Patent Publication Number: US-2010121721-A1

Title: Premium bidding of online advertisements

Description:
BACKGROUND 
     1. Field 
     The subject matter disclosed herein relates to data processing, and more particularly to methods and apparatuses that utilize one or more premium rules to associate a premium markup to a base bid for online advertising. 
     2. Information 
     Data processing tools and techniques continue to improve. Information in the form of data is continually being generated or otherwise identified, collected, stored, shared, and analyzed. Databases and other like data repositories are common place, as are related communication networks and computing resources that provide access to such information. 
     The Internet is ubiquitous; the World Wide Web provided by the Internet continues to grow with new information seemingly being added every second. With so much information being available, advertising on the Internet often allows advertisers to target audiences viewing their advertisements. Use of the Internet for online advertising facilitates a two way flow of information between end users and advertisers. For example, an end user may request an ad and in doing so may provide information in the form of data that describes the end user in some manner. Conversely, traditional print and “hard copy” advertising may constitute a one-way flow of information from advertisers to end users. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
       Claimed subject matter is particularly pointed out and distinctly claimed in the concluding portion of the specification. However, both as to organization and/or method of operation, together with objects, features, and/or advantages thereof, it may best be understood by reference to the following detailed description when read with the accompanying drawings in which: 
         FIG. 1  is a flow diagram showing a process for publishing of online advertising in accordance with one or more embodiments. 
         FIG. 2  is a diagram illustrating a hierarchical node structure for online advertising in accordance with one or more embodiments. 
         FIG. 3  is a diagram illustrating a premium rule hierarchical target node structure in accordance with one or more embodiments. 
         FIG. 4  is a diagram illustrating a premium rule hierarchical target node structure in accordance with one or more embodiments. 
         FIG. 5  is a flow diagram illustrating a process for utilizing one or more premium rules to associate a premium markup to a base bid for online advertising in accordance with one or more embodiments. 
         FIG. 6  is a block diagram illustrating an exemplary embodiment of a computing environment system in accordance with one or more embodiments. 
         FIG. 7  is a diagram illustrating premium rules associated with a hierarchical node structure for online advertising in accordance with one or more embodiments. 
     
    
    
     Reference is made in the following detailed description to the accompanying drawings, which form a part hereof, wherein like numerals may designate like parts throughout to indicate corresponding or analogous elements. It will be appreciated that for simplicity and/or clarity of illustration, elements illustrated in the figures have not necessarily been drawn to scale. For example, the dimensions of some of the elements may be exaggerated relative to other elements for clarity. Further, it is to be understood that other embodiments may be utilized and structural and/or logical changes may be made without departing from the scope of claimed subject matter. It should also be noted that directions and references, for example, up, down, top, bottom, and so on, may be used to facilitate the discussion of the drawings and are not intended to restrict the application of claimed subject matter. Therefore, the following detailed description is not to be taken in a limiting sense and the scope of claimed subject matter defined by the appended claims and their equivalents. 
     DETAILED DESCRIPTION 
     In the following detailed description, numerous specific details are set forth to provide a thorough understanding of claimed subject matter. However, it will be understood by those skilled in the art that claimed subject matter may be practiced without these specific details. In other instances, well-known methods, procedures, components and/or circuits have not been described in detail. 
     Some exemplary methods and systems are described herein that may be used to determine premium bidding of online advertisements. As used herein the phrase “online advertisements,” “advertising,” and/or the like may refer to pop-up ads, banner ads, and/or the like. Such online advertisements may be described below as an ad unit. Such an ad unit may include a keyword term and a creative component. For example, such an ad unit may include text, graphic or video data (herein referred to as “creative component”). Additionally, metadata associated with a banner ad (and/or other like creative components) may include one or more keyword terms associated with the ad unit. Such ad units may be delivered to an end user based at least in part on one or more forms of online marketing processes, such as on contextual advertising, search advertising, search engine marketing, sponsored listings, and/or the like, and/or combinations thereof, for example. As will be described in greater detail below, certain exemplary embodiments described herein may provide mechanisms for advertisers (and/or other like entities) to control how certain ads may be targeted online to publishers, pages and/or end users. Similarly, certain exemplary embodiments described herein may provide mechanisms for advertisers to control additional premium markups associated with a targeting of certain publishers, pages and/or end users. 
     Such methods and systems that may be used to determine premium bidding of online advertisements, as described herein, may be utilized to provide a flexible and structured mechanism for advertisers (and/or other like entities) to control premium markups associated with a targeting of certain publishers, pages and/or end users. For example, such methods and system may allow for a flexible and extensive taxonomy for target dimensions to include various combinations of the following: an end user demographic, end user location, time, end user interests, publication content, publication Uniform Resource Locator (URL), publication domain, publication site, and/or the like, and/or combinations thereof. For example, in some embodiments, premium rules may be formed as decision trees associated with a single target dimension (referred to herein as basic premium rules) and/or as decision trees associated with two or more target dimensions (referred to herein as composite premium rules). Additionally, such methods and system may provide a conflict resolution between various premium rules. For example, a group of two or more premium rules may be forbidden from reusing identical target dimension sets. Further, a hierarchical target node structure may include one or more target nodes associated with a first target dimension and one or more nodes associated with a second target dimension. In such a case, such a first and second target dimensions may be restrained so that a comparison of a current context to such two or more target dimensions may match no more than one target node associated with a first target dimension and no more than one target node associated with a second target dimension. 
