1. 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.