18545762. METHODS AND APPARATUS TO CONSTRUCT GRAPHS FROM COALESCED FEATURES simplified abstract (Intel Corporation)

METHODS AND APPARATUS TO CONSTRUCT GRAPHS FROM COALESCED FEATURES

Organization Name

Intel Corporation

Inventor(s)

Ravi H. Motwani of Fremont CA (US)

Ke Ding of Saratoga CA (US)

Jian Zhang of Shanghai (CN)

Chendi Xue of Austin TX (US)

Poovaiah Manavattira Palangappa of San Jose CA (US)

Rita Brugarolas Brufau of Hillsboro OR (US)

Xinyao Wang of Shanghai (CN)

Yu Zhou of Shanghai (CN)

Aasavari Dhananjay Kakne of Santa Clara CA (US)

METHODS AND APPARATUS TO CONSTRUCT GRAPHS FROM COALESCED FEATURES - A simplified explanation of the abstract

This abstract first appeared for US patent application 18545762 titled 'METHODS AND APPARATUS TO CONSTRUCT GRAPHS FROM COALESCED FEATURES

Simplified Explanation

The patent application describes a system that uses interface circuitry, machine readable instructions, and programmable circuitry to associate data points with nodes, construct a graph, and compare the accuracy of the graph with a baseline accuracy.

• Interface circuitry, machine readable instructions, and programmable circuitry are used to perform tasks related to data association, graph construction, and accuracy comparison.
• The system can associate data points of different features with nodes in a graph.
• It constructs a graph using the associated data points.
• The system then compares the accuracy of the constructed graph with a baseline accuracy.

Potential Applications

This technology could be applied in various fields such as data analysis, machine learning, and network optimization.

Problems Solved

This technology helps in efficiently organizing and analyzing large datasets, improving decision-making processes, and enhancing accuracy in data-driven tasks.

Benefits

The system offers a more structured approach to data analysis, enables better visualization of relationships between data points, and enhances the accuracy of predictive models.

Potential Commercial Applications

Potential commercial applications include data analytics software, predictive modeling tools, and network optimization solutions.

Possible Prior Art

One possible prior art could be existing graph construction and comparison algorithms used in data analysis and machine learning applications.

How does this technology handle noisy or incomplete data?

The system's ability to handle noisy or incomplete data is not explicitly mentioned in the abstract. It would be important to understand how robust the system is in real-world scenarios where data may not be perfect.

What computational resources are required to implement this system?

The abstract does not provide information on the computational resources needed to instantiate or execute the machine readable instructions. Understanding the system's resource requirements would be crucial for assessing its practicality and scalability.

Original Abstract Submitted

Systems, apparatus, articles of manufacture, and methods are disclosed that include interface circuitry, machine readable instructions, and programmable circuitry to at least one of instantiate or execute the machine readable instructions to associate first datapoints of a first feature with a first node, associate second datapoints of a second feature with a second node, construct a graph from the first datapoints and the second datapoints, and perform a comparison of a graph accuracy with a baseline accuracy.