Patent Publication Number: US-2021191953-A1

Title: Universal repository for holding repeatedly accessible information

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of U.S. patent application Ser. No. 14/928,367, filed Oct. 30, 2015, which is incorporated by reference as if fully set forth. 
    
    
     FIELD OF INVENTION 
     The present invention is related to an outlier system for grouping of characteristics and a universal repository for holding repeatedly accessible information. 
     BACKGROUND 
     An entity, such as an insurance company, may want to analyze or “mine” large amounts of data. For example, an insurance company might want to analyze tens of thousands of insurance claim files to look for patterns (e.g., a particular type of injury has occurred more frequently for employees who work in a particular industry). An entity might analyze this data in connection with different types of applications, and, moreover, different applications may need to analyze the data differently. For example, the term “IV” might referent to an “Insured Vehicle” when it appears in an automobile accident report and to “Intra-Venous” when it appears in a medical file. It can be difficult to identify patterns across such large amounts of data and different types of applications. In addition, manually managing the different needs and requirements (e.g., different business logic rules) associated with different applications can be a time consuming and error prone process. As a result, it would be desirable to provide systems and methods for efficiently and accurately preparing data for analysis, integrating the data to the workflow of the business, and inputting rules of users. 
     SUMMARY 
     A device and method is disclosed for automatically grouping data based on characteristics. The device and method include a communication interface for receiving data mining for component information, a storage medium for storing the received data, and a processor for performing an analysis on the received data to determine parameters included within the data and processing the data using relationships within the data to group or isolate data point. The relationships enable the processor to identify attributes associated with particular groups of the data. 
     A system and method includes a plurality of different data sources providing data stored in a plurality of storage devices, at least a first processor for searching each of the plurality of data sources using a plurality of data mining techniques and identifying selected portions of each data source, wherein the first processor further identifies at least one pattern in at least one of the selected portions, wherein the data mining techniques comprise at least machine learning and text mining, a database for staging the selected portions and patterns, wherein the staging includes preparation, reorganization an management of the selected portions and patterns, and at least a second processor for assembling the staged materials by incorporating analytics within the staged data, the second processor executing instructions stored in a storage medium to run computer models on the assembled staged data to provide information about the assembled staged data, and based on defined criteria produce a ranking added the assembled staged data and store the assembled staged data with the associated ranking to universally reposit repeated access to the information and automatically activating at least two systems by providing the associated ranking directly via at least one interface to the at least two systems to allow access to the stored information with the associated ranking. 
     A device and method are described for universally repositing repeatedly accessible information within the workflow of a business. A system including a plurality of raw materials stored in a plurality of storage devices, heavy industry mining the plurality of raw materials into a useable form, a database for staging the mined raw materials, wherein the staging includes preparation, reorganization an management of the mined materials, and at least one processor for assembling the staged materials by incorporating analytics within the staged mined materials, the at least one processor executing instructions stored on the storage medium to run models on the incorporated materials, and based on defined criteria produce a ranking in the materials and automatically activating systems within the business by providing the associated ranking directly via at least one interface to the systems to allow access to the stored materials with the associated ranking. 
     A device and method are described for universally repositing repeatedly accessible information within the workflow of a business. The device and method includes a communication interface for receiving input data from a plurality of devices, wherein the received input data includes analytics incorporated within the data, a storage medium for storing data based on analytics performed on the data, the storage medium further storing instructions adapted to be executed by the processor, and a processor executing the instructions stored on the storage medium to run models on the stored data, and based on defined criteria produce a ranking in the data and store the data with the associated ranking. The communications interface publishes the associated ranking with the data to allow access to the accessible information. 
     A system for evaluating data to support multiple insurance business applications is disclosed. The system includes a communication device to receive input data from a plurality of sources, wherein the received input data includes analytics incorporated within the data, a processor coupled to the communication device, and a storage device in communication with the processor and storing instructions adapted to be executed by the processor. The instructions predictively model the data to represent the data within a workflow of the business, and publish scores to tie data together and allow access to the multiple insurance business applications. 
     A system for evaluating data is also provided. The system includes a communication device to receive input data from a plurality of sources, a processor coupled to the communication device, and a storage device in communication with the processor and storing instructions adapted to be executed by the processor, The instructions identify and derive characteristics of the data, plot the identified and derived characteristics, determine groupings of data, profile the determined groupings of data, and generate referrals based on inclusion in a group. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       A more detailed understanding may be had from the following description, given by way of example in conjunction with the accompanying drawings wherein: 
         FIG. 1  illustrates a system block diagram of the insight supply chain; 
         FIG. 2  illustrates data grouped based on exemplary events; 
         FIG. 3  illustrates data grouped based on exemplary actors; 
         FIG. 4  illustrates data grouped based on exemplary places; 
         FIG. 5  illustrates data grouped based on exemplary things; 
         FIG. 6  illustrates several data storage elements for storing raw materials of  FIG. 1 ; 
         FIG. 7  heavy industry is illustrated as including data mining, machine learning and text mining; 
         FIG. 8  illustrates that parts may include ADM, ARE, outlier engine and text factory; 
         FIG. 9  illustrates elements of a predictive model; 
         FIG. 10  illustrates predictor variables, source systems, and text mined characteristics; 
         FIG. 11  illustrates the outlier engine with normative area, areas of interest and an outlier; 
         FIG. 12  illustrates a block diagram of the text factory; 
         FIG. 13  illustrates the assembly of the data; 
         FIG. 14  illustrates a dashboard representing the relationship of scores for events; 
         FIG. 15  illustrates a dashboard representing total dollars and outlier percentage based on year; 
         FIG. 16  illustrates a dashboard of volume of a characteristic plotted against score for selected groups; 
         FIG. 17  illustrates a dashboard of a pie chart of outlier traits within characteristics; 
         FIG. 18  illustrates the components of product; 
         FIG. 19  illustrates exemplary items assigned a ranking in the scoring mart; 
         FIG. 20  illustrates a method performed in the scoring mart; 
         FIG. 21  illustrates a data set associated with the scoring mart; 
         FIG. 22  illustrates an example batch lookup of scores within the scoring mart; 
         FIG. 23  illustrates an example batch lookup of scores within the scoring mart; 
         FIG. 24  illustrates an aspect of the scoring mart allowing identifying the speed of change based scores; 
         FIG. 25  illustrates an aspect of the scoring mart allowing a return of entities with more than one score; 
         FIG. 26  illustrates an aspect of the scoring mart allowing the aggregation of scores from one entity onto another entity; 
         FIG. 27  illustrates the output through distribution; 
         FIG. 28  illustrates a method performed in an insight supply chain; 
         FIG. 29  illustrates an example computing device that may be used to implement features described herein with reference to  FIGS. 1-28 ; and 
         FIG. 30  illustrates a mobile device that is a more specific example of the computing device of  FIG. 29 . 
