Patent Publication Number: US-8538911-B2

Title: Model-driven event detection, implication, and reporting system

Description:
PRIORITY CLAIM 
     This is a continuation application of U.S. patent application Ser. No. 11/387,173, now U.S. Pat. No. 7,523,137, filed on Mar. 22, 2006, which is incorporated by reference herein and which claims the benefit of priority from U.S. Provisional Application No. 60/669,850, filed on Apr. 8, 2005. 
    
    
     COPYRIGHT NOTICE 
     A portion of the disclosure of this patent document contains material which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent file or records, but otherwise reserves all copyright rights whatsoever. The following notice applies to any software and data as described below and in the drawings hereto: Copyright © 2005-2006, Accenture, All Rights Reserved. 
     BACKGROUND 
     1. Technical Field 
     This invention relates to processing systems which intelligently process information received from a wide range of sources. In particular, this invention relates to an automated analysis system customized to a particular organization&#39;s operating environment, and which gathers information from relevant sources, detects events represented in the information that are likely to be relevant to the organization, determines implications of the events to the organization, processes those implications as inferred events, and reports the events. 
     2. Background Information 
     Modern communication technology has delivered unprecedented growth in information, sources of information, and electronic access to information. However, it is difficult, if not impossible, for an individual to obtain, search, and interpret the information for events of interest and their potential meaning or impact. For example, newspapers from almost every country in the world are available online. Yet, from a practical standpoint, the immense amount of time required to retrieve and read each newspaper dictates that a much smaller subset of newspapers are actually reviewed for pertinent information. 
     Accordingly, despite the general availability of vast information resources, a business often obtains an incomplete view of their operating environment, fails to understand or identify patterns in information, and does not or cannot properly interpret the patterns as they relate to the dynamics of that business. As examples, the past, present, and/or predicted future resource availability, as well as changes in the availability are known with only partial accuracy and without a clearly defined impact on the business. Formulating business strategy based on incomplete information subjects the business to undue risk and may limit profits, growth, and other desirable goals. 
     There is a need for addressing the problems noted above and others previously experienced. 
     BRIEF SUMMARY 
     An event analysis system equipped with a customized model of a particular business&#39; concerns, analyzes information received from many different sources, and stored in an information database. The event analysis system detects relevant events, filters the events, infers new events from the detected events, and reports the events. Flexible models established in the event analysis system tailor the operation of the event analysis system to specific entities (including organizations, individuals, or other entities) and to relationships between entities. 
     An information source model identifies and characterizes the information sources from which the event analysis system may obtain information. An entity relationship model provides a representation of a particular entity (e.g., a business) as well as relationships of that entity to other entities. An event type model allows the event analysis system to define event types which are relevant to any particular entity. In addition, an event implication model defines implication rules. The event analysis system applies the implication rules to detected events. As a result, the event analysis system may determine new inferred events which may impact the entity for which event analysis is occurring. The entity for which event analysis is occurring is referred to as the event focus. 
     An event processing control program in the event analysis system coordinates the analysis performed by the event analysis system. The control program periodically scans information sources to retrieve and store, in an information database, new information potentially describing relevant events. The event processing control program implements a filtering step during which the control program recognizes and retains information relevant to entities defined in the environment model. Other information may be discarded, when it is not relevant to the entities established in the environment model. The control program initiates execution of an event detection engine to the newly retrieved information. 
     The event detection engine produces an event record which follows a standardized format. The format may include information about the event&#39;s event type, attributes, referenced organizations, the importance or priority of the event, the source text that describes the event, an address for the source text, or other information. The format may also include information specific to the data type that the event is based on. As an example, if the event is based on unstructured text, then the event detection engine may generate a tokenized or parsed version of the unstructured text. 
     The control program initiates execution of an implication engine to the event record. The implication engine produces two results. The first result is a description of an implied event which may be added to the event record. The implication engine prepares the description when the event record includes characteristics which signal the implied event. The second result is a separate event description for the implied event represented by a new event record. 
     The event analysis system stores all of the event records in the event database, including the originally detected events, as well as the event records describing inferred events. The control program also signals any processes which are consuming events. The processes may then retrieve the event records which represent the newly added events and inferred events from the event database. The processes may then report the events by updating a user interface or other information presentation. 
     Additionally, the control program may accept modifications to the data extracted by the automated event detection and implication processes. The modifications may come from any process that consumes the events, from a system operator, or from another source. In response, the event analysis system updates the event database to reflect the modifications, and re-applies the implication engine to the modified event. The event analysis system  100  may learn from the modifications, and, for example, suggest similar modifications in the future, thereby leading to enhanced future performance of the system. 
     Other systems, methods, features and advantages of the invention will be, or will become, apparent to one with skill in the art upon examination of the following figures and detailed description. It is intended that all such additional systems, methods, features and advantages be included within this description, be within the scope of the invention, and be protected by the following claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  illustrates an event analysis system for gathering information, identifying events, and interpreting the events. 
         FIG. 2  shows information sources which the event analysis system  100  may systematically monitor to obtain information. 
         FIG. 3  shows an event model including event records defined in a hierarchical tree. 
         FIG. 4  illustrates an entity node and an entity relationship which may be defined in an environment model. 
         FIG. 5  illustrates an implication item including a trigger constraint and a resulting implication. 
         FIG. 6  shows the acts which the event analysis system may take to detect and infer events. 
         FIG. 7  shows an event object defined according to a common event structure. 
         FIG. 8  shows an example of the acts which an event detection engine may take to detect an event in an article. 
         FIG. 9  shows an example of the acts which an event implication engine may take to infer new events from detected events. 
         FIG. 10  illustrates an example of the acts which an event display preparation engine may take to create output files. 
         FIG. 11  shows a competitor display rendered on a user interface. 
         FIG. 12  shows an event detail window. 
         FIG. 13  shows a graphical user interface front end to an environment model design tool. 
         FIG. 14  shows a graphical user interface front end to an event model design tool. 
         FIG. 15  shows two different windows of a graphical user interface front end to an implication model design tool. 
     
    
    
     DETAILED DESCRIPTION 
     The discussion below, regardless of the particular implementation being described, is exemplary in nature, rather than limiting. For example, although selected aspects, features, or components of the implementations are depicted as stored in program, data, or multipurpose system memories, all or part of systems and methods consistent with the event analysis system may be stored on or read from other machine-readable media, for example, secondary storage devices such as hard disks, floppy disks, and CD-ROMs; electromagnetic signals; or other forms of machine readable media either currently known or later developed. 
     Furthermore, although this specification describes specific components of an event analysis system, methods, systems, and articles of manufacture consistent with the event analysis system may include additional or different components. For example, a processor may be implemented as a microprocessor, microcontroller, application specific integrated circuit (ASIC), discrete logic, or a combination of other types of circuits acting as explained above. Databases, tables, and other data structures may be separately stored and managed, incorporated into a single memory or database, or generally logically and physically organized in many different ways. The programs discussed below may be parts of a single program, separate programs, or distributed across several memories and processors. 
     In the discussion below, event detection steps include the collection of raw data (e.g., from news articles), converting the data into event objects, and filtering (and discarding) events that are not relevant. In addition, event detection steps also include classifying the events which were not discarded, extracting information from the data which characterizes the events, and building an event template. Extracting information about the events includes obtaining event attribute values, while building the event template includes adding the attribute values into attribute fields in event objects. 
     An event implication engine applies an event implication model to infer new events from existing detected events. The new events may be fed back into the implication engine, resulting in additional inferred events. The feedback process may continue iterating to generate additional new events, all of which are maintained in the event database, and any of which may also be fed back into the implication engine. 
       FIG. 1  shows an event analysis system  100 . As an overview, the event analysis system  100  monitors information available from information sources connected to both publicly and privately distributed networks. The event analysis system  100  retrieves the information, such as news articles, blog entries, web site content, and electronic documents (e.g., word processor or spreadsheet documents) from the information sources for analysis. Although the example of a news article from a Really Simple Syndication (RSS) feed is used below, it is noted that the event analysis system  100  may process information in any other format from any other source. 
     Once retrieved, the event analysis system  100  analyzes the article for events. The event details are extracted from the article and represented in a standardized way for further processing. The system  100  discards articles which are not relevant with respect to the environment model  130 , and classifies events described in the remaining articles according to the event model. In particular, the event analysis system  100  determines relevant events, and alerts other systems, individuals, or other entities of the new event and its relevance. 
     The event analysis system  100  includes a processor  102 , a memory  104 , and a display  106 . In addition, a network interface  108 , an information database  110 , and an event database  112  are present. The information database  110  stores articles received over the network  114  from the information sources  116 . The event database  112  stores event objects constructed using information obtained from the articles, and modified and extended by further processing in the event analysis system  100 . The event objects may share a common event structure which is independent of the information sources  116  from which the articles are received. The common format of the event structure facilitates subsequent processing of the event objects by a wide range of analysis tools, described below. 
     The event analysis system  100  may communicate the detected events, implications of the events (e.g., in the form of a newly created event flowing from an implication of a previously detected event), or both, for further processing by external entities.  FIG. 1  shows an example in which an automated alert system  118  consumes originally detected events and inferred events produced by the event analysis system  100 . The automated alert system  118  may include comparison logic which watches for specific types of events and produces an alert. The alert system  118  may send the alert to an individual or other system (e.g., a PDA, a personal computer, or pager) to perform a notification that the event has occurred or that the event has the potential implication determined by the event analysis system  100 . As an additional example, the enterprise data integration system  120  may include a database management system or other information processing system which integrates event data into other data maintained for an enterprise. 
     An event portal  122  provides a remote external interface into the event analysis system  100 . The event portal  122  may implement a portal user interface  124  which supports login and communication with the event analysis system  100 . The event portal  122  may provide a representation (including text and/or or graphical elements) of the events (including inferred events arising from implications of existing events). The representation may assist, for example, a decision support role of the operator of the event portal  122 . 
     The memory  104  stores one or more information source models  126 , event models  128 , environment models  130 , and event implication models  132 , which are explained in more detail below. The memory  104  also stores analysis engines  134 . The analysis engines  134  may include an event detection engine  136 , an event implication engine  138 , and buzz and/or sentiment monitoring engines  140 . 
     The processor  102  generates a user interface  142  on the display  106 . The user interface  142  may locally provide graphical representations of events and their implications (e.g., in the form of inferred events) organized by company, competitor, or in another manner to an operator using the event analysis system  100 . To that end, the event analysis system  100  may include a rendering engine  144 . The rendering engine  144  may be implemented with programs which generate text and/or graphical representations (as examples, dashboards, charts, or text reports) of the events and their inferred events in the user interface  142 . The rendering engine  144  may include a program such as Crystal Reports™ available from Business Objects of San Jose Calif., or any other drawing, report generation, or graphical output program. The rendering engine  144  may parse output files generated by the event display preparation engine  146 . An event processing control program  148  coordinates the processing of the event analysis system  100 , as described in more detail below. 
     The network interface  108  connects the event analysis system  100  to the networks  114 . The networks  114  may be internal or external networks, including, as examples, company intranets, local area networks, and the Internet. The networks  114  connect, in turn, to the information sources  116 . The system  100  connects to the information sources  116  specified by the information source model  126  in the memory  104 . Accordingly, the processor  102  reads the information source model  102 , determines which information sources  116  to contact, then retrieves articles from the information sources  116  through the networks  114 . 
     The system  100  also includes graphical modeling tools  150 . The graphical modeling tools  150  display user interfaces through which an operator may establish and modify the models  126 - 132  without the burden of writing code. In one implementation, the modeling tools  150  include an event modeling tool, an implication modeling tool, and an environment modeling tool which support the definition and modification of the event model  128 , the event implication model  132 , and the environment model  130 , respectively. An information source modeling tool may also be provided to provide a graphical user interface for modifying the information source model  126 . The modeling tools  150  are described in more detail below. 
       FIG. 2  shows several examples of the information sources  116 . The information sources  116  may include government publication information sources  202 , online price information sources  204 , financial report information sources  206 , and local and national news information sources  208 . The information sources may also include one or more blog, online discussion, or USENET information sources  210 , analyst report information sources  212 , product review information sources  214 , and trade press information sources  216 . 
     The information sources  202 - 216  are exemplary only, and the event analysis system  100  may connect to any other information source. The information sources  202 - 216  may be driven by web sites, free or subscription electronic databases (e.g., the Lexis/Nexis™ databases), news groups, electronic news feeds, journal article databases, manual data entry services, or other sources. The event analysis system  100  may access the information sources  202 - 216  using a Hypertext Transport Protocol (HTTP) interface, File Transfer Protocol (FTP) interface, web service calls, message subscription service, or using any other retrieval mechanism. 
     The networks  114  may adhere to a wide variety of network topologies and technologies. For example, the networks  132  may include Ethernet and Fiber Distributed Data Interconnect (FDDI) networks. The network interface  108  is assigned one or more network addresses. The network address may be a packet switched network identifier such as a Transmission Control Protocol/Internet Protocol (TCP/IP) address (optionally including port numbers), or any other communication protocol address. Thus, the networks  114  may represent a transport mechanism or interconnection of multiple transport mechanisms for data exchange between the event analysis system  100  and the information sources  202 - 216 , the automated alert system  118 , the enterprise data integration system  120 , and the event portals  122 . 
     The information source model  126  may establish, define, or otherwise identify information sources. The information source model  126  may use include (e.g., “news.abcbnewspaper.com”), identifiers (e.g., an IP address and port number), or other identifiers to specify information sources which the event analysis system  100  will monitor. The processor  102  may then systematically monitor and gather articles from one or more of the information sources  116  to build the information compilation in the information database  110 . The processor  102  may supplement the information compilation at any time, such as on a periodic schedule (e.g., twice per day), when instructed by an operator, or when receiving a message that a new article is available. 
     In other implementations, the information source model  126  includes configuration information. The configuration information may specify how to access a given information source  116 , as well as information characterizing the information source  116 . The characterizing information may include weighting values for individual information sources which record the reputation, reliability, or quality of the information source (e.g., a weighting value between 1 and 5). The event analysis system  100  may use the configuration information in subsequent processing stages. For example, the event analysis system  100  may determine measures of event accuracy or probability based on the weighting values. 
     Table 1 shows an example of a source model instance. The example shown in Table 1 is an eXtensible Markup Language (XML) instance, with tags which specify the name, location, connection method, update frequency (e.g., once per day), and weighting value for the information source. The information source models  126  may represent a collection of such instances. 
     
