Abstract:
Embodiments of the invention generally relate to a method of processing data. The method includes receiving at least one structured data item and applying at least one processing rule to said at least one structured data item. The method also includes determining an anomaly associated with the at least one structured data item in response to the at least one structured data item matching a condition in said at least one processing rule. The method also includes appending the anomaly to a database of anomalies.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS  
       [0001]     This application relates to co-pending U.S. patent application Ser. No. 10/______, entitled, “SYSTEM AND METHOD FOR MIXED-LANGUAGE EDITING” filed concurrently herewith and co-pending U.S. patent application Ser. No. 10/______, entitled, “SYSTEM AND METHOD FOR DOCUMENT VALIDATION”, filed concurrently herewith, all co-pending applications are hereby incorporated by reference in their entirety.  
     
    
     BACKGROUND OF THE RELATED ART  
       [0002]     Many companies use business performance management (“BPM”) as a way to focus on core competencies and to lower costs. These companies initially outsource human resources (“HR”) then payroll and then rapidly move benefits, time and expense and other non-core, e.g., administrative, business functions to BPM companies. Companies have also used BMP in insurance, i.e., processing claims in disaster recovery, property, casualty, etc. The processing of insurance claims is very similar to processing benefit claims in the HR space. Other companies have started using BPM in other areas of business, e.g., enterprise resource planning (“ERP”), customer relationship management (“CRM”), supply chain management (“SCM”).  
         [0003]     The BPM companies typically set up service centers in a remote location to service their clients. The remote location is selected based on finding lower costs for personnel, software, and/or hardware. For example, the BPM companies have used countries with a low cost of living, e.g., India, as a way to lower personnel, hardware, and/or transmission costs. Since BPM companies typically purchase large quantities of software in servicing their companies, BPM companies use their bulk-purchasing power as another way to lower costs on software and/or hardware. The BPM companies typically earn their profit margins from the reselling of the per-seat licenses of the purchased of software and/or hardware systems.  
         [0004]     However, there are drawbacks and disadvantages to this approach for BPM companies. For example, BPM companies may have trouble being competitive with each other and against in-house services of large organizations. Large organizations can achieve similar deals as BPM companies for software, hardware, and software. A large organization may have many smaller branch offices that cannot afford to purchase off-the-shelf software directly or hosted by a BPM company. Moreover, a substantial portion of the profit margin of a BPM company may be balanced against the integration costs of back-end systems at the clients and/or customizing the BPM&#39;s systems to match the needs of the client.  
         [0005]     A BPM company has to resolve several issues of efficiency in order to remain a profitable business model. For example, a BPM company has to be able increase the efficiency of service center personnel without increasing the need for personnel as the number of clients increase. The BPM company also has to be able to integrate to a variety of backend systems of the customers quickly and without relying on third party expertise. The BPM company further has to be able to provide an alternative to expensive software and/or hardware systems for small customers and/or small satellite offices of large clients.  
         [0006]     One solution to the increasing employee efficiency requires systems in the service center that permit an employee to work on many clients at the same time, where each client often has specific software requirements. Most service center employees spend a majority of their time identifying and responding to bad data and transactions for the client. Thus, in order to serve multiple clients, the service center employee has to be familiar with various types of software packages. Accordingly, a consolidated and consistent management interface and software processing that identifies errors automatically has to be achieved in order to provide a solution to increasing employee efficiency.  
         [0007]     The solution to integrating quickly with backend systems of clients requires specialized data integration driven by client requirements. This solution also requires the creation of specialized user interfaces and processing rules to find errors in the incoming data. Enterprise application integration (“EAI”) solutions are a method to resolve integration issues. EAI platforms, e.g., BEA&#39;s Weblogic Integration Server, can assist BPM companies connect to a client&#39;s backend system and transform the data. However, EAI solutions require the use of developers to define the data formats for the client and the BPM company. Thus, the developers add time and costs for the BPM company. Moreover, the EAI solutions are limited in their capabilities to detect errors or generate user interfaces for service center employees to input transactions, independent of the customer or client backend system.  
