Source: https://patents.google.com/patent/US7346606B2/en
Timestamp: 2018-09-22 21:41:15
Document Index: 363070105

Matched Legal Cases: ['§ 1', '§ 1', '§ 4', '§ 4', '§ 4', '§ 4', '§ 4', '§ 4', 'Application No. 200425799', 'Application No. 10']

US7346606B2 - Rendering advertisements with documents having one or more topics using user topic interest - Google Patents
Rendering advertisements with documents having one or more topics using user topic interest Download PDF
US7346606B2
US7346606B2 US10610322 US61032203A US7346606B2 US 7346606 B2 US7346606 B2 US 7346606B2 US 10610322 US10610322 US 10610322 US 61032203 A US61032203 A US 61032203A US 7346606 B2 US7346606 B2 US 7346606B2
US10610322
US20040267723A1 (en )
Ambiguities with respect to a user topic interest may be resolved so that useful topic-relevant ads can be presented. Such ambiguities may be resolved by monitoring user behavior, determining a user topic interest (e.g., from a plurality of different candidate topics) based on the monitored behavior, and serving ads relevant to the determined user topic interest.
The present invention concerns advertising. In particular, the present invention concerns the targeted serving and rendering of ads.
Regardless of the strategy, Web site-based ads (also referred to as “Web ads”) are typically presented to their advertising audience in the form of “banner ads”—i.e., a rectangular box that includes graphic components. When a member of the advertising audience (referred to as a “viewer” or “user” 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 cover 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.
A “conversion” is said to occur when a user consummates a transaction related to a previously served ad. What constitutes a conversion may vary from case to case and can be determined in a variety of ways. For example, it may be the case that a conversion occurs when a user clicks on an ad, is referred to the advertiser's web page, and consummates a purchase there before leaving that web page. Alternatively, a conversion may be defined as a user being shown an ad, and making a purchase on the advertiser's web page within a predetermined time (e.g., seven days). In yet another alternative, a conversion may be defined by an advertiser to be any measurable/observable user action such as, for example, downloading a white paper, navigating to at least a given depth of a Website, viewing at least a certain number of Web pages, spending at least a predetermined amount of time on a Website or Web page, etc. Often, if user actions don't indicate a consummated purchase, they may indicate a sales lead, although user actions constituting a conversion are not limited to this. Indeed, many other definitions of what constitutes a conversion are possible. The ratio of the number of conversions to the number of impressions of the ad (i.e., the number of times an ad is displayed) is commonly referred to as the conversion rate. If a conversion is defined to be able to occur within a predetermined time since the serving of an ad, one possible definition of the conversion rate might only consider ads that have been served more than the predetermined time in the past.
Despite the initial promise of Web site-based advertisement, there remain several problems with existing approaches. Although advertisers are able to reach a large audience, they are frequently dissatisfied with the return on their advertisement investment.
Search engines, such as Google for example, have enabled advertisers to target their ads so that they will be rendered with a search results page and so that they will be relevant, presumably, to the query that prompted the search results page. Although search result pages afford advertisers a great opportunity to target their ads to a more receptive audience, some queries may have alternative interpretations. For example, the query term “jaguar” could refer to the car by that name, the animal by that name, the NFL football team by that name, etc. If the user is interested in the animal, then the user might not be interested in search results which pertain to the car or NFL football team. Similarly, the user might not be interested in advertisements, targeted to the keyword “Jaguar,” but that pertain to the car or NFL football team. Therefore, it would be useful to resolve ambiguities, such as those like the one described above, so that ads presented will more likely be of interest to the user.
Other targeted advertising systems, such as those that target ads based on e-mail information (See, e.g., the systems described in U.S. patent application Ser. No. 10/452,830 (incorporated herein by reference), entitled “SERVING ADVERTISEMENTS USING INFORMATION ASSOCIATED WITH E-MAIL”, filed on Jun. 2, 2003 and listing Jeffrey A. Dean, Georges R. Harik and Paul Bucheit); or those that target ads based on content (See, e.g., U.S. patent application Ser. No. 10/375,900 (incorporated herein by reference), entitled “SERVING ADVERTISEMENTS BASED ON CONTENT”, filed on Feb. 26, 2003 and listing Darrell Anderson, Paul Bucheit, Alex Carobus, Claire Cui, Jeffrey A. Dean, Georges R. Harik, Deepak Jindal, and Narayanan Shivakumar) may have similar challenges. That is, advertising systems would like to present advertisements that are relevant to the user requested information in general, and related to the current user interest in particular.
When ads are shown in association with a document (e.g., a Web page,) it is desirable that the ads be targeted based on the topic or topics suggested by the content of the document. Often, however, the document content may suggest different topics. Consequently, the targeting of advertisements may be sub-optimal because it is not clear which of the applicable topics the user is currently interested in. In general, if a document has content concerning different topics, it may be unclear which of the topics the user is most interested in at the moment. Consequently, if topic-relevant ads are to be presented, it may be unclear which of a number of candidate topic-relevant ads would be most useful to the user. Therefore, if a document has different topics, it would be useful to determine which of the different topics the user is most interested in at the moment so that topic-relevant ads presented will more likely be of interest to the user.
