Source: https://patents.google.com/patent/US8762203B2/en
Timestamp: 2019-10-14 11:00:09
Document Index: 743590242

Matched Legal Cases: ['§119', '§112', '§4', '§4', '§4', '§4', '§4']

US8762203B2 - Automated price maintenance for use with a system in which advertisements are rendered with relative preference based on performance information and price information - Google Patents
Automated price maintenance for use with a system in which advertisements are rendered with relative preference based on performance information and price information Download PDF
US8762203B2
US8762203B2 US12/911,605 US91160510A US8762203B2 US 8762203 B2 US8762203 B2 US 8762203B2 US 91160510 A US91160510 A US 91160510A US 8762203 B2 US8762203 B2 US 8762203B2
US12/911,605
US20110040614A1 (en
2002-11-08 Priority to US42479202P priority Critical
2003-01-10 Priority to US10/340,542 priority patent/US7844493B1/en
2010-10-25 Application filed by Google LLC filed Critical Google LLC
2010-10-25 Priority to US12/911,605 priority patent/US8762203B2/en
2011-02-17 Publication of US20110040614A1 publication Critical patent/US20110040614A1/en
2014-06-24 Publication of US8762203B2 publication Critical patent/US8762203B2/en
2014-09-04 Assigned to GOOGLE INC. reassignment GOOGLE INC. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: KAMANGAR, SALAR ARTA, VEACH, ERIC
231100000756 time-weighted average Toxicity 0 description 3
This application is a continuation of U.S. patent application Ser. No. 10/340,542 (referred to as “the '542 application” and incorporated herein by reference), titled “AUTOMATED PRICE MAINTENANCE FOR USE WITH A SYSTEM IN WHICH ADVERTISEMENTS ARE RENDERED WITH RELATIVE PREFERENCE BASED ON PERFORMANCE INFORMATION AND PRICE INFORMATION,” filed on Jan. 10, 2003 now U.S. Pat. No. 7,844,493, and listing Eric Veach and Salar Arta Kamangar, as the inventors, which is based upon and claims benefit under 35 U.S.C. §119(e)(1), to the filing date of provisional patent application Ser. No. 60/424,792 (referred to as “the '792 provisional” and incorporated herein by reference), titled “AUTOMATED PRICE MAINTENANCE FOR USE WITH A SYSTEM IN WHICH ADVERTISEMENTS ARE RENDERED WITH RELATIVE PREFERENCE BASED ON PERFORMANCE INFORMATION AND PRICE INFORMATION”, filed on Nov. 8, 2002 and listing Eric Veach as the inventor, for any inventions disclosed in the manner provided by 35 U.S.C. §112, ¶ 1.
Regardless of the strategy, Web site-based ads (also referred to as “Web ads”) are typically presented to their advertising audience in the form “banner ads”—i.e., a rectangular box that includes graphic components. When a member of the advertising audience (referred to as a “viewer” in the Specification without loss of generality) selects one of these banner ads by clicking on it, embedded hypertext links typically direct the viewer to the advertiser's Web site. This process, wherein the viewer selects an ad, is commonly referred to as a “click-through”. (“Click-through” is intended to include any user selection.) The ratio of the number of click-throughs to the number of impressions of the ad (i.e., the number of times an ad is displayed) is commonly referred to as the “click-through rate” of the ad.
Similarly, the hosts of Web sites on which the ads are presented (referred to as “Web site hosts” or “ad consumers”) have the challenge of maximizing ad revenue without impairing their users' experience. Some Web site hosts have chosen to place advertising revenues over the interests of users. One such Web site is “Overture.com”, which hosts a so-called “search engine” service returning purported “search results” in response to user queries. The Overture.com web site permits advertisers to pay to position an ad for their Web site (or a target Web site) higher up on the list of search results. If such schemes in which the advertiser only pays if a user clicks on the ad (i.e., cost-per-click) are implemented, the advertiser lacks incentive to target their ads effectively, since a poorly targeted ad will not be clicked and therefore will not require payment. As a result, high cost-per-click ads show up near or at the top, but do not necessarily translate into real revenue for the ad publisher because viewers don't click on them. Furthermore, ads that viewers would click on are further down the list, or not on the list at all, and so relevancy of ads is compromised.