     Referring to  FIG. 1 , a flow diagram illustrates a process for publishing of online advertising in accordance with one or more embodiments.  FIG. 1  depicts a process  100  for providing up-to-date constraint based advertising content. In process  100 . Although process  100 , as shown in  FIG. 1 , comprises one particular order of actions, the order in which the actions are presented does not necessarily limit claimed subject matter to any particular order. Likewise, intervening actions not shown in  FIG. 1  and/or additional actions not shown in  FIG. 1  may be employed and/or actions shown in  FIG. 1  may be eliminated, without departing from the scope of claimed subject matter. 
     Process  100  depicted in  FIG. 1  may in alternative embodiments be implemented in software, hardware, and/or firmware, and may comprise discrete operations. As illustrated, an ad manager  106  may be coupled in communication with one or more publisher devices  108  associated with one or more publishers. Such publisher devices  108  may comprise general purpose computing devices having a central processing unit, memory unit, permanent storage, optical drive(s), universal serial bus port(s), audio/video output devices, network interfaces, etc. Ad manager  106  may include an ad server operative to handle requests from publisher devices  108  and transmit data to publisher devices  108 . 
     During typical online activity, an end user  110  may request a page and/or other like data file(s) of content from publisher device  108 , as illustrated at action  112 . Publisher device  108  may, in turn, return a content page to the end user, where the content page may contain a link and/or the like to a request for an advertisement from ad manager  106 , as illustrated at action  114 . In the illustrated embodiment, ad server  112  handles requests for advertisements from end users  110 , as illustrated at action  116 . Such a request for advertisement may include an HTTP request for advertising content initiated by a content page provided by publisher devices  108  to end users  110 . For example, a request for advertisements may contain one or more current contexts associated with a given end user including a taxonomy of user centric data and/or publisher centric data. Such user centric data may include or otherwise be associated with an end user demographic (e.g. age, gender, income, and/or the like), end user location (e.g. continent, country, state/providence, city, zip, and/or the like), time (e.g. end user time, advertiser time, coordinated universal time (UTC), and/or the like), end user interests (e.g. sports, politics, and/or the like), and/or the like, and/or combinations thereof. Such user publisher centric data may include or otherwise be associated with publication content (e.g. shopping, search, and/or the like), publication Uniform Resource Locator (URL), publication domain, publication site, and/or the like, and/or combinations thereof. For example, a request for advertisement may specify a current context including end user gender, such as male or female, and/or the like. Similarly, a request for advertisement may specify a current context including end user age, such as age in years, by birthday, and/or the like, for example. Likewise, a request for advertisement may specify a current context including end user location, such as a geographic location, address, latitude and longitude, Global Positioning System location, and/or the like, for example. Further, a request for advertisement may specify a current context including end user time, such as a time of day, time zone, and/or the like, for example. Similarly, a request for advertisement may specify a current context including coordinated universal time (UTC), such as Greenwich Mean Time (GMT), other non-location dependent time measures, and/or the like, for example. Likewise, a request for advertisement may specify a current context including publication content, such as topic areas associated with such content, key words associated with such content and/or the like, for example. Further, a request for advertisement may specify a current context including publication URL, publication domain, and/or publication site that may refer to all or a portion of a string of characters used to represent a resource available on the Internet, for example. For example, a request for advertisement may specify that the requesting content page is directed towards “sports”, located on the domain “example.com”, that the end user is a male between the ages 18 and 25, that the end user is located in California, and that the end user device&#39;s screen resolution is 800 by 600 pixels. 
     As used herein, the term “content page” may include any information in a digital format, of which at least a portion may be perceived in some manner (e.g., visually, audibly) by a end user if reproduced by a digital device, such as, for example, a computing platform. For one or more embodiments, a content page may comprise a web page coded in a markup language, such as, for example, HTML (hypertext markup language), and/or the like. However, the scope of claimed subject matter is not limited in this respect. Also, for one or more embodiments, the content page may comprise one or more elements. The elements in one or more embodiments may comprise text, for example, as may be displayed as part of a web page presentation. Also, for one or more embodiments, the elements may comprise a graphical object, such as, for example, a digital image. Unless specifically stated, a content page may refer to either the source code for a particular web page or the web page itself. Each web page may contain embedded references to images, audio, video, other web documents, etc. One common type of reference used to identify and locate resources on the web is a Uniform Resource Locator (URL). 
