Patent Publication Number: US-9900237-B2

Title: Spam flood detection methodologies

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of Ser. No. 14/021,941, filed Sep. 9, 2013, which claims the benefit of U.S. provisional patent application No. 61/701,508, filed Sep. 14, 2012. 
    
    
     TECHNICAL FIELD 
     Embodiments of the subject matter described herein relate generally to a computer implemented methodology for detecting the occurrence of a spam flood that involves content posted on websites, blogs, forums, and social networking sites. 
     BACKGROUND 
     The Internet is a source of much useful information. However, the content of the internet is also polluted with spam data. Spam filtering is therefore crucial as it helps to reduce the level of noise contained in information obtained by web crawling. A “spam flood” refers to a phenomena where hundreds or thousands of spam web pages are introduced into the Internet over the span of a few hours (in turn, a spam flood can propagate into a web crawler system and cause unwanted responses). 
     Accordingly, it is desirable to have a computer implemented methodology for detecting the occurrence of a spam flood that involves spam web pages, blog posts, or the like. In addition, it is desirable to provide and maintain a system that is capable of dynamically responding to a spam flood in an efficient and effective manner. Furthermore, other desirable features and characteristics will become apparent from the subsequent detailed description and the appended claims, taken in conjunction with the accompanying drawings and the foregoing technical field and background. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       A more complete understanding of the subject matter may be derived by referring to the detailed description and claims when considered in conjunction with the following figures, wherein like reference numbers refer to similar elements throughout the figures. 
         FIG. 1  is a simplified schematic representation of a portion of a webcrawler system that incorporates a spam flood detection feature; 
         FIG. 2  is a simplified schematic representation of an exemplary computing system suitable for implementing spam flood detection; 
         FIG. 3  is a flow chart that illustrates an exemplary embodiment of a spam flood detection process; 
         FIG. 4  is a flow chart that illustrates another exemplary embodiment of a spam flood detection process; and 
         FIG. 5  is a flow chart that illustrates yet another exemplary embodiment of a spam flood detection process. 
     
    
    
     DETAILED DESCRIPTION 
     The subject matter presented here generally relates to webcrawling technology that analyzes websites, webpages, website content, blogs, and the like. A blog is a discussion or collection of information published on the Internet, consisting of a series of discrete entries, called posts, typically displayed in reverse chronological order so the most recent post appears first. Crawlers can obtain updated information from a blog through its Rich Site Summary (RSS) feed. An RSS feed normally includes information such as summarized text, the publication date of a post, and the name of the author. RSS data can be utilized to support the techniques and methodologies described in more detail below. 
     Instead of considering each individual post, certain “overall” or “global” features or characteristics of a blog or website may be analyzed to detect spam floods. Specifically, two methods are presented here: (1) a methodology that eliminates blogs with a high volume and high frequency of posts; and (2) a methodology that investigates the hypertext markup language (HTML) signatures of blog sites. Regarding the first approach, a blog will be considered to be spam if it generates a very high number of posts in a short period of time. Research has shown that most (if not all) blogs are spam if the number of posts is greater than 700 and if the average amount of time between two posts is less than a minute. Accordingly, the first approach determines these factors for a given blog and flags or filters the blog if the factors satisfy the criteria associated with a spam blog. Regarding the second approach, HTML signatures are useful to address the scenario where many spam webpages are generated with spam content embedded into a fixed HTML template. This trick allows spammers to create thousands of different blogs within a short period of time, with each blog having only a few posts. The first approach described above may not be effective in detecting these types of spam blogs, because the volume and frequency of posts in each individual spam blog are not high. However, the HTML signature (which is a characterization of the website or webpage formatting) of each blog can be used to detect this form of spam flood. In accordance with this second methodology, a subset of HTML tags is selected for use in generating the signature of the particular webpage. A hashed version of the selected tags is stored in a shared cache for a fast lookup. Posts are marked as spam if they have the same hashed signature as one whose volume is greater than a threshold within a predefined time interval. 
