US 10,241,992 B1 · Table item information extraction with continuous machine learning through local and global models

Abstract

A bipartite application implements a table auto-completion (TAC) algorithm on the client side and the server side. A client module runs a local model of the TAC algorithm on a user device and a server module runs a global model of the TAC algorithm on a server machine. The local model is continuously adapted through on-the-fly training, with as few as a negative example, to perform TAC on the client side, one document at a time. Knowledge thus learned by the local model is used to improve the global model on the server side. The global model can be utilized to automatically and intelligently extract table information from a large number of documents with significantly improved accuracy, requiring minimal human intervention even on complex tables.

A method, comprising:

displaying, on a user device through a user interface of a bipartite application, a database table and an image, the database table having a plurality of columns, the image containing a table, the bipartite application implemented on the user device and a server machine in a backend of an enterprise computing environment, the displaying performed by a client module of the bipartite application executing on the user device, the client module including a local model of a table auto-completion algorithm, the user interface including a user interface element associated with the table auto-completion algorithm;
responsive to a user selecting the user interface element displayed on the user device, performing, by the client module running the local model of the table auto-completion algorithm:
analyzing a portion of the table highlighted by the user on the user interface, the portion of the table highlighted by the user on the user interface defining initial coordinates on the user interface;
determining a data point for each column of the database table using the initial coordinates;
automatically extracting data points thus determined from the table utilizing the local model;
entering the data points automatically extracted from the table utilizing the local model into the plurality of columns of the database table;
and
storing information about the data points in the local model as positive examples;
determining, by the client module running the local model of the table auto-completion algorithm utilizing the positive examples in the local model, a plurality of additional data points in the table;
automatically extracting the plurality of additional data points from the table utilizing the local model and entering the plurality of additional data points extracted from the table utilizing the local model into the plurality of columns of the database table;
receiving, by the client module running the local model of the table auto-completion algorithm, a correction to a data point of the plurality of additional data points automatically extracted from the table utilizing the local model;
correcting the local model utilizing the correction to the data point and including the data point in the local model as a negative example, the correcting performed by the client module running the local model of the table auto-completion algorithm;
automatically continuously extracting table information from the table utilizing the positive and negative examples in the local model until extraction of the table information from the table is completed;
communicating the local model from the client module to a server module of the bipartite application running on the server machine in the backend of the enterprise computing environment, the server module including a global model of a table auto-completion algorithm;
updating the global model of the table auto-completion algorithm utilizing the local model;
automatically extracting table information from a plurality of documents, the automatically extracting performed by the server module executing on the server machine utilizing the global model;
and
automatically entering into database fields the table information extracted from the plurality of documents utilizing the global model, the automatically entering performed by the server module executing on the server machine.

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Table item information extraction with continuous machine learning through local and global models

Life of this patent

Table item information extraction with continuous machine learning through local and global models

Life of this patent

Abstract

Claims20