DETAILED ACTION
This final Office action is responsive to Applicant’s amendment filed January 2, 2026. Claims 1, 12, and 16 have been amended. Claims 1-20 are presented for examination.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant's arguments filed January 2, 2026 have been fully considered but they are not persuasive.
Applicant makes a general assertion that the claim amendments overcome the rejection under 35 U.S.C. § 101 (page 7 of Applicant’s response). The Examiner respectfully disagrees. The rejection has been updated to address the language of the claim amendments.
Regarding the art rejections, Applicant submits that Sharma does not train a model on positive events and negative events corresponding to post-click conversions that occur after a click or provide a bid based on the conversion-given-click probability, content provider bid, and a target CPA (pages 13-14 of Applicant’s response). The Examiner respectfully disagrees. As seen in ¶ 40, Sharma explains, “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 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). Many other definitions of what constitutes a conversion are possible. For example, a conversion also may include signing up to be a member of a web site, filling out an online form, making a purchase upon contacting an advertiser via online creatives and the like.” A purchase made after clicking on an ad is an example of a post-click conversion, i.e., a positive event. A conversion rate may convey a number of sales results from a total number of clicks (¶ 41), which means that both positive events and negative events are used to evaluate post-click conversions. When a conversion is defined as the purchase, not making the purchase is an example of a negative event. The ads may be correlated with products (¶¶ 5, 34); therefore, a purchase of an advertised product is an example of a post-click conversion related to an ad, i.e., item content, recommending the at least one product, i.e., the product that is the subject of the ad. A correction factor is computed using an iterative process and is automatically adjusted adaptively as part of a machine learning process to adjust bids and predictions over time and in response to changing prediction conditions (¶¶ 11, 53-55, 61-64, 70). This information is used to rank ads for an ad campaign (¶ 42). The rejections are maintained.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claimed invention is directed to “providing conversion prospecting for dynamic content recommendation” (Spec: ¶ 2) without significantly more.
Step
Analysis
1: Statutory Category?
Yes – The claims fall within at least one of the four categories of patent eligible subject matter. Process (claims 1-11), Apparatus (claims 16-20), Article of Manufacture (claims 12-15)
Independent claims:
Step
Analysis
2A – Prong 1: Judicial Exception Recited?
Yes – Aside from the additional elements identified in Step 2A – Prong 2 below, the claims recite:
[Claims 1, 12, 16] A method comprising:
forming a model to predict conversion-given-click probabilities of users and products, where the forming comprises incrementally forming the model on (i) sets of positive events corresponding to post-click conversions occurring after a user clicks on a content with recommending at least one product and (ii) sets of negative events corresponding to post-click conversions not occurring after a user clicks on a content item recommending at least one product;
generating, utilizing the model, a prediction of a conversion-given-click probability that a user will perform an action in relation to a product;
generating a bid for the user and the product based upon the conversion-given-click probability that the user will perform the action in relation to the product, a content provider bid for the product, and a target cost per action corresponding to a performance goal of a content provider;
determining whether the product is to compete in an auction hosted by the content serving platform based upon the bid; and
transmitting content items of products selected for display to users.
Aside from the additional elements, the aforementioned claim details exemplify the abstract idea(s) of a mental process (since the details include concepts performed in the human mind, including an observation, evaluation, judgment, and/or opinion). As explained in MPEP § 2106.04(a)(1)(III), “[t]he courts consider a mental process (thinking) that ‘can be performed in the human mind, or by a human using a pen and paper’ to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).” The limitations reproduced above, as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting the additional elements identified in Step 2A – Prong 2 below, nothing in the claim elements precludes the steps from practically being performed in the mind and/or by a human using a pen and paper. For example, but for the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the respectively recited steps/functions of the claims, as drafted and set forth above, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind and/or with the use of pen and paper. A human user can form a model, generate the recited information, make the recited determinations, perform the recited calculations, evaluate the recited mathematical relationships, set bids, make the recited determinations, evaluate the costs, transmit information for display, etc. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind (and/or with pen and paper) but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Aside from the additional elements, the aforementioned claim details exemplify a method of organizing human activity (since the details include examples of commercial or legal interactions, including advertising, marketing or sales activities or behaviors, and/or business relations and managing personal behavior or relationships or interactions between people, including social activities, teaching, and following rules or instructions). More specifically, the evaluated process is related to “providing conversion prospecting for dynamic content recommendation” (Spec: ¶ 2), which (under its broadest reasonable interpretation) is an example of marketing and managing personal behavior (i.e., organizing human activity); therefore, aside from the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the limitations identified in the more detailed claim listing above encompass the abstract idea of organizing human activity.
