US6847966B1 — Method and system for optimally searching a docum…

Claim 1

Independent Claim 141 words

A method for comparing documents for similarity by representative latent semantic analysis comprising: generating a weighted term-by-document matrix for a group of selected documents, said group of selected documents being representative of a similarity criteria; decomposing said weighted term-by-document matrix into a term matrix of terms occurring said group of selected documents and a reduced concepts matrix of concepts, at least a portion of said concepts indicative of latent semantics in at least one of said selected documents; generating a first pseudo-document vector utilizing a first document and said reduced concepts matrix; generating a second pseudo-document vector utilizing a second document and said reduced concepts matrix; comparing said first pseudo-document vector to said second pseudo-document vector; and determining similarity of said first document and said second document based on the comparison of said first pseudo-document vector to said second pseudo-document vector.

Claim 2

Independent Claim 141 words

A method for comparing documents for similarity by representative latent semantic analysis comprising: generating a weighted term-by-document matrix for a group of selected documents, said group of selected documents being representative of a similarity criteria; decomposing said weighted term-by-document matrix into a term matrix of terms occurring said group of selected documents and a reduced concepts matrix of concepts, at least a portion of said concepts indicative of latent semantics in at least one of said selected documents; generating a first pseudo-document vector utilizing a first document and said reduced concepts matrix; generating a second pseudo-document vector utilizing a second document and said reduced concepts matrix; comparing said first pseudo-document vector to said second pseudo-document vector; and determining similarity of said first document and said second document based on the comparison of said first pseudo-document vector to said second pseudo-document vector.

Claim 3

Dependent Claim 19 words

The method recited in claim 1 above wherein generating the first pseudo-document vector further comprises utilizing the term matrix.

Claim 4

Dependent Claim 19 words

The method recited in claim 1 above wherein generating the second pseudo-document vector further comprises utilizing the term matrix.

Claim 5

Dependent Claim 21 words

The method recited in claim 1 above wherein generating the first and the second pseudo-document vectors comprise utilizing the term matrix.

Claim 6

Dependent Claim 27 words

The method recited in claim 1 above wherein the similarity criteria is one of document language, document type, subject matter and a category of a subject matter.

Claim 7

Dependent Claim 127 words

The method recited in claim 1 above wherein generating the weighted term-by-document matrix further comprises: compiling the group of selected documents representative of a similarity criteria; identifying terms occurring in the group of documents; finding a global term weight for each identified term based on a frequency of occurrences for said each term in the group of documents; finding a local term weight for each identified term based on a frequency of occurrences for said each term in each document in the group of documents; organizing said local term weights in a term-by-document matrix for the identified terms; and creating a weighted term-by-document matrix by applying a global term weight to a corresponding local term weight for each of the respective identified terms in the term-by-document matrix.

Claim 8

Dependent Claim 25 words

The method recited in claim 6 above, wherein identifying terms occurring in the group of documents further comprises eliminating stop words from the identified terms.

Claim 9

Dependent Claim 69 words

The method recited in claim 7 above wherein said weighted term-by-document matrix is comprised of a product of said term matrix, said reduced concepts matrix S and the transpose of an other matrix, wherein said term matrix is a t row by k column matrix, said reduced concepts matrix is a k row by k column matrix, and said other matrix is a v row by k column matrix.

Claim 10

Dependent Claim 99 words

The method recited in claim 8 above, wherein generating a first pseudo-document vector D further comprises: finding the product of a transpose of a term vector A d , said term matrix T, and an inverse of said reduced concepts matrix S, such that a D=A d T TS −1 , wherein said term vector A d being formed from identified terms occurring in said first document and said identified occurring terms being weighted locally based on frequency of occurrence in said first document and scaled based on the global weight of said each of said identified occurring term.

Claim 11

Dependent Claim 112 words

The method recited in claim 9 above further comprises: compiling a collection of documents; generating a pseudo-document vector for each of the documents in the collection of documents, each of said pseudo-document vectors being formed utilizing a document from the collection of documents, said reduced concepts matrix and said term matrix; comparing said first pseudo-document vector to each of the pseudo-document vectors for each of the documents in the collection of documents; and determining similarity of each of the documents in the collection of documents to said first document based on the comparison of said first pseudo-document vector to each of the pseudo-document vectors for the documents in the collection of documents.

Claim 12

Dependent Claim 60 words

The method recited in claim 10 further comprises: constructing an index of term-to-document mappings, each of said mappings correlate a term occurring in the collection of documents to documents in which said term occurs; and finding a subset of documents in the collection of document in which a query term occurs by searching the inverted index for the query term.

Claim 13

Dependent Claim 98 words

The method recited in claim 11 further comprises: generating a pseudo-document vector for each document in the subset of documents utilizing a document from the subset, said term matrix, and said reduced concepts matrix; comparing said first pseudo-document vector to each of the pseudo-document vectors for each of the documents in the subset of documents; and determining document similarity of each of the documents in the subset of documents to said first document based on the comparison of said first pseudo-document vector to each of the pseudo-document vectors for each of the documents in the subset of documents.

Claim 14

Dependent Claim 16 words

The method recited in claim 12 above, wherein the first document is a list of terms.

Claim 15

Dependent Claim 15 words

The method recited in claim 12 above, wherein the first document is a query document.

