Query-oriented approximate query processing based on machine learning techniques

US11544281B2 · kind B2 · utility

Assignee

• Adobe Inc. · US

Inventors

• Subrata Mitra · Whitefield, IN
• Nikhil Sheoran · Chandigarh, IN
• Anup Rao · San Jose, US
• Tung Mai · San Jose, US
• Sapthotharan Krishnan Nair · Santa Clara, US
• Shivakumar Vaithyanathan · San Jose, US
• Thomas William Randall Jacobs · Cupertino, US
• Ghetia Siddharth · Sayla, IN
• Jatin Varshney · Atrauli, IN
• Vikas Maddukuri · Altrincham, GB
• Laxmikant Mishra · Ashti, IN

Key dates

• Filing date: Nov 20, 2020
• Grant date: Jan 3, 2023
• Expiry date: Jan 1, 2041

Classification

• Technology area (CPC G): Physics
• CPC primary: G06N20/10
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

In some embodiments, a model training system trains a sample generation model configured to generate synthetic data entries for a dataset. The sample generation model includes a prior model for generating an estimated latent vector from a partially observed data entry, a proposal model for generating a latent vector from a data entry of the dataset and a mask corresponding to the partially observed data entry, and a generative model for generating the synthetic data entries from the latent vector and the partially observed data entry. The model training system trains the sample generation model to optimize an objective function that includes a first term determined using the synthetic data entries and a second term determined using the estimated latent vector and the latent vector. The trained sample generation model can be executed on a client computing device to service queries using the generated synthetic data entries.