2A – Prong 2: Integrated into a Practical Application?
No – The judicial exception(s) is/are not integrated into a practical application.
Claim 1 recites a method executing on a processor of a computing device that causes the computing device to perform the recited operations. Claim 1 also recites training a model to predict conversion-given-click probabilities of users and products, where the training comprises incrementally training the model on sets of positive events from a content serving platform and sets of negative events from a content serving platform, and transmitting content items of products selected by the content serving platform to devices for display to users.
Claim 12 recites a non-transitory machine readable medium having stored thereon processor-executable instructions that when executed cause performance of the recited operations. Claim 12 also recites training a model to predict conversion-given-click probabilities of users and products, where the training comprises incrementally training the model on sets of positive events from a content serving platform and sets of negative events from a content serving platform, and transmitting content items of products selected by the content serving platform to devices for display to users.
Claim 16 recites a computing device comprising: a processor; and memory comprising processor-executable instructions that when executed by the processor cause performance of the recited operations. Claim 16 also recites training a model to predict conversion-given-click probabilities of users and products, where the training comprises incrementally training the model on sets of positive events from a content serving platform and sets of negative events from a content serving platform, and transmitting content items of products selected by the content serving platform to devices for display to users.
The claims as a whole merely describe how to generally “apply” the abstract idea(s) in a computer environment. The claimed processing elements are recited at a high level of generality and are merely invoked as a tool to perform the abstract idea(s). Simply implementing the abstract idea(s) on a general-purpose processor is not a practical application of the abstract idea(s); Applicant’s specification discloses that the invention may be implemented using general-purpose processing elements and other generic components (Spec: ¶¶ 17-31, 61).
The use of a processor/processing elements (e.g., as recited in all of the claims) facilitates generic processor operations. The use of a memory or machine-readable media with executable instructions facilitates generic processor operations.
The additional elements are recited at a high-level of generality (i.e., as generic processing elements performing generic computer functions) such that the incorporation of the additional processing elements amounts to no more than mere instructions to apply the judicial exception(s) using generic computer components. There is no indication in the Specification that the steps/functions of the claims require any inventive programming or necessitate any specialized or other inventive computer components (i.e., the steps/functions of the claims may be implemented using capabilities of general-purpose computer components). Accordingly, the additional elements do not integrate the abstract ideas into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea(s).
The processing components presented in the claims simply utilize the capabilities of a general-purpose computer and are, thus, merely tools to implement the abstract idea(s). As seen in MPEP § 2106.05(a)(I) and § 2106.05(f)(2), the court found that accelerating a process when the increased speed solely comes from the capabilities of a general-purpose computer is not sufficient to show an improvement in computer-functionality and it amounts to a mere invocation of computers or machinery as a tool to perform an existing process (see FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)).
Considering that the implementation of the machine learning model and/or the training of the model is performed using generic processing elements, such an implementation is presented as a generic recitation of machine learning in the claims and as a general link to technology. The machine learning-based processing elements are simply tools to generally automate the underlying process that could be performed by a human. It is further noted that, as described in Applicant’s Specification, the machine learning operations are generic machine learning operations (Spec: ¶¶ 35, 39, 44). The Specification presents no assertion that there is any improvement in the automated machine learning process itself. Such a generic recitation of machine learning, as recited in the claims, is little more than automating an analogous process that can be performed by a human.