       
         
           
               
             
               
                 TABLE 1 
               
               
                   
               
             
            
               
                 &lt;source&gt; 
               
               
                   &lt;name&gt;ABC Newspaper &lt;/name&gt; 
               
               
                   &lt;url&gt;http://www.abcnewspaper.com/news_drop/netcenter/netcenter.- 
               
               
                 rdf&lt;/url&gt; 
               
               
                   &lt;connectionMethod&gt;RSS&lt;/connectionMethod&gt; 
               
               
                   &lt;updateFrequency&gt;1.0&lt;/updateFrequency&gt; 
               
               
                   &lt;reputation&gt;4&lt;/reputation&gt; 
               
               
                 &lt;/source&gt; 
               
               
                   
               
            
           
         
       
     
     The name tag provides a descriptive string to describe the source. The url tag indicates where to access the requested information. The connectionMethod tag defines how to access the source. In this example, the event analysis system  100  uses an RSS feed. For other sources, the connectionMethod tag may specify other access mechanisms, including ftp, http, or other mechanisms. 
     The updateFrequency tag determines how often the event analysis system  100  accesses a particular information source  116 . In this example, the value ‘1.0’ specifies one access per day. The reputation tag assigns a weighting value to each source, on a scale of 1-5 (5 being most reputable). The event analysis system may use this information to resolve conflicting stories between different information sources and to build a measure of event accuracy or likelihood. Additional extensions include configuration parameters which specify login name and password, timeout constraints, and data transfer size limits or time limits. 
     The event models  128  define the type of events that can occur and the attributes which belong to instances of each event type. For example, “Hire” is an event type with attributes of “New employer”, “Previous employer”, “New position”, and “Manager name”. Thus, when the event analysis system  100  detects a “Hire” event in an article obtained from an information source  116 , the event analysis system  100  will scan through the article text to determine who the previous employer was, what the new position is, and what the manager&#39;s name is. 
       FIG. 3  shows that the event model  128  may include event nodes defined as a hierarchical tree  300 , starting with a root node  302  and root attributes  304 . In one implementation, the root node has three child nodes: an organization-centered node  306 , a society-centered node  308 , and a product-centered node  310 . The organization-centered node  306  is the parent node for child nodes that represent events that directly affect an organization, or are generated by the organization. A new ad campaign, a labor dispute, and a stock price change are examples of organization-centered events. The tree  300  separates event types into organization-centered, society-centered, and product-centered events, but any other organization may be implemented. The society-centered noted  308  categorizes changes that are external to the company, such as environmental changes or demographic changes. The product-centered node  310  categorizes events which are relevant to a particular product that is created by a company or organization. A product recall, a manufacturing difficulty that affects a product, and a rebate on a product are examples of product-centered events. The categories are not rigid. Instead, different system implementations may classify the same event into different categories and may define the fewer, more, or different categories and event nodes. 
     Each child node inherits the attributes from the root node, and each child node may optionally include additional attributes individually associated with that child node. Each child node  306 - 310  may have children nodes as well, which inherit the attributes from parent, grandparent, and further prior nodes. The tree  300  ends in leaf nodes (e.g., the leaf node  312 ), a node with no child nodes. The leaf nodes are associated with expressions which help the system  100  determine that article text includes an event of the event type represented in a leaf node. The tree  300  provides a structure in which similar events may be grouped together (e.g., for ease of comprehension). The tree  300  also increases efficiencies by avoiding duplication of information that is shared by children of the same parent. By allowing children to inherit attributes of their parents, the attributes may be specified only once (in the parent) instead of more than once (in all of the children). 
     When the event analysis system  100  classifies an event, the event analysis system  100  creates an event object and builds the event object according to one of the event types represented by a leaf node. As an example, an event may be classified as a “Hire” event, including the inherited attributes from prior nodes such as the organization-centered node  306  and the root node  302 . Thus, the event model  128  defines the form and content of the event objects for many different types of events. 
     Table 2 shows an example of the implementation of a root event type in the event model  128  corresponding to the root node  302 . 
     
       
         
           
               
               
             
               
                   
                 TABLE 2 
               
               
                   
                   
               
             
            
               
                   
                 &lt;EventType label=“Root” color=“DarkGray”&gt; 
               
               
                   
                 &lt;toolData&gt; 
               
               
                   
                 &lt;childrenShareColor&gt;False&lt;/childrenShareColor&gt; 
               
               
                   
                 &lt;/toolData&gt; 
               
               
                   
                 &lt;categoryDetectionInfo&gt; 
               
               
                   
                 &lt;newsStoryDetectionPatterns/&gt; 
               
               
                   
                 &lt;/categoryDetectionInfo&gt; 
               
               
                   
                 &lt;eventInstanceAttributes&gt; 
               
               
                   
                 &lt;eventInstanceAttribute&gt; 
               
               
                   
                 &lt;name&gt;Date&lt;/name&gt; 
               
               
                   
                 &lt;dataType&gt;&lt;dataType&gt; 
               
               
                   
                 &lt;dataType&gt;&lt;/dataType&gt; 
               
               
                   
                 &lt;preRegex&gt; 
               
               
                   
                 &lt;/preRegex&gt; 
               
               
                   
                 &lt;postRegex&gt; 
               
               
                   
                 &lt;/postRegex&gt; 
               
               
                   
                 &lt;display&gt; 
               
               
                   
                 &lt;summary&gt;True&lt;/summary&gt; 
               
               
                   
                 &lt;detail&gt;True&lt;/detail&gt; 
               
               
                   
                 &lt;/display&gt; 
               
               
                   
                 &lt;/eventInstanceAttribute&gt; 
               
               
                   
                 &lt;eventInstanceAttribute&gt; 
               
               
                   
                 &lt;name&gt;Time&lt;/name&gt; 
               
               
                   
                 &lt;dataType&gt;&lt;dataType&gt; 
               
               
                   
                 &lt;dataType&gt;&lt;/dataType&gt; 
               
               
                   
                 &lt;preRegex&gt; 
               
               
                   
                 &lt;/preRegex&gt; 
               
               
                   
                 &lt;postRegex&gt; 
               
               
                   
                 &lt;/postRegex&gt; 
               
               
                   
                 &lt;display&gt; 
               
               
                   
                 &lt;summary&gt;True&lt;/summary&gt; 
               
               
                   
                 &lt;detail&gt;True&lt;/detail&gt; 
               
               
                   
                 &lt;/display&gt; 
               
               
                   
                 &lt;/eventInstanceAttribute&gt; 
               
               
                   
                 &lt;eventInstanceAttribute&gt; 
               
               
                   
                 &lt;name&gt;Tense&lt;/name&gt; 
               
               
                   
                 &lt;dataType&gt; 
               
               
                   
                 &lt;/dataType&gt; 
               
               
                   
                 &lt;preRegex&gt; 
               
               
                   
                 &lt;/preRegex&gt; 
               
               
                   
                 &lt;postRegex&gt; 
               
               
                   
                 &lt;/postRegex&gt; 
               
               
                   
                 &lt;display&gt; 
               
               
                   
                 &lt;summary&gt;True&lt;/summary&gt; 
               
               
                   
                 &lt;detail&gt;True&lt;/detail&gt; 
               
               
                   
                 &lt;/display&gt; 
               
               
                   
                 &lt;/eventInstanceAttribute&gt; 
               
               
                   
                 &lt;eventInstanceAttribute&gt; 
               
               
                   
                 &lt;name&gt;Confidence&lt;/name&gt; 
               
               
                   
                 &lt;dataType&gt; 
               
               
                   
                 &lt;/dataType&gt; 
               
               
                   
                 &lt;preRegex&gt; 
               
               
                   
                 &lt;/preRegex&gt; 
               
               
                   
                 &lt;postRegex&gt; 
               
               
                   
                 &lt;/postRegex&gt; 
               
               
                   
                 &lt;display&gt; 
               
               
                   
                 &lt;summary&gt;True&lt;/summary&gt; 
               
               
                   
                 &lt;detail&gt;True&lt;/detail&gt; 
               
               
                   
                 &lt;/display&gt; 
               
               
                   
                 &lt;/eventInstanceAttribute&gt; 
               
               
                   
                 &lt;/eventInstanceAttributes&gt; 
               
               
                   
                 &lt;/EventType&gt; 
               
               
                   
                   
               
            
           
         
       
     
     In Table 2, the EventType (event type) tag includes a label attribute which specifies the name of the event type (e.g., Root), and the color used to display the node (e.g., in a model building tool on the user interface  142 ). The toolData tag specifies information which may be used by a model building tool. In this example, the toolData tag includes a color sharing tag that tells the model building tool that any children of the root event type should be displayed in the same color as the root node by default. 
     The categoryDetectionInfo (category definition) tags specify text strings which the event analysis system  100  uses to detect events which match the event type. In this example, events are not assigned to the root event type, and no text strings are defined. However, the event model  128  will specify text strings for leaf event nodes. 
     The eventInstanceAttributes (event instance attribute) tags specify the attributes for which the event analysis system  100  will search for values in the article, for any event that belongs to the event type, or any of the event type&#39;s children. In this example, the attributes ‘date’, ‘time’, ‘tense’, and ‘confidence’ are defined. Because the root node includes ‘date’, ‘time’, ‘tense’, and ‘confidence’ attributes, the event analysis system  100  will search the article to determine the date this event occurred or will occur, what time the event occurred or will occur, whether the event occurred in the past or will occur in the future, and how confident the event analysis system  100  is are about the analysis of the event, regardless of the specific type of event. The attributes may vary widely in form, number, and type between implementations. In particular, two different implementations of the system may define the same event or events, yet use similar or very different attributes to characterize the events. 
     Within the event instance attribute, the “name” field contains the name of the event instance attribute. The “dataType” field contains a description of the data types that serve as a value for the attribute. This field may specify, as examples, that a “person&#39;s name”, “geographical location”, “quantity”, “currency”, “company name”, “job title”, or other data type may provide the value. Knowing what data type provides the value assists the event analysis system  100  with identifying the value in the text itself. 
     The “preRegex” field specifies a regular expression that identifies text that event analysis system  100  may search for prior to (e.g., immediately prior to) the text that serves as the value for the event instance attribute. For example, the phrase “will be leaving” found before a company&#39;s name may point to the company name as value for the “Previous employer” event instance attribute for the “Hire” event type. The “postRegex” field is similar to the “preRegex” field, but the event analysis system  100  uses the postRegex field to specify a regular expression which identifies strings expected to come immediately after the value for the event instance attribute. 
     The “display” field contains information about whether the event instance attribute will be shown on either a “summary” display of the event, a “detailed” display of the event, both, or neither. For example, the user interface  142  may display either or both of a broad-view or summary window, as well as pop-up windows giving more details about an individual event (i.e., a detailed display). 
     Table 3 shows a definition for the organization centered event type, a child of the root node. 
     
       
         
           
               
             
               
                 TABLE 3 
               
               
                   
               
             
            
               
                 &lt;EventType label=“Organization-Centered” color=“Silver” 
               
               
                 acronym=“OAA” scope=“general” focus=“organization” parent=“Root”&gt; 
               
               
                 &lt;toolData&gt; 
               
               
                 &lt;childrenShareColor&gt;False&lt;/childrenShareColor&gt; 
               
               
                 &lt;/toolData&gt; 
               
               
                 &lt;categoryDetectionInfo&gt; 
               
               
                 &lt;newsStoryDetectionPatterns/&gt; 
               
               
                 &lt;/categoryDetectionInfo&gt; 
               
               
                 &lt;eventInstanceAttributes/&gt; 
               
               
                 &lt;/EventType&gt; 
               
               
                   
               
            
           
         
       
     
     The event type provides an acronym for the event type (e.g., “OAA”), a scope specifier, and focus specifier, and a parent specifier which links the event type to a parent event type (i.e., the root event type). The scope and focus specifiers provide fields for future implementations which further increase the flexibility and capabilities of the system. 
     Table 4 shows an example of a ‘hire’ event type. Other events may share the same or similar tags and structure. 
     
       
         
           
               
             
               
                 TABLE 4 
               
               
                   
               
             
            
               
                 &lt;EventType label=“Hire” color=“DeepSkyBlue” parent=“Management”&gt; 
               
               
                 &lt;toolData&gt; 
               
               
                 &lt;childrenShareColor&gt;True&lt;/childrenShareColor&gt; 
               
               
                 &lt;/toolData&gt; 
               
               
                 &lt;categoryDetectionInfo&gt; 
               
               
                 &lt;newsStoryDetectionPatterns&gt; 
               
               
                 &lt;pattern regularExpression=“name(s|d)” weight=“1”/&gt; 
               
               
                 &lt;pattern regularExpression=“appoint(s|ed)?” weight=“2”/&gt; 
               
               
                 &lt;pattern regularExpression=“appointment(s)?” weight=“2”/&gt; 
               
               
                 &lt;pattern regularExpression=“election of” weight=“2”/&gt; 
               
               
                 &lt;pattern regularExpression=“elect(s|ed)?” weight=“2”/&gt; 
               
               
                 &lt;pattern regularExpression=“hire(s|d)?” weight=“2”/&gt; 
               
               
                 &lt;pattern regularExpression=“promoted to” weight=“2”/&gt; 
               
               
                 &lt;pattern regularExpression=“assume(s)? control” weight=“2”/&gt; 
               
               
                 &lt;pattern regularExpression=“(of the (year|world))|most|best|greatest” 
               
               
                 weight=“−100”/&gt; 
               
               
                 &lt;pattern regularExpression=“service appointment(s)?” weight=“−100”/&gt; 
               
               
                 &lt;pattern regularExpression=“code(-| )?name(s|d)?” weight=“−100”/&gt; 
               
               
                 &lt;/newsStoryDetectionPatterns&gt; 
               
               
                 &lt;/categoryDetectionInfo&gt; 
               
               
                 &lt;eventInstanceAttributes&gt; 
               
               
                 &lt;eventInstanceAttribute&gt; 
               
               
                 &lt;name&gt;Previous Employer&lt;/name&gt; 
               
               
                 &lt;dataType&gt; 
               
               
                 company 
               
               
                 &lt;/dataType&gt; 
               
               
                 &lt;preRegex&gt; 
               
               
                 “(is leaving)|(will be leaving)” 
               
               
                 &lt;/preRegex&gt; 
               
               
                 &lt;postRegex&gt; 
               
               
                 “has fired” 
               
               
                 &lt;/postRegex&gt; 
               
               
                 &lt;display&gt; 
               
               
                 &lt;summary&gt;True&lt;/summary&gt; 
               
               
                 &lt;detail&gt;True&lt;/detail&gt; 
               
               
                 &lt;/display&gt; 
               
               
                 &lt;/eventInstanceAttribute&gt; 
               
               
                 &lt;eventInstanceAttribute&gt; 
               
               
                 &lt;name&gt;New Position&lt;/name&gt; 
               
               
                 &lt;dataType&gt; 
               
               
                 &lt;/dataType&gt; 
               
               
                 &lt;preRegex&gt; 
               
               
                 &lt;/preRegex&gt; 
               
               
                 &lt;postRegex&gt; 
               
               
                 &lt;/postRegex&gt; 
               
               
                 &lt;display&gt; 
               
               
                 &lt;summary&gt;True&lt;/summary&gt; 
               
               
                 &lt;detail&gt;True&lt;/detail&gt; 
               
               
                 &lt;/display&gt; 
               