Claim 16

Dependent Claim 43 words

The method recited in claim 10 further comprises: adding a document to the collection of documents; and generating a pseudo-document vector for the added document, said added pseudo-document vector being formed utilizing said added document, said reduced concepts matrix and said term matrix.

Claim 17

Dependent Claim 73 words

The method recited in claim 15 above further comprises: comparing said first pseudo-document vector to each of the pseudo-document vectors for each of the documents in the collection of documents; and determining document similarity of each of the documents in the collection of documents to said first document based on the comparison of said first pseudo-document vector to each of the pseudo-document vectors for each of the documents in the collection of documents.

Claim 18

Dependent Claim 25 words

The method recited in claim 10 further comprises: deleting a document from the collection of documents; and eliminating a pseudo-document vector for the deleted document.

Claim 19

Dependent Claim 73 words

The method recited in claim 17 above further comprises: comparing said first pseudo-document vector to each of the pseudo-document vectors for each of the documents in the collection of documents; and determining document similarity of each of the documents in the collection of documents to said first document based on the comparison of said first pseudo-document vector to each of the pseudo-document vectors for each of the documents in the collection of documents.

Claim 20

Dependent Claim 37 words

The method recited in claim 1 above, wherein decomposing said weighted term-by-document matrix further comprises: establishing a predetermined quantity of strong concepts; deriving the predetermined quantity of strong concepts from said weighted term-by-document matrix; and ceasing decomposition.

Claim 21

Dependent Claim 26 words

The method recited in claim 1 above, wherein decomposing said weighted term-by-document matrix further comprises applying a singular value decomposition algorithm to said weighted term-by-document matrix.

Claim 22

Independent Claim 119 words

A method for comparing documents for similarity by representative latent semantic analysis comprising: selecting documents based on a similarity criteria; generating a weighted term-by-document matrix for said selected documents; decomposing said weighted term-by-document matrix into a reduced concepts matrix, a term matrix, and a document matrix; discarding said document matrix; generating a first pseudo-document vector utilizing a first document, said reduced concepts matrix and said term matrix; generating a second pseudo-document vector utilizing a second document, said reduced concepts matrix and said term matrix; comparing said first pseudo-document vector to said second pseudo-document vector, and determining similarity of said first document and said second document based on the comparison of said first pseudo-document vector to said second pseudo-document vector.

Claim 23

Independent Claim 119 words

A method for comparing documents for similarity by representative latent semantic analysis comprising: selecting documents based on a similarity criteria; generating a weighted term-by-document matrix for said selected documents; decomposing said weighted term-by-document matrix into a reduced concepts matrix, a term matrix, and a document matrix; discarding said document matrix; generating a first pseudo-document vector utilizing a first document, said reduced concepts matrix and said term matrix; generating a second pseudo-document vector utilizing a second document, said reduced concepts matrix and said term matrix; comparing said first pseudo-document vector to said second pseudo-document vector, and determining similarity of said first document and said second document based on the comparison of said first pseudo-document vector to said second pseudo-document vector.

Claim 24

Dependent Claim 26 words

The method recited in claim 21 above, wherein decomposing said weighted term-by-document matrix further comprises applying a singular value decomposition algorithm to said weighted term-by-document matrix.

Claim 25

Dependent Claim 37 words

The method recited in claim 21 above, wherein decomposing said weighted term-by-document matrix further comprises: establishing a predetermined quantity of strong concepts; deriving the predetermined quantity of strong concepts from said weighted term-by-document matrix; and ceasing decomposition.

Claim 26

Dependent Claim 19 words

The method recited in claim 23 above, wherein the predetermined quantity of strong concepts is k number of concepts.

Claim 27

Dependent Claim 68 words

The method recited in claim 24 above wherein said weighted term-by-document matrix is comprised of a product of said term matrix, said reduced concepts matrix, and the transpose of an other matrix, wherein said term matrix is a t row by k column matrix, said reduced concepts matrix is a k row by k column matrix, and said other matrix is a v row by k column matrix.

Claim 28

Dependent Claim 24 words

The method recited in claim 21 above wherein the similarity criteria is one of document language, document type, subject matter and subject matter category.

Claim 29

Dependent Claim 21 words

The method recited in claim 21 above wherein said selected documents exclude said first document and further exclude said second document.

Claim 30

Dependent Claim 114 words

The method recited in claim 21 above wherein generating the weighted term-by-document matrix further comprises: identifying terms occurring in the selecting documents; finding a global term weight for each identified term based on a frequency of occurrences for said each identified term in the selected documents; finding a local term weight for each identified term based on a frequency of occurrences in each of the plurality of documents in the selected documents; organizing said local term weights in a term-by-document matrix for the identified terms; and creating a weighted term-by-document matrix by applying a global term weight to a corresponding local term weight for each of the respective identified terms in the term-by-document matrix.

Claim 31

Dependent Claim 25 words

The method recited in claim 28 above, wherein identifying terms occurring in the group of documents further comprises eliminating stop words from the identified terms.

Claim 32

Dependent Claim 67 words

The method recited in claim 29 above wherein said weighted term-by-document matrix is comprised of a product of said term matrix, said reduced concepts matrix, and the transpose of a document matrix, wherein said term matrix is a t row by k column matrix, said reduced concepts matrix is a k row by k column matrix, and said matrix is a d row by k column matrix.