There is no transformation or reduction of a particular article to a different state or thing recited in the claims.
2B: Claim(s) Provide(s) an Inventive Concept?
No – The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception(s). As discussed above with respect to integration of the abstract idea(s) into a practical application, the use of the additional elements to perform the steps identified in Step 2A – Prong 1 above amounts to no more than mere instructions to apply the exceptions using a generic computer component(s). Mere instructions to apply an exception using a generic computer component(s) cannot provide an inventive concept. The claims are not patent eligible.
Dependent claims:
Step
Analysis
2A – Prong 1: Judicial Exception Recited?
Yes – Aside from the additional elements identified in Step 2A – Prong 2 below, the claims recite:
[Claim 2] wherein a positive event corresponds to a first user performing the action after interacting with a first content item, and wherein a negative event corresponds to the first user interacting with a second content item without subsequently performing the action.
[Claim 3] wherein the model represents products with product features that include product identifiers, product set identifiers, and content provider identifiers.
[Claim 4] wherein the model comprises a multi-value feature containing a list of content item campaigns with a highest click through rate of a first user during a time period.
[Claim 5] wherein the model comprises a content serving platform experiment identifier.
[Claim 6] wherein the model comprises a page section corresponding to a webpage identifier where a user viewed a content item.
[Claim 7] adjusting, during forming of the model, predictions generated by the model based upon the model under-predicting due to the model being formed on an additional negative event for each positive event.
[Claim 8] calculating the target cost per action by applying a factor to an average spend divided by a number of conversions of products of a content provider of the product, wherein the products are part of retargeting impressions.
[Claim 9] setting the bid to guarantee that the user is eligible for the product if an expected value satisfies a content provider target.
[Claim 10] setting the bid to guarantee that a value of the bid does not exceed an amount the content provider will pay for a click.
[Claim 11] determining that the user is eligible for the product based upon the bid exceeding a floor price.
[Claim 13] bounding the model by a set number of products selected to include products having higher amounts of conversions than other products and that exceed a predefined minimum number of conversions.
[Claim 14] reducing the target cost per action to reduce a value of the bid and to decrease a pool of eligible users for the product.
[Claim 15] increasing the target cost per action to increase the value of the bid and to increase a pool of eligible users for the product.
[Claim 17] bounding the model by a set number of products selected to include products having higher amounts of conversions than other products and that exceed a predefined minimum number of conversions.
[Claim 18] reducing the target cost per action to reduce a value of the bid and to decrease a pool of eligible users for the product.
[Claim 19] increasing the target cost per action to increase a value of the bid and to increase a pool of eligible users for the product.
[Claim 20] calculating the target cost per action by applying a factor to an average spend divided by a number of conversions of products of a content provider of the product.
The dependent claims further recite details of the abstract ideas identified in the independent claims above.
Aside from the additional elements, the aforementioned claim details exemplify the abstract idea(s) of a mental process (since the details include concepts performed in the human mind, including an observation, evaluation, judgment, and/or opinion). As explained in MPEP § 2106.04(a)(1)(III), “[t]he courts consider a mental process (thinking) that ‘can be performed in the human mind, or by a human using a pen and paper’ to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).” The limitations reproduced above, as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting the additional elements identified in Step 2A – Prong 2 below, nothing in the claim elements precludes the steps from practically being performed in the mind and/or by a human using a pen and paper. For example, but for the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the respectively recited steps/functions of the claims, as drafted and set forth above, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind and/or with the use of pen and paper. A human user can form a model, generate the recited information, make the recited determinations, perform the recited calculations, evaluate the recited mathematical relationships, set bids, make the recited determinations, evaluate the costs, transmit information for display, etc. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind (and/or with pen and paper) but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Aside from the additional elements, the aforementioned claim details exemplify a method of organizing human activity (since the details include examples of commercial or legal interactions, including advertising, marketing or sales activities or behaviors, and/or business relations and managing personal behavior or relationships or interactions between people, including social activities, teaching, and following rules or instructions). More specifically, the evaluated process is related to “providing conversion prospecting for dynamic content recommendation” (Spec: ¶ 2), which (under its broadest reasonable interpretation) is an example of marketing and managing personal behavior (i.e., organizing human activity); therefore, aside from the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the limitations identified in the more detailed claim listing above encompass the abstract idea of organizing human activity.
