Microsoft technology licensing, llc (20240119050). METHOD AND SYSTEM FOR EXTENDING QUERY PROCESSING WITH DIFFERENTIABLE OPERATORS simplified abstract

METHOD AND SYSTEM FOR EXTENDING QUERY PROCESSING WITH DIFFERENTIABLE OPERATORS

Organization Name

microsoft technology licensing, llc

Inventor(s)

Matteo Interlandi of Torrance CA (US)

Apurva Sandeep Gandhi of Union City CA (US)

Yuki Asada of Arlington MA (US)

Advitya Gemawat of Cambridge MA (US)

Victor Renjie Fu of Boston MA (US)

Lihao Zhang of Quincy MA (US)

Rathijit Sen of Redmond WA (US)

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 20240119050 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. These techniques involve receiving a query with a query operator and a trainable user-defined function, determining a query representation, creating an initial neural network program based on the query representation, executing the neural network program to generate a query result, training the initial neural network program to determine a trained neural network program, and executing the trained neural network program to generate inference information.

• Query processing techniques for deep neural network runtimes
• Receiving queries with operators and user-defined functions
• Determining query representations
• Creating initial neural network programs with differentiable operators
• Executing neural network programs to generate query results
• Training neural network programs to determine trained programs
• Generating inference information with trained neural network programs

Potential Applications

The technology described in the patent application could be applied in various fields such as natural language processing, image recognition, and data analysis.

Problems Solved

This technology addresses the challenge of efficiently processing queries over deep neural network runtimes, allowing for faster and more accurate results.

Benefits

The benefits of this technology include improved query processing speed, enhanced accuracy of query results, and the ability to train neural network programs for specific tasks.

Potential Commercial Applications

Potential commercial applications of this technology include search engines, recommendation systems, and data analytics platforms.

Possible Prior Art

One possible prior art for this technology could be techniques for query processing in traditional database systems or machine learning models.

How does this technology compare to existing query processing methods in deep neural network runtimes?

This article does not provide a direct comparison with existing methods in deep neural network runtimes. It would be helpful to understand the specific advantages and limitations of this technology compared to others.

What are the potential limitations or challenges in implementing this technology in real-world applications?

The article does not address the potential obstacles or difficulties that may arise when implementing this technology in practical settings. It would be valuable to explore the practical implications and challenges of deploying this innovation.

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.