View source for 17963809. METHOD AND SYSTEM FOR EXTENDING QUERY PROCESSING WITH DIFFERENTIABLE OPERATORS simplified abstract (Microsoft Technology Licensing, LLC)

=METHOD AND SYSTEM FOR EXTENDING QUERY PROCESSING WITH DIFFERENTIABLE OPERATORS=


==Organization Name==

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


[[Category:Microsoft Technology Licensing, LLC]]

==Inventor(s)==

[[:Category:Matteo Interlandi of Torrance CA (US)|Matteo Interlandi of Torrance CA (US)]][[Category:Matteo Interlandi of Torrance CA (US)]]

[[:Category:Apurva Sandeep Gandhi of Union City CA (US)|Apurva Sandeep Gandhi of Union City CA (US)]][[Category:Apurva Sandeep Gandhi of Union City CA (US)]]

[[:Category:Yuki Asada of Arlington MA (US)|Yuki Asada of Arlington MA (US)]][[Category:Yuki Asada of Arlington MA (US)]]

[[:Category:Advitya Gemawat of Cambridge MA (US)|Advitya Gemawat of Cambridge MA (US)]][[Category:Advitya Gemawat of Cambridge MA (US)]]

[[:Category:Victor Renjie Fu of Boston MA (US)|Victor Renjie Fu of Boston MA (US)]][[Category:Victor Renjie Fu of Boston MA (US)]]

[[:Category:Lihao Zhang of Quincy MA (US)|Lihao Zhang of Quincy MA (US)]][[Category:Lihao Zhang of Quincy MA (US)]]

[[:Category:Rathijit Sen of Redmond WA (US)|Rathijit Sen of Redmond WA (US)]][[Category:Rathijit Sen of Redmond WA (US)]]

[[:Category:Dalitso Hansini Banda of Mountain View CA (US)|Dalitso Hansini Banda of Mountain View CA (US)]][[Category:Dalitso Hansini Banda of Mountain View CA (US)]]

==METHOD AND SYSTEM FOR EXTENDING QUERY PROCESSING WITH DIFFERENTIABLE OPERATORS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17963809 titled 'METHOD AND SYSTEM FOR EXTENDING QUERY PROCESSING WITH DIFFERENTIABLE OPERATORS

==Simplified Explanation==

The patent application describes techniques for query processing over deep neural network runtimes. 

* Receiving a query with a query operator and a trainable user-defined function (UDF)
* Determining a query representation based on the query
* Creating an initial neural network program with differentiable operators corresponding to the query operator
* Executing the neural network program in the runtime to generate a query result
* Training the initial neural network program to determine a trained neural network program
* Executing the trained neural network program in the runtime to generate inference information

== Potential Applications ==
This technology could be applied in various fields such as natural language processing, image recognition, and recommendation systems.

== Problems Solved ==
This technology helps in efficiently processing complex queries over deep neural network runtimes, improving performance and accuracy.

== Benefits ==
The benefits of this technology include faster query processing, improved accuracy in results, and the ability to handle complex queries effectively.

== Potential Commercial Applications ==
Potential commercial applications of this technology include search engines, e-commerce platforms, and data analytics companies.

== Possible Prior Art ==
One possible prior art could be techniques for query processing in traditional database systems, which may not be as efficient or accurate as the methods described in this patent application.

=== What are the specific neural network runtimes used in this technology? ===

The specific neural network runtimes used in this technology are not explicitly mentioned in the abstract. Further details may be provided in the full patent application document.

=== How does this technology compare to existing query processing techniques in terms of efficiency and accuracy? ===

The abstract does not provide a direct comparison between this technology and existing query processing techniques. Further information on comparative studies or performance evaluations would be needed to answer this question accurately.


==Original Abstract Submitted==

Example aspects include techniques for query processing over deep neural network runtimes. These techniques include receiving a query including a query operator and a trainable user defined function (UDF). In addition, the techniques include determining a query representation based on the query, and determining, for performing the query in a neural network runtime, an initial neural network program based on the query representation, the initial neural network program including a differentiable operators corresponding to the query operator. and executing the neural network program in the neural network runtime over the neural network data structure to generate a query result. Further, the techniques include training the initial neural network program via the neural network runtime to determine a trained neural network program, and executing the trained neural network program in the neural network runtime to generate inference information.



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