Abstract:
Systems, methods, and computer program products to perform an operation comprising receiving a policy document specifying a plurality of criteria, identifying, in a segment of unstructured text in the policy document, a criteria delimiter, creating a first portion and a second portion of the segment of unstructured text, wherein the first and second portions are created based on the criteria delimiter, wherein the first and second portions comprise a first criterion and a second criterion, respectively, and responsive to identifying an anaphora in the second portion of the segment of unstructured text that is related to a term in the first portion of the segment of unstructured text, combining the first criterion and the second criterion into a composite criterion.

Description:
BACKGROUND 
       [0001]    The present disclosure relates to computer software, and more specifically, to computer software which modifies criteria in a policy document during an ingestion phase to improve subsequent runtime analysis. 
         [0002]    Unstructured text is not always provided in an ideal form for ingestion by software applications. Policy documents (such as insurance policies, clinical trial/study documents, and the like) contain individual criteria that must be ingested, evaluated, and scored before the applications can later compare cases against the criteria to determine whether the case meets the criteria. However, many problems arise when ingesting unstructured text in policy documents. For example, multiple criteria that are tightly related generally should be aggregated into one criterion for processing. Similarly, a single delimited paragraph or list may contain more criteria that can be properly evaluated as an aggregate criterion, and should be split into individual criteria. However, during the ingestion process, these opportunities to combine or split the criteria are missed. This causes detrimental results to ingestion processing time, runtime processing time, and accuracy of evaluation/scoring when processing cases. 
       SUMMARY 
       [0003]    Embodiments disclosed herein provide systems, methods, and computer program products to perform an operation comprising receiving a policy document specifying a plurality of criteria, identifying, in a segment of unstructured text in the policy document, a criteria delimiter, identifying a first portion and a second portion of the segment of unstructured text, wherein the first and second portions are identified based on the criteria delimiter, wherein the first and second portions comprise a first criterion and a second criterion, respectively, and responsive to identifying an anaphora in the second portion of the segment of unstructured text that is related to a term in the first portion of the segment of unstructured text, combining the first criterion and the second criterion into a composite criterion. 
     
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS 
         [0004]      FIG. 1  illustrates a logical view of a system configured to perform criteria modification to improve analysis, according to one embodiment. 
           [0005]      FIG. 2  is a flow chart illustrating a method for criteria modification to improve analysis, according to one embodiment. 
           [0006]      FIG. 3  is a flow chart illustrating a method to use anaphora to identify criteria, according to one embodiment. 
           [0007]      FIG. 4  is a flow chart illustrating a method to determine whether to expand a criterion into multiple criteria, according to one embodiment. 
           [0008]      FIG. 5  is a flow chart illustrating a method to determine whether to combine a criterion with other criteria, according to one embodiment. 
           [0009]      FIG. 6  is a block diagram illustrating a system which performs criteria modification to improve analysis, according to one embodiment. 
       
    
    
     DETAILED DESCRIPTION 
       [0010]    Embodiments disclosed herein provide techniques to intelligently modify policy criteria to improve system analysis. Generally, embodiments disclosed herein leverage anaphora and concept associations to determine whether to split a single criterion into multiple criteria or join multiple criteria into a single criterion. Anaphora is the repetition of a word (or group of words), or a synonym (or substitute) for a preceding word (or group of words). Written works often use anaphoric words among multiple statements to create relationships between the statements. Concepts may generally be defined as a type or generalized type of thing. Examples of concepts include, without limitation, age, primary diagnosis, and medications. 
         [0011]    For example, a policy document may specify criteria that are used to determine whether a patient can join a medical study or receive a particular treatment. The policy document may include the following text: 
         [0012]    1) Fasting cholesterol&lt;=300 mg/dL and triglycerides&lt;=2.5 times I ULN; 2) patients may be on lipid-lowering agents to reach these values. 