     
    
    
     DETAILED DESCRIPTION OF THE EMBODIMENTS 
     An enterprise may want to analyze or “mine” large amounts of data, such as text data, images, documents, and voice data that may be received from various sources. By way of example, an insurance company might want to analyze tens of thousands of insurance claim text files including images that may be included with a claim to look for patterns (e.g., to find patterns of factors that may help indicate a likely recovery period length will be associated with a particular type of injury). An entity might analyze this data in connection with different types of applications and that different applications may need to analyze the data differently. An outlier engine may be used analyze the data to determine data points that may fit with other data points and be grouped together and/or determine data points that do not fit with other data points. Based on these grouping or exclusions, characteristics may be identified and derived from the data. A scoring mart may be used to provide integration into the workflow and input rules/business rules of users. 
     A device and method is disclosed for automatically grouping data based on characteristics. The device and method include a communication interface for receiving data mining for component information, a storage medium for storing the received data, and a processor for performing an analysis on the received data to determine parameters included within the data and processing the data using relationships within the data to group or isolate data point. The relationships enable the processor to identify attributes associated with particular groups of the data. 
     A system and method includes a plurality of different data sources providing data stored in a plurality of storage devices, at least a first processor for searching each of the plurality of data sources using a plurality of data mining techniques and identifying selected portions of each data source, wherein the first processor further identifies at least one pattern in at least one of the selected portions, wherein the data mining techniques comprise at least machine learning and text mining, a database for staging the selected portions and patterns, wherein the staging includes preparation, reorganization an management of the selected portions and patterns, and at least a second processor for assembling the staged materials by incorporating analytics within the staged data, the second processor executing instructions stored in a storage medium to run computer models on the assembled staged data to provide information about the assembled staged data, and based on defined criteria produce a ranking added the assembled staged data and store the assembled staged data with the associated ranking to universally reposit repeated access to the information and automatically activating at least two systems by providing the associated ranking directly via at least one interface to the at least two systems to allow access to the stored information with the associated ranking. 
     A system for evaluating data to support multiple insurance business applications is disclosed. The system includes a communication device to receive input data from a plurality of sources, wherein the received input data includes analytics incorporated within the data, a processor coupled to the communication device, and a storage device in communication with the processor and storing instructions adapted to be executed by the processor. The instructions predictively model the data to represent the data within a workflow of the business, and publish scores to tie data together and allow access to the multiple insurance business applications. 
     A system for evaluating data is also provided. The system includes a communication device to receive input data from a plurality of sources, a processor coupled to the communication device, and a storage device in communication with the processor and storing instructions adapted to be executed by the processor, The instructions identify and derive characteristics of the data, plot the identified and derived characteristics, determine groupings of data, profile the determined groupings of data, and generate referrals based on inclusion in a group. 
       FIG. 1  illustrates a system block diagram of the insight supply chain  100 . System  100  includes raw materials  110 , heavy industry  120 , parts  130 , assembly  140  product  150  and distribution  160  with names by way of analogy and not of requirement as discussed herein. As with a supply chain, system  100  begins with raw materials  110 . These raw materials  110  may generally include data that may be in one form or another or a myriad of different forms. Raw materials  110  generally refer to data and other information that may be beneficial or provide insight to a business or corporate environment. Generally, the data may be stored within databases and other rigid structures, such as data warehouses  610 , data lakes  630  that allow storage of varying forms of data including images, documents, and voice files, for example, and data supplied from third parties  620 . Data contained within the insurance industry may include claim documents, policies, doctor reports, images of damage to vehicles, and the like. Data warehouses  610 , data from third parties  620 , and data lakes  630  are further described with respect to  FIG. 6  below. 
     Raw materials  110 , (the data), may be mined by heavy industry  120 . Mining the data  710  includes the steps of taking the raw data and manipulating the data into a useable form. This manipulation may include formatting the data, relating the data, and structuring the data as well as other conventional data mining techniques. 
     Additionally or alternatively, the manipulation may include machine learning  720  which may include pattern recognition of the data. This may include pattern/sequence labeling for a claim contained within the data. 
     Other mining may include text mining  730 . This type of mining includes generally unstructured mining of data in the form of text. Text mining may include analyzing the claim handler narrative where the narrative is written in a free form. Text mining, as will be described in more detail below, may include evaluating text data received from a myriad of potential sources, and aggregating and mapping the data to create composite text input data. Data mining  710 , machine learning  720 , and text mining  730  are further described with respect to  FIG. 7  below. 
     After the data is mined by heavy industry  120 , the data may be staged with parts  130 . In a similar fashion to the manufacturing industry, parts  130  become the building blocks for further manufacturing. In the analysis being performed as described herein, parts  130  including the preparation, reorganization and management of the data. This staging may include reorganizing the data to accelerate access to the data. Data management  810  may be included in the staging. This may include claim notes, documents, social media and call transcripts consolidated based on a specific feature, such as consolidating all of the data surrounding a claim for a car crash. This may include texts by witnesses, police reports, 911 calls, and the like organized based on the automobile claim. Staging may also take the form of analytical analysis of data. This analysis may include the “what if” question, answer and associated analysis. 
     Staging may also include using an outlier engine  830  on the data to group or isolate data points, and based on relationships within the data conclude additional data about specific data points. For example, if a data point is grouped with other data points and the group includes high doctor bills associated with back injuries, the data point may have attributes associated with it based on the other members of the group. In this example, the grouping may allow a conclusion that the duration of doctor visits may also be above a threshold. Data management  810 , outlier engine  830  and text factory  840  are further described with respect to  FIG. 8  below. 
     Staging the data includes grouping the mined observations by common central components. For example, and with reference to  FIG. 2 , there is illustrated data grouped based on exemplary events  210 . As may be seen in  FIG. 2 , the events  210  may be grouped according to auto claims  220  property claims  230 , invoices for repairs and parts  240 , medical bills  250 , and handling milestones  260 . Each of auto claims  220  property claims  230 , invoices for repairs and parts  240 , medical bills  250 , and handling milestones  260  may be anchored to a common event  210 . Event  210  may be any meaningful concept within the company, for example. 
     Further in the example, and with reference to  FIG. 3 , there is illustrated data grouped based on exemplary actors  310 . As may be seen in  FIG. 3 , people  310  may be grouped according to insureds  320 , repairers  330 , lawyers  340 , and physicians  350 . Each of insureds  320 , repairers  330 , lawyers  340 , and physicians  350  may be anchored to a common person  310 . Person  310  may be any meaningful concept within the company, for example. 
     With reference to  FIG. 4 , there is illustrated data grouped based on exemplary places  410 . As may be seen in  FIG. 4 , places  410  may be grouped according to insured location  420 , damage path  430 , and loss location  440 . Each of insured location  420 , damage path  430 , and loss location  440  may be anchored to a common place  410 . Place  410  may be any meaningful concept within the company, for example. 
     With reference to  FIG. 5 , there is illustrated data grouped based on exemplary things  510 . As may be seen in  FIG. 5 , things  510  may be grouped according to automobiles  520  and machinery  530 . Each of automobiles  520  and machinery  530  may be anchored to things  510 . Thing  510  may be any meaningful concept within the company, for example. 
