Patent Publication Number: US-2023154220-A1

Title: Pre-processing a table in a document for natural language processing

Description:
BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The present invention relates to a computer program product, system, and method for pre-processing a table in a document for natural language processing. 
     2. Description of the Related Art 
     A Natural Language Processing (NLP) program processes documents including tables, comprised of columns and rows of data cells, by performing a table flattening pre-process to extract information on the table for the NLP program to use to interpret the document and tables. Table flattening involves parsing the table to extract tokens comprising headers and sub-headers in the table associated with the data cells. Table flattening may involve table segmentation to segment text into rows, columns, and headers and then interpret the text. 
     The table flattening process may identify three types of elements, including a main element, which defines the primary entity whose value to be extracted, a conditional element, which refines the primary entity, and a value element, which contains the value for the entity. Concatenation rules may then be used to form natural language sentences based on the main, conditional and value elements. Information extracted during the table flattening process is then provided to the NLP program to interpret the document and tables embedded in the document. 
     There is a need in the art for improved techniques for pre-processing of tables in a document for the NLP program to process. 
     SUMMARY 
     Provided are a computer program product, system, and method for pre-processing a table in a document for natural language processing (NLP). A table in a document is parsed to extract column headers, row headers, and data cells. The column headers, row headers, and data cells are processed to determine an initial set of a main element comprising an entity whose value is to be extracted, a conditional element that refines the entity, and a value element comprising a value for the entity from the processed column headers, row headers, and data cells. User selection is received of at least one of the column headers, row headers, and data cells for at least one of the main element, conditional element, and the value element in the initial set to produce a modified set of the main element, conditional element, and value element. The modified set of the main element, conditional element, and the value element are provided to an NLP engine to perform natural language processing of the document including the table, using the modified set of the main element, conditional element, and the value element to interpret the table. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    illustrates an embodiment of a natural language computing system. 
         FIG.  2    illustrates an embodiment of table item metadata for table items in a table. 
         FIG.  3    illustrates an embodiment of element classifications and lexical connectors for a table. 
         FIG.  4    illustrates an embodiment of an instance of a display screen coordinate mapping. 
         FIG.  5    illustrates an embodiment of operations to parse a table to classify elements in the table. 
         FIG.  6    illustrates an embodiment of operations to generate a graphical user interface (GUI) to enable a user to modify the classified elements for the table. 
         FIG.  7    illustrates an example of classification of table items in a table. 
         FIGS.  8   a ,  8   b , and  8   c    illustrate examples of a graphical user interface (GUI) in which a user may modify the classified elements of the table. 
         FIG.  9    illustrates an embodiment of operations to update the classification of elements in the table with user input. 
         FIG.  10    illustrates an embodiment of operations to process user selection of a table item rendered on a display screen for table pre-processing. 
         FIG.  11    depicts a computing environment in which the components of  FIG.  1    may be implemented. 
     
    
    
     DETAILED DESCRIPTION 
     When performing natural language processing (NLP) of a document, the NLP program may provide a visualization of annotations output from the NLP processing layer. This visualizations allow users to evaluate the accuracy of detected concepts against the full context from which the concepts were detected in the document. 
     However, one or more pre-processing layers preceding the NLP processing layer of the document can greatly impact the accuracy of the NLP layer output. 
     Described embodiments provide improved computer technology for NLP pre-processing operations including the table flattening process to provide users information and visualization of the pre-processing table flattening results to allow the user to inspect the results for accuracy and edit classifications of the table elements for subsequent improved processing and understanding by the NLP program. With described embodiments, a table interpreter may process column headers, row headers, and data cells in a table in a document to determine an initial set of a main element comprising an entity whose value is to be extracted, a conditional element that refines the entity, and a value element comprising a value for the entity from the processed column headers, row headers, and data cells. User selection may then be received of at least one of the column headers, row headers, and data cells for at least one of the main element, conditional element, and the value element in the initial set to produce a modified set of the main element, conditional element, and value element. The modified set of the main element, conditional element, and the value element is provided to an NLP engine to perform natural language processing of the document including the table, using the modified set of the main element, conditional element, and the value element to interpret the table. 
