Dell products l.p. (20240134562). AUTOMATED DATA ARCHIVAL FRAMEWORK USING ARTIFICIAL INTELLIGENCE TECHNIQUES simplified abstract
Contents
- 1 AUTOMATED DATA ARCHIVAL FRAMEWORK USING ARTIFICIAL INTELLIGENCE TECHNIQUES
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 AUTOMATED DATA ARCHIVAL FRAMEWORK USING ARTIFICIAL INTELLIGENCE TECHNIQUES - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
AUTOMATED DATA ARCHIVAL FRAMEWORK USING ARTIFICIAL INTELLIGENCE TECHNIQUES
Organization Name
Inventor(s)
Bijan Kumar Mohanty of Austin TX (US)
Barun Pandey of Bangalore (IN)
Sabu K. Syed of Austin TX (US)
Hung T. Dinh of Austin TX (US)
AUTOMATED DATA ARCHIVAL FRAMEWORK USING ARTIFICIAL INTELLIGENCE TECHNIQUES - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240134562 titled 'AUTOMATED DATA ARCHIVAL FRAMEWORK USING ARTIFICIAL INTELLIGENCE TECHNIQUES
Simplified Explanation
The abstract describes a patent application for an automated data archival framework using artificial intelligence techniques. The method involves obtaining data from storage systems, determining storage-related features, predicting data archival classes, and performing automated actions based on the predicted classes.
- Obtaining data from storage systems
- Determining storage-related features within the data
- Predicting data archival classes using artificial intelligence techniques
- Performing automated actions based on predicted classes
Potential Applications
This technology could be applied in various industries such as healthcare, finance, and e-commerce for efficient data management and archival processes.
Problems Solved
This technology solves the problem of manual data archival processes, which can be time-consuming and error-prone. By automating the archival framework, organizations can save time and resources.
Benefits
The benefits of this technology include improved data management, increased efficiency, reduced human error, and cost savings for organizations implementing the automated archival framework.
Potential Commercial Applications
The potential commercial applications of this technology include data storage companies, cloud service providers, and organizations with large amounts of data that require efficient archival solutions.
Possible Prior Art
One possible prior art for this technology could be traditional data archival methods that rely on manual processes and lack the use of artificial intelligence techniques for predicting data archival classes.
Unanswered Questions
How does this technology ensure data security during the archival process?
The patent abstract does not mention specific security measures implemented in the automated data archival framework to ensure the protection of sensitive information. Additional details on data encryption, access controls, and compliance with data protection regulations would be helpful in understanding the security aspects of this technology.
What types of storage systems are compatible with this automated data archival framework?
The abstract does not specify the compatibility of the framework with different types of storage systems such as on-premises servers, cloud storage, or hybrid storage environments. Understanding the flexibility and adaptability of the technology to various storage systems would be beneficial for potential users looking to implement the automated archival solution.
Original Abstract Submitted
methods, apparatus, and processor-readable storage media for implementing an automated data archival framework using artificial intelligence techniques are provided herein. an example computer-implemented method includes obtaining data associated with one or more storage systems; determining one or more storage-related features within the obtained data by processing at least a portion of the obtained data; predicting at least one data archival class, from a set of multiple predetermined data archival classes, for at least a portion of the obtained data by processing the one or more storage-related features using one or more artificial intelligence techniques; and performing one or more automated actions based at least in part on the at least one predicted data archival class.