Patent Publication Number: US-11392764-B2

Title: Classifying text to determine a goal type used to select machine learning algorithm outcomes

Description:
BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The present invention relates to a computer program product, system, and method for classifying text to determine a goal type used to select machine learning algorithm outcomes. 
     2. Description of the Related Art 
     A machine learning program may be trained to produce outcomes based on input text. For instance, text in a medical record may be processed using natural language processing to detect words related to medical conditions which are then inputted into a machine learning algorithm to classify a medical diagnosis based on the detected medical conditions in the text, such as in an electronic patient record. In ensemble learning, multiple machine learning models may be used to classify outcomes from input text to improve the predictive outcome to decrease variance, bias and improve predictions 
     There is a need in the art for improved techniques to use an ensemble of machine learning algorithms to determine predictive outcomes from input. 
     SUMMARY 
     Provided are a computer program product, system, and method for classifying text to determine a goal type used to select machine learning algorithm outcomes. Natural language processing of text is performed to determine features in the text and their relationships. A classifier classifies the text based on the relationships and features of the text to determine a goal type of a plurality of goal types. The determined features and relationships from the text are inputted into a plurality of different machine learning algorithms to generate outcomes for the text. For each of the machine learning algorithms, a determination is made of performance measurements resulting from the machine learning algorithms generating the outcomes. A determination is made of at least one machine learning algorithm of the machine learning algorithms having performance measurements that are highly correlated to the determined goal type. An outcome is determined from at least one of the outcomes of the determined at least one machine learning algorithm. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  illustrates an embodiment of a computing device. 
         FIG. 2  illustrates an embodiment of a goal type providing information on goals classified from input text. 
         FIG. 3  illustrates an embodiment of machine learning output produced by a machine learning algorithm. 
         FIGS. 4 a  and 4 b    illustrates an embodiment of operations to determine a goal type from input text. 
         FIG. 5  illustrates an embodiment of operations to use the determined goal type to determine outcomes from machine learning algorithms. 
         FIG. 6  illustrates a computing environment in which the components of  FIG. 1  may be implemented 
     
    
    
     DETAILED DESCRIPTION 
     In an ensemble of machine learning algorithms, multiple models are used to generate outcomes from the input text or other input data. However, understanding whether different of the classifiers, or machine learning algorithms, are most suitable for the type of input text being processed requires deep knowledge of how each machine learning algorithm works, and such expertise for a specific domain may not be available. In order to select among different machine learning algorithms to use in a particular context, an understanding of the machine learning algorithms used to generate the outcome and the underlying data is required. 
     Described embodiments provide improvements to the computer technology for machine learning processing by providing computer technology to improve selection of machine learning algorithms from an ensemble of algorithms to use to provide the outcomes for input text. Described embodiments use a classifier to classify text to classify based on relationships and features determined in the text to determine a goal type of the text. Features and relationships of the input text are inputted to machine learning algorithms that produce outcomes along with performance measurements. A determination is made of at least one machine learning algorithm having performance measurements most highly correlated with the determined goal type so that outcomes may be selected from the determined at least one machine learning algorithm most correlated to the goal type of the input text. 
     With the described embodiments, machine learning algorithms from an ensemble of learning algorithms are automatically selected to provide the outcome based on a goal type or criteria of the input text and the performance measurements of the machine learning algorithms. In this way, more optimal selection of the machine learning algorithm is made because outcomes are selected from those machine learning algorithms having performance measurements most correlated to the determined goal types of the input text as determined by a classifier algorithm that classifies a goal or criteria of the text, such as a scope of a question, whether the question requires accuracy, precision or recall, i.e., a large field of answers. 
       FIG. 1  illustrates an embodiment of a computing device  100  in which embodiments are implemented. The computing device  100  includes a processor  102 , a main memory  104 , and a storage  106 . The main memory  104  includes various program components including an operating system  108 , a goal classifier  110  to process input text  112 , such as sentences, questions, etc., to determine a goal vector  200  from the text, such as an accuracy score, recall score, precision score, execution time score, etc., where the goal of the text would be the highest score in the vector; an outcome generator  116  to supply the input text  112  and/or a modified feature set  114  to machine learning algorithms  118 , such as an ensemble of machine learning algorithms implementing different algorithms, to process input text  112  or features from the input text  112  to generate outcomes  300  for which the machine learning algorithms  118  are trained. The modified feature set  114  may exclude features determined from the input text  112  having features unrelated to the determined goal type from the goal vector  200 . The outcome generator  116  receives the outcomes  300  and uses the goal type, determined from the goal vector  200 , to select outcomes  300  having performance measurements most highly correlated with the determined goal type and then produces a final outcome  120  based on the outcomes  300  most highly correlated to the goal type in the goal vector  200 .  FIG. 1  shows arrows to illustrate the program flow of processing and data among the program components  110 ,  116 , and  118  executing in the memory  104 . 
