View source for 18122667. MULTI-LEVEL RICH AUTOSUGGESTIONS FOR MULTI-INTENT SEARCH QUERY simplified abstract (Microsoft Technology Licensing, LLC)

=MULTI-LEVEL RICH AUTOSUGGESTIONS FOR MULTI-INTENT SEARCH QUERY=


==Organization Name==

[[:Category:Microsoft Technology Licensing, LLC|Microsoft Technology Licensing, LLC]]


[[Category:Microsoft Technology Licensing, LLC]]

==Inventor(s)==

[[:Category:Kishor Chamua of Hyderabad (IN)|Kishor Chamua of Hyderabad (IN)]][[Category:Kishor Chamua of Hyderabad (IN)]]

[[:Category:Shveta Verma of Hyderabad (IN)|Shveta Verma of Hyderabad (IN)]][[Category:Shveta Verma of Hyderabad (IN)]]

[[:Category:Saksham Saxena of Noida (IN)|Saksham Saxena of Noida (IN)]][[Category:Saksham Saxena of Noida (IN)]]

[[:Category:Sushil Kumar Chordia of Hyderabad (IN)|Sushil Kumar Chordia of Hyderabad (IN)]][[Category:Sushil Kumar Chordia of Hyderabad (IN)]]

[[:Category:Biju Venugopal of Hyderabad (IN)|Biju Venugopal of Hyderabad (IN)]][[Category:Biju Venugopal of Hyderabad (IN)]]

[[:Category:Puneet Agrawal of Jaipur (IN)|Puneet Agrawal of Jaipur (IN)]][[Category:Puneet Agrawal of Jaipur (IN)]]

[[:Category:Karthik Rg of Bengaluru (IN)|Karthik Rg of Bengaluru (IN)]][[Category:Karthik Rg of Bengaluru (IN)]]

==MULTI-LEVEL RICH AUTOSUGGESTIONS FOR MULTI-INTENT SEARCH QUERY - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18122667 titled 'MULTI-LEVEL RICH AUTOSUGGESTIONS FOR MULTI-INTENT SEARCH QUERY

The abstract describes a system that provides multi-level rich autosuggestions for multi-intent search queries. The system uses machine-learning derived intent clusters to generate smart suggestions for search queries.

* The system identifies a top-ranking machine-learning derived intent cluster based on the query prefix entered by the user.
* A smart suggestion, which is a first level suggestion of the top-ranking intent cluster, is provided to the user in the autosuggestion box.
* When the user selects the smart suggestion, the system replaces the first level autosuggestions with second level autosuggestions, which include subcategories related to the selected intent category.

Potential Applications:
- This technology can be used in search engines to improve user experience by providing more relevant and accurate search suggestions.
- E-commerce platforms can utilize this system to enhance product search functionality and increase user engagement.
- Online advertising platforms can benefit from improved search query predictions to deliver targeted ads to users.

Problems Solved:
- Helps users find relevant information quickly and efficiently.
- Improves search accuracy and reduces the time spent on refining search queries.
- Enhances user satisfaction by providing personalized and contextually relevant search suggestions.

Benefits:
- Increased user engagement and retention.
- Improved search functionality and user experience.
- Enhanced targeting and personalization in online platforms.

Commercial Applications:
Title: Enhanced Search Suggestions System for Improved User Experience
This technology can be commercially applied in search engines, e-commerce platforms, and online advertising systems to optimize search functionality, improve user engagement, and enhance targeted advertising strategies.

Questions about the technology:
1. How does this system improve the search experience for users?
- The system uses machine-learning derived intent clusters to provide relevant and accurate search suggestions, helping users find information quickly and efficiently.

2. What are the potential applications of this technology in online platforms?
- This technology can be applied in search engines, e-commerce platforms, and online advertising systems to enhance user experience, improve search functionality, and increase user engagement.


==Original Abstract Submitted==

Systems and methods are directed to providing multi-level rich autosuggestions for multi-intent search queries. The system receives a query prefix entered in a search box and accesses a database of machine-learning derived intent clusters. Based on the query prefix, a top-ranking machine-learning derived intent cluster is identified, and the search user interface is updated to provide a plurality of first level autosuggestions in an autosuggestion box including a smart suggestion. The smart suggestion is a first level suggestion of the top-ranking machine-learning derived intent cluster. The system receives a selection of the smart suggestion and, in response, replaces the plurality of first level autosuggestions in the autosuggestion box with second level autosuggestions. The second level autosuggestions comprise one or more intent categories that are subsets of the smart suggestion, whereby each intent category comprises a plurality of subcategory suggestions.



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