       SUMMARY OF THE INVENTION  
       [0008]     An embodiment of the invention generally relates to a method of processing data. The method includes receiving at least one structured data item and applying at least one processing rule to said at least one structured data item. The method also includes determining an anomaly associated with the at least one structured data item in response to the at least one structured data item matching a condition in the at least one processing rule. The method also includes appending the anomaly to a database of anomalies.  
         [0009]     Another embodiment of the invention generally pertains to a system for processing structured data. The system includes a processing rule module configured to store at least one processing rule, each processing rule configured to detect an anomaly. The system also includes an anomaly engine configured to receive at least one structured data element. The anomaly engine is also configured to determine a nearness vector for the at least one structured data element. The anomaly engine is further configured to select a subset of processing rules based on a comparison of the nearness vector for the at least one structured data element and the respective nearness vectors of the subset of processing rules being within a predetermined value.  
         [0010]     Yet another embodiment of the invention generally relates to a computer readable storage medium on which is embedded one or more computer programs. The one or more computer programs implement a method of processing structured data. The one or more computer programs include a set of instructions for receiving at least one structured data element and maintaining a plurality of nearness vector for a plurality of processing rules. Each nearness vector is associated with a respective processing rule. The set of instructions also include determining a nearness vector for at least one structured data element. The set of instructions further include selecting a subset of processing rules based on the nearness vector for the at least one structure data element and the associated nearness vectors for the subset of processing rules being within a predetermined value.  
         [0011]     Yet another embodiment of the invention generally pertains to a means for processing data. The apparatus includes means for receiving at least one structured data item and means for applying at least one processing rule to said at least one structured data item. The apparatus also includes means for determining an anomaly associated with said at least one structured data item in response to said at least one structured data item matching a condition in said at least one processing rule. The apparatus further includes means for appending the anomaly to a database of anomalies.  
     
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0012]     While the specification concludes with claims particularly pointing out and distinctly claiming the present invention, it may be believed the same will be better understood from the following description taken in conjunction with the accompanying drawings, which illustrate, in a non-limiting fashion, the best mode presently contemplated for carrying out the present invention, and in which like reference numerals designate like parts throughout the figures, wherein:  
         [0013]      FIG. 1  illustrates a block diagram of a system using an intelligent processor module (IPM) in accordance with an embodiment of the invention;  
         [0014]      FIG. 2  illustrates a more detailed block diagram of the IPM, shown in  FIG. 1 , in accordance with another embodiment of the invention;  
         [0015]      FIG. 3  illustrates a block diagram of the anomaly engine, shown in  FIG. 2 , in accordance with yet another embodiment of the invention;  
         [0016]      FIG. 4  illustrates a flow diagram for the processing of structured data by the anomaly engine processor, shown in  FIG. 3 , in accordance with yet another embodiment of the invention;  
         [0017]      FIG. 5  illustrates a flow diagram for the pattern-matching module, shown in  FIG. 3 , in accordance with yet another embodiment of the invention;  
         [0018]      FIG. 6  illustrates a flow diagram for the IVA, shown in  FIG. 3 , in accordance with yet another embodiment of the invention; and  
         [0019]      FIG. 7  illustrates a computer system implementing the anomaly engine in accordance with yet another embodiment of the invention.  
     
    
     DETAILED DESCRIPTION OF THE EMBODIMENTS  
       [0020]     For simplicity and illustrative purposes, the principles of the present invention are described by referring mainly to exemplary embodiments thereof. However, one of ordinary skill in the art would readily recognize that the same principles are equally applicable to, and can be implemented in, many types of systems for processing structured data, and that any such variations do not depart from the true spirit and scope of the present invention. Moreover, in the following detailed description, references are made to the accompanying figures, which illustrate specific embodiments. Electrical, mechanical, logical and structural changes may be made to the embodiments without departing from the spirit and scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense and the scope of the present invention is defined by the appended claims and their equivalents.  
