US Patent Application 17659318. PREDICTING MATCHING DENSITY WITH STRUCTURAL CAUSAL MODEL simplified abstract
Contents
PREDICTING MATCHING DENSITY WITH STRUCTURAL CAUSAL MODEL
Organization Name
Microsoft Technology Licensing, LLC
Inventor(s)
Ruofei Zhang of Mountain View CA (US)
PREDICTING MATCHING DENSITY WITH STRUCTURAL CAUSAL MODEL - A simplified explanation of the abstract
- This abstract for appeared for US patent application number 17659318 Titled 'PREDICTING MATCHING DENSITY WITH STRUCTURAL CAUSAL MODEL'
Simplified Explanation
This abstract describes a computing device that can analyze data related to a specific category of queries over a certain period of time. The device uses this data to create a model and estimate the accuracy of the data. It then updates a specific variable in the model and predicts the matching density (number of matches per query) based on this updated variable. The device finally outputs the predicted matching density.
Original Abstract Submitted
A computing device including a processor configured to receive data indicating, for a query category within a sampled time period, a matching density defined as a number of matches per query. The processor may generate a structural causal model (SCM) of the data within the sampled time period. The SCM may include a plurality of structural equations. Based at least in part on the plurality of structural equations, the processor may estimate a structural equation error value for the matching density. The processor may update a value of a target SCM output variable to a counterfactual updated value. Based at least in part on the SCM, the counterfactual updated value, and the structural equation error value, the processor may compute a predicted matching density when the target SCM output variable has the counterfactual updated value. The processor may output the predicted matching density.
