Patent Publication Number: US-2005120009-A1

Title: System, method and computer program application for transforming unstructured text

Description:
CROSS-REFERENCE TO RELATED APPLICATION  
      The present application claims the benefit of a provisional application entitled “System and Method for Transforming Unstructured Text” which was filed with the U.S. Patent Office on Nov. 21, 2003 and assigned Ser. No. 60/524,274. The entire contents of the foregoing provisional patent application are hereby incorporated by reference. 
    
    
     BACKGROUND OF THE INVENTIONS  
      1. Technical Field  
      The present disclosure relates to systems, methods and computer program applications for using information. More particularly, the present disclosure relates to an improved system, method and computer program product for gathering, organizing and presenting information to allow efficient review and use.  
      2. Background of the Invention  
      Modern workers, professional individuals, and the like have an unprecedented ability to access information. Frequently, the success of these individuals relies on their ability to effectively process the large amounts of available information. Often, when collecting information analysis in support of making a decision, so many documents are available that the typical individual and/or staff cannot effectively and/or accurately review everything. Typically, these documents are in unmanageable formats that prevent reformatting. Moreover, modern searching methods, such as search engines used on the Internet, yield many irrelevant documents. Drawbacks of these types extend across many platforms and business areas such as information technology, law, healthcare and others.  
      Accordingly, there is a need for a system, method, and computer program application for searching available information sources, which includes unstructured text sources of information, interpreting and/or analyzing information contents, and presenting relevant information to the user.  
     SUMMARY OF THE INVENTION  
      The present disclosure provides a system, method and computer program that overcomes many of the prior art problems associated with gathering, sorting and using information, and more particularly unstructured information. The present disclosure is directed to an exemplary system, method and computer program application whose contents cause a computer system to perform a method for transforming unstructured text.  
      A system for transforming unstructured text, according to an exemplary embodiment of the present disclosure, includes (i) means for defining a search query, (ii) means for searching a plurality of information sources so as to collect data relevant to the search query, (iii) means for processing the collected data so as to identify and transform unstructured text into structured text, (iv) means for reporting results to an end user, and (v) means for storing results for reuse.  
      In another exemplary embodiment of the present disclosure, a method for performing event analysis is provided including the steps of: (i) defining at least one event (e.g., a business related event), (ii) searching a plurality of information sources including unstructured text sources, (iii) collecting data from said information sources, (iv) processing said collected data using a plurality of passes, (v) identifying occurrences of said event, (vi) analyzing identified occurrences of said event, and (vii) generating output based on relevant information pertaining to said event. Advantageously, the information sources include public, semi-public and private sources. In addition, the processing passes are selected from a group consisting of tokenizing, initializing, converting sub-trees to prose-like strings, resolving company names, setting XML tags, tagging white spaces, setting regions, marking HTML tags, removing HTML tags, identifying entities, tokenizing domains, reducing text, marking common entities, converting time, marking common words, converting numbers, recognizing simple business events, marking copyrights, marking headlines, extracting business entities, revising the knowledge base, generating and unifying metadata, categorizing companies, building causal phrases, correcting abbreviations, preparing event sentences, categorizing events, sub-segmenting information into output chosen by an end user, outputting a report to XML, and consuming XML.  
      According to still another exemplary embodiment of the present disclosure, a computer program application for advantageously causing a computer system to perform a method for transforming unstructured text is provided. The application includes (i) an identifying feature for finding one or more defined events in various sources of information, (ii) an analyzing feature for analyzing identified events so as to filter out extraneous information, (iii) a transforming feature for reducing any form of text in any sentence structure to subject-verb-object, and (iv) an output feature for outputting the identified events in a manner prescribed by an end user.  
