View source for 17850754. SYSTEM AND METHOD FOR REDUCTION OF DATA TRANSMISSION IN DYNAMIC SYSTEMS THROUGH REVISION OF RECONSTRUCTED DATA simplified abstract (Dell Products L.P.)

=SYSTEM AND METHOD FOR REDUCTION OF DATA TRANSMISSION IN DYNAMIC SYSTEMS THROUGH REVISION OF RECONSTRUCTED DATA=


==Organization Name==

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


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

==Inventor(s)==

[[:Category:Ofir Ezrielev of Be'er Sheva (IL)|Ofir Ezrielev of Be'er Sheva (IL)]][[Category:Ofir Ezrielev of Be'er Sheva (IL)]]

[[:Category:Jehuda Shemer of Kfar Saba (IL)|Jehuda Shemer of Kfar Saba (IL)]][[Category:Jehuda Shemer of Kfar Saba (IL)]]

==SYSTEM AND METHOD FOR REDUCTION OF DATA TRANSMISSION IN DYNAMIC SYSTEMS THROUGH REVISION OF RECONSTRUCTED DATA - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17850754 titled 'SYSTEM AND METHOD FOR REDUCTION OF DATA TRANSMISSION IN DYNAMIC SYSTEMS THROUGH REVISION OF RECONSTRUCTED DATA

==Simplified Explanation==

Methods and systems for managing data collection are disclosed in this patent application. The innovation involves using a twin inference model to reduce computing resources used for data aggregation. Here are the key points:

* A data aggregator collects and aggregates data from a data collector.
* Instead of receiving copies of data from the data collector, the data aggregator and data collector use inferences provided by a twin inference model.
* The twin inference model replaces the need for actual data collected by the data collector, reducing computing resources required for aggregation.
* Over time, the aggregated data can be revised using revised inference models.
* The revised inference models are updated with subsequently obtained data from the data collector.
* The revised inferences obtained from the revised inference models can replace the original inferences in the aggregated data.
* The revised inferences are expected to be more accurate due to differences in the data used for inference and the revised inference models.

Potential applications of this technology:

* Data management and aggregation systems.
* Internet of Things (IoT) networks where data collection and aggregation are crucial.
* Machine learning and artificial intelligence systems that rely on accurate data.

Problems solved by this technology:

* Reduces the computing resources required for data aggregation.
* Provides a more efficient way to update and revise aggregated data.
* Improves the accuracy of inferences made from the aggregated data.

Benefits of this technology:

* Saves computing resources, leading to cost savings and improved efficiency.
* Allows for real-time updates and revisions of aggregated data.
* Enhances the accuracy of inferences made from the aggregated data.


==Original Abstract Submitted==

Methods and systems for managing data collection are disclosed. A data aggregator may aggregate data collected by a data collector. To reduce computing resources used for aggregation, the data aggregator and data collector may use inferences provided by a twin inference model in place of data collected by the data collector rather than receiving copies of data from the data collector. Over time, the aggregated data may be revised using revised inference models that are revised using subsequently obtained data from the data collector. The revised inference models may be used to obtain revised inferences that may replace original inferences in the aggregated data. The revised inferences may be of higher accuracy due to differences in the data upon which the inference and revised inference models are based.



[[Category:G06N5/04]]