View source for 17933247. SYSTEMS AND METHODS FOR DETERMINING VIDEO SIMILARITY, RISK SCORES, AND TEXTUAL DESCRIPTIONS simplified abstract (Verizon Patent and Licensing Inc.)

=SYSTEMS AND METHODS FOR DETERMINING VIDEO SIMILARITY, RISK SCORES, AND TEXTUAL DESCRIPTIONS=


==Organization Name==

[[:Category:Verizon Patent and Licensing Inc.|Verizon Patent and Licensing Inc.]]


[[Category:Verizon Patent and Licensing Inc.]]

==Inventor(s)==

[[:Category:Matteo Simoncini of Florence (IT)|Matteo Simoncini of Florence (IT)]][[Category:Matteo Simoncini of Florence (IT)]]

[[:Category:Stefano Caprasecca of Firenze (IT)|Stefano Caprasecca of Firenze (IT)]][[Category:Stefano Caprasecca of Firenze (IT)]]

[[:Category:Leonardo Sarti of Florence (IT)|Leonardo Sarti of Florence (IT)]][[Category:Leonardo Sarti of Florence (IT)]]

==SYSTEMS AND METHODS FOR DETERMINING VIDEO SIMILARITY, RISK SCORES, AND TEXTUAL DESCRIPTIONS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17933247 titled 'SYSTEMS AND METHODS FOR DETERMINING VIDEO SIMILARITY, RISK SCORES, AND TEXTUAL DESCRIPTIONS

==Simplified Explanation==

The abstract describes a device that can analyze video data to identify unsafe driving events, assign tags based on classifications, calculate event severity scores, and determine video risk scores for display.

* The device receives video data identifying unsafe driving events by a driver.
* It processes the video data using a machine learning model to determine classifications for the videos.
* Tags are assigned to the videos based on the classifications.
* Event severity scores are calculated based on the classifications.
* Tag scores, time-to-contact scores, box cross scores, day/night scores, weather scores, and road condition scores are calculated based on the video data.
* Video risk scores are calculated for the videos based on various factors.
* The video risk scores are provided for display.

== Potential Applications ==
This technology could be used in:
* Fleet management systems to monitor and improve driver behavior.
* Insurance companies to assess risk and adjust premiums accordingly.

== Problems Solved ==
This technology helps in:
* Identifying and addressing unsafe driving behavior.
* Improving road safety by providing insights into driving habits.

== Benefits ==
The benefits of this technology include:
* Enhanced driver safety.
* Reduced accidents and associated costs.
* Improved risk assessment for insurance purposes.

== Potential Commercial Applications ==
The potential commercial applications of this technology include:
* Integration into dash cams and telematics systems.
* Licensing to fleet management companies and insurance providers.

== Possible Prior Art ==
One possible prior art could be the use of dash cams for recording driving behavior, but the specific analysis and scoring system described in this patent application may be novel.

=== Unanswered Questions ===

=== How does the device determine the severity of an unsafe driving event? ===
The device calculates event severity scores based on the classifications obtained from processing the video data with a machine learning model.

=== What factors are considered in calculating the video risk scores? ===
The video risk scores are calculated based on event severity scores, tag scores, time-to-contact scores, box cross scores, day/night scores, weather scores, and road condition scores.


==Original Abstract Submitted==

A device may receive video data identifying videos associated with one or more unsafe driving events by a driver of a vehicle, and may process the video data, with a machine learning model, to determine classifications for the videos. The device may assign tags to the videos based on the classifications, and may calculate event severity scores based on the classifications. The device may calculate tag scores based on the tags assigned to the videos, and may calculate time-to-contact scores, box cross scores, day/night scores, weather scores, and road condition scores based on the video data. The device may calculate video risk scores for the videos based on the event severity scores, the tag scores, the time-to-contact scores, the box cross scores, the day/night scores, the weather scores, and the road condition scores, and may provide one or more of the video risk scores for display.



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