     The goal classifier  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 determine a goal type based on the input text  112 . In certain embodiments the goal types may include accuracy—a ratio of correctly predicted observation to the total observations; precision—a ratio of correctly predicted positive observations to the total predicted positive observations; recall—a ratio of correctly predicted positive observations to all observations in an actual class; an F1 score—the weighted average of precision and recall; and execution time, indicating a permissible time to complete execution to produce the outcome. An outcome may be determined to be most highly correlated to the goal type, if the outcome has a highest performance score for the goal type or a performance score satisfying a high threshold for the goal type. For instance, if the goal type is precision for a question in the input text having a high degree of specificity, then the outcome having a highest precision score or precision score satisfying a threshold among performance measurements would be determined to be most highly correlated. 
     The machine learning algorithms  118  may comprise an ensemble of different machine learning models that are capable of processing input text  112  to determine outcomes  300  based on training of the machine learning algorithms  118  to produce specific results. The machine learning algorithms  118  may determine outcomes for input text  112  using different techniques such as decision tree learning, association rule learning, neural network, inductive programming logic, support vector machines, Bayesian network, etc. 
     The computing device  100  may store program components, such as  108 ,  110 ,  116 , and  118 , input text  112 , and the output, such as the outcomes  300  and final outcome  120  in a non-volatile storage  106 , which may comprise one or more storage devices known in the art, such as a solid state storage device (SSD) comprised of solid state electronics, NAND storage cells, EEPROM (Electrically Erasable Programmable Read-Only Memory), flash memory, flash disk, Random Access Memory (RAM) drive, storage-class memory (SCM), Phase Change Memory (PCM), resistive random access memory (RRAM), spin transfer torque memory (STM-RAM), conductive bridging RAM (CBRAM), magnetic hard disk drive, optical disk, tape, etc. The storage devices may further be configured into an array of devices, such as Just a Bunch of Disks (JBOD), Direct Access Storage Device (DASD), Redundant Array of Independent Disks (RAID) array, virtualization device, etc. Further, the storage devices may comprise heterogeneous storage devices from different vendors or from the same vendor. 
     The memory  104  may comprise a suitable volatile or non-volatile memory devices, including those described above. 
     Generally, program modules, such as the program components  108 ,  110 ,  116 , and  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 computing device  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 program components  108 ,  110 ,  116 , and  118  may be accessed by the processor  102  from the memory  104  to execute. Alternatively, some or all of the program components  108 ,  110 ,  116 , and  118  may be implemented in separate hardware devices, such as Application Specific Integrated Circuit (ASIC) hardware devices. 
     The functions described as performed by the program  108 ,  110 ,  116 , and  118  may be implemented as program code in fewer program modules than shown or implemented as program code throughout a greater number of program modules than shown. 
     Although embodiments are described as classifying input text, in alternative embodiments may provide input in other media formats, such as audio, video, graphics, etc. 
       FIG. 2  illustrates an embodiment of an instance of goal vector  200  produced by the goal classifier  110  and includes an input text  202  identifier and different weighted goal scores to satisfy a criteria of the text, such as accuracy  204  (number of correct predictions made by the model over all kinds predictions made); precision  206  (percentage of positives that were true positives); recall  208  (ratio of correctly predicted results); an execution time  210  (an extent to which determining the outcome requires greater execution time); and a goal type  212 , which may comprise the highest goal score of the goals  204 - 210 . Other goal scores may also be determined from the text, such as F1 and others. Further, there may be goal scores specified that provide a combination of multiple of the goals  204 ,  206 ,  208 ,  210 , such as the F1 goal score that provides a weighted average of precision and recall. The determined goal type  212  of the classified input text may comprise those one or more goals, such as precision, recall, accuracy, etc., having a highest score  204 ,  206 ,  208 ,  210 . 
