Source: http://patents.com/us-10198746.html
Timestamp: 2019-02-23 08:44:36
Document Index: 459927239

Matched Legal Cases: ['Application No. 201110054976', 'Application No. 2', 'Application No. 03822749', 'Application No. 03822749', 'Application No. 2009', 'Application No. 2008201934', 'Application No. 201110054976', 'Application No. 2009', 'Application No. 10', 'Application No. 2005', 'Application No. 038227495', 'Application No. 2', 'Application No. 2', 'Application No. 03', 'Application No. 2', 'Application No. 2003275252', 'Application No. 10', 'Application No. 2005', 'Application No. 2', 'Application No. 03824459', 'Application No. 2', 'Application No. 03824459', 'Application No. 03824459', 'Application No. 03', 'Application No. 2']

US Patent # 1,019,8746. Methods and apparatus for serving relevant advertisements - Patents.com
United States Patent 10,198,746
Dean , et al. February 5, 2019
Dean; Jeffrey Adgate (Palo Alto, CA), Harik; Georges (Palo Alto, CA), Buchheit; Paul (Los Altos, CA)
Family ID: 1000003805797
15/704,445
US 20180005266 A1 Jan 4, 2018
14816205 Aug 3, 2015 9799052
14249146 Apr 9, 2014
13420801 Mar 15, 2012
12729054 May 1, 2012 8171034
10314427 May 11, 2010 7716161
60413536 Sep 24, 2002
Current CPC Class: G06F 16/951 (20190101); G06F 16/35 (20190101); G06Q 30/0256 (20130101); G06Q 30/0242 (20130101); G06Q 30/0251 (20130101); G06Q 30/0263 (20130101); G06Q 30/02 (20130101); Y10S 707/944 (20130101)
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This application is a continuation of U.S. patent application Ser. No. 14/816,205, titled "METHODS AND APPARATUS FOR SERVING RELEVANT ADVERTISEMENTS," filed on Aug. 3, 2015 and listing Jeffrey A. Dean, Georges R. Harik and Paul Buchheit as the inventors, which is a continuation application of U.S. patent application Ser. No. 14/249,146, titled "METHODS AND APPARATUS FOR SERVING RELEVANT ADVERTISEMENTS," filed on Apr. 9, 2014 and listing Jeffrey A. Dean, Georges R. Harik and Paul Buchheit as the inventors, which is a continuation of U.S. patent application Ser. No. 13/420,801, titled "METHODS AND APPARATUS FOR SERVING RELEVANT ADVERTISEMENTS," filed on Mar. 15, 2012 and listing Jeffrey A. Dean, Georges R. Harik and Paul Buchheit as the inventors, which is a continuation of U.S. patent application Ser. No. 12/729,054 (now U.S. Pat. No. 8,171,034), titled "METHODS AND APPARATUS FOR SERVING RELEVANT ADVERTISEMENTS," filed on Mar. 22, 2010 and listing Jeffrey A. Dean, Georges R. Harik and Paul Buchheit as the inventors, which is a continuation of U.S. patent application Ser. No. 10/314,427 (now U.S. Pat. No. 7,716,161), titled "METHODS AND APPARATUS FOR SERVING RELEVANT ADVERTISEMENTS," filed on Dec. 6, 2002 and listing Jeffrey A. Dean, Georges R. Harik and Paul Buchheit as the inventors, which claimed benefit under 35 U.S.C. .sctn. 119(e)(1), to the filing date of provisional patent application Ser. No. 60/413,536, titled "METHODS AND APPARATUS FOR SERVING RELEVANT ADVERTISEMENTS", filed on Sep. 24, 2002 and listing Jeffrey A. Dean, Georges R. Harik and Paul Buchheit as the inventors, for any inventions disclosed in the manner provided by 35 U.S.C. .sctn. 112, 1. Each of the foregoing provisional and utility applications is expressly incorporated herein by reference.
1. A computer implemented method comprising: obtaining a set of rules that specify weights for each term of a plurality of terms extracted from a resource, with a weight of a term being a function of a frequency of the term in the resource; extracting a plurality of terms from a target resource; generating, based on the set of rules, a term vector that represents weights for the plurality of terms that appear within the target resource; determining that one or more terms of the term vector each have one or more respective weights that exceed a defined threshold; identifying targeting data for a content item that is available for presentation with various different resources; determining that the content item is relevant to the target resource, the determining including: comparing the identified targeting data for the content item to the determined one or more terms of the term vector for the target resource; and determining, based on the comparison, that the identified targeting data for the content item and the determined one or more terms of the term vector have a degree of similarity that is greater than a similarity threshold; and serving the content item based on determining that the identified targeting data for the content item has the degree of similarity to the determined one or more terms of the term vector for the target resource.