Claim 33

Dependent Claim 103 words

The method recited in claim 30 above, wherein generating a first pseudo-document vector D l further comprises: finding the product of a transpose of a term vector A d1 , said term matrix T, and an inverse of said reduced concepts matrix S, such that a D l =A d1 T S −1 , wherein said term vector A d1 being formed from identified terms occurring in said first document, wherein said identified occurring terms are terms being weighted locally based on frequency of occurrence in said first document and scaled based the global weight of said each of said identified occurring term.

Claim 34

Independent Claim 263 words

A method for comparing documents for similarity by representative latent semantic analysis comprising: creating a concept database comprising: compiling a corpus from a plurality of documents representative of a similarity criteria; identifying terms occurring in the corpus; finding a global term weight for each identified term based on a frequency of occurrences in the corpus; finding a local term weight for each identified term based on a frequency of occurrences in each of the plurality of documents in the corpus; organizing said local term weights in a term-by-document matrix for the identified terms; creating a weighted term-by-document matrix by applying global term weights to corresponding local term weights for each of the respective identified terms in the term-by-document matrix; decomposing the weighted term-by-document matrix into at least a term matrix and a concepts matrix; forming a reduced concepts matrix by eliminating indices in said concept matrix identifying weak concepts; and forming a reduced term matrix by eliminating indices in said term matrix corresponding to the indices eliminated from the concept matrix; creating a document database from a collection of documents and said concept database comprising: compiling said collection of documents; generating a plurality of pseudo-document vectors utilizing said collection of documents and each of said reduced concepts matrix and said term matrix of said concept database; and determining document similarity between documents in the collection of documents comprising: comparing pseudo-document vectors from the document database for respective documents in the collection of documents; and determining similarity of said respective documents based on the comparison of the respective pseudo-document vectors from the document database.

Claim 35

Independent Claim 263 words

A method for comparing documents for similarity by representative latent semantic analysis comprising: creating a concept database comprising: compiling a corpus from a plurality of documents representative of a similarity criteria; identifying terms occurring in the corpus; finding a global term weight for each identified term based on a frequency of occurrences in the corpus; finding a local term weight for each identified term based on a frequency of occurrences in each of the plurality of documents in the corpus; organizing said local term weights in a term-by-document matrix for the identified terms; creating a weighted term-by-document matrix by applying global term weights to corresponding local term weights for each of the respective identified terms in the term-by-document matrix; decomposing the weighted term-by-document matrix into at least a term matrix and a concepts matrix; forming a reduced concepts matrix by eliminating indices in said concept matrix identifying weak concepts; and forming a reduced term matrix by eliminating indices in said term matrix corresponding to the indices eliminated from the concept matrix; creating a document database from a collection of documents and said concept database comprising: compiling said collection of documents; generating a plurality of pseudo-document vectors utilizing said collection of documents and each of said reduced concepts matrix and said term matrix of said concept database; and determining document similarity between documents in the collection of documents comprising: comparing pseudo-document vectors from the document database for respective documents in the collection of documents; and determining similarity of said respective documents based on the comparison of the respective pseudo-document vectors from the document database.

Claim 36

Dependent Claim 66 words

The method recited in claim 32 above wherein the weighted term-by-document matrix is comprised of a product of said term matrix, said concepts matrix, and the transpose of a document matrix, wherein said term matrix is a t row by m column matrix, said concepts matrix is an m row by m column matrix, and said document matrix is a d row by m column matrix.

Claim 37

Dependent Claim 75 words

The method recited in claim 33 above wherein forming a reduced concepts matrix by eliminating indices in said concept matrix identifying weak concepts further comprises; selecting the k strongest concepts; eliminating (k+1) th through m th column indices of said concepts matrix; and eliminating (k+1) th through m th row indices of said concepts matrix, thereby truncating said m row by m column concepts matrix to a k row by k column reduced concepts matrix.

Claim 38

Dependent Claim 57 words

The method recited in claim 34 above wherein forming a reduced term matrix by eliminating indices in said concept matrix identifying weak concepts further comprises: eliminating (k+1) th through m th column indices of said term matrix, thereby truncating said t row by m column term matrix to a t row by k column reduced term matrix.

Claim 39

Dependent Claim 47 words

The method recited in claim 33 above wherein forming a reduced concepts matrix by eliminating indices in said concept matrix identifying weak concepts further comprises; establishing a predetermined quantity of strong concepts; deriving the predetermined quantity of strong concepts from said weighted term-by-document matrix; and ceasing decomposition.

Claim 40

Dependent Claim 118 words

The method recited in claim 36 above, wherein generating a plurality of pseudo-document vectors further comprises: finding the product of a transpose of a term vector A di , said term matrix T, and an inverse of said reduced concepts matrix S, for each document d i in the collection of documents such that a D i =A d T TS 1 , said term vector A di being formed from identified terms occurring in an i th document, wherein each of said identified occurring terms are weighted locally based on occurrences in said each document d i of the collection of documents and scaled based on the global weight of said each of said identified occurring term.

Claim 41

Independent Claim 97 words

A method for comparing documents for similarity by representative latent semantic analysis comprising: defining a representative semantic space; creating a first pseudo-document vector for a first document using at least a portion of the definition for the representative semantic space; creating a second pseudo-document vector for a second document using at least a portion of the definition for the representative semantic space; comparing the first pseudo-document vector to the second pseudo-document vector, and determining similarity of the first document and the second document based on the comparison of the first pseudo-document vector to the second pseudo-document vector.

