17957069. SYSTEMS AND METHODS FOR PREDICTING CHANGE POINTS simplified abstract (Capital One Services, LLC)
Contents
- 1 SYSTEMS AND METHODS FOR PREDICTING CHANGE POINTS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SYSTEMS AND METHODS FOR PREDICTING CHANGE POINTS - 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
SYSTEMS AND METHODS FOR PREDICTING CHANGE POINTS
Organization Name
Inventor(s)
Aamer Charania of Flower Mound TX (US)
Abhisek Jana of Herndon VA (US)
Jiankun Liu of Flower Mound TX (US)
Behrouz Saghafi Khadem of Frisco TX (US)
SYSTEMS AND METHODS FOR PREDICTING CHANGE POINTS - A simplified explanation of the abstract
This abstract first appeared for US patent application 17957069 titled 'SYSTEMS AND METHODS FOR PREDICTING CHANGE POINTS
Simplified Explanation
The patent application describes systems and methods for predicting change points in tabular data by generating time-stamped graphs based on data entries and corresponding time stamps, and using machine learning models to predict occurrences of change points in the data entries.
- Time-stamped graphs are generated based on data entries and corresponding time stamps.
- Each graph represents one or more events associated with a time stamp.
- Graphs are independent of events before or after the time stamp.
- Graph embeddings are generated for each graph and processed using a machine learning model to predict change points in the data entries.
Potential Applications
The technology could be applied in various fields such as finance, healthcare, and manufacturing for predicting changes in data patterns.
Problems Solved
This technology helps in identifying change points in tabular data, which can be crucial for making informed decisions and taking timely actions.
Benefits
The system provides a predictive tool for detecting potential changes in data trends, allowing for proactive decision-making and risk management.
Potential Commercial Applications
The technology could be utilized in financial forecasting software, healthcare analytics platforms, and quality control systems in manufacturing.
Possible Prior Art
One possible prior art could be traditional statistical methods for detecting change points in time series data, which may not be as efficient or accurate as the proposed system.
What are the specific machine learning models used in the system for predicting change points?
The specific machine learning models used in the system for predicting change points are not mentioned in the abstract. It would be helpful to know which algorithms or techniques are employed for this predictive task.
How does the system handle noisy or incomplete data entries when generating time-stamped graphs?
The abstract does not address how the system handles noisy or incomplete data entries when generating time-stamped graphs. Understanding the approach to dealing with data quality issues would be important for assessing the reliability of the predictions made by the system.
Original Abstract Submitted
Systems and methods for predicting change points in tabular data. In some aspects, the systems and methods provide for generating time-stamped graphs based on data entries and corresponding time stamps. Each graph of the time-stamped graphs corresponds to a data entry and is representative of one or more events associated with a time stamp corresponding to the data entry. The graph is independent of any events before or after the time stamp. For each graph of the time-stamped graphs, a set of graph embeddings is generated based on the graph and processed using a machine learning model to predict an occurrence of a change point in the data entries.