         [0021]     Embodiments of the present invention generally relate to a system for processing multiple types of structured data and semi-structured data, e.g., a document (or XML fragment) that has at least one element referring to a binary large object, that allows for dynamic adaptation and defect management of the structured data. More particularly, an intelligent processor module (IPM) may be configured to receive many types of structured data, e.g., an XML document. The IPM may process the received structured data against a set of processing rules.  
         [0022]     The processing rules may be configured to detect defects, errors, or anomalies in the syntax and structure as well as perform higher logic functions to detect anomalies in the received data. The processing rules may be predetermined and dynamically adapted as the IPM processes the received structured data.  
         [0023]     The IPM may also be configured to output the detected anomalies for a user to view the data. The IPM may be further configured to communicate with third party computer systems that may provide the data and/or consume the processed data.  
         [0024]     Another embodiment of the invention generally pertains to a method, apparatus and/or system for dynamic adaptation of the processing rules. More specifically, the IPM includes an anomaly engine configured to analyze the data for anomalies. The anomaly engine may interface with a processing rules module configured to store the processing rules for the IPM. The anomaly engine may access the processing rules module to process received structured data. The processing rules module may also interface with a pattern-matching module, an intelligent virtual agent and a schema editor.  
         [0025]     The anomaly engine may also implement a classification model for the received structured and/or semi-structured data. More particularly, the anomaly engine may apply XML techniques to generate a hierarchal abstraction of the received data. The pattern-matching module may then use the classification model to determine the nearest self-organized domain map. In one embodiment, the domain maps are a hierarchal representation of the grammar, processing rules and data for a particular application being serviced by the IPM. The detection process may be implemented using neural nets, graph theory or other similar pattern recognition algorithms known to those skilled in the art. The use of the hierarchal abstraction enables a greater chance of matching a known pattern against the domain maps.  
         [0026]     The pattern-matching module may also be configured to develop rules based on the detected patterns. For example, when the pattern-matching module detects that employees of a company have salaries within a range, the pattern-matching module creates a rule where the employees of the company are within the range. The pattern-matching module then forwards the rule to the processing rules module to be included in future processing by the anomaly engine. In another embodiment, the frequency of certain structured data (fragment or document) may generate exceptions by the anomaly engine processor. Policies that are generated from the analysis of a series of recommendations and the workflows to implement the recommendations may then be implemented by the IPM. Accordingly, the IPM may be biased into a learned habit or behavior by implementing the generated policies.  
         [0027]     The intelligent virtual agent (“IVA”) may be configured to dynamically create additional processing rules by monitoring a human agent. The IVA may mimic the action as the human agent responds to an anomaly generated by the anomaly engine. From the course of actions of the human agent, the IVA may create a rule. The IVA may then forward the rule to the processing rules module to be included in subsequent processing of the structured data by the anomaly engine. In other embodiments, the IVA may query the human agent in order to develop processing rules.  
         [0028]     The schema editor may be configured to provide a mechanism for users to enter processing rules into the processing rules module. The schema editor may be implemented using a what-you-see-is-what-you-get (“WYSIWYG”) mixed-language editor as described by U.S patent application Ser No., 10/______, entitled “System and Method for Mixed-Language Editing”, filed concurrently herewith, and is incorporated in its entirety.  
         [0029]     Yet another embodiment of the invention generally pertains to a method, system and/or apparatus for processing structured data against processing rules by an anomaly engine. The anomaly engine may be configured to determine a nearness vector for an incoming structured data, e.g., an XML document, an HTML document, an XHTML document, etc. The anomaly engine may also be configured to maintain a nearness vector for each processing rule stored in the processing rules module. The anomaly engine may then compare the nearness vector the incoming data with the nearness vectors of the processing rules. The anomaly processes the incoming data against the rules that are nearest to the incoming data. Accordingly, the IPM may receive different types of structured data and efficiently process the structured data.  