               
                 &lt;/eventInstanceAttribute&gt; 
               
               
                 &lt;/eventInstanceAttributes&gt; 
               
               
                 &lt;/EventType&gt; 
               
               
                   
               
            
           
         
       
     
     Table 4 shows that the ‘Hire’ event type is a child of the ‘Management’ event type, and that ‘Hire’ events are displayed as DeepSkyBlue boxes in the user interface  142  (and in other interfaces, such as a model building tool). The newsStoryDetectionInfo tags establish the regular expressions, under the newsStoryDetectionPatterns tag, which the event analysis system  100  uses to identify events which are ‘Hire’ events. Each regular expression (bounded by the ‘pattern’ tag) may specify the regular expression and a weight. 
     The event analysis system  100  uses the patterns and weights to determine whether a particular event belongs to a particular event type. Thus, the event analysis system  100  may distinguish between events when regular expressions from different event types are located in the same article. When regular expressions from multiple different event types are found in a single article, the system  100  adds the weights for the expressions. The highest resulting weight is the event type to which the system  100  classifies the event. Note that weights can be positive or negative. Negative weights may be applied when a regular expression matches text which points away from the event type. For example, matching the phrase “service appointment” or “of the year” would be clear indicators that the article does not describe a ‘hire’ event. Accordingly, large negative weights are assigned to those regular expressions. 
     Note that the ‘hire’ event type specifies that the value of the “Previous Employer” event instance attribute should be a company name. The “preRegex” (i.e., prior regular expression) field defines a regular expression that the event analysis system  100  uses to identify phrases that are expected or likely to come before (e.g., immediately before) the value for the Previous Employer attribute. In this example, when the event analysis system  100  finds the text “is leaving” followed by a company name, the event analysis system  100  determines that the company name should be the value for the Previous Employer attribute. 
     The “postRegex” (i.e., post regular expression) fields define a regular expression that is likely to occur after the value for the Previous Employer attribute. For example, the event analysis system  100  may establish the text “has fired” as text expected to come immediately after a company name to suggest that that company name is the appropriate value for the Previous Employer attribute. 
     The event model may also specify variables instead of text strings that the event analysis system  100  uses to match an event. The variables may be flagged by a leading character (e.g., ‘$’), followed by a variable, for example, ‘${changephrase}’. The event analysis system  100  may expand a variable using rules defined according to a specific grammar. 
     Table 5 shows an example of a grammar. 
     
       
         
           
               
               
             
               
                   
                 TABLE 5 
               
               
                   
                   
               
             
            
               
                   
                 increasephrase:(will |have |has )?(increase(d)?|((go |went 
               
               
                   
                 )(way)?up)|r(i|o)se|skyrocket(ed)?|jump(ed)?|gain(ed?)|buoy(ed)?| 
               
               
                   
                 inch(ed)?|recover(ed)?) 
               
               
                   
                 decreasephrase:(will |have |has )?(decrease(d)?|((go |went 
               
               
                   
                 )(way)?down)|drop(ped)?|fall|fell|sink|sank|slid(e)?|hit 
               
               
                   
                 bottom|dip(ped)?|sag(ged)?|peak(ed)?|inch(ed)?|falter(ed)?) 
               
               
                   
                 changephrase:((${increasephrase})|(${decreasephrase})) 
               
               
                   
                   
               
            
           
         
       
     
     The grammar shown in Table 5 specifies how to expand variables into regular expressions for pattern matching. In particular, the grammar shown in Table 5 specifies that “${changephrase}” should be expanded into “${increasephrase}|${decreasephrase}”. In turn, the grammar specifies that “${increasephrase} is expanded into “(will |have |has) ?(increase(d)?|((go|went)(way)?up)|r(i|o)se|skyrocket(ed)?|jump(ed)?|gain(ed?)|buo y(ed)?|inch(ed)?|recover(ed)?). Similarly, the grammar specifies that “${decreasephrase}” is expanded into “(will |have |has)?(decrease(d)?|((go|went)(way)?down)|drop(ped)?|fall|fell|sink|sank|slid(e)?|hit bottom|dip(ped)?|sag(ged)?|peak(ed)?|inch(ed)?|falter(ed)?)”. 
     The event analysis system  100  thereby implements macros which allow shorter forms to be used in the event model, allow re-use of common phrases, and that significantly increases the flexibility of the event model. The event analysis system  100  may store the grammar in the memory  104 , in a file on disk, or in any other location for reference when parsing the event model. 
     Tables 6 and 7 show examples of the product centered node  306  and the society centered node  308 . 
     
       
         
           
               
             
               
                 TABLE 6 
               
               
                   
               
             
            
               
                 &lt;EventType label=“Product-Centered” color=“Silver” acronym=“OPC” 
               
               
                 scope=“general” focus=“organization” parent=“Root”&gt; 
               
               
                 &lt;toolData&gt; 
               
               
                 &lt;childrenShareColor&gt;False&lt;/childrenShareColor&gt; 
               
               
                 &lt;/toolData&gt; 
               
               
                 &lt;categoryDetectionInfo&gt; 
               
               
                 &lt;newsStoryDetectionPatterns/&gt; 
               
               
                 &lt;/categoryDetectionInfo&gt; 
               
               
                 &lt;eventInstanceAttributes&gt; 
               
               
                 &lt;eventInstanceAttribute&gt; 
               
               
                 &lt;name&gt;Product&lt;/name&gt; 
               
               
                 &lt;dataType&gt; 
               
               
                 &lt;/dataType&gt; 
               
               
                 &lt;preRegex&gt; 
               
               
                 &lt;/preRegex&gt; 
               
               
                 &lt;postRegex&gt; 
               
               
                 &lt;/postRegex&gt; 
               
               
                 &lt;display&gt; 
               
               
                 &lt;summary&gt;True&lt;/summary&gt; 
               
               
                 &lt;detail&gt;True&lt;/detail&gt; 
               
               
                 &lt;/display&gt; 
               
               
                 &lt;/eventInstanceAttribute&gt; 
               
               
                 &lt;/eventInstanceAttributes&gt; 
               
               
                 &lt;/EventType&gt; 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
             
               
                 TABLE 7 
               
               
                   
               
             
            
               
                 &lt;EventType label=“Society-Centered” color=“Silver” parent=“Root”&gt; 
               
               
                 &lt;toolData&gt; 
               
               
                 &lt;childrenShareColor&gt;False&lt;/childrenShareColor&gt; 
               
               
                 &lt;/toolData&gt; 
               
               
                 &lt;categoryDetectionInfo&gt; 
               
               
                 &lt;newsStoryDetectionPatterns/&gt; 
               
               
                 &lt;/categoryDetectionInfo&gt; 
               
               
                 &lt;eventInstanceAttributes&gt; 
               
               
                 &lt;eventInstanceAttribute&gt; 
               
               
                 &lt;name&gt;Entities&lt;/name&gt; 
               
               
                 &lt;dataType&gt; 
               
               
                 &lt;/dataType&gt; 
               
               
                 &lt;preRegex&gt; 
               
               
                 &lt;/preRegex&gt; 
               
               
                 &lt;postRegex&gt; 
               
               
                 &lt;/postRegex&gt; 
               
               
                 &lt;display&gt; 
               
               
                 &lt;summary&gt;True&lt;/summary&gt; 
               
               
                 &lt;detail&gt;True&lt;/detail&gt; 
               
               
                 &lt;/display&gt; 
               
               
                 &lt;/eventInstanceAttribute&gt; 
               
               
                 &lt;eventInstanceAttribute&gt; 
               
               
                 &lt;name&gt;Status&lt;/name&gt; 
               
               
                 &lt;dataType&gt; 
               
               
                 &lt;/dataType&gt; 
               
               
                 &lt;preRegex&gt; 
               
               
                 &lt;/preRegex&gt; 
               
               
                 &lt;postRegex&gt; 
               
               
                 &lt;/postRegex&gt; 
               
               
                 &lt;display&gt; 
               
               
                 &lt;summary&gt;True&lt;/summary&gt; 
               
               
                 &lt;detail&gt;True&lt;/detail&gt; 
               
               
                 &lt;/display&gt; 
               
               
                 &lt;/eventInstanceAttribute&gt; 
               
               
                 &lt;/eventInstanceAttributes&gt; 
               
               
                 &lt;/EventType&gt; 
               
               
                   
               
            
           
         
       
     
     Table 8 shows an example of the events established in the tree  300  and which may be defined in the event model  128 . In the example, Root is the root event, Organization-centered, Product-centered, and Society-centered are children of the Root event. As examples, the Financial, Image, Labor Relations, Legal, Management, Marketing, and Partnering events are children of the organization-centered event. The Analyst Report, Earnings guidance, Earnings report, Market report, and Stock price change are examples of leaf nodes under the Financial node. Any other attributes or regular expressions may be defined for the events or used to locate the events. Event types are very flexible and may be organized, defined, or established into multiple event types in many different ways. For example, one implementation of the system  100  may include a Product Price Change event, while another implementation may define separate Product Price Increase and Product Price Decrease events. 
     
       
         
           
               
             
               
                 TABLE 8 
               
               
                   
               
             
            
               
                 &lt;EventTypes&gt; 
               
               
                 &lt;EventType label=“Root” color=“DarkGray”&gt; 
               
               
                 &lt;EventType label=“Organization-Centered” color=“Silver” acronym=“OAA” 
               
               
                 scope=“general” focus=“organization” parent=“Root”&gt; 
               
               
                 &lt;EventType label=“Financial” color=“Lime” parent=“Organization-Centered”&gt; 
               
               
                 &lt;EventType label=“Analyst report” color=“Lime” parent=“Financial”&gt; 
               
               
                 &lt;EventType label=“Earnings guidance” color=“Lime” parent=“Financial”&gt; 
               
               
                 &lt;EventType label=“Earnings report” color=“Lime” parent=“Financial”&gt; 
               
               
                 &lt;EventType label=“Market report” color=“Lime” parent=“Financial”&gt; 
               
               
                 &lt;EventType label=“Stock price change” color=“Lime” parent=“Financial”&gt; 
               
               
                 &lt;EventType label=“Image” color=“LavenderBlush” parent=“Organization- 
               
               
                 Centered”&gt; 
               
               
                 &lt;EventType label=“Accident” color=“LavenderBlush” parent=“Image”&gt; 
               
               
                 &lt;EventType label=“Ad campaign (image)” color=“LavenderBlush” 
               
               
                 parent=“Image”&gt; 
               
               
                 &lt;EventType label=“Scandal” color=“LavenderBlush” parent=“Image”&gt; 
               
               
                 &lt;EventType label=“Labor relations” color=“MediumSeaGreen” 
               
               
                 parent=“Organization-Centered”&gt; 
               
               
                 &lt;EventType label=“Labor demands” color=“MediumSeaGreen” parent=“Labor 
               
               
                 relations”&gt; 
               
               
                 &lt;EventType label=“Public demonstration” color=“MediumSeaGreen” 
               
               
                 parent=“Labor relations”&gt; 
               
               
                 &lt;EventType label=“Strike” color=“MediumSeaGreen” parent=“Labor 
               
               
                 relations”&gt; 
               
               
                 &lt;EventType label=“Workforce size change” color=“MediumSeaGreen” 
               
               
                 parent=“Labor relations”&gt; 
               
               
                 &lt;EventType label=“Legal” color=“Khaki” parent=“Organization-Centered”&gt; 
               
               
                 &lt;EventType label=“Bankruptcy” color=“Khaki” parent=“Legal”&gt; 
               
               
                 &lt;EventType label=“Criminal” color=“Khaki” parent=“Legal”&gt; 
               
               
                 &lt;EventType label=“Lawsuit” color=“Khaki” parent=“Legal”&gt; 
               
               
                 &lt;EventType label=“Management” color=“DeepSkyBlue” 
               
               
                 parent=“Organization-Centered”&gt; 
               
               
                 &lt;EventType label=“Departure” color=“DeepSkyBlue” parent=“Management”&gt; 
               
               
                 &lt;EventType label=“Hire” color=“DeepSkyBlue” parent=“Management”&gt; 
               
               
                 &lt;EventType label=“Position change” color=“DeepSkyBlue” 
               
               
                 parent=“Management”&gt; 
               
               
                 &lt;EventType label=“Marketing” color=“MediumSeaGreen” 
               
               
                 parent=“Organization-Centered”&gt; 
               
               
                 &lt;EventType label=“Ad campaign (marketing)” color=“MediumSeaGreen” 
               
               
                 parent=“Marketing”&gt; 
               
               
                 &lt;EventType label=“Charitable donation” color=“MediumSeaGreen” 
               
               
                 parent=“Marketing”&gt; 
               
               
                 &lt;EventType label=“Event sponsorship” color=“MediumSeaGreen” 
               
               
                 parent=“Marketing”&gt; 
               
               
                 &lt;EventType label=“Press release” color=“MediumSeaGreen” 
               
               
                 parent=“Marketing”&gt; 
               
               
                 &lt;EventType label=“Public presentation” color=“MediumSeaGreen” 
               
               
                 parent=“Marketing”&gt; 
               
               
                 &lt;EventType label=“Partnering” color=“LightCoral” parent=“Organization-Centered”&gt; 
               
               
                 &lt;EventType label=“Agreement” color=“LightCoral” parent=“Partnering”&gt; 
               
               
                 &lt;EventType label=“Co-branding” color=“LightCoral” parent=“Partnering”&gt; 
               
               
                 &lt;EventType label=“Joint venture” color=“LightCoral” parent=“Partnering”&gt; 
               
               
                 &lt;EventType label=“Merger” color=“LightCoral” parent=“Partnering”&gt; 
               
               
                 &lt;EventType label=“Product-Centered” color=“Silver” acronym=“OPC” 
               
               
                 scope=“general” focus=“organization” parent=“Root”&gt; 
               
               
                 &lt;EventType label=“Product attribute change” color=“AliceBlue” 
               
               
                 parent=“Product-Centered”&gt; 
               
               
                 &lt;EventType label=“Feature change” color=“AliceBlue” parent=“Product 
               
               
                 attribute change”&gt; 
               
               
                 &lt;EventType label=“Price change” color=“AliceBlue” parent=“Product attribute 
               
               
                 change”&gt; 
               
               
                 &lt;EventType label=“Product discontinuation” color=“Tomato” parent=“Product 
               
               
                 line change”&gt; 
               
               
                 &lt;EventType label=“Product introduction” color=“Tomato” parent=“Product line 
               
               
                 change”&gt; 
               
               
                 &lt;EventType label=“Product production change” color=“−12550016” 
               
               
                 parent=“Product-Centered”&gt; 
               
               
                 &lt;EventType label=“Production capacity change” color=“−12550016” 
               
               
                 parent=“Product production change”&gt; 
               
               
                 &lt;EventType label=“Production cost change” color=“−12550016” 
               
               
                 parent=“Product production change”&gt; 
               
               
                 &lt;EventType label=“Production location change” color=“−12550016” 
               