2. The computer implemented method of claim 1, wherein the identified targeting data is provided by a content provider for the content item and comprises at least one of (A) a keyword and (B) a phrase, and wherein the identified targeting data for the content item is received via a content item campaign entry and management component of a server system.
3. The computer implemented method of claim 1, further comprising determining one or more topics that correspond to the target resource by determining that one or more terms of the term vector each have one or more respective weights that exceed a defined threshold.
4. The computer implemented method of claim 3, wherein the one or more topics corresponding to the target resource comprise at least one topic from another resource linked to the target resource.
5. The computer implemented method of claim 3, wherein the one or more topics corresponding to the target resource comprise anchor text in a link from another resource to the target resource.
6. The computer implemented method of claim 3, wherein the one or more topics corresponding to the target resource comprise text from queries to a search engine that returned a search result comprising the target resource.
7. The computer implemented method of claim 3, wherein the one or more topics corresponding to the target resource comprise text from queries to a search engine, wherein the search engine is configured to return a search result including the target resource, the search result being subsequently selectable by a user.
8. A non-transitory computer readable medium storing instructions that are executable by one or more processors to perform operations comprising: obtaining a set of rules that specify weights for each term of a plurality of terms extracted from a resource, with a weight of a term being a function of a frequency of the term in the resource; extracting a plurality of terms from a target resource; generating, based on the set of rules, a term vector that represents weights for the plurality of terms that appear within the target resource; determining that one or more terms of the term vector each have one or more respective weights that exceed a defined threshold; identifying targeting data for a content item that is available for presentation with various different resources; determining that the content item is relevant to the target resource, the determining including: comparing the identified targeting data for the content item to the determined one or more terms of the term vector for the target resource; and determining, based on the comparison, that the identified targeting data for the content item and the determined one or more terms of the term vector have a degree of similarity that is greater than a similarity threshold; and serving the content item based on determining that the identified targeting data for the content item has the degree of similarity to the determined one or more terms of the term vector for the target resource.
9. The non-transitory computer readable medium of claim 8, wherein the identified targeting data is provided by a content provider for the content item and comprises at least one of (A) a keyword and (B) a phrase, and wherein the identified targeting data for the content item is received via a content item campaign entry and management component of a server system.
10. The non-transitory computer readable medium of claim 8, the operations further comprising determining one or more topics that correspond to the target resource by determining that one or more terms of the term vector each have one or more respective weights that exceed a defined threshold.
11. The non-transitory computer readable medium of claim 10, wherein the one or more topics corresponding to the target resource further comprise at least one topic from another resource linked to the target resource.
12. The non-transitory computer readable medium of claim 10, wherein the one or more topics corresponding to the target resource further comprise anchor text in a link from another resource to the target resource.
13. The non-transitory computer readable medium of claim 10, wherein the one or more topics corresponding to the target resource further comprise text from queries to a search engine that returned a search result including the target resource.
14. The non-transitory computer readable medium of claim 10, wherein the one or more topics corresponding to the target resource further comprise text from queries to a search engine, wherein the search engine returns a search result including the target resource, the search result being subsequently selectable by a user.
15. A computer implemented method comprising: obtaining a set of rules that specify weights for each term of a plurality of terms extracted from a resource, with a weight of a term being a function of a frequency of the term in the resource; extracting a plurality of terms from a target resource; generating, based on the set of rules, a term vector that represents weights for the plurality of terms that appear within text of the target resource, wherein the weights are based on at least one of (A) whether the term appears in the text of the target resource more frequently, relative to one or more other terms that each appear less frequently, and (B) whether the term appears less frequently, relative to one or more other terms that each appear more frequently, across a collection of resources to which the target resource belongs; identifying targeting data for a content item that is available for presentation with various different resources; determining that the content item is relevant to the target resource, the determining including: comparing the identified targeting data for the content item to the determined one or more terms of the term vector for the target resource; and determining, based on the comparison, that the identified targeting data for the content item and the determined one or more terms of the term vector have a degree of similarity that is greater than a similarity threshold; and serving the content item based on determining that the identified targeting data for the content item has the degree of similarity to the determined one or more terms of the term vector for the target resource.