     Once the data is staged using parts  130 , the data may be assembled  140 . Assembly of the data includes performing analytics on the data. The analytics may include descriptive analytics  1310  including the management and reporting of the data. This may be relevant to the business intelligence team, the finance group, actuarial data, and product data, for example. 
     Analytics may also include diagnostics analytics  1320  on the data. Diagnostics analysis of the data may include the “why” and “what if” type of data analysis. This type of data analysis may be relevant in the insurance industry for actuarial forensics, for example. 
     Predictive/prescriptive analytics  1330  may also be performed on the data. This may include actions that should be taken to prevent something from happening. For example, in the insurance industry, severe medical issues may be lessened or eliminated by having yearly physicals. This type of analytics may be used in forensics research in the insurance industry, for example. Descriptive analytics  1310 , diagnostic analytics  1320 , and predictive/prescriptive analytics are further described with respect to  FIG. 13  below. 
     Once the data is assembled, it may be packaged as a product  150 . The product may include business intelligence reporting  1810  of the assembled data. For example, a trigger based on yearly physicals may provide a discount on the insurance premiums since severe medical issues are reduced by the occurrence of physicals. This type of reporting may include human delivery reporting, for example. Human delivery reporting is the reporting of metrics found in the data in a form to be used by a human. For example, after analyzing data the probability of an event occurring may be reported to a human so that that person may act in a certain way based on the probability. If the likelihood of getting in a car accident is 90% at a certain time, and that information is communicated to a human, it is likely that the human would not travel during that certain time so as to reduce the likelihood of being involved in an accident. 
     The assembled data may also be packaged via a scoring mart  1820 . The scoring mart may provide a machine to machine product that integrates into the workflow of the insurance company. Such workflow input may include being input into rules of users and business rules, for example. Again using the physical example, if the business rules of issuing life insurance are input, a discount based on yearly physicals may automatically be implemented via the scoring mart  1820 . Business intelligence reporting  1810  and scoring mart  1820  are further described with respect to  FIG. 18  below. 
     After the product  150  is prepared, the data may be distributed  160  to the various corporate systems. These may include workflow platforms for fraud investigation  2730 , underwriting  2720 , and claims administration  2740 , for example. Again with the physical example, the underwriting system may be provided the product that related to the use of yearly physicals providing a discount in life insurance premiums. 
     Distribution  160  may also include reporting the product through SharePoint or self-help. This may include a web interface on the insurance company&#39;s web page that allows insureds to access information that may provide information on reducing their insurance premiums. In the physical example discussed throughout, the web page may provide the insured information that directs the insured to seek yearly physicals in order to lower their life insurance premium. Internal reporting  2710 , underwriting  2720 , fraud investigation  2730 , and claim administration  2740  are described below with respect to  FIG. 27 . 
     For example, and with reference to  FIG. 6 , there is illustrated several data storage elements for storing raw materials  110  of  FIG. 1 . Raw materials  110  may be included in a data warehouse (DW)  610 . DW  610 , which may also be referred to as an enterprise data warehouse (EDW), is a central repository or repositories of integrated data from one or more disparate sources of data. DW  610  stores current and historical data and may be relied upon to create analytical reports for knowledge workers throughout an enterprise. DW  610  included structured data. Examples of reports could range from annual and quarterly comparisons and trends to detailed daily sales analyses. Generally, the data stored in DW  610  is uploaded from the operational systems, including data from marketing and sales. The data may pass through an operational data store for additional operations before it is used in DW  610 . Often DW  610  refers to rigid structure data. 
     DW  610  may include data from portions of the company  612  that may include sales, claims, marketing, and supply, for example. This raw data may be stored in an integration layer  614  that prepares for staging. The integration layer  614  may output structured data for staging. The data may be held in a data warehouse  616  and stored in data marts  618 . 
     Raw materials  110  may also include third party data  620 . Third party data  620  may include any data that is purchased, or comes from outside the system  100 . This may include data from external providers and may be aggregated from numerous websites. System  100  may be provided purchased third-party data  620  and data  220  may be shared within system  100 . 
     Third party data  620  may include data that is stored in a data storage device  622 . This data may be accumulated in an accessible data device  624 . The data may be received via a communication link  626  and stored within the company in a data warehouse  628 . Communication link  626  may provide a point-to-point portal or marketplace for third party data and/or aggregated data such as from an aggregator. 
     Raw materials  110  may also include data stored in a data lake  630 . Data lake  630  may include a storage repository or a repository that holds a vast amount of data generally in its native format. While DW  610  stores data in files or folders, data lake  630  may use a flat architecture to store data. Each data element in data lake  630  may be assigned an identifier and tagged with a set of tags. When a business question arises, data lake  630  may be queried for relevant data, and that smaller set of data may then be analyzed to help answer the question. More specifically, data lake  630  houses data that is in large data pool in which the schema and data requirements are not defined until the data is queried. 
     Data lake  630  may include data storage  632  that holds the data. This data may then be stored in a bunch of disks (JBOD)  634 . The data in bunch of disks  634  may be accessed and linked via SSA/flash  636  and stored within a bunch of flash memory (JBOF)  638 . 
     Raw materials  110  are provided to or accessed by heavy industry  120 . Referring now also to  FIG. 7 , heavy industry  120  is illustrated as including data mining  710 , machine learning  720  and text mining  730 . 
     Data mining  710  may include the classical types of data manipulation including relational data, formatted and structured data. Data mining  710  generally involves the extraction of information from raw materials  110  and transformation into an understandable structure. Data mining  710  may be performed on the raw materials  110 . Data mining  710  may be used to analyze large quantities of data to extract previously unknown, interesting patterns such as groups of data records, unusual records, and dependencies. Data mining involves six common classes of tasks: 1) anomaly detection; 2) dependency modeling; 3) clustering; 4) classification; 5) regression, and 6) summarization. 
     Anomaly detection, also referred to as outlier/change/deviation detection may provide the identification of unusual data records, that might be interesting or data errors that require further investigation. 
     Dependency modeling, also referred to as association rule learning, searches for relationships between variables, such as gathering data on customer purchasing habits. Using association rule learning, associations of products that may be bought together may be determined and this information may be used for marketing purposes. 
     Clustering is the task of discovering groups and structures in the data that are in some way or another “similar”, without using known structures in the data. 
     Classification is the task of generalizing known structure to apply to new data. For example, an e-mail program might attempt to classify an e-mail as “legitimate” or as “spam”. 
     Regression attempts to find a function which models the data with the least error. 
     Summarization provides a more compact representation of the data set, including visualization and report generation. 
     Data mining  710  may include data stored in storage devices  712  and accumulated in data  714 . This data may be mined and combined in database or table  715 , for example. This data may then be correlated in a correlator  717 . The correlated data may then be analyzed and reported  719 . 
     Machine learning  720  may include performing pattern recognition on data or data sets contained within raw materials  110 . This can be a review for pattern or sequence labels for claims for example. Machine learning explores the construction and study of raw materials  110  using algorithms that can learn from and make predictions on such data. Such algorithms operate using a model such as from example inputs in order to make data-driven predictions or decisions, rather than strictly using static program instructions. 