      A beneficial feature provided by the computer program application of the present disclosure is found in its utility in predicting an event. That is, in accordance with another exemplary embodiment of the present disclosure, a method for predicting an event is provided including the steps of: (i) inputting a query so as to defining at least one event, (ii) conducting a search among a number of different information sources, (iii) collecting data from the information sources, (iv) providing the resulting data to the computer program application of the present disclosure, which employs computational linguistics to identify subject-verb-object in any form of text in any sentence construction, (v) analyzing the data via a plurality of passes so as to identify relevant information and transform the relevant information into a raw text format, and (vi) exporting the relevant information to a database so as to be usable as input for a discrete choice probability model for predicting the probability of the event.  
      It should be appreciated that the system, method and computer program application of the present disclosure can be implemented and utilized in numerous ways, including without limitation as a process, an apparatus, and/or a device for applications now known and later developed. These and other features, advantages and benefits of the disclosed system, method and computer program application will be apparent from the detailed description which follows, particularly when reviewed in conjunction with the figures appended hereto. 
    
    
     BRIEF DESCRIPTION OF THE FIGURES  
      To assist those of skill in the art to which the subject matter of the present disclosure appertains to make and use the disclosed system, method and computer program application, reference is made to the accompanying figures, wherein:  
       FIG. 1  is an overview of an environment in which an exemplary embodiment of the system, method and/or computer program application of the present disclosure may be used;  
       FIG. 2  is a layered architecture of an exemplary embodiment in accordance with the present disclosure;  
       FIG. 3  is a block diagram of the components of a system in accordance with another illustrative embodiment of the present disclosure;  
       FIG. 4  is a simplified diagram of the feature components of a computer program application according to an illustrative embodiment of the present disclosure;  
       FIG. 5  is flowchart diagram providing an overview of an exemplary text processing operation in accordance with an illustrative aspect of the present disclosure;  
       FIG. 6  is a graphical representation of a probability model for predicting a business related event according to an illustrative aspect of the present disclosure;  
       FIG. 7  is a flowchart diagram according to an exemplary utilization of the probability model of  FIG. 6  for predicting a business related event according to an illustrative aspect of the present disclosure;  
       FIG. 8  is an exemplary architecture view illustrating an implementation of the predictive functionality according to an illustrative aspect of the present disclosure;  
       FIG. 9  is an exemplary user interface input screen according to the present disclosure;  
       FIG. 10  is a screenshot of an exemplary input/output screen according to the present disclosure;  
       FIG. 11  is a screenshot of an exemplary output screen in accordance with the present disclosure;  
       FIG. 12  is a screenshot of another exemplary output screen according to the present disclosure;  
       FIG. 13  is another exemplary output according to the present disclosure;  
       FIG. 14  is another illustrative screenshot of an exemplary input/output screen according to the present disclosure;  
       FIG. 15  is still another exemplary output according to the present disclosure;  
       FIG. 16  is still another illustrative screenshot of an exemplary input/output screen according to the present disclosure;  
       FIG. 17  is yet another illustrative screenshot of an exemplary input/output screen according to the present disclosure; and  
      FIGS.  18  to  25  are still further exemplary outputs according to other illustrative aspects of the present disclosure. 
    
    
     DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENT(S)  
      As noted above, the present disclosure is directed to an exemplary system, method and computer program that may be used to gather, sort and use unstructured information. The advantages, and other features associated with the exemplary system, method and computer program disclosed herein, will become more readily apparent to those having ordinary skill in the art from the following detailed description of certain preferred embodiments taken in conjunction with the drawings which set forth representative embodiments of the present invention and wherein like reference numerals identify similar structural elements.  
      For the purpose of explanation rather than limitation, specific details are set forth such as the particular environment, architecture, interfaces, techniques, etc., in an effort to provide a thorough understanding of the present invention. For purposes of simplicity and clarity, detailed descriptions of well-known devices, circuits and methods have been omitted.  