         [0030]      FIG. 1  illustrates a block diagram of a system  100  using an intelligent processor module (IPM) in accordance with an embodiment of the invention. It should be readily apparent to those of ordinary skill in the art that the system  100  depicted in  FIG. 1  represents a generalized schematic illustration and that other components may be added or existing components may be removed or modified. Moreover, the system  100  may be implemented using software components, hardware components, or a combination thereof.  
         [0031]     As shown in  FIG. 1 , the system  100  includes an intelligent processor module (labeled as “IPM” in  FIG. 1 )  110 , clients  120 , and third party processors  130 . The IPM  110  may be configured to receive data from the clients  120  and to determine whether anomalies exist in the received data. After anomaly processing, the IPM  110  may route the data to the appropriate third party processor  130  for subsequent processing. The IPM  110  may also provide a platform to create user interfaces for creating of processing rules for detecting anomalies and to input data.  
         [0032]     The IPM  110  may also dynamically adapt to the received data. More specifically, in certain embodiments, the IPM  110  may create new rules based on detecting patterns in the data and/or a human service center agent responding to an anomaly. Accordingly, the IPM may dynamically reconfigure itself to changing conditions to improve the detection of anomalies and thereby reduce the need for additional personnel in the service center.  
         [0033]     The clients  120  may interface with the IPM  110  over local area networks, wide area networks or some combination thereof. The clients  120  may use the IPM  110  to outsource business processes such as payroll processing, insurance claims processing, benefits processing, etc. Each client  120  may be an individual company or divisions of a large organization located in multiple jurisdictions, i.e., many countries.  
         [0034]     The third party processors  130  may also interface with the IPM  110 . The third party processors  130  may provide services, e.g., payroll, electronic find transfers, claim processing, etc. to the IPM  110 . The IPM  110  may function as an intermediary between clients  120  and the third party processors  130 , thus providing economies of scale by reusing integrations to external third party processors  130  and calculation engines.  
         [0035]      FIG. 2  illustrates a block diagram a system  200  utilizing the IPM  110 , shown in  FIG. 1 , in accordance with another embodiment of the invention. It should be readily apparent to those of ordinary skill in the art that the system  200  depicted in  FIG. 2  represents a generalized schematic illustration and that other components may be added or existing components may be removed or modified. Moreover, the IPM  110  may be implemented using software components, hardware components, or a combination thereof.  
         [0036]     As shown in  FIG. 2 , the system includes an analyst  205 , a service center representative  210 , a client administrator  215 , a client employee  220 , and the IPM  110 . The IPM  110  may include a schema editor  225 , a management portal  230 , a self-service portal  235 , a processing engine  240 , and an anomaly engine  245 .  
         [0037]     The analyst  205  may be an employee of a service center that implements the integration of new clients into the service center. The analyst  205  may use the schema editor  225  to define the metadata for the new customers.  
         [0038]     The service center representative  210  may be an employee of the service center that administers the business processes that are outsourced. The service center representative  210  may interact with the IPM  110  by using the management portal  230 . For example, the service center representative  210  may enter transactions and/or view reports generated by the IPM  110 .  
         [0039]     The client administrator  215  may be an individual at a client of the service center responsible for managing the outsourcing relationship. For example, in the outsourcing of human resources, the client administrator  215  is typically a human resource person. The client administrator  215  may enter transactions and/or view reports generated by the IPM  110 .  
         [0040]     The client employee  220  may be an employee at the client of the service center. The client employee  220  interacts with the IPM  110  by using the self-service portal  235 . The client employee  220  may be constrained with a limited set of transactions. For example, a client employee  220  may submit a request to view cumulative pay for the year or to view a payroll stub for human resources outsourcing.  
         [0041]     The schema editor  225  may be configured to allow analysts and developers to create the metadata and configuration information for the IPM  110 . The schema editor  225  may be implemented using a mixed-language WYSIWYG editor as described by U.S. patent application Ser. No. 10/______, entitled “System and Method For Mixed Language Editing”, filed concurrently herewith, and is hereby incorporated in its entirety.  
         [0042]     The management portal  230  may be configured as a tool for the service center representative  210  to manage the processing of data and the actions based on anomalies found in the data.  