     Machine learning  720  may be performed using data stored on a storage device  722 . This data may then be accumulated in a storage device  724 . Machine learning may then be performed including supervised and unsupervised learning  725 . This learning may include processing using clustering, associating, regression analysis, and classifying in a processor  727 . The processed data may then be analyzed and reported  729 . 
     Text mining  730  includes using text from raw materials  110  such as claim handling narrative. Generally, text mining  730  involves unstructured fields. Text mining  730  involves the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning. Text mining  730  generally involves structuring the input data from raw materials  110 , deriving patterns within the structured data, and finally evaluation and interpretation of the output. Text analysis involves information retrieval, lexical analysis to study word frequency distributions, pattern recognition, tagging/annotation, information extraction, data mining techniques including link and association analysis, visualization, and predictive analytics. The overarching goal is, essentially, to turn text into data from raw materials  110  for analysis, via application of natural language processing (NLP) and analytical methods. 
     Text mining  730  may be performed on data stored on data storage devices  732 . This data may be accumulated in data storage  734 . The data may be scanned for words or sets of words and the word count may be monitored  737 . The monitored and counted data may then be analyzed and reported  739 . 
     A typical application is to scan a set of documents written in a natural language and either model the document set for predictive classification purposes or populate a database or search index with the information extracted. 
     Parts  130  may be the staging area for the analytics. For example, in parts  130  the data may be reorganized to accelerate those queries that are frequently requested. Parts  130  may include analytical data mart (ADM)  810 , ARE  820 , outlier engine  830  and text factory  840  as depicted in  FIG. 8 . 
     Analytical data mart, sometimes referred to as ADM,  810  may include an interface  812  for receiving data from heavy industry  120 . This received data may be processed using processors and servers  814 . The processed data, or unprocessed data, may be stored in repository storage  816 . The data may then be accumulated on a data storage device  818 . 
     Text factory  840  may include an interface  842  for receiving data from heavy industry  120 . This received data may be processed as described herein using processors and servers  844 . The processed data, or unprocessed data, may be stored in repository storage  846 . The data may then be accumulated on a data storage device  848 . 
     Outlier engine  830  receives data input from a machine learning unit  320  that establishes pattern recognition and pattern/sequence labels for a claim, for example. This may include billing, repair problems, and treatment patterns, for example. This data may be manipulated within outlier engine  830  such as by providing a multiple variable graph as will be described herein below. Outlier engine  830  may provide the ability to identify or derive characteristics of the data, find clumps of similarity in the data, profile the clumps to find areas of interest within the data, generate referrals based on membership in an area of interest within the data, and generate referrals based on migration toward and area of interest in the data. These characteristics may be identified or derived based on relationships with other data points that are common with a given data point. For example, if a data point is grouped with another data point, the attributes of the other data point may be derived to be with the data point. Such derivation may be based on clumps of similarity, for example. Such an analysis may be performed using a myriad of scores as opposed to a single variable. 
     Outlier engine  830  may include an interface  832  for receiving data from heavy industry  120 . This received data may be processed as described herein using processors and servers  834 . The processed data, or unprocessed data, may be stored in repository storage  836 . The data may then be accumulated on a data storage device  838 . 
     Outlier analysis is generally performed on unweighted data with no variable to model to. This analysis may include identifying and/or calculating a set of classifying characteristics. Referring now to  FIG. 9 , in the case of a loss or claim  910  the classifying characteristics  920  may include loss state  930 , claimant age  940 , injury type  950 , and reporting  955 . 
     Additionally, these classifying characteristics  920  may be calculated by comparing a discrete observation  960  against a benchmark  970  and use the differences  980  as the characteristic. For example, the number of line items on a bill compared to the average for bills of the type may be determined. A ratio may be used so that if the average number of line items is 4 and a specific bill has 8, the characteristic may be the ratio, in the example a value of 2. 
     An algorithm may be used to group the target, such as claims for example, into sets with shared characteristics. Each group or cluster of data may be profiled and those that represent sets of observations that are atypical are labeled as outliers or anomalies. A record is made for each observation with all of the classifying characteristics, and values used to link the record back to the source data. The label for the cluster that the observation belonged to, whether it is normal or an outlier with a data of classification is recorded. 
     Outlier engine  830  may be used, for example, to utilize characteristics such as binary questions, claim duration, and peer group metric to measure the relative distance from a peer group. This enables outlier engine  830  to identify claims that have high ratio and K means clustering. 
     For example, when performing invoice analytics on doctor invoices to check for conformance including determining if doctors are performing the appropriate testing, a ratio of duration of therapy to average duration therapy may be utilized. A score of 1 may be assigned to those ratios that are the same as the average, a score of 2 may be assigned to those ratios that are twice as long and 0.5 assigned to the ratios that are half as long. Outlier engine  830  may then group data by the score data point to determine if a score of 2 finds similarity with other twice as long durations, which classification enables the data to provide other information that may accompany this therapy including, by way of example, a back injury. 
     The ratio of billed charges may also be compared to the average. A similar scoring system may be utilized where a score of 1 is assigned to those ratios that are the same as the average, a score of 2 may be assigned to those ratios that are twice as high and 0.5 assigned to the ratios that are half as much. Similarly, the ratio of the number of bills/claim to average may also be compared and scored. The measure of whether a procedure matches a diagnosis may also be compared and scored. The billed charges score may be used based on the diagnosis to determine if a given biller is consistently providing ratios that are twice as high as others. 
     According to one aspect, things that do not correlate may be dropped as unique situations. In a perfect scenario, collinearity may be achieved with mutually exclusive independent variables. That is duplicative variables that correlate in their outcomes may be dropped. Outlier engine  830  may also utilize a predictive model. As is generally understood in the art, a predictive model is a model that utilizes statistics to predict outcomes. Alternatively, in an implementation, predictive modeling may not be based on statistics, instead including association rules, sequence mining, and linear/non-linear optimization. Outlier engine  830  may use a predictive model that may be embedded in workflow, for example. Such a model may include biases of end users and priorities and/or may include a leap of faith. Such a model may benefit from reuse because the model may be honed based on previous uses. The output of outlier engine  830  may be provided to the analytics of the supply chain. 
     Referring now also to  FIG. 10 , there is illustrated an example data system for the outlier engine  830 . The outlier engine  830  becomes, along with the data available from source systems and characteristics derived through text mining, a source of information describing a characteristic  1010  including an event  210 , person  310 , place  410  and/or thing  510  that is the subject of a predictive model. Predictor variables may include source systems  1022 , text mine  1030 , and outlier  1040 . Using claim as an example, source systems  1020  may include loss state  930 , claimant age  940 , injury type  950  and reporting  955  including the channel the claim was reported through such as phone, web, or attorney call, for example. 
     Data may be considered standard data from text mine  1030 . Using claim as an example, prior injury  1033 , smoking history  1035 , and employment status  1038  may be included. 
