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US 11,776,291 B1Granted
Document analysis architecture
AssigneeAon Risk Services, Inc. of Maryland
InventorsSamuel Cameron Fleming, David Craig Andrews, John E. Bradley, III, Lewis C. Lee, Jared Dirk Sol, Timothy Seegan, Scott Buzan
Life of this patent
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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.
Claims18
A method, comprising:
- generating machine-learning models configured to identify a document as in class or out of class, the machine-learning models trained utilizing user input data indicating a first portion of documents as in class and a second portion of documents as out of class, wherein the documents are intellectual property documents;
- determining, for individual ones of the machine-learning models, a category associated with individual ones of the machine-learning models, the category associated with a classification system associated with the documents;
- generating a taxonomy of the machine-learning models, the taxonomy indicating categorical relationships between the machine-learning models, wherein generating the taxonomy is based at least in part on the category associated with the individual ones of the machine-learning models;
- receiving, for a machine-learning model of the machine-learning models, a training dataset configured to train the machine-learning model to determine which of the documents are in class to the machine-learning model;
- receiving an indication that a portion of the training dataset includes confidential information;
- generating a modified machine-learning model corresponding to the machine-learning model trained without the portion of the training dataset that includes the confidential information;
- and
- wherein generating the taxonomy comprises generating the taxonomy based at least in part on the modified machine-learning model.
5 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 machine-learning models configured to identify a document as in class or out of class, the machine-learning models trained utilizing user input data indicating a first portion of documents as in class and a second portion of documents as out of class, wherein the documents are intellectual property documents;
- determining, for individual ones of the machine-learning models, a category associated with individual ones of the machine-learning models, the category associated with a classification system associated with the documents;
- generating a taxonomy of the machine-learning models, the taxonomy indicating categorical relationships between the machine-learning models, wherein generating the taxonomy is based at least in part on the category associated with the individual ones of the machine-learning models;
- receiving data indicating a restriction associated with the user input data for a machine-learning model of the machine-learning models;
- determining that the restriction is user specific;
- determining, based at least in part on the restriction being user specific, a portion of a training dataset configured to train the machine-learning model that is associated with the restriction;
- and
- generating a modified machine-learning model corresponding to the machine-learning model trained without the portion of the training dataset.
8 more elements
One or more non-transitory computer-readable media storing computer-executable instructions that, when executed, cause one or more processors to perform operations comprising:
- generating machine-learning models configured to identify a document as in class or out of class, the machine-learning models trained utilizing user input data indicating a first portion of documents as in class and a second portion of documents as out of class, wherein the documents are intellectual property documents;
- determining, for individual ones of the machine-learning models, a category associated with individual ones of the machine-learning models, the category associated with a classification system associated with the documents;
- generating a taxonomy of the machine-learning models, the taxonomy indicating categorical relationships between the machine-learning models, wherein generating the taxonomy is based at least in part on the category associated with the individual ones of the machine-learning models
- receiving data indicating a restriction associated with the user input data for a machine-learning model of the machine-learning models;
- determining that the restriction is user specific;
- determining, based at least in part on the restriction being user specific, a portion of a training dataset configured to train the machine-learning model that is associated with the restriction;
- and
- generating a modified machine-learning model corresponding to the machine-learning model trained without the portion of the training dataset.
5 more elements
Dependencies are inferred from the claim text.