Abstract: Example embodiments of the present disclosure include a method, system and computer-readable medium including receiving input data that is organized into a set of rows and a set of columns, maintaining a machine learning header model that is trained on tabular data with header rows, supplying the input data to generate header row identification data that identifies a set of header rows that is a subset of the set of rows, to generate column label data that applies a set of defined labels to the set of columns, predicting one of the set of defined labels for the column, determining a column confidence score, generating a notification seeking user feedback when the column confidence score is below a threshold for the column, applying user feedback to predict the one of the set of defined labels for the column, and generating output data that is organized into rows and columns.

Abstract: A computer-implemented method includes receiving input data that is organized into a set of rows and a set of columns. A column of the set of columns includes data associated with a set of product types. A first row of the set of rows includes data associated with an individual of a set of individuals and a first product type of the set of product types. A second row of the set of rows includes data associated with the individual and a second product type of the set of product types. The method includes splitting the input data into a first data structure associated with the first product type and a second data structure associated with the second product type. The method includes generating output data that is organized into rows and columns by merging the first data structure and the second data structure into a unified data structure.

Abstract: Example embodiments of the present disclosure include a method, system and computer-readable medium including receiving input data that is organized into a set of rows and a set of columns, maintaining a machine learning header model that is trained on tabular data with header rows, supplying the input data to generate header row identification data that identifies a set of header rows that is a subset of the set of rows, to generate column label data that applies a set of defined labels to the set of columns, predicting one of the set of defined labels for the column, determining a column confidence score, generating a notification seeking user feedback when the column confidence score is below a threshold for the column, applying user feedback to predict the one of the set of defined labels for the column, and generating output data that is organized into rows and columns.

Abstract: A computer-implemented method includes receiving input data that is organized into a set of rows and a set of columns. A column of the set of columns includes data associated with a set of product types. A first row of the set of rows includes data associated with an individual of a set of individuals and a first product type of the set of product types. A second row of the set of rows includes data associated with the individual and a second product type of the set of product types. The method includes splitting the input data into a first data structure associated with the first product type and a second data structure associated with the second product type. The method includes generating output data that is organized into rows and columns by merging the first data structure and the second data structure into a unified data structure.

Abstract: A computer-implemented method includes receiving input data that is organized into a set of rows and a set of columns. The method includes maintaining a machine learning header model that is trained on tabular data with header rows. The method includes supplying the input data as input to the machine learning header model to generate header row identification data that identifies a set of header rows that is a subset of the set of rows. The method includes maintaining a machine learning column model that is trained on tabular data. The method includes supplying the header row identification data and features of the input data to the machine learning column model to generate column label data that applies a set of defined labels to the set of columns. The method includes generating output data that is organized into rows and columns.

Abstract: A computer system includes memory hardware and processor hardware configured to execute stored instructions. The instructions include training a machine learning model with the historical feature vector inputs including multiple audio data entries and multiple claims data entries, to generate a condition likelihood output indicative of a specified condition associated with one of multiple historical database entities. The instructions include for each of a set of multiple database entities, generating a feature vector input according to audio data and the claims data associated with the entity, processing the feature vector input with the machine learning model to generate the condition likelihood output, and assigning the database entity to an identified condition subset in response to determining that the condition likelihood output is greater than a specified likelihood threshold.