     Outlier  1040  characteristics may also be included. Outlier characteristics  1040  may include physician/billing information  1043 , such as if the physician is a 60-70% anomaly biller, treatment pattern  1045 , such as if the treatment pattern is an anomaly, and the agency  1048 , such as if the agency is an outlier for high loss ratio insureds. 
     Referring now also to  FIG. 11 , outlier engine  830  is illustrated with a normative area  1110  wherein all target characteristics are typical, a first area of interest  1120  wherein there is an unusual procedure for the provider specialty and an unusual pattern of treatment for the injury, a second area of interest  1130  wherein there is an unusual number of invoices and the presence of co-morbidity/psycho-social condition, and outlier  1140  that is too far from any clump and includes a unique profile. 
     For example, an invoice belonging to a set may be analyzed and presented with characteristics of that invoice including doctor and treatment for example as well as the injury suffered. The axes shown in  FIG. 11  may be defined by attributes of the group of invoices. Data may be grouped based on sharing attributes or qualities, like duration of treatment for an injury for example. Other data may fall in between groups as described. The groupings of data become an important attribute of the data fitting that group. 
     Referring now also to  FIG. 12 , a block diagram of text factory  840  is shown. Text factory  840  includes a text mining platform  1250  that receives information from a semantic rules database  1210 , a claim system  1220 , one or more text input data sources  1230  (e.g., internal to an insurance enterprise), and/or external third party text data  1232  (e.g., reports issued by the National Highway Safety Board). The text mining platform  1250  might be, for example, associated with a personal computer (PC), laptop computer, an enterprise server, a server farm, and/or a database or similar storage devices. The text mining platform  1250  may, according to some embodiments, be associated with a business organization or an insurance provider. 
     According to some embodiments, an “automated” text mining platform  1250  may access semantic rules in the semantic rules database  1210  to mine the information from the claim system  1220  and/or the other text input data sources  1230 . As used herein, the term “automated” may refer to, for example, actions that can be performed with little or no human intervention. 
     The text mining platform  1250  may store information into and/or retrieve information from the semantic rules database  1210  and/or a text mining result database that is output to various external insurance applications  1260  (e.g., software applications or devices associated with subrogation, fraud detection, and/or recovery factor analysis). The semantic rules database  1210  may be a locally stored relational database or reside remote from the text mining platform  1250 . The term “relational” may refer to, for example, a collection of data items organized as a set of formally described tables from which data can be accessed. Moreover, a Relational Database Management System (“RDBMS”) may be used in connection with any of the database tables described herein. According to some embodiments, a graphical administrator interface  1270  may provide the ability to access and/or modify the semantic rules database  1210  via the text mining platform  1250 . The administrator interface  1270  might, for example, let an administrator define terms, dictionaries, mapping rules, etc. associated with text mining. The data sources  1230 ,  1232  may be thought of as “publishers” of information to be consumed by the text mining platform  1250 , and the insurance applications  1260  may be considered “subscribers” to information created by the text mining platform  1250 . Moreover, note that the text mining platform  1250  may operate asynchronously and/or independently of any insurance application  1260 . 
     Although a single text mining platform  1250  is shown in  FIG. 12 , any number of such devices may be included. Moreover, various devices described herein might be combined according to embodiments of the present invention. For example, in some embodiments, the text mining platform  1250  and semantic rules database  1210  might be co-located and/or may comprise a single apparatus. 
       FIG. 13  illustrates the assembly  140  of the data. This includes the many levels of analytics used in business including business intelligence and data mining, optimization and simulation, and forecasting. Descriptive analytics  1310  may be used. Descriptive analytics  1310  is the business intelligence and data mining aspect of analytics and includes the management and reporting of the analytics. Generally, descriptive analytics  1310  may view data and analyze past events for insight about approaching the future. Descriptive analytics  1310  may analyze past performance by mining historical data to look at the reasons for past success and failures, such as post-mortem analysis. Descriptive models may be used to quantify relationships in data to groups by grouping portions of the data including the many different relationships that may exist within the data. 
     Descriptive analytics  1310  may include standard management reports and information referred to as management information systems (MIS). This reports what is and what has happened. The descriptive analytics  1310  may be metric heavy having many measures of performance of the business with a limited set of attributes used to filter and display the metrics. Common examples include daily performance dashboards, financial results reports, inventory and aging reports, scorecards of relative performance among a set of employees, partners with a shared perspective. 
     Specifically,  FIG. 13  may include data  1312  in repositories that may be accumulated into one or a singularly accessible storage  1314  to be processed according to the descriptive analytics method on a processor or other mainframe  1316 . The resulting analytics  1318  result from this processing. 
     Similarly,  FIG. 13  may include data  1322  in repositories that may be accumulated into one or a singularly accessible storage  1324  to be processed according to the diagnostic analytics method on a processor or other mainframe  1326 . The resulting analytics  1328  result from this processing. 
       FIG. 13  may include data  1332  in repositories that may be accumulated into one or a singularly accessible storage  1334  to be processed according to the predictive/prescriptive analytics method on a processor or other mainframe  1336 . The resulting analytics  1338  result from this processing. 
     The present system may use dashboards to represent performance by showing revenue for a given year, debt-to-equity and return on equity as well as net profit margin and gross profit margin comparisons, represent financial results report showing revenue, earnings and dividends, profitability balance sheet, cash flow, industry, and other facts, represent inventory and aging report showing invoices and balances due, and represent a scorecard of relative performance page showing signups, conversions and % visitors, by way of non-limiting examples only. Additionally, outputs and scorecards may represent other facets of the present system. For example,  FIG. 14  illustrates a scorecard for events, such as events  210 , for example, being plotted based on a first score, score 1, against a second score, score 2. Score 1 is illustrated as ranging from 0 to 1000. Similarly, score 2 ranges from 0 to 1000. Groupings of the events may be understood or realized using such a dashboard. Similar dashboards and scorecards may be presented for person  310 , place  410 , and thing  510 , by way of example. 
       FIG. 15  illustrates a dashboard representing total dollars plotted against service year and includes as an overlay outlier percent by year. This dashboard may enable a relationship to be determined between outlier percent and total dollars. As would be understood, other variables may be presented in such a dashboard to enable relationship there between to be realized. One such relationship shown in  FIG. 15  includes outlier percentage decreasing as total dollars increases in year 2014. 
       FIG. 16  illustrates a dashboard representing volume of a characteristic  1010  plotted against score for three respective groups. In this illustration, group 1 demonstrates a bell curve of volume centered approximately centered on a score of 400. Group 2 demonstrates increased volume at lower scores. Group 3 demonstrates a fluctuating volume related to score. 
       FIG. 17  illustrates a dashboard of a pie chart of outlier traits. In this illustration, the pie chart is distributed between two portions represented as blue and orange. The orange portion includes three outlier traits, trait 1, trait 2, and trait 3, with trait 3 being most represented followed by trait 2 at approximately 50% of trait 3, and trait 1 at approximately 50% of trait 2. 
     Assembly  140  may also include diagnostic analytics  1320 . Diagnostic analytics  1320  includes the “why” and “what if” aspects of the data analytics. 