       FIG.  1    illustrates an embodiment of a computer system  100  having a processor  102  to execute program components stored in a memory/storage  104 . The memory/storage  104  includes program components executed by the processor  102 , including a table parser  106  and a table interpreter  108  to perform table pre-processing flattening operations, and a natural language processor (NLP) engine  110 . The table parser  106  parses a document  112  including one or more tables  114 , having rows and columns of data, to produce extracted table items  116 , including determined row headers, column headers, and data cells, and table item metadata  200  having metadata on the extracted row headers, column headers, and data cells. A table interpreter  108  receives the extracted table items  116  and classifies the table items as an initial set of element classifications  300 i including a main element, comprising the primary entity whose value is to be extracted as part of natural language understanding of the table, a conditional element, which refines or places a condition on the primary entity, a value element having the value for the primary entity, and lexical connector tokens to connect the elements in a concatenated natural language statement. The table interpreter  108  may use classification techniques known in the art, such as machine learning technology to classify table items as the main element, conditional element(s), and value element. Machine learning or concatenation rules may be used to determine the lexical connectors for the classified elements in the initial set  300   I . 
     The initial set of element classifications  300   I  and table item metadata  200  are provided to a graphical user interface (GUI) generator  118  to generate a table pre-processing GUI  120  to allow the user to modify the table cells initially determined to be the main element, conditional element, and value element used in interpreting the table to produce a modified set of element classifications and lexical connectors  300   M .The table flattening GUI generator  118  further generates a display coordinate mapping  400  that maps coordinates of the table  114 , rendered in the table pre-processing GUI  120  displayed on a user computer display, to coordinates of the table  114  in the document  112 . A user, such as a subject matter expert, reviewing the initial classified table element classifications and lexical connectors  300   I , may use the table pre-processing GUI  120  to change the table items comprising the table element classifications and lexical connectors to output a user modified set of element classifications and lexical connectors  300   M  that are provided to the NLP engine  110 . The NLP engine  110  performs natural language processing of the document  112  and tables  114  subject to table flattening preprocessing. 
     With the described embodiments, the table flattening preprocessing allows the user to review the initial set of machine determined element classifications and lexical connectors  300   I  and determine whether the concatenation rules, classifications, and lexical connectors should be changed to improve the understanding of the meaning of the table  114  to optimize processing by the NLP engine  110 . 
     The document  112  may comprise a structured document, such as a Microsoft® Word file, Portable Document Format (“PDF)”, Extensible Markup Language (XML) document, a Hypertext Markup Language (HTML) document, as well as an unstructured document, etc. (Microsoft is a trademark of Microsoft Corporation throughout the world). 
     The memory/storage  104  may comprise a suitable volatile or non-volatile memory for storing programs to execute and information used by the processor  102  to execute. 
     Generally, program modules, such as the program components  106 ,  108 ,  110 ,  118  may comprise routines, programs, objects, components, logic, data structures, and so on that perform particular tasks or implement particular abstract data types. The program components and hardware devices of the computer system  100  of  FIG.  1    may be implemented in one or more computer systems, where if they are implemented in multiple computer systems, then the computer systems may communicate over a network 
     The programs  106 ,  108 ,  110 ,  118  may comprise program code loaded into memory and executed by a processor. Alternatively, some or all of the functions may be implemented in hardware devices, such as in Application Specific Integrated Circuits (ASICs) or executed by separate dedicated processors. 
     In one embodiment, the programs  106 ,  108 ,  110  may implement a machine learning technique such as decision tree learning, association rule learning, neural network, inductive programming logic, support vector machines, Bayesian network, etc., to perform their specific tasks in the natural language processing pipeline. The programs  106 ,  108 ,  110  may comprise artificial neural network programs. Each neural network may be trained using backward propagation to adjust weights and biases at nodes in a hidden layer to produce the computed output. In backward propagation used to train a neural network machine learning module, biases at nodes in the hidden layer are adjusted accordingly to produce the desired outcome based on specified confidence levels. Backward propagation may comprise an algorithm for supervised learning of artificial neural networks using gradient descent. Given an artificial neural network and an error function, the method may calculate the gradient of the error function with respect to the neural network&#39;s weights and biases. 
     In  FIG.  1   , arrows are shown between components in the memory/storage  104 . These arrows represent information flow to and from the program components  106 ,  108 ,  110 ,  118  and do not represent data structures in the memory  104 . 