U.S. patent application Ser. No. 10/112,654, entitled “METHODS AND APPARATUS FOR ORDERING ADVERTISEMENTS BASED ON PERFORMANCE INFORMATION AND PRICE INFORMATION”, filed on Mar. 29, 2002, and listing Salar Kamangar, Eric Veach and Ross Koningstein as the inventors (hereafter referred to as “the Kamangar application”, and incorporated herein by reference), provides a better scheme, in which ads are positioned (or otherwise rendered with relative preference) as a function of both price and at least one performance parameter (such as click-through rate for example).
It would be useful to help advertisers manage their bidding in any of the foregoing schemes (that is, the foregoing schemes in which ad position (or other relative preferential rendering) is, at least in part, based on a bid price). Software which automates tracking bids, bidding, and updating bids (known as “robots”) are known. However, such robots allow sophisticated bidders to have an unfair advantage over others. That is, sophisticated bidders can repeatedly win a bid by increasing their bid by the minimum increment (e.g., $0.01) over the highest bid automatically and without exposing the maximum that they are willing to bid. Therefore, it would be desirable to make the bidding process more fair, while permitting a winning bidder to avoid “winner's remorse” (i.e., allowing the winner to pay the least amount of money to maintain the position (or other relative rendering preference of their ad).
An advertising program may include information concerning accounts, campaigns, creatives, targeting, etc. The term “account” relates to information for a given advertiser (e.g., a unique email address, a password, billing information, etc.). A “campaign” or “ad campaign” refers to one or more groups of one or more advertisements, and may include a start date, an end date, budget information, geo-targeting information, syndication information, etc. For example, Honda may have one advertising campaign for its automotive line, and a separate advertising campaign for its motorcycle line. The campaign for its automotive line have one or more ad groups, each containing one or more ads. Each ad group may include a set of keywords, and a maximum cost bid (cost per click-though, cost per conversion, etc.). Alternatively, or in addition, each ad group may include an average cost bid (e.g., average cost per click-through, average cost per conversion, etc.). Therefore, a single maximum cost bid and/or a single average cost bid may be associated with one or more keywords. As stated, each ad group may have one or more ads or “creatives” (That is, ad content that is ultimately rendered to an end user).
Recall that various aspects of the cost determination operation(s) 227 may depend on techniques used in the presentation ordering operations 250. FIG. 5 is a flow diagram of an exemplary method 250′ that may be used to effect such presentation ordering operation(s). This exemplary method corresponds to the method described in the Kamangar application. As indicated by block 510, a set (e.g., a list) of candidate ads is obtained. Referring to the Table of FIG. 6, suppose that this list contains the five ads—A, B, C, D, and E. Then, as indicated by block 520, one or more performance parameters (or more generally, “performance information”) for each candidate ad is identified. In one implementation, the performance parameter is a windowed, time-weighted average click-through rate for the ad. As shown in the Table of FIG. 6, assume that ads A, B, C, D, and E have the following windowed, time-weighted average click-through rates, respectively: 1%, 4%, 4%, 3% and 12%.
Similarly, as indicated by block 530, a price parameter (or more generally, “price information”) is identified for each candidate ad. Examples of a price parameter include a maximum cost-per-impression bid, a maximum cost-per-selection (e.g., click-through) bid, a maximum cost-per-conversion bid, etc. In the example illustrated in FIG. 6, this price is a maximum cost-per-click bid, which is preferably defined in advance as a negotiated or auction-based bid. As shown in the Table of FIG. 6, assume that at a given point in time, ads A, B, C, D, and E have the following maximum costs-per-click bids, respectively: $5.00 per click, $1.50 per click, $1.00 per click, $0.90 per click, and $0.25 per click.
In the exemplary method 250′ of FIG. 5, as indicated by block 540, a placement value (or more generally, “an ad preference value”) is determined for each candidate ad based on the one or more performance parameters and the price. In one implementation, this placement value is a product of the windowed, time-weighted average click-through rate and the maximum cost-per-click bid. Accordingly, as shown in the Table of FIG. 6, ad A has a score of 0.050 (0.01 multiplied by 5.00), ad B has a score of 0.060 (0.04 multiplied by 1.50), ad C has a score of 0.040 (0.04 multiplied by 1.00), ad D has a score of 0.027 (0.03 multiplied by 0.90), and ad E has a score of 0.030 (0.12 multiplied by 0.25). Finally, as indicated by block 550, the ads may be ordered based on their placement values, and the method 250′ may then be left via RETURN node 560. In the present example, the order would be B, A, C, E, D.