The present invention helps resolve ambiguities with respect to a user topic interest so that useful topic-relevant ads can be presented. The present invention may do so by tracking user behavior, determining a user topic interest (e.g., from a plurality of different candidate topics) based on the monitored behavior, and serving ads relevant to the determined user topic interest.
FIG. 2 illustrates an environment in which advertisers can target their ads on search results pages generated by a search engine, documents served by content servers, and/or e-mail.
FIG. 3 illustrates information that may be used and associated in a manner consistent with the present invention.
FIG. 4 is a bubble diagram of operations that may be performed, and information that may be generated, used, and/or stored, to generate document region and/or topic to ad(s) associations in a manner consistent with the present invention.
FIG. 5 is a bubble diagram of ad optimization operations that may be performed, and information that may be generated, used, and/or stored, in a manner consistent with the present invention.
FIGS. 6-10 are exemplary data structures that may be generated and/or used in a manner consistent with the present invention.
FIG. 11 is a flow diagram of an exemplary method that may be used to associate document regions and/or topics with one or more ads, in a manner consistent with the present invention.
FIG. 12 is a flow diagram of an exemplary method that may be used to perform optimized client-side ad rendering, in a manner consistent with the present invention.
FIG. 13 is a high-level block diagram of apparatus that may be used to perform at least some of the various operations that may be used and store at least some of the information that may be used and/or generated in a manner consistent with the present invention.
FIGS. 14-17 are diagrams illustrating examples of operations of exemplary embodiments of the present invention.
The present invention may involve novel methods, apparatus, message formats and/or data structures for resolving ambiguities with respect to a user topic interest so that useful topic-relevant ads can be presented. 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. 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. Thus, the present invention is not intended to be limited to the embodiments shown and the inventors regard their invention as any patentable subject matter described.
FIG. 1 is a high level diagram of an advertising environment. The environment may include an ad entry, maintenance and delivery system (simply referred to 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.
The ad server 120 may be similar to the one described in FIG. 2 of U.S. patent application Ser. No. 10/375,900, mentioned in § 1.2 above. 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 e-mail 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 targeting information (e.g., a set of keywords, a set of one or more topics, etc.), and price information (e.g., maximum cost (cost per click-though, cost per conversion, etc.)). Alternatively, or in addition, each ad group may include an average cost (e.g., average cost per click-through, average cost per conversion, etc.). Therefore, a single maximum cost and/or a single average cost may be associated with one or more keywords, and/or topics. 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.). Each ad may also include a link to a URL (e.g., a landing Web page, such as the home page of an advertiser, or a Web page associated with a particular product or server). Naturally, the ad information may include more or less information, and may be organized in a number of different ways.
FIG. 2 illustrates an environment 200 in 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 or the Navigator browser from AOL/Time Warner), 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 (e.g., 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, 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.
Thus, one example of an ad consumer 130 is a general content server 230 that receives requests for documents (e.g., articles, discussion threads, music, video, graphics, search results, Web page listings, etc.), and retrieves the requested document in response to, or otherwise services, the request. The content server may submit a request for ads to the ad server 120/210. Such an ad request may include a number of ads desired. The ad request may also include document request information. This information may include the document itself (e.g., page), a category or topic corresponding to the content of the document or the document request (e.g., arts, business, computers, arts-movies, arts-music, etc.), part or all of the document request, content age, content type (e.g., text, graphics, video, audio, mixed media, etc.), geolocation information, document information, etc.
The content server 230 may combine the requested document with one or more of the advertisements provided by the ad server 120/210. This combined information including the document content and advertisement(s) is then forwarded towards the end user device 250 that requested the document, for presentation to the user. Finally, the content server 230 may transmit information about the ads and how, when, and/or where the ads are to be rendered (e.g., position, click-through or not, impression time, impression date, size, conversion or not, etc.) back to the ad server 120/210. Alternatively, or in addition, such information may be provided back to the ad server 120/210 by some other means.
Finally, the search engine 220 may transmit information about the ad and when, where, and/or how the ad was to be rendered (e.g., position, click-through or not, impression time, impression date, size, conversion or not, etc.) back to the ad server 120/210. Alternatively, or in addition, such information may be provided back to the ad server 120/210 by some other means.
As discussed in U.S. patent application Ser. No. 10/375,900 (introduced in § 1.2 above), ads targeted to documents served by content servers may also be served.
§ 4.1.2 Definitions
“User information” may include user behavior information and/or user profile information, such as that described in U.S. patent application Ser. No. 10/452,791, entitled “SERVING ADVERTISEMENTS USING USER REQUEST INFORMATION AND USER INFORMATION,” filed on the Jun. 2, 2003, and listing Steve Lawrence, Mehran Sahami and Amit Singhal as inventors. This application is incorporated herein by reference.