     Assembly  140  may also include predictive and/or prescriptive analytics  1330 . Predictive and/or prescriptive analytics  1330  may include the type of information that may be included to make or prevent an event. Predictive and/or prescriptive analytics  1330  may include analytics involving forecasting. Predictive and/or prescriptive analytics  1330  includes the optimization and simulation portion of the analytics. 
     Predictive analytics  1330  includes the processes that turn data into actionable information. Predictive analytics  1330  may use data to determine the probable future outcome of an event of the likelihood of certain things occurring. Predictive analytics  1330  may include any number of statistical techniques including modeling, machine learning, data mining, game theory where there is current and historical facts that are analyzed to make predictions about future events. This may include using models to synthesize patterns in the data to identify risks and opportunities. 
     Prescriptive analytics  1330  may include the synthetization of data, rules, to make predictions and provide suggestions to take advantage of the predictions. Prescriptive analytics  1330  goes beyond predictive analytics  1330  by not only predicting future outcomes but also suggesting action to leverage the predictions and identifying the implications of each decision. 
     Diagnostic analytics  1320  starts during the descriptive analytics  1310  and extends into predictive analytics  1330 . Diagnostic analytics  1320  gets into root cause analysis and data discovery and exploration. Diagnostic analytics  1320  aids in understanding why things happened to enable improvement in business operations and processes. Diagnostic analytics  1320  provides the ability to drill down into all types of data coming from many different sources. 
       FIG. 18  illustrates the components of product  150 . Specifically, product  150  may include business intelligence (BI) reporting  1810  and scoring mart  1820 . 
     BI reporting  1810  may include forms of human delivery reporting. BI reporting  1810  may include housing the data in data storage  1812  and accumulating the data in a singular storage  1814 . The data may be provided in spreadsheets  1816  and/or other known reporting mechanisms. Spreadsheets  1816  may include other reporting that provides the business intelligence to humans, for example. 
     Scoring mart  1820  may contain the final output of a broad set of scoring processes used in assembly  140 . This scoring mart  1820  may include the predictive/prescriptive analytics  1330  described hereinabove. Scoring mart  1820  may utilize a rules-based scoring system and may aggregate the scores summarizing from one entity to another. Scoring mart  1820  may include attributes or metadata associated with the score as will be discussed herein below. 
     Scoring mart  1820  may include data storage  1822  for housing data from assembly  140 . The data may be accumulated within a data repository  1824  and scores may be output per queries from a database or by reporting  1829 . Reporting  1829  may generally take the form of any transfer to other computers or systems. 
     Referring now additionally to  FIG. 19 , which illustrates exemplary items that may be assigned a ranking in the scoring mart  1820 . Items may include claims  1910 , policies  1920 , agents  1930 , claimants  1940 , and providers  1950 . Scoring mart  1820  may be the repository to archive the rank of any item within the system. As an example, a fraud model may score claims  1910  based on the priority to engage a special investigator to investigate that claim for fraud, waste and abuse. Claims  1910  may be distributed based on priority, and that priority may then be converted into a score and stored in scoring mart  1820 . Scoring mart  1820  may hold scores for any item that may be assigned a ranking within the present system. 
     Additionally, scoring mart  1820  may hold more than one score per item. For example, a fraud score may be stored for an item based on a 30 day score and another on a 3 year score, as the fraud score may be different. This may occur because the score for the first 30 days was lower because the likelihood or level of suspected fraud was low, and then the score for fraud increased as additional actions occurred over the next 3 years. The scoring mart  1820  may be used to rank doctors within the system each quarter, for example. Underwriting may wish to score agencies using 12 reasons. 
       FIG. 20  illustrates a method  2000  performed in the scoring mart  1820 . Method  2000  may include running models at step  2010 . Once the models are run at step  2010 , method  2000  may produce a ranking based on the purpose of that model at step  2020 . Records are created in the scoring mart  1820  for each item scored with the date of the, the identity of what is scored, the identity of the specific scoring process, the raw score, a normalized score to simplify consumption rules, a value of whether this score is part of a holdback sample, a date range over which the score is considered to be valid at step  2030 . At step  2040 , method  2000  may determine what qualifies for holdback. The score may be verified against a prior score at step  2050  when the scoring mart  1820  searches its history to determine if there is a prior score for the scored item and scoring process. If the scoring mart  1820  determines there is a prior a score, that prior score may be expired as of the day prior to the new record. At step  2060 , scoring mart  1820  may insert the new record with a valid date range, generally starting with the insert date and potentially have no end date. 
     Referring now to  FIG. 21 , there is illustrated a data set associated with scoring mart  1820 . As shown in  FIG. 21 , the data may include a date of the score  2110 , the model  2120 , the line of business  2130 , the entity type  2140 , the entity identification  2150 , the normalized score  2160 , and the holdback  2170 . The line of business  2130  may provide a tag related to the line of business being represented and the model being used. Entity type  2140  identifies the business entity associated with the score. Entity type  2140  may include the claim, policy, TIN, agency, or the like. Entity identification  2150  identifies the element that the score attaches to in the format determined by entity type  2140 . Normalized score  2160  may be a score within the range 0-999. In addition, a holdback flag  2170  allowing scores to be reserved as control for measurement. 
       FIG. 22  illustrates an example batch lookup of scores within scoring mart  1820 . As illustrated in  FIG. 22 , scores that meet a set of criteria may be looked up in batch. Further, those scores may be used for workflow triggers and reporting. For example, in  FIG. 22 , a search for all scores for line of business  2130  of worker&#39;s compensation (WC) and a model  2120  of large losses having a normalized score  2160  of a value greater than 500 not identified with a holdback  2170  may be performed. The result of a batch lookup given these parameters may be data points  2210 . In this batch lookup, the other data points  2220 ,  2230 ,  2240 ,  2250 ,  2260 ,  2270  may not be included in the result of the batch lookup  2210 , based on at least of model  2120 , line of business  2130 , normalized score  2160 , and holdback  2170 . Data points  2220 ,  2270  may not be included in the batch lookup result because of each of model  2220 , line of business  2130 , normalized score  2160  is not positively identified. Data point  2230  may not be included in the batch lookup result because the normalized score  2160  is not greater than the requested  500 . Data point  2240  is not included at least because holdback  2170  is positive. Data points  2250 ,  2260  may not be included in the batch lookup result because normalized score  2160  is not positively identified as being greater than 500. 
       FIG. 23  illustrates an example batch lookup of scores within scoring mart  1820 . As illustrated in  FIG. 23 , a search for all scores that meet a set of criteria may be looked up in real-time. Further, those scores may be used for workflow triggers and reporting. For example, in  FIG. 23 , a search for all scores for line of business  2130  of WC and a model  2120  of large loss having a normalized score  2160  of a value greater than 500 not identified with a holdback  2170  may be performed. The result of a real-time lookup given these parameters may be data points  2310 . In this real-time lookup, the other data points  2320 ,  2330 ,  2340 ,  2350 ,  2360 ,  2370  may not be included in the result of the real-time lookup  2310 , based on at least of model  2120 , line of business  2130 , normalized score  2160 , and holdback  2170 . Data points  2320 ,  2370  may not be included in the real-time lookup result because of each of model  2120 , line of business  2130 , normalized score  2160  is not positively identified. Data point  2330  may not be included in the real-time lookup result because the normalized score  2160  is not greater than the requested  500 . Data point  2340  is not included at least because holdback  2170  is positive. Data points  2350 ,  2360  may not be included in the real-time lookup result because normalized score  2160  is not positively identified as being greater than 500. 