Various calculating steps are recited throughout claims 8 and 20 and these are examples of mathematical concepts.
2A – Prong 2: Integrated into a Practical Application?
No – The judicial exception(s) is/are not integrated into a practical application.
The dependent claims incorporate the additional elements of the independent claims from which they depend.
Claims 1-11 recite a method executing on a processor of a computing device that causes the computing device to perform the recited operations. Claim 1 also recites training a model to predict conversion-given-click probabilities of users and products, where the training comprises incrementally training the model on sets of positive events and sets of negative events from a content serving platform, and transmitting content items of products selected by the content serving platform to devices for display to users.
Claim 7 recites adjusting, during training of the model, predictions generated by the model based upon the model under-predicting due to the model being trained on an additional negative event for each positive event.
Claims 12-15 recites a non-transitory machine readable medium having stored thereon processor-executable instructions that when executed cause performance of the recited operations. Claim 12 also recites training a model to predict conversion-given-click probabilities of users and products, where the training comprises incrementally training the model on sets of positive events and sets of negative events from a content serving platform, and transmitting content items of products selected by the content serving platform to devices for display to users.
Claims 16-20 recites a computing device comprising: a processor; and memory comprising processor-executable instructions that when executed by the processor cause performance of the recited operations. Claim 16 also recites training a model to predict conversion-given-click probabilities of users and products, where the training comprises incrementally training the model on sets of positive events and sets of negative events from a content serving platform, and transmitting content items of products selected by the content serving platform to devices for display to users.
The claims as a whole merely describe how to generally “apply” the abstract idea(s) in a computer environment. The claimed processing elements are recited at a high level of generality and are merely invoked as a tool to perform the abstract idea(s). Simply implementing the abstract idea(s) on a general-purpose processor is not a practical application of the abstract idea(s); Applicant’s specification discloses that the invention may be implemented using general-purpose processing elements and other generic components (Spec: ¶¶ 17-31, 61).
The use of a processor/processing elements (e.g., as recited in all of the claims) facilitates generic processor operations. The use of a memory or machine-readable media with executable instructions facilitates generic processor operations.
The additional elements are recited at a high-level of generality (i.e., as generic processing elements performing generic computer functions) such that the incorporation of the additional processing elements amounts to no more than mere instructions to apply the judicial exception(s) using generic computer components. There is no indication in the Specification that the steps/functions of the claims require any inventive programming or necessitate any specialized or other inventive computer components (i.e., the steps/functions of the claims may be implemented using capabilities of general-purpose computer components). Accordingly, the additional elements do not integrate the abstract ideas into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea(s).
The processing components presented in the claims simply utilize the capabilities of a general-purpose computer and are, thus, merely tools to implement the abstract idea(s). As seen in MPEP § 2106.05(a)(I) and § 2106.05(f)(2), the court found that accelerating a process when the increased speed solely comes from the capabilities of a general-purpose computer is not sufficient to show an improvement in computer-functionality and it amounts to a mere invocation of computers or machinery as a tool to perform an existing process (see FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)).
Considering that the implementation of the machine learning model and/or the training of the model is performed using generic processing elements, such an implementation is presented as a generic recitation of machine learning in the claims and as a general link to technology. The machine learning-based processing elements are simply tools to generally automate the underlying process that could be performed by a human. It is further noted that, as described in Applicant’s Specification, the machine learning operations are generic machine learning operations (Spec: ¶¶ 35, 39, 44). The Specification presents no assertion that there is any improvement in the automated machine learning process itself. Such a generic recitation of machine learning, as recited in the claims, is little more than automating an analogous process that can be performed by a human.
