Microsoft Technology Licensing, LLC

Abhineet Mishra

Venkata Madhu Sravanth Kurumaddali

Rashi Anand

Sravanth Venkata Madhu Kurumaddali

Venkata Kurumaddali

The discussion relates to providing more relevant search results to a user based upon information relating to IoT devices associated with the user. One example can receive search results for a search query associated with the user. The example can obtain entities from IoT devices associated with the user and rank the search results utilizing the entities from the IoT devices.

IoT enhanced search results

Representative embodiments disclose mechanisms to assign search results associated with a user query to locations based in part on features associated with the user's Internet of Things (IoT) devices. This results in a search page that presents results in an order and prominence that improves user understanding and information accessibility. Features from IoT devices are used to train a machine learning model that receives as an input information from an IoT entity index, the query, and results from the search engine along with their static rankings. The resultant IoT positioning scores are used to assign an initial location on the search results page. A second machine learning model trained using IoT features is then used to assign a final location on the page. The system causes display of the page to the user.

Personalized whole search page organization and relevance

A system for generating sensor data based query results can include a processor to detect sensor data from a plurality of sensors and store the sensor data as aggregated sensor data. The sensor data can be aggregated based on a time corresponding to the sensor data, wherein the sensor data is stored with a notification based push operation in response to a change in value of the sensor data exceeding a predetermined threshold. The processor can also convert the aggregated sensor data to an indexable data format (IDF) and provide a low latency query result to a query based on the aggregated sensor data in the IDF.

Sensor data based query results

Techniques for providing a recommendation engine for personal digital assistant functionality on hardware devices. In an aspect, sensor data and other signals for a user are collected and processed to extract user patterns. A user profile is further constructed using machine learning techniques. The insights obtained from the user patterns and user profile are combined by digital service routines to generate customized recommendations for users. In a further aspect, user feedback may be utilized to improve the accuracy and relevance of the recommendations.

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Bothell, WA, United States of America

Abhineet Mishra