               
                 parent=“Product production change”&gt; 
               
               
                 &lt;EventType label=“Production method change” color=“−12550016” 
               
               
                 parent=“Product production change”&gt; 
               
               
                 &lt;EventType label=“Ad campaign (product promotion)” color=“−12829441” 
               
               
                 parent=“Product promotion”&gt; 
               
               
                 &lt;EventType label=“Rebate” color=“−12829441” parent=“Product promotion”&gt; 
               
               
                 &lt;EventType label=“Special financing” color=“−12829441” parent=“Product 
               
               
                 promotion”&gt; 
               
               
                 &lt;EventType label=“Product quality” color=“Orange” parent=“Product- 
               
               
                 Centered”&gt; 
               
               
                 &lt;EventType label=“Award” color=“Orange” parent=“Product quality”&gt; 
               
               
                 &lt;EventType label=“Design flaw” color=“Orange” parent=“Product quality”&gt; 
               
               
                 &lt;EventType label=“Manufacturing flaw” color=“Orange” parent=“Product 
               
               
                 quality”&gt; 
               
               
                 &lt;EventType label=“Recall” color=“Orange” parent=“Product quality”&gt; 
               
               
                 &lt;EventType label=“Review” color=“Orange” parent=“Product quality”&gt; 
               
               
                 &lt;EventType label=“Society-Centered” color=“Silver” parent=“Root”&gt; 
               
               
                 &lt;EventType label=“Cultural trend” color=“PaleVioletRed” parent=“Society- 
               
               
                 Centered”&gt; 
               
               
                 &lt;EventType label=“Attitude change” color=“PaleVioletRed” parent=“Cultural 
               
               
                 trend”&gt; 
               
               
                 &lt;EventType label=“Demographic change” color=“PaleVioletRed” 
               
               
                 parent=“Cultural trend”&gt; 
               
               
                 &lt;EventType label=“Economic trend” color=“Cornsilk” parent=“Society- 
               
               
                 Centered”&gt; 
               
               
                 &lt;EventType label=“Economic news” color=“Cornsilk” parent=“Economic 
               
               
                 trend”&gt; 
               
               
                 &lt;EventType label=“Regulatory change” color=“PaleTurquoise” 
               
               
                 parent=“Society-Centered”&gt; 
               
               
                 &lt;EventType label=“Environmental” color=“PaleTurquoise” parent=“Regulatory 
               
               
                 change”&gt; 
               
               
                 &lt;EventType label=“Labor” color=“PaleTurquoise” parent=“Regulatory 
               
               
                 change”&gt; 
               
               
                 &lt;EventType label=“Privacy” color=“PaleTurquoise” parent=“Regulatory 
               
               
                 change”&gt; 
               
               
                 &lt;EventType label=“Safety” color=“PaleTurquoise” parent=“Regulatory 
               
               
                 change”&gt; 
               
               
                 &lt;EventType label=“Technology trend” color=“LimeGreen” parent=“Society- 
               
               
                 Centered”&gt; 
               
               
                 &lt;EventType label=“Obsolesence” color=“LimeGreen” parent=“Technology 
               
               
                 trend”&gt; 
               
               
                 &lt;EventType label=“Technology development” color=“LimeGreen” 
               
               
                 parent=“Technology trend”&gt; 
               
               
                 &lt;/EventTypes&gt; 
               
               
                   
               
            
           
         
       
     
     Table 9 shows examples of the attributes (in single quotes) and regular expressions (in double quotes) defined for specific events. 
     
       
         
           
               
               
             
               
                 TABLE 9 
               
               
                   
               
             
            
               
                 Event 
                 Attributes and Regular Expressions 
               
               
                 Financial 
                 ‘value’ and ‘valence’ 
               
               
                 Analyst Report or a 
                 ‘analyst’ and ‘issues raised’ 
               
               
                 Market Report 
               
               
                 earnings guidance or 
                 ‘timespan’ 
               
               
                 an earnings report 
               
               
                 Stock price change 
                 ‘percent change’, and ‘split’ 
               
               
                   
                 “stock price ${changephrase}” 
               
               
                   
                 “shares ${changephrase}” 
               
               
                 Image 
                 ‘issue’ 
               
               
                 ad campaign 
                 ‘focus’ and ‘channel’ 
               
               
                 scandal 
                 ‘person’ 
               
               
                 labor relations 
                 ‘labor organization’ 
               
               
                 labor demands 
                 ‘demands’ and ‘issues’ 
               
               
                 public 
                 ‘location’, ‘message’, and ‘size’ 
               
               
                 demonstration 
               
               
                 strike 
                 ‘management positions’ and ‘labor positions’ 
               
               
                 workforce size 
                 ‘number effected’, ‘location’, and ‘reason’ 
               
               
                 change 
               
               
                 criminal 
                 ‘charges’, ‘prosecution stage’, and ‘defendant’ 
               
               
                 lawsuit 
                 ‘suit type’, ‘dollar value’, ‘issues’, ‘prosecution 
               
               
                   
                 stage’, ‘plaintiff’ and ‘defendant’ 
               
               
                 management 
                 ‘manager name’ 
               
               
                 departure 
                 ‘reason’ 
               
               
                   
                 “quit(s)?” 
               
               
                   
                 “resign(s|ed|ing)” 
               
               
                   
                 “fired” 
               
               
                   
                 “retir(e|es|ing|ement)” 
               
               
                   
                 “leaving” 
               
               
                   
                 “leave(s)?(?!( a))” 
               
               
                 position change 
                 ‘old position’ and ‘new position’ 
               
               
                 marketing 
                 ‘scope’ 
               
               
                 ad campaign 
                 ‘topic’ and ‘channel’ 
               
               
                 charitable donation 
                 ‘charity’ 
               
               
                 event sponsorship 
                 ‘event type’ 
               
               
                   
                 “event(?=(.*sponsorship))” 
               
               
                   
                 “sponsor(?=(.*event))” 
               
               
                 press release 
                 ‘topic’ 
               
               
                 public presentation 
                 ‘presenter’ and ‘location’ 
               
               
                 partnering 
                 ‘organizations’ 
               
               
                 agreement 
                 ‘duration’ and ‘promises’ 
               
               
                 co-branding 
                 ‘product’ 
               
               
                 joint venture 
                 ‘type’, ‘purpose’ and ‘size’ 
               
               
                   
                 “hooks up with” 
               
               
                   
                 “joint venture” 
               
               
                   
                 “joint company” 
               
               
                   
                 “partner” 
               
               
                   
                 “teaming” 
               
               
                   
                 “teams(s) ? (up )?with” 
               
               
                   
                 “join(s) forces” 
               
               
                   
                 “combines(s)? forces” 
               
               
                   
                 “alliance” 
               
               
                 merger 
                 ‘acquirer’ and ‘price’ 
               
               
                 feature 
                 ‘feature’ 
               
               
                 price 
                 ‘old price’ and ‘new price’ 
               
               
                 product 
                 ‘reason’ 
               
               
                 discontinuation 
                 “discontinu(e|es|ed|ing)” 
               
               
                   
                 “exit(s|ed|ing)? the market” 
               
               
                 product introduction 
                 ‘features’, ‘competitors’, and ‘price’ 
               
               
                 production location 
                 ‘old location’ and ‘new location’ 
               
               
                 change 
               
               
                 product promotion 
                 ‘type’, ‘duration’, ‘target’, and ‘objective’ 
               
               
                 ad campaign 
                 ‘focus’ and ‘channels’ 
               
               
                 rebate 
                 ‘amount’ 
               
               
                   
                 “rebate(s)?” 
               
               
                   
                 “mail-in” 
               
               
                 award 
                 ‘award’ and ‘award source’ 
               
               
                   
                 “award(?!(-| )?winning)” 
               
               
                   
                 “award(s)?” 
               
               
                   
                 “winner(s)?” 
               
               
                   
                 “win(s)?(?=.*(prize|award|honor|best))” 
               
               
                   
                 “rates(?=.*(top|best|highest))” 
               
               
                   
                 “name(s|d)(?=.*of the year)” 
               
               
                   
                 “name(s|d)(?=.*best)” 
               
               
                   
                 “best(?=.*(of 20\d\d))” 
               
               
                   
                 “best(?=.*(of the year))” 
               
               
                   
                 “honor(ed|s)?” 
               
               
                   
                 “most respected” 
               
               
                   
                 “prize(s)?” 
               
               
                   
                 “world(-| )record” 
               
               
                   
                 “excellence in” 
               
               
                 design flaw or 
                 ‘plants’ 
               
               
                 manufacturing flaw 
               
               
                 recall 
                 ‘problem type’, ‘number’, and ‘cost of remedy’ 
               
               
                   
                 “recall” 
               
               
                   
                 “recall(ing|s)” 
               
               
                 review 
                 “(magazine|product) review(s)?” 
               
               
                   
                 “comparo” 
               
               
                   
                 “comparison” 
               
               
                   
                 “round(-)?up” 
               
               
                 cultural trend 
                 ‘demographic’, and ‘strength’ 
               
               
                 attitude change 
                 ‘target’ and ‘change direction’ 
               
               
                 demographic change 
                 “demographic change” 
               
               
                   
                 “demographic trend” 
               
               
                   
                 “change in demographic(s)?” 
               
               
                   
                 “changing demographic(s)?” 
               
               
                 environmental 
                 ‘scope’, ‘regulatory agency’, ‘new restriction’, ‘old 
               
               
                   
                 restriction’, and ‘estimated cost’ 
               
               
                   
                 “kyoto treaty” 
               
               
                   
                 “global warming” 
               
               
                   
                 “environmental” 
               
               
                   
                 “epa” 
               
               
                   
                 “e.p.a.” 
               
               
                   
                 “greenhouse” 
               
               
                   
                 “greenpeace” 
               
               
                   
                 “fuel economy” 
               
               
                   
                 “energy consumption” 
               
               
                   
                 “emission(s)?” 
               
               
                   
                 “eco-” 
               
               
                 technology trend 
                 ‘technology’ 
               
               
                 obsolescence 
                 ‘reason’ and ‘replacement technology’ 
               
               
                 technology 
                 ‘technology replaced’ 
               
               
                 development 
               
               
                   
               
            
           
         
       
     
     The environment model  130  defines entities and the relationships between entities. Table 10 shows an example of an XML definition of entities. 
     
       
         
           
               
             
               
                 TABLE 10 
               
               
                   
               
             
            
               
                 &lt;entityDefs&gt; 
               
               
                  &lt;orgDef ID=“CBA” OneSourceID=“153329” Identifiers=“Brilliance China” 
               
               
                 FullName=“Brilliance China Automotive Holding” DisplayedName=“Brilliance China” /&gt; 
               
               
                  &lt;orgDef ID=“CTB” OneSourceID=“8035” Identifiers=“Cooper” 
               
               
                 FullName=“Cooper Tire and Rubber company” DisplayedName=“Cooper” /&gt; 
               
               
                  &lt;orgDef ID=“DCX” OneSourceID=“88129” Identifiers=“DaimlerChrysler; 
               
               
                 Chrysler” FullName=“DaimlerChrysler AG” DisplayedName=“DaimlerChrysler” 
               
               
                 /&gt; 
               
               
                  &lt;orgDef ID=“DENSO” OneSourceID=“91196” Identifiers=“Denso” 
               
               
                 FullName=“Denso Corporation” DisplayedName=“Denso” /&gt; 
               
               
                  &lt;orgDef ID=“DPH” OneSourceID=“42787936” Identifiers=“Delphi” 
               
               
                 FullName=“Delphi Corporation” DisplayedName=“Delphi” /&gt; 
               
               
                  &lt;orgDef ID=“F” OneSourceID=“12338” Identifiers=“Ford Motor Company; 
               
               
                 Ford; FoMoCo” FullName=“Ford Motor Company” DisplayedName=“Ford” /&gt; 
               
               
                  &lt;orgDef ID=“FIA” OneSourceID=“63645” Identifiers=“Fiat” FullName=“Fiat 
               
               
                 S.p.A.” DisplayedName=“Fiat” /&gt; 
               
               
                  &lt;orgDef ID=“FSS” OneSourceID=“10965” Identifiers=“Federal Signal” 
               
               
                 FullName=“Federal Signal Corporation” DisplayedName=“Federal Signal” /&gt; 
               
               
                  &lt;orgDef ID=“GM” OneSourceID=“13001” Identifiers=“General Motors Corp.; 
               
               
                 General Motors Corporation; General Motors Corp; General Motors; GM” 
               
               
                 FullName=“General Motors Corporation” DisplayedName=“GM” /&gt; 
               
               
                  &lt;orgDef ID=“GT” OneSourceID=“13495” Identifiers=“Goodyear” 
               
               
                 FullName=“Goodyear” DisplayedName=“Goodyear” /&gt; 
               
               
                  &lt;orgDef ID=“HMC” OneSourceID=“14737” Identifiers=“Honda” 
               
               
                 FullName=“Honda Motor Company, LTD” DisplayedName=“Honda” /&gt; 
               
               
                  &lt;orgDef ID=“MNC” OneSourceID=“175532” Identifiers=“Monaco” 
               
               
                 FullName=“Monaco Coach Corporation” DisplayedName=“Monaco” /&gt; 
               
               
                  &lt;orgDef ID=“NAV” OneSourceID=“169908” Identifiers=“Navistar” 
               
               
                 FullName=“Navistar International” DisplayedName=“Navistar” /&gt; 
               
               
                  &lt;orgDef ID=“NSANY” OneSourceID=“20835” Identifiers=“Nissan” 
               
               
                 FullName=“Nissan Motor Co., Ltd” DisplayedName=“Nissan” /&gt; 
               
               
                  &lt;orgDef ID=“OSK” OneSourceID=“21800” Identifiers=“Oshkosh” 
               
               
                 FullName=“Oshkosh Truck Corporation” DisplayedName=“Oshkosh” /&gt; 
               
               
                  &lt;orgDef ID=“PCAR” OneSourceID=“21980” Identifiers=“PACCAR” 
               
               
                 FullName=“PACCAR Incorporated” DisplayedName=“PACCAR” /&gt; 
               
               
                  &lt;orgDef ID=“PEUGY.PK” OneSourceID=“88711” Identifiers=“PSA; Peugeot; 
               
               
                 Citroen” FullName=“PSA Peugeot Citroen S.A.” 
               
