Optum, Inc.

Unitedhealth Group Incorporated

Vinit Garg

Ayush Tomar

Laura D Hamilton

Lun Yu

Fazle Shahnawaz Muhibul Karim

Chenwei Liu

Preet Kamal S Bawa

David Armbrust

Martin R Linenweber

Rupesh Kartha

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing personalized autocomplete predictions. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform personalized autocomplete predictions using a general search corpus and/or individual curated search corpus.

Machine learning techniques for generating personalized autocomplete prediction

Various embodiments of the present disclosure provide query processing techniques for generating optimized query results. The techniques include generating using a machine learning framework, one or more predictions for a natural language query. The one or more predictions may include an intent prediction and an event prediction. The technique may include generating an intent classification for the natural language query based on the intent prediction and the event prediction. The techniques may include in response to the intent classification corresponding to a target query intent: generating, a plurality of candidate data objects based on an identifier associated with the natural language query, identifying one or more relevant data objects from the plurality of candidate data objects based on a relevancy score for each of the plurality of candidate data object; and providing, via a user interface, a natural language query result.

Methods, apparatuses and computer program products for intent-driven query processing

Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for retrieving relevant items for user queries by generating, using a search engine machine learning model, a prediction-based action for the query input wherein query input embeddings of the query input are generated. For each query input embedding, a k-Nearest-Neighbor (KNN) search is performed with respect to search engine repository item embeddings to generate initial search results, and for each initial set result, performing N hops within a semantic graph starting from nodes associated with the initial search result to generate related search results. The search engine machine learning model is trained by generating a search engine repository item embeddings according to embedding techniques for respective content categories and generating the semantic graph based at least in part on a measure of similarity for pairs of search engine repository item embeddings.

Search analysis and retrieval via machine learning embeddings

Systems and methods for improved spelling checking are disclosed. A method includes receiving a search query from a user device and determining that the search query does not exist in a data store that stores (a) a corpus of correctly spelled words or (b) forced correction mapping data. The method further includes, in response to the determining, determining a plurality of suggested search queries generated by a plurality of respective spell corrector models, selecting a suggested search query determined using a spell corrector model from the plurality of spell corrector models based on at least one of a frequency of the suggested search query in historical search data or a weightage associated with the spell corrector model, and causing the suggested search query to be displayed on the user device.

Systems and methods for improved spell check

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Fremont, CA, United States of America

Vinit Garg