Document relationship analysis system
AssigneeVortext Analytics, Inc.
InventorsMatthew Cody Lambert, Peter Joseph Angerani, Gregory David Ostermayr
Life of this patent
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Abstract
A document relationship analysis system. Aspects of the system include ingesting, discovering, recommending, analyzing, and exporting documents of interest. The system dynamically searches large or streaming datasets using a tiered, multi-step approach that includes discovery techniques and recommender components to filter and refine these larger datasets to smaller datasets of documents of interest. The system dynamically selects and renders an appropriate visualization for result datasets based on predetermined measures that allow for facilitate analysis of the documents of interest.
Claims18
A system for analyzing relationships between documents, the system comprising:
- a user interface;
- an ingest memory configured to store source documents retrieved from an external document source;
- a text index memory configured to store a text index;
- a cluster index memory configured to store document vectors associated with each source document;
- a text extraction pipeline automatically extracting text from source documents added to the ingest memory;
- a document vector calculator automatically computing document vectors for source documents by applying term weights to the extracted text associated with the source document as a mathematical representation of a frequency of extracted terms;
- an indexer automatically building an index of the extracted text and storing the text index in the text index memory;
- a dataset manager component generating a result dataset containing documents of interest from a target dataset containing selected source documents based on a query;
- a relationship analyzer component automatically selecting a visualization model for clustering the documents of interest based at least in part up on a number of documents of interest in the result dataset and rendering the result dataset using selected visualization model in the user interface,
- a user property memory configured to store profile documents, wherein the document vector calculator automatically computes document vectors for profile documents added to the user property memory;
- and
- a combined profile document vector generated from document vectors calculated for a plurality of profile documents selected for use in a query against a target dataset.
A method of analyzing relationships between documents, the method comprising the acts of:
- extracting text from source documents received from an external document source including automatically routing each source document to a text extraction component selected from a plurality of text extraction components based on an evaluation of features of the source document against at least one scheduling parameter, wherein the automatically routing is based at least in part upon a file size the source documents;
- storing the extracted text;
- creating an index of the extracted text;
- computing a document vector for each source document using the extracted text automatically when the extracted text is stored, wherein the document vector is a mathematical representation of a frequency of extracted terms;
- storing the document vectors for each source document;
- receiving a selection of a plurality of source documents as a target dataset and parameters of a query via a user interface;
- generating a result dataset containing documents of interest from the target dataset based on the query;
- and
- automatically selecting a visualization model for clustering the documents of interest based at least in part upon a number of documents of interest in the result dataset and rendering the result dataset using selected visualization model in a user interface.
A computer readable medium containing computer executable instructions which, when executed by a computer, perform a method for analyzing relationships between documents, the method comprising the acts of:
- extracting text from source documents received from an external document source;
- storing the extracted text;
- creating an index of the extracted text;
- indexing the extracted text from each source document automatically when the extracted text is stored;
- storing the indexed text;
- computing a document vector for each source document using the extracted text automatically when the extracted text is stored, wherein the document vector is a mathematical representation of a frequency of extracted terms;
- storing the document vectors for each source document;
- receiving a selection of a plurality of source documents as a target dataset and parameters of a query via a user interface;
- generating a result dataset containing documents of interest from the target dataset based on the query;
- automatically selecting a visualization model for clustering the documents of interest based at least in part upon a number of documents of interest in the result dataset and rendering the result dataset using selected visualization model in a user interface;
- extracting text from profile documents received from an external document source;
- computing a document vector for each profile document using the extracted text automatically;
- and
- generating a result dataset by evaluating similarities between each profile document vector and the document vector calculated for each source document in the target dataset.
Dependencies are inferred from the claim text.