Source: https://patents.google.com/patent/US8666820
Timestamp: 2018-04-20 03:18:50
Document Index: 423463970

Matched Legal Cases: ['§4', '§4', '§4', '§4', '§4', '§4', '§4', '§4', '§4', '§4', '§4', '§4']

US8666820B2 - Ad rendering parameters, such as size, style, and/or layout, of online ads - Google Patents
Ad rendering parameters, such as size, style, and/or layout, of online ads Download PDF
US8666820B2
US8666820B2 US11026329 US2632904A US8666820B2 US 8666820 B2 US8666820 B2 US 8666820B2 US 11026329 US11026329 US 11026329 US 2632904 A US2632904 A US 2632904A US 8666820 B2 US8666820 B2 US 8666820B2
US11026329
US20060149622A1 (en )
Ad rendering parameters for a set of two or more ads may be determined by (a) accepting, for a set of two or more ads, ad information which includes at least one ad feature having a value that depends on ad rendering parameters, and (b) determining ad rendering parameters for at least one ad from the set of two or more ads using the accepted ad information. The act of determining ad rendering parameters may use accepted ad rendering constraints. The ad rendering constraints may include space available for rendering the ads, a footprint available for rendering the ads, and/or a maximum number of ads permitted to be rendered. The act of determining ad rendering parameters may include maximizing a value associated with serving at least one ad from the set of two or more ads with ad rendering parameters subject to the ad rendering constraints. The ad rendering parameters may include sizes of the served ads, and/or a layout of the served ads.
The present invention concerns online advertising. In particular, the present invention concerns improving the size and/or layout of online ads.
Traditional online, tabular yellow page listings have some limitations. For example, since they are all the same size, they can't convey an implicit message to the user about how much money the company can afford to spend on ads and, therefore, how large it may be (larger companies often being thought of as more reliable). Further, some may find them to be difficult to read because of limited typefaces. Furthermore, most have little or no additional information on products carried by the company because of limited sizes.
In view of the foregoing, it would be useful to improve online ads.
Embodiments consistent with the present invention may determine ad rendering parameters for a set of two or more ads by (a) accepting, for a set of two or more ads, ad information, wherein the ad information includes at least one ad feature having a value that depends on ad rendering parameters, and (b) determining ad rendering parameters for at least one ad from the set of two or more ads using the accepted ad information.
At least some embodiment consistent with the present invention may accept ad rendering constraints, wherein the act of determining ad rendering parameters further uses the accepted ad rendering constraints. In at least some such embodiments, the ad rendering constraints may include space available for rendering the ads, a footprint available for rendering the ads, and/or a maximum number of ads permitted to be rendered.
In at least some embodiments consistent with the present invention, the act of determining ad rendering parameters includes maximizing a value associated with serving at least one ad from the set of two or more ads with ad rendering parameters subject to the ad rendering constraints.
In at least some embodiments consistent with the present invention, the ad rendering parameters may include sizes of the served ads, and/or a layout of the served ads
FIG. 2 is a diagram illustrating an exemplary environment in which, or with which, the present invention may operate.
FIG. 3 is a bubble diagram of exemplary operations for determining ad parameters which may be performed in a manner consistent with the present invention, as well as information that may be used and/or generated by such operations.
FIG. 4 is a flow diagram of an exemplary method for performing an ad and ad parameter determination operation in a manner consistent with the present invention
FIG. 5 is a block diagram of apparatus that may be used to perform at least some operations, and store at least some information, consistent with the present invention.
FIGS. 6-8 illustrate exemplary advertisement layout pages that are used to illustrate how an exemplary embodiment consistent with the present invention may be used to improve ad size and/or layout.
The present invention may involve novel methods, apparatus, message formats, and/or data structures for improving online advertising. The following description is presented to enable one skilled in the art to make and use the invention, and is provided in the context of particular applications and their requirements. Thus, the following description of embodiments consistent with the present invention provides illustration and description, but is not intended to be exhaustive or to limit the present invention to the precise form disclosed. Various modifications to the disclosed embodiments will be apparent to those skilled in the art, and the general principles set forth below may be applied to other embodiments and applications. For example, although a series of acts may be described with reference to a flow diagram, the order of acts may differ in other implementations when the performance of one act is not dependent on the completion of another act. Further, non-dependent acts may be performed in parallel. No element, act or instruction used in the description should be construed as critical or essential to the present invention unless explicitly described as such. Also, as used herein, the article “a” is intended to include one or more items. Where only one item is intended, the term “one” or similar language is used. Thus, the present invention is not intended to be limited to the embodiments shown and the inventors regard their invention to include any patentable subject matter described.
