17970262. DUPLICATE INCIDENT DETECTION USING DYNAMIC SIMILARITY THRESHOLD simplified abstract (Dell Products L.P.)
Contents
- 1 DUPLICATE INCIDENT DETECTION USING DYNAMIC SIMILARITY THRESHOLD
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 DUPLICATE INCIDENT DETECTION USING DYNAMIC SIMILARITY THRESHOLD - 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
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 17970262 titled 'DUPLICATE INCIDENT DETECTION USING DYNAMIC SIMILARITY THRESHOLD
Simplified Explanation
The abstract describes a method for detecting duplicate incidents in a database by comparing similarity scores between incidents and a dynamic threshold.
- Obtaining a request with information about a first incident
- Generating a representation of the first incident
- Computing similarity scores between the first incident and other incidents in the database
- Detecting duplicates based on similarity scores and a dynamic threshold
- Updating the database in response to duplicate detection
Potential Applications
This technology could be applied in various industries where duplicate incident detection is crucial, such as fraud detection in financial services, plagiarism detection in academia, and identifying duplicate medical records in healthcare.
Problems Solved
This technology solves the problem of efficiently identifying duplicate incidents in a database, reducing manual effort and improving data accuracy and integrity.
Benefits
The benefits of this technology include improved data quality, reduced risk of errors, increased efficiency in incident management, and enhanced decision-making based on accurate and reliable data.
Potential Commercial Applications
A potential commercial application of this technology could be in software development for database management systems, where companies can integrate this duplicate incident detection method to enhance the functionality and reliability of their products.
Possible Prior Art
One possible prior art for this technology could be existing duplicate detection algorithms used in various fields such as data deduplication in storage systems, plagiarism detection software, and fraud detection systems in financial institutions.
Unanswered Questions
How does the dynamic similarity threshold adapt to changing data patterns over time?
The abstract mentions that the similarity threshold is updated over time, but it does not specify the mechanism or algorithm used for this adaptation. It would be interesting to know how the threshold is adjusted to account for evolving data patterns.
What is the computational complexity of computing similarity scores for a large database of incidents?
The abstract describes computing similarity scores for the first incident against a plurality of additional incidents in the database. Understanding the computational complexity of this process, especially for large databases, would provide insights into the scalability of the method.
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.