     Turning now to the drawings,  FIG. 1  is a simplified schematic representation of a portion of a webcrawler system  100  that incorporates a spam flood detection feature. The system is computer-based and computer-implemented in that it may include any number of computing systems, devices, or components.  FIG. 1  depicts a data acquisition module  102  of the system  100 , which is suitably configured to obtain data and information from any number of online sources via a network  104 . It should be appreciated that the network  104  may utilize any number of network protocols and technologies (e.g., a local area network, a wireless network, the Internet, a cellular telecommunication network, or the like). Although not always required, the network  104  provides Internet access to the data acquisition module  102  such that the system  100  can perform webcrawling to obtain content data, metadata, and other types of information regarding different websites, blogs, webpages, and other online sources as needed. 
     The system  100  includes or cooperates with one or more databases  106  and one or more indices  108  that are utilized to store and index information obtained and processed by the data acquisition module  102 . Although not shown in  FIG. 1 , the databases  106  and indices  108  may communicate with other processing modules, software, application program interfaces, user interfaces, and the like for purposes of organizing, formatting, and presenting information to a user. Such elements, components, and functionality are commonly utilized with conventional webcrawling systems and applications and, therefore, they will not be described in detail here. 
     The data acquisition module  102  may be implemented as a suitably configured module of a computing system. In this regard, the data acquisition module  102  can be realized as a software-based processing module or logical function of a host computer system. The data acquisition module  102  performs a number of conventional data acquisition and processing functions that need not be described in detail here. In addition to such conventional functionality, the data acquisition module  102  also performs certain noise filtering techniques, which are schematically depicted as a noise filtering module  110  in  FIG. 1 . In accordance with some embodiments, the noise filtering module  110  is configured to detect and manage spam floods. This functionality is schematically depicted in  FIG. 1  as a spam flood detection module  112 . The spam flood detection module  112  determines a spam flood and, in response to the determination, flags, marks, or otherwise identifies the source (e.g., a blog, a website, user posts, or other online content items) as spam. The data related to the spam content can be saved and processed as usual, but the flagging of spam content allows the user to filter spam content if so desired while viewing, organizing, or analyzing webcrawler data. 
       FIG. 2  is a simplified schematic representation of an exemplary computing system  200  that is suitable for implementing the spam flood detection techniques described herein. In this regard, the spam flood detection module  112  may be implemented as software-based processing logic that is written and configured to support the various spam flood detection techniques and methodologies described in more detail below. In certain embodiments, therefore, the spam flood detection module  112  is realized using a computer readable medium having appropriately written computer-executable instructions stored thereon. When the computer-executable instructions are executed by a processor of the computing system  200 , the spam flood detection methods described herein are performed. Thus, the computing system  200  described here could be used to implement the data acquisition module  102  shown in  FIG. 1 . Moreover, a client device or a user device could be configured in accordance with the general architecture shown in  FIG. 2 . 
     The computing system  200  is only one example of a suitable operating environment and is not intended to suggest any limitation as to the scope of use or functionality of the inventive subject matter presented here. Other well-known computing systems, environments, and/or configurations that may be suitable for use with the embodiments described here include, but are not limited to, personal computers, server computers, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. 
     The computing system  200  and certain aspects of the exemplary spam flood detection module  112  may be described in the general context of computer-executable instructions, such as program modules, executed by one or more computers or other devices. Generally, program modules include routines, programs, objects, components, data structures, and/or other elements that perform particular tasks or implement particular abstract data types. Typically, the functionality of the program modules may be combined or distributed as desired in various embodiments. 
     The computing system  200  typically includes at least some form of computer readable media. Computer readable media can be any available media that can be accessed by the computing system  200  and/or by applications executed by the computing system  200 . By way of example, and not limitation, computer readable media may comprise tangible and non-transitory computer storage media. Computer storage media includes volatile, nonvolatile, removable, and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by the computing system  200 . Combinations of any of the above should also be included within the scope of computer readable media. 
     Referring again to  FIG. 2 , in its most basic configuration, the computing system  200  typically includes at least one processor  202  and a suitable amount of memory  204 . Depending on the exact configuration and type of platform used for the computing system  200 , the memory  204  may be volatile (such as RAM), non-volatile (such as ROM, flash memory, etc.) or some combination of the two. This most basic configuration is identified in  FIG. 2  by reference number  206 . Additionally, the computing system  200  may also have additional features/functionality. For example, the computing system  200  may also include additional storage (removable and/or non-removable) including, but not limited to, magnetic or optical disks or tape. Such additional storage is represented in  FIG. 2  by the removable storage  208  and the non-removable storage  210 . The memory  204 , removable storage  208 , and non-removable storage  210  are all examples of computer storage media as defined above. 