               
                 DisplayedName=“Peugeot/Citroen” /&gt; 
               
               
                  &lt;orgDef ID=“SPAR” OneSourceID=“26622” Identifiers=“Spartan” 
               
               
                 FullName=“Spartan Motors Incorporated” DisplayedName=“Spartan Motors” 
               
               
                 /&gt; 
               
               
                  &lt;orgDef ID=“TRW” OneSourceID=“48618062” Identifiers=“TRW” 
               
               
                 FullName=“TRW Automotive Holdings Corp.” DisplayedName=“TRW” /&gt; 
               
               
                  &lt;orgDef ID=“TM” OneSourceID=“28470” Identifiers=“Toyota” 
               
               
                 FullName=“Toyota Motor Corporation” DisplayedName=“Toyota” /&gt; 
               
               
                  &lt;orgDef ID=“VC” OneSourceID=“43489502” Identifiers=“Visteon” 
               
               
                 FullName=“Visteon Corporation” DisplayedName=“Visteon” /&gt; 
               
               
                  &lt;orgDef ID=“VLKAY.PK” OneSourceID=“88276” Identifiers=“Volkswagen; 
               
               
                 VW” FullName=“Volkswagen AG” DisplayedName=“Volkswagen” /&gt; 
               
               
                  &lt;orgDef ID=“VOLVY” OneSourceID=“30176” Identifiers=“Volvo” 
               
               
                 FullName=“Volvo AB” DisplayedName=“Volvo” /&gt; 
               
               
                  &lt;orgDef ID=“MAZDA” OneSourceID=“91679” Identifiers=“Mazda” 
               
               
                 FullName=“Mazda Motor Corporation” DisplayedName=“Mazda” /&gt; 
               
               
                  &lt;orgDef ID=“JAGUAR” DisplayedName=“Jaguar” Identifiers=“Jaguar” 
               
               
                 FullName=“Jaguar Cars Ltd” OneSourceID=“42245870” /&gt; 
               
               
                  &lt;orgDef ID=“KIA” DisplayedName=“Kia” Identifiers=“Kia” FullName=“Kia 
               
               
                 Motors Corporation” OneSourceID=“91824” /&gt; 
               
               
                  &lt;orgDef ID=“BMW” DisplayedName=“BMW” Identifiers=“Bayerische 
               
               
                 Motoren Werke; BMW; Bayerische Motoren Werke AG” 
               
               
                 FullName=“Bayerische Motoren Werke AG” OneSourceID=“88104” /&gt; 
               
               
                  &lt;orgDef ID=“HYUNDAI” DisplayedName=“Hyundai” Identifiers=“Hyundai” 
               
               
                 FullName=“Hyundai Motor Company” OneSourceID=“91815” /&gt; 
               
               
                  &lt;orgDef ID=“AICO” DisplayedName=“Amcast” Identifiers=“Amcast” 
               
               
                 FullName=“Amcast Industrial Corp” OneSourceID=“1325” /&gt; 
               
               
                  &lt;typeDef ID=“steel” Identifiers=“steel” FullName=“steel” 
               
               
                 EconomicCategory=“commodity” DisplayedName=“steel” /&gt; 
               
               
                  &lt;typeDef ID=“cars” Identifiers=“\bcar\b | automobile” FullName=“Cars / 
               
               
                 Automobiles” EconomicCategory=“” DisplayedName=“cars” /&gt; 
               
               
                  &lt;typeDef ID=“glass” Identifiers=“glass” FullName=“glass” 
               
               
                 EconomicCategory=“commodity” DisplayedName=“glass” /&gt; 
               
               
                  &lt;typeDef ID=“gg” Identifiers=“greenhouse gas; global warming; kyoto treaty” 
               
               
                 FullName=“Greenhouse Gasses” EconomicCategory=“” 
               
               
                 DisplayedName=“greenhouse” /&gt; 
               
               
                  &lt;typeDef ID=“gasoline” Identifiers=“Gasoline; Crude oil; oil well” 
               
               
                 FullName=“Gasoline” EconomicCategory=“” DisplayedName=“gasoline” /&gt; 
               
               
                  &lt;typeDef ID=“motoroil” Identifiers=“motor oil” FullName=“Motor Oil” 
               
               
                 EconomicCategory=“” DisplayedName=“motoroil” /&gt; 
               
               
                  &lt;typeDef ID=“smog” Identifiers=“smog; air pollution; clean air” 
               
               
                 FullName=“Smog / air polution” EconomicCategory=“” 
               
               
                 DisplayedName=“smog” /&gt; 
               
               
                  &lt;typeDef ID=“trucks” Identifiers=“truck” FullName=“Trucks” 
               
               
                 EconomicCategory=“” DisplayedName=“trucks” /&gt; 
               
               
                  &lt;typeDef ID=“tires” Identifiers=“tires; tire” FullName=“tires” 
               
               
                 EconomicCategory=“” DisplayedName=“tires” /&gt; 
               
               
                  &lt;brandDef ID=“Mustang” OneSourceID=“0” Identifiers=“” FullName=“” 
               
               
                 DisplayedName=“Mustang” /&gt; 
               
               
                  &lt;segmentDef ID=“Male 35-45” OneSourceID=“0” Identifiers=“” FullName=“” 
               
               
                 DisplayedName=“Male 35-45” /&gt; 
               
               
                  &lt;segmentDef ID=“Male” DisplayedName=“Male” Identifiers=“” FullName=“” 
               
               
                 OneSourceID=“0” /&gt; 
               
               
                 &lt;/entityDefs&gt; 
               
               
                   
               
            
           
         
       
     
     The XML definition shown in Table 9 specifies the list of entities in the environment model  130  and their attributes. The &lt;orgDef&gt; contains the definition of an Organization entity, the &lt;typeDef&gt; contains the definition of an ProductType entity, the &lt;brandDef&gt; contains the definition of an Brand entity, the &lt;segmentDef&gt; contains the definition of an ConsumerSegment entity, the &lt;modelDef&gt; contains the definition of a ProductModel entity, and the &lt;personDef&gt; contains the definition of an Person entity. By dividing entities into these entity types, the system  100  may specify different default relationships for each entity type. For instance, a Consumer Segment entity would generally not have a ‘competitor’ relationship with any other entity, but a Product Type or Organizational entity may. Furthermore, the distinction between entity types provides more flexibility when displaying events in the user interface  142 , which may then display, as examples, events that involve people, or products, or organizations. For each of different kind of entity (i.e., Organization, Product Types, Brand, Consumer Segments, Product Models, and Person), the environment model  130  may specify an identifier (ID), a FullName, a DisplayName, and a list of Identifiers. 
     The Identifiers are the strings that event analysis system  100  searches for to recognize the entity when parsing through an article. For example, for Acme Motors, the identifiers may include “ACME” and “Acme Motors”, both specify the same entity. The DisplayName field stores the value for the name which is displayed on the user interface  142 , while FullName gives the proper full name of the entity. The ID (e.g., a stock symbol or a short form of the entity name) is the name by which the event analysis system  100  refers to the entity. 
     For Organization entities, the environment model  130  may also specify a OneSourceID attribute. This attribute may provide a numerical identifier for the organization which the event analysis system  100  may use to look up information in OneSource online business information (www.onesource.com). The event analysis system  100  may also define, for ProductType entities, an EconomicCategory attribute. This attribute may influence how the event analysis system  100  processes events involving different kinds of products and materials. 
       FIG. 4  illustrates an entity node  400  for Acme Motor Company, the ID  402  is “A” (the stock ticker for Acme). The OneSourceID  404  of 99999 allows the event analysis system  100  to connect to OneSource to find more information about Acme. The entity node  400  defines the identifiers  406  as “Acme Motor Company,” “Acme,” or “AcMoCo.” The full name field  408  gives the full name “Acme Motor Company” and the displayed name field  410  specifies “Acme”. For each entity, the event analysis system  100  may store (e.g., in an entity specific file, database entry, or other storage), a relationship entry for the entity. The ID field  402  may be used as an index or file identifier to locate the relationship entry in a database or in a file system. 
     The environment model  130  also defines relationships between entities. Table 11 shows an example of an entity relationship file for XYZ Motor, which is a competitor of Acme. 
     
       
         
           
               
             
               
                 TABLE 11 
               
               
                   
               
             
            
               
                 &lt;busNet&gt; 
               
               
                  &lt;CMNet&gt; 
               
               
                  &lt;focus&gt; 
               
               
                   &lt;organization ID=“XYZ” OneSourceID=“99999” Identifiers=“XYZ 
               
               
                 Motor Company; XYZ; XYZMoCo” FullName=“XYZ Motor Company” 
               
               
                 DisplayedName=“XYZ” /&gt; 
               
               
                  &lt;/focus&gt; 
               
               
                  &lt;Suppliers&gt; 
               
               
                   &lt;product ID=“steel” /&gt; 
               
               
                   &lt;product ID=“glass” /&gt; 
               
               
                   &lt;product ID=“tires” /&gt; 
               
               
                   &lt;organization ID=“DENSO” /&gt; 
               
               
                   &lt;organization ID=“TRW” /&gt; 
               
               
                   &lt;organization ID=“GT” /&gt; 
               
               
                   &lt;organization ID=“CTB” /&gt; 
               
               
                   &lt;organization ID=“VC” /&gt; 
               
               
                  &lt;/Suppliers&gt; 
               
               
                  &lt;Products&gt; 
               
               
                   &lt;product ID=“cars” /&gt; 
               
               
                   &lt;product ID=“trucks” /&gt; 
               
               
                  &lt;/Products&gt; 
               
               
                 &lt; Byproducts&gt; 
               
               
                   &lt;product ID=“smog” /&gt; 
               
               
                   &lt;product ID=“emissions” /&gt; 
               
               
                  &lt;/Byproducts&gt; 
               
               
                  &lt;Channels /&gt; 
               
               
                  &lt;Competitors&gt; 
               
               
                   &lt;organization ID=“Acme” /&gt; 
               
               
                   &lt;organization ID=“GM” /&gt; 
               
               
                   &lt;organization ID=“DCX” /&gt; 
               
               
                   &lt;organization ID=“TM” /&gt; 
               
               
                   &lt;organization ID=“HMC” /&gt; 
               
               
                   &lt;organization ID=“FIA” /&gt; 
               
               
                   &lt;organization ID=“NSANY” /&gt; 
               
               
                   &lt;organization ID=“PEUGY.PK” /&gt; 
               
               
                   &lt;organization ID=“VLKAY.PK” /&gt; 
               
               
                   &lt;organization ID=“CBA” /&gt; 
               
               
                   &lt;organization ID=“KIA” /&gt; 
               
               
                   &lt;organization ID=“BMW” /&gt; 
               
               
                   &lt;organization ID=“HYUNDAI” /&gt; 
               
               
                  &lt;/Competitors&gt; 
               
               
                  &lt;Substitutes /&gt; 
               
               
                  &lt;Consumers /&gt; 
               
               
                  &lt;Complements&gt; 
               
               
                   &lt;product ID=“gasoline” /&gt; 
               
               
                   &lt;product ID=“motoroil” /&gt; 
               
               
                  &lt;/Complements&gt; 
               
               
                  &lt;Issues /&gt; 
               
               
                  &lt;Subsidiaries&gt; 
               
               
                   &lt;organization ID=“VOLVY” /&gt; 
               
               
                   &lt;organization ID=“JAGUAR” /&gt; 
               
               
                   &lt;organization ID=“MAZDA” /&gt; 
               
               
                  &lt;/Subsidiaries&gt; 
               
               
                  &lt;ParentCompany /&gt; 
               
               
                  &lt;/CMNet&gt; 
               
               
                 &lt;/busNet&gt; 
               
               
                   
               
            
           
         
       
     
     The busNet and CMNet nodes are reserved for future expansion, and  FIG. 4  (in conjunction with Table 11) shows the configuration of an entity relationship  412 . The focus node  414  verifies that the content of the relationship entity file is for XYZ Motor Company (and not some other entity). The remainder of the relationship entity file is subdivided by relationship type. Examples are shown in Table 11; any other relationships may be defined and established in the relationship entity file.  FIG. 4  shows that the entity relationship  412  may define relationships of other entities, places, or things to Acme using a Suppliers field  416  (e.g., “steel”), Products field  418  (e.g., “cars”), ByProducts field  420  (e.g., “emissions”), Competitors field  422  (e.g., “ABC motor”), Substitutes field  424 , Consumers field  426 , Complements field  428  (e.g., “gasoline”), Issues field  430 , Subsidiaries field  432  (e.g., “Volvo”, “Jaguar”, and “Mazda”), and Parent company field  434 . 
     In the examples shown in Table 11, each relationship section is populated with the IDs of the entities that fulfill that relationship. For example, Table 11 establishes that Acme has three defined subsidiaries, Volvo, Jaguar, and Mazda. In other words, Acme is related to Volvo, Jaguar, and Mazda by the relationship of parent to subsidiary. As another example, the entity relationship establishes Acme as a competitor to XYZ using the Competitors field. The event analysis system  100  may employ the IDs as a database key, search term, or filename to navigate through the model  130  and to locate additional information about the entities. For example, knowing that XYZ Motor is a competitor of Acme Motor, the event analysis system may then open a file keyed off of the ID (e.g., “XYZ.xml”) to determine XYZ&#39;s suppliers. 
     The event analysis system  100  uses the event implication model  132  to determine when certain types of events with particular attributes signal the possibility of other events occurring in the future. As one example, if a CEO of a competitor is recruited to head another competitor, it is reasonable to infer that there is an increased chance of the two competitors merging, sharing technology, or otherwise working together. The event implication model  132  establishes rules for making the inferences. 
     As an overview, the event analysis system  100  adds messages to events. The messages explain that an inference can be made from the event, and/or describe the inference. In addition, the event analysis system  100  creates new detected events which the event analysis system  100  may display along with events directly determined from an original article. Furthermore, the event analysis system  100  may inject the inferred events back into the implication processing flow so that the inferred event may generate additional detected events. 
     The event implication engine  138  matches trigger events to possible implications defined in the implication models  132 . An example of an implication model  132  is shown below in Table 12. 
     