Claim 42

Independent Claim 97 words

A method for comparing documents for similarity by representative latent semantic analysis comprising: defining a representative semantic space; creating a first pseudo-document vector for a first document using at least a portion of the definition for the representative semantic space; creating a second pseudo-document vector for a second document using at least a portion of the definition for the representative semantic space; comparing the first pseudo-document vector to the second pseudo-document vector, and determining similarity of the first document and the second document based on the comparison of the first pseudo-document vector to the second pseudo-document vector.

Claim 43

Dependent Claim 65 words

The method recited in claim 38 above, wherein defining a representative semantic space further comprises: determining a similarity criteria; selecting parameters for said similarity criteria; compiling a corpus of documents representative of said similarity criteria based on said selected parameters, wherein said corpus of documents excludes said first and said second documents; and deriving concepts from the corpus of documents representative of said similarity criteria.

Claim 44

Dependent Claim 130 words

The method recited in claim 39 above, wherein deriving concepts from the corpus of documents further comprises: identifying terms occurring in the corpus of documents; finding a global term weight for each identified term based on a frequency of occurrences for said each term in the corpus of documents; finding a local term weight for each identified term based on a frequency of occurrences in each of the plurality of documents in the corpus of documents; organizing said local term weights in a term-by-document matrix for the identified terms; creating a weighted term-by-document matrix by applying a global term weight to a corresponding local term weight for each of the respective identified terms in the term-by-document matrix; and decomposing the weighted term-by-document matrix into concepts representative of said similarity criteria.

Claim 45

Dependent Claim 28 words

The method recited in claim 40 above wherein said concepts representative of said similarity criteria further comprise a concept database including a concept index and a term index.

Claim 46

Dependent Claim 25 words

The method recited in claim 41 above, wherein said concept index and term index being organized as a concept matrix and a term matrix respectively.

Claim 47

Dependent Claim 66 words

The method recited in claim 42 above, wherein said concept matrix and said term matrix being products of the decomposition of said weighted term-by-document matrix, said decomposition comprises: establishing a predetermined quantity of strongest concepts; decomposing said weighted term-by-document matrix into said term matrix, said concept matrix and a document matrix; deriving the predetermined quantity of strong concepts from said weighted term-by-document matrix; and ceasing decomposition.

Claim 48

Dependent Claim 120 words

The method recited in claim 43 above, wherein creating a first pseudo-document vector for a first document using at least a portion of the definition for the representative semantic space further comprises: creating a first document vector for the first document based on the co-occurrence of terms in the first document and the corpus of documents, the local term weight of each co-occurring term based on a frequency of occurrences in said first document and a global term weight for each co-occurring term based on a frequency of occurrences for said each term in the corpus of documents; and transposing said first document vector to a first pseudo-document vector using a product of said concept matrix and said term matrix.

Claim 49

Independent Claim 149 words

A method for comparing resumès for similarity by representative latent semantic analysis comprising: generating a weighted term-by-resumè matrix for a group of resumès; decomposing said weighted term-by-resumè matrix into a term matrix of terms occurring in said group of resumès and a reduced concepts matrix of concepts, at least a portion of said concepts indicative of latent semantics in at least one of said resumès; identifying a collection of resumès, wherein at least one resumè in the collection of resumès is not included in said group of resumès; generating a plurality of pseudo-resumè vectors utilizing said collection of resumès and said reduced concepts matrix; generating a query pseudo-document vector utilizing a query document and said reduced concepts matrix; comparing said query pseudo-document vector to said plurality of pseudo-resumè vectors; and ranking said plurality of resumè based on similarity between said query pseudo-document vector to said plurality of pseudo-resumè vectors.

Claim 50

Independent Claim 149 words

A method for comparing resumès for similarity by representative latent semantic analysis comprising: generating a weighted term-by-resumè matrix for a group of resumès; decomposing said weighted term-by-resumè matrix into a term matrix of terms occurring in said group of resumès and a reduced concepts matrix of concepts, at least a portion of said concepts indicative of latent semantics in at least one of said resumès; identifying a collection of resumès, wherein at least one resumè in the collection of resumès is not included in said group of resumès; generating a plurality of pseudo-resumè vectors utilizing said collection of resumès and said reduced concepts matrix; generating a query pseudo-document vector utilizing a query document and said reduced concepts matrix; comparing said query pseudo-document vector to said plurality of pseudo-resumè vectors; and ranking said plurality of resumè based on similarity between said query pseudo-document vector to said plurality of pseudo-resumè vectors.

Claim 51

Dependent Claim 18 words

The method recited in claim 45 above wherein generating the pseudo-document vector further comprises utilizing the term matrix.

Claim 52

Dependent Claim 20 words

The method recited in claim 45 above wherein generating the plurality of pseudo-resumè vectors further comprises utilizing the term matrix.

Claim 53

Dependent Claim 24 words

The method recited in claim 45 above wherein generating the query pseudo-document vector and the plurality of pseudo-resumè vectors comprise utilizing the term matrix.

Claim 54

Dependent Claim 14 words

The method recited in claim 45 above wherein said query document is a resumè.

Claim 55

Dependent Claim 17 words

The method recited in claim 45 above wherein said query document is a list of query terms.