     The computing system  200  may also contain communications connection(s)  212  that allow the computing system  200  to communicate with other devices. For example, the communications connection(s) could be used to establish data communication between the computing system  200  and devices or terminals operated by developers or end users, and to establish data communication between the computing system  200  and the Internet. The communications connection(s)  212  may also be associated with the handling of communication media as defined above. 
     The computing system  200  may also include or communicate with various input device(s)  214  such as a keyboard, mouse or other pointing device, pen, voice input device, touch input device, etc. Although the exemplary embodiment described herein utilizes a mouse device, certain embodiments can be equivalently configured to support a trackball device, a joystick device, a touchpad device, or any type of pointing device. The computing system  200  may also include or communicate with various output device(s)  216  such as a display, speakers, printer, or the like. All of these devices are well known and need not be discussed at length here. 
     As mentioned above, an exemplary embodiment of the system  100  includes or cooperates with at least one processor and a suitable amount of memory that stores executable instructions that, when executed by the processor, support various data acquisition and spam flood detection functions. In this regard,  FIG. 3  is a flow chart that illustrates an exemplary embodiment of a spam flood detection process  300 , which may be performed by the system  100 . The various tasks performed in connection with a process described herein may be performed by software, hardware, firmware, or any combination thereof. In practice, portions of a described process may be performed by different elements of the described system. It should be appreciated that an illustrated process may include any number of additional or alternative tasks, the tasks shown in a figure need not be performed in the illustrated order, and a described process may be incorporated into a more comprehensive procedure or process having additional functionality not described in detail herein. Moreover, one or more of the tasks shown in a figure could be omitted from an embodiment of the described process as long as the intended overall functionality remains intact. 
     The process  300  collects online web data from any number of various sources (task  302 ) using any appropriate technique or technology. In this regard, the process  300  may utilize conventional webcrawling methodologies to acquire the web data, which may include content, metadata, RSS data, HTML code, and/or other information associated with: blog sites; blog posts; websites; webpages; videos; news items; social media sites; user posts (e.g., posts on a social media site such as FACEBOOK); user comments; user messages (e.g., short messages such as those generated by the TWITTER service); and/or other forms of online content items. It should be appreciated that the foregoing list is merely exemplary, and that the list is not intended to be exhaustive, restrictive, or limiting in any way. 
     The process  300  may continue by analyzing certain characteristics of a website, blog, forum, webpage, or other online content of interest (task  304 ). For the sake of simplicity and brevity, the following description assumes that the online content under investigation is a blog site. The analysis performed during task  304  is intended to determine whether the blog site represents a potential source of spam (rather than a legitimate blog site that contains valid, original, or other content that might be of interest to an end user of the webcrawling system). In certain embodiments, task  304  analyzes non-content based characteristics of the online item under analysis. Moreover, task  304  need not review or analyze the actual content being conveyed by the online item. Rather, task  304  may focus on particular global or overall characteristics of the online item that do not necessarily depend on the actual content being conveyed. In this way, the spam flood detection process  300  is agnostic with respect to the actual content conveyed by the blog site. For example, the process  300  need not capture and analyze the actual written content of blog posts. Instead, the process  300  concentrates on other detectable characteristics or metrics associated with the blog site, such as the volume of blog posts, the publication frequency of blog posts, the HTML formatting of the blog site, the arrangement or layout of HTML tags utilized by the blog site, and the like (see the following description related to  FIG. 4  and  FIG. 5 ). 
     This example assumes that the process  300  determines that at least some of the content of the blog site is spam. Accordingly, in response to the analyzing, the process  300  flags or identifies at least some content of the blog site (or website, webpage, etc.) as spam content (task  306 ). In certain situations, task  306  flags the blog site or website in its entirety as spam content. If the blog site or website under analysis includes a plurality of webpages (which is often the case), then task  306  may flag one or more webpages of the site as spam content, while leaving other webpages of the same site unflagged. In other situations, task  306  flags at least one individual post, comment, user message, or other content item (of a given blog site, website, or webpage) as spam content. Task  306  flags, marks, or otherwise indicates spam content in any suitable manner that enables the host system to quickly and efficiently identify and distinguish spam content from non-spam content. For example, task  306  may create or update metadata that designates spam content. As another example, task  306  may update the indices utilized by the host system for purposes of identifying spam content. As yet another example, task  306  could update a status property of a data object that stores content items as a way to identify the items as spam. 