       
         
           
               
             
               
                 TABLE 12 
               
               
                   
               
             
            
               
                 &lt;ThreatAndOpportunityModel&gt; 
               
               
                  &lt;ThreatAndOpportunityItem item=“IF company hires new CFO THEN 
               
               
                 possible merger”&gt; 
               
               
                  &lt;Constraints&gt; 
               
               
                   &lt;EventConstraint eventType=“Recruit”&gt; 
               
               
                   &lt;AttributeConstraints&gt; 
               
               
                    &lt;AttributeConstraint name=“New Position” type=“Match”&gt; 
               
               
                    &lt;Value&gt;CFO&lt;/Value&gt; 
               
               
                    &lt;Value&gt;Chief Financial Officer&lt;/Value&gt; 
               
               
                    &lt;Value&gt;C.F.O.&lt;/Value&gt; 
               
               
                    &lt;/AttributeConstraint&gt; 
               
               
                    &lt;AttributeConstraint name=“Previous Employer” 
               
               
                    type=“NonEmpty” /&gt; 
               
               
                    &lt;AttributeConstraint name=“Organization” type=“NonEmpty” /&gt; 
               
               
                   &lt;/AttributeConstraints&gt; 
               
               
                   &lt;/EventConstraint&gt; 
               
               
                  &lt;/Constraints&gt; 
               
               
                  &lt;Implications&gt; 
               
               
                   &lt;ImplicationMessage&gt;$Organization may be interested in purchasing 
               
               
                 $Previous_Employer.&lt;/ImplicationMessage&gt; 
               
               
                   &lt;ImpliedEvent eventType=“Merger”&gt; 
               
               
                   &lt;Attributes&gt; 
               
               
                    &lt;Attribute name=“Acquirer” value=“$Organization” /&gt; 
               
               
                    &lt;Attribute name=“Price” value=“Unknown” /&gt; 
               
               
                    &lt;Attribute name=“Organizations” value=“Unknown” /&gt; 
               
               
                    &lt;Attribute name=“Organization” value=“$Previous Employer” /&gt; 
               
               
                    &lt;Attribute name=“Date” value=“Unknown” /&gt; 
               
               
                    &lt;Attribute name=“Time” value=“1 year” /&gt; 
               
               
                    &lt;Attribute name=“Tense” value=“Unknown” /&gt; 
               
               
                    &lt;Attribute name=“Confidence” value=“Unknown” /&gt; 
               
               
                   &lt;/Attributes&gt; 
               
               
                   &lt;/ImpliedEvent&gt; 
               
               
                  &lt;/Implications&gt; 
               
               
                  &lt;/ThreatAndOpportunityItem&gt; 
               
               
                  &lt;ThreatAndOpportunityItem item=“IF competitor changes price THE 
               
               
                 change in demand”&gt; 
               
               
                  &lt;Constraints&gt; 
               
               
                   &lt;EventConstraint eventType=“ Price Change”&gt; 
               
               
                   &lt;AttributeConstraints&gt; 
               
               
                    &lt;AttributeConstraint name=“Company” type=“Relationship”&gt; 
               
               
                    &lt;Origin&gt;$FOCUS&lt;/Origin&gt; 
               
               
                    &lt;Value&gt;competitors&lt;/Value&gt; 
               
               
                    &lt;/AttributeConstraint&gt; 
               
               
                   &lt;/AttributeConstraints&gt; 
               
               
                   &lt;/EventConstraint&gt; 
               
               
                  &lt;/Constraints&gt; 
               
               
                  &lt;Implications&gt; 
               
               
                   &lt;ImplicationMessage&gt;$FOCUS may experience a change in product 
               
               
                 demand.&lt;/ImplicationMessage&gt; 
               
               
                   &lt;ImpliedEvent eventType=“Update”&gt; 
               
               
                   &lt;Attributes&gt; 
               
               
                    &lt;Attribute name=“Feature Change” value=“change in demand” /&gt; 
               
               
                    &lt;Attribute name=“Company” value=“Unknown” /&gt; 
               
               
                    &lt;Attribute name=“Products” value=“Unknown” /&gt; 
               
               
                    &lt;Attribute name=“Date” value=“Unknown” /&gt; 
               
               
                    &lt;Attribute name=“Time” value=“Unknown” /&gt; 
               
               
                    &lt;Attribute name=“Tense” value=“Unknown” /&gt; 
               
               
                    &lt;Attribute name=“Confidence” value=“Unknown” /&gt; 
               
               
                   &lt;/Attributes&gt; 
               
               
                   &lt;/ImpliedEvent&gt; 
               
               
                  &lt;/Implications&gt; 
               
               
                  &lt;/ThreatAndOpportunityItem&gt; 
               
               
                  &lt;ThreatAndOpportunityItem item=“IF company&#39;s supplier changes prices 
               
               
                 THEN company may change product price”&gt; 
               
               
                  &lt;Constraints&gt; 
               
               
                   &lt;EventConstraint eventType=“Price Change”&gt; 
               
               
                   &lt;AttributeConstraints&gt; 
               
               
                    &lt;AttributeConstraint name=“Company” type=“Relationship”&gt; 
               
               
                    &lt;Origin&gt;$FILL_Company_IS_supplier&lt;/Origin&gt; 
               
               
                    &lt;Value&gt;supplier&lt;/Value&gt; 
               
               
                    &lt;/AttributeConstraint&gt; 
               
               
                   &lt;/AttributeConstraints&gt; 
               
               
                   &lt;/EventConstraint&gt; 
               
               
                  &lt;/Constraints&gt; 
               
               
                  &lt;Implications&gt; 
               
               
                   &lt;ImplicationMessage&gt;$Company product price change (supplier) may 
               
               
                 cause $FILL_Company_IS_supplier to change product 
               
               
                 price.&lt;/ImplicationMessage&gt; 
               
               
                   &lt;ImpliedEvent eventType=“Price Change”&gt; 
               
               
                   &lt;Attributes&gt; 
               
               
                    &lt;Attribute name=“Old Price” value=“Unknown” /&gt; 
               
               
                    &lt;Attribute name=“New Price” value=“Unknown” /&gt; 
               
               
                    &lt;Attribute name=“Company” 
               
               
                    value=“$FILL_Company_IS_supplier” /&gt; 
               
               
                    &lt;Attribute name=“Products” value=“Unknown” /&gt; 
               
               
                    &lt;Attribute name=“Time” value=“$Time” /&gt; 
               
               
                    &lt;Attribute name=“Tense” value=“Unknown” /&gt; 
               
               
                    &lt;Attribute name=“Confidence” value=“Unknown” /&gt; 
               
               
                   &lt;/Attributes&gt; 
               
               
                   &lt;/ImpliedEvent&gt; 
               
               
                  &lt;/Implications&gt; 
               
               
                  &lt;/ThreatAndOpportunityItem&gt; 
               
               
                 &lt;/ThreatAndOpportunityModel&gt; 
               
               
                   
               
            
           
         
       
     
     The event implication model  132  defines individual implication items, shown in  FIG. 5 , each tagged with a ‘ThreatAndOpportunityItem’ tag. The implication items  500  establish one or more trigger constraints  502  for the triggering event to meet, and a resulting implication  504  which holds when the constraints were met. The trigger constraints  502  may be established as an event constraint  506  and one or more attribute constraints  508  for that event. The event constraint  506  searches for a match on the event type. 
     The implication items  500  may distinguish between multiple different types of attribute constraints  508 . One example is an ‘Optional’ attribute constraint. The optional attribute constraint signifies that the value of the attribute is not pivotal and may even be unknown or undefined. Optional attributes may be included so that the implications may refer to the attribute by name. 
     A second example is ‘NonEmpty’. The nonempty attribute constraint signifies that the event extraction process has returned a value for the attribute. The specific value is not pivotal. 
     A third example is ‘Match’. The match attribute constraint signifies that the extracted value for the attribute matches a listed value. There may be multiple listed values which can match the extracted value. 
     A fourth example is ‘Relationship’. The relationship attribute constraint signifies that the extracted value for the attribute conforms to a relationship defined in the environment model  130 . The extracted value serves as a target for the relationship, and the origin of the relationship is listed in the constraint. The origin of the relationship may refer to another attribute of the event. 
     The origin may include special parameters, such as “focus” and “fill” parameters. One example is: $FOCUS. The $FOCUS parameter specifies that the origin will be the focus entity of the application. For a particular relationship attribute constraint, the system  100  may define both parties in the relationship. For some event types, there may not be an attribute to define both parties. For example, in the event type definition of the Hire event, there may not be both of the companies defined as attributes. Instead, there may only be the company that is involved in the hiring. Nevertheless, the system  100  may then “lookup” the competitor relationship by specifying that the hiring company is the FOCUS. The system  100  may then check detected hire events, and when the company involved in the hiring event is a competitor to the FOCUS, the system  100  may make a match. For example, the FOCUS may be defined as Acme Motors, Inc. A particular hire event at XYZ Motors may not mention Acme, but the FOCUS entity lets the system  100  make the connection, since XYZ is a competitor of Acme. A second example is $FILL_&lt;entity&gt;_IS_&lt;relationship&gt;. This parameter specifies that the event analysis system  100  will fill in the entity from the environment model  130 . Generally, the system  100  will generate one inferred event for a triggering threat event. With the FILL functionality, however, the system  100  may generate multiple inferred events from a single triggering threat event. For example, assume an event involving company SupplierX which may have implications for several other companies (namely, for each company which has a supplier relationship with SupplierX). The system  100  may use the FILL functionality to look at the relationship model and pick out each entity that has the supplier relationship with SupplierX. Then the system  100  may generate inferred event for each of those companies. 
     The resulting implication  504  is the second part of each implication item  500 . The resulting implication  504  specifies an action to take when the attribute constraints are met. Each resulting implication  504  may specify an implication message  510  and an implication event  512 . 
     The implication message  510  may be implemented as a string (e.g., a human readable text string) that the event analysis system  100  stores in the event object for the inferred event and outputs through the user interface when there is an event match. The string may embed variables, which may specify the named attributes from the attribute constraints  508  in the trigger constraint portion  502 . In addition, the “focus” and “fill” parameters may provide variables for the implication message  510 . 
     The inferred event  512  specifies an output event which may enter the event stream, according to the format described above for events. Thus, the inferred event objects may be saved in the event database  112 , and subject to further implication as well. The implication item  500  specifies the event type for the output event and the attributes for the event. Any attribute may be left empty, may be set to a specific value, or may be filled using the attribute variable described above. 
       FIG. 6  presents an example of the acts  600  which the event processing control program  148  may take to coordinate the processing of the event analysis system  100 . The event processing control program  148  scans the information sources  116  and retrieves new articles (Act  602 ). The event processing control program  148  filters (e.g., removes) articles which are not relevant to the entities defined in the environment model  130  (Act  604 ). As a result, the analysis system  100  eliminates a significant percentage (e.g., 99% or more) of retrieved information prior to applying the event detection engine on the new articles. The event processing control program  148  initiates execution of the event detection engine  136  on the retained articles. The event detection engine  136  processes each article according to the source and type of data (e.g., text) in the article. The event detection engine  136  produces an event record according to the event model  128  for the events defined in the article, including event type, event attributes, event importance or priority, the source article which describes the event, an address for the article, and referenced organizations, and any other extracted event information (Act  605 ). Prior to performing event implication, additional filtering may be performed to retain those events which reference entities defined in the environment model  130 . In addition, the event detection engine  136  may output information specific to the type of data in which the event was detected. As one example, when the article is a text article, the output of the conversion engine may include a tokenized or parsed version of the text article. 
     The event processing control program  148  also initiates execution of the event implication engine  138  on the event records (Act  606 ). The implication engine  138  produces a description of an implied event which is added to the original event record. The implication engine  138  also generates a new event record for an inferred event. 
     The event processing control program  148  may signal other entities that newly detected events and inferred events exist (Act  608 ). The entities may be processes which consume and display events on a graphical user interface, for example. The event analysis system  100  may employ a message publication/subscription engine, web services, direct messaging or signaling, email, file transfer, or other communication techniques to notify other entities. The event analysis system  100  may thereby create a stream of events (e.g., a stream of event object data) in a form for consumption by client applications. The stream may include each event detected, or may include subsets of detected events, as specified or requested by the client application. 
     In addition, the event processing control program  148  may query for and accept manual corrections to any of the automated event detection processing (Act  610 ). For example, the event analysis system  100  may accept a correction to a time, date, or place where an event occurred from the user interface  142 . The event analysis system  100  may then update the event database  112  with the corrected event, and re-apply the implication engine  138  to the corrected event. (Note that we can have manual corrections to the attributes of an event (date, time, place, etc., like you mention), but also to the event type itself. For example, something that is actually a Hire event may be incorrectly machine classified as a Joint Venture event. Accordingly, manual corrections may also apply to the event type itself, and an operator may, for example, change a Joint Venture event to a Hire event, or may make any other change to an event classification. 
       FIG. 7  shows an event object  700  which the event analysis system  100  may create, save in the event database  112 , and update as a new article is processed. The event object  700  may include a title field  702 , which stores a title associated with the article; a link field  704 , which stores a link (e.g., a url link) which specifies where the article may be found; and a description field  706 , which stores a description of the event. In addition, the event object  700  may include a source type field  708 , which identifies the type of source from which the article was obtained; an entity identifier field  710  which stores an identifier of an entity involved in the event; and an event type field  712 , which stores an identifier of the type of event represented in the article. Different, fewer, or additional fields may be provided in any particular implementation. As examples, the additional fields may specify full article text, numerical data, or other data. 
     The event object  700  also includes an event type probability field  714 , which identifies how certain the event analysis system  100  is that the event occurred; an importance field  716 , which specifies how important the event is; and a public interest field  718 , which specifies the level of public interest in the event. The event object  700  further includes a tokenized title  720 , which stores the title of the article broken down into tokens; a tokenized description  722 , which stores the description of the article broken down into tokens; and attribute fields such as an attribute list  724 , which holds the attribute values for the event represented by the event object. The event object  700  may also include an implication message list  726 , which stores messages returned by the implication process; and an extracted entities list  728 , which stores identifiers of entities involved in the event. 
     The fields shown in  FIG. 7  are examples only. The event object  700  may include additional, fewer, or different fields. 
       FIG. 8  shows an example of the acts  800  which the event detection engine  136  may take. The event detection engine  136  analyzes each article and responsively prepares a new event object  700 . An example of the processing is given below, assuming an article received from an RSS information source  116  illustrated in Table 13. 
     
       
         
           
               
             
               
                 TABLE 13 
               
               
                   
               
             
            
               
                 &lt;item&gt; 
               
               
                    &lt;title&gt;ACME Cutting Jobs&lt;/title&gt; 
               
               
                    &lt;link&gt;http://us.rd.abcnewspaper.com/dailynews/rss/business/ 
               
               
                 20051121/autos/Acme01.html&lt;/link&gt; 
               
               
                    &lt;description&gt; ACME Motors Corp. said on Monday it would cut 
               
               
                 5,000 manufacturing jobs and close two plants in Asia as it struggles to 
               
               
                 compete with XYZ Motors.&lt;/description&gt; 
               
               
                    &lt;author&gt;&lt;/author&gt; 
               
               
                    &lt;pubDate&gt;11/21/2005 2:44 PM&lt;/pubDate&gt; 
               
               
                    &lt;comments&gt; &lt;/comments&gt; 
               
               
                    &lt;read&gt;False&lt;/read&gt; 
               
               
                    &lt;date&gt;11/21/2005 5:22 PM&lt;/date&gt; 
               
               
                    &lt;importance&gt;0&lt;/importance&gt; 
               
               
                 &lt;/item&gt; 
               
               
                   
               
            
           
         
       
     
     The event detection engine  136  parses the article and extracts the title, link, description, and source type information by locating the corresponding xml tags in the article (Act  802 ). The event detection engine  136  then completes the title field  702 , link field  704 , description field  706 , and source type field  708 . Table 14 shows the newly created event object. 
     
       
         
           
               
             
               
                 TABLE 14 
               
               
                   
               
             
            
               
                 businessEvent class 
               
               
                 title = ACME Cutting Jobs (Reuters) 
               
               
                 link = 
               
               
                 http://us.rd.abcnewspaper.com/dailynews/rss/business/20051121/autos/ 
               
               
                 Acme01.html 
               
               
                 description = ACME Motors Corp. said on Monday it would cut 5,000 
               
               
                 manufacturing jobs and close two plants in Asia as it struggles to compete 
               
               
                 with XYZ Motors.. 
               