As shown in FIG. 3, the present invention teaches two features—document region (or topic) to ad(s) association 310, and topic-relevant ad optimization 320 using such associations 310. The first feature is described below with reference to FIGS. 4 and 6, while the second feature is described below with reference to FIGS. 5 and 7. Still referring to FIG. 3, for each of one or more document regions, one or more topics are determined. For each of the one or more topics, a set one or more topic-relevant ads are determined. These associations between document regions, topics and ads 310 may be predetermined if the document content is relatively static. As indicated by 320, at the user device (client), user behavior is monitored and user interest in a document region (and therefore a topic) is determined using such user behavior. These associations between user interests and topics may be determined in real time. Scores of topic-relevant ads may be adjusted using determined user topic interest.
As used in this description, the term “document region” is to be broadly construed as a part of a document. For example, a document region may include a spatial portion (e.g., a box, a column, an image, etc.), a section of a Web page, a temporal (e.g., audio and/or video) segment, a link, etc. The term “topic” is to be broadly construed as a concept, subject, or theme. The term “user behavior” is to include any observable or measurable user interaction with a document. Examples of user behavior include (a) cursor positioning, (b) cursor dwell time, (c) document item (e.g., link, control button, etc.) selection, (d) user eye direction relative to the document, (e) user facial expressions, (f) user expressions, and/or (g) express user input (e.g., increasing the volume of an audio segment), etc. User behavior could be used to infer topic interest or disinterest. For example, if an action was immediately reversed, then it may be interpreted as evidence of disinterest. Suppose, for example, the user clicked on a hyperlink, visited a Web page and immediately returned. This sequence of user actions might imply that the user was not interested in a topic or topics included in the Web page. As another example, if an ad made available to the user was not selected while it was being rendered for a certain duration, this might imply that the user was not interested in a topic or topics associated with the ad.
FIG. 4 is a bubble diagram of operations that may be performed, and information that may be generated, used, and/or stored, to generate document region and/or topic to ad(s) associations in a manner consistent with the present invention. Region/topic to ad(s) association operations 420 may include region topic determination operations 430 and topic-relevant ad determination operations 450. A document (e.g., a Web page) 410 may have more than one region, and different regions may concern different topics. The document 410 itself, or the regions of the document 415, may be provided to the region topic determination operations 430. The region topic determination operations determine at least one topic of each of one or more regions 440. Various ways of determining topics from content are known. See, for example, U.S. Provisional Patent Application Ser. No. 60/413,536 (incorporated herein by reference), entitled “METHODS AND APPARATUS FOR SERVING RELEVANT ADVERTISEMENTS”, filed on Sep. 24, 2002, and listing Jeffrey A. Dean, Georges R. Harik and Paul Bucheit as inventors; U.S. patent application Ser. No. 10/314,427 (incorporated herein by reference), entitled “METHODS AND APPARATUS FOR SERVING RELEVANT ADVERTISEMENTS”, filed on Dec. 6, 2002 and listing Jeffrey A. Dean, Georges R. Harik and Paul Bucheit as inventors; and U.S. patent application Ser. No. 10/375,900 (incorporated herein by reference), entitled “SERVING ADVERTISEMENTS BASED ON CONTENT”, filed on Feb. 26, 2003, listing Darrell Anderson, Paul Bucheit, Alex Carobus, Claire Cui, Jeffrey A. Dean, Georges R. Harik, Deepak Jindal, and Narayanan Shivakumar as inventors. This determined information 440 is then provided to topic-relevant ad determination operations 450 which use it 440, along with ad information 460 (which may include topic targeting), to determine, as an output 470, (i) a mapping of each of a number of topics and/or regions to one or more ads 480, and (ii) a mapping of each of one or more ads to an ad interest score 490. Exemplary region and/or topic to ad association operations 420 are described in § 4.2.1 below.
FIG. 5 is a bubble diagram of (e.g., client-side) ad optimization operations that may be performed, and information that may be generated, used, and/or stored, in a manner consistent with the present invention. Ad optimization operations 510 may include document (e.g., Web page) and ad combination operations 520, user behavior monitoring and user interest determination operations 540, ad interest score adjustment operations 550 and score adjustment policies 560. Basically, the document/ad combination operations 520 may select at least one of the ads 490′, using at least their respective ad interest scores, and provide the selected ad(s), along with the document (e.g., Web page) 410′ to rendering operations 530 for presentation to the user.