In the following definitions of terms that may be used in the specification are provided in §4.1. Then, environments in which, or with which, the present invention may operate are described in §4.2. Exemplary embodiments of the present invention are described in §4.3. Thereafter, a specific example illustrating the usefulness of one embodiment of the present invention is provided in §4.4. Finally, some conclusions regarding the present invention are set forth in §4.5.
Online ads, such as those used in the exemplary systems described below with reference to FIGS. 1 and 2, or any other system, may have various intrinsic attributes. Such attributes may be specified by an application and/or an advertiser. These attributes are referred to as “ad attributes” below. For example, in the case of a text ad, ad attributes may include a title line, ad text, and an embedded link. In the case of an image ad, ad attributes may include images, executable code, and an embedded link. Depending on the type of online ad, ad attributes may include one or more of the following: text, a link, an audio file, a video file, an image file, executable code, embedded information, etc.
“Ad rendering parameters” may include the size(s) of one or more ads, the layout of one or more ads, the styles of one or more ads, etc. “Styles” may include font types, font sizes, background color, foreground color, distance and extent of audio/image/animation/video, etc.
Although serving parameters may be extrinsic to ad attributes, they may be associated with an ad as serving conditions or constraints. When used as serving conditions or constraints, such serving parameters are referred to simply as “serving constraints” (or “targeting criteria”). For example, in some systems, an advertiser may be able to target the serving of its ad by specifying that it is only to be served on weekdays, no lower than a certain position, only to users in a certain geolocation, etc. As another example, in some systems, an advertiser may specify that its ad is to be served only if a page or search query includes certain keywords or phrases (referred to generally as “keyword targeting criteria”). As yet another example, in some systems, an advertiser may specify that its ad is to be served only if a document being served includes certain topics or concepts, or falls under a particular cluster or clusters, or some other classification or classifications.
“Ad information” may include any combination of ad attributes, ad serving constraints, information derivable from ad attributes or ad serving constraints (referred to as “ad derived information”), and/or information related to the ad (referred to as “ad related information”), as well as an extension of such information (e.g., information derived from ad related information).
§4.2 Environments in which, or with which, the Present Invention may Operate §4.2.1 Exemplary Advertising Environment
FIG. 1 is a diagram of an advertising environment 100. The environment 100 may include an ad entry, maintenance and delivery system (simply referred to as an “ad server”) 120. Advertisers 110 may directly, or indirectly, enter, maintain, and track ad information in the system 120. The ads may be in the form of graphical ads such as so-called banner ads, text only ads, image ads, audio ads, video ads, ads combining one of more of any of such components, etc. The ads may also include embedded information, such as a link, and/or machine executable instructions. Ad consumers 130 may submit requests for ads to, accept ads responsive to their request from, and provide usage information to, the system 120. An entity other than an ad consumer 130 may initiate a request for ads. Although not shown, other entities may provide usage information (e.g., whether or not a conversion or click-through related to the ad occurred) to the system 120. This usage information may include measured or observed user behavior related to ads that have been served.
FIG. 2 illustrates an exemplary environment 200 in which, or with which, the present invention may be used. A user device (also referred to as a “client” or “client device”) 250 may include a browser facility (such as the Explorer browser from Microsoft, the Opera Web Browser from Opera Software of Norway, the Navigator browser from AOL/Time Warner, the Firefox browser from the Mozilla, etc.), an e-mail facility (e.g., Outlook from Microsoft), etc. A search engine 220 may permit user devices 250 to search collections of documents (e.g., Web pages). A content server 210 may permit user devices 250 to access documents. An e-mail server (such as GMail from Google, Hotmail from Microsoft Network, Yahoo Mail, etc.) 240 may be used to provide e-mail functionality to user devices 250. An ad server 210 may be used to serve ads to user devices 250. The ads may be served in association with search results provided by the search engine 220. However, content-relevant ads may be served in association with content provided by the content server 230, and/or e-mail supported by the e-mail server 240 and/or user device e-mail facilities.
FIG. 3 is a bubble diagram of exemplary operations which may be performed in a manner consistent with the present invention, as well as information that may be used and/or generated by such operations. Ad and ad parameter determination operations 350 may be used to determine rendering parameters (e.g., sizes, layouts, and/or styles) for online ads. The layouts are not limited to a simple linear list of results. For example; layouts of the ads may be more similar to page layouts used in yellow pages.