      Referring to  FIG. 1 , there is shown an overview of an exemplary client/server environment in which one or more client work stations or computers are utilized to access an application  10  hosted on a server, for example. While the client computers of  FIG. 1  are of general purpose, it will be appreciated that custom hardware and/or software also may be employed for the purpose of implementing different operative aspects of the present disclosure. As shown, the application  10  is operatively connected to public sources, e.g., via the Internet, semi-public sources, e.g., via subscription services, and private sources, e.g., via internal e-mail, hard-drive, network, applications, databases and the like.  
      Referring to  FIG. 2 , there is shown a layered architecture in accordance with an illustrative aspect of the present disclosure. As shown, a storage layer  12 , a logic layer  14 , a communications/collaboration layer  16 , and a user interface layer  18  are operatively associated with a web server  20  and a web service engine  22 . The web server  20  and web service engine  22  are, in turn, operatively associated with auxiliary network applications  24  and the application  10  is integrally associated with the user interface layer  18 .  
      The environment and architecture of  FIGS. 1 and 2  preferably make use of computer systems that include at least means for processing information, means for storing static and/or dynamic information, means for communicating and/or receiving information, a user input interface, and an operating system for controlling the allocation of system resources and performing tasks, such as processing, scheduling, memory management, networking, and I/O services, among other things. It should be understood that other processing systems equally may be used, including systems having architecture dissimilar, at least in part, to that which is shown in  FIGS. 1 and 2 . In addition, it should be noted that client computers within the context of the present disclosure includes, but is not limited to, laptop computers, mobile phones, and other mobile computing devices, such as personal digital assistants (PDAs), personal communication assistants (PCAs), electronic organizers, interactive TV/set-top box remote control, or any duplex interactive devices capable of Internet access.  
      Referring to  FIG. 3 , there is shown an exemplary embodiment of the present invention which includes a system  30  having a user interface  32  enabling a user to define a search query, one or more public, semi-public and/or private information source connections  34  (e.g., databases, networks, Internet, etc.) to collect data relevant to a search query, a processor  36  for processing the collected data so as to, among other things, identify and transform unstructured text into structured text, a graphical output  38  (e.g., a display) for presenting results to the user, a memory  40  for storing results for reuse, and a bus  42  or other communication means facilitating communication by and between the various components of the system  30 .  
      In operation, the application  10  directs the processor  36  to search and collect information form the various information sources, then identify and interpret the collected data and impose structure on data that is otherwise unstructured. In the search mode, the user inputs one or more queries via the user interface  32  to access relevant information from the information sources. The inputting of a query may be accomplished in different ways. For example, in a preferred aspect of the present disclosure, the user may use a pointing device (e.g., a keyboard or mouse) to select predefined searching parameters. In alternative aspects of the present disclosure, the searching parameters are defined by the user and/or can be indirectly (e.g., voice recognition) entered via the user interface  32 . A search engine or browser conducts a federated search identifying and collecting information related to the query and any unstructured text is transformed to structured text so as to be more effectively utilized. More about the text transformation feature will be discussed hereinafter.  
      In a preferred aspect of the present disclosure, the application  10  is implemented as a computer program, executable within a computer system. The computer program (or computer control logic) of the present disclosure, when executed, enables the computer system to perform at least the above-discussed operations.  FIG. 4  schematically illustrates the preferred features of the computer program, which include an identifying feature  44 , an analyzing feature  46  and a transforming feature  48 . The identifying feature  44  finds one or more defined events in various sources of information  43  (e.g., web pages, news, e-mails, network, etc.). Once the events are identified, which events are defined via text, the analyzing feature  46  analyzes the results filtering out extraneous information and the transforming feature  48  reduces any form of text in any sentence structure to subject-verb-object (SVO). After the events have been identified, interpreted and transformed as appropriate, such events can be counted, graphed, charted, modeled, predicted and/or otherwise quantified or manipulated so as to enable the user to obtain any of a variety of different outputs  49 , as desired.  