       FIG. 24  illustrates an aspect of scoring mart  1820  allowing identification of the “speed of change” based scores. As shown in  FIG. 24 , scores that exhibit a rate of change over time that is faster than a determined criterion may be identified. For example, as identified in  FIG. 24 , a search for all scores for line of business  2130  of WC and a model  2120  of large loss having a normalized score  2160  with a slope greater than 10 not identified with a holdback  2170  may be performed. The result in this example may include data points  2410 ,  2420  having identical entity identification  2150  value of 23456. Data point  2410  identifies the normalized score  2160  as a value of 400 on 6/1/2014. Data point  2420  identifies the normalized score  2160  as a value of 700 on 6/30/2014. The slope of entity identification  2150  value of 23456 may be calculated based on the equation: 
       RISE/RUN=(700−400)/(6/30/2014−6/1/2014)=300/29=10.3.
 
     Since a slope of 10.3 is greater than the requested slope of 10, data points  2410 ,  2420  entity identification  2150  value of 23456 are returned. 
       FIG. 25  illustrates an aspect of scoring mart  1820  allowing a return of entities with more than one score. As shown in  FIG. 25 , claims that have a high score on more than one model at a time may be identified. For example, as identified in  FIG. 25 , a search for all claims for line of business  2130  of WC and a model  2120  of large loss having a normalized score  2160  greater than 500 and a model  2120  of fraud having a normalized score  2160  greater than 600. The result in this example may include data points  2510 ,  2520  having identical entity identification  2150  value of 23456. Data point  2510  includes a model  2120  of large loss having a normalized score  2160  value of 700 (greater than 500) while data point  2520  includes a model  2120  of fraud having a normalized score  2160  value of 650 (greater than 600). 
       FIG. 26  illustrates an aspect of scoring mart  1820  allowing the aggregation of scores from one entity onto another entity. As shown in  FIG. 26 , scores that exhibit a rate of change over time that is faster than a criterion may be referred. For example, as identified in  FIG. 26 , the normalized score  2160  of data points included in group  2610  is 250, 700, 200, 650, and 300. The average of these normalized scores  2160  is a value of 420 as shown for data point  2620 . The average normalized score  2160  of a model  2120  of large loss with entity type  2140  of claims for a given policy in a time period may calculated and that normalized score  2160  may be entered as a score at the policy level, data point  2620 . Similarly, although not shown, the average fraud score across all claims for an agent in a time period may be performed and that average score may be inserted as a score at the agency level. 
       FIG. 27  illustrates the output through distribution  160 . Distribution  160  may include internal reporting  2710 , underwriting  2720 , fraud investigation  2730 , and claims administration  2740 . 
     Internal reporting  2710  may be the external interface to clients of a given business. Internal reporting  2710  may include the web interface portal of a client, for example. Alternatively or additionally, internal reporting  2710  may include representatives that work in offices and interact with potential clients. 
     Internal reporting  2710  may include a communications interface  2712  for transceiving data from product  150 . This data may be included in a format from product  150  such as spreadsheets  2714  and/or other known reporting formats. The internal reporting  2710  may then be used to process an output  2719 . 
     Underwriting  2720 , fraud investigation  2730 , and claim administration  2740  represent specific systems within a client. These systems include and represent any of a myriad of workflow platforms that may exist in a business or corporate environment. Underwriting  2720  may represent the fraud system within an insurance company. Fraud investigation  2730  may represent the underwriting system found within an insurance company. Claim administration  2740  represents the claims handling system found within an insurance company. 
     Underwriting  2720  may include an interface  2722  for transceiving information with product  150 . This information may then be stored in a storage unit  2724 . Processors  2725  may act on the stored data and output underwriting conclusions  2729 . 
     Fraud investigation  2730  may include an interface  2732  for transceiving information with product  150 . This information may then be stored in a storage unit  2734 . Processors  2735  may act on the stored data and output Fraud conclusions  2739 . 
     Claim administration  2740  may include an interface  2742  for transceiving information with product  150 . This information may then be stored in a storage unit  2744 . Processors  2745  may act on the stored data and output claim administration  2749 . 
       FIG. 28  illustrates a method  2800  performed in an insight supply chain. Method  2800  includes capturing data at step  2810 . This data may be data used within an organization and may be stored within the computer systems of the organization. Data may also include information gleaned during method  2800 , for example. This data may be housed within a data warehouse, a data lake, or even provided by a third party. 
     Method  2800  may also include mining the data at step  2820 . The mining of the data  2820  is the heavy industry of determining the contents of the data. Mining the data  2820  may include traditional data mining techniques, machine learning and text mining, for example. 
     Method  2800  may include staging the data at step  2830 . Staging the data may include reorganizing the data to accelerate the use of the data or the analytics performed on the data. This may include an analytical data mart (ADM), an outlier engine, and a text factory. Staging the data at step  2830  is the data management step of method  2800 . 
     Method  2800  may include assembling the data at step  2840 . Assembling at step  2840  may include performing analytics on the data. 
     Method  2800  may include producing the data at step  2850 . Producing the data at step  2850  may include reporting the data, including traditional human delivery reporting, or providing the data in a scoring mart. 
     Method  2800  may include distributing the data at step  2860 . Distributing at step  2860  may include reporting the SharePoint or self-help, or providing the information to the myriad of workflow platforms included within the business organization, such as a fraud system, underwriting system, and claims handling system. 
     As used herein, devices, such as the score mart and outlier engine and any other device described herein, may exchange information via any communication network which may be one or more of a telephone network, a Local Area Network (“LAN”), a Metropolitan Area Network (“MAN”), a Wide Area Network (“WAN”), a proprietary network, a Public Switched Telephone Network (“PSTN”), a Wireless Application Protocol (“WAP”) network, a Bluetooth network, a wireless LAN network, and/or an Internet Protocol (“IP”) network such as the Internet, an intranet, or an extranet. Note that any devices described herein may communicate via one or more such communication networks. 
       FIG. 29  shows an example computing device  2910  that may be used to implement features describe above with reference to  FIGS. 1-28 . The computing device  2910  includes a processor  2918 , memory device  2920 , communication interface  2922 , peripheral device interface  2912 , display device interface  2914 , and storage device  2916 .  FIG. 29  also shows a display device  2924 , which may be coupled to or included within the computing device  2910 . 
     The memory device  2920  may be or include a device such as a Dynamic Random Access Memory (D-RAM), Static RAM (S-RAM), or other RAM or a flash memory. The storage device  2916  may be or include a hard disk, a magneto-optical medium, an optical medium such as a CD-ROM, a digital versatile disk (DVDs), or Blu-Ray disc (BD), or other type of device for electronic data storage. 
