US 11,893,505 B1Granted

Document analysis architecture

AssigneeAon Risk Services, Inc. of Maryland

InventorsSamuel Cameron Fleming, David Craig Andrews, John E. Bradley, III, Scott Buzan, Jared Dirk Sol, Timothy Seegan, Joseph Henderson Ashmore, Christopher Ali Mirabzadeh

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Abstract

Systems and methods for generation and use of document analysis architectures are disclosed. A model builder component may be utilized to receiving user input data for labeling a set of documents as in class or out of class. That user input data may be utilized to train one or more classification models, which may then be utilized to predict classification of other documents. Trained models may be incorporated into a model taxonomy for searching and use by other users for document analysis purposes.

Claims20

Dependent claims

A method comprising:

receiving documents from one or more databases, the documents including at least one of patents or patent applications;
generating first data representing the documents and components of the documents, the components including at least a title portion, an abstract portion, and a claims portion;
generating a user interface configured to display:
the components of individual ones of the documents;
and
an element configured to accept user input indicating whether the individual ones of the documents are in class or out of class;
generating a positive training dataset indicating positive vectors from a first portion of documents marked as in class in response to the user input;
generating a negative training dataset indicating negative vectors from a second portion of documents marked as out of class in response to the user input;
generating a machine learning classification model based at least in part on user input data corresponding to the user input, the machine learning classification model configured to determine whether a sample vector representing a document is closer to the positive vectors than the negative vectors in a coordinate system;
causing the user interface to display an indication of:
the first portion of the documents marked as in class in response to the user input;
the second portion of the documents marked as out of class in response to the user input;
a third portion of the documents determined to be in class utilizing the machine learning classification model;
and
a fourth portion of the documents determined to be out of class utilizing the machine learning classification model.

12 more elements

A system, comprising:

one or more processors;
and
non-transitory computer-readable media storing computer-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
generating first data representing documents received from one or more databases, the first data including components of the documents;
generating a user interface configured to display:
at least one of the components of individual ones of the documents;
and
an element configured to accept user input indicating whether the individual ones of the documents are in class or out of class;
generating a positive training dataset indicating positive vectors from a first portion of documents marked as in class in response to the user input;
generating a negative training dataset indicating negative vectors from a second portion of documents marked as out of class in response to the user input;
generating a machine learning model based at least in part on user input data corresponding to the user input, the machine learning model configured to determine whether a sample vector representing a document is closer to the positive vectors than the negative vectors in a coordinate system;
determining, utilizing the machine learning model, a third portion of the documents that are in class and a fourth portion of the documents that are out of class;
causing the user interface to display:
an indication of the third portion and the fourth portion of the documents;
a first section indicating first keywords determined to be statistically relevant by the machine learning model for identifying the third portion of the documents, wherein the first keywords are displayed in a manner that indicates a first ranking of statistical importance of the first keywords, wherein the first keywords are selectable via user input to be removed from the first section;
and
a second section indicating second keywords determined to be statistically relevant by the machine learning model for identifying the fourth portion of the documents, wherein the second keywords are displayed in a manner that indicates second ranking of the statistical relevance of the second keywords, wherein the second keywords are selectable via user input to be removed from the second section;
and
based at least in part on receiving the user input indicating that at least one of the first keywords or the second keywords should be removed, retraining the machine learning model to account for removal of the at least one of the first keywords or the second keywords.

16 more elements

A method, comprising:

generating first data representing documents received from one or more databases, the first data including components of the documents;
generating a user interface configured to display:
the components of individual ones of the documents;
and
an element configured to accept user input indicating whether the individual ones of the documents are in class or out of class;
generating a positive training dataset indicating positive vectors from a first portion of documents marked as in class in response to the user input;
generating a negative training dataset indicating negative vectors from a second portion of documents marked as out of class in response to the user input;
generating a machine learning model based at least in part on user input data corresponding to the user input, the machine learning model configured to determine whether a sample vector representing a document is closer to the positive vectors than the negative vectors in a coordinate system;
determining, based at least in part on the machine learning model, a third portion of the documents that are in class and a fourth portion of the documents that are out of class;
and
causing the user interface to display:
an indication of the third portion and the fourth portion of the documents;
a first section indicating first keywords determined to be statistically relevant by the machine learning model for identifying the third portion of the documents, wherein the first keywords are displayed in a manner that indicates a first ranking of statistical importance of the first keywords, wherein the first keywords are selectable via user input to be removed from the first section;
and
a second section indicating second keywords determined to be statistically relevant by the machine learning model for identifying the fourth portion of the documents, wherein the second keywords are displayed in a manner that indicates second ranking of the statistical relevance of the second keywords, wherein the second keywords are selectable via user input to be removed from the second section;
and
based at least in part on receiving the user input indicating that at least one of the first keywords or the second keywords should be removed, retraining the machine learning model to account for removal of the at least one of the first keywords or the second keywords.

14 more elements

Dependencies are inferred from the claim text.