       FIG. 3  illustrates an embodiment of an instance of machine learning output  300 , from one of the machine learning algorithms  118  providing the outcome for the input text  112 , such as an answer to a question, etc., and includes a machine learning algorithm identifier  302 ; an outcome  304  based in the input text  112 ; and different weighted performance measurements for the outcome  304 , such as accuracy  306 , precision  308 , recall  310  and execution time  312 . 
       FIGS. 4 a  and 4 b    illustrate an embodiment of operations performed by the goal classifier  110  to generate a goal vector  200  and goal type  212  based on the input text  112 . Upon receiving (at block  400 ) the input text  112 , the goal classifier  110  parses (at block  402 ) input text, e.g., question, to determine features, entity types, relationships, feature weights, comparison terms, numerical quantities of features. A loop of operations is performed at block  404  through  424  for each feature i determined from the input text  112 , where a feature comprises an individual measurable property or characteristic of the text  112 , and may be extracted using filtration, fusion, mapping, and clustering. Features may provide information on the content words in the text  112 , and weight of the feature. To determine the goal scores  204 - 210 , the goal classifier  110  determines (at block  406 ) whether feature i has a high degree of specificity or generality in an ontology depth of the feature i. If (at block  406 ) the feature i has a high degree of specificity, e.g., a more specific instance of a class of terms or entity, then an increased weighting is provided (at block  408 ) to the precision score  206 . If (at block  406 ) the feature i has a high degree of generality, e.g., a more general instance of a class of terms, then an increased weighting is provided (at block  408 ) to a recall score  208 . For instance, a question in the input text  112  such as “Who and what kind of people entered the Post office?” has a high level of generality in the term “people”, because it concerns a general class of humans, as opposed to specific class or person. Thus, because the goal is to find lots of “people”, the goal would be “recall”, to search for as many members as possible of the group or having the feature, e.g., persons. If the input text  122  has a question such as “get me the patients living in District  32 ”, this has a high level of specificity in the term people satisfying a particular criteria. Thus, because the goal is to find a specific class or limited group of people, the goal is precision. 
     From block  408 ,  410  or if the feature i does not have a high degree of specificity or ontology, then a determination is made (at block  412 ) as to whether the feature i is a comparison. If (at block  412 ) the feature i is a comparison, then an increased weighting is provided (at block  414 ) to the precision score  206  because the comparison narrows the requested class to one matching the subject of the comparison. From block  414  or if (from the no branch of block  412 ) the feature i is not a comparison, then a determination is made (at block  416 ) as to whether feature i has a high weighting, meaning the feature is determined to be of particular determinative relevance in the input text  112 . If (at block  416 ) the feature has a high weighting, then an increased weighting is provided (at block  418 ) to the accuracy score  204  to provide outcomes more specific to that highly weighted feature. If (at block  420 ) the feature i does not have a high weighting or from block  418 , then a determination is made (at block  420 ) whether the feature i has a numerical quantity related to a measurement of the feature i. If so (at block  420 ), an increased weighting is provided (at block  422 ) to the accuracy score  204  weighting because a feature provided with an associated numerical quantity in the input text  112 , e.g., X number of elements, so the outcome is specific to this information. In medical domains, a numerical quantity may be specified for an observed condition in a patient, such as the dimensions of a tumor or measurement, measured components in a blood test, etc. In such case, accuracy as to the medical outcome/diagnosis related to such observations is paramount to avoid misdiagnosis. 
     After processing the features in the input text  112 , control proceeds (at block  426 ) to block  430  in  FIG. 4 b    where the goal classifier  110  determines whether the features indicate a deep search, such as if the recall score  208  is high or a specific answer, associated with a high precision score  206 . If (at block  430 ) the features indicate a deep search, then a lower weighting is provided (at block  432 ) to the execution time score  210  because determining the outcome does not necessarily require a greater amount of time to get an answer because the scope of answers is large. If (at block  430 ) the features indicate a specific answer, then a higher weighting is provided (at block  434 ) to the execution time score  210  because a specific answer is required, which may take more time to search to provide results. The goal classifier  110  may then determine (at block  436 ) a specific goal type  212  from the goal vector  200  scores by the score  204 ,  206 ,  208 , and  210  having the highest value. The goal classifier  110  provides (at block  438 ) the determined goal vector  200  to the outcome generator  116  to use to process outcomes  300  from the machine learning algorithms  118 . 
