Aon Risk Services, Inc. of Maryland

Moat Metrics, Inc.

John E Bradley, Iii

Daniel Crouse

Lewis C Lee

Samuel Cameron Fleming

Jared Dirk Sol

William Michael Edmund

Timothy Seegan

Scott Buzan

David Craig Andrews

Michael John Tobias

Louise Janice Luvina Bowman

Jeffrey Brendan Ryan

Scott Randolph Brewer

Christopher Ali Mirabzadeh

Benjamin A Keim

Joseph Henderson Ashmore

Systems and methods for generation and use of intellectual-property (IP) landscaping platform architectures are disclosed. A landscaping component may be utilized to produce refined clusters of IP assets using user seeded searches in varying areas of interest, such as, for example, target technical fields, targeted publications, targeted products, and/or competitor entity portfolios. The landscaping component may be further utilized to produce an interactive graphical element including a spatial representation of the clusters of IP assets. The interactive graphical element may include various functionalities and/or information associated with the clusters of IP assets. An exposure assessment component may be utilized to assess the level of exposure associated with a targeted entity by analyzing a mapping between IP assets and allocated funds of the targeted entity, generate a score representing the level of exposure, make determinations as to insurance offerings and/or alter rates associated with the insurance offerings.

Intellectual-property landscaping platform

Systems and methods for document analysis using model intersections are disclosed. Predictive models are built and trained to predict whether a given document is in class with respect to a given model. Each predictive model may be associated with a subcategory of an identified technology. Documents that are determined to be in class by multiple ones of the predictive models may be identified and grouped into subsets. These subsets of documents may be identified as relevant to the technology in question.

Document analysis using model intersections

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.

Document analysis architecture

Manual human processing of documents often generates results that are subjective and include human-error. The cost and relatively slow speed of manual, human analysis makes it effectively impossible or impracticable to perform document analysis at the scale, speed, and cost desired in many industries. Accordingly, it may be advantageous to employ objective, accurate rule-based techniques to evaluate and process documents. This application discloses data processing equipment and methods specially adapted for a specific application: analysis of the breadth of documents. The processing may include context-dependent pre-processing of documents and sub-portions of the documents. The sub-portions may be analyzed based on word count and commonality of words in the respective sub-portions. The equipment and methods disclosed herein improve upon other automated techniques to provide document processing by achieving a result that quantitatively improves upon manual, human processing.

Automated document analysis for varying natural languages

This disclosure describes, in part, techniques for performing automatic document analysis. For instance, one or more computing devices may obtain a plurality of documents for analysis. The one or more computing devices may then analyze the documents to determine a respective comparative breadth score representing a breadth of each document, a respective comparative portion count score representing a number of documents portions included in each document, and a respective comparative differentiation score representing differentiation between the documents portions included in each document. In some instances, the one or more computing devices determine each of the scores based on comparing the respective breadth, respective number of document portions, and respective differentiation for each of the documents under analysis. The one or more computing devices can then determine a comprehensive score for each document using the respective scores, and generate a user interface that includes the overall scores.

Aggregating procedures for automatic document analysis

Automatic processing of documents often generates results far different from those obtained by manual human processing. For a given document processing task, many different techniques can be tried but it is often not known which will best emulate manual, human processing. This application discloses data processing equipment and methods specially adapted for a specific application: analysis of the breadth of documents. The processing may include context-dependent pre-processing of documents and sub-portions of the documents. The sub-portions may be analyzed based on word count and commonality of words in the respective sub-portions. Preambles may be identified and analyzed. The equipment and methods disclosed herein improve upon other automated techniques to provide document processing by achieving a result that is quantitatively closer to manual, human processing.

Automatic human-emulative document analysis enhancements

Growing community of inventors

Duvall, WA, United States of America

John E Bradley, Iii