16. The computer implemented method of claim 15, further comprising determining one or more topics that correspond to the target resource by determining that one or more terms of the term vector each have one or more respective weights that exceed a defined threshold.
17. The computer implemented method of claim 16, wherein comparing comprises scoring a similarity between the identified targeting data for the content item and the one or more topics that correspond to the target resource and determining that the content item is relevant to the target resource when the similarity is above a threshold score.
18. The computer implemented method of claim 15, wherein the identified targeting data is provided by a content provider for the content item and comprises at least one of (A) a keyword and (B) a phrase, and wherein the identified targeting data for the content item is received via a content item campaign entry and management component of a server system.
19. A system comprising: one or more processing devices; and at least one non transitory computer readable medium storing instructions operable to cause the one or more processing devices to perform operations comprising: obtaining a set of rules that specify weights for each term of a plurality of terms extracted from a resource, with a weight of a term being a function of a frequency of the term in the resource; extracting a plurality of terms from a target resource; generating, based on the set of rules, a term vector that represents weights for the plurality of terms that appear within the target resource; determining that one or more terms of the term vector each have one or more respective weights that exceed a defined threshold; identifying targeting data for a content item that is available for presentation with various different resources; determining that the content item is relevant to the target resource, the determining including: comparing the identified targeting data for the content item to the determined one or more terms of the term vector for the target resource; and determining, based on the comparison, that the identified targeting data for the content item and the determined one or more terms of the term vector have a degree of similarity that is greater than a similarity threshold; and serving the content item based on determining that the identified targeting data for the content item has the degree of similarity to the determined one or more terms of the term vector for the target resource.
20. The system of claim 19, wherein the operations further comprise: determining one or more topics that correspond to the target resource by determining that one or more terms of the term vector each have one or more respective weights that exceed a defined threshold.
Advertising using traditional media, such as television, radio, newspapers and magazines, is well known. Advertisers have used these types of media to reach a large audience with their advertisements ("ads"). To reach a more responsive audience, advertisers have used demographic studies. For example, advertisers may use broadcast events such as football games to advertise beer and action movies to a younger male audience. However, even with demographic studies and entirely reasonable assumptions about the typical audience of various media outlets, advertisers recognize that much of their ad budget is simply wasted because the target audience is not interested in the ad they are receiving.
Ad entry and management component 210 is the component by which the advertiser enters information required for an advertising campaign and manages the campaign. An ad campaign contains one or more advertisements that are related in some manner. For example, the Ford Motor Company may have an ad campaign for zero percent financing, which could contain a series of advertisements related to that topic. Among the other things that could be provided by an advertiser through ad entry and management component 210 are the following: one or more advertising creatives (simply referred to as "ads" or "advertisements"), one or more set of keywords or topics associated with those creatives (which may be used as targeting information for the ads), geographic targeting information, a value indication for the advertisement, start date, end date, etc. The data required for, or obtained by, ad entry and management component 210 resides in one of the databases 240.
Client processors 308 and server processor 311 can be any of a number of well known micro-processors, such as processors from Intel Corporation, of Santa Clara, Calif. In general, client device 302 may be any type of computing platform connected to a network and that interacts with application programs, such as a digital assistant or a "smart" cellular telephone or pager. Server 310, although depicted as a single computer system, may be implemented as a network of computer processors.
FIG. 4 is a flow diagram of an exemplary method for determining if an advertisement is relevant to a document, consistent with the present invention. As used herein, the term "document" includes any type of paper or electronic document or file, including audio, video, image, text, etc. That is, as will be appreciated by one skilled in the art, a "document" as used in the specification is any machine-readable and machine-storable work product. A document may be a file, a combination of files, one or more files with embedded links to other files, etc. For the sake of illustration, it may be understood that the process described herein takes place as part of the ad selection component 260, although those skilled in the art will recognize that it need not take place in that component alone.
The exemplary method is not limited by the order shown in the flow diagram. The process identifies targeting information for an advertisement. (Stage 410). The targeting information may be in the form of a list of keywords or phrases associated with the advertisement (e.g., "honda", "honda cars", "cars", etc.), as provided by advertiser 110 through ad campaign entry and management component 210. Alternatively, or in addition, the targeting information may be determined algorithmically, based on the content of the advertisement, the goods or services being advertised, the targeting of other related advertisements, etc. For example, if the content of the advertisement includes "Buy honda cars at the lowest prices of the year!", the terms "honda" or "honda cars" may be extracted from that content. The targeting information may also include other demographic information, such as geographic location, affluence, etc. Thus, the targeting information is simply some information from which a topic may be derived.