Initially, the document 410′ may be rendered in association with an initial set of one or more ads (e.g., before attempting to resolve any ambiguities in the user's present interest). However, as stated above, the present invention may be used to resolve ambiguities to determine a user's present topic interest, such that ads relevant to the user's present topic interest may be served. In this regard, user behavior monitoring and user interest determination operations 540 may be used to (i) monitor user behavior (e.g., with respect to the document 410′), and (ii) to determine the user's present interest (e.g., to resolve ambiguities about which of a number of different topics the user is presently interested in) using at least the observed user behavior. The ad interest score adjustment operations 550 may use such determinations about the user's present topic interest to adjust ad interest scores of one or more ads 490′, in accordance with policies 560. Since, as described above, the document/ad combination operations 520 may use such scores 490′ in determining which ads to serve with the document 410′, adjusting the ad interest scores 490′ may lead to changes in the ads presented in association with the document 410′. In this way, as user topic interests are discerned using their behavior, ads more relevant to a user's present topic interest may be presented to the user.
FIGS. 6-10 are exemplary data structures that may be used in a manner consistent with the present invention. For example, referring back to FIG. 4, an association or mapping of document topics and/or document regions to ads 480 may be generated. FIG. 6 is an exemplary table data structure 480′ which includes entries, each entry including a document region identifier 610 and one or more associated ads 620. Referring back to FIG. 4, an association or mapping of ads to ad interest scores 490 may be generated. FIG. 7 is an exemplary table data structure 490′ which includes entries, each entry including an ad (or ad identifier) 710 and an associated ad interest score 720.
FIGS. 8-10 illustrate alternative data structures to those of FIGS. 6 and 7. Instead of providing a region-ad table 480′ in which a mapping between regions of the document (e.g., Web page) and one or more ads is maintained, a region-topic table 800 shown in FIG. 8, which stores mappings between document regions 810 and topics 820, is provided. Correspondingly, as shown in FIG. 9, a topic-interest score table 900, which stores mappings between topics 910 and interest scores 920, is maintained. As shown in FIG. 10, a further table 1000 stores mappings between topics 1010 and one or more ads 1020 (e.g., on a per-document basis). A database “join” allows an ad-interest score table (such as the one 490′ illustrated in FIG. 7) to be recovered from the topic-interest score table 900 and the topic-ads table 1000 whenever it is needed. Since the topic-interest score table 900 is more general than the ad-interest score table 490′, it can be used, advantageously, with many documents. Thus, interest scores gathered for one document (e.g., one search results Web page) can be used to show topic-relevant ads on a subsequent document, thereby increasing the scope and usefulness of the present invention.
FIG. 11 is a flow diagram of an exemplary method 1100 that may be used to associate document topics/regions with one or more ads, in a manner consistent with the present invention. This method 1100 may be performed by an ad server for example. The document (e.g., Web page) regions are analyzed to determine topics. (Step 1110) Different regions of the document may be defined by links, section tags; etc., for example. Document regions/topics can be defined using known “topic detection” techniques, such as statistical text analysis. (See, e.g., Doug Beeferman, Adam Berger, John Lafferty, “Statistical Models for Text Segmentation”, at www.cs.cmu.edu/˜aberger/pdf/ml.pdf, and Jay M Ponte and W. Bruce Croft, “Text Segmentation by Topic,” at ciir.cs.umass.edu/pubfiles/ir-103.pdf, both incorporated herein by reference.) If the document region is a document section, content of the section may be analyzed to determine a topic. If the document region is a link, the document linked to may be analyzed to determine a topic. If document regions and/or region topics have already been determined, such predetermined information may simply be accepted. Then, for each determined topic, one or more acts are performed. (Loop 1120-1150) For example, for each topic, a set of topic-relevant ads is selected (Block 1130) and document regions are mapped to the sets of topic relevant ads (Block 1140). Finally, region to ad(s) mappings and ad to ad score mappings are provided, in association with the document, to the client device that requested the document (Block 1160) before the method 1100 is left (Node 1170). These mappings, together with the document, may be referred to as an “optimizable document.”
FIG. 12 is a flow diagram of an exemplary method 1200 that may be used to perform optimized client-side ad rendering, in a manner consistent with the present invention. This method 1200 may be performed by a client device that requested the document (e.g., a Web page such as a search result page for example). An optimizable document is loaded. (Block 1210) An initial set of ads may then be rendered in association with the document. (Block 1220) The initial set of ads may be determined using, at least, their interest scores for example. User activity is monitored. (Block 1230) Other information such as ad performance information, price information, and/or advertiser information may be used in this determination. Generally, user activity that may be used to determine a user interest is monitored. For example, activity in a particular region of a document may be used to infer user topic interest and/or a user selection may be used to express a user topic interest in the topic associated with that region.
Certain user activities may be events that trigger further processing. (Block 1240) For example, if the user activity indicates a “close” event, the method 1200 may be left. (Node 1299) If the user activity is related to user interest in a certain topic, user topic interest may be determined and/or recorded. (Block 1250) Then, the score(s) of the ad(s) associated with the topic may be adjusted using, at least, the determined user topic interest. (Block 1260) Then, whether or not a different set of ads should be rendered in association with the document using at least the adjusted score(s) of the ad(s) (and possibly previous ad scores) may be determined in accordance with a policy. (1270). If it was determined to update the ads (Decision block 1280), a new set of one or more ads is rendered in association with the document (Block 1290) and user activity monitoring is continued (Block 1230). If, on the other hand, it was not determined to update the ads (Decision block 1280), user activity monitoring is continued (Block 1230).