         [0043]     The self-service portal  235  may be configured as a programmable database and portal for self-service for the client administrator  215  and the client employee  220 . In some embodiments, the self-service portal  235  may be created using a mixed-language WYSIWYG editor as described in the U.S. patent application Ser. No. 10/______, entitled “System and Method for Mixed-Language Editing”, filed concurrently, and hereby incorporated in its entirety.  
         [0044]     The processing engine  240  may be configured to communicate with the different backend systems of the third party processors and clients. The processing engine  240  may also be configured to store transactions and to use the anomaly engine  245  to process the transactions.  
         [0045]     The anomaly engine  245  may be, but not limited to being, configured to be a component used to execute a variety of processing rules on the data to detect anomalies. The anomalies may be in the syntax and structure in the data as well as in the data, i.e., a data value inconsistent with other similar data values. Portions of the anomaly engine  245 , if not all, may be implemented using the validation component as described in U.S. patent application Ser. No. 10/______, entitled, “System and Method For Document Validation”, filed concurrently herewith and is incorporated in its entirety. The anomaly engine  245  may also be configured to dynamically add processing rules as it processes data as described above and herein below.  
         [0046]      FIG. 3  illustrates a block diagram of the anomaly engine  245 , shown in  FIG. 2 , in accordance with yet another embodiment of the invention. It should be readily apparent to those of ordinary skill in the art that the anomaly engine  245  depicted in  FIG. 3  represents a generalized schematic illustration and that other components may be added or existing components may be removed or modified. Moreover, the anomaly engine  245  may be implemented using software components, hardware components, or a combination thereof.  
         [0047]     As shown in  FIG. 3 , the anomaly engine  245  may include an anomaly engine processor  305 , a processing rules module  310 , and a pattern-matching module  315 . The anomaly engine processor  305  may be, but not limited to being, configured to receive data in a structured form, e.g., an XML document, from the processing engine  240 . The anomaly engine processor  305  may also be configured to determine the “closest” or “nearest” rules that may apply to the received structured document. The anomaly engine processor  305  may then apply the nearest rule(s) to the received structured document without processing every processing rule, thereby increasing efficiency.  
         [0048]     One advantage of embodiments of the present invention is that processing rules for a variety of applications, e.g., human resources, CRM, SCM, insurance, etc., may be entered into the processing rules module  310 . The processing engine  240  may accept all types of structured documents or pieces of structured data, i.e., at least one metadata and associated value, for all the programmed applications and process the structured document without reconfiguration. Thus, the processing engine  240  may increase its availability and efficiency.  
         [0049]     In one embodiment, the anomaly engine processor  305  may also be configured to form a nearness vector for the received structured data. More specifically, the received structured data may be abstracted into a graph representation by equating the metadata and associated data as nodes and segments, respectively. Weights may be assigned to the node/segments based on a predetermined algorithm, historical data, etc.  
         [0050]     The anomaly engine processor  305  may then use the nearness vector to search for processing rules that are within a predetermined “nearness” of the nearness vector in the processing rules module  310 . The anomaly engine processor  305  may apply the selected processing rules to the received structured data to determine anomalies.  
         [0051]     Subsequently, the anomaly engine processor  305  may use the nearness vector of the structured data to determine any recommendations and/or rules. More particularly, the anomaly engine processor  305  may also maintain self-domain maps (or templates) for the applications being served by the IPM  110 . For example, for an insurance application, the anomaly engine processor  305  may have a template for processing car claims, home claims, disaster claims, etc. Each of the templates may contain a grammar, processing rules, and historical data for the respective application. Since data contained in the templates may also be structured data, a template may be abstracted to a graph.  