There is no transformation or reduction of a particular article to a different state or thing recited in the claims.
2B: Claim(s) Provide(s) an Inventive Concept?
No – The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception(s). As discussed above with respect to integration of the abstract idea(s) into a practical application, the use of the additional elements to perform the steps identified in Step 2A – Prong 1 above amounts to no more than mere instructions to apply the exceptions using a generic computer component(s). Mere instructions to apply an exception using a generic computer component(s) cannot provide an inventive concept. The claims are not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-5, 9-13, and 16-17 are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by Sharma et al. (US 2008/0275757).
[Claim 1] Sharma discloses a method executing on a processor of a computing device that causes the computing device to perform operations (¶¶ 92-103) comprising:
training a model to predict conversion-given-click probabilities of users and products, where the training comprises incrementally training the model on sets of positive events, from a content serving platform, corresponding to post-click conversions occurring after a user clicks on a content item recommending at least one product and (ii) sets of negative events, from the content serving platform, corresponding to post-click conversions not occurring after a user clicks on a content item recommending at least one product (¶ 45 – “the pCTR [predicted clickthrough rate] may be derived by the learning model 202 using historical data (e.g., clickthrough data).”; ¶ 11 – “In some implementations, a correction factor may be computed to better predict the conversion rate. For example, the correction factor may be computed using an iterative process (e.g., by a learning model) that compensates for the deviation error of the predicted conversion rate within a bidding period. The iterative process may employ historical performance data to obtain an accurate estimated click-based bid. The correction factor may be automatically adjusted in an adaptive way to mitigate changes or fluctuations of the predicted conversion rate so as to yield an accurate estimated click-based bid as a function of the target bid.”; ¶ 63 – “ In some implementations, the correction factor γ in [11] may be computed (e.g., by the learning model 202) using an iterative process (e.g., a feedback loop) that compensates for the deviation error of the pCVR within a bidding period. The iterative process may employ historical performance data to obtain an accurate eCPC. The correction factor γ may be automatically adjusted in an adaptive way to mitigate changes or fluctuations of the predicted conversion rate so as to yield an accurate estimated CPC as a function of a CPA bid.”; ¶ 64 – “In some implementations, the iterations can be performed until a predetermined, dynamically determined, or other optimal value is reached for the correction factor. In certain implementations, a correction factor may be approximated (e.g., by the learning model 202) before iterations are performed, such as before a threshold number of iterations are performed. In other implementations, no further iteration is performed if the correction factor changes by less than a threshold amount after a particular iteration.”; ¶ 30 – “The advertisement data may include data relating to ads previously provided to users 218 and whether the ads were selected or not selected by the users 218.”; ¶ 33 – “Ads presented to the user, whether the user selected or did not select the ad(s), and the document(s) that the user accessed when presented with the ad(s) may be forwarded to the ad ranking model to create an empirical model so as to improve ad ranking.”; ¶ 52 – “In some implementations, the target bid specified by the advertisers 214 may be provided by the ad server 206 to the learning model 204 where the target bid can be combined with a predicted conversion rate to produce a new or adjusted maximum CPC bid. The learning model 202, for example, can be used to compute the pCVR [predicted conversion rate] for a potential ad impression by collecting the number of clicks and conversions for each impression context feature of interest. As discussed above, a conversion rate defines a ratio of the number of conversions (e.g., # of sales generated by a given ad) to the number of clicks of the ad (i.e., # of visits to advertiser's web property from the ad). Thus, statistics can be calculated based on these numbers for use in predicting a conversion rate. Once the pCVR is determined, this parameter may be used with the target bid (e.g., multiplied by the target bid) to automatically adjust the advertiser's default click-based bid (e.g., maximum CPC bid) or compute a new click-based bid.”; ¶ 40 – “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 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). Many other definitions of what constitutes a conversion are possible. For example, a conversion also may include signing up to be a member of a web site, filling out an online form, making a purchase upon contacting an advertiser via online creatives and the like.” A purchase made after clicking on an ad is an example of a post-click conversion, i.e., a positive event. A conversion rate may convey a number of sales results from a total number of clicks (¶ 41), which means that both positive events and negative events are used to evaluate post-click conversions. When a conversion is defined as the purchase, not making the purchase is an example of a negative event. The ads may be correlated with products (¶¶ 5, 34); therefore, a purchase of an advertised product is an example of a post-click conversion related to an ad, i.e., item content, recommending the at least one product, i.e., the product that is the subject of the ad. A correction factor is computed using an iterative process and is automatically adjusted adaptively as part of a machine learning process to adjust bids and predictions over time and in response to changing prediction conditions (¶¶ 11, 53-55, 61-64, 70). This information is used to rank ads for an ad campaign (¶ 42).);