Claim 56

Dependent Claim 138 words

The method recited in claim 45 above wherein generating the weighted term-by-resumè matrix for a group of resumès further comprises: selecting the group of resumè s; identifying terms occurring in the group of resumès; finding a global term weight for each term occurring in the group of resumès based on a frequency of occurrences in the group of resumès; finding a local term weight for each term identified as occurring in the group of resumès based on a frequency of occurrences in each of the selected resumès of the group of resumès; creating a term-by-document matrix of local term weights for the terms identified as occurring in the group of resumès; and creating a weighted term-by-document matrix by applying a global term weight to corresponding local term weight for each of the respective terms in the term-by-document matrix.

Claim 57

Dependent Claim 44 words

The method recited in claim 51 further comprises: adding a resumè to the collection of resumès; and generating a pseudo-resumè vector for said added resumè, said added pseudo-resumè vector being formed by utilizing said added resumè, said reduced concepts matrix and said term matrix.

Claim 58

Dependent Claim 72 words

The method recited in claim 52 above further comprises: comparing said query pseudo-document vector to each of the pseudo-resumè vectors for each of the resumès in the collection of resumès; and determining similarity of each of the resumès in the collection of resumès to said query document based on the comparison of said query pseudo-resumè vector to each of the pseudo-resume vectors for each of the resumès in the collection of resumès.

Claim 59

Dependent Claim 25 words

The method recited in claim 45 further comprises: deleting a resumè from the collection of resumès; and eliminating a pseudo-resumè vector for the deleted resumè.

Claim 60

Dependent Claim 72 words

The method recited in claim 54 above further comprises: comparing said query pseudo-document vector to each of the pseudo-resumè vectors for each of the resumès in the collection of resumès; and determining similarity of each of the resumès in the collection of resumès to said query document based on the comparison of said query pseudo-document vector to each of the pseudo-resumè vectors for each of the resumès in the collection of resumès.

Claim 61

Dependent Claim 61 words

The method recited in claim 45 further comprises: constructing an index of term-to-resumè mappings, each of said mappings correlate a term occurring in the collection of resumès to resumès in which said each term occurs; and finding a subset of resumès in the collection of resumè in which a query term occurs by searching the inverted index for the query term.

Claim 62

Dependent Claim 100 words

The method recited in claim 56 further comprises: generating a pseudo-resumè vector for each resumè in the subset of resumès utilizing a resumè from the subset, said term matrix, and said reduced concepts matrix; comparing said first pseudo-resumè vector to each of the pseudo-resumè vectors pseudo-resumè vectors for each of the resumès in the subset of resumès; and determining resumè similarity of each of the resumès in the subset of resumès to said first resumè based on the comparison of said first pseudo-resumè vector to each of the pseudo-resumè vectors for each of the resumès in the subset of resumès.

Claim 63

Independent Claim 134 words

A method for comparing patent disclosures for similarity by representative latent semantic analysis comprising: generating a weighted term-by-patent matrix for a group of patent disclosures; decomposing said weighted term-by-patent disclosure matrix into a term matrix of terms occurring in said group of patent disclosures and a reduced concepts matrix of concepts, at least a portion of said concepts indicative of latent semantics in at least one of said patent disclosures; generating a plurality of pseudo-patent vectors utilizing said reduced concepts matrix and a plurality of patent disclosures; generating a query pseudo-document vector utilizing a query document and said reduced concepts matrix; comparing said query pseudo-document vector to said plurality of pseudo-patent disclosure vectors; and ranking said plurality of patent disclosures based on similarity between said query pseudo-document vector to said plurality of pseudo-patent vectors.

Claim 64

Independent Claim 134 words

A method for comparing patent disclosures for similarity by representative latent semantic analysis comprising: generating a weighted term-by-patent matrix for a group of patent disclosures; decomposing said weighted term-by-patent disclosure matrix into a term matrix of terms occurring in said group of patent disclosures and a reduced concepts matrix of concepts, at least a portion of said concepts indicative of latent semantics in at least one of said patent disclosures; generating a plurality of pseudo-patent vectors utilizing said reduced concepts matrix and a plurality of patent disclosures; generating a query pseudo-document vector utilizing a query document and said reduced concepts matrix; comparing said query pseudo-document vector to said plurality of pseudo-patent disclosure vectors; and ranking said plurality of patent disclosures based on similarity between said query pseudo-document vector to said plurality of pseudo-patent vectors.

Claim 65

Dependent Claim 18 words

The method recited in claim 58 above wherein generating the pseudo-document vector further comprises utilizing the term matrix.

Claim 66

Dependent Claim 20 words

The method recited in claim 58 above wherein generating the plurality of pseudo-patent vectors further comprises utilizing the term matrix.

Claim 67

Dependent Claim 24 words

The method recited in claim 58 above wherein generating the query pseudo-document vector and the plurality of pseudo-patent vectors comprise utilizing the term matrix.

Claim 68

Dependent Claim 15 words

The method recited in claim 58 above wherein said query document is a patent disclosure.

Claim 69

Dependent Claim 15 words

The method recited in claim 58 above wherein said query document is a patent application.

Claim 70

Dependent Claim 17 words

The method recited in claim 58 above wherein said query document is a list of query terms.

