Microsoft Technology Licensing, LLC

Nathan Novielli

Sean O Lyndersay

Marcelo Medeiros De Barros

David Joel Sheldon

Rahul Lal

Paul Baecke

Malik Mehdi Pradhan

Daniel Hill

Yan Zhong

Hazim Macky

Dheeraj Mehta

Abuzer Mirac Ozcan

Representative embodiments disclose mechanisms to improve the perceived responsiveness of a search engine. As a user types a query prefix into a browser or other interface to the search engine, the search engine returns query completion suggestions to the browser. The query completion suggestions, user history, user favorites and/or other information are presented to a trained machine learning model on the client device to predict a desired location that the user is attempting to navigate to. When the confidence level of the predicted location surpasses a threshold, content from the desired location is preloaded into a hidden tab in the browser. When the user submits a query, the browser submits feedback to a system responsible for updating and refining the machine learning model. Updated machine learning model coefficients can be received by the browser from the system to make predictions more accurate.

Machine learning model to preload search results

Aspects of the technology described herein can detect visual regressions in a computer-generated document, such as a web page, caused by programming errors. The technology can be used to detect errors in programs coded in a variety of different languages and used in a variety of applications that generate user interfaces. In one aspect, the technology is used to validate an updated program module prior to replacing a production version of the program module with the updated version. The technology described herein can compare a baseline version of a user interface with a test version of the user interface generated using the updated program module. In an aspect, a visual parity analysis is run to detect a visual regression.

Visual regression analysis

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Kirkland, WA, United States of America

Nathan Novielli