# US 2013/0159313 A1 · Multi-Concept Latent Semantic Analysis Queries > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2013/0159313 A1 - Document ID: US20130159313A1 - Status: Published application - Kind: A1 - Application number: 13/326,284 - Filed: 2011-12-14 - Published: 2013-06-20 - Earliest claimed: 2011-12-14 - Assignees: PureDiscovery Corporation - Inventors: Paul A. Jakubik - Web page: https://patentgrep.com/patent/US20130159313A1 ## CPC - G06F 40/30: Semantic analysis - G06F 16/35: Clustering; Classification - G06F 16/355: Creation or modification of classes or clusters - G06F 16/358: Browsing; Visualisation therefor - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract A method includes accessing text, identifying a plurality of terms from the text, determining a plurality of term vectors associated with the identified plurality of terms, and clustering the determined plurality of term vectors into a plurality of clusters, the plurality of clusters comprising a first and a second cluster, the first and second clusters each comprising two or more of the determined term vectors. The method further includes creating a first pseudo-document according to the first cluster, creating a second pseudo-document according to the second cluster, identifying a first set of terms associated with the first cluster using latent semantic analysis (LSA) of the first pseudo-document, identifying a second set of terms associated with the second cluster using LSA of the second pseudo-document, and combining the first and second sets of terms into a list of output terms. ## Claims ### Claim 1 A system, comprising: one or more memory units; and one or more processing units operable to: access text; identify a plurality of terms from the text; determine a plurality of term vectors associated with the identified plurality of terms; cluster the determined plurality of term vectors into a plurality of clusters, the plurality of clusters comprising a first and a second cluster, the first and second clusters each comprising two or more of the determined term vectors; create a first pseudo-document according to the first cluster; create a second pseudo-document according to the second cluster; identify, using latent semantic analysis (LSA) of the first pseudo-document, a first set of terms associated with the first cluster; identify, using LSA of the second pseudo-document, a second set of terms associated with the second cluster; and combine the first and second sets of terms into a list of output terms. ### Claim 2 (depends on 1) The system of claim 1, wherein identifying the plurality of terms from the text comprises using a vocabulary driven parser. ### Claim 3 (depends on 1) The system of claim 1, wherein clustering the determined plurality of term vectors into one or more clusters comprises using agglomerative clustering. ### Claim 4 (depends on 1) The system of claim 1, wherein combining the first and second sets of terms into the list of output terms comprises using log-entropy mixing. ### Claim 5 (depends on 1) The system of claim 1, wherein: the one or more processing units are further operable to: create a query pseudo-document from the determined plurality of term vectors; create a first leaned pseudo-document using the first pseudo-document and the query pseudo-document; and create a second leaned pseudo-document using the second pseudo-document and the query pseudo-document; wherein: identifying the first set of terms associated with the first cluster comprises using LSA of the first leaned pseudo-document; and identifying the second set of terms associated with the second cluster comprises using LSA of the second leaned pseudo-document. ### Claim 6 (depends on 5) The system of claim 5, wherein creating the first and second leaned pseudo-documents comprises: determining a query vector according to the query pseudo-document; determining a first cluster vector according to the first pseudo-document; determining a second cluster vector according to the second pseudo-document; normalizing the query vector and the first cluster vector to the same length; normalizing the query vector and the second cluster vector to the same length; determining a first leaned cluster vector that points to a location that is between the query vector and the first cluster vector; and determining a second leaned cluster vector that points to a location that is between the query vector and the second cluster vector. ### Claim 7 (depends on 1) The system of claim 1, wherein combining the first and second sets of terms into a list of output terms comprises: determining a log-entropy weight of each of the term vectors; determining a first log-entropy weight for the first cluster, the first log-entropy weight being a sum of the determined log-entropy weights of the term vectors of the first cluster; determining a second log-entropy weight for the second cluster, the second log-entropy weight being a sum of the determined log-entropy weights of the term vectors of the second cluster; determining a summed log-entropy weight by summing the first and second log-entropy weights; determining a first percentage associated with the first cluster by dividing the first log-entropy weight by the summed log-entropy weight; and determining a second percentage associated with the second cluster by dividing the second log-entropy weight by the summed log-entropy weight; wherein the list of output terms comprises terms from the first and second set of terms according to the first and second percentages. ### Claim 8 A computer-implemented method, comprising: accessing text by a processing system; identifying, by the processing system, one or more terms from the text; determining, by the processing system, one or more term vectors associated with the identified terms; clustering, by the processing system, the determined term vectors into one or more clusters, each cluster comprising at least one of the determined term vectors; creating, by the processing system, one or more pseudo-documents, each pseudo-document created according to a particular one of the one or more clusters; identifying, by the processing system using latent semantic analysis (LSA), a set of terms for each of the created pseudo-documents; and creating, by the processing system, a list of output terms using the identified sets of terms. ### Claim 9 (depends on 8) The computer-implemented