The ad and ad parameter determination operations 350 may use ad information 310, query information (which may include user information) 320, and/or constraint information 330 to determine a set of ads and their parameters 360. Ad information 310 may include ad features 340 having “values” that depend on ad rendering parameters. Further ad information 310 may include one or more of offer information (e.g., price per impression, selection or conversion, maximum offer per impression, selection or conversion, etc.), location information, product information, targeting information, performance information (e.g., selection rate, conversion rate, etc.), etc. Query information 320 may include one or more of search terms, content topics, end user location or location of interest, user profile, user behavior, etc.
Constraint information 330 may include one or more of screen space (or footprint) for the ads, maximum number of ads permitted, etc.
The constraint information 330 and ad features 340 may be used to determine ads and their rendering parameters. Query information 320 and/or other ad information 310 may also be used in this determination. Thus, the operations 350 can be used to determine a “best” combination of size, style, and/or layout of ads.
FIG. 4 is a flow diagram of an exemplary method 400 that may be used to perform ad rendering parameter (and perhaps ad) determination operations in a manner consistent with the present invention. The method 400 may accept a variety of information to determine an optimized set of rendered ads. In particular, the method 400 may accept ad information (that includes at least one ad feature having a “value” that depends on ad rendering parameters) (Block 405), query information (Block 410), and/or constraint information (Block 415). This obtained information may then be processed to determine an optimized set of ads, including their rendering parameters (Block 420).
Examples of ad information, query information, and constraint information were described earlier in §4.3. Hence, exemplary feature values will be described here. As described above with reference to block 420, ad rendering parameter determination method 400 may accept ad features having values that depend on the ad rendering parameters. These ad features are used by the method 400 to define a constrained optimization problem where the optimum rendering parameters (e.g., size, style, and/or layout) of ads are determined. (Block 420) Features that may be used to optimize the ad rendering parameters, may include: features having a value that depends on ad rendering parameters (such as selection rate, conversion rate, offer for impression, offer for selection, offer for conversion, ad size (absolute or relative), ad style (absolute or relative), ad layout, etc).
Referring back to block 420, an example of an optimization problem may be defined as maximizing a weighted combination of the feature values for an advertisement relative to the size of the ad. The number of features, constraints, and calculations that may be involved in such a constrained optimization function may be very large and complex. Therefore, standard methods of solving such problems (e.g., linear programming, non-linear programming, integer programming, simulated annealing, etc.) can be used as appropriate (depending on the sort of additional constraints used). A simple constrained optimization problem will be described in §4.4 below.
§4.3.2 Exemplary Appartatus
The one or more processors 510 may execute machine-executable instructions (e.g., C or C++ running on the Solaris operating system available from Sun Microsystems Inc. of Palo Alto, Calif. or the Linux operating system widely available from a number of vendors such as Red Hat, Inc. of Durham, N.C.) to perform one or more aspects of the present invention. At least a portion of the machine executable instructions may be stored (temporarily or more permanently) on the one or more storage devices 520 and/or may be received from an external source via one or more input interface units 530.
Although many of the foregoing discussions and examples concern determining rendering parameters (e.g., styles, sizes, and/or layouts (e.g., yellow page-like layouts)) for ads, there are many other applications in which the principles of the present invention could be applied. For instance, embodiments consistent with the present invention could be used for determining the layout of news stories. For example, the amount of space and size of the font for the news headline could be controlled by a similar method. Also, embodiments consistent with the present invention could be refined to support the creation of dynamic yellow book-like pages that work with concept-targeted ads rather than keyword-targeted ads. For instance, if a user conducts a search on the key-word “plumbers”, instead of just providing a list of plumbers, it could also provide a list of hardware stores.
Furthermore, the invention may be able to further refine the optimized ad sizes and layout by using a variety of optimization algorithms (VLSI layout, stochastic search, dynamic programming, etc.) to determine placement alternatives and may further refine the weights used in the optimization over time, using ad selection rate for different media, different locations, and/or different classes of users.
Another refinement can be to provide constraints as to how ads with different sizes and shapes can be mixed with respect to one another, thereby preventing a layout that is jumbled (e.g., due to too many sizes on the layout, ad size set discontinuities, etc.). For instance, once sizes for each ad are determined by the methods discussed earlier, the invention can then quantize the ads as necessary to fit the “closest” available ad size. Such quantization can be incorporated into the original optimization problem through additional constraints.
Computations taking place during the operations of the present invention may be complicated and extensive. However, other common methods for tackling non-linear result sets may be used, such as showing a topographic map.