Claim 71

Dependent Claim 146 words

The method recited in claim 58 above wherein generating the weighted term-by-patent matrix for a group of patent disclosures further comprises: selecting the group of patent disclosures; identifying terms occurring in the group of patent disclosures; finding a global term weight for each term occurring in the group of patent disclosures based on a frequency of occurrences in the group of patent disclosures; finding a local term weight for each term identified as occurring in the group of patent disclosures based on a frequency of occurrences in each of the selected patent disclosures of the group of patent disclosures; creating a term-by-document matrix of local term weights for the terms identified as occurring in the group of patent disclosures; and creating a weighted term-by-document matrix by applying a global term weight to corresponding local term weight for each of the respective terms in the term-by-document matrix.

Claim 72

Dependent Claim 47 words

The method recited in claim 65 further comprises: adding a patent disclosure to the collection of patent disclosures; and generating a pseudo-patent vector for said added patent disclosure, said added pseudo-patent vector being formed utilizing said added patent disclosure, said reduced concepts matrix and said term matrix.

Claim 73

Dependent Claim 78 words

The method recited in claim 66 above further comprises: comparing said query pseudo-document vector to each of the pseudo-patent vectors for each of the patent disclosures in the collection of patent disclosures; and determining similarity of each of the patent disclosures in the collection of patent disclosures to said query document based on the comparison of said query pseudo-document vector to each of the pseudo-patent vectors for each of the patent disclosures in the collection of patent disclosures.

Claim 74

Dependent Claim 26 words

The method recited in claim 58 further comprises: deleting a patent from the collection of patent disclosures; and eliminating a pseudo-patent vector for the deleted patent.

Claim 75

Dependent Claim 78 words

The method recited in claim 68 above further comprises: comparing said query pseudo-document vector to each of the pseudo-patent vectors for each of the patent disclosures in the collection of patent disclosures; and determining similarity of each of the patent disclosures in the collection of patent disclosures to said query document based on the comparison of said query pseudo-document vector to each of the pseudo-patent vectors for each of the patent disclosures in the collection of patent disclosures.

Claim 76

Dependent Claim 64 words

The method recited in claim 58 further comprises: constructing an index of term-to-patent mappings, each of said mapping correlates a term occurring in the collection of patent disclosures to patent disclosures in which said each term occurs; and finding a subset of patent disclosures in the collection of patent in which a query term occurs by searching the inverted index for the query term.

Claim 77

Dependent Claim 106 words

The method recited in claim 70 further comprises: generating a pseudo-patent vector for each patent in the subset of patent disclosures utilizing a patent from the subset, said term matrix, and said reduced concepts matrix; comparing said first pseudo-patent vector to each of the pseudo-patent vectors pseudo-patent vectors for each of the patent disclosures in the subset of patent disclosures; and determining similarity of each of the patent disclosures in the subset. of patent disclosures to said first patent based on the comparison of said first pseudo-patent vector to each of the pseudo-patent vectors for each of the patent disclosures in the subset of patent disclosures.

Claim 78

Independent Claim 165 words

A system for comparing documents for similarity by representative latent semantic analysis comprising: a network containing a plurality of network elements; a server connected to said network, said server comprising: an interface for interfacing with the network; a memory, said memory containing instructions for: identifying terms occurring in the plurality of documents; determining a global term weight for each identified term; determining a local term weight for each identified term; organizing said local term weights in a term-by-document matrix for the identified terms; creating a weighted term-by-document matrix for global term weights and local term weight for each of the respective identified terms in the weighted term-by-document matrix; decomposing the weighted term-by-document matrix into at least a term matrix and a reduced concepts matrix; generating a pseudo-document vector utilizing a document, said reduced concepts matrix, and said term matrix; comparing pseudo-document vectors; and determining similarity of respective documents based on the comparison of their respective pseudo-document vectors; and a processor for executing instructions from memory.

Claim 79

Independent Claim 165 words

A system for comparing documents for similarity by representative latent semantic analysis comprising: a network containing a plurality of network elements; a server connected to said network, said server comprising: an interface for interfacing with the network; a memory, said memory containing instructions for: identifying terms occurring in the plurality of documents; determining a global term weight for each identified term; determining a local term weight for each identified term; organizing said local term weights in a term-by-document matrix for the identified terms; creating a weighted term-by-document matrix for global term weights and local term weight for each of the respective identified terms in the weighted term-by-document matrix; decomposing the weighted term-by-document matrix into at least a term matrix and a reduced concepts matrix; generating a pseudo-document vector utilizing a document, said reduced concepts matrix, and said term matrix; comparing pseudo-document vectors; and determining similarity of respective documents based on the comparison of their respective pseudo-document vectors; and a processor for executing instructions from memory.

Claim 80

Dependent Claim 28 words

The system recited in claim 72 above, wherein said server further comprises: a client connected to said network, said client passing the plurality of documents to said server.

Claim 81

Dependent Claim 37 words

The system recited in claim 72 above further comprises: a client connected to said network, said client passing a document to said server, said client indicating to said server to generate a pseudo-document vector for said document.

Claim 82

Dependent Claim 44 words

The system recited in claim 72 above further comprises: a client connected to said network, said client passing a plurality of documents to said server, said client indicating for said server to generate a plurality of pseudo-document vectors for respective said plurality of documents.

Claim 83

Dependent Claim 24 words

The system recited in claim 72 above, wherein said server further comprises: a local interface for passing the plurality of documents to said server.