         [0013]    By including the semicolon and/or numerals, the text appears to indicate that there are two distinct criteria. Traditional ingestion techniques may indeed consider these criteria as two separate criteria. However, embodiments disclosed herein may identify the anaphora of “these values” which refers back to “cholesterol” and “triglyceride” levels, and combine these criteria into a single criterion. 
         [0014]    As another example, the policy document may include the following text: 
         [0015]    Patients must not have an organ allograft or other history of immune compromise; patients must not be receiving chronic, systemic treatment with corticosteroids or other immunosuppressive agents. 
         [0016]    This portion of text does not include anaphora or related concepts. Therefore, embodiments disclosed herein would separate these criteria into distinct criteria, whereas traditional techniques may consider them as a single criterion. 
         [0017]    As used herein, a “policy document” includes any document which specifies criteria. The use of a specific type of policy document as a reference example herein should not be considered limiting of the disclosure. Examples of policy documents include, without limitation, medical studies, clinical trials, insurance policies, federal regulations, laws, and the like. For example, a policy document for a health insurance policy may include criteria used to determine whether the insurance policy covers a particular medical treatment. As another example, a policy document for a clinical trial may specify criteria used to determine whether a patient can participate in the clinical trial. 
         [0018]    As previously indicated, several problems arise when policy documents contain individual criteria in unstructured form. For example, separate criteria may be tightly related, and should be combined into one criterion for processing. Similarly, a single delimited paragraph (or list item) may contain more criteria that can be properly be evaluated as an aggregate, and should be split into individual criteria. The way criteria are broken up or kept together can be detrimental to processing time and accuracy of evaluation and scoring. For example, processing A and B and C (where A, B, and C are individual criteria) has different characteristics than processing (A and B) and C. There is a grouping of A, B, and C which provides optimal processing even though any grouping gives the same result. Advantageously, embodiments disclosed herein leverage anaphora and concepts to determine optimal groupings of criteria, improving processing time and accuracy of evaluation and scoring. 
         [0019]      FIG. 1  illustrates a logical view of a system  100  configured to perform criteria modification to improve analysis, according to one embodiment. As shown, the system  100  includes an ingestion component  101 , policy criteria  110 , and a runtime component  120 . The ingestion component  101  is configured to extract criteria  107  from policy document  102 . The ingestion component may then store the extracted criteria  107  in the policy criteria  110 . The runtime component  120  may then determine whether a case submitted by a user satisfies the policy document  102  by referencing the stored policy criteria  110 . A “case,” as used herein, refers to a collection of data attributes submitted for analysis. Stated differently, a case may be considered a question and a set of supporting data. Examples of questions in a case may be “does this patient meet the criteria of this clinical trial,” or “does the insurance policy require payment for an incident based on guideline X.” Examples of supporting data of a case include a patient&#39;s medical records, which specifies detailed information about the patient (such as age, gender, weight, history of disease, and the like), or a report describing the incident submitted under the insurance policy. 
         [0020]    For example, the policy document  102  may specify criteria  107  for determining whether a patient can take a certain medication. The ingestion component  101  may extract the criteria  107  and store the criteria in the policy criteria  110 . In at least one embodiment, the criteria in the policy criteria  110  are stored as Unstructured Information Management Architecture (UIMA) Common Analysis System (CAS) objects. The runtime component  120  may then receive a case which specifies attributes for a patient. The runtime component  120  may process the case to determine whether the patient&#39;s attributes satisfy the policy criteria  110 . If the runtime component  120  determines that the patient&#39;s attributes satisfy the policy criteria  110 , the patient may be eligible to take the medication. However, if the patient&#39;s attributes do not satisfy the policy criteria  110 , the patient may not be eligible to take the medication. One example of a system including an ingestion component  101  and a runtime component  120  is Watson by the IBM Corporation of Armonk, N.Y. 
