Utilizing machine learning models to process low-results web queries and generate web item deficiency predictions and corresponding user interfaces

US12423371B2 · kind B2 · utility

Assignee

• Adobe Inc. · US

Inventors

• Irgelkha Mejia · Round Rock, US
• Michele Saad · Austin, US
• Eunyee Koh · San Jose, US
• Andrew Thomson · Moraga, US
• Lauren Dest · Austin, US
• Dustin Ground · Austin, US
• Anna Hammond · Austin, US
• Arjun Prasanna Athreya · Palo Alto, US
• Catherine Chiodo · Austin, US

Key dates

• Filing date: May 2, 2022
• Grant date: Sep 23, 2025
• Expiry date: Oct 26, 2042

Classification

• Technology area (CPC G): Physics
• CPC primary: G06F16/958
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

Methods, systems, and non-transitory computer readable media are disclosed for utilizing machine learning models to extract digital signals from low-results web queries and generate item demand deficiency predictions for digital item lists corresponding to websites. In one or more embodiments, the deficiency identification system identifies a low-results query submitted by client devices navigating a website. The deficiency identification system generates features for the low-results query and the digital item list to generate a deficiency prediction relative to demand indicated by the low-results query. In some embodiments, the deficiency identification system utilizes a deficiency prediction model to process the extracted signals and generate a deficiency confidence score corresponding to the low-results query. Based on the deficiency confidence score, the deficiency identification systemd can generate and provide demand notifications via one or more graphical user interfaces.