       FIG.  2    illustrates an embodiment of table item metadata  200   i  for one of the table items extracted from the table  114 , and includes a table item identifier  202 , comprising a unique identifier of the table item; a table item type  204 , such as a row header, column header, or data cell; the table item text  206 ; a location  208  of the table item in the document  112 , such as page number, horizontal and vertical offsets and coordinates within that page for the start and end of the table item  202 , such as defining a boundary region from the top left corner of the extracted text to its bottom right corner; location  210  of table item in the table  114 , such as row and column numbers within the table  114 . Table item metadata  200   i  may be generated for each cell in the table  114  being processed. 
       FIG.  3    illustrates an embodiment of element classifications and lexical connectors  300   i  which may comprise an initial set  300   I  generated by the table interpreter  108  or a modified set  300   M  selected by the user using the table pre-processing GUI  120 , and includes: a main element  302 , comprising the primary entity whose value is to be extracted as part of natural language understanding of the table; a main lexical connector  304  to provide a lexical token to connect the main element  302  to a conditional element  306   1 , which refines or places a condition on the main element  302 . If there are multiple conditional elements  306   n , i.e., n is greater than one, then there would be n−1 lexical connectors, one additional conditional lexical connector  308   j  for each added conditional element  306   i . There is a value lexical connector  310  to connect the final  306   n  or only conditional element  3061  to the value element  312 , which contains the value for the entity. If there is only one conditional element  306   I , i.e., n=1, then there would be no conditional lexical connector and only a value lexical connector  310  between the sole conditional element  306   I  and the value element  312 . 
     Lexical connectors may comprise strings such as “for”, “is”, “and”, etc. 
     The element classifications and lexical connectors provide  300   i  a concatenation rule for combining main elements comprising values in a row or column for the main element and conditional element(s) comprising values in a row or column that results in the value element  312 . 
       FIG.  4    illustrates an embodiment of an instance of a display screen coordinate mapping  400   i  that maps display screen coordinates  402  showing the table  114  on the user display screen to the document coordinates  404  of the table  114  in the document. 
       FIG.  5    illustrates an embodiment of operations performed by the table parser  106  to initiate the table flattening pre-processing of tables  114  in the document  112 . Upon receiving (at block  500 ) a document  112  including a table  114 , the parser  106  parses (at block  502 ) the table  114  to extract table items of column headers, row headers, and data cells in a table  114  in the document  112 . For each extracted table item i, the parser  106  performs the operations at blocks  504  through  510 . The parser  106  determines (at block  506 ) coordinates in the table  114  in the document  112  comprising the boundary region including the table item i. The coordinates may include the page number of the table item i within the document  112  as well as horizontal and vertical offsets and coordinates within that page for the start and end of the table item i. The horizontal and vertical offsets may define a boundary region or bounding box from the top left corner of the extracted table item i to its bottom right corner. The determined coordinates may also include information about the row and column coordinates of the table item i within the containing table  114 . The parser  106  generates (at block  508 ) table item i metadata  200   i  indicating table item ID  202 , table item type  204  (row, column, data), table item  202  location in the document  208  (page and horizontal and vertical offsets in the page), and location  210  of table item  202  in the table  114 , such as row and column location. 
     The table interpreter  108  processes (at block  512 ) the extracted table items  116 , including table item metadata  200  for the table items, to determine an initial set  300   I  of element classifications, e.g., main element  302 , conditional element(s)  306   I  . . .  306   n , and value element  312  of the table  114 , from the extracted column headers, row headers, and data cells, and lexical connectors  304 ,  308   I  . . .  308   n−1 ,  310  between the main element  302 , conditional element(s)  306   I  . . .  306   n , and value element  312 . The determined initial classifications and lexical connectors may then be saved in the initial set of element classifications and lexical connectors  3001   I . 
     With the embodiment of  FIG.  5   , the table flattening processes  106 ,  108  determines an initial set of element classifications and lexical connectors to form concatenation rules to construct natural language sentences describing the information in the cells of the table. 