Note that the exemplary ordering method 250′ is advantageous in that it can be used to “normalize” cost per different types of result and different performance parameters to some monetary or revenue unit. For example, if a first advertiser wanted to pay based on conversions, while a second advertiser wanted to pay based on click-throughs, the placement values could be normalized to dollars as shown:
dollars conversion × conversions 100 ⁢ ⁢ impressions ⇒ dollars 100 ⁢ ⁢ impressions ; and dollars click ⁢ - ⁢ through × click ⁢ - ⁢ throughs 100 ⁢ ⁢ impressions ⇒ dollars 100 ⁢ ⁢ impressions
Such normalization permits advertisers that want to pay for their advertising using different metrics to compete on a common basis.
§4.2.1 Exemplary Cost Determination Methods
FIG. 7 is a flow diagram of an exemplary method 227′ that may be used to effect a cost determination operation(s) 227. This exemplary method 227′ ensures that each “winner” does not pay more than is necessary to maintain the determined position (or other relative rendering preference) of its ad. This method 227′ may be invoked, for example, after the ads that are to be served are determined and ordered, and is preferably invoked for each ad (to be) served. As indicated by blocks 710 and 720, for a given ad, the next lower placement value (or ad preference value) is accepted, and the performance parameter(s), used in the determination of the placement value, is also accepted. Then, as indicated by block 730, a cost is determined based on the accepted next lower placement (or ad preference) value and the accepted performance parameter.
In another embodiment of the invention, the cost charged for an ad may be determined by dividing the next lower placement (or ad preference) value by the performance value(s), and adding an amount. The amount may be a nominal value so that the ad with a higher placement (or ad preference) value costs at least the amount (or perhaps slightly more than) necessary to render the ad in a higher placement or preference position than the next lower-placed (or preferred) ad. For example, with respect to the example illustrated in the Table of FIG. 6 above, one cent ($0.01) may be added to the cost per result billed to each of the rendered ads B, A, and C, so that the cost of placing the ads in their preference order is ($1.25+$0.01)=$1.26 per click to place ad B first, ($4.00+$0.01)=$4.01 to place ad A second, and ($0.75+$0.01)=$0.76 to place ad C third. The amounts added to the ad costs for ads A, B, and C need not be the same, and may also be negative (such that, for example, the cost billed to ads B, A, and C may be ($1.25+$0.00)=$1.25, ($4.00−$0.01)=$3.99, and ($0.75-$0.01)=$0.74, respectively). One of ordinary skill will further recognize that numerous additional or substitute methods may be employed to achieve the same effect, such as by rounding the cost per result billed to a nearest convenient increment (such as the nearest penny for transactions in United States dollars), adding an amount to the maximum bid cost used to determine preference order, adjusting or rounding the performance parameter, etc.
Note that in each case, the advertiser usually pays a lower rate, e.g. the lowest rate, while still maintaining the position of its ad. The cost of the lowest placed ad can be similarly determined, unless it did not “beat out” another ad that was not served. In this case, the cost of the lowest placed ad can simply be some predetermined minimum cost (referred to as a “reserve price”), or some predetermined minimum cost modified by the performance parameter(s) of the ad. Although bids can correspond to multiple keywords (of a given ad or ad group), each particular keyword preferably has its own (system-wide) reserve price, though all keywords or groups of keywords can be associated with a single reserve price.
§4.2.2 Exemplary Billing Methods
§4.2.3 Exemplary Apparatus and Data Structures
FIG. 10 illustrates a relationship among ad identification information 1010, keyword(s) 1020, ad content 1030, a maximum cost per result bid 1040 and an average cost per result bid 1050. Various data structures for associating such information are possible. In one embodiment, the ad identifier 1010 may be unique and therefore used as a “primary key” to associate various pieces of information.
§4.2.4 Alternatives and Refinements
In the exemplary embodiment of ordering ads described above with reference to FIG. 5, ordering was strictly a matter of relative position (or ad preference) values. However, in one exemplary embodiment, advertisers can define constraints. For example, if an advertiser sets a “top position only” constraint, if their ad does not have the top position value, it will not be served and ads with lower position (or ad preference) values will move up. Referring to the example provided in FIG. 6, suppose ad C has such a “top position only” constraint. In this example, ad C would be dropped and ads E and D would each move up one spot. In this case, the cost for placing ad A second could be dropped from $4.00 to $3.00 (ad E's placement value divided by ad A's performance parameter=0.030/0.01). In another example, if an advertiser sets a “no higher than position three” constraint, if the position value of their ad is the highest, the ad with the second highest position value will move up to the first position, the ad with the third highest position value will move up to the second highest position, and the “constrained” ad will move down two positions to the third highest position. Referring to the example provided in FIG. 6, if ad A had a “no higher than third position” constraint, ad C would take ad A's second position, and ad A would move to the third position. In this case, since ad C could not fall below ad A, but could fall below ad E, its actual cost could remain at $0.75. However, ad A's actual cost could be $3.00 (ad E's placement value divided by ad A's performance parameter=0.030/0.01).
