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 textual information selected from the group consisting of patent specification, abstract, title and combinations thereof to be analyzed and wherein said analyzing step includes: processing the textual information to strip out delimiters that designate different fields within the textual information and thereby produce processed textual information; defining an eigenspace representing a training population of training textual information; representing at least a portion of said training textual information in said eigenspace and associating a predefined category with each training textual information in said eigenspace; and projecting the processed textual information to be analyzed into said eigenspace and associating with said projected processed textual information the predefined category of the training textual information to which said projected textual information is closest within the eigenspace.