Optum Technology, Inc.

Optum, Inc.

Swapna Sourav Rout

Sruti Rallapalli

Vineet Shukla

Kartik Chaudhary

Sharlene L Tan

Vamsi Bhandaru

Akshaya D N

Mrinalini M

Kirk Michael Wroblewski

Bhuvaneshwari Atti Janakiram

Ankit Varshney

Snigdha Sree Borra

Sudeep Choudhary

Sreedhar Terala

Ravi Kumar Raju Gottumukkala

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis operations. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data transformation operations by machine learning models, where the predictive data transformation is performed based at least in part on a cross comparison of a pair of columns, accounting for the similarity of both the column names and the column values, inferred by the outputs of a machine learning model. Additionally, certain embodiments of the present invention utilize systems, methods, and computer program products that perform anomaly detection by using machine learning models that operate based at least in part on a comparison of the fixed-size representation of the column values resulting from the machine learning model.

Machine learning techniques for enhanced data mapping

There is a need for more effective and efficient detection of cross-data-column relationships. This need can be addressed by, for example, techniques for detecting cross-data-column data relationships that utilize at least one of feature-based similarity models and deep-learning-based similarity models. The cross-data-column data relationships may be displayed to an end-user using a cross-column relationship detection user interface.

Detecting relationships across data columns

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis with respect to structured data objects. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis with respect to structured data objects by utilizing at least one of cross-table data similarity score generation machine learning models and various machine learning frameworks for cross-database similarity determination.

Machine learning techniques for efficient data pattern recognition across databases

Systems and methods are configured to generate a set of related document objects for a predictive entity and/or to generate an optimal document sequence for a set of related document objects. In one embodiment, a set of related document objects for a predictive entity is generated by processing entity metadata features associated with the predictive entity using an entity-document correlation machine learning model, and an optimal document sequence is generated for the set of related document objects by processing the set of related document objects using a document sequence optimization machine learning model.

Dynamic detection of cross-document associations

Systems, methods, and computer program products may be configured to generate a set of related document objects for a predictive entity and/or to generate an optimal document sequence for a set of related document objects. In one embodiment, for example, a set of related document objects for a predictive entity is generated by processing entity metadata features associated with the predictive entity using an entity-document correlation machine learning model, and an optimal document sequence is generated for the set of related document objects by processing the set of related document objects using a document sequence optimization machine learning model.

In general, embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for software process failure prevention are provided herein. Such embodiments may include steps of: generating error logs at one or more phases of a software process lifecycle; preprocessing each error log to standardize error log data; grouping the preprocessed error logs based on similar characteristics of the preprocessed error logs; associating each group of preprocessed error logs with one or more discrete events of the software process lifecycle; converting each preprocessed error log into a sequence of associated discrete events of the software process lifecycle; merging redundant sequences of associated discrete events of the software process lifecycle; and identifying one or more error causing patterns for software process failure prediction from the sequences of associated discrete events of the software process lifecycle.

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Bengaluru, India

Swapna Sourav Rout