In yet another alternative, advertisers could specify a maximum average cost per result bid. In such a case, in some instances, the costs billed to the advertiser could be above this maximum average cost per result bid, so long as the average of all of the costs billed is below the maximum average cost per result bid. In a conservative implementation of this embodiment, initial instances would be at or less than the maximum average cost per result bid. In this way, a “surplus” could accrue and be applied to later instances which could be greater than the maximum average cost per result bid. In a less conservative implementation of this embodiment, initial instances could be above the maximum average cost per bid, perhaps subject to some limit, under the assumption that later costs would bring the average back to, or below, the maximum average cost per result bid. In this less conservative implementation, if later costs did not bring the average back to, or below, the maximum average cost per result bid by the time of the billing cycle, the amount billed to the advertiser could be limited such that its maximum average cost per result bid is not exceeded.
§4.3 Operations
FIG. 12 is a messaging diagram illustrating exemplary operations of a second exemplary embodiment of the present invention. This second embodiment is useful when billing is subject to a condition precedent. As indicated by communication 1210, an ad serving operation(s) 230 receives an ad request from an ad consumer 130. The ad request may include the number of ads desired, as well as other information. In response to the request, the ad serving operation 230 may get ordered, relevant ads (Recall, e.g., operations 235 and 250 of FIG. 2). As shown by communication 1220, the ad serving operation(s) 230 may then provide, for each ad determined in response to the request 1210, ad information (preferably including at least an ad identifier and an ad placement value) to a cost determination operation(s) 227. In response, the cost determination operation(s) 227 may determine, for each ad to be served, a cost that may, perhaps subject to a condition precedent, be billed to an advertiser. As indicated by communication 1230, the cost determination operation(s) 227 may then provide ad information (preferably including at least an ad identifier and the determined cost) back to the ad serving operation(s) 230. In response, the ad serving operation(s) 230 provides, for each ad, the ad content (or an identifier for obtaining the ad content) and the determined cost information and placement information, as indicated by communication 1240. In this case, it is assumed that billing is subject to a condition precedent (such as a click-through or a conversion for example). Thus, the billing operation(s) will await result information from the result interface operation(s) 260 as indicated by message 1250. Here, the communication 1250 preferably includes the previously determined ad cost information, in addition to an ad identifier and result information. The result information may be implied, for example from the presence or absence of a result message. If the condition precedent is met, the billing operation(s) 228 may then generate billing information to be charged to the relevant advertiser(s), as indicated by communication 1260.
As can be appreciated from the foregoing disclosure, the advertising techniques described above account for the interests of users (e.g., users of a search engine) by providing them with ads that are of interest and that are not easily confused with noncommercial requested content (such as search results), if any. Further, the interests of the advertisers are well served since users will presumably be more likely to click-through on ads that are of interest to them. Moreover, the present invention allows advertisers to manage their “bidding” for placing their advertisement easily, while being assured that they won't pay more than they need to.
a) for each of a plurality of ads, determining, by a computer system including a least one computer, an ad preference value as a function of performance information associated with the ad and a maximum cost for a result bid associated with the ad, wherein the performance information associated with the ad includes an actual or expected selection rate of the ad; and
b) determining, by the computer system, a cost for each of the plurality of ads using performance information of the ad and the maximum cost for a result bid associated with an ad having a next lower ad preference value to the ad, the determined cost being lower than the maximum cost for a result bid associated with the ad.
2. The computer-implemented method of claim 1 wherein, for each of the plurality of ads, the maximum cost for a result bid associated with the ad is a maximum cost per selection bid.
c) accepting, by the computer system, result information indicating whether or not a selection on one of the plurality of ads occurred; and
d) billing, by the computer system, the cost determined to an advertiser associated with the one of the plurality of ads only if the accepted result information indicates that a selection occurred.