         [0052]     The anomaly engine processor  305  may use the pattern-matching module  315  to select the appropriate template. More specifically, the patter-matching module  315  may comprise of neural nets to select the appropriate template for the nearness vector and to provide automated defect management. More specifically, the neural nets may be configured to determine how “near” the nearness vector is to the selected template. From the differences, the neural nets may be configured to provide actions (or recommendations) based on, in part, of the historical data contained in the template. For example, a structure data element containing expense data is analyzed by the pattern-matching module  315  against an expense template. The data may have a value, e.g., a meal expense that is three times the historical value of meal expense contained in the expense template. The neural nets of the pattern-matching module  315  may generate an action identifying the anomaly as well as a recommendation for the anomaly. For example, the recommendation may be paying the historical average and requesting additional justification for the expense.  
         [0053]     In another embodiment, the anomaly engine processor  305  may use vector space analysis to determine the nearness to processing rules. More particularly, the anomaly engine processor  305  may convert the received structured document into a vector representation. The vector representation may be based on binary weights, raw term frequency, derived thesaurus terms, etc. The anomaly engine processor  305  may determine a similarity score for the vector representation of the received structured document with vector representation of the processing rules. The vector representations of the processing rules may be stored with the rules processing module  310  in some embodiments. The similarity score may be determined using simple matching, Dice&#39;s coefficient, Jaccard&#39;s coefficient, Cosine coefficient, Overlap coefficient, or other quantitative process. The processing rules with a similarity score within a predetermined value (or range) are selected for processing by the anomaly engine processor  305 .  
         [0054]     In yet another embodiment, the anomaly engine processor  305  may also use vector space processing to determine the template. More specifically, the data elements in a template may also be represented in vector representation. Accordingly, a template may then comprise a group of similar vectors. The vector representation of the structured data may then be hashed to select the correct template.  
         [0055]     The anomaly engine processor  305  may be configured to interface with the processing rules module  310 . The processing rules module  310  may be, but not limited to being, configured to store processing rules for the anomaly engine  245 . The processing module  310  may store a plurality of processing rules. In some embodiments, the each processing rule may have an associated nearness vector, which may be calculated by the anomaly engine processor  305  as described above or predetermined during configuration of the processing engine  240 . The processing rules and associated nearness vector may be stored and accessed using conventional database techniques, a linked list or other similar data structure.  
         [0056]     The processing rules module  310  may also be configured to interface with a schema editor  320 . The schema editor  320  may provide a means for users to input processing rules into the processing rules module  310 .  
         [0057]     The anomaly engine processor  305  may be further configured to interface with the pattern-matching module  315 . The pattern-matching module  315  may be, but not limited to being, configured to detect patterns in the structured data processed by the anomaly engine processor  305 . The pattern-matching module  315  may be implemented using conventional data mining processors and neural nets.  
         [0058]     The pattern-matching module  315  may also be configured to develop rules based on the detected patterns. The newly developed rules are then forwarded to the processing rules module  310  to be included in subsequent processing of data by the anomaly engine processor  305 .  
         [0059]     The processing rules module  310  may be further configured to interface with an intelligent virtual agent (“IVA”)  325 . The IVA  325  may be configured to monitor the human agent  330 . More particularly, the WVA  325  may monitor how the expert, i.e., human agent  330  responds to anomalies presented to by the anomaly engine processor  305 . The IVA  325  may mimic the actions of the human response, i.e., screen capture, keystroke capture, etc., and develop processing rules based on the mimicked actions. Alternatively, the WVA  325  may query the human a gent  330  on the response to the anomaly and develop additional processing rules based on the response. The IVA  325  may then forward the developed processing rules to the processing rules module  310  for subsequent processing of data by the anomaly engine processor  305 .  
         [0060]      FIG. 4  illustrates a flow diagram  400  for the processing of structured data by the anomaly engine processor  305 , shown in  FIG. 3 , in accordance with yet another embodiment of the invention. It should be readily apparent to those of ordinary skill in the art that this flow diagram  400  shown in  FIG. 4  represents a generalized illustration and that other steps may be added or existing steps may be removed or modified.  
         [0061]     As shown in  FIG. 4 , the anomaly engine processor  305  may be in an idle state, in step  405 . The processing engine  245  (shown in  FIG. 2 ) may forward structured data, e.g., an XML document, XHTML document, etc., comprising of at least one data element. In step  410 , the anomaly engine processor  305  receives the structured data for processing.  