§4.4 Example of Operations
FIGS. 6-8 illustrate exemplary candidate layouts for displaying yellow page-like ads, used to illustrate exemplary operations in an exemplary embodiment of the invention. FIGS. 6-8 serve as layout constraints to an optimization problem since only three (3) ads can be displayed on each layout, and each ad must fit to one of the spaces defined in a layout. In FIG. 6 since the allotted space for ads has been divided evenly into three pieces, each ad will have a size of one third of the total allotted space allowed for ads. In FIG. 7 each ad could be sized to fit in any of the three (3) divided spaces. Hence, one (1) of the ads could have a size of one half, while the other two (2) ads would have a size of one fourth. In FIG. 8, again each ad could be sized to fit in any of three (3) divided spaces. Hence, one of the ads could have a size of two thirds, while the other two (2) ads would have a size of one sixth. Thus, there are three potential layouts for the ads to be displayed on, and one of five possible sizes each ad could have. These possible parameters serve as constraints to the following example.
In the following, a detailed example of the optimization operation that may occur at the ad and ad parameter determination operation will be presented. In particular, the optimization problem concerns maximizing a constrained function of weighted combinations of feature values for an advertisement relative to the size of the ad.
Specifically, consider having a set of advertisers who are targeting the query “leather jacket”. Each of these advertisers has some features associated with them. Consider the following advertisers:
Advertiser—1: Macy's
CTR: 0.05*size of ad
Advertiser—2: Nordstrom
CTR: 0.03*size of ad
Advertiser—3: Target
CTR: 0.06*size of ad
The feature values from these advertisers are distance and CTR (click-through rate) and are represented by the following:
Feature_val—1=Distance
Feature_val—2=CTR
Here, distance refers to the distance from the user to the advertised location and CTR refers to the click-through rate of the ad. No units are shown for values of distance since they serve no significance for this illustration (they could be miles, kilometers, etc). Also note that the click-through rate is a linear function of the size of the ad which is the variable to be optimized.
Now consider the optimization problem of maximizing the function:
∑ α ⁢ ∑ λ ⁢ ( weight_λ × Feature_val ⁢ _λ × size_α ) + ∑ γ ⁢ ( reward_γ )
where α is an index of ads, λ is an index of features and γ is an index of conditions, subject to the following constraints:
∑ α ⁢ ( size_α ) = 1 size_α ≥ 1 κ , ⁢ ∀ ⁢ α ∈ [ 1 , 3 ]
where κ is a constant and κ≧(number of ads). In this case, κ=6 since the smallest size available on the three layouts depicted by FIGS. 6-8 is one sixth.
Also, consider the additional constraints mentioned earlier depicted by FIGS. 6-8. In particular, only three (3) ads can be displayed on each layout, and each ad must fit to one of the divided spaces from a layout. Thus, there are three potential layouts and one of five possible sizes each ad could have.
Note that some implementations could involve a large number of features and constraints in the optimization problem, making it computationally expensive. However, this simple example is useful for purposes of illustration.
In the above optimization problem weight_λ denotes the weight of the λth feature of an ad, Feature_val_λ denotes the value of the λth feature for the ad, and size_α denotes the size of an ad. Specifically, α=1 refers to advertiser—1, α=2 refers to advertiser—2, and α=3 refers to advertiser—3.
The following exemplary weights are assigned to each feature. Since Feature_val—1 depends on distance, a function that decreases values as the distance between the user and the advertised location increases is used. Hence a possible weight function may be: weight
weight_ ⁢ 1 = 30 ( distance + 0.5 ) .
This weight function will be assigned to Feature_val—1 and will be used in this example. The weight given to Feature_val—2 which refers to the click-through weight of an ad will be defined as a constant specifically, weight—2=100.
Furthermore, the reward_γ included in the optimization problem denotes a reward given for satisfying condition γ. The reward values and their respective conditions can be represented by the function_satisfied_γ which indicates the degree to which condition γ is satisfied.
Specifically, in this example, the rewards are defined as follows:
γ=1(refers to advertiser—1)
function_satisfied—1:
If size—1≧0.5, then reward—1=5 else reward—1=0.
γ=2(refers to advertiser—2)
function_satisfied—2:
If size—2≧0.25, then reward—2=3 else reward—2=0.
γ=3(refers to advertiser—3)
function_satisfied—3:
If size—3≧⅓, then reward—3=4 else reward—3=0.
The above conditions and results can be thought of as additional constraints of the optimization problem.
Having defined all features, weights, constraints, as well as a function to maximize, the optimization problem can now be solved. Again the goal is to maximize the constrained function:
∑ α ⁢ ∑ λ ⁢ ( weight_λ × Feature_val ⁢ _λ × size_α ) + ∑ γ ⁢ ( reward_γ ) .
