Capital one services, llc (20240111989). SYSTEMS AND METHODS FOR PREDICTING CHANGE POINTS simplified abstract
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 20240111989 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. Graph embeddings are then generated and processed using a machine learning model to predict occurrences of change points in the data entries.
- Time-stamped graphs are created based on data entries and time stamps.
- Each graph represents events associated with a specific 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.
- The machine learning model predicts occurrences of change points in the data entries.
Potential Applications
This technology can be applied in various fields such as finance, healthcare, and manufacturing for predicting changes in data trends and patterns.
Problems Solved
1. Predicting change points in tabular data accurately. 2. Identifying significant events in data entries efficiently.
Benefits
1. Improved decision-making based on predicted change points. 2. Enhanced data analysis and trend forecasting capabilities.
Potential Commercial Applications
Predictive analytics software for businesses. SEO Optimized Title: "Commercial Applications of Predictive Analytics Software for Businesses"
Possible Prior Art
One possible prior art could be traditional statistical methods for identifying change points in data, which may not be as accurate or efficient as the system described in the patent application.
What are the limitations of the machine learning model used in this system?
The limitations of the machine learning model used in this system could include overfitting to the training data, potential bias in the predictions, and the need for continuous updates to adapt to new data patterns.
How does this system handle outliers in the tabular data when predicting change points?
This system may address outliers in the tabular data by incorporating robust statistical techniques or preprocessing steps to minimize their impact on the prediction of change points.
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.
