View source for 20240013064. MACHINE LEARNING TECHNIQUES USING MODEL DEFICIENCY DATA OBJECTS FOR TENSOR-BASED GRAPH PROCESSING MODELS simplified abstract (Optum, Inc.)

=MACHINE LEARNING TECHNIQUES USING MODEL DEFICIENCY DATA OBJECTS FOR TENSOR-BASED GRAPH PROCESSING MODELS=


==Organization Name==

[[:Category:Optum, Inc.|Optum, Inc.]]


[[Category:Optum, Inc.]]

==Inventor(s)==

[[:Category:Paul J. Godden of London (GB)|Paul J. Godden of London (GB)]][[Category:Paul J. Godden of London (GB)]]

[[:Category:Erik A. Nystrom of Durham NC (US)|Erik A. Nystrom of Durham NC (US)]][[Category:Erik A. Nystrom of Durham NC (US)]]

[[:Category:Gregory J. Boss of Saginaw MI (US)|Gregory J. Boss of Saginaw MI (US)]][[Category:Gregory J. Boss of Saginaw MI (US)]]

==MACHINE LEARNING TECHNIQUES USING MODEL DEFICIENCY DATA OBJECTS FOR TENSOR-BASED GRAPH PROCESSING MODELS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240013064 titled 'MACHINE LEARNING TECHNIQUES USING MODEL DEFICIENCY DATA OBJECTS FOR TENSOR-BASED GRAPH PROCESSING MODELS

==Simplified Explanation==

The present invention relates to methods, apparatus, systems, computing devices, computing entities, and/or the like for generating a model deficiency data object for a tensor-based graph processing machine learning model. The invention utilizes systems, methods, and computer program products that generate a model deficiency data object for a tensor-based graph processing machine learning model using holistic graph links generated by utilizing a graph representation machine learning model.

* The invention provides a method for generating a model deficiency data object for a tensor-based graph processing machine learning model.
* The method utilizes holistic graph links generated by utilizing a graph representation machine learning model.
* The generated model deficiency data object helps in identifying and addressing deficiencies in the machine learning model.
* The invention can be implemented using various apparatus, systems, computing devices, and computing entities.
* The invention can be implemented using computer program products that facilitate the generation of the model deficiency data object.
* The tensor-based graph processing machine learning model can benefit from the generated model deficiency data object to improve its performance and accuracy.

Potential applications of this technology:

* Improving the performance and accuracy of tensor-based graph processing machine learning models.
* Identifying and addressing deficiencies in machine learning models.
* Enhancing the capabilities of graph representation machine learning models.
* Optimizing the training and learning process of tensor-based graph processing machine learning models.

Problems solved by this technology:

* Lack of efficient methods for identifying and addressing deficiencies in machine learning models.
* Difficulty in optimizing the training and learning process of tensor-based graph processing machine learning models.
* Inadequate capabilities of graph representation machine learning models in generating holistic graph links.

Benefits of this technology:

* Improved performance and accuracy of tensor-based graph processing machine learning models.
* Enhanced capabilities of graph representation machine learning models.
* Efficient identification and resolution of deficiencies in machine learning models.
* Optimized training and learning process for tensor-based graph processing machine learning models.


==Original Abstract Submitted==

various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for generating a model deficiency data object for a tensor-based graph processing machine learning model. certain embodiments of the present invention utilize systems, methods, and computer program products that generate a model deficiency data object for a tensor-based graph processing machine learning model using holistic graph links generated by utilizing a graph representation machine learning model.



[[Category:G06N3/12]]