A series of calculations will follow:
1st Layout (FIG. 6):
For ⁢ ⁢ size_ ⁢ 1 = 1 3 size_ ⁢ 2 = 1 3 size_ ⁢ 3 = 1 3 } ,
entering these values in the optimization function generates:
For α=1 (advertiser—1):
⁢ For ⁢ ⁢ λ = 1 ⁢ ( first ⁢ ⁢ feature ⁢ ⁢ value ) , γ = 1 ⁢ ( refers ⁢ ⁢ to ⁢ ⁢ advertiser_ ⁢ 1 ) ⁢ : ⁢ ( weight_ ⁢ 1 × Feature_val ⁢ _ ⁢ 1 × size_ ⁢ 1 ) + 0 ( 1 ) ⁢ For ⁢ ⁢ λ = 2 ⁢ ( second ⁢ ⁢ feature ⁢ ⁢ value ) ⁢ : ⁢ ( weight_ ⁢ 2 × Feature_val ⁢ _ ⁢ 2 × size_ ⁢ 1 ) ( 2 ) ( 1 ) + ( 2 ) ⇒ ( ( 30 0.1 + 0.5 ) × 0.1 × size_ ⁢ 1 ) + ( 100 × 0.05 × size_ ⁢ 1 × size_ ⁢ 1 ) ⇒ 5 × ( size_ ⁢ 1 ) + 5 × ( size_ ⁢ 1 ) 2 ( a )
For α=2 (advertiser—2):
⁢ For ⁢ ⁢ λ = 1 ⁢ ( first ⁢ ⁢ feature ⁢ ⁢ value ) , γ = 2 ⁢ ( refers ⁢ ⁢ to ⁢ ⁢ advertiser_ ⁢ 2 ) ⁢ : ⁢ ( weight_ ⁢ 1 × Feature_val ⁢ _ ⁢ 1 × size_ ⁢ 2 ) + 3 ( 1 ) ⁢ For ⁢ ⁢ λ = 2 ⁢ ( second ⁢ ⁢ feature ⁢ ⁢ value ) ⁢ : ⁢ ( weight_ ⁢ 2 × Feature_val ⁢ _ ⁢ 2 × size_ ⁢ 2 ) ( 2 ) ( 1 ) + ( 2 ) ⇒ ( ( 30 0.3 + 0.5 ) × 0.3 × size_ ⁢ 2 ) + ( 100 × 0.03 × size_ ⁢ 2 × size_ ⁢ 2 ) + 3 ⇒ 11.25 × ( size_ ⁢ 2 ) + 3 × ( size_ ⁢ 2 ) 2 + 3 ( b )
For α=3 (advertiser—3):
⁢ For ⁢ ⁢ λ = 1 ⁢ ( first ⁢ ⁢ feature ⁢ ⁢ value ) , γ = 3 ⁢ ( refers ⁢ ⁢ to ⁢ ⁢ advertiser_ ⁢ 3 ) ⁢ : ⁢ ( weight_ ⁢ 1 × Feature_val ⁢ _ ⁢ 1 × size_ ⁢ 3 ) + 4 ( 1 ) ⁢ For ⁢ ⁢ λ = 2 ⁢ ( second ⁢ ⁢ feature ⁢ ⁢ value ) ⁢ : ⁢ ( weight_ ⁢ 2 × Feature_val ⁢ _ ⁢ 2 × size_ ⁢ 3 ) ( 2 ) ( 1 ) + ( 2 ) ⇒ ( ( 30 0.9 + 0.5 ) × 0.9 × size_ ⁢ 3 ) + ( 100 × 0.06 × size_ ⁢ 3 × size_ ⁢ 3 ) + 4 ⇒ 19.29 × ( size_ ⁢ 3 ) + 6 × ( size_ ⁢ 3 ) 2 + 4 ( c )
Summing parts (a)+(b)+(c) provides:
5×(size—1)+5×(size—1)2+11.25×(size—2)+3×(size—2)2+3+19.29×(size—3)+6×(size13 3)2+4
Substituting the size values for this orientation and layout provides:
value=20.40
2ND Layout (FIG. 7):
first orientation of ads
For ⁢ ⁢ size_ ⁢ 1 = 1 2 size_ ⁢ 2 = 1 4 size_ ⁢ 3 = 1 4 } ,
∑ α ⁢ ∑ λ ⁢ ( weight_ ⁢ λ ⨯ Feature_val ⁢ _λ ⨯ size_α ) + ∑ γ ⁢ ( reward_γ )
⁢ For ⁢ ⁢ λ = 1 ⁢ ( first ⁢ ⁢ feature ⁢ ⁢ value ) , γ = 1 ⁢ ( refers ⁢ ⁢ to ⁢ ⁢ advertiser_ ⁢ 1 ) ⁢ : ⁢ ( weight_ ⁢ 1 × Feature_val ⁢ _ ⁢ 1 × size_ ⁢ 1 ) + 5 ( 1 ) ⁢ For ⁢ ⁢ λ = 2 ⁢ ( second ⁢ ⁢ feature ⁢ ⁢ value ) ⁢ : ⁢ ( weight_ ⁢ 2 × Feature_val ⁢ _ ⁢ 2 × size_ ⁢ 1 ) ( 2 ) ( 1 ) + ( 2 ) ⇒ ( ( 30 0.1 + 0.5 ) × 0.1 × size_ ⁢ 1 ) + ( 100 × 0.05 × size_ ⁢ 1 × size_ ⁢ 1 ) + 5 ⇒ 5 × ( size_ ⁢ 1 ) + 5 × ( size_ ⁢ 1 ) 2 + 5 ( a )
⁢ For ⁢ ⁢ λ = 1 ⁢ ( first ⁢ ⁢ feature ⁢ ⁢ value ) , γ = 3 ⁢ ( refers ⁢ ⁢ to ⁢ ⁢ advertiser_ ⁢ 3 ) ⁢ : ⁢ ( weight_ ⁢ 1 × Feature_val ⁢ _ ⁢ 1 × size_ ⁢ 3 ) ( 1 ) ⁢ For ⁢ ⁢ λ = 2 ⁢ ( second ⁢ ⁢ feature ⁢ ⁢ value ) ⁢ : ⁢ ( weight_ ⁢ 2 × Feature_val ⁢ _ ⁢ 2 × size_ ⁢ 3 ) ( 2 ) ( 1 ) + ( 2 ) ⇒ ( ( 30 0.9 + 0.5 ) × 0.9 × size_ ⁢ 3 ) + ( 100 × 0.06 × size_ ⁢ 3 × size_ ⁢ 3 ) ⇒ 19.29 × ( size_ ⁢ 3 ) + 6 × ( size_ ⁢ 3 ) 2 ( c )
5×(size—1)+5×(size—1)2+5+11.25×(size—2)+3×(size—2)2+3+19.29×(size—3)+6×(size13 3)2
value=19.95
second orientation of ads
For ⁢ ⁢ size_ ⁢ 1 = 1 4 size_ ⁢ 2 = 1 2 size_ ⁢ 3 = 1 4 } ,
