category including the item. To optimize application of pricing policies to categories, the online concierge system categorizes items offered by the retailer and applies an outcome model to combinations of categories and pricing policies. From the output of the outcome model, the online concierge system selects a set of categories and corresponding pricing policies. Using a price adjustment model, the online concierge system determines modifications to one or more of the pricing policies of the set to enforce one or more constraints across multiple pricing policies.