     The communication interface  2922  may be, for example, a communications port, a wired transceiver, a wireless transceiver, and/or a network card. The communication interface  2922  may be capable of communicating using technologies such as Ethernet, fiber optics, microwave, xDSL (Digital Subscriber Line), Wireless Local Area Network (WLAN) technology, wireless cellular technology, and/or any other appropriate technology. 
     The peripheral device interface  2912  may be an interface configured to communicate with one or more peripheral devices. The peripheral device interface  2912  may operate using a technology such as Universal Serial Bus (USB), PS/2, Bluetooth, infrared, serial port, parallel port, and/or other appropriate technology. The peripheral device interface  2912  may, for example, receive input data from an input device such as a keyboard, a mouse, a trackball, a touch screen, a touch pad, a stylus pad, and/or other device. Alternatively or additionally, the peripheral device interface  2912  may communicate output data to a printer that is attached to the computing device  2910  via the peripheral device interface  2912 . 
     The display device interface  2914  may be an interface configured to communicate data to display device  2924 . The display device  2924  may be, for example, a monitor or television display, a plasma display, a liquid crystal display (LCD), and/or a display based on a technology such as front or rear projection, light emitting diodes (LEDs), organic light-emitting diodes (OLEDs), or Digital Light Processing (DLP). The display device interface  2914  may operate using technology such as Video Graphics Array (VGA), Super VGA (S-VGA), Digital Visual Interface (DVI), High-Definition Multimedia Interface (HDMI), or other appropriate technology. The display device interface  2914  may communicate display data from the processor  2918  to the display device  2924  for display by the display device  2924 . As shown in  FIG. 29 , the display device  2924  may be external to the computing device  2910 , and coupled to the computing device  2910  via the display device interface  2914 . Alternatively, the display device  2924  may be included in the computing device  2900 . 
     An instance of the computing device  2910  of  FIG. 29  may be configured to perform any feature or any combination of features described above as performed. In such an instance, the memory device  2920  and/or the storage device  2916  may store instructions which, when executed by the processor  2918 , cause the processor  2918  to perform any feature or any combination of features described above as performed. Alternatively or additionally, in such an instance, each or any of the features described above may be performed by the processor  2918  in conjunction with the memory device  2920 , communication interface  2922 , peripheral device interface  2912 , display device interface  2914 , and/or storage device  2916 . 
       FIG. 30  shows a mobile device  3000  that is a more specific example of the computing device  2910  of  FIG. 29 . The mobile device  3000  may include a processor (not depicted), memory device (not depicted), communication interface (not depicted), peripheral device interface (not depicted), display device interface (not depicted), storage device (not depicted), and touch screen display  3024 , which may possess characteristics of the processor  2918 , memory device  2920 , communication interface  2922 , peripheral device interface  2912 , display device interface  2914 , storage device  2916 , and display device  2924 , respectively, as described above with reference to  FIG. 29 . The touch screen display  3024  may receive user input using technology such as, for example, resistive sensing technology, capacitive sensing technology, optical sensing technology, or any other appropriate touch-sensing technology. As shown in  FIG. 30 , the touch screen display  3024  may display a notification/alert document, with characteristics of any or any combination of documents described above with reference to  FIGS. 1-28 . The touch screen display  3024  may receive input from a user of the mobile device  3000 , for filling in a notification/alert document shown in the touch screen display  3024 . 
     Referring again to  FIG. 29 , an instance of the computing device  2910  may alternatively or additionally be configured to perform any feature or any combination of features described above as performed. In such an instance, the memory device  2920  and/or the storage device  2916  may store instructions which, when executed by the processor  2918 , cause the processor  2918  to perform any feature or any combination of features. In such an instance, the processor  2918  may perform the feature or combination of features in conjunction with the memory device  2920 , communication interface  2922 , peripheral device interface  2912 , display device interface  2914 , and/or storage device  2916 . 
     Alternatively or additionally, an instance of the computing device  2910  may be configured to perform any feature or any combination of features described above as performed. In such an instance, the memory device  2920  and/or the storage device  2916  may store instructions which, when executed by the processor  2918 , cause the processor  2918  to perform any feature or any combination of features described above as performed. In such an instance, the processor  2918  may perform the feature or combination of features in conjunction with the memory device  2920 , communication interface  2922 , peripheral device interface  2912 , display device interface  2914 , and/or storage device  2916 . 
     Alternatively or additionally, an instance of the computing device  2910  may be configured to perform any feature or any combination of features described above as performed. In such an instance, the memory device  2920  and/or the storage device  2916  may store instructions which, when executed by the processor  2918 , cause the processor  2918  to perform any feature or any combination of features described above as performed. In such an instance, the processor  2918  may perform the feature or combination of features in conjunction with the memory device  2920 , communication interface  2922 , peripheral device interface  2912 , display device interface  2914 , and/or storage device  2916 . 
     Although  FIG. 29  shows that the computing device  2910  includes a single processor  2918 , single memory device  2920 , single communication interface  2922 , single peripheral device interface  2912 , single display device interface  2914 , and single storage device  2916 , the computing device may include multiples of each or any combination of these components  2918 ,  2920 ,  2922 ,  2912 ,  2914 ,  2916 , and may be configured to perform, mutatis mutandis, analogous functionality to that described above. 
     As used to herein, the term “document” broadly refers to and is not limited to a paper document, an electronic file defining a paper document, a social media post, an SMS, an email, or any electronic medium of communication used to deliver a message. 
     As used herein, the term “processor” broadly refers to and is not limited to a single- or multi-core processor, a special purpose processor, a conventional processor, a Graphics Processing Unit (GPU), a digital signal processor (DSP), a plurality of microprocessors, one or more microprocessors in association with a DSP core, a controller, a microcontroller, one or more Application Specific Integrated Circuits (ASICs), one or more Field Programmable Gate Array (FPGA) circuits, any other type of integrated circuit (IC), a system-on-a-chip (SOC), and/or a state machine. 
     As used to herein, the term “computer-readable medium” broadly refers to and is not limited to a register, a cache memory, a ROM, a semiconductor memory device (such as a D-RAM, S-RAM, or other RAM), a magnetic medium such as a flash memory, a hard disk, a magneto-optical medium, an optical medium such as a CD-ROM, a DVDs, or BD, or other type of device for electronic data storage. 
     Although the methods and features described above with reference to  FIGS. 1-18  are described above as performed using the system  100  of  FIG. 1 , the methods and features described above may be performed, mutatis mutantis, using any appropriate architecture and/or computing environment. Although features and elements are described above in particular combinations, each feature or element can be used alone or in any combination with or without the other features and elements. For example, each feature or element as described above with reference to  FIGS. 1-18  may be used alone without the other features and elements or in various combinations with or without other features and elements. Sub-elements of the methods and features described above with reference to  FIGS. 1-18  may be performed in any arbitrary order (including concurrently), in any combination or sub-combination.