∑ α ⁢ ∑ λ ⁢ ( weight_λ ⨯ Feature_val ⁢ _λ ⨯ size_α ) + ∑ γ ⁢ ( reward_γ )
⁢ For ⁢ ⁢ λ = 1 ⁢ ( first ⁢ ⁢ feature ⁢ ⁢ value ) , γ = 1 ⁢ ( refers ⁢ ⁢ to ⁢ ⁢ advertiser_ ⁢ 1 ) ⁢ : ⁢ ( weight_ ⁢ 1 × Feature_val ⁢ _ ⁢ 1 × size_ ⁢ 1 ) ( 1 ) ⁢ For ⁢ ⁢ λ = 2 ⁢ ( second ⁢ ⁢ feature ⁢ ⁢ value ) ⁢ : ⁢ ( weight_ ⁢ 2 × Feature_val ⁢ _ ⁢ 2 × size_ ⁢ 1 ) ( 2 ) ( 1 ) + ( 2 ) ⇒ ( ( 30 0.1 + 0.5 ) × 0.1 × size_ ⁢ 1 ) + ( 100 × 0.05 × size_ ⁢ 1 × size_ ⁢ 1 ) ⇒ 5 × ( size_ ⁢ 1 ) + 5 × ( size_ ⁢ 1 ) 2 ( a )
5×(size—1)+5×(size—1)2+11.25×(size—2)+3×(size—2)2+3+19.29×(size—3)+6×(size13 3)2
value=16.14
third orientation of ads
For ⁢ ⁢ size_ ⁢ 1 = 1 4 size_ ⁢ 2 = 1 4 size_ ⁢ 3 = 1 2 } ,
value=22.71
3RD Layout (FIG. 8):
For ⁢ ⁢ size_ ⁢ 1 = 2 3 size_ ⁢ 2 = 1 6 size_ ⁢ 3 = 1 6 } ,
⁢ For ⁢ ⁢ λ = 1 ⁢ ( first ⁢ ⁢ feature ⁢ ⁢ value ) , γ = 2 ⁢ ( refers ⁢ ⁢ to ⁢ ⁢ advertiser_ ⁢ 2 ) ⁢ : ⁢ ( weight_ ⁢ 1 × Feature_val ⁢ _ ⁢ 1 × size_ ⁢ 2 ) ( 1 ) ⁢ For ⁢ ⁢ λ = 2 ⁢ ( second ⁢ ⁢ feature ⁢ ⁢ value ) ⁢ : ⁢ ( weight_ ⁢ 2 × Feature_val ⁢ _ ⁢ 2 × size_ ⁢ 2 ) ( 2 ) ( 1 ) + ( 2 ) ⇒ ( ( 30 0.3 + 0.5 ) × 0.3 × size_ ⁢ 2 ) + ( 100 × 0.03 × size_ ⁢ 2 × size_ ⁢ 2 ) ⇒ 11.25 × ( size_ ⁢ 2 ) + 3 × ( size_ ⁢ 2 ) 2 ( b )
5×(size—1)+5×(size—1)2+5+11.25×(size—2)+3×(size—2)2+19.29×(size—3)+6×(size13 3)2
value=15.90
For ⁢ ⁢ size_ ⁢ 1 = 1 6 size_ ⁢ 2 = 2 3 size_ ⁢ 3 = 1 6 } ,
5×(size—1)+5×(size—1)2+11.25×(size—2)+3×(size—2)2+3+19.29×(size—3)+6×(size—3)2
value=16.19
For ⁢ ⁢ size_ ⁢ 1 = 1 6 size_ ⁢ 2 = 1 6 size_ ⁢ 3 = 2 3 } ,
5×(size—1)+5×(size—1)2+11.25×(size—2)+3×(size—2)2+19.29×(size—3)+6×(size—3)2+4
value=22.46
As can be concluded by looking at the generated values in each layout, the optimum configuration is achieved by choosing the second layout (FIG. 7) with the third orientation of ads, which gives value=22.71.
In this configuration advertiser—1 gets an ad space value of two thirds of the total layout space, advertiser—2 gets an ad space value of one sixth of the total layout space, and advertiser—3 gets an ad space of one sixth of the total layout space.
§4.5 Conclusion
As can be appreciated from the foregoing, the present invention can be used to improve ads by optimizing the size and/or layout of a set of ads.
1. A non-transitory computer-readable medium with computer-executable instructions stored thereon, that when executed, perform operations including:
for each ad in a set of two or more ads, accepting ad information including two or more ad feature values for two or more ad features of the ad, the ad information including at least one ad feature having an ad feature value that depends on an ad rendering parameter;
accepting, by one or more processors and for available candidate ad layouts and corresponding ad orientations, ad rendering constraints, the ad rendering constraints for each candidate ad layout including space available for rendering the ads, an advertisement layout available for rendering the ads, and a maximum number of available ads permitted to be rendered, each corresponding ad orientation specifying presentation positions of each ad relative to presentation positions of other ads presented in a candidate ad layout;
