View source for 18049502. ADAPTIVE LOGISTICS NAVIGATION ASSISTANCE BASED ON PACKAGE FRAGILITY simplified abstract (Dell Products L.P.)

=ADAPTIVE LOGISTICS NAVIGATION ASSISTANCE BASED ON PACKAGE FRAGILITY=


==Organization Name==

[[:Category:Dell Products L.P.|Dell Products L.P.]]


[[Category:Dell Products L.P.]]

==Inventor(s)==

[[:Category:Eric L. Caron of Ottawa (CA)|Eric L. Caron of Ottawa (CA)]][[Category:Eric L. Caron of Ottawa (CA)]]

[[:Category:Eric Bruno of Shirley NY (US)|Eric Bruno of Shirley NY (US)]][[Category:Eric Bruno of Shirley NY (US)]]

[[:Category:Jason Bonafide of Winter Garden FL (US)|Jason Bonafide of Winter Garden FL (US)]][[Category:Jason Bonafide of Winter Garden FL (US)]]

[[:Category:Nalinkumar Mistry of Ottawa (CA)|Nalinkumar Mistry of Ottawa (CA)]][[Category:Nalinkumar Mistry of Ottawa (CA)]]

[[:Category:Vinicius Michel Gottin of Rio de Janeiro (BR)|Vinicius Michel Gottin of Rio de Janeiro (BR)]][[Category:Vinicius Michel Gottin of Rio de Janeiro (BR)]]

==ADAPTIVE LOGISTICS NAVIGATION ASSISTANCE BASED ON PACKAGE FRAGILITY - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18049502 titled 'ADAPTIVE LOGISTICS NAVIGATION ASSISTANCE BASED ON PACKAGE FRAGILITY

==Simplified Explanation==

The abstract describes a method for analyzing sensor data to detect anomalous driving patterns in a movable edge node based on the fragility level of transported packages.

* Receiving datasets from sensors with fragility level data
* Extracting features from the datasets
* Determining events indicating anomalous driving patterns
* Generating alarms based on predetermined fragility level thresholds

== Potential Applications ==
This technology could be applied in various industries such as logistics, transportation, and supply chain management to improve the safety and security of transported packages.

== Problems Solved ==
1. Detection of anomalous driving patterns in real-time
2. Enhancing the monitoring and protection of fragile packages during transportation

== Benefits ==
1. Early detection of potential risks to fragile packages
2. Improved decision-making for route optimization and driver behavior monitoring

== Potential Commercial Applications ==
"Enhancing Package Transportation Safety and Security: Applications of Fragility-Based Anomaly Detection Technology"

== Possible Prior Art ==
There may be existing systems or methods for monitoring driving patterns in transportation systems, but the specific focus on fragility levels of packages for anomaly detection may be a novel aspect of this technology.

== Unanswered Questions ==

=== How does the system differentiate between normal driving patterns and anomalous ones based on fragility levels? ===
The system likely uses machine learning algorithms to analyze the extracted features and compare them to predefined thresholds to determine anomalous events.

=== What types of sensors are commonly used in this technology, and how do they contribute to detecting anomalous driving patterns? ===
Sensors such as accelerometers, GPS trackers, and temperature sensors may be utilized to collect data on the movement and conditions of the packages during transportation. The data from these sensors is crucial in determining the fragility levels and detecting anomalies in driving patterns.


==Original Abstract Submitted==

One example method includes receiving datasets based on one or more instances of sensor data that are received from one or more sensors. The sensor data is associated with an aggregate fragility level that indicates how fragile one or more packages being transported by a movable edge node in an edge environment are. Features that are based on the datasets are extracted. Based on the extracted features, events that indicate anomalous driving patterns for the movable edge node are determined. In response to determining the events, an alarm based on a predetermined threshold that is based on the aggregate fragility level is generated.



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