Data analysis system using artificial intelligence

Abstract

A data analysis system utilizing custom unsupervised machine learning processes over a communications network is disclosed, the system including a repository of data, a web application deployed on a web server, the web application including a data collection interface, wherein the web application is configured for providing a graphical user interface for modifying threshold parameters of a clustering algorithm for clustering the data, executing the clustering algorithm with the threshold parameters that were modified, thereby producing a set of results, providing a graphical user interface for reviewing the set of results of the clustering algorithm and re-executing previous steps if the set of results are not useful and, executing a deep learning algorithm in a deep learning software framework on the set of results, thereby establishing relationships between the data, and providing generalizations of the data.

We claim: 
     
         1 . A data analysis system utilizing custom unsupervised machine learning processes over a communications network, the system comprising:
 a repository of data connected to the communications network;   a web application deployed on a web server connected to the communications network, the web application including a data collection interface between the web server and the repository of data, wherein the web application is configured for:   a) providing a graphical user interface for modifying, by a user, a plurality of threshold parameters of a clustering algorithm for clustering the data, wherein the clustering algorithm comprises at least a machine learning logistic regression function;   b) executing the clustering algorithm with the plurality of threshold parameters that were modified by the user, thereby producing a set of results;   c) providing a graphical user interface for reviewing, by the user, the set of results of the clustering algorithm and re-executing steps a) through c) if the set of results are not useful;   d) copying the set of results into a deep learning software framework; and   e) executing a deep learning algorithm in the deep learning software framework on the set of results, thereby establishing relationships between the data, and providing generalizations of the data.   
     
     
         2 . The data analysis system of  claim 1 , further comprising:
 wherein the machine learning logistic regression function is configured for identifying a set of functional data comprised within the data.   
     
     
         3 . The data analysis system of  claim 2 , further comprising:
 wherein the machine learning logistic regression function is configured to calculate a metric of influence for each feature of a plurality of features associated with the data.   
     
     
         4 . The data analysis system of  claim 3 , further comprising:
 wherein the plurality of threshold parameters comprises a plurality of numerical values, wherein each numerical value comprises a decimal number.   
     
     
         5 . The data analysis system of  claim 4 , further comprising:
 wherein the graphical user interface for reviewing, by the user, the set of results comprises a supportive graphical user interface.   
     
     
         6 . The data analysis system of  claim 5 , further comprising:
 wherein the web application is further configured for generating a downloadable report comprising the set of results for review by the user.   
     
     
         7 . The data analysis system of  claim 6 , further comprising:
 wherein the set of results comprises a plurality of scores associated with each of the plurality of features associated with the data.   
     
     
         8 . A method for data analysis utilizing custom unsupervised machine learning processes over a communications network, the method comprising:
 storing data in a repository connected to the communications network;   providing a web application deployed on a web server connected to the communications network, the web application including a data collection interface between the web server and the repository, wherein the web application is configured for:   a) providing a graphical user interface for modifying, by a user, a plurality of threshold parameters of a clustering algorithm for clustering the data, wherein the clustering algorithm comprises at least a machine learning logistic regression function;   b) executing the clustering algorithm with the plurality of threshold parameters that were modified by the user, thereby producing a set of results;   c) providing a graphical user interface for reviewing, by the user, the set of results of the clustering algorithm and re-executing steps a) through c) if the set of results are not useful;   d) copying the set of results into a deep learning software framework; and   e) executing a deep learning algorithm in the deep learning software framework on the set of results, thereby establishing relationships between the data, and providing generalizations of the data.   
     
     
         9 . The method of  claim 8 , further comprising:
 wherein the machine learning logistic regression function is configured for identifying a set of functional data comprised within the data.   
     
     
         10 . The method of  claim 9 , further comprising:
 wherein the machine learning logistic regression function is configured to calculate a metric of influence for each feature of a plurality of features associated with the data.   
     
     
         11 . The data method of  claim 10 , further comprising:
 wherein the plurality of threshold parameters comprises a plurality of numerical values, wherein each numerical value comprises a decimal number.   
     
     
         12 . The method of  claim 11 , further comprising:
 wherein the graphical user interface for reviewing, by the user, the set of results comprises a supportive graphical user interface.   
     
     
         13 . The method of  claim 12 , further comprising:
 wherein the web application is further configured for generating a downloadable report comprising the set of results for review by the user.   
     
     
         14 . The method of  claim 13 , further comprising:
 wherein the set of results comprises a plurality of scores associated with each of the plurality of features associated with the data.   
     
     
         15 . A data analysis system utilizing custom unsupervised machine learning processes over a communications network, the system comprising:
 a repository of data connected to the communications network;   a web application deployed on a web server connected to the communications network, the web application including a data collection interface between the web server and the repository of data, wherein the web application is configured for:   a) providing a graphical user interface for modifying, by a user, a plurality of threshold parameters of a clustering algorithm for clustering the data, wherein the clustering algorithm comprises at least a machine learning logistic regression function;   b) executing the clustering algorithm with the plurality of threshold parameters that were modified by the user, thereby producing a set of results including a plurality of scores associated with at least one component of the data;   c) providing a graphical user interface for reviewing, by the user, the set of results of the clustering algorithm and re-executing steps a) through c) with an adjusted set of the plurality of threshold parameters, if the set of results are not useful;   d) copying the set of results into a deep learning software framework; and   e) executing a deep learning algorithm in the deep learning software framework on the set of results, thereby establishing relationships between the data, and providing generalizations of the data.   
     
     
         16 . The data analysis system of  claim 15 , further comprising:
 wherein the machine learning logistic regression function is configured for identifying a set of functional data comprised within the data.   
     
     
         17 . The data analysis system of  claim 16 , further comprising:
 wherein the machine learning logistic regression function is configured to calculate a metric of influence for each feature of a plurality of features associated with the data.   
     
     
         18 . The data analysis system of  claim 17 , further comprising:
 wherein the plurality of threshold parameters comprises a plurality of numerical values, wherein each numerical value comprises a decimal number.   
     
     
         19 . The data analysis system of  claim 18 , further comprising:
 wherein the graphical user interface for reviewing, by the user, the set of results comprises a supportive graphical user interface.   
     
     
         20 . The data analysis system of  claim 19 , further comprising:
 wherein the web application is further configured for generating a downloadable report comprising the set of results for review by the user.