     In the illustrated embodiment, ad manager  106  may be operative to receive advertising data associated with one or more advertisers, as illustrated at action  118 . In one embodiment, advertising data may comprise text, graphic or video data (herein referred to as “creative component”) related to a given ad unit. Such an ad unit may include a keyword term and a creative component. For example, ad manager  106  may receive metadata associated with a banner ad or other like ad including, but not limited to, one or more keyword terms associated with the ad unit. In addition, advertising data also may comprise one or more rules associated with a given ad unit. As will be described in greater detail below, such rules may formed as, or be used to form, a premium rule including one or more constraints or rules that may determine the cost of such advertising based on a determination of one or more target dimensions. By way of example but not limitation, such target dimensions may be associated with and/or include a taxonomy of user centric data and/or publisher centric data. Such user centric data may include or otherwise be associated with an end user demographic (e.g. age, gender, income, and/or the like), end user location (e.g. continent, country, state/providence, city, zip, and/or the like), time (e.g. end user time, advertiser time, coordinated universal time (UTC), and/or the like), end user interests (e.g. sports, politics, and/or the like), and/or the like, and/or combinations thereof. Such user publisher centric data may include or otherwise be associated with publication content (e.g. shopping, search, and/or the like), publication Uniform Resource Locator (URL), publication domain, publication site, and/or the like, and/or combinations thereof. 
     For a given request for advertisement, an associated current context may be modeled as a map of a given target dimension portion and given target value portion. Likewise, for a given premium rule, an associated target dimension may be modeled as a map of a given target dimension portion and given target value portion. Such a target dimension may identify a particular current context in the above taxonomy of a given request for advertisement. For example, such a target dimension may identify a time-type current context. Such a target value may be a computed value representing a quantification of the given target dimension of a given request for advertisement. For example, such a target value may be computed to represent a quantification of a time-type current context. For example, end user time could be represented as a number of minutes within a week, such as one for 0:01 AM Monday, sixty-one for 1:01 AM Monday, and/or the like. Such a map of a current context may be compared with target dimensions associated with one or more premium rules. Based at least in part on such a comparison, such premium rules may determine at least a portion of the cost of such advertising. As will be described in greater detail below, in some embodiments such premium rules may be formed as decision trees associated with a single target dimension (referred to herein as basic premium rules) or may be formed as decision trees associated with two or more target dimensions (referred to herein as composite premium rules). 
     Ad manager  106  may determine which ads to send to end users  110  based at least in part on information received from advertisers. Such ad units may be filtered and/or ranked by ad manager  106  based on one or more criteria. For example such ad units may be filtered and/or ranked based on criteria from advertiser device  104 , publisher device  108 , and/or based on end user data. Additionally, ad manager  106  may determine a cost of such advertising based at least in part on information received from advertisers, as illustrated at action  120 . As will be described in greater detail below, such information may include premium rule related information associated with a given ad unit. Such premium rule related information may include one or more constraints or rules that may determine the cost of such advertising based on a determination of one or more target dimensions. Ad manager  106  may send a response to user requests for advertisements to end users  110 , as illustrated at action  122 . For example, such a response to user requests for advertisements may include an ad, and may also include an associated premium value that may be embedded within the response. 
     Referring to  FIG. 2 , a diagram illustrates a hierarchical node structure for online advertising in accordance with one or more embodiments. As discussed above, an ad manager  106  (see  FIG. 1 ) may receive an ad data from one or more advertisers. As illustrated in  FIG. 2 , this ad data may be organized into a hierarchical node structure  200 . For example, hierarchical node structure  200  may include any number of hierarchical nodes. Such hierarchical nodes may include account-type nodes  202 , campaign-type nodes  204 , ad-group-type nodes  206 , keyword-term-type nodes  208 , and/or creative-component-type nodes  210 , and/or the like. For example, a given advertiser may organize advertising expenditures by one or more accounts that may be established with ad manager  106  ( FIG. 1 ). Such accounts may have one or more ad campaigns that may include a series of advertisement messages that may share a common idea, goal, and/or theme. Such ad campaigns may have one or more ad units including a keyword term and a creative component. For example, a given keyword-term-type node  208  may be associated with a keyword component of an ad unit while a given creative-component-type node  210  may be associated with a creative component of such an ad unit. A given ad-group-type node  206  may be associated with a given ad unit which may belong to a given ad campaign. A given campaign-type node  204  may be associated with a given ad campaign which belong to a given account. Additionally, a given account-type node  202  may be associated with a given advertising account. 