Claim 84

Dependent Claim 36 words

The system recited in claim 72 above, wherein said server further comprises: a local interface for passing a document to said server, said client indicating to said server to generate a pseudo-document vector for said document.

Claim 85

Dependent Claim 43 words

The system recited in claim 72 above, wherein said server further comprises: a local interface for passing a plurality of documents to said server, said client indicating for said server to generate a plurality of pseudo-document vectors for respective said plurality of documents.

Claim 86

Dependent Claim 160 words

A search engine method comparing documents for similarity by representative latent semantic analysis comprising: receiving a corpus of documents; creating a concept database from said corpus of documents, said concept database including at least a term matrix and a concepts matrix; receiving a collection of documents, wherein at least one document in said collection of documents is not in said corpus of documents; creating a document database from said collection of documents and said concept database, said documents database including at least one pseudo-document vector for each of at least two documents in said collection of documents, said at least one pseudo-document vector being created utilizing said each of at least two documents in said collection of documents and each of said reduced concepts matrix and said term matrix of said concept database; and determining document similarity between at least said each of at least two documents in said collection of documents using said pseudo-document vectors in said document database.

Claim 87

Dependent Claim 160 words

A search engine method comparing documents for similarity by representative latent semantic analysis comprising: receiving a corpus of documents; creating a concept database from said corpus of documents, said concept database including at least a term matrix and a concepts matrix; receiving a collection of documents, wherein at least one document in said collection of documents is not in said corpus of documents; creating a document database from said collection of documents and said concept database, said documents database including at least one pseudo-document vector for each of at least two documents in said collection of documents, said at least one pseudo-document vector being created utilizing said each of at least two documents in said collection of documents and each of said reduced concepts matrix and said term matrix of said concept database; and determining document similarity between at least said each of at least two documents in said collection of documents using said pseudo-document vectors in said document database.

Claim 88

Dependent Claim 149 words

The system recited in claim 79 above, wherein creating a concept database from said corpus of documents further comprises: identifying terms occurring in the corpus of documents; finding a global term weight for each identified term based on a frequency of occurrences for said each term in the corpus of documents; finding a local term weight for each identified term based on a frequency of occurrences in each of the plurality of documents in the corpus of documents; organizing said local term weights in a term-by-document matrix for the identified terms; creating a weighted term-by-document matrix by applying a global term weight to a corresponding local term weight for each of the respective identified terms in the term-by-document matrix; determining a number of stronger concepts; and decomposing the weighted tern-by-document matrix into at least a term matrix and a concepts matrix, said concepts matrix having indices for stronger concepts.

Claim 89

Dependent Claim 163 words

A data processing system implemented program product for performing a method for comparing documents for similarity by representative latent semantic analysis, the program product comprising: instructions for generating a weighted tern-by-document matrix for a group of selected documents, said group of selected documents being representative of a similarity criteria; instructions for decomposing said weighted term-by-document matrix into a term matrix of terms occurring said group of selected documents and a reduced concepts matrix of concepts, at least a portion of said concepts indicative of latent semantics in at least one of said selected documents; instructions for generating a first pseudo-document vector utilizing a first document and said reduced concepts matrix; instructions for generating a second pseudo-document vector utilizing a second document and said reduced concepts matrix; comparing said first pseudo-document vector to said second pseudo-document vector; and instructions for determining similarity of said first document and said second document based on the comparison of said first pseudo-document vector to said second pseudo-document vector.

Claim 90

Dependent Claim 163 words

A data processing system implemented program product for performing a method for comparing documents for similarity by representative latent semantic analysis, the program product comprising: instructions for generating a weighted tern-by-document matrix for a group of selected documents, said group of selected documents being representative of a similarity criteria; instructions for decomposing said weighted term-by-document matrix into a term matrix of terms occurring said group of selected documents and a reduced concepts matrix of concepts, at least a portion of said concepts indicative of latent semantics in at least one of said selected documents; instructions for generating a first pseudo-document vector utilizing a first document and said reduced concepts matrix; instructions for generating a second pseudo-document vector utilizing a second document and said reduced concepts matrix; comparing said first pseudo-document vector to said second pseudo-document vector; and instructions for determining similarity of said first document and said second document based on the comparison of said first pseudo-document vector to said second pseudo-document vector.

Claim 91

Dependent Claim 25 words

The program product recited in claim 81 above wherein said instructions for generating the first pseudo-document vector further comprises instructions for utilizing the term matrix.

Claim 92

Dependent Claim 25 words

The program product recited in claim 81 above wherein said instructions for generating the second pseudo-document vector further comprises instructions for utilizing the term matrix.

Claim 93

Dependent Claim 27 words

The program product recited in claim 81 above wherein said instructions for generating the first and the second pseudo-document vectors comprise instructions for utilizing the term matrix.

Claim 94

Dependent Claim 28 words

The program product recited in claim 81 above wherein the similarity criteria is one of document language, document type, subject matter and a category of a subject matter.

Claim 95

Dependent Claim 143 words

The program product recited in claim 81 above wherein said instructions for generating the weighted term-by-document matrix further comprises: instructions for compiling the group of selected documents representative of a similarity criteria; instructions for identifying terms occurring in the group of documents; instructions for finding a global term weight for each identified term based on a frequency of occurrences for said each term in the group of documents; instructions for finding a local term weight for each identified term based on a frequency of occurrences for said each term in each document in the group of documents; instructions for organizing said local term weights in a term-by-document matrix for the identified terms; and instructions for creating a weighted term-by-document matrix by applying a global term weight to a corresponding local term weight for each of the respective identified terms in the term-by-document matrix.

