Computer-implemented patent portfolio analysis method and apparatus
OwnerGregory A Stobbs, John V Biernackiinventors
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Abstract
A computer-implemented apparatus and method for performing patent portfolio analysis. The patent portfolio analysis apparatus and method clusters a group of patents based upon one or more techniques. The clustering techniques include linguistic clustering techniques (e.g., eigenvector analysis), claim meaning, and patent classification techniques. Different aspects of the clusters are analyzed, including financial, claim breadth, and assignee patent comparisons. Moreover, patents and/or their clusters are linked to the Internet in order to determine what products might be covered by the claims of the patents or whether materials on the Internet might render patent claims invalid.
Claims8
A computer-implemented patent portfolio analysis method comprising:
- providing user-prescribed categories which were specified by a user;
- retrieving a corpus of patent information from a database, wherein the patent information is information from multiple patent documents;
- analyzing said patent information to generate a category model corresponding to at least one of said user-prescribed categories;
- and
- applying said model against said patent information to select from said patent information a subset that fits said model and storing said subset in association with a label corresponding to said at least one of said user-prescribed categories in a computer-readable dataset,
- wherein said patent information includes claim text information to be analyzed and wherein said analyzing step includes:
- defining an eigenspace representing a training population of training claims each training claim having associated training text;
- representing at least a portion of said training claims in said eigenspace and associating a predefined category with each training claim in said eigenspace;
- and
- projecting the claim text information to be analyzed into said eigenspace and associating with said projected claim text the predefined category of the training claim to which said projected claim text is closest within the eigenspace.
A computer-implemented patent portfolio analysis method comprising:
- retrieving patent information from a database, wherein the patent information is from a plurality of patent documents;
- analyzing said patent information to generate at least one eigenspace category model;
- and
- applying said category model to said patent information to select from said patent information a subset that fits said model and storing said subset in a computer-readable dataset,
- wherein said patent information includes claim text information to be analyzed and wherein said analyzing step includes:
- defining an eigenspace representing a training population of training claims each training claim having associated training text;
- representing at least a portion of said training claims in said eigenspace and associating a predefined category with each training claim in said eigenspace;
- and
- projecting the claim text information to be analyzed into said eigenspace and associating with said projected claim text the predefined category of the training claim to which said projected claim text is closest within the eigenspace.
Dependencies are inferred from the claim text.