     Here, hierarchical node structure  200  may express and/or represent hierarchical information within one or more computing platforms by digital electronic signals, and/or the like, for example. By way of example but not limitation, information in such a hierarchical node structure  200  may be expressed as a finite, rooted, connected, acyclic graph. Such a hierarchical ad node structure  200  may include a node (e.g., a root node) that may not have any preceding parent nodes. For example, an account-type node  202  associated with a given advertising account may be utilized as such a root node. Additionally, hierarchical node structure  200  may be traversed via edges  212  to reach a given leaf node. A leaf node may refer to a node that may not have any subsequent child nodes. For example, ad-group-type nodes  206  and/or keyword-term-type nodes  208  may be associated with a given advertising account to be utilized as a leaf node to represent a possible base bid data  212  associated with a given ad unit. Thus, a path through hierarchical node structure  200  may pass from a root node to a given leaf node. Additionally, hierarchical node structure  200  also may include interior nodes located between a root node to a given leaf node that may have a preceding parent node, such as a root node or another interior node, and also may have subsequent child nodes, such as leaf nodes or other interior nodes. For example, campaign-type nodes  204  associated with a given advertising account may be utilized as such an interior node (additional ad-group-type nodes  206 , keyword-term-type nodes  208 , and/or creative-component-type nodes  210  associated with individual campaign-type nodes  204  may not be illustrated here). 
     Such hierarchical node structures  200  may be utilized to access base bid data associated with a given item of advertising. For example, such base bid data may be utilized to sorts various ad units according to a baseline price associated with a given ad unit. Ad manager  106  ( FIG. 1 ) may comprise a cost algorithm wherein a value of an ad may be determined based at least in part on one or more hierarchical node structures  200 . For example, base bid data may be associated with a leaf node of hierarchical node structure  200 , such as an ad-group-type node  206  and/or a keyword-term-type node  208 . For example, base bid data may be established based on a bid associated with a keyword term that is associated with ad-group-type node  206  and/or a keyword-term-type node  208 . 
     Referring to  FIG. 7 , a diagram illustrates premium-rules associated with a hierarchical node structure for online advertising in accordance with one or more embodiments. Individual premium rules  216  may be identified climbing up hierarchical node structure  200  from an ad-group-type node  206  and/or a keyword-term-type node  208 . For example, a first type of premium rule  216  may be associated with an account  202 , campaign  204 , or ad-group  206 , while a second different type of premium rule  216  may be associated with an account  202 , campaign  204 , or ad-group  206 . Multiple premium rules  216  may identified by climbing up hierarchical node structure  200 . Such multiple premium rules  216  may factor into the determination of premium markup data associated with a given base bid data. For example, such premium markup data may be utilized to provide a further cost that is in addition to a baseline price of base bid data associated with a given ad unit. 
     Additionally, one or more premium rules  216  may be associated with one or more nodes  202 / 204 / 206  of hierarchical node structure  200 . Such premium rules  216  may be utilized to determine one or more premium markup values associated with a given item of advertising. Ad manager  106  ( FIG. 1 ) may comprise a cost algorithm wherein a value of an ad may be determined based at least in part on one or more premium rules  216 . For example, a premium markup value may be associated with a leaf node of hierarchical node structure  200 , such as an ad-group-type node  206  and/or a keyword-term-type node  208 . As will be described in greater detail below, such premium rules  216  may include a hierarchical target node structure associated with two or more target dimensions for use in determining a premium markup for a given item of advertising. 
     Referring to  FIG. 3 , a diagram illustrates a premium rule hierarchical target node structure in accordance with one or more embodiments. As illustrated, in some embodiments premium rules may be formed as decision trees associated with a single target dimension (referred to herein as basic premium rules). As illustrated here, hierarchical target node structure  300  may include a target dimension  302 . For example, target dimension  302  may refer to an end user location. Here a decision node  304  may be associated with two outcomes, illustrated here as either the state of California at node  308  or the state of New York at node  310 , and/or the like. In cases where an additional premium rule  216  ( FIG. 2 ) is identified as being associated with hierarchical node structures  200  (See  FIG. 2 ), a decision path through an associated a hierarchical target node structure  300  to a given premium markup  314 / 316  may be identified based at least in part on comparing a current context associated with a given end user to such a target dimension. As illustrated, such basic premium rules represented a hierarchical target node structure  300  may be described as follows: 
                                (target_dimension_ID, { (target_min_value, [ target_max_value,]       premium_value) } ),                    
where “target_dimension_ID” refers to a target dimension, “target_min_value” refers to a minimum target value, “target_max_value” refers to a maximum target value, and “premium_value” refers to a monetary-value-type and/or a percentage-type premium markup. In the above example, such a maximum target value may be optional. As discussed above, for a given request for advertisement, an associated current context may be modeled as a map of a given target dimension and given target value. Likewise, for a given premium rule, an associated target dimension may be modeled as a map of a given target dimension and given target value. As illustrated, such a hierarchical target node structure  300  may be described as follows:
         (geo::State, {(“CA”, 1), (“NY”, 2)})
 
where “geo::State” refers to a target dimension of a state-type user location, “CA” refers to a target value of the state of California associated with a monetary-value-type premium markup of one dollar, and “NY” refers to a target value of the state of New York associated with a monetary-value-type premium markup of two dollars.