§ 4.2.1 Exemplary Techniques for Associating a Region with a Set of One or More Topic Relevant Ads
Recall from block 1110 of FIG. 11 that document regions are analyzed to determine a related topic. Topics may be determined from documents by analyzing their content. For example, U.S. Provisional Patent Application Ser. No. 60/413,536 and U.S. patent application Ser. Nos. 10/314,427 and 10/375,900, as mentioned in § 4.2, described ways to determine one or more topics.
Document regions can be accepted. Alternatively, or in addition, the present invention may be used to determine document regions. For example, meta data and/or structured data can define different document sections (or regions), each having different content. The different content of different sections may concern different topics, the different topics being associated with different sets of one or more ads. Accordingly, if user interest in a particular section of the document is inferred (e.g., by the user hovering a pointer cursor over the section, centering the section on a display, highlighting the section, etc.), interest scores for ads of the set of one or more ads associated with the topic of the particular section may be increased. A link such as a hyper-text link may define a region. The linked document may concern a topic being associated with a set of one or more ads. Accordingly, if user interest in the link is express (e.g., by the user selecting it), interest scores of the set of one or more ads associated with the topic of the linked document may be increased.
In the context of Web pages, the HTML of the Web page being transferred may be modified so that user activity with respect to the Web page can be monitored. In one implementation of the present invention, this is done by providing javascript code in the Web page that is provided to the user device (client). In this way, various user activities can be monitored and user interests can be determined using at least the monitored activities. For example, if a pointer enters a certain HTML region then it may be inferred that the user is interested in the contents of the region.
§ 4.2.2 Exemplary Techniques for Client-side Ad Optimization
Recall from blocks 1230, 1240 and 1250 of FIG. 12 that user activity is monitored and user interest is determined and/or recorded using at least the monitored activity.
Recall from block 1260 that score(s) of one or more ads may be adjusted using at least the user interest. For example, if the region-ad table has an association between the region and an ad, then the ad-interest of the ad is adjusted. An ad interest score may be adjusted by various amounts corresponding to various degrees of user interest (e.g., from casual interest to intense interest) and various confidence levels associated with user interest (e.g., from inferred interest to express interest). For example, if a cursor hovers over a given region concerning a given topic (casual interest inferred), ad interest scores of one or more ads associated with this topic may be incremented by a first amount (e.g., 1 unit). If the user selects a link to a document concerning a topic (express interest), ad interest scores of one or more ads associated with the topic may be incremented by a second amount (e.g., 5 units). If the pointer cursor stays within a given region for a considerable length of time (interest inferred), then the interest scores of corresponding ads may be incremented again (e.g., by 3 units). If a section of text is blocked and “copied” (strong express interest), then the interest scores of corresponding ads may be incremented (e.g., by 10 units).
Recall from blocks 1270, 1280 and 1290 that a new set of ads may be rendered (or the same or different ads may be rendered with different enhanced features and/or in a different order) in association with the document using the adjusted ad scores and a policy. In one example, the policy may be that if the ad-interest scores of the ads deviate significantly from the predicted interest scores of the ads, then the rendering of the ads is changed to reflect the user's interest. For example, an old ad may be replaced with a new one.
A number of alternatives exist for monitoring user activity and changing the rendering of ads. For example, software on the client device or an extension to the browser can record activity and change a display through the supported programming interface. In the one implementation of the present invention, the monitoring of usage and redrawing of the ads is done by Javascript in the client HTML inserted specifically for ad optimization before the Web page was sent to the client device. In one implementation of the present invention, the ad-interest scores known to the Javascript code are additionally saved in a cookie which is a persistent means of storage on the client computer. This allows the (e.g., ad and/or topic) interest scores to be recovered after a user browses over a hyperlink to a target document and returns to the original page.
In a variant of the above scheme, the region-ad table, which stores a mapping between regions of the document and ads is replaced with a region-topic table which stores a mapping between regions of the Web page and topics. Correspondingly a topic-interest table of scores is maintained. A second table records the mapping between topics and ads (e.g., on a per-page basis). A database join allows the ad-interest table to be recovered from these ad-topic and topic-interest tables whenever it is needed. The advantage of this scheme is that the topic-interest table is more general than the ad-interest table and can apply to many documents. Thus, interest scores gathered on one document (e.g., one search results page) can be used to show ads on a subsequent document where ads for the same topics exist. This increases the scope and usefulness of the present invention. Generally, any technique that determines topic-relevant ads to render or enhance using user topic interest (which may be determined using user behavior) may be used.
In another variant all user behavior tracking and ad insertion can be done by software residing on the client device (e.g., the Google Toolbar).
