17966812. Processing of Queries using a Consolidated Matching Architecture simplified abstract (Microsoft Technology Licensing, LLC)

Processing of Queries using a Consolidated Matching Architecture

Organization Name

Microsoft Technology Licensing, LLC

Inventor(s)

Jian Jiao of Redmond WA (US)

Eren Manavoglu of Menlo Park CA (US)

Processing of Queries using a Consolidated Matching Architecture - A simplified explanation of the abstract

This abstract first appeared for US patent application 17966812 titled 'Processing of Queries using a Consolidated Matching Architecture

Simplified Explanation

The query-processing technique described in the abstract involves matching input queries against candidate target items to produce a set of candidate query-item pairings. This matching is performed by a computer processing architecture using class-agnostic logic and indexes. Each pairing is then assigned a matching class to produce a set of classified pairings, from which a particular output item is chosen for the end user based on the results of the matching and assigning. Some implementations include a filtering operation to conform candidate-item pairings to specified selection strategies.

• Explanation of the patent/innovation:

   - Matching input queries against candidate target items
   - Using class-agnostic logic and indexes for matching
   - Assigning a matching class to each pairing
   - Choosing a particular output item based on the results
   - Implementing a filtering operation for selection strategies

Potential Applications

This technology could be applied in various fields such as e-commerce, information retrieval systems, recommendation systems, and personalized content delivery platforms.

Problems Solved

1. Efficiently matching input queries against candidate target items 2. Providing relevant and personalized output items to end users

Benefits

1. Improved user experience through personalized recommendations 2. Increased efficiency in processing queries and selecting output items

Potential Commercial Applications

Optimizing search engines, enhancing e-commerce platforms, improving recommendation systems, and streamlining content delivery services.

Possible Prior Art

One possible prior art could be the use of machine learning algorithms for query processing and matching in information retrieval systems.

How does this technology handle large datasets efficiently?

The abstract does not provide information on the scalability of the query-processing technique and how it manages large volumes of data effectively.

What security measures are in place to protect user data during the query-processing operation?

The abstract does not mention any security protocols or measures implemented to safeguard user data during the matching and assigning operations.

Original Abstract Submitted

A query-processing technique includes an operation of matching the input query against a plurality of candidate target items, to produce a set of candidate query-item pairings. The matching is applicable to different classes of matching, but is performed by a computer processing architecture that uses a class-agnostic instance of query-processing logic and a class-agnostic target item index. After the matching operation, the technique assigns a matching class to each candidate query-item pairing in the set of candidate query-item pairings, to produce a set of classified pairings. The technique ultimately serves a particular output item to an end user, where the particular output item is chosen based on the results of the matching and assigning. Some implementations of the technique include a filtering operation whereby the candidate-item pairings are filtered to conform to a specified selection strategy or strategies. This filtering operation supplements or replaces the assigning operation.