       
     Referring to  FIG. 4 , a diagram illustrates a premium rule hierarchical target node structure in accordance with one or more embodiments. As illustrated, in some embodiments premium rules may be formed as decision trees associated with two or more target dimensions (referred to herein as composite premium rules). A hierarchical target node structure  400  may be similar in form to a first hierarchical target node structure  300 , but may contain a different and/or more complex organization of target dimensions. As illustrated, a given premium rule  216  ( FIG. 2 ) may include a second hierarchical target node structure  400  including two or more target dimensions  402 / 403 . For example, target dimension  402  may include end user interest (e.g. sports), while target dimension  403  may include end user time, and/or the like. Here a decision node  404  may be associated with two outcomes. For example, decision node  404  may be associated with either a “no” result that continues hierarchical target node structure  400  to a second decision node  406 , or a “yes” result that continues hierarchical target node structure  400  to a second decision node  405 , and/or the like, for example. Such a second decision node  403  may be associated with one or more outcomes. For example, decision node  406  may be associated with a 9 AM-5 PM result associated with a premium markup. Similarly, decision node  405  may be associated with one or more outcomes. For example, decision node  405  may be associated with either an 11 AM-12:59 PM result or a 1 PM-4:59 PM result. Accordingly, such a hierarchical target node structure  400  may be designed so that one or more target nodes  405 / 406  may be associated with a first target dimension  402  and one or more nodes  408 / 410  may be associated with a second target dimension  403 . In some cases, for simplicity, such first and second target dimensions  402 / 403  may be restrained so that a comparison of a current context associated with an end user to such two or more target dimensions  402 / 403  may match no more than one target node  405 / 406  associated with first target dimension  402  and no more than one target node  408 / 410 / 412  associated with second target dimension  403 . For example, in the illustration, second target dimension  403  may be restrained so that no more than one target node  408 / 410  (11 AM-12:59 PM/1 PM-4:59 PM) associated with a time target dimension  403  can match any given time of a given current context. In such a situation, only a single premium markup  414 / 416 / 418  may result from comparing a current context associated with a given end user to such target dimensions. Once a premium rule  216  ( FIG. 2 ) is identified as being associated with hierarchical node structures  200  (See  FIG. 2 ), a decision path through premium rules  216  ( FIG. 2 ) to a given premium markup  414 / 416 / 418  may be identified based at least in part on comparing a current context associated with a given end user to such two or more target dimensions. 
     As illustrated, such composite premium rules represented in a hierarchical target node structure  400  may be described as follows: 
                                (target_dimension_ID, { (target_min_value, [ target_max_value,]       premium_rule) } )                    
where “target_dimension_ID” refers to a target dimension, “target_min_value” refers to a minimum target value, “target_max_value” refers to a maximum target value, and “premium_rule” refers to an additional embedded rule. As illustrated, such a hierarchical target node structure  400  may be described as follows:
 
                                  (interests::Sports, { (0, (time:: localTime, { (“9am”, “5pm”, 0.25)}),       (1, (time::localTime, { (“11am”, “12:59pm”, 1), (“1pm”, “4:59pm”,       2)})} )                    
where “interests::Sports” refers to a target dimension of an end user interest (e.g. sports) associated with negative minimum target value of zero and a positive maximum target value of one. With a negative minimum target value of zero, “time::localTime” refers to a second target dimension of an end user time where “9 am-5 pm” refers to a target value of time associated with a monetary-value-type premium markup of twenty five cents. Similarly, with a positive maximum target value of one, “time:: localTime” refers to a second target dimension of an end user time where (“11 am”, “12:59 pm”) refers to a target value range of time associated with a monetary-value-type premium markup of one dollar and where (“1 pm”, “4:59 pm”) refers to a target value range of time associated with a monetary-value-type premium markup of two dollars.
 
     Referring back to  FIGS. 2-4 , in operation, base bid data may be established based on a bid associated with a keyword term via an ad-group-type node  206  and/or a keyword-term-type node  208 . A monetary-value-type and/or a percentage-type premium markup may be set to a zero value. Individual premium rules  216  may be identified climbing up a hierarchical node structure  200  from an ad-group-type node  206  and/or a keyword-term-type node  208 . Once a premium rule  216  is identified, a decision path through premium rules  216  to a given premium markup  414 / 416 / 418  may be identified based at least in part on comparing a current context associated with a given end user to two or more target dimensions. Multiple premium rules  216  may identified by climbing up hierarchical node structure  200 . Such multiple premium rules  216  may factor into the determination of premium markup data associated with a given base bid data. A total bid may be determined based at least in part on premium markup data and base bid data (e.g., one or more premium markups may be combined in some manner with base bid data). For example, such a total bid may be utilized to sort various ad units according to a combined total of both a baseline price associated with a given ad unit and/or a markup price associated with such an ad unit. Additionally, in situations where there is not an applicable premium markup, such a total bid may be based only on base bid data. Additional details regarding such operation may be found below with reference to  FIG. 5 . 