     Marking the spam content is desirable to enable the host system to quickly and easily filter spam content from the stored webcrawler data (task  308 ). Notably, task  308  could filter and disregard the flagged spam content (if, for example, a user is only interested in non-spam items), or it could filter and disregard the non-spam content (if, for example, a user is interested in viewing the spam content). Regardless of the specific form of filtering performed at task  308 , the process  300  may continue by outputting the filtered webcrawler data for presentation to a user (task  310 ). The filtered data can be output in any desired human-readable format, e.g., a displayed or printed report, a saved computer-readable file, or the like. In practice, task  308  and task  310  could be performed at any time while the remaining tasks of the process  300  continue in an ongoing manner. In this way, the process  300  can monitor for occurrences of spam floods in a continuous manner while flagging spam and updating the databases and indices of the host system (see  FIG. 1 ) to enable users to view, format, and analyze the stored and flagged data at their convenience. 
       FIG. 4  is a flow chart that illustrates an embodiment of a spam flood detection process  400  that considers the post count and publication frequency of posts when checking for spam. The process  400  may be performed in conjunction with the generalized spam flood detection process  300  described above. Thus, the process  400  analyzes a blog site or a website that contains a plurality of content items (task  402 ). This example assumes that the content items are posts that appear on or in association with a blog site of interest. The process  400  computes or otherwise determines the total number of posts, N, associated with the blog site (task  404 ). In accordance with certain embodiments, the total number of posts can be computed by reviewing the information in the RSS feed of the blog site under analysis. 
     The illustrated embodiment uses the post count as one factor for determining whether or not the blog site in its entirety is spam content. To this end, the process  400  compares the computed value of N to a threshold number, N MIN  (query task  406 ). If N is less than N MIN  (the “Yes” branch of query task  406 ), then the process  400  identifies the blog site in its entirety as non-spam content (task  408 ). The process  400  utilizes the post count threshold because research has revealed that blog sites having a very high number of posts are likely to be “spam farms” that include automatically generated posts. The actual value of N MIN  may vary from one embodiment to another, and the value may be user-configurable. In certain non-limiting implementations, N MIN  has a value within the range of about 500 to 1000. 
     If N is greater than or equal to N MIN  (the “No” branch of query task  406 ), then the process  400  assumes that the blog site might be a source of spam. Accordingly, the process  400  continues by calculating a publication frequency, F PUB , for the total number of posts under consideration (task  410 ). The publication frequency can be calculated using any suitable formula or algorithm. This non-limiting example calculates the publication frequency in accordance with the expression 
               F   PUB     =       N       T   2     -     T   1         .           
In this expression: F PUB  is the calculated publication frequency (expressed in posts per minute); T 1  is a publication time of an oldest content item (e.g., blog post) in the website; T 2  is a publication time of a newest content item (e.g., blog post) in the website; and N is the computed total number of content items.
 
     The illustrated embodiment uses the publication frequency as another factor for determining whether or not the blog site in its entirety is spam content. Accordingly, in certain scenarios the process  400  determines whether a website in its entirety represents spam content, based on the computed total number of posts and the calculated publication frequency. To this end, the process  400  compares the calculated publication frequency to a threshold frequency, F MAX  (query task  412 ). If F PUB  is less than or equal to F MAX  (the “No” branch of query task  412 ), then the process  400  identifies the blog site in its entirety as non-spam content (task  408 ). The process  400  utilizes the publication frequency in this way because research has revealed that blog sites having a very high post publication frequency are likely to be “spam farms” that include a large number of posts that are automatically published in a very short period of time. The actual value of F MAX  may vary from one embodiment to another, and the value may be user-configurable. In certain non-limiting implementations, F MAX  has a value that is less than about five posts per minute. 