       FIG. 5  is a flow chart illustrating the operation steps or transformation passes that are incorporated in the software application  10  according to a preferred aspect of the present disclosure. Although the events in this illustration are defined as business events, one skilled in the pertinent art will appreciate from the present disclosure that any of a variety of other event types equally may defined and/or used. The analyzing sequence is a series of steps or passes, each containing its own pass algorithm. As the analyzer is run over the text, each pass is taken in the order it occurs in the sequence and executes the code and rules that are contained in it. The rectangle elements represent a state, the oval elements represent tags, the diamond elements represent transform, and the octagon elements represent consume.  
      The first step or pass  50  in the analyzer sequence is tokenize by default, which is a system pass that converts the input text to a parse tree. The parse tree converts the text into a common structure so that later passes can work more efficiently with the text.  
      An initialize pass  51  sets external parameters such file types and locations for input to the analyzer as well as output types and locations as XML, relational databases and meta information. The initialize pass  51  also makes a connection to a knowledge base containing common and custom terms, proper nouns, names and other information specific to the end user. Subsequent passes can then update the knowledge base to improve the accuracy and efficiency thereof.  
      A fundamentals pass  52  converts sub-trees to prose-like strings, as in, for example, “IBM buys Lotus” to contain concepts. Finds and marks spaces and punctuation to know where sentences begin and end. The fundamental pass  52  also converts months to numbers (e.g., Jan. . . . Dec. to 1 . . . 12, respectively) for later classifying or filtering with more efficiency.  
      A name recognition pass  53  finds and resolves formal names in the text. In the present embodiment, this involves locating business names by, for example, identifying capitalizations, proper nouns or other business designators (e.g., Inc., LLP, LLC, PC, Co., etc.). Matching company names in the knowledge base is also accomplished by this pass. The name recognition pass  53  will increase references to names if new and register in the knowledge base for increased accuracy, as is “IBM” also is the same as “IBM, Inc.”, “International Business Machines”, etc.  
      A tag setting pass  54  adds tags to resolved company names for later output. The tags preferably adhere to XML standards for consumption by graphics programs outside of the application  10 . This pass is the first cut at identifying full concept business events under the hypothesis that companies as entities take action that affects others. Subsequent passes will identify the actions and then group the actions into categories.  
      A white space tagging pass  55  identifies non-essential marks such as dashes, commas, etc. that do not convey information for the purposes of the application  10 . Once these non-essential marks are tagged no further processing of theses text characters is necessary and thus processing time is saved.  
      A region setting pass  56  works to identify the main body of the text relative to other text. For example, in web pages, this means finding the central region body text and not sidebars. In news articles, it means finding the central region body text and not publisher or declaratory statements such as company or individual backgrounds or explanations.  
      Having identified the main body text, an HTML marking pass  57  operates to find web page HTML anchors so as to keep track of the main body text for processing. The HTML marking pass  57  also delineates HTML style tags for exclusion as HTML style tags do not cover information but are only about presentation. With areas marked for processing unnecessary and confusing HTML tags (e.g., the extraneous HTML style tags) can be removed by a HTML tag removing pass  58 . The application  10  efficiency is further improved by not processing text that provides no relevant information.  
      An entity identifying pass  59  through the entire text operates to identify other entities (e.g., stock exchanges, regulatory bodies, etc.). Such entities may be specified in a variety of forms (e.g., acronym or spelled out). For example, the “New York Stock Exchange” may equally be specified as “NYSE”. The entity identifying pass  59  is preferably able to determine whether the entity is a subject (an entity taking action) or an object (an entity on which an action was intended to affect). The entity identifying pass  59  also operates to resolve entities with the knowledge base and adds to the knowledge base as appropriate any new information.  
      A domain tokenization pass  60  operates to pick up industry, source author and/or date range end user cues to form domains. For example, a company such as “Johnson &amp; Johnson” can be in the industry domain “Pharmaceutical” as well as in the domain “Consumer Products” since the company makes products that fall within both industries. Source and author information provides cues for the quality of information. For example, a page from the “Wall Street Journal” would have a higher factual confidence rating than a web page of chat.  