         [0062]     In step  415 , the anomaly engine processor  305  may calculate a nearness vector for the structured data. The anomaly engine processor  305  may abstract the metadata and associated data value of the received structured data into nodes and segments, respectively. The anomaly engine processor  305  may assign weights to the nodes and segments based on a predetermined heuristic, historical data, or other similar manner.  
         [0063]     In step  420 , the anomaly engine processor  305  may access the processing rules module  310  to search for a set of processing rules that are within a predetermine value of the calculated nearness vector for the structured data. In some embodiments, each of the processing rules stored in the processing rules module  310  may have an associated nearness vector. Thus, the anomaly engine processor  305  may use a hash function to determine at least one processing rule that is applicable to the structured data.  
         [0064]     In step  425 , the anomaly engine processor  305  may apply the set of processing rules near to the structured data. In one embodiment, anomaly engine processor  305  may execute each processing rule sequentially. In other embodiments, the processing rules may be linked for execution in a predetermined order.  
         [0065]     In step  430 , the anomaly engine processor  305  may determine whether an anomaly has been detected by the applied processing rule. If an anomaly has been detected, the anomaly engine processor  305  may append the anomaly to a listing of anomalies or to a database of anomalies, in step  435 . Subsequently, the list of anomalies may be formatted to a single predetermined format for a user to analyze. The anomaly engine processor  305  may then proceed to the processing of step  440 , as described herein below.  
         [0066]     Otherwise, if an anomaly has not been detected for the selected processing rule, the anomaly engine processor  305  may be configured to determine whether the last rule in the set of processing rules has been reached, in step  440 . If the last processing rule has been reached the anomaly engine processor  305  returns to the idle state of step  405 . Otherwise, if the anomaly engine processor  305  has not applied the last processing rule, the anomaly engine processor  305  returns to the processing of step  420 , described above.  
         [0067]      FIG. 5  illustrates a flow diagram  500  for the pattern-matching module  315 , shown in  FIG. 3 , in accordance with yet another embodiment of the invention. It should be readily apparent to those of ordinary skill in the art that this flow diagram  500  shown in  FIG. 5  represents a generalized illustration and that other steps may be added or existing steps may be removed or modified.  
         [0068]     As shown in  FIG. 5 , the pattern-matching module  315  may be configured to be in an idle state, in step  505 . The anomaly engine processor  305  may receive structured data forwarded by the processing engine  240 .  
         [0069]     In step  510 , the pattern-matching module  315  may be configured to analyze the structured data. In some embodiments, the pattern-matching module  315  may maintain a database that tracks previous instances of the structured data.  
         [0070]     In step  515 , the pattern-matching module  315  may be configured to determine any patterns in the structured data by data mining and/or neural nets. In step  520 , the pattern-matching module  315  may determine whether there has been a pattern detected. If the pattern-matching module  315  has not detected a pattern, the pattern-matching module  315  may return to the idle state of step  505 .  
         [0071]     Otherwise, if the pattern-matching module  315  determines a pattern, the pattern-matching module  315  may be configured to develop a rule in response to the detected pattern, in step  525 . For example, neural nets may be trained to develop rules based on detected pattern between the nearness vector and its selected template.  
         [0072]     In step  530 , the pattern-matching module  315  may be configured to forward the developed processing rule to the processing rules module  310  for subsequent processing by the anomaly engine processor  305 . Subsequently, the pattern-matching module  315  may return to the idle state of step  505 .  
         [0073]      FIG. 6  illustrates a flow diagram  600  for the IVA  325 , shown in  FIG. 3 , in accordance with yet another embodiment of the invention. It should be readily apparent to those of ordinary skill in the art that this flow diagram  600  shown in  FIG. 6  represents a generalized illustration and that other steps may be added or existing steps may be removed or modified.  