     If F PUB  is greater than F MAX  (the “Yes” branch of query task  412 ), then the process  400  assumes that the blog site is a source of spam content. Accordingly, the process  400  continues by identifying the website (e.g., the blog site) in its entirety as spam content (task  414 ). Thus, a blog site will be flagged as a spam site if: (1) it exhibits a large number of blog posts; and (2) it exhibits a high post publication frequency. Otherwise, the blog site will be flagged as non-spam content. 
     It should be appreciated that alternative thresholding schemes and/or criteria could be used for the decisions made during query task  406  and query task  412 . For example, different threshold values could be used to accommodate different operating conditions, days of the week, categories of genres of content under investigation, or the like. As another example, more complicated decision algorithms could be implemented rather than the simple single-value thresholds mentioned above. These and other options are contemplated by this description. 
       FIG. 5  is a flow chart that illustrates an embodiment of a spam flood detection process  500  that considers the HTML formatting or arrangement of a webpage when checking for spam. The process  500  may be performed in conjunction with the generalized spam flood detection process  300  described above, and it could also be executed concurrently with the process  400 . Thus, the process  500  analyzes a webpage that contains at least one content item (task  502 ). The webpage under analysis may be, for example, a blog page or any page having one or more posts, messages, articles, comments, or the like. This example assumes that the content items are posts that appear on or in association with a blog site of interest. 
     The illustrated embodiment of the process  500  selects or identifies at least some of the HTML tags of the webpage under analysis (task  504 ). In this regard, the HTML tags represent one type of webpage formatting data that is associated with the webpage under investigation. In accordance with commonly used HTML syntax, most if not all HTML elements begin with a tag, which is written as a text label surrounded by angle brackets. The actual HTML content follows the starting tag, and is usually followed by an ending tag (which also has a slash after the opening angle bracket to distinguish it from starting tags). Thus, task  504  can be performed by analyzing the underlying HTML code, elements, and data to find and select the desired number of HTML tags. In certain embodiments, task  504  simply identifies and selects all of the HTML start tags. In more complex implementations, the selection performed at task  504  may be governed by selection criteria or a selection algorithm that chooses some, but not all, of the HTML tags of the webpage. For instance, task  504  may select every other HTML tag in the order of appearance. 
     As one simple example, assume that the webpage under analysis includes the following selected HTML start tags:
         &lt;html xxxxxxxxxxxxxx xxxxxx xxxxx xxx   &lt;head&gt; xxxxxxxxxxxxx xxx x xxxx xxxxx   &lt;title&gt; xxxxxxxxxxxxxxx xxxxxx xxxx   &lt;meta xxxxxxxxxxxxxxxxx xxxx xxx xxxxxxxxx
 
The “x” characters represent dummy text, which is unimportant for purposes of this description. The HTML start tags for this example are: “&lt;html”, “&lt;head&gt;”, “&lt;title&gt;”, and “&lt;meta”. In certain embodiments, the process  500  will select a plurality of HTML tags at task  504  and then continue by creating an ordered sequence of the selected HTML tags (task  506 ). In accordance with one straightforward approach, task  506  places the selected HTML tags into their order of appearance. For this particular example, therefore, the ordered sequence will be as follows: “&lt;html &lt;head&gt; &lt;title&gt; &lt;meta”. Of course, the specific syntax may be different, depending on the particular embodiment. For instance, the process  500  may strip the angle brackets away before creating the ordered sequence, or it may remove all spaces, or it may add characters or spaces if so desired.
       

     Next, the process  500  generates a characterizing signature of the webpage, using the selected HTML tags. More specifically, the process  500  applies a hash function, a translation algorithm, an encoding algorithm, or any suitable transformation formula to the ordered sequence of HTML tags to map the selected HTML tags to the characterizing signature (task  508 ). Although any appropriate algorithm or formula may be utilized at task  508 , in certain non-limiting embodiments, task  508  applies the well-known SHA-1 hash function to the ordered sequence of HTML tags (which results in a 160-bit hash value). Notably, given the same string of HTML tags, task  508  will generate the same characterizing signature (hash value). As explained below, this allows the host system to keep track of how often the same ordered sequence of HTML tags appears in webpages under analysis. 