         [0021]    As shown in  FIG. 1 , the ingestion component  101  includes a criteria optimizer  103 , a set of annotators  104 , a set of scorers  105 , and one or more ontologies  106 . The criteria optimizer  103  is configured to join or separate criteria  107  in a policy document using natural language processing (or parsing) that leverages anaphora and/or concepts in the policy document  102 . For example, “cholesterol” may be a concept in a medical record, and “these values” may be an anaphora that refers back to the patient&#39;s cholesterol. More generally, the ingestion component  101  may initially determine individual criteria by considering the location of structural constructs (e.g., line breaks, indentation, header information), symbolic characters (e.g., bullets such as hyphens or asterisks), or the presence of list prefix delimiters (e.g., ordered list numbers, letters, Roman numerals, or predefined labels). 
         [0022]    The criteria optimizer  103  may further determine to leave some criteria unmodified. Generally, the criteria optimizer  103  is configured to process each criterion  107  in the policy document by annotating concepts over each criterion  107  and annotating candidate anaphoric words over each criterion  107 . The criteria optimizer  103  may then determine if a criterion  107  contains indications that it may be a candidate for splitting into separate criterion. The criteria optimizer  103  may identify candidates using natural language parsing, such as detecting semicolons, periods, tabs, returns, or other delimiters in text. For example, the criteria optimizer  103  may use natural language processing to identify the semicolon in the following text as a delimiter that separates two criteria: “primary tumor is unresectable; it must be stable or asymptomatic.” The criteria optimizer  103  may then split criteria  107  into separate criteria, or combine separate criteria  107  into a single criterion. Generally, the criteria optimizer  103  splits criteria when anaphora are not present in the criteria or a similarity score computed for two concepts in the criteria does not exceed a similarity threshold (i.e., the concepts are not sufficiently similar). The criteria optimizer  103  may combine criteria with other criteria when the criteria include overlapping concepts or anaphoric words that establish an anaphoric relationship to other criteria. 
         [0023]    The annotators  104  include analysis modules configured to extract structured information from unstructured data. Examples of annotators  104  include annotators configured to perform natural language processing, identify delimiters in text, identify criteria, identify anaphora, and identify concepts. The scorers  105  are generally configured to compute similarity scores for concepts based on the distance between concepts in the ontologies  106 . The ontologies  106  provide a structural framework for organizing information. An ontology formally represents knowledge as a set of concepts within a domain, and the relationships between those concepts. 
         [0024]      FIG. 2  is a flow chart illustrating a method  200  for criteria modification to improve analysis, according to one embodiment. The steps of the method  200  provide enhanced techniques to extract criteria from a policy document to improve ingestion processing time, improve runtime processing time, and the accuracy of evaluation and/or scoring of criteria. As shown, the method  200  begins at step  210 , where the ingestion component  101  receives a policy document which includes criteria in the form of unstructured text. At step  220 , described in greater detail in  FIG. 3 , the criteria optimizer  103  may use anaphora and/or concepts in the policy document to extract optimized criteria from the policy document. Generally, the criteria optimizer  103  may identify criteria in the text of the policy document, and determine to join related criteria, break up a single criterion into multiple criteria, or determine to leave certain criteria unmodified. For example, the policy document may include the following text: “Primary tumor is unresectable; it must be stable or asymptomatic.” The criteria optimizer  103  may identify the anaphora “it” in the text, and determine that the text only includes a single criterion. In such a scenario, if the text was considered to include multiple criteria, the criteria optimizer  103  would join the criteria into a single criterion. However, if the text was considered to include a single criterion, the criteria optimizer  103  would not modify the status of being a single criterion. 
         [0025]    At step  230 , the criteria optimizer  103  and/or the ingestion component  101  may store the extracted criteria in the policy criteria  110 . At step  240 , the runtime component  120  may receive a case from a user, and process the case against the criteria to determine whether the case satisfies the policy. Continuing with the tumor example, the runtime component  120  may determine whether the case (which may include a patient&#39;s detailed medical history) indicates that the patient&#39;s tumor is unresectable and stable or asymptomatic. The policy document may, for example, determine whether a patient can receive a treatment. If the patient&#39;s tumor satisfies all the criteria of the policy document, the runtime component  120  may return an indication that the patient can receive the treatment. 
