View source for 17982994. DEVICE AND COMPONENT STATE PREDICTION AND FAILURE PREVENTION simplified abstract (Dell Products L.P.)

=DEVICE AND COMPONENT STATE PREDICTION AND FAILURE PREVENTION=


==Organization Name==

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


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

==Inventor(s)==

[[:Category:Parminder Singh Sethi of Ludhiana (IN)|Parminder Singh Sethi of Ludhiana (IN)]][[Category:Parminder Singh Sethi of Ludhiana (IN)]]

[[:Category:Lakshmi Saroja Nalam of Bangalore (IN)|Lakshmi Saroja Nalam of Bangalore (IN)]][[Category:Lakshmi Saroja Nalam of Bangalore (IN)]]

[[:Category:Atishay Jain of Meerut (IN)|Atishay Jain of Meerut (IN)]][[Category:Atishay Jain of Meerut (IN)]]

==DEVICE AND COMPONENT STATE PREDICTION AND FAILURE PREVENTION - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17982994 titled 'DEVICE AND COMPONENT STATE PREDICTION AND FAILURE PREVENTION

==Simplified Explanation==

The patent application describes techniques for state prediction and failure prevention using machine learning algorithms based on data corresponding to the operation of elements in a system.

* Data is received for a plurality of elements, such as devices and device components, containing operational states.
* Machine learning algorithms predict future operational states of elements based on the received data.
* Actions to prevent elements from transitioning to predicted future states are identified using machine learning algorithms.

== Potential Applications ==
The technology can be applied in various industries such as manufacturing, healthcare, and transportation to predict and prevent failures in systems and equipment.

== Problems Solved ==
This technology helps in proactively identifying potential failures in systems, allowing for preventive maintenance and minimizing downtime.

== Benefits ==
The benefits of this technology include increased system reliability, reduced maintenance costs, and improved operational efficiency by preventing unexpected failures.

== Potential Commercial Applications ==
The technology can be utilized in predictive maintenance solutions for industrial equipment, smart healthcare devices, and autonomous vehicles to enhance performance and reliability.

== Possible Prior Art ==
One possible prior art could be predictive maintenance systems that use historical data to predict equipment failures, but the use of machine learning algorithms for real-time state prediction and failure prevention is a novel approach.

=== Unanswered Questions ===

=== How does the accuracy of the state prediction compare to traditional methods? ===
The article does not provide information on the accuracy of the state prediction compared to traditional methods. This could be crucial in understanding the effectiveness of the proposed technology in real-world applications.

=== What are the potential limitations or challenges in implementing this technology? ===
The article does not address potential limitations or challenges in implementing this technology, such as data quality requirements, computational resources, or integration with existing systems. Understanding these factors is essential for successful adoption of the innovation.


==Original Abstract Submitted==

Techniques for state prediction and failure prevention are disclosed. For example, a method comprises receiving data corresponding to operation of a plurality of elements, wherein the plurality of elements comprise at least one of a plurality of devices and a plurality of device components. The data corresponding to the operation of the plurality of elements comprises one or more operational states for respective ones of the plurality of elements. Using one or more machine learning algorithms, a future operational state of one or more elements of the plurality of elements is predicted. The prediction is based, at least in part, on the data corresponding to the operation of the plurality of elements. Using the one or more machine learning algorithms, one or more actions to prevent the one or more elements from transitioning to the future operational state are identified.



[[Category:G05B19/418]]