for each candidate ad layout and corresponding ad orientation:
computing, by one or more processors, a weight for each feature value of each ad, the weight being computed using a weight function corresponding to the feature value; and
computing a result of a function of weighted combinations of feature values for each ad, the result being computed using the ad feature values for each ad, the computed weights corresponding to the ad feature values, and the ad rendering constraints; and
selecting, by one or more processors, ad rendering parameters for at least one ad from the set of two or more ads based on the result of the function of weighted combinations of feature values, the ad rendering parameters including a candidate ad layout and corresponding ad orientation for which the result of the function of weighted combinations exceeds a specified value; and
providing the at least one ad according to the layout and the ad orientation for which the result of the function of weighted combinations of feature values exceeds the specified value.
2. The computer-readable medium of claim 1 wherein the ad rendering parameters include sizes of provided ads.
3. The computer-readable medium of claim 1 wherein the ad rendering parameters include styles of the ads.
4. The computer-readable medium of claim 3 wherein the styles of the ads include at least one of ad background color and ad foreground color.
5. The computer-readable medium of claim 3 wherein the styles of the ads include at least one of font size and font style.
6. The computer-readable medium of claim 3 wherein the styles of the ads include amount of text.
7. The computer-readable medium of claim 3 wherein the styles of the ads include type and degree of graphical elements.
8. The computer-readable medium of claim 1 wherein the ad information includes feature values for at least one ad feature selected from the set of ad features including a language of the ad, ad selection rate, ad conversion rate, cost of ad, price of items advertised, profit on items being advertised, number of related items that an advertiser sells, offer information, location information, product information, targeting information, and performance information, and wherein the result of the function is computed using the feature values for the at least one selected ad feature.
9. The computer-readable medium of claim 1 wherein the at least one ad feature having an ad feature value that depends on an ad rendering parameter is one of a click-through-rate and a conversion rate.
10. The computer-readable medium of claim 1 wherein the at least one ad feature having an ad feature value that depends on an ad rendering parameter is one of an offer for impression, an offer for selection and an offer for conversion.
