Wide-spectrum information search engine
Abstract
A method and computer program product for comparing documents includes segmenting a judgment matrix into a plurality of information sub-matrices where each submatrix has a plurality of classifications and a plurality of terms relevant to each classification; evaluating a relevance of each term of the plurality of terms with respect to each classification of each information submatrix of the information submatrices; calculating an information spectrum for a first document based upon at least some of the plurality of terms; calculating an information spectrum for a second document based upon at least some of the plurality of terms; and identifying the second document as relevant to the first document based upon a comparison of the calculated information spectrums.
Claims
exact text as granted — not AI-modified1 . A method for processing information, comprising:
receiving a segmented judgment matrix, the segmented judgment matrix being a numerical matrix pairing each of a set of terms to each of a set of classifications, each term being a word or phrase, the segmented judgment matrix having a plurality of information submatrices, each element of each information submatrix representing a rating of a relevance of the term of the element to the classification of the element, each information submatrix being a numerical matrix representing the relevance of each of a subset of the set of terms to each of a subset of the set classifications; and using the segmented judgment matrix to calculate an information spectrum.
2 . The method of claim 1 , wherein at least some of the elements of the information submatrices represent ratings of relevance made by a human being.
3 . The method of claim 1 wherein the segmented judgment matrix has rows and columns and each column of the segmented judgment matrix represents a classification and each row of the segmented judgment matrix represents a term.
4 . A computer program product comprising instructions operable to cause data processing apparatus to:
receive a segmented judgment matrix, the segmented judgment matrix being a numerical matrix pairing each of a set of terms to each of a set of classifications, each term being a word or phrase, the segmented judgment matrix having a plurality of information submatrices, each element of each information submatrix representing a rating of a relevance of the term of the element to the classification of the element, each information submatrix being a numerical matrix representing the relevance of each of a subset of the set of terms to each of a subset of the set classifications; and use the segmented judgment matrix to calculate an information spectrum.
5 . The product of claim 4 wherein at least some of the elements of the information submatrices represent ratings of relevance made by a human being.
6 . The product of claim 4 wherein the segmented judgment matrix has rows and columns and each column of the segmented judgment matrix represents a classification and each row of the segmented judgment matrix represents a term.
7 . A computer program product for processing text information, the product comprising instructions operable to cause data processing apparatus to perform the operations of:
receiving a judgment matrix that is segmented into a plurality of information submatrices where each submatrix has a plurality of classifications and a plurality of terms relevant to each classification; evaluating a relevance of each term of the plurality of terms with respect to each classification of each information submatrix of the information submatrices; calculating an information spectrum for each of a plurality of documents based upon at least some of the plurality of terms; receiving a search request; calculating an information spectrum of the search request based upon at least some of the plurality of terms; and identifying at least some documents of the plurality of documents as relevant to the request based upon a comparison of the calculated information spectrums.Join the waitlist — get patent alerts
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