Claim 96

Dependent Claim 31 words

The program product recited in claim 86 above, wherein said instructions for identifying terms occurring in the group of documents further comprises instructions for eliminating stop words from the identified terms.

Claim 97

Dependent Claim 69 words

The program product recited in claim 87 above wherein said weighted term-by-document matrix is comprised of a product of said term matrix, said reduced concepts matrix and the transpose of an other matrix, wherein said term matrix is a t row by k column matrix, said reduced concepts matrix is a k row by k column matrix, and said other matrix is a v row by k column matrix.

Claim 98

Dependent Claim 104 words

The program product recited in claim 88 above, wherein said instructions for generating a first pseudo-document vector D further comprises: instructions for finding the product of a transpose of a term vector A d , said term matrix T, and an inverse of said reduced concepts matrix S, such that a D=A d T TS −1 , and said term vector A d being formed from identified terms occurring in said first document, wherein said identified occurring terms are weighted locally based on frequency of occurrence in said first document and scaled by the global weight of said each of said identified occurring term.

Claim 99

Dependent Claim 127 words

The program product recited in claim 89 above further comprises: instructions for compiling a collection of documents; instructions for generating a pseudo-document vector for each of the documents in the collection of documents, each of said pseudo-document vectors being formed utilizing a document from the collection of documents d i , said reduced concepts matrix S and said term matrix T; instructions for comparing said first pseudo-document vector to each of the pseudo-document vectors for each of the documents in the collection of documents; and instructions for determining document similarity of each of the documents in the collection of documents to said first document based on the comparison of said first pseudo-document vector to each of the pseudo-document vectors for the documents in the collection of documents.

Claim 100

Dependent Claim 66 words

The program product recited in claim 90 further comprises: instructions for constructing an index of term-to-document mappings, each of said mapping correlates a term occurring in the collection of documents to documents in which said each term occurs; and instructions for finding a subset of documents in the collection of documents in which a query term occurs by searching the inverted index for the query term.

Claim 101

Dependent Claim 107 words

The program product recited in claim 91 further comprises: instructions for generating a pseudo-document vector for each document in the subset of documents utilizing a document from the subset, said term matrix, and said reduced concepts matrix; instructions for comparing said first pseudo-document vector to each of the pseudo-document vectors pseudo-document vectors for each of the documents in the subset of documents; and instructions for determining document similarity of each of the documents in the subset of documents to said first document based on the comparison of said first pseudo-document vector to each of the pseudo-document vectors for each of the documents in the subset of documents.

Claim 102

Dependent Claim 17 words

The program product recited in claim 92 above, wherein the first document is a list of terms.

Claim 103

Dependent Claim 16 words

The program product recited in claim 92 above, wherein the first document is a query document.

Claim 104

Dependent Claim 48 words

The program product recited in claim 90 further comprises: instructions for adding a document to the collection of documents; and instructions for generating a pseudo-document vector for the added document, said added pseudo-document vector being formed utilizing said added document, said reduced concepts matrix and said term matrix.

Claim 105

Dependent Claim 78 words

The program product recited in claim 95 above further comprises: instructions for comparing said first pseudo-document vector to each of the pseudo-document vectors for each of the documents in the collection of documents; and instructions for determining document similarity of each of the documents in the collection of documents to said first document based on the comparison of said first pseudo-document vector to each of the pseudo-document vectors for each of the documents in the collection of documents.

Claim 106

Dependent Claim 30 words

The program product recited in claim 90 further comprises: instructions for deleting a document from the collection of documents; and instructions for eliminating a pseudo-document vector for the deleted document.

Claim 107

Dependent Claim 78 words

The program product recited in claim 97 above further comprises: instructions for comparing said first pseudo-document vector to each of the pseudo-document vectors for each of the documents in the collection of documents; and instructions for determining document similarity of each of the documents in the collection of documents to said first document based on the comparison of said first pseudo-document vector to each of the pseudo-document vectors for each of the documents in the collection of documents.

Claim 108

Dependent Claim 47 words

The program product recited in claim 81 above, wherein said instructions for decomposing said weighted term-by-document matrix further comprises: instructions for establishing a predetermined quantity of strong concepts; instructions for deriving the predetermined quantity of strong concepts from said weighted term-by-document matrix; and instructions for ceasing decomposition.

Claim 109

Dependent Claim 32 words

The program product recited in claim 81 above, wherein said instructions for decomposing said weighted term-by-document matrix further comprises instructions for applying a singular value decomposition algorithm to said weighted term-by-document matrix.

Annuity Stage	Window Opens	Payment Deadline	Surcharge Period Until	Estimated Fee	Status
3.5 Years (Year 4)	2008-01-25	2008-07-26	2009-01-25	$2000.00	Window Closed / Lapsed
7.5 Years (Year 8)	2012-01-25	2012-07-26	2013-01-25	$3760.00	Window Closed / Lapsed
11.5 Years (Year 12)	2016-01-25	2016-07-26	2017-01-25	$7700.00	Window Closed / Lapsed

Method and system for optimally searching a document database using a representative semantic space

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