      A text reduction pass  61  operates to reduce the overall size of the text to be processed for greater efficiency. The text reduction pass  61  prevents any extraneous text identified in any of the previous passes, and particularly, in the white space tagging pass  55 , the HTML tag removing pass  58 , and/or the domain tokenization pass  60  so as to provide improved processing efficiency.  
      Using the knowledge base and cues such as capitals, periods and the number of characters allows for a common entity marking pass  62  to find and mark common entities such as countries, cities, postal codes, days, years, etc. The common entity marking pass  62  enables an analytical use of business events to be sorted, for example, by geography and/or time.  
      A time conversion pass  63  operates to find all forms of date and times and converts them to common number form for use in a relational database. For example, MM/DD/YY may also be expressed MM/DD/YYYY or in the European version of dates YY(YY)/MM/DD, MM/DD/YYYY, etc. This pass facilitates organizing business events by time.  
      A common language marking pass  64  operates to find a number of common language words or terms. For example, in the English language terms like “it, and, the, they, them”, etc. are marked relying on an extensive set of such terms found in the knowledge base, which is updated as appropriate. A recursive portion of the common language marking pass  64  is used to determine if a preposition refers to a subject (e.g., a company) or an object (e.g., a focus of an action).  
      A number conversion pass  65  finds and converts to common basic all numerical references as in integers, thousands, millions, billions, etc. These numerical references can include contextual identifiers such as monetary symbols (e.g., $), percentages (e.g., %) or other unit symbols (e.g., 000&#39;s, m, b etc.). The common basic conversion may include division or multiplication so that filtering or sub-segmenting in the relational database can be done with accuracy and without delay.  
      A simple event pass  66  takes a first quick pass at simple events that are quite common (e.g., sell, buy, merge, launch, advertise, hire, fire, etc.) and variations on these events as identified in the knowledge base. If the event is in the same sentence as an identified company the sentence is marked for further processing towards an export of the full concept event.  
      A copyright marking pass  67  operates to find and mark copyright instructions or identifiers so as to allow end users to adhere to legal and/or contractual use of materials public or private, internal or external during processing.  
      A headline marking pass  68  uses a number of techniques to find and mark headlines or lead ideas in text. These techniques include identifying HTML tags that directly contain a headline and/or employing a technique that relies on how business content is typically written. For example, the technique can look at text taking as a default that first and last paragraphs are more important than all others, and that first and last sentences per paragraph are more important than all others. A headline summary or other general idea version of the information can thus be generated via the content-construction technique.  
      Having identified companies in prior passes, an entity extraction pass  69  operates to find and mark, for example, company products, executives, or locations. These may be the objects to which the subjects address their actions but they may also be the context or focus of the actions or events under examination. For instance, the text “IBM buys Lotus for its Notes software”, has the subject IBM taking the event buy on the object Lotus with a focus or context on “Notes software” (i.e., the reason IBM bought Lotus). The use of these entities is in sub-segmenting or further contextualizing captured business events.  
      An improve knowledge base pass  70  is used to make improvements in the knowledge base after all entities have been identified. With companies, executives, products, source, time, and geography identified, matching and variation improvements to the knowledge base are completed as appropriate.  
      A unification pass  71  is needed since the present algorithm is working across heterogeneous sources of information (e.g., web pages, news articles, emails, etc.), identification, and unification and if necessary unification of meta information is needed, including information about author, source, publication date, etc. For example, with respect to web pages, which often do not include a publication date but rather only reflect the date the reader reviews the material, the unification pass  72  identifies cues that can be found in the text body that allow a publication date to be determined with a fair degree of accuracy. For instance, if the text mentions an event in the “1 st  quarter” a publication date of February 15 th  can be used as a reasonable estimate. The more cues provided in the text, the better the estimation.  
