18522982. METHOD AND APPARATUS FOR PROCESSING DATA simplified abstract (Samsung Electronics Co., Ltd.)
Contents
- 1 METHOD AND APPARATUS FOR PROCESSING DATA
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHOD AND APPARATUS FOR PROCESSING DATA - 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
METHOD AND APPARATUS FOR PROCESSING DATA
Organization Name
Inventor(s)
METHOD AND APPARATUS FOR PROCESSING DATA - A simplified explanation of the abstract
This abstract first appeared for US patent application 18522982 titled 'METHOD AND APPARATUS FOR PROCESSING DATA
Simplified Explanation
The patent application describes a method of processing data by identifying sparsity among information in input data, rearranging the data based on this sparsity, and generating output data through a neural network operation.
- The method involves identifying valid and invalid information in input data.
- The input data is rearranged based on the distribution of invalid values.
- The rearranged data is processed in a neural network to generate output data.
Potential Applications
This technology could be applied in:
- Data cleaning and preprocessing in machine learning tasks.
- Anomaly detection in large datasets.
Problems Solved
This technology helps in:
- Improving data quality by handling invalid values effectively.
- Enhancing the accuracy of neural network predictions.
Benefits
The benefits of this technology include:
- Streamlining data processing tasks.
- Increasing the reliability of neural network outputs.
Potential Commercial Applications
The technology could be utilized in:
- Data analytics software for businesses.
- Fraud detection systems in financial institutions.
Possible Prior Art
One possible prior art could be:
- Techniques for data preprocessing in machine learning algorithms.
What are the potential limitations of this method in handling extremely large datasets?
The method may face challenges in processing extremely large datasets efficiently due to computational constraints and memory limitations.
How does this method compare to existing data preprocessing techniques in terms of accuracy and efficiency?
This method may offer improved accuracy and efficiency compared to traditional data preprocessing techniques by specifically targeting and rearranging data based on the distribution of invalid values.
Original Abstract Submitted
A method of processing data includes identifying a sparsity among information, included in input data, based on valid information or invalid information included in the input data, rearranging the input data based on the sparsity among the information indicating a distribution of the invalid values included in the input data, and generating, by performing an operation on the rearranged input data in the neural network, an output data.