     If the signature is new to the host system (i.e., it has not been previously generated), then the process  500  may exit after task  508 . At this point, however, the new signature is saved (in, for example, cache memory for easy access) and the corresponding occurrence count for the new signature is initiated. In this way, the host system maintains an occurrence count for each unique signature. This example assumes that the generated signature already has an occurrence count associated therewith. Accordingly, the process  500  obtains or accesses the occurrence count for the generated characterizing signature (task  510 ). At this point, the process  500  may increment the occurrence count if so desired (optional task  512 ). Task  512  is considered optional here because the process  500  may increment or adjust the occurrence count at any desired time. For example, the count could be incremented immediately upon generating the signature, or after each iteration of the process  500 . 
     The illustrated embodiment uses the occurrence count as one factor for determining whether or not to indicate spam content. To this end, the process  500  compares the obtained occurrence count, C O , to a threshold count, C TH  (query task  514 ). If C O  is less than or equal to C TH  (the “No” branch of query task  514 ), then the process  500  identifies the content item as non-spam content (task  516 ). Thereafter, the process  500  may increment the occurrence count if so desired (optional task  518 , which is performed in lieu of optional task  512 ). Tasks  512 ,  518  are optional in  FIG. 5  because the process  500  could be designed to increment the occurrence count at any convenient time. 
     If the obtained occurrence count is greater than the threshold count (the “Yes” branch of query task  514 , then the process  500  assumes that the content item is spam content. Accordingly, the process  500  continues by identifying the content item (e.g., a post, an online article, a webpage, or the like) as spam content (task  520 ). Thus, online content will be flagged as spam site if: (1) the characterizing signature of the associated webpage matches a saved signature; and (2) the host system has counted a high enough number of occurrences of the same signature. Otherwise, the content item will be flagged or passed as non-spam content. Thus, until the threshold occurrence count has been reached for a given characterizing signature, the process  500  might pass along some content that would otherwise be marked as spam. 
     The process  500  utilizes the occurrence count threshold because research has revealed that spam websites are often generated from a common, fixed, and repeated HTML template. Consequently, the same HTML template will utilize an identical and repeatable HTML tag format and arrangement. The process  500  takes advantage of this observation by generating the characterizing signature from the HTML tags. The actual value of C TH  may vary from one embodiment to another, and the value may be user-configurable. In certain non-limiting implementations, C TH  has a value within the range of about 80 to 120 (per minute). 
     It should be appreciated that alternative thresholding schemes and/or criteria could be used for the decisions made during query task  514 . For example, different threshold count values could be used to accommodate different operating conditions, days of the week, categories of genres of content under investigation, or the like. As another example, more complicated decision algorithms could be implemented rather than the simple single-value threshold mentioned above. These and other options are contemplated by this description. 
     Techniques and technologies may be described herein in terms of functional and/or logical block components, and with reference to symbolic representations of operations, processing tasks, and functions that may be performed by various computing components or devices. Such operations, tasks, and functions are sometimes referred to as being computer-executed, computerized, software-implemented, or computer-implemented. It should be appreciated that the various block components shown in the figures may be realized by any number of hardware, software, and/or firmware components configured to perform the specified functions. For example, an embodiment of a system or a component may employ various integrated circuit components, e.g., memory elements, digital signal processing elements, logic elements, look-up tables, or the like, which may carry out a variety of functions under the control of one or more microprocessors or other control devices. 
     The foregoing detailed description is merely illustrative in nature and is not intended to limit the embodiments of the subject matter or the application and uses of such embodiments. As used herein, the word “exemplary” means “serving as an example, instance, or illustration.” Any implementation described herein as exemplary is not necessarily to be construed as preferred or advantageous over other implementations. Furthermore, there is no intention to be bound by any expressed or implied theory presented in the preceding technical field, background, or detailed description. 
     While at least one exemplary embodiment has been presented in the foregoing detailed description, it should be appreciated that a vast number of variations exist. It should also be appreciated that the exemplary embodiment or embodiments described herein are not intended to limit the scope, applicability, or configuration of the claimed subject matter in any way. Rather, the foregoing detailed description will provide those skilled in the art with a convenient road map for implementing the described embodiment or embodiments. It should be understood that various changes can be made in the function and arrangement of elements without departing from the scope defined by the claims, which includes known equivalents and foreseeable equivalents at the time of filing this patent application.