method of claim 8, wherein identifying the one or more terms from the text comprises using a vocabulary driven parser. ### Claim 10 (depends on 8) The computer-implemented method of claim 8, wherein clustering the determined term vectors into one or more clusters comprises using agglomerative clustering. ### Claim 11 (depends on 8) The computer-implemented method of claim 8, wherein creating the list of output terms using the identified sets of terms comprises using log-entropy mixing. ### Claim 12 (depends on 8) The computer-implemented method of claim 8, further comprising: creating a query pseudo-document from the determined term vectors; and creating a leaned pseudo-document using a particular one of the one or more created pseudo-documents and the query pseudo-document; wherein identifying the set of terms for each of the created pseudo-documents comprises using LSA of the leaned pseudo-document. ### Claim 13 (depends on 12) The computer-implemented method of claim 12, wherein creating the leaned pseudo-document comprises: determining a query vector according to the query pseudo-document; determining a cluster vector according to the particular pseudo-document; normalizing the query vector and the cluster vector to the same length; and determining a leaned cluster vector that points to a location that is between the query vector and the cluster vector. ### Claim 14 (depends on 8) The computer-implemented method of claim 8, wherein creating the list of output terms using the identified sets of terms comprises: determining a log-entropy weight of each of the term vectors; determining a first log-entropy weight for a first cluster of the one or more clusters, the first log-entropy weight being a sum of the determined log-entropy weights of the term vectors of the first cluster; determining a second log-entropy weight for a second cluster of the one or more clusters, the second log-entropy weight being a sum of the determined log-entropy weights of the term vectors of the second cluster; determining a summed log-entropy weight by summing the first and second log-entropy weights; and determining a percentage associated with the cluster by dividing the first log-entropy weight by the summed log-entropy weight; wherein the list of output terms comprises terms from the first and second set of terms according to the first and second percentages. ### Claim 15 A non-transitory computer-readable medium comprising software, the software when executed by one or more processing units operable to perform operations comprising: accessing text; identifying a plurality of terms from the text; determining a plurality of term vectors associated with the identified plurality of terms; clustering the determined plurality of term vectors into a plurality of clusters, the plurality of clusters comprising a first and a second cluster, the first and second clusters each comprising two or more of the determined term vectors; creating a first pseudo-document according to the first cluster; creating a second pseudo-document according to the second cluster; identifying, using latent semantic analysis (LSA) of the first pseudo-document, a first set of terms associated with the first cluster; identifying, using LSA of the second pseudo-document, a second set of terms associated with the second cluster; and combining the first and second sets of terms into a list of output terms. ### Claim 16 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein clustering the determined plurality of term vectors into one or more clusters comprises using agglomerative clustering. ### Claim 17 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein combining the first and second sets of terms into the list of output terms comprises using log-entropy mixing. ### Claim 18 (depends on 15) The non-transitory computer-readable medium of claim 15, the one or more processing units further operable to perform operations comprising: creating a query pseudo-document from the determined plurality of term vectors; creating a first leaned pseudo-document using the first pseudo-document and the query pseudo-document; and creating a second leaned pseudo-document using the second pseudo-document and the query pseudo-document; and wherein: identifying the first set of terms associated with the first cluster comprises using LSA of the first leaned pseudo-document; and identifying the second set of terms associated with the second cluster comprises using LSA of the second leaned pseudo-document. ### Claim 19 (depends on 18) The non-transitory computer-readable medium of claim 18, wherein creating the first and second leaned pseudo-documents comprises: determining a query vector according to the query pseudo-document; determining a first cluster vector according to the first pseudo-document; determining a second cluster vector according to the second pseudo-document; normalizing the query vector and the first cluster vector to the same length; normalizing the query vector and the second cluster vector to the same length; determining a first leaned cluster vector that points to a location that is between the query vector and the first cluster vector; and determining a second leaned cluster vector that points to a location that is between the query vector and the second cluster vector. ### Claim 20 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein combining the first and second sets of terms into a list of output terms comprises: determining a log-entropy weight of each of the term vectors; determining a first log-entropy weight for the first cluster, the first log-entropy weight being a sum of the determined log-entropy weights of the term vectors of the first cluster; determining a second log-entropy weight for the second cluster, the second log-entropy weight being a sum of the determined log-entropy weights of the term vectors of the second cluster; determining a summed log-entropy weight by summing the first and second log-entropy weights; determining a first percentage associated with the first cluster by dividing the first log-entropy weight by the summed log-entropy weight; and determining a second percentage associated with the second cluster by dividing the second log-entropy weight by the summed log-entropy weight; wherein the list of output terms comprises terms from the first and second set of terms according to the first and second percentages.