         [0026]      FIG. 3  is a flow chart illustrating a method  300  corresponding to step  220  to use anaphora to identify criteria, according to one embodiment. In at least one embodiment, the criteria optimizer  103  performs the steps of the method  300 . At step  310 , the criteria optimizer  103  may parse the policy document to identify criteria in the document text. In at least one embodiment, the criteria optimizer  103  uses one or more annotators  104  to identify the criteria. In some embodiments, a set of criteria may be identified based on delimiters in the text, such as semicolons, line breaks, periods, and the like. At step  320 , the criteria optimizer  103  executes a loop including steps  330 - 380  for each criterion identified at step  310 . At step  330 , the criteria optimizer  103  may invoke an annotator  104  to identify one or more concepts in each criterion. For example, in the text “patient has ECOG performance status of 0-2, patient&#39;s performance was determined within the previous 30 days” the annotator  104  may identify “ECOG performance status” as a concept. At step  340 , the criteria optimizer  103  may invoke an annotator  104  to identify candidate anaphoric terms in the current criterion. Continuing with the previous example, the annotator  104  may identify “performance” as an anaphora for “ECOG performance status.” At step  350 , the criteria optimizer  103  may invoke an annotator  104  to identify indications of multiple (or compound) criteria in the current criterion. Examples of indications of multiple criteria include delimiters such as sentence boundaries (e.g., periods and semicolons), line breaks, or numbering (or other list formatting). 
         [0027]    At step  360 , described in greater detail with reference to  FIG. 4 , the criteria optimizer  103  determines whether to expand the current criterion into multiple criteria. Generally, the criteria optimizer  103  executes step  360  responsive to identifying indications of multiple (or composite) criteria at step  350 . Once the indications are identified, the criteria optimizer  103  may break up the criteria according to the delimiters found in the text. At step  370 , described in greater detail with reference to  FIG. 5 , the criteria optimizer  103  determines whether to combine the current criterion with other criteria. The criteria optimizer  103  may combine the current criterion with nearby criteria (in the text), or criteria from other locations in the text. At step  380 , the criteria optimizer  103  determines whether more criteria remain in the policy text. If more criteria remain, the criteria optimizer  103  returns to step  320 , otherwise the criteria optimizer  103  proceeds to step  390 , where the criteria optimizer  103  stores indications of the optimized criteria. 
         [0028]      FIG. 4  is a flow chart illustrating a method  400  corresponding to step  360  to determine whether to expand a criterion into multiple criteria, according to one embodiment. In at least one embodiment, the criteria optimizer  103  performs the steps of the method  400 . As shown, the method  400  begins at step  410 , where the criteria optimizer  103  expands the compound criteria into multiple distinct criteria. In at least one embodiment, the criteria optimizer  103  may use the delimiters identified at step  350  to break up criteria. For example, if the text includes three sentences all ending in periods, the criteria optimizer  103  may identify the periods and break the text into three individual sentences, each of which is considered to have at least one respective distinct criterion. At step  420 , the criteria optimizer  103  executes a loop including steps  430 - 470  for each distinct criterion created at step  410 . At step  430 , the criteria optimizer  103  may invoke an annotator  104  that identifies overlapping concepts (e.g., similar concepts which are present in the current criterion as well as other criteria in the policy). At step  440 , the criteria optimizer  103  may invoke a scorer  105  to compute a similarity score for the overlapping concepts. In at least one embodiment, the similarity score may be computed based on a distance between the concepts in an ontology. Generally, the shorter the distance between the concepts in the ontology, the more likely the concepts are related, producing a higher similarity score. For example, the concepts “cancer” and “carcinoma” may be relatively close in an ontology, thereby reflecting a similarity between the concepts. Therefore, the criteria optimizer  103  and/or the scorer  105  may compute a high similarity score for the concepts. Conversely, the concepts “hamburger” and “earmuffs” are likely to be very distant in an ontology, reflecting a low degree of similarity. Therefore, a low similarity score may be computed for these concepts. Generally, the criteria optimizer  103  may compare the computed similarity score to a similarity threshold, and recombine (or keep intact) criteria having a similarity score that exceeds the threshold. 