generating, utilizing the model, a prediction of a conversion-given-click probability that a user will perform an action in relation to a product (¶ 52 – “In some implementations, the target bid specified by the advertisers 214 may be provided by the ad server 206 to the learning model 204 where the target bid can be combined with a predicted conversion rate to produce a new or adjusted maximum CPC bid. The learning model 202, for example, can be used to compute the pCVR [predicted conversion rate] for a potential ad impression by collecting the number of clicks and conversions for each impression context feature of interest. As discussed above, a conversion rate defines a ratio of the number of conversions (e.g., # of sales generated by a given ad) to the number of clicks of the ad (i.e., # of visits to advertiser's web property from the ad). Thus, statistics can be calculated based on these numbers for use in predicting a conversion rate. Once the pCVR is determined, this parameter may be used with the target bid (e.g., multiplied by the target bid) to automatically adjust the advertiser's default click-based bid (e.g., maximum CPC bid) or compute a new click-based bid.”; ¶ 40 – “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 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). Many other definitions of what constitutes a conversion are possible. For example, a conversion also may include signing up to be a member of a web site, filling out an online form, making a purchase upon contacting an advertiser via online creatives and the like.”; The ads may be correlated with products (¶ 34). By nature of the association of the products with the ads, a ranking of the ads and conversion rates may be seen as also reflecting a ranking of the advertised products.);
generating a bid for the user and the product based upon the conversion-given-click probability that the user will perform the action in relation to the product, a content provider bid for the product, and a target cost per action corresponding to a performance goal of a content provider (¶ 29 – “The learning model 202, which can be coupled to the ad server 206 and the conversion data repository 208, may include a statistical and probability model constructed using statistical techniques. Such techniques may include, for example, logistic regression, regression trees, boosted stumps, or any other statistical modeling techniques. In some implementations, the learning model 202 provides a predicted conversion rate ("pCVR"), which can be used to perform a metric conversion, as described below with reference to FIGS. 3 and 4.”; ¶ 53 – “In some implementations, the learning model 202 is a machine learning system model that includes rules for mapping impression context features to conversion rate predictions. The rules may include, for example, a probability multiplier for each context feature. For example, a user from USA may be assigned a probability multiplier of 0.85, and an ad appearing on a particular news web site may be assigned a probability multiplier of 1.1. To predict a conversion rate, the default conversion rate can be aggregated with the probability multiplier for each relevant feature. Using the above example, for an ad having a default conversion rate of 0.2% that is shown on the particular news web site to a user from USA, the predicted conversion rate for the ad would be 0.187% (0.2%*1.1*0.85).”; ¶ 13 – “In some implementations, a method includes: obtaining input specifying a first metric value associated with an advertisement; determining a predicted conversion rate for a potential impression of the advertisement; estimating a second metric value based on the first metric value and the predicted conversion rate; compensating based on the second metric value; and debiting based on the first metric value. The first metric value may be a value based on a Cost-Per-Action model, and the second metric value may be based on a Cost-Per-Click model. Alternatively, the first metric value may be a value based on one of Cost-Per-Click model or Cost-Per-Action model, and the second metric value may be based on a Cost-Per-Impression model.”; ¶ 35 – “Each ad or ad group may include individual price information (e.g., cost, average cost, or maximum cost (per impression, per selection, per conversion, etc.)). For example, the advertiser 102 may specify a maximum monetary value with the advertising management system 104 as to how much the advertiser 102 is willing to pay per user click, impression or conversion per ad or ad group. The maximum monetary value may be based on the number of impressions (e.g., CPM bidding), the number of clicks on an ad (e.g., CPC bidding), or the number of conversions (e.g., CPA bidding) generated in response to an ad. For example, if the advertiser 102 has selected a CPC bidding model, the advertiser 102 can enter a maximum CPC bid which represents the highest amount the advertiser 102 is willing to pay if an ad