Ad scores may be used instead of ad interest scores. Such scores may reflect a user topic interest, but may also reflect one of more of (a) ad price information, (b) ad performance information, (c) targeting criteria match information, and (d) advertiser quality information.
Although the foregoing exemplary embodiments described a topic that a user has a present interest in, the present invention is also applicable to subtopics. For example, recall that the term “jaguar” can have three interpretations—automobile, animal, an football team—and that a set of ads can be associated with each corresponding topic. Alternatively, or in addition, there can be subtopics of the same topic that are worth distinguishing for the purpose of targeting ads. For example, for the topic “flowers” there can be the subtopics “buying flowers,” “growing flowers” and “arranging flowers.” There can be different sets of one or more ads associated with one or more of these subtopics.
Further, a given topic might be divided based on degrees of expertise or sophistication. For example, a user interested in the topic “volcano” could be interested in a basic introduction to volcanoes (a novice), a technical understanding of volcanoes (an expert), or be interested in a tourism information regarding volcanoes (some sophistication). Different sets of one or more ads can be associated with the different levels of user expertise in a give topic. Using evidence of sophistication determined using user actions for example, ads targeted for novices, average sophistication, or experts (e.g., children, tourists, scientists) may be served and rendered.
§ 4.2.5 Exemplary Apparatus
FIG. 13 is high-level block diagram of a machine 1300 that may effect one or more of the operations discussed above. The machine 1300 basically includes one or more processors 1310, one or more input/output interface units 1330, one or more storage devices 1320, and one or more system buses and/or networks 1340 for facilitating the communication of information among the coupled elements. One or more input devices 1332 and one or more output devices 1334 may be coupled with the one or more input/output interfaces 1330.
The one or more processors 1310 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 effect 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 1320 and/or may be received from an external source via one or more input interface units 1330.
In one embodiment, the machine 1300 may be one or more conventional personal computers. In this case, the processing units 1310 may be one or more microprocessors. The bus 1340 may include a system bus. The storage devices 1320 may include system memory, such as read only memory (ROM) and/or random access memory (RAM). The storage devices 1320 may also include a hard disk drive for reading from and writing to a hard disk, a magnetic disk drive for reading from or writing to a (e.g., removable) magnetic disk, and an optical disk drive for reading from or writing to a removable (magneto-) optical disk such as a compact disk or other (magneto-) optical media.
A user may enter commands and information into the personal computer through input devices 1332, such as a keyboard and pointing device (e.g., a mouse) for example. Other input devices such as a microphone, a joystick, a game pad, a satellite dish, a scanner, or the like, may also (or alternatively) be included. These and other input devices are often connected to the processing unit(s) 1310 through an appropriate interface 1330 coupled to the system bus 1340. The output devices 1334 may include a monitor or other type of display device, which may also be connected to the system bus 1340 via an appropriate interface. In addition to (or instead of) the monitor, the personal computer may include other (peripheral) output devices (not shown), such as speakers and printers for example.
Operations of an ad server 210, such as the region/topic to ad(s) association operations 410 and/or user device (client) operations such as the ad optimization and operations 510 may be performed by, and the various information may be stored on, one or more machines 1300. The user device (client) 250, search engine 220, content server 230, and/or e-mail server 240 may also be one or more machines 1300.
FIG. 14 illustrates a document region to topic mapping for documents such as Web pages in general. In this example 1400, a document 1410 is a Web page including content 1412, links 1414 to other documents 1420, 1430, 1440 and one or more ad spots 1416. The content 1412 may suggest a first topic (topic 1). Linked document B 1420 may include content 1422 that suggests another topic (topic 2). Similarly, linked document C 1430 may include content 1432 that suggests yet another topic (topic 3). Finally, linked document D 1440 may include content 1442 that suggests the first topic (topic 1).
In this example, one or more ads associated with topic 1 might originally be rendered in association with the document 1410. If a user were to follow the link 1414 a, interest scores of one or more ads associated with topic 2 could be increased. In this case, upon returning back to document A 1410 from document B 1420, one or more ads associated with topic 2 might now be rendered in association with the document 1410. Similarly, if a user were to follow the link 1414 b, interest scores of one or more ads associated with topic 3 could be increased. In this case, upon returning back to document A 1410 from document C 1430, one or more ads associated with topic 3 might now be rendered in association with the document 1410. Finally, if a user were to follow the link 1414 c, interest scores of the one or more ads already associated with topic 1 could be increased, further increasing an interest score differential between theses ad(s) and ad(s) associated with other topics.