         [0029]    At step  450 , the criteria optimizer  103  may invoke an annotator  104  to identify anaphoric relationships between the current criterion and an adjacent criterion (relative to their positions in the text). An example portion of policy text may read: “Patients with a current condition of osteopenia or osteoporosis via a Dual Energy X-ray Absorptiometry (DEXA) scan; patients with a history of either are allowed.” The criteria optimizer  103  may have split the text into two criteria, namely “Patients with a current condition of osteopenia or osteoporosis via a Dual Energy X-ray Absorptiometry (DEXA) scan” and “patients with a history of either are allowed.” At step  450 , the criteria optimizer  103  may identify the term “either” when processing the second criterion, and determine that the term “either” is an anaphora for osteopenia and/or osteoporosis. Because of this relationship, the criteria optimizer  103  may determine to recombine these criteria into a single criterion. Therefore, at step  460 , the criteria optimizer  103  may recombine the criteria into a single criterion upon determining the similarity score computed at step  440  exceeds a similarity threshold, or upon determining that the anaphoric relationship exists. 
         [0030]      FIG. 5  is a flow chart illustrating a method  500  corresponding to step  370  to determine whether to combine a criterion with other criteria, according to one embodiment. In at least one embodiment, the criteria optimizer  103  performs the steps of the method  500 . As shown, the method  500  begins at step  510 , where the criteria optimizer  103  determines whether a criterion has overlapping concepts with other criteria. For example, the criteria optimizer  103  may invoke an annotator  104  to identify concepts in a criterion that are similar to other concepts in other criteria. In at least one embodiment, the criteria optimizer  103  computes a score for the overlapping concepts based on distance between the concepts in an ontology. At step  520 , the criteria optimizer  103  may determine whether the current criterion has anaphoric words that have an anaphoric relationship to a preceding (or subsequent) criterion. For example, the following two criteria may be in the policy text: “patient has cholesterol&gt;=200” and “it was taken within the previous 30 days.” By identifying the anaphora “it,” the criteria optimizer  103  may determine an anaphoric relationship exists between the two criteria, and combine the criteria into a single criterion. At step  530 , the criteria optimizer  103  combines criteria that have overlapping concepts and/or have anaphoric relationships. In at least one embodiment, the criteria optimizer  103  determines that the concepts are “overlapping” upon determining that the similarity score for the concepts exceeds a similarity threshold. The criteria optimizer  103  may combine the criteria upon determining the concepts are overlapping based on the similarity score exceeding the threshold. 
         [0031]      FIG. 6  is a block diagram illustrating a system  600  which performs criteria modification to improve analysis, according to one embodiment. The networked system  600  includes a computer  602 . The computer  602  may also be connected to other computers via a network  630 . In general, the network  630  may be a telecommunications network and/or a wide area network (WAN). In a particular embodiment, the network  630  is the Internet. 
         [0032]    The computer  602  generally includes a processor  604  which obtains instructions and data via a bus  620  from a memory  606  and/or a storage  608 . The computer  602  may also include one or more network interface devices  618 , input devices  622 , and output devices  624  connected to the bus  620 . The computer  602  is generally under the control of an operating system (not shown). Examples of operating systems include the UNIX operating system, versions of the Microsoft Windows operating system, and distributions of the Linux operating system. (UNIX is a registered trademark of The Open Group in the United States and other countries. Microsoft and Windows are trademarks of Microsoft Corporation in the United States, other countries, or both. Linux is a registered trademark of Linus Torvalds in the United States, other countries, or both.) More generally, any operating system supporting the functions disclosed herein may be used. The processor  604  is a programmable logic device that performs instruction, logic, and mathematical processing, and may be representative of one or more CPUs. The network interface device  618  may be any type of network communications device allowing the computer  602  to communicate with other computers via the network  630 . 