associated with an ad group receives a click. As another example, if the advertiser 102 has selected a CPA bidding model, the advertiser 102 can enter a maximum CPA bid which represents the highest amount an advertiser is willing to pay if an ad associated with that ad group generates a conversion. Based on the defined bidding model, the publishers 106 can be credited and the advertisers 102 can be debited accordingly.”; ¶ 43 – “Based on the ranking (and any adjustment thereof as a result of the weighted scores) determined during the auction, the identified ads can be selected for presentation.”; The ads may be correlated with products (¶ 34). By nature of the association of the products with the ads, a ranking of the ads and conversion rates may be seen as also reflecting a ranking of the advertised products and actions in relation to the advertised products.; ¶ 10 – “An advertiser specifies a target bid (e.g., CPA target bid or other target) for a conversion event associated with an ad. A predicted conversion rate or value is determined (e.g., empirically) for potential impressions of the ad based on conversion data (e.g., historical conversion data) for the ad and impression context data (e.g., current impression context data). The predicted conversion rate and target bid can be used to estimate a click-based bid. Publishers can be compensated based on the estimated click-based bid, while advertisers can be debited using the target bid originally specified.” Considering that an ad may have multiple impressions, this implies that the ads may be retargeted impressions. The ads may be correlated with products (¶ 34). By nature of the association of the products with the ads, a ranking of the ads and conversion rates may be seen as also reflecting a ranking of the advertised products and actions in relation to the advertised products.; ¶ 27 – “In some implementations, when the user 218 clicks an ad served by the ad server 206, the user 218 is directed to a landing page on the web property (e.g., a web site) of the advertiser 214. The user 218 may then perform a conversion event at the website (e.g., make a purchase, register). The conversion event generates conversion data which is sent to the system 200 and stored in a repository (e.g., MySQL® database). In this manner, a conversion history can be accumulated and maintained for each ad or ad group in an advertiser's ad campaign.”).; ¶ 40 – “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 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). Many other definitions of what constitutes a conversion are possible. For example, a conversion also may include signing up to be a member of a web site, filling out an online form, making a purchase upon contacting an advertiser via online creatives and the like.” A purchase made after clicking on an ad is an example of a post-click conversion, i.e., a positive event. A conversion rate may convey a number of sales results from a total number of clicks (¶ 41), which means that both positive events and negative events are used to evaluate post-click conversions. When a conversion is defined as the purchase, not making the purchase is an example of a negative event. The ads may be correlated with products (¶¶ 5, 34); therefore, a purchase of an advertised product is an example of a post-click conversion related to an ad, i.e., item content, recommending the at least one product, i.e., the product that is the subject of the ad. A correction factor is computed using an iterative process and is automatically adjusted adaptively as part of a machine learning process to adjust bids and predictions over time and in response to changing prediction conditions (¶¶ 11, 53-55, 61-64, 70). This information is used to rank ads for an ad campaign (¶ 42).);
determining whether the product is to compete in an auction hosted by the content serving platform based upon the bid (¶ 43 – “Based on the ranking (and any adjustment thereof as a result of the weighted scores) determined during the auction, the identified ads can be selected for presentation.”; ¶ 38 – “As another example, auction factors such as clickthrough rates (CTRs) and conversion rates (CVRs) can be ranked from largest to smallest.” The ads may be correlated with products (¶ 34). By nature of the association of the products with the ads, a ranking of the ads and conversion rates may be seen as also reflecting a ranking of the advertised products.); and
transmitting content items of products selected by the content serving platform to devices for display to users (¶ 26 – “In some implementations, a publisher 216 can request an ad from the ad server 206. In response to the request, one or more ads (e.g., image ads) are sent to the publisher 216. The ad(s) can be placed on, for example, a web property owned or operated by the publisher 216 (e.g., a web site). In some implementations, the web page can have a page content identifier (ID), which can be used by the ad server 206 to determine ad context for targeting ads. These implementations target ads in the hope that the users, e.g., user 218, will be more receptive to targeted ads than to untargeted ads.”; ¶ 27 – “In some implementations, when the user 218 clicks an ad served by the ad server 206, the user 218 is directed to a landing page on the web property (e.g., a web site) of the advertiser 214. The user 218 may then perform a conversion event at the website (e.g., make a purchase, register).” It is understood that the user would need a display device to view content on a website.; The ads may be correlated with products (¶ 34).).