11. The computer-readable medium of claim 1 wherein the ad information further includes at least one ad feature having an ad feature value that is independent of ad rendering parameters.
12. The computer-readable medium of claim 1 wherein the at least one ad feature having an ad feature value that depends on an ad rendering parameter depends on a presentation size of an ad.
13. The computer-readable medium of claim 1 wherein the at least one ad feature having an ad feature value that depends on an ad rendering parameter depends on a presentation size of an ad relative to at least one other presentation size of at least one other ad.
14. The computer-readable medium of claim 1 wherein the at least one ad feature having an ad feature value that depends on an ad rendering parameter depends on a presentation style of an ad.
15. The computer-readable medium of claim 1 wherein the at least one ad feature having an ad feature value that depends on an ad rendering parameter depends on a presentation style of an ad relative to at least one other presentation style of at least one other ad.
16. The computer-readable medium of claim 1 wherein the at least one ad feature having an ad feature value that depends on ad an rendering parameter depends on an ad layout in which an ad is presented.
17. The computer-readable medium of claim 1 further comprising accepting user information,
wherein determining ad rendering parameters for the at least one ad comprises determining the ad rendering parameters for the at least one ad based on the accepted user information.
18. The computer-readable medium of claim 17 wherein the user information includes at least one of language of a user to which the ad is to be presented, distance of the user to an advertised location, user browser preferences, user browser capabilities, client device capabilities, user demographic information, user ethnographic information, user behavior, location of the user, and browser history information.
19. The computer-readable medium of claim 1 further comprising accepting query information,
wherein determining ad rendering parameters for the at least one ad comprises determining the ad rendering parameters for the at least one ad based on the accepted query information.
20. The computer-readable medium of claim 19 wherein the accepted query information includes at least one of query time, query date, query season, and query source location.
receiving, by at least one processor and for each of a plurality of ads, ad information including feature values for the ad, each feature value representing a characteristic of the ad;
receiving, by at least one processor and for each of a plurality of candidate ad layouts, constraint information specifying a number of ads permitted to be presented in the ad layout and available space for presenting each of the number of ads;
selecting, by at least one processor and for each ad, rendering parameters for the ad, the rendering parameters including a candidate ad layout selected form the candidate ad layouts, the selecting comprising:
for each candidate layout:
computing, for each ad and by at least one processor, a weight value for each feature value of the ad using a weight function corresponding to the feature value;
computing, by at least one processor, a result value of a function of weighted combinations of feature values of the ads, the result value being computed using, as input to the function, the feature values of the ads and the computed weights corresponding to the feature values; and
selecting, as a rendering parameter, the candidate layout for which a result value of the function is greater than another result value corresponding to another candidate layout; and
providing, by at least one processor, the ads for presentation according to the ad rendering parameters.
US11026329 2004-12-30 2004-12-30 Ad rendering parameters, such as size, style, and/or layout, of online ads Active 2030-09-19 US8666820B2 (en)
US11026329 US8666820B2 (en) 2004-12-30 2004-12-30 Ad rendering parameters, such as size, style, and/or layout, of online ads
US14160664 US20140136304A1 (en) 2004-12-30 2014-01-22 Ad rendering parameters, such as size, style, and/or layout, of online ads
US14160664 Continuation US20140136304A1 (en) 2004-12-30 2014-01-22 Ad rendering parameters, such as size, style, and/or layout, of online ads
US20060149622A1 true US20060149622A1 (en) 2006-07-06
US8666820B2 true US8666820B2 (en) 2014-03-04
ID=36641824
US11026329 Active 2030-09-19 US8666820B2 (en) 2004-12-30 2004-12-30 Ad rendering parameters, such as size, style, and/or layout, of online ads
US14160664 Pending US20140136304A1 (en) 2004-12-30 2014-01-22 Ad rendering parameters, such as size, style, and/or layout, of online ads
US (2) US8666820B2 (en)
US20160117305A1 (en) * 2005-01-14 2016-04-28 Callahan Cellular L.L.C. Method and apparatus for form automatic layout
US20060149622A1 (en) 2006-07-06 application
US20140136304A1 (en) 2014-05-15 application
US20040162760A1 (en) 2004-08-19 Method and system for advertisement using internet browser to insert advertisements
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:BALUJA, SHUMEET;MITTAL, VIBHU;SAHAMI, MEHRAN;REEL/FRAME:016313/0764;SIGNING DATES FROM 20050501 TO 20050506
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:BALUJA, SHUMEET;MITTAL, VIBHU;SAHAMI, MEHRAN;SIGNING DATES FROM 20050501 TO 20050506;REEL/FRAME:016313/0764