4. The computer-implemented method of claim 1 wherein the ad preference value is a placement value.
5. The computer-implemented method of claim 1 wherein each of the plurality of ads is associated with a group of one or more keywords.
6. The computer-implemented method of claim 1 wherein, for each of the plurality of ads, the maximum cost for a result bid is a maximum average cost per result associated with the ad.
7. The computer-implemented method of claim 1 wherein, for each of the plurality of ads, the maximum cost for a result bid is a maximum cost for an incremental result bid associated with the ad.
1) for each of a plurality of ads, determining an ad preference value as a function of performance information associated with the ad and a maximum cost for a result bid associated with the ad, wherein the performance information associated with the ad includes an actual or expected selection rate of the ad, and
2) determining a cost for each of the plurality of ads using performance information of the ad and the maximum cost for a result bid associated with an ad having a next lower ad preference value to the ad, the determined cost being lower than the maximum cost for a result bid associated with the ad.
9. The apparatus of claim 8 wherein, for each of the plurality of ads, the maximum cost for a result bid associated with the ad is a maximum cost per selection bid.
10. The apparatus of claim 9 wherein the method further includes
3) accepting result information indicating whether or not a selection on one of the plurality of ads occurred, and
4) billing the cost determined to an advertiser associated with the one of the plurality of ads only if the accepted result information indicates that a selection occurred.
11. The apparatus of claim 8 wherein the ad preference value is a placement value.
12. The apparatus of claim 8 wherein each of the plurality of ads is associated with a group of one or more keywords.
13. The apparatus of claim 8 wherein, for each of the plurality of ads, the maximum cost for a result bid is a maximum average cost per result associated with the ad.
14. The apparatus of claim 8 wherein, for each of the plurality of ads, the maximum cost for a result bid is a maximum cost for an incremental result bid associated with the ad.
a) for each of a plurality of ads, determining, by a computer system including a least one computer, an ad preference value as a function of performance information associated with the ad and a maximum cost for a result bid associated with the ad, wherein the performance information associated with the ad includes an actual or expected conversion rate of the ad; and
16. The computer-implemented method of claim 15 wherein, for each of the plurality of ads, the maximum cost for a result bid associated with the ad is a maximum cost per conversion bid.
c) accepting, by the computer system, result information indicating whether or not a conversion on one of the plurality of ads occurred; and
d) billing, by the computer system, the cost determined to an advertiser associated with the one of the plurality of ads only if the accepted result information indicates that a conversion occurred.
18. The computer-implemented method of claim 15 wherein the ad preference value is a placement value.
19. The computer-implemented method of claim 15 wherein each of the plurality of ads is associated with a group of one or more keywords.
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US42479202P true 2002-11-08 2002-11-08
US10/340,542 US7844493B1 (en) 2002-11-08 2003-01-10 Automated price maintenance for use with a system in which advertisements are rendered with relative preference based on performance information and price information
US12/911,605 US8762203B2 (en) 2002-11-08 2010-10-25 Automated price maintenance for use with a system in which advertisements are rendered with relative preference based on performance information and price information
US14/306,436 US20140351062A1 (en) 2002-11-08 2014-06-17 Automated price maintenance for use with a system in which advertisements are rendered with relative preference based on performance information and price information
US10/340,542 Continuation US7844493B1 (en) 2002-11-08 2003-01-10 Automated price maintenance for use with a system in which advertisements are rendered with relative preference based on performance information and price information
US14/306,436 Continuation US20140351062A1 (en) 2002-11-08 2014-06-17 Automated price maintenance for use with a system in which advertisements are rendered with relative preference based on performance information and price information
US20110040614A1 US20110040614A1 (en) 2011-02-17
US8762203B2 true US8762203B2 (en) 2014-06-24
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US12/846,648 Active 2023-10-26 US8447653B2 (en) 2002-11-08 2010-07-29 Automated price maintenance for use with a system in which advertisements are rendered with relative preferences
US12/911,605 Active 2025-05-27 US8762203B2 (en) 2002-11-08 2010-10-25 Automated price maintenance for use with a system in which advertisements are rendered with relative preference based on performance information and price information
US14/306,436 Abandoned US20140351062A1 (en) 2002-11-08 2014-06-17 Automated price maintenance for use with a system in which advertisements are rendered with relative preference based on performance information and price information
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US20100293060A1 (en) 2010-11-18
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Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:VEACH, ERIC;KAMANGAR, SALAR ARTA;REEL/FRAME:033671/0835