     Referring back to  FIG. 7 , one or more premium rules  216  may be associated with one or more nodes  202 / 204 / 206  of hierarchical node structure  200 . For example, premium rules  216  may be associated at one or more levels hierarchical node structure  200  (e.g., account-type node  202 , campaign-type node  204  and/or ad-group-type node  206 ). As discussed above, individual premium rules  216  may be identified and/or evaluated in a bottom-up order climbing up hierarchical node structure  200 , such as starting at an ad-group-type node  206  and/or a keyword-term-type node  208 , for example. Multiple premium rules  216  may identified by climbing up hierarchical node structure  200 . Potential conflicts between such multiple premium rules  216  and/or potential conflicts multi-level association of premium rules  216  may be resolved by providing one or more conflict resolution features for premium rules  216 . One such conflict resolution feature may include forbidding a group of two or more premium rules  216  from reusing identical target dimension sets. Such a group of two or more premium rules  216  may include those premium rules  216  identified and/or evaluated climbing up hierarchical node structure  200 , for example. Such target dimensions from identified and/or evaluated premium rules  216  may be remembered. Precedence may be given to premium rules  216  in the order that such premium rules  216  were identified and/or evaluated, such as in a bottom-up order climbing up hierarchical node structure  200 , for example. For example, for a premium rule  216  that has been evaluated to be relevant to a given current context, an associated target dimension set may be remembered. A subsequently identified and/or evaluated premium rule  216  may be ignored. For example, a first premium rule  216  may be associated with a target dimension set of user-time-type time and sports-type interest, and a second premium rule  216  may be ignored in cases where the second premium rule  216  is associated with an identical target dimension set of user-time-type time and sports-type interest. 
     Process  500 , as illustrated in  FIG. 5 , may utilize one or more premium rules to associate a premium markup to base bid data for online advertising in accordance with one or more embodiments, for example, although the scope of claimed subject matter is not limited in this respect. Additionally, although process  500 , as shown in  FIG. 5 , comprises one particular order of blocks, the order in which the blocks are presented does not necessarily limit claimed subject matter to any particular order. Likewise, intervening blocks shown in  FIG. 5  and/or additional blocks not shown in  FIG. 5  may be employed and/or blocks shown in  FIG. 5  may be eliminated, without departing from the scope of claimed subject matter. 
     Process  500 , depicted in  FIG. 5 , may in alternative embodiments be implemented in software, hardware, and/or firmware, and may comprise discrete operations. As illustrated, process  500  may utilize one or more premium rules to associate a premium markup to base bid data for online advertising. Starting at block  502 , advertising data may be received, where such advertising data may associate an online advertising budget among one or more ad units in a hierarchical node structure. Such an online advertising budget may comprise prices associated with one or more ad units. As discussed above, each ad unit may comprise a keyword term and a creative component. 
     At block  504 , advertising data may be received, where such advertising data may associate one or more premium rules with one or more nodes of such a hierarchical node structure. As discussed above, such premium rules may include a hierarchical target node structure including two or more target dimensions. For example, two or more premium rules may be associated to an ad unit at one or more of the following hierarchical nodes: an account-type node, a campaign-type node, and/or an ad-group-type node and/or the like. To avoid potential conflict among premium rules, a group of two or more premium rules  216  may be forbidden from reusing identical target dimension sets. 
     For a premium rule, its hierarchical target node structure may include one or more target nodes associated with a first target dimension and one or more nodes associated with a second target dimension. For example, such a first and second target dimensions may be restrained so that a comparison of a current context to such two or more target dimensions may match no more than one target node associated with a first target dimension and no more than one target node associated with a second target dimension. 
     At block  506 , base bid data associated with a node of such a hierarchical ad node structure may be accessed. As discussed above, such a node may be associated with at least a portion of an ad unit. For example, such a node may include a keyword-term-type node associated with a keyword term portion of an ad unit. Additionally or alternatively, such a node may include an ad-group-type node associated with an ad unit. As discussed above, such target dimensions may include one or more of the following: an end user demographic, end user location, time, end user interests, publication content, publication Uniform Resource Locator (URL), publication domain, publication site, and/or the like, and/or combinations thereof. 
     At block  507 , preliminary conditions may be set. For example, an initial total bid may be set as equal to an accessed base bid value. Additionally, a current node of the hierarchical ad node structure may be specified. For example, such a current node may be specified as a keyword-term-type node associated with a keyword term portion of an ad unit. Further, a set of tested target dimensions may be reset. 