       FIG.  6    illustrates an embodiment of operations performed by the GUI generator  118  to initiate the review process of the generated initial set  300   I  of element classifications and concatenation rules to allow editing by a subject matter expert managing the pre-processing and optimization of the NLP engine  110 . Upon initiating (at block  600 ) the process, the GUI generator  118  generates (at block  602 ) a table pre-processing GUI  120  including a representation of the table  114  with the initial main element, conditional element(s), and value elements highlighted. The GUI generator  118  renders (at block  604 ) graphical controls in the GUI  120 , such as drop-down menus, to enable the user to select one of the extracted column headers, row headers, and data cells as the main element  302 , conditional element(s)  306   1  . . .  306   n , and value element  312  different from what the table interpreter  108  determined. The GUI generator  118  renders (at block  606 ) graphical controls in the GUI  120 , such as drop-down menus, to enable the user to select lexical connectors  304 ,  308   1  . . .  308   n−1 ,  310  between the main element  302  and one of the conditional elements  306   1 , between any pair of conditional elements, and between the last conditional element  306   n  and the value element  312 , respectively. The GUI generator  118  may further render (at block  608 ) an add graphical control to enable the user to add an additional conditional element  306   n+1  to the concatenation rules and a lexical connector  308   n ,  310  for the new conditional element  306   n+1 . 
     The GUI generator  118  maps (at block  610 ) display screen coordinates  402  of the display screen in which the table  114  is displayed to document coordinates  404  in the document including the extracted table items to form display screen coordinate mappings  400 , to map the coordinates in which the table  114  is rendered on the display screen to coordinates of the table  114  in the document  112 . Upon receiving (at block  612 ) a resizing of the GUI  120  rendered on the display screen changing the dimensions of the displayed table  114  in the GUI  120 , control proceeds back to block  610  to remap the display screen coordinates  402  to document coordinates  404  based on the resizing. 
     The embodiment of  FIG.  6    renders the table pre-processing GUI  120  to enable the subject matter expert to modify the initial set  300   I  of element classifications and lexical connectors to improve the NLP engine  110  processing of the document  112  and table  114 . 
     In generating the display screen to document  112  mappings, the GUI generator  118  translates the screen coordinates to document coordinates, e.g., PDF coordinates. The coordinate mapping may consider the location on-screen of the rendered page, the magnification in effect, the location of the user&#39;s click, etc. Straightforward geometrical calculations using these factors may be used to translate the screen coordinates to document  112 , e.g., PDF coordinates, i.e., the page number and horizontal and vertical offsets within the document  112  of the coordinates. 
       FIG.  7    illustrates an example of a table  700 , such as table  114 , including rows and columns. Highlighted table item  702  comprises the main element  302 , highlighted column header  704  comprises a conditional element  306 , and highlighted data cell  706  comprises the value element by way of example. 
       FIG.  8   a    illustrates an example of a table pre-processing GUI  800   a,  such as an embodiment of GUI  120 , in which the table  802   a  is rendered with columns and headers. A main element drop-down menu  804   a,  a conditional element drop-down menu  806   a,  and value element drop-down menu  808   a  enables the user to select any of the table items as the main element  302 , conditional element  306   1  (only one is shown), and value element  312 . Further, the lexical connector controls  810   a,    812   a  enable the user to select a main lexical connector  304  and value lexical connector  310 , respectively. The panel  814   a  shows the result of the selected concatenation rule selected in the drop-down menus  804   a - 812   a.  The drop-down menus  804   a - 812   a  may initially be set to the element classifications and lexical connectors in the initial set  300   I  determined by the table interpreter  108 . The add column graphical control  816   a  allows the user to add an additional condition element  306   i  and conditional lexical connector  308   j  to the concatenation rules. In  FIG.  8   a   , table elements  818   a,    820   a,  and  822   a  are set as the main element  302 , sole conditional element  3061 , and value element  312 , respectively, and they are highlighted with a graphic to identify their element classification. 
       FIG.  8   b    illustrates the table pre-processing GUI  800   b,  such as an embodiment of GUI  120 , after the user has modified the selection of the main element  804   b  and conditional element  806   b  presented in GUI  800   b  to be the table items  820   b  and  818   b,  respectively. This changed selection results in a new textual concatenation in panel  814   b.  This new selection modifies the main element  302  and conditional element  306   1  in the settings  300   2  to produce the modified set  300   M  of element classifications. 