               
                 sourceType = RSS 
               
               
                   
               
            
           
         
       
     
     The event detection engine  136  then filters the events generated by the initial analysis phase (Act  804 ). In particular, the event detection engine  136  retains those event objects which include an entity defined in the environment model  130  (e.g., Acme motor company) and that does not include any exclusion phrases (e.g., “Acme modeling agency”) which may also be defined in the event model  130 . The event detection engine  136  may perform regular expression pattern matching to search the event object (e.g., the description field  706 ) for entities defined in the environment model  130 . Accordingly, the event detection engine  136  applies a filter to the articles received from the information sources  116 . Specifically, the event detection engine  136  retains those articles and corresponding event objects which are relevant to the entities defined in the environment model  130 . 
     During the filtering process, the pattern matching process identifies entities in the event object which are defined in the environment model  130 . As a result, the event detection engine  136  may set the entity identifier field  710  to an entity identifier (e.g., “Acme”) located in the description field  706  (i.e., entityID=“Acme”). The system  100  may assign the entityID for an event to the first entity found in that event. For example, an event created by the text “Acme and XYZ Motors to merge” may have only Acme as its entityID. In an alternate embodiment, the system  100  may implement each event&#39;s entity field as a list including each entity discovered in the article (e.g., a list with two members: Acme and XYZ). 
     Next, the event detection engine  136  removes duplicate event objects (Act  806 ). Because an article may appear multiple times across multiple dates from multiple RSS feeds, the same article may create multiple duplicate event objects. The event detection engine  136  may eliminate duplicates by computing a hash value based on the fields of the event object (e.g., the title and description fields). The hash function may be the SHA-1 hash function, or another function which converts a string into a fixed-length (hexadecimal) number. The event detection engine  136  determines that an event object is a duplicate when hash values collide. In one implementation, each duplicate event object is removed, leaving one event object which captures the event. 
     The event detection engine  136  continues by classifying an event represented in the event object (Act  808 ). In one implementation, the event detection engine  136  may apply a classification algorithm to the description field  706 . The classification algorithm may determine, given the description, whether the description belongs to a specified class (e.g., a particular event defined in the event model  128 ). The classification algorithm may implement, for example, the naïve Bayes algorithm for document classification. The classification algorithm may be implemented with the opensource Rainbow classification engine, or any other classification engine. 
     The classification algorithm may provide not only the classification of the event, but also the reliability or probability of a correct classification. Thus, the classification algorithm may provide information for the event type field  712  and the event type probability field  714 . As examples: eventType=‘workforce size change’ and eventTypeProbability=0.99. 
     The event detection engine  136  also applies an attribute extraction program to extract attributes for the event (e.g., obtained from the description field  706 ) (Act  810 ). The extracted attributes build the attribute list  724 . In one implementation, the event detection engine  136  tokenizes the title (Act  812 ) and tokenizes the description (Act  814 ) as part of the attribute extraction process. 
     To that end, a tokenizing engine breaks the title and event description into a tokenized description, including words, numbers, punctuation, and/or other tokens, and adds the tokens to the tokenized title  720  and tokenized description  722 . A natural language processing engine may perform the tokenizing operation. As an example, the natural language processing engine may be the opensource Natural Language Tool Kit (NLTK). In addition, a tagging engine may assign a part-of-speech tag to each token in the tokenized representations (At  816 ) to provide a tagged tokenized description. The NLTK may implement the tagging engine. 
     The event detection engine  136  also performs named entity recognition (Act  818 ). In that regard, a named entity recognition engine accepts as input an event object (e.g., including a tokenized tagged description) and performs named entity recognition on the text included in the event object. As examples, the named entity recognition engine may identify personal names, company names, or geographical locations within the text. The named entity recognition engine adds information tags to the event object which describe which named entities exist and where they are located to the event object. In one implementation, the named entity recognition engine may be the ClearForest engine available from ClearForest Corp. of Waltham, Mass. 
     A parsing engine, which takes as input an event object, identifies the grammatical structure of the text (Act  818 ). For example, the parsing engine may identify noun phrases, verb phrases, or other grammatical structure within a sentence. The parsing engine adds grammatical structure tags to identify the detected structures and identifies which words make up the structures. The tags are added to the event object  700 . The parsing engine may be implemented with a statistical natural language parser, such as the Collins parser. 
     Next, the event detection engine  136  performs template matching (Act  820 ). The event detection engine  136  may process an event object and determine a matching event type in the event model  128 . The matching event type defines the attributes for the event. The template matching may then add each attribute and a detected value for the attribute to the event object (Act  822 ). In one implementation, the system  100  performs template filling as described next. Assume that the system  100  has already identified all instances of all data types within the event text, where the data types may be a pre-defined “class” of words or terms. For instance, “person” is a data type, and “John Doe” is an instance of the “person” data type. Other examples of data types include date, company name, and job title. 
     Assume also that preMarkers and postMarkers for all event types have been identified. These markers are phrases that are defined in the business event model  128 , and indicate possible positions of event attribute values. 
     The system  100  then examines the event model  128  to determine what attributes the system  100  should search for, for each event, based on each event&#39;s event type. For instance, if the event has been classified as belonging to the “Hire” event type, then the system  100  may search for five attributes: person, new employer, previous employer, new job title, and the date that the hire becomes effective. 
     The event model  128  specifies what data type each of these attributes will have. As examples, the person attribute will be filled with a value that belongs to the person data type, and the new employer and previous employer attributes will be filled with values that belong to the company name data type. 
     For each attribute, the system  100  determines whether it has identified any instances of the data type associated with that attribute. For instance, if the system  100  is searching to find the value for the “New Employer” attribute, the system  100  determines if it has identified any instances of the “company” data type. If the system  100  has identified 0 instances of that data type, the system assigns the value ‘unknown’. 
     If the system  100  has identified one instance of that data type, the system  100  checks whether that instance has already been determined to be the value of a different attribute. If so, the system  100  assigns the value ‘unknown’. If the instance has not been used for another attribute, the system  100  may assume that that instance is the value for our current attribute, and the system  100  may assign the value to the current attribute. If the system  100  has identified more than one instance of the right kind of data type, then the system  100  determines whether there are any preMarkers that fall immediately before an instance of the data type, or if there are any postMarkers that fall immediately after an instance of the data type. For example, the phrase “will be joining” may be a preMarker for the “New Employer” attribute. Thus, if an instance of the “company name” data type comes immediately after the phrase “will be joining” in the event text, the system  100  may assume that that instance is the value for the “New Employer” attribute. Similarly, if “will join” is a postMarker for the “Person” attribute, and if the system  100  sees an instance of the “Person” data type followed by the phrase “will join”, the system  100  may assume that that instance of the “Person” data type is the value for the “Person” attribute. Thus, the term “Person” is used both as a data type and as an attribute name. If the system  100  does not find any cases of an instance of the right data type either preceded by a preMarker or followed by a postMarker, then the system  100  may assign the value of ‘unknown’. 
     Table 15 shows an event object, continuing the example above, processed through the template matching phase. 
     
       
         
           
               
             
               
                 TABLE 15 
               
               
                   
               
             
            
               
                 businessEvent class 
               
               
                 title = ACME Cutting Jobs (Reuters) 
               
               
                 link = 
               
               
                 http://us.rd.abcnewspaper.com/dailynews/rss/business/20051121/autos/ 
               
               
                 Acme01.html 
               
               
                 description = ACME Motors Corp. said on Monday it would cut 5,000 
               
               
                 manufacturing jobs and close two plants in Asia as it struggles to compete 
               
               
                 with XYZ Motors.. 
               
               
                 sourceType = RSS 
               
               
                 entityId = ACME 
               
               
                 eventType = Workforce size change 
               
               
                 eventTypeProbability = .99 
               
               
                 tokenizedTitle = &lt;list of tokens in title&gt; 
               
               
                 tokenizedDescription = &lt;list of tokens in description&gt; 
               
               
                 attributes = { 
               
               
                       Company: Acme Motors 
               
               
                       Labor Organization: Unknown 
               
               
                       Number Affected: 5,000 
               
               
                       Location: Asia 
               
               
                       Reason: “struggles to compete” 
               
               
                       Date: 11-21-2005 
               
               
                       Time: Unknown 
               
               
                       Tense: Report 
               
               
                       Confidence: Unknown 
               
               
                       } 
               
               
                 extractedEntities = &lt;list (dictionary) of extracted entities &gt; 
               
               
                   
               
            
           
         
       
     
     Optionally, the event analysis system  100  determines and assigns importance and/or public interest levels (e.g., from 1 to 5) to the detected event (Act  824 ). The buzz and sentiment engines  140  may provide estimates of the importance and public interest levels. The buzz and sentiment engines  140  may be provided by the Sentiment Monitoring System or Online Analysis System available from Accenture Technology Labs of Chicago II. Alternatively, the event analysis system  100  may communicate with an external system which performs the analysis of importance or public interest and returns the importance or public interest level, given event objects which the event analysis system  100  sends to the external system. The event analysis system  100  may thereby receive the importance and public interest levels from the external system and accordingly populate the event object in the event database  112 . In determining the importance level and public interest level, the buzz and sentiment engines  140  may consider factors such as organization size, event likelihood, number of articles reporting the same event, length of article, amount of money or personnel at issue, or other factors. 
       FIG. 9  shows an example of the acts which the event implication engine  138  may take to interpret events, starting with an input of a list of event objects. The implication engine  138  reads the implication model  138  (Act  902 ). The implication engine  138  thereby obtains a list of the implication items, trigger constraints, and resulting implications stored in the implication model  138 . 
     The event implication engine  138  obtains the next event object from the list of event objects (Act  904 ) and searches for a match. To that end, the event implication engine  138  searches for a match between the event type defined in the event type field  712  of the event object and the event type specified in the event constraints  506  of the trigger constraints  502 . 
     If the event type matches, then the event implication engine  138  also searches for a match to the attribute constraints  508  in the attribute list  724  (Act  908 ). When the event type and attributes match, the event implication engine  138  activates the resulting implications  504 . For example, the event implication engine  138  may generate an implication message (Act  910 ) and then insert the implication message into the event object which triggered the implication (Act  912 ). In addition, the implication engine  138  generates one or more inferred events flowing from the matched trigger constraint  502  (Act  914 ). The inferred events give rise to new event objects which are inserted into the event database  112 . Thus, a single originally detected event may result in many inferred events, as each new inferred event is processed by the implication engine, established as a new event object, stored in the event database  112 , and processed by the implication engine. 
     In the process of matching attributes, the event implication engine  138  creates a temporary variable list, and creates an attribute variable for each attribute named in the constraints. In addition, the event implication engine  138  creates the focus and fill variables as they are encountered. The focus and fill variables are bound with the actual attribute values from the triggering event (or from the entity-relationship model for the fill variables). Thus, the event implication engine  138  may use the variables in the implications analysis, having bound the variable values in the constraints analysis. 
     Table 16 shows detailed pseudo-code for the event implication engine  138 . 
     
       
         
           
               
             
               
                 TABLE 16 
               
               
                   
               
             
            
               
                 (Input: event list) 
               
               
                 read interpretation model 
               
               
                 create empty impliedEvents list 
               
               
                 for each trigger event in event list: 
               
               
                  for each implication item in implication model: 
               
               
                   if trigger event type matches implication item event type: 
               
               
                    eventMatch = True 
               
               
                    create empty attributes variable dictionary 
               
               
                    create empty fill variables dictionary 
               
               
                    for each constraint in implication item: 
               
               
                     get event attribute value for this constraint item 
               
               
                     if constraint type = “NonEmpty”: 
               
               
                      if event attribute value is unknown: 
               
               
                       eventMatch = False 
               
               
                      else: 
               
               
                       insert attribute-value pair into attribute variable dictionary 
               
               
                     if constraint type = “Optional”: 
               
               
                      insert attribute-value pair into attribute variable dictionary 
               
               
                     if constraint type = “Match”: 
               
               
                      if event attribute value does not match one of the listed values: 
               
               
                       eventMatch = False 
               
               
                      else: 
               
               
                       insert attribute-value pair into attribute variable dictionary 
               
               
                     if constraint type = “Relationship”: 
               
               
                      if origin is not “fill” type: 
               
               
                       get origin and relationship from implication item 
               
               
                       lookup origin-target relationship in entity-relationship 
               
               
                       model 
               
               
                       if relationship does not exist: 
               
               
                        eventMatch = False 
               
               
                       else: 
               
               
                        insert attribute-value pair into attribute variable 
               
               
                        dictionary 
               
               
                      if origin is “fill” type: 
               
               
                       lookup all possible relationship origins for given target and 
               
               
                 relationship 
               
               
                       if no results: 
               
               
                        eventMatch = False 
               
               
                       else: 
               
               
                        insert values into fill variables dictionary 
               
               
                    if eventMatch = True: 
               
               
                     generate implication message, populating variables from variable 
               
               
                 dictionaries 
               
               
                     insert implication message string into original trigger event 
               
               
                     create generated event list with one event 
               
               
                     fill out non-attribute event info 
               
               
                     for each attribute in implication event: 
               
               
                      if value is not “fill” type: 
               
               
                       if value is a variable: 
               
               
                        look up in variable dictionary 
               
               
                       else: 
               
               
                        use string as written in model 
               
               
                       set corresponding attribute for each event in generated event 
               
               
                 list 
               
               
                      if value is “fill” type: 
               
               
                       create empty temp event list 
               
               
                       for each event in generated event list: 
               
               
                        for each entry in fill variable list in fill variable 
               
               
                        dictionary: 
               
               
                         create event copy 
               
               
                         set corresponding attribute to fill entry 
               
               
                         append new event copy to temp event list 
               
               
                       replace generated event list with temp event list 
               
               
                     append items in generated event list to main impliedEvents list 
               
               
                 insert impliedEvents back into original event list 
               
               
                   
               
            
           
         
       
     
     The event analysis system  100  also facilitates the display of events, including inferred events. To that end, the event analysis system  100  may produce output files which drive the display of events on the user interface  142 , or which the event portal  122  may use to display event and event information on the portal user interface  124 . 
       FIG. 10  shows an example of the acts which the event display preparation engine  146  may take to create the output files. The event display preparation engine  146  obtains the list of event objects, including each event detected by the event detection engine  136  and the inferred events determined by the event implication engine  138  (Act  1002 ). The event display preparation engine  146  writes the contents of the event objects in the list to specific XML display files. In other implementations, however, the event display preparation engine  146  writes the contents of the event objects into database tables and fields organized, for example, by entity relationship type for further processing by the rendering engine  144  or event portal  122 . 
     In one implementation, the event display preparation engine  146  adds the contents of the event objects to specific XML display files created for each relationship type established in the environment model  130 . As examples, there may be an XML display file for Competitors, Suppliers, Products, Consumers, Subsidiaries, or any other defined relationship. The event analysis system  100  particular display file chosen to hold the event data depends on the relationship between the event entity and the focus. 
     The focus entity is the entity on behalf of which the event analysis system  100  detects and infers events. For example, the focus may be XYZ motors, a competitor of Acme motors. The event analysis system  100  may then detect, infer, and display events as they affect XYZ motors. The focus may be set before event detection and implication occurs. In other implementations, the focus may be selected through the user interface  142 , and the processing system  100  will initiate the corresponding changes to the reports generated on the display  106  or communicated through the event portal  122 . 
     Accordingly, the display preparation engine  146  determines the focus (Act  1004 ), obtains the next event object in the event object list (Act  1006 ), and determines the entity specified in the event object (Act  1008 ). Knowing the focus and the event entity, the display preparation engine  146  may determine the relationship between the focus and the event entity (Act  1010 ). To that end, the display preparation engine  146  may search the environment model  130  for an entity relationship between the focus and the event entity. In the example above, Table 11 defined the environment for XYZ Motor Company. In particular, the environment established a Competitors relationship between XYZ and Acme. More generally, the display preparation engine  146  determines the relationship between the focus and the event entity by searching the environment model  130  (Act  1010 ). 
     As a result, the display preparation engine  146  identifies the specific XML display file in which to write the event data. In the example above, the XML display file is the Competitors display file. The display preparation engine  146  writes the event data from the event object into the display file (Act  1012 ). Specifically, the display preparation engine  146  may save the event type, source type, link, description, entity identifier and name, entity attribute names and values, implications and any other event data in the display file. 
     Table 17 shows an example of the contents of the display file, continuing the example above regarding the layoffs at Acme Motor Company. 
     