      A categorization pass  73  operates to collect identified companies and variation of companies into common groups. For example, a common group of pharmaceutical companies such as Merck, Amgen and Roche may be the buyers of a common group of suppliers of industrial gases as in Air Products and Air L&#39;Quide. The categories established via the categorization pass  73  help to refine the directional relationship in the particular business environment under examination.  
      A causal phrase building pass  74  matches companies and events to company categories providing in a stepwise build towards the goal of capturing sentences that answer “who did what to whom”. The causal phrasing puts the final identification on the event (i.e., who is the subject and who is the object).  
      An abbreviation correction pass  75  is a recursive pass to capture and correct for company name abbreviations and match events again. The abbreviation correction pass  75  operates recursively by finding events first and locating the subject and checking if it is a new abbreviation or company. The abbreviation correction pass  75  adds new company events to categories. For example, if an event for “International Business Machines” was previously identified but “IBM” was missed as the abbreviation for this company, the abbreviation correction pass  75  may find the event “buy” and note the subject “IBM” matching it to “International Business Machines” in the knowledge base.  
      Once a full event is prepared with subject, event and object identified, a prepare event sentence pass  76  is employed to prepare the sentence and its identifiers in an XML format for consumption outside of the application  10  as, for example, in a graphics program and/or a relational database. It is noted that source, dates, author, time, and/or geography identifiers may also be present as appropriate.  
      An event categorization pass  77  operates to collect and count all events. Statistical processing and generation then follows. For example, means, modes and standard deviations among any selected group of companies and/or events allows for testing a hypothesis for level of effort in a market place between selected companies by focusing on the number of standard deviations above or below the mean for each company. Testing results can be reported or represented in a graphical manner, for example.  
      A final traverse pass  78  of the text is performed to sub-segment into user selected output. For example, if the user caress only about events to do with “mergers and acquisitions”, these types of events are identified and output as specified by the user while other events in the text such as events concerning financial reporting, are not identified and/or reported to the user.  
      An output to XML pass  79  writes out in XML format the desired companies, events, categories, dates, sources, author, etc. and full sentences for consumption by graphics routines. A consume XML pass  80  can then call various graphic routines to “see” events tabled by event categories and/or companies, by time, by source, and/or any of a variety of other parameters including statistically generated parameters or tests using the XML output provided by the output to XML pass 79 .  
      The various passes identified and described hereinabove may be implemented by programming them into functions incorporated within application programs, and programmers of ordinary skill in the pertinent art based on the teachings herein can implement such operations using customary programming techniques in languages such as, for example, C, Visual Basic, Java, Perl, C++, and the like.  
      As is apparent from the foregoing, the present disclosure provides, in part, for a computer program application to accomplish, among other things, collecting, processing and presenting a large amount of unstructured information so that an end user associated with a client computer can quickly and efficiently monitor and/or utilize a business related event. In addition, the software program of the present disclosure is preferably capable of eliminating duplicative information by, for example, comparing URL sources, employing pattern matching techniques, and/or utilizing date matching methods.  
      The end user may customize the event, entity, subject matter, combinations thereof and the like that are of interest and the knowledge base for interpreting the information may be built up from various public, semi-public and/or private sources. An example of a public source is WORDNET® available from Princeton University of Princeton, N.J. Additional examples of public sources include, the Internet (e.g., Google®, Yahoo®), news and PR (e.g., Google News®), science (e.g., Scientific America®), legal (e.g., FindLaw®), government (e.g., Patent &amp; Trademark Office, Department of Defense), and/or chat (e.g., BlogWise®). Example semi-public sources include, subscription news sources (e.g., Factiva®, Thomson®, Hoovers®, Wall Street Journal®) and example private sources include personal e-mail (e.g., Outlook®, Notes®), databases (e.g., SQL®, Lotus Notes®), group folders (e.g., shared drives), and/or personal folders (e.g., “My Documents”). It will be readily apparent to those of skill in the pertinent art from the present disclosure that any of a variety of other information sources equally may be used.  