         [0074]     As shown in  FIG. 6 , the IVA  325  may be in an idle state, in step  605 . The IVA  325 , in step  610 , may monitor a human agent respond to an anomaly. The anomaly may originate from the anomaly engine processor  305  or from a service call to the human agent. The IVA  325  may track the capture the screens and/or keystrokes used by the human agent responding to the anomaly.  
         [0075]     In step  615 , the IVA  325  may be configured to develop a processing rule based on the response by the human agent. For example, the IVA  325  may monitor the expert, human agent  330  may update the templates manually or accept anomalies and provide a rule to fix the anomaly. The IVA may also monitor the expert constantly repair the data free of anomalies, i.e., monitor the patterns of data being fixed, to develop a rule to detect an anomaly.  
         [0076]     In step  620 , the IVA  325  may be configured to forward the processing rule to the processing rules module  310  for subsequent processing by the anomaly engine processor  305 .  
         [0077]      FIG. 7  illustrates a computer system implementing the anomaly engine in accordance with yet another embodiment of the invention. The functions of the anomaly engine be implemented in program code and executed by the computer system  700 . The anomaly engine may be implemented in computer languages such as PASCAL, C, C++, JAVA, etc. Using any procedural or AI language.  
         [0078]     As shown in  FIG. 7 , the computer system  700  includes one or more processors, such as processor  702 , that provide an execution platform for embodiments of the anomaly engine. Commands and data from the processor  702  are communicated over a communication bus  704 . The computer system  700  also includes a main memory  706 , such as a Random Access Memory (RAM), where the software for the anomaly engine may be executed during runtime, and a secondary memory  708 . The secondary memory  708  includes, for example, a hard disk drive  720  and/or a removable storage drive  722 , representing a floppy diskette drive, a magnetic tape drive, a compact disk drive, or other removable and recordable media, where a copy of a computer program embodiment for the anomaly engine may be stored. The removable storage drive  722  reads from and/or writes to a removable storage unit  724  in a well-known manner. A user interfaces with the anomaly engine with a keyboard  726 , a mouse  728 , and a display  720 . The display adaptor  722  interfaces with the communication bus  704  and the display  720  and receives display data from the processor  702  and converts the display data into display commands for the display  720 .  
         [0079]     Certain embodiments may be performed as a computer program. The computer program may exist in a variety of forms both active and inactive. For example, the computer program can exist as software program(s) comprised of program instructions in source code, object code, executable code or other formats; firmware program(s); or hardware description language (HDL) files. Any of the above can be embodied on a computer-readable medium, which include storage devices and signals, in compressed or uncompressed form. Exemplary computer readable storage devices include conventional computer system RAM (random access memory), ROM (read-only memory), EPROM (erasable, programmable ROM), EEPROM (electrically erasable, programmable ROM), and magnetic or optical disks or tapes. Exemplary computer readable signals, whether modulated using a carrier or not, are signals that a computer system hosting or running the present invention can be configured to access, including signals downloaded through the Internet or other networks. Concrete examples of the foregoing include distribution of executable software program(s) of the computer program on a CD-ROM or via Internet download. In a sense, the Internet itself, as an abstract entity, may be a computer-readable medium. The same may be true of computer networks in general.  
         [0080]     While the invention has been described with reference to the exemplary embodiments thereof, those skilled in the art will be able to make various modifications to the described embodiments without departing from the true spirit and scope. The terms and descriptions used herein are set forth by way of illustration only and are not meant as limitations. In particular, although the method has been described by examples, the steps of the method may be performed in a different order than illustrated or simultaneously. Those skilled in the art will recognize that these and other variations are possible within the spirit and scope as defined in the following claims and their equivalents.  
         [0081]     For the convenience of the reader, the above description has focused on a representative sample of possible embodiments, a sample that teaches the principles of the invention and conveys the best mode contemplated for carrying it out. The description has not attempted to exhaustively enumerate all possible variations. Further undescribed alternative embodiments are possible. It will be appreciated that many of those undescribed embodiments are within the literal scope of the following claims, and others are equivalent.