FIG. 15 illustrates a document 1520 including search results 1525 returned in response to the search query “jaguar” 1510. In this example, the search results 1525 include links to documents relevant to the query 1510. These linked documents can be analyzed, in real-time or in advance, to determine a topic. As shown, search results 1 1525 a, 4 1525 d and 5 1525 e are linked to documents concerning the animal jaguar. This first topic 1540 a is associated with a first set of one or more ads 1545 a relevant to the first topic. Search results 2 1525 b, 6 1525 f, 7 1525 g and 9 1525 i are linked to documents concerning the car jaguar. This second topic 1540 b is associated with a set of one or more ads 1545 b relevant to the second topic. Finally, search results 3 1525 c, 8 1525 h and 10 1525 j are linked to documents concerning the NFL team, the jaguars. This third topic 1540 c is associated with a set of one or more ads 1545 c relevant to the third topic. Recall from FIG. 6 that a table that maps regions (e.g., content sections within a table, or specific hyperlinks, etc.) of the document (e.g., a Web page) to a set of one or more ads may be generated. Such a “region-ad” table may be used to (i) map search results 1, 4 and 5 to ad set 1 1545 a, (ii) map search results 2, 6, 7 and 9 to ad set 2 1545 b, and (iii) map search results 3, 8 and 10 to ad set 3 1545 c. Recall from FIG. 7 that an interest score may be computed for each ad. This information may be stored in an “ad-interest” table. These tables are transmitted to the client in conjunction with the search results Web page 1520. In one implementation, the tables are embedded within Javascript code which is part of the Web page 1520 being transmitted.
Referring to FIG. 16, when the optimized Web page 1520′ is loaded into the client device, an initial ad or arrangement of ads 1610 is presented based on ad interest scores. Then, client device user activity is monitored and interest in regions that suggest a certain topic, and therefore a certain set of one or more ads, is recorded. In the example illustrated in FIG. 16, the user selects (e.g., clicks on) search result eight 1525 h and is taken to that page 1620 by a browser for example. Since the eighth search result 1525 h is associated with a Web page that has a topic concerning the NFL football team, 1540 c, the interest scores of one or more ads of a third set are incremented. If the user then returns back to the search results Web page 1520″, one or more ads 1630 from the third set 1630 have replaced the initial set of one or more ads 1610 (assuming that their incremented scores are greater than those of the ads originally presented).
Naturally, a position ranking or some enhanced feature of the ads may be changed based on the updated ad interest scores. For example, as shown in FIG. 17, in the search results Web page 1520′, initially, an ad from a first set associated with a first topic is displayed above an ad from a third set associated with the third topic as shown by 1710. After the ad interest scores of the one or more ads of the third set are increased because the user selected the link to the eighth page 1720, when the user returns to the search results Web page 1520″, the ad form the third set associated with the third topic is displayed above the ad from the first set associated with the first topic as shown by 1730 (assuming that their incremented scores are greater than those of the ads originally presented).
In other implementations, the application of enhanced ad features may be determined using at least the ad interest score.
As can be appreciated from the foregoing disclosure, the present invention can be used to resolve ambiguities with respect to a user topic interest so that useful topic-relevant ads may be presented to the user.
1. A computer-implemented method for generating a document with optimizable topic-relevant ads, the method comprising:
a) for each of a plurality of document regions of the document, determining a topic, wherein the plurality of document regions are intrinsic to the document, and thereby independent of how the document is being viewed and of a user viewing the document;
b) using at least the determined topics, associating, with each of the plurality of document regions, a different set of one or more topic-relevant ads;
c) determining, from among the plurality of topics, a topic interest of a user;
d) adjusting the scores of the ads using at least the determined topic interest of the user; and
e) serving, for rendering to the user, an updated set of ads in association with the document using at least the adjusted scores,
wherein each of the ads of each of the sets of one or more topic relevant ads includes a score,
wherein an initial set of ads to be rendered in association with the document is determined using at least the scores of the ads,
wherein the topic interest of the user is determined using at least monitored actions of the user with respect to the document,
wherein an amount by which scores of ads are adjusted is determined using, at least, a confidence level that an observed monitored action of the user indicates a user topic interest,
wherein an amount by which scores of ads are adjusted is greater for a first type of monitored user actions with respect to the document than a second type of monitored user actions with respect to the document, and
wherein the second type of monitored user actions includes hovering a pointer cursor over a particular region, centering a particular region on a display, and highlighting at least a portion of a particular region.
2. The computer-implemented method of claim 1 wherein the scores are interest scores.
storing a first set of mappings, each associating a document region and a topic;
storing a second set of mappings, each associating a topic and a topic interest score; and
storing a third set of mappings, each associating a topic and one or more topic-relevant ads.
storing a first set of mappings, each associating a document region and a set of one or more ads, wherein each document region is associated with a topic and its associated set of one or more ads are relevant to the topic.
5. The computer-implemented method of claim 1 wherein the document regions are defined as links to other documents.
6. The computer-implemented method of claim 1 wherein the document regions are defined by meta data.
7. The computer-implemented method of claim 1 wherein the document regions are defined by at least one of (a) a document-defined section, (b) a document-defined column, and (c) a document-defined box.
