Patent · US Active

Method and system for adaptively imputing sparse and missing data for predictive models

US10409789B2 · kind B2 · utility

10Cited by

36References

15Claims

0Family size

Assignee

Oracle International Corporation · US

Inventors

Michael Zoll · Foster City, US
Yaser I. Suleiman · Santa Clara, US
Subhransu Basu · Fremont, US
Angelo Pruscino · Los Angeles, US
Wolfgang Lohwasser · Munich, DE
Wataru Miyoshi · Redwood City, US
Thomas Breidt · Munich, DE
Thomas Herter · Santa Clara, US
Klaus Thielen · Munich, DE
Sahil Kumar · Redwood City, US

Key dates

Filing date	Sep 18, 2017
Grant date	Sep 10, 2019
Priority date	—
Expiry date	Mar 1, 2038

Classification

Technology area (CPC H)Electricity
CPC primaryH04L43/02
WIPO fieldComputer technology
WIPO sectorElectrical engineering

Abstract

Described is an approach that provides an adaptive solution to missing data for machine learning systems. A gradient solution is provided that is attentive to imputation needs at each of several missingness levels. This multilevel approach treats data missingness at any of multiple severity levels while utilizing, as much as possible, the actual observed data.

Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.