Dell products l.p. (20240232048). DUPLICATE INCIDENT DETECTION USING DYNAMIC SIMILARITY THRESHOLD simplified abstract
Contents
DUPLICATE INCIDENT DETECTION USING DYNAMIC SIMILARITY THRESHOLD
Organization Name
Inventor(s)
David C. Sydow of Merrimack NH (US)
Anil Kumar Koluguri of Durham NC (US)
Jeremy Denis White of Londonderry NH (US)
Shobhit Nitinkumar Dutia of Westborough MA (US)
Duhita Mulky Avinash of Melrose MA (US)
DUPLICATE INCIDENT DETECTION USING DYNAMIC SIMILARITY THRESHOLD - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240232048 titled 'DUPLICATE INCIDENT DETECTION USING DYNAMIC SIMILARITY THRESHOLD
The patent application describes methods, apparatus, and processor-readable storage media for duplicate incident detection using a dynamic similarity threshold.
- Obtaining a request with information about tracking a first incident in a database.
- Generating a representation of the first incident.
- Computing similarity scores for the first incident by comparing it to other incidents in the database.
- Detecting duplicate incidents based on the similarity scores and a dynamic threshold.
- Updating the database in response to the detection.
- Potential Applications:
This technology can be applied in various fields such as fraud detection, security monitoring, and data deduplication.
- Problems Solved:
This technology addresses the challenge of efficiently identifying duplicate incidents in a database, leading to improved data accuracy and streamlined processes.
- Benefits:
- Enhanced accuracy in identifying duplicate incidents.
- Increased efficiency in database management.
- Reduction in errors and redundancies in data processing.
- Commercial Applications:
The technology can be utilized by companies in sectors such as finance, healthcare, and e-commerce to improve data quality and streamline operations.
- Prior Art:
Readers can explore prior art related to duplicate incident detection algorithms, database management systems, and similarity threshold techniques.
- Frequently Updated Research:
Stay informed about the latest advancements in duplicate incident detection algorithms, database optimization strategies, and dynamic similarity threshold methodologies.
- Questions about Duplicate Incident Detection:
1. How does the dynamic similarity threshold improve the accuracy of duplicate incident detection? 2. What are the potential challenges in implementing this technology in large-scale databases?
- A relevant generic question not answered by the article, with a detailed answer:
How does the dynamic similarity threshold adapt to changing data patterns to maintain accuracy in duplicate incident detection? The dynamic similarity threshold is updated over time based on the evolving nature of the data in the database. By adjusting the threshold according to the changing patterns of incidents, the system can effectively differentiate between true duplicates and unique incidents, ensuring accurate detection results.
Original Abstract Submitted
methods, apparatus, and processor-readable storage media for duplicate incident detection using a dynamic similarity threshold are provided herein. an example computer-implemented method includes obtaining a request including information associated with tracking at least a first incident in a database; generating a first representation of the first incident that encodes at least a portion of the information; computing a set of similarity scores for the first incident, where a given similarity score is based on a comparison between the first representation and a second representation generated for one of a plurality of additional incidents in the database; detecting that the first incident is a duplicate of at least one of the plurality of additional incidents based on a comparison of the set of similarity scores to a similarity threshold, where the similarity threshold is updated over time; and initiating an update in the database in response to the detecting.
