Microsoft technology licensing, llc (20240135413). Processing of Queries using a Consolidated Matching Architecture simplified abstract
Contents
- 1 Processing of Queries using a Consolidated Matching Architecture
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Processing of Queries using a Consolidated Matching Architecture - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
Processing of Queries using a Consolidated Matching Architecture
Organization Name
microsoft technology licensing, llc
Inventor(s)
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 20240135413 titled 'Processing of Queries using a Consolidated Matching Architecture
Simplified Explanation
The query-processing technique described in the abstract involves matching an input query against a variety of candidate target items to generate a set of candidate query-item pairings. This matching process is performed by a computer system using class-agnostic query-processing logic and a class-agnostic target item index. Each candidate query-item pairing is then assigned a matching class, resulting in a set of classified pairings. The technique ultimately presents a specific output item to the user based on the matching and assigning results, with the option of filtering the candidate-item pairings based on specified selection strategies.
- Matching input queries against candidate target items
- Using class-agnostic query-processing logic and target item index
- Assigning matching classes to candidate query-item pairings
- Presenting specific output item to user based on results
- Filtering candidate-item pairings based on selection strategies
Potential Applications
This technology can be applied in various fields such as e-commerce, information retrieval systems, recommendation systems, and search engines.
Problems Solved
1. Efficient matching of input queries with candidate target items 2. Automated classification and selection of query-item pairings
Benefits
1. Improved accuracy in matching and assigning query-item pairings 2. Enhanced user experience through personalized output item selection
Potential Commercial Applications
Optimizing search engine results, enhancing recommendation systems in e-commerce platforms, improving information retrieval systems in databases.
Possible Prior Art
One potential prior art could be the use of machine learning algorithms for query-item matching and classification in recommendation systems.
Unanswered Questions
How does this technology handle large datasets during the matching process?
The abstract does not provide details on the scalability of the query-processing technique when dealing with extensive datasets. It would be essential to understand how the system manages and processes large volumes of candidate target items efficiently.
What security measures are in place to protect user data during the matching and assigning operations?
The abstract does not mention any security protocols or measures implemented to safeguard user data during the query-processing operations. It would be crucial to address the data privacy and security aspects of the technology to ensure user trust and compliance with data protection regulations.
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.