      Turning now to FIGS.  6  to  8 , in a preferred aspect of the present disclosure, the application  10  allows users to predict events, and more particularly business related events. As demonstrated by  FIG. 6 , as the number of preparatory events increases over time towards an action horizon, the degrees of freedom (DOF) to act decreases while the probability or certainty of the action occurring increases. Hence, there is a trade off between waiting for enough events to occur (i.e., the probability of action to increase) and the decrease in the DOF to act. Typically there is a lag between an action (indicated by line  82 ) and the public becoming aware of the action (indicated by line  84 ). An increase in the speed of information decreases the lag time and thus improves the DOF to act in response to the action by the area  86  under the DOF curve  88  between line  84  and the action line  82 . However, there is also a distance between what is private awareness (indicated by line  90 ) and the action line  82 . That is, there is a lag between the time the decision makers decide to take action and the action itself. As shown, the DOF can again be increased by modeling so as to predict the private awareness horizon. In fact, the area  92  under the DOF curve  88  between line  90  and the action line  82 , which reflects the accurate prediction benefit, is far greater than the simpler area  86  reflecting the faster alert benefit. According to a preferred aspect of the present disclosure both benefits are captured.  
      By way of illustration, consider the drug industry. Prior to a new drug being launched companies must plan and expend time and resources along at least three tracks, a scientific preparation track  94 , a marking preparation track  96 , and a medical affairs preparation track  98 . As shown in  FIG. 7 , the scientific preparation track  94  includes a discovery step  100  in which a novel compound is discovered typically as a by-product of research and development, a number of intermediate phase trial steps  102 ,  104 ,  106  proving the safety and efficacy of the new drug to regulators, and a final step  108  of gaining regulatory approval. The marking preparation track  96 , for example, might include an initial step  110  of making a human trial press release, an intermediate step  112  of educating/training a sales force, and a final step  114  launching the new drug. The medical affairs preparation track  98  can include an initial step  116  of publishing a paper about the newly discovered compound, intermediate steps  118 ,  120  of presenting the new compound at an Expo and providing doctors with reprints of the published article, and a final step  122  of conducting doctor education meetings. The goal of the medical affairs preparation is to convince medical professionals of the scientific merits of the new drug.  
      Each preparatory step along the preparation tracks  94 ,  96 ,  98  either happens or it does not. Should a step happen, it is typically recorded and known publicly. If all the steps happen the probability of the new drug launch is 1. If none of the steps happen then the probability of the new drug launch is 0. Hence, the probabilities between 0 and 1 are dependent on the number of observed steps. The probability of a drug company&#39;s action (i.e., the launch of a new drug) with their preparatory steps can be modeled as follows: 
 
 S ( 1 )+ S ( 2 )+ S ( 3 )+ . . .  S ( X )= P ( A ) 
 
 where S is preparatory steps, A is the action intended by the preparatory step, and P is the probability of the action. 
 
      The quality of the input of the model is dependent on correctly identifying preparatory steps in text (structured and unstructured). Accordingly, in a preferred aspect of the present disclosure, subject-verb-object (SVO) computational linguistics is employed to look for subject-verb-object in any form of text in any sentence construction. This SVO feature depends on not just identification of words but understanding the direction of the action represented in the sentence. In essence, the SVO detection correctly interprets the direction in addition to locating the “actors” in the sentence and answering the generic question “who is doing what to whom”. Additional entities such as time and place can be extracted as well so as to supplement the where and when aspects of the action. Thus, information processed according to the present disclosure is preferably relied on as input to a discrete choice model which estimates the probability of a future company action. This approach is beneficial at least in that the input for such predictions is raw text, which has the effect of improving the quality of the output.  