The targeting information identified in stage 410 is compared to the one or more topics identified in stage 420 to determine if a match exists. (Stage 430). A "match" need not be an exact match. Instead, a match is an indication of a relatively high degree of similarly, and/or a predetermined (e.g., absolute) degree of similarity. If a match exists, the advertisement is determined to be relevant to the target document (stage 440) and may be provided to ad ordering component 270, for eventual provision to ad consumer 130 via ad consumer interface component 250.
One way to identify a topic corresponding to the target document is by analyzing some or all text within the target document, which shall be illustrated in reference to FIG. 5. FIG. 5 shows a sample document, entitled "Travels in Italy", which contains a collection of travel-related information pertaining to Italy. The document text contains the term "restaurant" (appearing 20 times), "chianti" (appearing 10 times), and "the" (appearing 100 times). It could be determined that one or more of each term (word or phrase) that appears in the title of the target document corresponds to a topic of the target document. On this basis, the topics for this document may be "travels", "in", and/or "italy."
Alternatively, it could be determined that one or more of each term that appears in the body of the target document corresponds to a topic of the target document. In the simplest case, each term within the target document would be identified as a topic. A slightly more complex approach would be to identify a term as a topic if it appears in the target document more than N times, such as N=2 (and indeed such a threshold-based approach could be used whenever terms within text are being analyzed). Even more complex analysis could be performed, such as by using a term vector for the target document, which assigns weights to each term. For example, terms that appear frequently in the target document may be assigned a relatively higher weight than those that appear less frequently. And so the term "the" would have a higher weight than "restaurant", which would have a higher weight than "chianti".
In addition, the weighting could be adjusted to give higher weight to terms that appear less frequently in a collection, such as a collection to which the document belongs or the general collection of documents. For example, the term "chianti" does not appear very commonly across the general collection of documents and so its weight may be boosted. Conversely, the term "the" appears so frequently across a collection of documents that its weight may be reduced or eliminated altogether.
Alternatively, or in addition to using text or other information within the target document, meta-information associated with the target document may be used. For example, a reference to the target document by another document may contain a brief description of the target document. Assume a document called "Entertainment" that contains a reference to the target document and describes it as "For a description of restaurants and wine in Italy, see `Travels in Italy`." In the context of a web page, this is often described as anchor text. One or more such brief descriptions may be used to revise (figuratively) the target document by supplementing or replacing some or all of its content with the brief descriptions. So, for example, the topic could be identified from the combination of the target document's title and the brief descriptions of the target document.
Alternatively, or in addition to the brief descriptions from these references, the references themselves may be used. For example, a reference from another document to the target document may be used as an indication that the two documents are similar. Alternatively, or in addition, a reference from the target document to another document may be used as an indication that the two documents are similar. So a reference between the "Entertainment" document and the "Travels in Italy" document may indicate that the two are related. In the context of web pages, these references occur in the form of links from one web page to another. On this basis, the content (or meta-information) of the other document may be used to revise (figuratively) the target document by supplementing or replacing its content with that of the other document. The revised target document's content may then be analyzed using the techniques described above to identify one or more topics.
Alternatively, or addition to using the content (including perhaps meta-data) associated with a target document, other techniques may be used to identify one or more topics for the target document. For example, the top N queries (or subset thereof) that result in a reference to the target document could be determined to constitute a topic for the target document, with N being some defined number. These may be, for example, text queries in a search engine that yield a result that links to the target document or web page. Alternatively, or in addition, the content of other similar documents (e.g., in the same collection as the target document, in the same category as the target document, etc.) may be used to revise (figuratively) the content of the target document. Any of the techniques described above may then be used to analyze the target document to identify one or more topics. In the context of web pages, this may be other web pages that are stored within a subdirectory of related pages on the same host as the target web page. Alternatively, or in addition, any technique for classifying the target document into a set of one or more topics or categories may be used. Even the search query history of one or more users who visit the target document (or target web page) may be used to identify a topic for the target document or web page, on the theory that a visit to the target document that is temporally proximate to that search query history indicates that the user thought the concepts were related. For example, if a user searched for "italian wine" and then soon afterwards visited the "Travels in Italy" document, the content of that prior search could be used to determine that "italian" and/or "wine" are potential topics for the "Travels in Italy" document.
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