       
         
           
               
             
               
                 TABLE 17 
               
               
                   
               
             
            
               
                 &lt;event eventType=“Labor relations: Workforce size change”&gt; 
               
               
                    &lt;sourceInfo sourceType=“RSS”&gt; 
               
               
                       &lt;title&gt; ACME Cutting Jobs (Reuters)&lt;/title&gt; 
               
               
                       &lt;link&gt; 
               
               
                    http://us.rd.abcnewspaper.com/dailynews/rss/business/20051121/ 
               
               
                 autos/Acme01.html 
               
               
                       &lt;/link&gt; 
               
               
                       &lt;description&gt;Reuters - ACME Motors Corp. said on 
               
               
                 Monday it would cut 5,000 manufacturing jobs and close a two plants in 
               
               
                 Asia as it struggles to compete with XYZ Motors..&lt;/description&gt; 
               
               
                    &lt;/sourceInfo&gt; 
               
               
                    &lt;entityIds&gt; 
               
               
                       &lt;entityId&gt;Acme&lt;/entityId&gt; 
               
               
                    &lt;/entityIds&gt; 
               
               
                    &lt;entityDisplayName&gt;Acme Motors&lt;/entityDisplayName&gt; 
               
               
                    &lt;generalAttributes&gt; 
               
               
                       &lt;importance&gt;5&lt;/importance&gt; 
               
               
                       &lt;publicInterestLevel&gt;4&lt;/publicInterestLevel&gt; 
               
               
                    &lt;/generalAttributes&gt; 
               
               
                    &lt;typeSpecificAttributes&gt; 
               
               
                       &lt;attribute name=“Company” value=“Acme Motors” 
               
               
                 detail=“True” summary=“True” /&gt; 
               
               
                       &lt;attribute name=“Labor Organization” value=“Unknown” 
               
               
                 detail=“True” summary=“True” /&gt; 
               
               
                       &lt;attribute name=“Number Affected” value=“5,000” 
               
               
                 detail=“True” summary=“True” /&gt; 
               
               
                       &lt;attribute name=“Location” value=“Asia” detail=“True” 
               
               
                 summary=“True” /&gt; 
               
               
                       &lt;attribute name=“Reason” value=“struggles to compete” 
               
               
                 detail=“True” summary=“True” /&gt; 
               
               
                       &lt;attribute name=“Date” value=“11-21-2005” detail=“True” 
               
               
                 summary=“True” /&gt; 
               
               
                       &lt;attribute name=“Time” value=“Unknown” detail=“True” 
               
               
                 summary=“True” /&gt; 
               
               
                       &lt;attribute name=“Tense” value=“Report” detail=“True” 
               
               
                 summary=“True” /&gt; 
               
               
                       &lt;attribute name=“Confidence” value=“Unknown” 
               
               
                 detail=“True” summary=“True” /&gt; 
               
               
                    &lt;/typeSpecificAttributes&gt; 
               
               
                    &lt;implications&gt; 
               
               
                       &lt;implication&gt;Possible reduction in Acme&#39;s production 
               
               
                 costs (0 other signals of this implication detected).&lt;/implication&gt; 
               
               
                    &lt;implication&gt;Possible reduction in Acme&#39;s production capacity 
               
               
                 (0 other signals of this implication detected).&lt;/implication&gt; 
               
               
                    &lt;/implications&gt; 
               
               
                 &lt;/event&gt; 
               
               
                   
               
            
           
         
       
     
     Given the display files or database entries, the event analysis system  100  renders the events on the user interface  142 .  FIG. 11  shows an example of a competitor display  1100  rendered on the user interface  142 . The event analysis system  100  may display one or more events associated with a particular entity, events of a particular type, events within a specific date range, events obeying a particular relationship, or any other set of events. The event analysis system  100  may read a configuration file of operator preferences to determine how many events to display, what date ranges to display, or to determine any other display parameter (including, as examples, color, font size, and data position on the display). The system  100  may also accept input from the operator to select and modify one or more of these parameters. 
     The user interface  142  includes a display selector  1102 , through which an operator may select the information which will be shown on the user interface  142 . As shown in  FIG. 11 , the display selector is set to Competitors. The event analysis system  100  responds to the display selector  1102  by responsively updating the display to show the requested information, such as events involving all Competitors, Products, Suppliers (or specific individual Competitors, Products, or Suppliers), or any other relationship defined in the environment model  130 . 
     In particular the competitor display  1100  includes the Acme competitor event window  1104  and the ZZZ Motor Company competitor event window  1106 . Additional competitor event windows may be displayed, one for each competitor defined in the environment model  130  with regard to the current focus. The Acme competitor event window  1104  includes three event panes  1108 ,  1110 , and  1112 . The ZZZ competitor event window  1106  includes four event panes  1114 ,  1116 ,  1118 , and  1120 . 
     Each event pane  1114 - 1120  displays the data for an event involving a particular competitor entity as detected by the event analysis system. In addition, each event pane may display an importance indicator. As an example, the importance indicator  1122  shows that the layoffs at Acme motor company have been assigned a level  5  importance. Navigation buttons  1124  (in this case arrow buttons) allow the operator to move between multiple pages of event panes. 
     The user interface  142  may also provide drill down links. For example, any of the event panes  1108 - 1120  may operate as a drill down link when clicked. In response, the user interface  142  may display an event detail window. 
       FIG. 12  shows an example of an event detail window  1200  for the Acme layoff event. The event detail window  1200  includes an overview pane  1202 , a buzz analysis pane  1204 , and a potential implications pane  1206 . The event detail window  1200  also includes a sources pane  1208  and a source text pane  1210 . The event detail window  1200  may vary widely in content, however, and is not limited to the form shown in  FIG. 12 . 
     The overview pane  1202  specifies the event type, event importance, and event attributes for the event. The operator may change any of the entries in the overview pane  1202  using the Edit/Confirm/Cancel interface buttons  1212 . When editing the entries, the user interface  142  may provide text entry fields, drop down selection menus, selection buttons, or any other user interface element which accepts modified event data. Any modifications may feed back into the analysis engines  134 , and initiate a revised analysis (e.g., a revised implication analysis) based on the modified information. 
     The buzz analysis pane  1204  displays the public interest level with regard to the event. As noted above, the buzz and sentiment engine  140  may gauge the public interest level. Alternatively, the event analysis system  100  may obtain the buzz and sentiment level from external measurement systems. 
     The sources pane  1208  displays the article title and the article source. In the example shown in  FIG. 12 , the article title is “Acme slashing production and jobs.” The information source  116  from which the article was retrieved is identified as ABC Newspaper. The source pane  1208  may include a drop down menu from which the operator may select from multiple sources reporting the same event. The source text window  1210  displays the text of the article received from the information source  116 , while the source link button  1212  provides a mechanism by which the event reporting system  100  may open and display the original article from the original information source (e.g., by opening a web page addressed to the link at which the article is found). 
     The event analysis system  100  may interact with or provide graphical tools  150  for building any of the models described above. For example, a graphical tool to build the environment model  130  may provide a focus selector, an entity relationship selector (e.g., to choose between Competitor, Consumers, and Products relationships), and a selection pane of entity types (e.g., organizations, brands, or product types) to add to the selected entity relationship. The environment model tool may also provide a pane with interface elements which display and/or accept input to set the attributes of the entities, such as displayed name, full name, ID, identifiers, OneSourceID, and other attributes. 
       FIG. 13  shows one example of a graphical user interface front end  1300  to an environment model tool. The front end  1300  shows a focus selector  1302 , an entity type pane  1304 , and an entity type selection pane  1306 . The attributes pane  1308  displays and accepts input to set specific attributes of the entities. 
     Similarly, an event model tool may provide a tree node editing pane in which the operator may add or delete leaf and non-leaf event nodes. The event model tool may also provide a pane which displays and accepts input which defines or modifies event type properties, including colors, parent and/or child nodes, event attributes, regular expressions, weights, and any other characteristic of an event. 
       FIG. 14  shows an example of a graphical user interface front end  1400  to an event model tool. The front end  1400  includes a node editing pane  1402  for adding, modifying, and deleting nodes in the event model  128 . The front end  1400  also includes an event properties pane  1404  which displays the properties assigned to an event type. In addition, an event type editing pane  1406  displays and accepts operator input to set, change, or delete regular expressions and weights assigned to the regular expressions. 
     An implication model tool provides a graphical mechanism for an operator to define and modify elements of the event implication model  132 . To that end, the implication model tool may provide a window in which new threat and opportunity items (i.e., trigger events) are established. To that end, the implication model tool accepts operator input for selecting, defining, or modifying a trigger constraint (e.g., a New position trigger type), one or more attribute constraints (e.g., a Match or Non-Empty constraint), and attribute constraint properties, including the values which will satisfy the constraint. Furthermore, the implication model tool may accept operator input to select a corresponding resulting implication, as well as define the implication messages and events. In addition, the implication model tool also provides a selection interface for assigning trigger events to specific events defined in the event model  128 . 
       FIG. 15  shows an example of the graphical user interface front end  1500 , including an implication model window  1502  and a threat/opportunity editing window  1504 . The implication model window  1502  displays events and implication rules defined for the events. The editing window  1504  displays the implication rules, and allows the operator to add, change, or delete trigger constraints  502  and resulting implications  504 . 
     Similarly, a graphical user interface may be provided for accepting, modifying, and deleting information which establishes the information source model  126 . To that end, the system  100  may provide input interface elements for accepting an information source name, url (or other locator or specifier), a connection method (e.g., RSS, FTP, HTTP), an update frequency, a reputation level, or any other information which the operator desires to add to characterize the information source. 
     Each of the modeling tools  150  separates the user from the underlying XML code in the models  126 - 132 . The modeling tools  150  convert from the graphical elements to corresponding entries in XML statements which compose the models  126 - 132 . For example, when an operator ads the “XYZ Motor” element to the competitors list for Acme motors, the modeling tool may insert a new organization ID tag into the Competitors list for Acme in the environment model  130 . Accordingly, the operator is not burdened with writing XML to define, modify, and change the models. 
     The event analysis system  100  solves several challenging technical problems surrounding event detection and implication. The fundamental problem is to automatically detect and relate to users information about external events. One associated problem was to determine how to design models which facilitate event detection and implication. Thus, for example, the environment model  130  stores descriptions of entities that make up the competitive ecosystem for a particular industry, as well as relationships between those entities. In order to filter out events that do not reference any entities in the model, the system  100  attaches key phrases to the entities in the model. The system  100  may thereby detect when an article mentions an entity which is relevant to the operator. The relationships stored in the model help the system infer how certain events may affect particular entities, based on the location of the entities in the business ecosystem. Furthermore, the environment model  130  helps the user interface  142  display events in an organized manner. 
     Additionally, the event model  128  was built to store descriptions of the event types which the system  100  will detect and process. Each description includes key phrases through which the system  100  detects instances of that event type (in other words, classify input data as belonging to a specific event type). In addition, the event model  128  instructs the system  100  about which key pieces of information (attributes) to extract from instances of that event type. The event model  128  also provides a structure for the inference rules applied by the implication engine, and includes information on how to display each event in the user interface  142 . 
     In addition, the event implication model  132  stores interpretive rules which the system  100  applies to determine what future events may occur based on existing events. Each rule in the model may specify which event type and attribute values should match in order for the implication engine to generate an implied event from an existing detected event. Finally, the information source model  126  stores descriptions of online data sources. The information source model  126  specifies how the system collects information from the data sources and how to resolve conflicting data pulled from the data sources. 
     Another problem was viewing and maintaining the models. The modeling tools described above allow the operator to view and maintain the models in a way that does not presuppose any understanding of XML or XML-editing tools. To that end, the modeling tools provide a custom display for each of the models, and provide a rich graphical user interface through which the operators may view, add, delete, or edit entries in each of the models. The modeling tool converts the graphical elements to well-formed XML that conforms to the appropriate schemas for the models. Accordingly, the modeling tools generate XML which the system  100  may parse without requiring manual coding. 
     Another technical challenge was providing a client-specific event processing application driven by the models. The technical challenge was addressed by breaking the event processing into several distinct steps. First, the system  100  consults the information source model  126  to determine from which online sources to obtain articles. This articles are augmented with information about the source itself, such as source reliability or source importance, and then converted into a standard format to be consumed by the application&#39;s processing engines. Next, the system  100  applies the environment model  130  to filter out content which is not of interest, according to the particular industry focus for the particular system implementation. 
     After the input data stream has been filtered, the system  100  uses the event model  128  to drive the classification of the data. The result is a classification into an event type. The event model  128  includes a list of possible event types and representative text phrases to aid in the classification process. The event model  128  also drives extraction of attribute information for each event. To that end, the event model  128  includes a list of particular attributes to extract for each event type and representative text patterns to aid in the extraction process. The system  100  may then apply the event implication model  132  to drive the inference of new potential events from existing events. In the implication engine, each inference rule contained in the model is applied to each input event to find matches. A match results in an implied event. As described above, the event display preparation engine  146  then creates output XML files which drive the user interface  142  to report the events. 
     It is therefore intended that the foregoing detailed description be regarded as illustrative rather than limiting, and that it be understood that it is the following claims, including all equivalents, that are intended to define the spirit and scope of this invention.