Abstract: 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.

Abstract: This disclosure describes, in part, techniques for performing automatic document analysis. For instance, a system may analyze documents to calculate respective coverage scores corresponding to coverage of the documents, where a respective coverage score is based on at least one of breadth of a document, portion count for the document, or differentiation between portions of the document. The system may further analyze the documents to calculate risk scores associated with risks of the documents, where a respective risk score is based on a number of other documents that predate a document. Furthermore, the system may analyze the documents to calculate market scores corresponding to market values of the documents. The system can then calculate comprehensive scores for the documents based on the coverage scores, the risk scores, and the market scores.

Abstract: 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.

Abstract: 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.

Abstract: 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.