       FIG.  8   c    illustrates the table pre-processing GUI  800   a  after the user has added two additional conditional element drop-down menus using the add column button  816   c,  resulting in three conditional element selection drop-down menus  806   C1 ,  806   C2 , and  806   c   3  and conditional lexical connector drop-down menus  824   c  and  826   c.  In the example of  FIG.  8   c   , the user has modified the main element  804   c  presented in GUI  800   c  to be the table items  820   c.  This user selections shown in  FIG.  8   c    selects three conditional elements  806   c1 ,  806   c2 ,  806   c3  to be table items  828   c,    818   c,  and  830   c  and selects new conditional lexical connectors  308   1  and  308   2  to be  824   c  and  826   c.  This changed selection results in a new textual concatenation in panel  814   c.  These user modifications of the main element  302  and conditional element  806   c   1 ,  806   c   2 ,  806   c   3  results in the modified set  300   M  of classified elements. 
       FIG.  9    illustrates an embodiment of operations performed by the GUI generator  118  to form the modified set  300   M  of element classifications and lexical connectors  300 . The GUI generator  118  receives from the table pre-processing GUI  120  user modifications of the main element  302 , conditional element(s)  306   1 , . . .  306   n , value element  312 , and/or new lexical connector(s)  304 ,  308   i,    310 . The GUI generator  118  then updates (at block  902 ) any of the main element  302 , conditional element(s)  306   i , value element  312 , and/or lexical connectors  304 ,  308   i ,  310  in the initial set  300   I  or a previously modified set  300   M  with the user modifications to form a new modified set  300   M  with the new values entered by the user via the GUI  120 . 
     The embodiment of  FIG.  9    allows a user, such as a subject matter expert, to modify the concatenation rules for the table  114  by updating the main element, conditional element(s),value element, and/or lexical connectors to provide a more optimized classification of the table items to improve subsequent NLP processing. 
       FIG.  10    illustrates an embodiment of operations performed by the GUI generator  118  to process user selection of table items in the table  114  via the table pre-processing GUI  120 . Upon receiving (at block  1000 ) coordinates of user selection of an area in the display screen in the table pre-processing GUI  120 , the GUI generator  118  determines (at block  1002 ), from the display screen coordinate mapping  400 i, document coordinates  404  in the document  112  mapping to the determined display screen coordinates  402 . The table item metadata  200 k is determined (at block  1004 ) that includes the determined document coordinates  404  in the document  112 . The display coordinate mappings  4001  are then used to determine display screen coordinates  402  forming a display screen boundary region that maps to the coordinates of the table item  208  in the determined table item metadata  200   1 . The GUI generator  118  renders (at block  1008 ) the display screen boundary region that includes the coordinates the user selected and the selected table item as highlighted. The GUI generator  118  may further update (at block  1010 ) the element classifications and lexical connectors  300   i  to indicate the highlighted table item as main element, conditional element or value element in response to user selection of that designation. 
     With the embodiment of  FIG.  10   , the GUI generator  118  translates, using geometrical calculations, a location of the user&#39;s click to document  112  coordinates, e.g., the page number and horizontal and vertical offsets within the document  112  of the user&#39;s click. The calculated document  112  coordinates (page number and offsets) of the user&#39;s click are used to look up the underlying table item by finding the extracted table item whose bounding box encloses the calculated offsets. In this way, the GUI generator  118  can locate any table element in the document  112  that the user clicks on, regardless of whether the text clicked on is (or is not) part of a value that was recognized and extracted from the document. Moreover, when the table item that the user clicks on contributed to a value recognized by the GUI generator  118  (be it a true positive or a false positive), the GUI generator  118  locates and renders the value recognized by the NLP engine  110  because all such values are linked to the extracted text elements that composed the flattened table item string that was sent to the NLP engine  110 . 
     The present invention may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention. 
     The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. 
     Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device. 
     Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention. 
     Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions. 
     These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions. 
     The computational components of  FIG.  1   , including the computer system  100 , may be implemented in one or more computer systems, such as the computer system  1102  shown in  FIG.  11   . Computer system/server  1102  may be described in the general context of computer system executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, and so on that perform particular tasks or implement particular abstract data types. Computer system/server  1102  may be practiced in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules may be located in both local and remote computer system storage media including memory storage devices. 
     As shown in  FIG.  11   , the computer system/server  1102  is shown in the form of a general-purpose computing device. The components of computer system/server  1102  may include, but are not limited to, one or more processors or processing units  1104 , a system memory  1106 , and a bus  1108  that couples various system components including system memory  1106  to processor  1104 . Bus  1108  represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnects (PCI) bus. 