         [0033]    The storage  608  is representative of hard-disk drives, solid state drives, flash memory devices, optical media and the like. Generally, the storage  608  stores application programs and data for use by the computer  602 . In addition, the memory  606  and the storage  608  may be considered to include memory physically located elsewhere; for example, on another computer coupled to the computer  602  via the bus  620 . 
         [0034]    The input device  622  may be any device for providing input to the computer  602 . For example, a keyboard and/or a mouse may be used. The input device  622  represents a wide variety of input devices, including keyboards, mice, controllers, and so on. Furthermore, the input device  622  may include a set of buttons, switches or other physical device mechanisms for controlling the computer  602 . The output device  624  may include output devices such as monitors, touch screen displays, and so on. 
         [0035]    As shown, the memory  606  contains the ingestion component  101 , which includes the criteria optimizer  103 , annotators  104 , and scorers  105 . The memory  606  further includes the runtime component  120 . As shown, the storage  608  contains data stores for policy documents  102 , policy criteria  110 , and one or more ontologies  106 . Generally, the computer  602  implements all of the methods and functionality described with respect to  FIGS. 1-5 . 
         [0036]    The descriptions of the various embodiments of the present disclosure have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. 
         [0037]    In the foregoing, reference is made to embodiments presented in this disclosure. However, the scope of the present disclosure is not limited to specific described embodiments. Instead, any combination of the recited features and elements, whether related to different embodiments or not, is contemplated to implement and practice contemplated embodiments. Furthermore, although embodiments disclosed herein may achieve advantages over other possible solutions or over the prior art, whether or not a particular advantage is achieved by a given embodiment is not limiting of the scope of the present disclosure. Thus, the recited aspects, features, embodiments and advantages are merely illustrative and are not considered elements or limitations of the appended claims except where explicitly recited in a claim(s). Likewise, reference to “the invention” shall not be construed as a generalization of any inventive subject matter disclosed herein and shall not be considered to be an element or limitation of the appended claims except where explicitly recited in a claim(s). 
         [0038]    Aspects of the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module” or “system.” 
         [0039]    The present disclosure 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 disclosure. 
         [0040]    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. 
         [0041]    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. 
         [0042]    Computer readable program instructions for carrying out operations of the present disclosure 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 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 disclosure. 
         [0043]    Aspects of the present disclosure 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 disclosure. 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. 
         [0044]    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. 
         [0045]    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. 
         [0046]    The flowchart and block diagrams in the FIGS. illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. 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. 
         [0047]    Embodiments of the disclosure may be provided to end users through a cloud computing infrastructure. Cloud computing generally refers to the provision of scalable computing resources as a service over a network. More formally, cloud computing may be defined as a computing capability that provides an abstraction between the computing resource and its underlying technical architecture (e.g., servers, storage, networks), enabling convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction. Thus, cloud computing allows a user to access virtual computing resources (e.g., storage, data, applications, and even complete virtualized computing systems) in “the cloud,” without regard for the underlying physical systems (or locations of those systems) used to provide the computing resources. 
         [0048]    Typically, cloud computing resources are provided to a user on a pay-per-use basis, where users are charged only for the computing resources actually used (e.g. an amount of storage space consumed by a user or a number of virtualized systems instantiated by the user). A user can access any of the resources that reside in the cloud at any time, and from anywhere across the Internet. In context of the present disclosure, a user may access applications or related data available in the cloud. For example, the ingestion component  101  could execute on a computing system in the cloud and process policy documents. In such a case, the ingestion component could extract optimized policy criteria and store the extracted criteria at a storage location in the cloud. Doing so allows a user to access this information from any computing system attached to a network connected to the cloud (e.g., the Internet). 
         [0049]    While the foregoing is directed to embodiments of the present disclosure, other and further embodiments of the disclosure may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow.