US2012321202A1PendingUtilityA1
Identifying information related to a particular entity from electronic sources, using dimensional reduction and quantum clustering

Assignee: FERTIK MICHAEL BENJAMIN SELKOWEPriority: Jun 20, 2011Filed: Jun 20, 2011Published: Dec 20, 2012
Est. expiryJun 20, 2031(~4.9 yrs left)· nominal 20-yr term from priority
Inventors:Michael Benjamin Selkowe Fertik Tony Scott Thomas Gerardo Dignan
G06F 18/2135G06F 16/355
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References
Claims
Abstract

Presented are systems and methods for identifying information about a particular entity including acquiring electronic documents having unstructured text, that are selected based on one or more search terms from a plurality of terms related to the particular entity. Tokenizing the acquired documents to form a data matrix and then calculating a plurality of eigenvectors, using the data matrix and the transpose of the data matrix. The variance is then acquired for determining the amount of intra-clustering between the documents and then the acquired documents are clustered using some of the eigenvectors and the variance.
Claims

exact text as granted — not AI-modified
1 . A system comprising:
 a collector module configured to acquire a plurality of eigenvectors, each having a corresponding eigenvalue, wherein
 the plurality of eigenvectors are based on a plurality of tokenized electronic documents having unstructured text, the plurality of tokenized electronic documents forming a data matrix, and 
 the unstructured text includes background terms and nonbackground terms; and 
   a dimensional reduction module configured to:
 classify the plurality of eigenvectors and their corresponding eigenvalues into one or more background eigenvectors and background eigenvalues, and one or more nonbackground eigenvectors and nonbackground eigenvalues, wherein the background eigenvectors correspond to the background terms and the nonbackground eigenvectors correspond to nonbackground terms, 
 acquire a threshold, 
 compare the nonbackground eigenvalues with the threshold, and 
 provide the nonbackground eigenvectors whose corresponding compared nonbackground eigenvalues exceed the threshold, wherein the provided nonbackground eigenvectors are used for clustering the plurality of documents. 
   
     
     
         2 . The system of  claim 1 , wherein the dimensional reduction module is further configured to set the threshold by a user, by a human operator other than a user, or from a data storage. 
     
     
         3 . The system of  claim 1 , further comprising a data processing module that is configured to:
 construct a guess eigenvector;   multiply the guess eigenvector by the transpose of the data matrix to obtain a first vector, wherein the data matrix corresponds to the plurality of tokenized electronic documents having unstructured text;   multiply the first vector with the data matrix to obtain a second vector; and   provide the second vector if the second vector is an eigenvector.   
     
     
         4 . The system of  claim 1 , further comprising a data processing module that is configured to:
 construct a guess eigenvector to a distributed matrix, wherein the distributed matrix is related to the data matrix and the transpose of the data matrix;   partition the guess eigenvector into one or more sub-vectors;   distribute the or more sub-vectors to one or more server slaves that contain at least one tokenized electronic document;   acquire from the one or more server slaves, a second vector that corresponds to the multiplication of the sub-vector, that was distributed to the server slave, with the tokenized electronic document contained on that server slave;   assemble the acquired vectors into a single reconstituted vector; and   provide the reconstituted vector if it is an eigenvector of the distributed matrix.   
     
     
         5 . An system for identifying information about a particular entity, comprising:
 a collector module configured to acquire electronic documents having unstructured text, wherein the electronic documents are selected based on one or more search terms from a plurality of terms related to the particular entity;   a tokenizer module configured to tokenize the acquired documents to form a data matrix;   a data processing module configured to calculate a plurality of eigenvectors, using the data matrix and the transpose of the data matrix;   a quantum clustering module configured to:
 acquire a variance for determining the amount of intra-clustering between the documents, and 
 cluster the acquired documents using some of the eigenvectors and the variance. 
   
     
     
         6 . The system of  claim 5 , further comprising a dimensional reduction module configured to perform dimensional reduction on the calculated eigenvectors to obtain some nonbackground eigenvectors used for clustering. 
     
     
         7 . The system of  claim 5 , wherein the quantum clustering module is configured to cluster the acquired documents using a designated quantum potential V(σ, x), some eigenvectors, and the variance. 
     
     
         8 . The system of  claim 5 , further comprising an output module configured to display the clustered documents if the eigenvectors used for clustering number are 3 or less. 
     
     
         9 . The system of  claim 5 , wherein the data processing module is configured to:
 construct a guess eigenvector;   multiply the guess eigenvector by the transpose of the data matrix to obtain a first vector;   multiply the first vector with the data matrix to obtain a second vector; and   provide the second vector if it is an eigenvector.   
     
     
         10 . The system of  claim 5 , wherein the data processing module is configured to:
 construct a guess eigenvector to a distributed matrix, wherein the distributed matrix is related to the data matrix and the transpose of the data matrix;   partition the guess eigenvector into one or more sub-vectors;   distribute the or more sub-vectors to one or more server slaves that contain at least one tokenized electronic document;   acquire from the one or more server slaves, a second vector that corresponds to the multiplication of the sub-vector that was distributed to the server slave with the tokenized electronic document contained on that server slave;   assemble the acquired vectors into a single reconstituted vector;   determine whether the reconstituted vector is an eigenvector of the distributed matrix; and   provide the reconstituted vector if it is an eigenvector of the distributed matrix.   
     
     
         11 . The system of  claim 5 , wherein the quantum clustering module is configured to set the variance by a user, by a human operator other than a user, or from a data storage. 
     
     
         12 . A method comprising:
 acquiring a plurality of eigenvectors, each having a corresponding eigenvalue, wherein
 the plurality of eigenvectors are based on a plurality of tokenized electronic documents having unstructured text, the plurality of tokenized electronic documents forming a data matrix, and 
 the unstructured text includes background terms and nonbackground terms; and 
   classifying the plurality of eigenvectors and their corresponding eigenvalues into one or more background eigenvectors and background eigenvalues, and one or more nonbackground eigenvectors and nonbackground eigenvalues, wherein the background eigenvectors correspond to the background terms and the nonbackground eigenvectors correspond to nonbackground terms;   acquiring a threshold;   comparing the nonbackground eigenvalues with the threshold; and   providing the nonbackground eigenvectors whose corresponding compared nonbackground eigenvalues exceed the threshold, wherein the provided nonbackground eigenvectors are used for clustering the plurality of documents.   
     
     
         13 . The method of  claim 12 , wherein the threshold is set by a user, by a human operator other than a user, or from a data storage. 
     
     
         14 . The method of  claim 12 , wherein at least one of the acquired plurality of eigenvectors is generated by:
 constructing a guess eigenvector;   multiplying the guess eigenvector by the transpose of the data matrix to obtain a first vector, wherein the data matrix corresponds to the plurality of tokenized electronic documents having unstructured text;   multiplying the first vector with the data matrix to obtain a second vector; and   providing the second vector if the second vector is an eigenvector.   
     
     
         15 . The method of  claim 12 , wherein at least one of the acquired plurality of eigenvectors is generated by:
 constructing a guess eigenvector to a distributed matrix, wherein the distributed matrix is related to the data matrix and the transpose of the data matrix;   partitioning the guess eigenvector into one or more sub-vectors;   distributing the or more sub-vectors to one or more server slaves that contain at least one tokenized electronic document;   acquiring from the one or more server slaves, a second vector that corresponds to the multiplication of the sub-vector that was distributed to the server slave with the tokenized electronic document contained on that server slave;   assembling the acquired vectors into a single reconstituted vector;   determining whether the reconstituted vector is an eigenvector of the distributed matrix; and   providing the reconstituted vector if it is an eigenvector of the distributed matrix.   
     
     
         16 . A method for identifying information about a particular entity comprising:
 acquiring electronic documents having unstructured text, that are selected based on one or more search terms from a plurality of terms related to the particular entity;   tokenizing the acquired documents to form a data matrix;   calculating a plurality of eigenvectors, using the data matrix and the transpose of the data matrix;   acquiring a variance for determining the amount of intra-clustering between the documents; and   clustering the acquired documents using some of the eigenvectors and the variance.   
     
     
         17 . The method of  claim 16 , further comprising performing dimensional reduction on the calculated eigenvectors to obtain some nonbackground eigenvectors used for clustering. 
     
     
         18 . The method of  claim 16 , wherein clustering the acquired documents is performed using a quantum potential V(σ, x), some eigenvectors, and the variance. 
     
     
         19 . The method of  claim 16 , further comprising displaying the clustered documents if the eigenvectors used for clustering number are 3 or less. 
     
     
         20 . The method of  claim 16 , wherein calculating a plurality of eigenvectors, using the data matrix and the transpose of the data matrix comprises:
 constructing a guess eigenvector;   multiplying the guess eigenvector by the transpose of the data matrix to obtain a first vector;   multiplying the guess eigenvector by the transpose of the data matrix to obtain a first vector;   multiplying the first vector with the data matrix to obtain a second vector; and   providing the second vector if it is an eigenvector.   
     
     
         21 . The method of  claim 16 , wherein calculating a plurality of eigenvectors, using the data matrix and the transpose of the data matrix comprises:
 constructing a guess eigenvector to a distributed matrix, wherein the distributed matrix is related to the data matrix and the transpose of the data matrix;   partitioning the guess eigenvector into one or more sub-vectors;   distributing the or more sub-vectors to one or more server slaves that contain at least one tokenized electronic document;   acquiring from the one or more server slaves, a second vector that corresponds to the multiplication of the sub-vector that was distributed to the server slave with the tokenized electronic document contained on that server slave;   assembling the acquired vectors into a single reconstituted vector;   determining whether the reconstituted vector is an eigenvector of the distributed matrix; and   providing the reconstituted vector if it is an eigenvector of the distributed matrix.   
     
     
         22 . The method of  claim 16 , wherein the variance is set by a user, by a human operator other than a user, or from a data storage. 
     
     
         23 . The method of  claim 16 , wherein the acquired electronic documents are provided by one or more public data sources. 
     
     
         24 . A non-transitory computer-readable medium comprising program code, the program code being operable, when executed by a computer system, to cause the computer system to perform a method comprising:
 acquiring a plurality of eigenvectors, each having a corresponding eigenvalue, wherein
 the plurality of eigenvectors are based on a plurality of tokenized electronic documents having unstructured text, the plurality of tokenized electronic documents forming a data matrix, and 
 the unstructured text includes background terms and nonbackground terms; and 
   classifying the plurality of eigenvectors and their corresponding eigenvalues into one or more background eigenvectors and background eigenvalues, and one or more nonbackground eigenvectors and nonbackground eigenvalues, wherein the background eigenvectors correspond to the background terms and the nonbackground eigenvectors correspond to nonbackground terms;   acquiring a threshold;   comparing the nonbackground eigenvalues with the threshold; and   providing the nonbackground eigenvectors whose corresponding compared nonbackground eigenvalues exceed the threshold, wherein the provided nonbackground eigenvectors are used for clustering the plurality of documents.   
     
     
         25 . The computer readable medium of  claim 24 , wherein the threshold is set by a user, by a human operator other than a user, or from a data storage. 
     
     
         26 . The computer readable medium of  claim 24 , wherein at least one of the acquired plurality of eigenvectors is generated by:
 constructing a guess eigenvector;   multiplying the guess eigenvector by the transpose of the data matrix to obtain a first vector, wherein the data matrix corresponds to the plurality of tokenized electronic documents having unstructured text;   multiplying the first vector with the data matrix to obtain a second vector; and   providing the second vector if the second vector is an eigenvector.   
     
     
         27 . The computer readable medium of  claim 24 , wherein at least one of the acquired plurality of eigenvectors is generated by:
 constructing a guess eigenvector to a distributed matrix, wherein the distributed matrix is related to the data matrix and the transpose of the data matrix;   partitioning the guess eigenvector into one or more sub-vectors;   distributing the or more sub-vectors to one or more server slaves that contain at least one tokenized electronic document;   acquiring from the one or more server slaves, a second vector that corresponds to the multiplication of the sub-vector that was distributed to the server slave with the tokenized electronic document contained on that server slave;   assembling the acquired vectors into a single reconstituted vector;   determining whether the reconstituted vector is an eigenvector of the distributed matrix; and   providing the reconstituted vector if it is an eigenvector of the distributed matrix.   
     
     
         28 . A non-transitory computer-readable medium comprising program code, the program code being operable, when executed by a computer system, to cause the computer system to perform a method for identifying information about a particular entity comprising:
 acquiring electronic documents having unstructured text, that are selected based on one or more search terms from a plurality of terms related to the particular entity;   tokenizing the acquired documents to form a data matrix;   calculating a plurality of eigenvectors, using the data matrix and the transpose of the data matrix;   acquiring a variance for determining the amount of intra-clustering between the documents; and   clustering the acquired documents using some of the eigenvectors and the variance.   
     
     
         29 . The computer readable medium of  claim 28 , further comprising performing dimensional reduction on the calculated eigenvectors to obtain some nonbackground eigenvectors used for clustering. 
     
     
         30 . The computer readable medium of  claim 28 , wherein clustering the acquired documents is performed by solving the Schrödinger Wave Equation, using some eigenvectors and the variance. 
     
     
         31 . The computer readable medium of  claim 28 , further comprising displaying the clustered documents if the eigenvectors used for clustering number are 3 or less. 
     
     
         32 . The computer readable medium of  claim 28 , wherein calculating a plurality of eigenvectors, using the data matrix and the transpose of the data matrix comprises:
 constructing a guess eigenvector;   multiplying the guess eigenvector by the transpose of the data matrix to obtain a first vector;   multiplying the guess eigenvector by the transpose of the data matrix to obtain a first vector;   multiplying the first vector with the data matrix to obtain a second vector; and   providing the second vector if it is an eigenvector.   
     
     
         33 . The computer readable medium of  claim 28 , wherein calculating a plurality of eigenvectors, using the data matrix and the transpose of the data matrix comprises:
 constructing a guess eigenvector to a distributed matrix, wherein the distributed matrix is related to the data matrix and the transpose of the data matrix;   partitioning the guess eigenvector into one or more sub-vectors;   distributing the or more sub-vectors to one or more server slaves that contain at least one tokenized electronic document;   acquiring from the one or more server slaves, a second vector that corresponds to the multiplication of the sub-vector that was distributed to the server slave with the tokenized electronic document contained on that server slave;   assembling the acquired vectors into a single reconstituted vector;   determining whether the reconstituted vector is an eigenvector of the distributed matrix; and   providing the reconstituted vector if it is an eigenvector of the distributed matrix.   
     
     
         34 . The computer readable medium of  claim 28 , wherein the variance is set by a user, by a human operator other than a user, or from a data storage. 
     
     
         35 . The computer readable medium of  claim 28 , wherein the acquired electronic documents are provided by one or more public data sources.
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