     Computer system/server  1102  typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer system/server  1102 , and it includes both volatile and non-volatile media, removable and non-removable media. 
     System memory  1106  can include computer system readable media in the form of volatile memory, such as random access memory (RAM)  1110  and/or cache memory  1112 . Computer system/server  1102  may further include other removable/non-removable, volatile/non-volatile computer system storage media. By way of example only, storage system  1113  can be provided for reading from and writing to a non-removable, non-volatile magnetic media (not shown and typically called a “hard drive”). Although not shown, a magnetic disk drive for reading from and writing to a removable, non-volatile magnetic disk (e.g., a “floppy disk”), and an optical disk drive for reading from or writing to a removable, non-volatile optical disk such as a CD-ROM, DVD-ROM or other optical media can be provided. In such instances, each can be connected to bus  1108  by one or more data media interfaces. As will be further depicted and described below, memory  1106  may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention. 
     Program/utility  1114 , having a set (at least one) of program modules  1116 , may be stored in memory  1106  by way of example, and not limitation, as well as an operating system, one or more application programs, other program modules, and program data. Each of the operating system, one or more application programs, other program modules, and program data or some combination thereof, may include an implementation of a networking environment. The components of the computer  1102  may be implemented as program modules  1116  which generally carry out the functions and/or methodologies of embodiments of the invention as described herein. The systems of  FIG.  1    may be implemented in one or more computer systems  1102 , where if they are implemented in multiple computer systems  1102 , then the computer systems may communicate over a network. 
     Computer system/server  1102  may also communicate with one or more external devices  1118  such as a keyboard, a pointing device, a display  1120 , etc.; one or more devices that enable a user to interact with computer system/server  1102 ; and/or any devices (e.g., network card, modem, etc.) that enable computer system/server  1102  to communicate with one or more other computing devices. Such communication can occur via Input/Output (I/O) interfaces  1122 . Still yet, computer system/server  1102  can communicate with one or more networks such as a local area network (LAN), a general wide area network (WAN), and/or a public network (e.g., the Internet) via network adapter  1124 . As depicted, network adapter  1124  communicates with the other components of computer system/server  1102  via bus  1108 . It should be understood that although not shown, other hardware and/or software components could be used in conjunction with computer system/server  1102 . Examples, include, but are not limited to, microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems, etc. 
     The terms “an embodiment”, “embodiment”, “embodiments”, “the embodiment”, “the embodiments”, “one or more embodiments”, “some embodiments”, and “one embodiment” mean “one or more (but not all) embodiments of the present invention(s)” unless expressly specified otherwise. 
     The terms “including”, “comprising”, “having” and variations thereof mean “including but not limited to”, unless expressly specified otherwise. 
     The enumerated listing of items does not imply that any or all of the items are mutually exclusive, unless expressly specified otherwise. 
     The terms “a”, “an” and “the” mean “one or more”, unless expressly specified otherwise. 
     Devices that are in communication with each other need not be in continuous communication with each other, unless expressly specified otherwise. In addition, devices that are in communication with each other may communicate directly or indirectly through one or more intermediaries. 
     A description of an embodiment with several components in communication with each other does not imply that all such components are required. On the contrary a variety of optional components are described to illustrate the wide variety of possible embodiments of the present invention. 
     When a single device or article is described herein, it will be readily apparent that more than one device/article (whether or not they cooperate) may be used in place of a single device/article. Similarly, where more than one device or article is described herein (whether or not they cooperate), it will be readily apparent that a single device/article may be used in place of the more than one device or article or a different number of devices/articles may be used instead of the shown number of devices or programs. The functionality and/or the features of a device may be alternatively embodied by one or more other devices which are not explicitly described as having such functionality/features. Thus, other embodiments of the present invention need not include the device itself. 
     The foregoing description of various embodiments of the invention has been presented for the purposes of illustration and description. It is not intended to be exhaustive or to limit the invention to the precise form disclosed. Many modifications and variations are possible in light of the above teaching. It is intended that the scope of the invention be limited not by this detailed description, but rather by the claims appended hereto. The above specification, examples and data provide a complete description of the manufacture and use of the composition of the invention. Since many embodiments of the invention can be made without departing from the spirit and scope of the invention, the invention resides in the claims herein after appended.