     At block  508 , a subset of premium rules associated with such a current node may be identified. As discussed above, potential conflicts between multiple premium rules and/or potential conflicts multi-level association of premium rules may be resolved by providing one or more conflict resolution features for premium rules. One such conflict resolution feature may include forbidding a group of two or more premium rules from reusing identical target dimension sets. Such a group of two or more premium rules may include those premium rules identified and/or evaluated climbing up a hierarchical node structure, for example. Such target dimensions from identified and/or evaluated premium rules may be remembered. Precedence may be given to premium rules in the order that such premium rules were identified and/or evaluated, such as in a bottom-up order climbing up a hierarchical node structure, for example. For example, for a premium rule that has been evaluated to be relevant to a given current context, an associated target dimension set may be remembered. A subsequently identified premium rule may be ignored. 
     Accordingly, a premium markup data may be determined based at least in part on such identified premium rules. For example such premium markup data may be determined based at least in part on one or more premium rules comparing a current context associated with a given end user to two or more target dimensions. For example, such premium markup data may be based at least in part on a percentage-type premium markup, such as a percentage of base bid data. Alternatively, such premium markup data may be based at least in part on monetary values independent of base bid data. As discussed above, such current contexts may include an end user demographic, end user location, time, end user interests, publication content, publication Uniform Resource Locator (URL), publication domain, publication site, and/or the like, and/or combinations thereof. 
     At block  510 , a total bid may, for example, be determined based at least in part on combining premium markup data with base bid data. In some cases, such base bid data may be represented by an initial total bid that was set to be equal to a base bid at block  507 , for example. Additionally, in situations where there is not applicable premium markup, such a total bid may be based only on base bid data. Such a total bid may be utilized to charge an advertiser for delivering a given ad to an end user. 
     At block  512 , a set of tested target dimensions may be updated. For example, a set of tested target dimensions may be updated to included those dimensions tested at block  508 . 
     At block  514 , a determination may be made to see if process  500  has reached a root of the hierarchical ad node structure. In cases where such a root has been reached, process  500  may end. In cases where such a root has not been reached, a parent node may be set to be the current node to be analyzed and process  500  may return to block  508  for further operations, as illustrated at block  516 , 
     In operation, process  500  may be utilized to provide a flexible and structured mechanism for advertisers (and/or other like entities) to control premium markups associated with a targeting of certain publishers, pages and/or end users. For example, the mechanism, illustrated by process  500 , may allow for a flexible and extensive taxonomy for target dimensions include various combinations of the following: an end user demographic, end user location, time, end user interests, publication content, publication Uniform Resource Locator (URL), publication domain, publication site, and/or the like, and/or combinations thereof. For example, in some embodiments, premium rules may be formed as decision trees associated with a single target dimension (referred to herein as basic premium rules) and/or as decision trees associated with two or more target dimensions (referred to herein as composite premium rules). Additionally, multi-level association of premium rules  216  with hierarchical node structure  200  may be facilitated by providing a conflict resolution between premium rules  216 . For example, a group of two or more premium rules  216  may be forbidden from reusing identical target dimension sets. Further, a hierarchical target node structure may include one or more target nodes associated with a first target dimension and one or more nodes associated with a second target dimension. In such a case, such a first and second target dimensions may be restrained so that a comparison of a current context to such two or more target dimensions may match no more than one target node associated with a first target dimension and no more than one target node associated with a second target dimension. 
       FIG. 6  is a block diagram illustrating an exemplary embodiment of a computing environment system  600  that may include one or more devices configurable to utilize one or more premium rules to associate a premium markup to base bid data for online advertising using one or more techniques illustrated above, for example. For example, computing environment system  600  may be operatively enabled to perform all or a portion of process  500  of  FIG. 5 . 
     Computing environment system  600  may include, for example, a first device  602 , a second device  604  and a third device  606 , which may be operatively coupled together through a network  608 . 
     First device  602 , second device  604  and third device  606 , as shown in  FIG. 6 , are each representative of any device, appliance or machine that may be configurable to exchange data over network  608 . By way of example, but not limitation, any of first device  602 , second device  604 , or third device  606  may include: one or more computing platforms or devices, such as, e.g., a desktop computer, a laptop computer, a workstation, a server device, storage units, or the like. 
     Network  608 , as shown in  FIG. 6 , is representative of one or more communication links, processes, and/or resources configurable to support the exchange of data between at least two of first device  602 , second device  604  and third device  606 . By way of example, but not limitation, network  608  may include wireless and/or wired communication links, telephone or telecommunications systems, data buses or channels, optical fibers, terrestrial or satellite resources, local area networks, wide area networks, intranets, the Internet, routers or switches, and the like, or any combination thereof. 
     As illustrated by the dashed lined box partially obscured behind third device  606 , there may be additional like devices operatively coupled to network  608 , for example. 
     It is recognized that all or part of the various devices and networks shown in system  600 , and the processes and methods as further described herein, may be implemented using or otherwise include hardware, firmware, software, or any combination thereof. 
