View source for Dell products l.p. (20240134774). DUPLICATE INCIDENT DETECTION USING DYNAMIC SIMILARITY THRESHOLD simplified abstract

=DUPLICATE INCIDENT DETECTION USING DYNAMIC SIMILARITY THRESHOLD=


==Organization Name==

[[:Category:dell products l.p.|dell products l.p.]]


[[Category:dell products l.p.]]

==Inventor(s)==

[[:Category:David C. Sydow of Merrimack NH (US)|David C. Sydow of Merrimack NH (US)]][[Category:David C. Sydow of Merrimack NH (US)]]

[[:Category:Anil Kumar Koluguri of Durham NC (US)|Anil Kumar Koluguri of Durham NC (US)]][[Category:Anil Kumar Koluguri of Durham NC (US)]]

[[:Category:Jeremy Denis White of Londonderry NH (US)|Jeremy Denis White of Londonderry NH (US)]][[Category:Jeremy Denis White of Londonderry NH (US)]]

[[:Category:Shobhit Nitinkumar Dutia of Westborough MA (US)|Shobhit Nitinkumar Dutia of Westborough MA (US)]][[Category:Shobhit Nitinkumar Dutia of Westborough MA (US)]]

[[:Category:Duhita Mulky Avinash of Melrose MA (US)|Duhita Mulky Avinash of Melrose MA (US)]][[Category: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 20240134774 titled 'DUPLICATE INCIDENT DETECTION USING DYNAMIC SIMILARITY THRESHOLD

==Simplified Explanation==

The patent application describes methods, apparatus, and processor-readable storage media for duplicate incident detection using a dynamic similarity threshold. The process involves obtaining information related to tracking an incident, generating representations of the incident, computing similarity scores with other incidents in the database, detecting duplicates based on a similarity threshold, and updating the database accordingly.

* The method involves obtaining information about an incident, generating representations of the incident, computing similarity scores with other incidents, detecting duplicates based on a dynamic similarity threshold, and updating the database.
* The apparatus includes components for obtaining incident information, generating representations, computing similarity scores, and updating the database.
* The processor-readable storage media stores instructions for implementing the duplicate incident detection process.

== Potential Applications ==
The technology can be applied in various fields such as fraud detection, security monitoring, and data deduplication.

== Problems Solved ==
This technology helps in efficiently identifying duplicate incidents in a database, reducing manual effort and improving data accuracy.

== Benefits ==
The benefits of this technology include improved data quality, faster incident detection, and automated database maintenance.

== Potential Commercial Applications ==
Commercial applications of this technology can be found in industries like cybersecurity, financial services, and healthcare for enhancing data management and security protocols.

== Possible Prior Art ==
Prior art in this field may include existing systems for incident tracking and duplicate detection, but the use of a dynamic similarity threshold for detecting duplicates may be a novel aspect of this technology.

=== Unanswered Questions ===

== How does the dynamic similarity threshold adapt to changing data patterns over time? ==
The patent application does not provide specific details on how the similarity threshold is updated in response to evolving data trends.

== What are the computational requirements for computing similarity scores between incidents in a large database? ==
The application does not delve into the computational resources needed to perform similarity score calculations for a significant number of incidents in a database.


==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.



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