      An exemplary architectural view illustrating the foregoing discussion is shown in  FIG. 8 . As shown, a query is made (box  124 ) whereby one or more events may be defined. A search is conducted (box  126 ) among a number of different information sources  128 ,  130 ,  132 . Data collected from the various information sources is provided to the application  10  so as to be analyzed (box  134 ) via a plurality of passes identifying relevant information and transforming such information into a raw text format. The relevant information is exported (box  136 ) in, for example, an XML format to a database (e.g., a relational database) (box  138 ). The relevant information may then be used as input (box  140 ) for a prediction model (box  142 ) and thereby improve the quality of output (box  144 ).  
      Turning to  FIGS. 9 through 12 , in addition to predictive functionality described above, in preferred aspects of the present disclosure, a variety of additional functionalities may be provided. For example, as shown in  FIG. 9 , in one aspect of the present disclosure, a user can define an input query using a number of predefined parameters (e.g., target type, event type, time period, etc.). Thus, the user may tailor the results by selecting the entity or entities, events, timeframe, relevancy or tolerance, and the like that are of interest. For example, as shown in  FIG. 10 , the results may be provided in a matrix summary taking into account multiple input factors. Also, as shown in  FIG. 11 , the results can be provided in the form of a hit list of hypertext links that may be sorted or ranked according to predefined descriptors. Still further, as shown in  FIG. 12 , the results may also be individually displayed with the relevant text highlighted for easy reference. It should be understood that the results may be provided in any of a variety other ways suitable to provide the user with effective and efficient means for evaluating and/or otherwise using the information.  
      With reference to  FIGS. 13 through 25 , in accordance with still another preferred aspect of the present disclosure, a variety of different outputs may be accomplished. For example, as shown in  FIG. 13 , relevant output pertaining to one or more companies can be relatively charted according to predefined parameters (e.g., marketing efforts, new product efforts, timeframe).  FIG. 14  is an exemplary screen display showing the relative space for four exemplary pharmaceutical companies (i.e., Amgen, Genzyme, Novartis, and Pfizer) demonstrating the degree to which such companies couple “New Products” with “Marketing and Sales” over a six month period. As shown, Amgen and Novartis, for the selected time frame, seem to support product launches with marketing and sales, whereas Pfizer and Genzyme appear to do notably less of such coupling. Additionally, as shown in  FIG. 15 , relevant output pertaining to one or more companies can be proportionally charted according to predefined parameters (e.g., marketing, M&amp;A, new products, technology, timeframe).  FIG. 16  is an exemplary screen display showing the proportion of effort for the four exemplary pharmaceutical companies as they execute in the marketplace over a six month period demonstrating how the companies have opted to allocate their resources to generate profits. Also, as shown via the exemplary screen display of  FIG. 17 , relevant output pertaining to one or more companies can also be trended over a defined period according to at least one selected parameter (e.g., marketing and sales, timeframe).  
      It will be readily apparent to one having skill in the pertinent art from the present disclosure that any of a variety of additional and/or alternative outputs equally may be provided and fall within the scope of the present disclosure. For example, as demonstrated via  FIGS. 18-25 , industry averages, priority changes, quality improvements, strategic direction and zoning, and/or public relations density, tone and ROI may equally be provided in a variety of different forms. In addition, any of the above-described output charts and/or graphs may be interactive so as to allow the user to modify the output and/or obtain more detailed information with respect to the displayed data (e.g., click on a charted line to obtain confirming details/information). Also, any of a variety of other conventional data manipulation tools (e.g., Word®, Excel®, Power Point®, etc.) may be integrally associated with the application of the present disclosure so as to facilitate providing relevant output.  
      While various preferred embodiments and/or implementations of the present disclosure have been illustrated and described, it is to be understood that the present disclosure is not limited to such exemplary embodiments and/or implementations. Rather, the present disclosure encompasses embodiments and implementations that fall within the spirit and scope of the present disclosure, including modifications, changes and/or enhancements that will be apparent to persons skilled in the art based on the foregoing disclosure.