     Thus, by way of example, but not limitation, second device  604  may include at least one processing unit  620  that is operatively coupled to a memory  622  through a bus  623 . 
     Processing unit  620  is representative of one or more circuits configurable to perform at least a portion of a data computing procedure or process. By way of example, but not limitation, processing unit  620  may include one or more processors, controllers, microprocessors, microcontrollers, application specific integrated circuits, digital signal processors, programmable logic devices, field programmable gate arrays, and the like, or any combination thereof. 
     Memory  622  is representative of any data storage mechanism. Memory  622  may include, for example, a primary memory  624  and/or a secondary memory  626 . Primary memory  624  may include, for example, a random access memory, read only memory, etc. While illustrated in this example as being separate from processing unit  620 , it should be understood that all or part of primary memory  624  may be provided within or otherwise co-located/coupled with processing unit  620 . 
     Secondary memory  626  may include, for example, the same or similar type of memory as primary memory and/or one or more data storage devices or systems, such as, for example, a disk drive, an optical disc drive, a tape drive, a solid state memory drive, etc. In certain implementations, secondary memory  626  may be operatively receptive of, or otherwise configurable to couple to, a computer-readable medium  628 . Computer-readable medium  628  may include, for example, any medium that can carry and/or make accessible data, code and/or instructions for one or more of the devices in system  600 . 
     Second device  604  may include, for example, a communication interface  630  that provides for or otherwise supports the operative coupling of second device  604  to at least network  608 . By way of example, but not limitation, communication interface  630  may include a network interface device or card, a modem, a router, a switch, a transceiver, and the like. 
     Second device  604  may include, for example, an input/output  632 . Input/output  632  is representative of one or more devices or features that may be configurable to accept or otherwise introduce human and/or machine inputs, and/or one or more devices or features that may be configurable to deliver or otherwise provide for human and/or machine outputs. By way of example, but not limitation, input/output device  632  may include an operatively enabled display, speaker, keyboard, mouse, trackball, touch screen, data port, etc. 
     Some portions of the detailed description are presented in terms of algorithms or symbolic representations of operations on data bits or binary digital signals stored within a computing system memory, such as a computer memory. These algorithmic descriptions or representations are examples of techniques used by those of ordinary skill in the data processing arts to convey the substance of their work to others skilled in the art. An algorithm is here, and generally, is considered to be a self-consistent sequence of operations or similar processing leading to a desired result. In this context, operations or processing involve physical manipulation of physical quantities. Typically, although not necessarily, such quantities may take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared or otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to such signals as bits, data, values, elements, symbols, characters, terms, numbers, numerals or the like. It should be understood, however, that all of these and similar terms are to be associated with appropriate physical quantities and are merely convenient labels. Unless specifically stated otherwise, as apparent from the following discussion, it is appreciated that throughout this specification discussions utilizing terms such as “processing,” “computing,” “calculating,” “determining” or the like refer to actions or processes of a computing platform, such as a computer or a similar electronic computing device, that manipulates or transforms data represented as physical electronic or magnetic quantities within memories, registers, or other information storage devices, transmission devices, or display devices of the computing platform. 
     In one implementation, premium rules may associate premium markup data to base bid data for online advertising via a computing platform. Such premium ruling may be performed via a computing platform that manipulates or transforms electronic signals employed to represent physical electronic or magnetic quantities, or other physical quantities, within the computing platform&#39;s memories, registers, or other information storage, transmission, or display devices. For example, a computing platform may be enabled to receive advertising data that associates an online advertising budget among one or more ad units in a hierarchical node structure represented within one or more computing platforms by digital electronic signals. Such a computing platform may additionally be enabled to receive advertising data that associates one or more premium rules represented within such computing platforms by digital electronic signals with one or more nodes of such a hierarchical node structure. Such a computing platform may additionally be enabled to access base bid data represented within such computing platforms by digital electronic signals associated with a node of such a hierarchical node structure. Such a computing platform may additionally be enabled to determine premium markup data represented within such computing platforms by digital electronic signals based at least in part on a comparison via such premium rules of a current context associated with a given end user to such target dimensions 
     Reference throughout this specification to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of claimed subject matter. Thus, the appearance of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. 
     The term “and/or” as referred to herein may mean “and”, it may mean “or”, it may mean “exclusive-or”, it may mean “one”, it may mean “some, but not all”, it may mean “neither”, and/or it may mean “both”, although the scope of claimed subject matter is not limited in this respect. 
     While certain exemplary techniques have been described and shown herein using various methods and systems, it should be understood by those skilled in the art that various other modifications may be made, and equivalents may be substituted, without departing from claimed subject matter. Additionally, many modifications may be made to adapt a particular situation to the teachings of claimed subject matter without departing from the central concept described herein. Therefore, it is intended that claimed subject matter not be limited to the particular examples disclosed, but that such claimed subject matter also may include all implementations falling within the scope of the appended claims, and equivalents thereof.