View source for Dell products l.p. (20240256371). SYSTEM AND METHOD FOR PREDICTING SYSTEM FAILURE AND TIME-TO-FAILURE BASED ON ATTRIBUTION SCORES OF LOG DATA simplified abstract

=SYSTEM AND METHOD FOR PREDICTING SYSTEM FAILURE AND TIME-TO-FAILURE BASED ON ATTRIBUTION SCORES OF LOG DATA=


==Organization Name==

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


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

==Inventor(s)==

[[:Category:DALE Wang of Hayward CA (US)|DALE Wang of Hayward CA (US)]][[Category:DALE Wang of Hayward CA (US)]]

[[:Category:MIN Gong of Shanghai (CN)|MIN Gong of Shanghai (CN)]][[Category:MIN Gong of Shanghai (CN)]]

[[:Category:ASHOK NARAYANAN Potti of Bangalore (IN)|ASHOK NARAYANAN Potti of Bangalore (IN)]][[Category:ASHOK NARAYANAN Potti of Bangalore (IN)]]

==SYSTEM AND METHOD FOR PREDICTING SYSTEM FAILURE AND TIME-TO-FAILURE BASED ON ATTRIBUTION SCORES OF LOG DATA - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240256371 titled 'SYSTEM AND METHOD FOR PREDICTING SYSTEM FAILURE AND TIME-TO-FAILURE BASED ON ATTRIBUTION SCORES OF LOG DATA

The abstract describes methods and systems for managing data processing systems based on indications of potential failures. This involves obtaining logs for components of the system to analyze historical and current operation, implementing inference models to predict future infrastructure issues, and generating health scores for the system.

* Data processing system manager obtains logs for system components
* Inference models predict future system infrastructure issues
* Attribution scores assigned to portions of logs to predict times-to-failures
* Health scores generated for the data processing system
* Remedial actions determined based on health scores

== Potential Applications ==
This technology can be applied in various industries where data processing systems are critical for operations, such as finance, healthcare, and telecommunications.

== Problems Solved ==
This technology addresses the challenge of proactively managing data processing systems to prevent potential failures and downtime, ultimately improving system reliability and performance.

== Benefits ==
The benefits of this technology include increased system uptime, reduced maintenance costs, improved operational efficiency, and enhanced decision-making based on predictive analytics.

== Commercial Applications ==
Title: Predictive Maintenance for Data Processing Systems
This technology can be commercially used by IT companies, data centers, and organizations with complex data processing systems to optimize system performance, minimize downtime, and enhance overall operational effectiveness.

== Prior Art ==
Further research can be conducted in the field of predictive maintenance for data processing systems, including studies on machine learning algorithms for failure prediction and real-time monitoring technologies.

== Frequently Updated Research ==
Researchers are constantly exploring new methods and technologies for predictive maintenance in data processing systems, including advancements in artificial intelligence, predictive analytics, and anomaly detection algorithms.

== Questions about Data Processing Systems ==
=== How can predictive maintenance benefit data processing systems? ===
Predictive maintenance can help prevent unexpected failures, reduce downtime, and optimize system performance by identifying potential issues before they occur.

=== What are the key components of a data processing system manager? ===
A data processing system manager typically includes software tools for collecting and analyzing system logs, inference models for predicting failures, and algorithms for generating health scores.


==Original Abstract Submitted==

methods and systems for managing data processing systems based on indications of a failure are disclosed. a data processing system may include and depend on the operation of hardware and/or software components. to manage the operation of the data processing system, a data processing system manager may obtain logs for components of the data processing system that reflect the historical and/or current operation of these components. inference models may be implemented to predict future system infrastructure issues (e.g., future component failures) using information recorded in the logs. the inference models may also predict times-to-failures associated with the future failures by assigning attribution scores to portions of logs. further analysis of attribution scores may be performed to generate health scores for the data processing system, providing additional information for the determination of remedial actions that may reduce the likelihood of the data processing system becoming impaired.



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