8. The computer-implemented method of claim 1 wherein the document regions are defined using statistical text analysis.
9. The computer-implemented method of claim 1 wherein the actions of the user monitored are selected from a group of user actions consisting of (a) cursor positioning, (b) cursor dwell time, (c) document item selection, (d) user eye direction, (e) user facial expressions, (f) user expressions, and (g) express user topic interest input.
10. The computer-implemented method of claim 1 wherein the second type of monitored user activity in a particular region of a document by the user is used to infer user topic interest in the topic associated with that region.
11. The computer-implemented method of claim 1 wherein the first type of monitored user action includes a selection by the user which is used to express a user topic interest in the topic associated with the region associated with the selection.
12. The computer-implemented method of claim 1 wherein the act of determining a topic interest of a user includes determining disinterest of the user in a topic using actions of the user.
13. The computer-implemented method of claim 12 wherein user disinterest in a topic is determined if the user selects a link, is brought to a Web page and returns back shortly.
a) accepting a document with different regions corresponding to different topics;
b) accepting, for each of the different topics, a set of one or more topic-relevant ads, each having a score;
c) determining, from among the different topics, a topic interest of a user;
d) adjusting the scores of at least some of the ads using at least the determined topic interest of the user; and
e) serving, for rendering to the user, one or more of the ads in association with the document using at least the adjusted scores,
wherein the topic interest of the user is determined using monitored actions of the user with respect to the document,
15. The computer-implemented method of claim 14 wherein actions of the user monitored are selected from a group of user actions consisting of (a) cursor positioning, (b) cursor dwell time, (c) document item selection, (d) user eye direction, (e) user facial expressions, (f) user expressions, and (g) express user topic interest input.
16. The computer-implemented method of claim 14 wherein the second type of monitored activity of the user in a particular region of a document is used to infer user topic interest in the topic associated with that region.
17. The computer-implemented method of claim 14 wherein the first type of monitored action by a user includes a selection by the user which is used to express a user topic interest in the topic associated with the region associated with the selection.
d) adjusting the scores of at least some of the ads using at least the determined topic interest of the user;
e) determining whether or not to apply at least one enhanced feature to one or more of the ads using at least the adjusted scores;
f) applying the at least one enhanced feature to the one or more of the ads only if it was determined to do so in act (e); and
g) serving, for rendering to the user, one or more of the ads in association with the document,
19. The computer-implemented method of claim 18 wherein actions of the user monitored are selected from a group of user actions consisting of (a) cursor positioning, (b) cursor dwell time, (c) document item selection, (d) user eye direction, (e) user facial expressions, (f) user expressions, and (g) express user topic interest input.
20. The computer-implemented method of claim 18 wherein the second type of monitored activity of the user in a particular region of a document is used to infer user topic interest in the topic associated with that region.
21. Apparatus for generating a document with optimizable topic-relevant ads, the apparatus comprising:
a) means for determining a topic for each of a plurality of document regions of the document, wherein the plurality of document regions are intrinsic to the document, and thereby independent of how the document is being viewed and of a user viewing the document;
b) means for associating, with each of the plurality of document regions, a different set of one or more topic-relevant ads using at least the determined topics of the document;
c) means for determining, from among the plurality of topics, a topic interest of a user;
d) means for adjusting the scores of the ads using at least the determined topic interest of the user; and
e) means for serving, for rendering to the user, an updated set of ads in association with the document using at least the adjusted scores,
f) a storage device for storing
1) a first set of mappings, each associating a document region and a topic,
2) a second set of mappings, each associating a topic and a topic interest score, and
3) a third set of mappings, each associating a topic and one or more topic-relevant ads.
f) a storage device for storing a first set of mappings, each associating a document region and a set of one or more ads, wherein each document region is associated with a topic and its associated set of one or more ads are relevant to the topic.
24. The apparatus of claim 21 wherein the document regions are defined as links to other documents.
25. The apparatus of claim 21 wherein the document regions are defined by meta data.
26. The apparatus of claim 21 wherein the document regions are defined by at least one of (a) a document-defined section, (b) a document-defined column, and (c) a document-defined box.
27. The apparatus of claim 21 wherein the document regions are defined using statistical text analysis.
28. The apparatus of claim 21 wherein actions of the user monitored are selected from a group of user actions consisting of (a) cursor positioning, (b) cursor dwell time, (c) document item selection, (d) user eye direction, (e) user facial expressions, (f) user expressions, and (g) express user topic interest input.
29. The apparatus of claim 21 wherein the second type of monitored user activity in a particular region of a document by the user is used to infer user topic interest in the topic associated with that region.
30. The apparatus of claim 21 wherein the first type of monitored user action includes a selection by the user which is used to express a user topic interest in the topic associated with the region associated with the selection.
31. The apparatus of claim 21 wherein the means for determining a topic interest of a user includes means for determining user disinterest in a topic using actions of the user.
32. The apparatus of claim 31 wherein user disinterest in a topic is determined if the user selects a link, is brought to a Web page and returns back shortly.
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