       FIGS. 4 a  and 4 b    provide improved computer technology to classify input text as having a goal type based on criteria of the input text that is related to performance measurements provided by the machine learning algorithm with outcomes, such as precision, recall, accuracy, etc. Described embodiments process the features of the input text  112  to determine weightings to goal type scores that relate to performance measurements provided by the machine learning algorithms  118 . This goal type may then be used to determine which machine learning algorithm outcomes have performance measurements most correlated and relevant to the goal type of the input text, such as a question asked in the input text. 
       FIG. 5  illustrates an embodiment of operations performed by the outcome generator  116  to generate outcomes  300  from the machine learning algorithms  118  and process to determine a final outcome  120 . The outcome generator  116  receives (at block  500 ) input text  112  and a goal vector  200  providing the goal type  212  for the text, e.g., precision, accuracy, recall, execution time, etc. The outcome generator  116  (or the goal classifier  110 ) may remove (at block  502 ) text features unrelated/not contributing to the goal type  212 , such as features having a chi-square (CHI) value indicating they do not contribute to the goal type classification, to produce a modified feature set  114 . The outcome generator  116  calls (at block  504 ) the machine learning algorithms  118  to process the input text  112  (or the modified feature set  114 ) to determine outcomes  300 . 
     The outcome generator  116  receives (at block  506 ) outcomes  300  from the machine learning algorithms  118 , including an outcome  304 , such as an answer to a question in the input text  112 , and performance measurements  306 ,  308 ,  310 ,  312 . The outcome generator  116  determines (at block  508 ) the machine learning algorithm  118  whose one or more performance measurements  306 ,  308 ,  310 ,  312  have the highest or threshold score for the goal type  212  from the goal classifier  110 . For instance, if the goal type  212  is precision, i.e., precision  206  has the highest score, then the outcome generator  118  looks for the machine learning output  300   i  having the highest or threshold precision score  308 . The outcome generator  118  then determines (at block  510 ) the outcome  304  from the determined machine learning algorithm  118  having the highest or threshold satisfying performance measurement  306 ,  308 ,  310 ,  312  matching the goal type  212 . There may be a plurality of machine learning algorithms  118  having performance scores most highly correlated to the goal type  212  if there are multiple outcomes  300   i  having a performance measurement corresponding to the goal type  212  that have a matching highest value or satisfy the threshold for the goal type  212 . In such case, the outcome generator  118  may use voting, boosting and other machine learning selection techniques to derive an outcome from a plurality of outcomes. 
     With the embodiment of  FIG. 5 , the determined goal type is used to select outcomes from machine learning algorithms or one machine learning algorithm having performance measurements highly correlated to the goal type determined from the input text, which goal type indicates the type of performance measurement that can produce the best outcome based on the outcome, e.g., answer, diagnosis, needed from the input text. For instance, for a question, different performance measurements may provide better outcomes/answers given the goals or criteria of the question. 
     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  602  shown in  FIG. 6 . Computer system/server  602  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  602  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. 6 , the computer system/server  602  is shown in the form of a general-purpose computing device. The components of computer system/server  602  may include, but are not limited to, one or more processors or processing units  604 , a system memory  606 , and a bus  608  that couples various system components including system memory  606  to processor  604 . Bus  608  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  602  typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer system/server  602 , and it includes both volatile and non-volatile media, removable and non-removable media. 
     System memory  606  can include computer system readable media in the form of volatile memory, such as random access memory (RAM)  610  and/or cache memory  612 . Computer system/server  602  may further include other removable/non-removable, volatile/non-volatile computer system storage media. By way of example only, storage system  613  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  608  by one or more data media interfaces. As will be further depicted and described below, memory  606  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  614 , having a set (at least one) of program modules  616 , may be stored in memory  606  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  602  may be implemented as program modules  616  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  602 , where if they are implemented in multiple computer systems  602 , then the computer systems may communicate over a network. 
     Computer system/server  602  may also communicate with one or more external devices  618  such as a keyboard, a pointing device, a display  620 , etc.; one or more devices that enable a user to interact with computer system/server  602 ; and/or any devices (e.g., network card, modem, etc.) that enable computer system/server  602  to communicate with one or more other computing devices. Such communication can occur via Input/Output (I/O) interfaces  622 . Still yet, computer system/server  602  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  624 . As depicted, network adapter  624  communicates with the other components of computer system/server  602  via bus  608 . It should be understood that although not shown, other hardware and/or software components could be used in conjunction with computer system/server  602 . 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 letter designators, such as i, is used to designate a number of instances of an element may indicate a variable number of instances of that element when used with the same or different elements. 
     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.