[Claim 2] Sharma discloses wherein a positive event corresponds to a first user performing the action after interacting with a first content item, and wherein a negative event corresponds to the first user interacting with a second content item without subsequently performing the action (¶ 30 – “In some implementations, the conversion data repository 208 may include one or more logical or physical memory devices configured to store a large data set (e.g., millions of instances and hundreds of thousands of features) that may be used, for example, to create and train the learning model 202. The data may include conversion data, advertisement information, such as advertisement data, user information, and document or content information, that may be used to create a model that may be used to determine a metric conversion rate. The advertisement data may include data relating to ads previously provided to users 218 and whether the ads were selected or not selected by the users 218.”).
[Claim 3] Sharma discloses wherein the model represents products with product features that include product identifiers, product set identifiers, and content provider identifiers (¶ 34 – “Referring back to FIG. 1, each advertiser 102 may establish an advertising program with the advertising management system 104. An advertising program may include, for example, ad campaigns, creatives, targeting and the like. The advertiser 102 may define an "ad campaign" which can include one or more ad groups each including one or more ads. An ad group may define, for example, a product type (e.g., Hats or Pants), and a creative may include an ad textually or graphically defining the product type Each ad group or ad may include a start date, an end date, budget information, geographical targeting information, and syndication information.”).
[Claim 4] Sharma discloses wherein the model comprises a multi-value feature containing a list of content item campaigns with a highest click through rate of a first user during a time period (¶ 42 – “An ad campaign parameter or auction factor also can be weighted to increase or decrease the impact that auction factor has on ad ranking. For example, an ad that has a higher CTR can be ranked above an ad that has a lower CTR, even if the ad with the lower CTR has an equal or greater default bid.”; ¶ 36 – “When an ad request is received, an ad that corresponds to the received ad request is identified. If more than one ad has been identified, an auction can be conducted to identify which ad to be served. During the auction, the ads may be ranked in accordance with one or more associated ad campaign parameters. The one or more ad campaign parameters may include, without limitation, default bids (e.g., CPC, CPA or CPM bids), daily budget defined by the advertisers 102 (e.g., at the time of registering an ad campaign), and relevance of an ad that can be determined by various methods, such as by inferring a high relevance for an ad with respect to, for example, a particular keyword query.”; ¶ 39 – “The CTR of an ad can be calculated by dividing the number of clickthroughs associated with the ad by the number of impressions of that ad over a given time period.”; ¶ 40 – “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). Many other definitions of what constitutes a conversion are possible.”).
[Claim 5] Sharma discloses wherein the model comprises a content serving platform experiment identifier (¶ 26 – “In some implementations, the web page can have a page content identifier (ID), which can be used by the ad server 206 to determine ad context for targeting ads. These implementations target ads in the hope that the users, e.g., user 218, will be more receptive to targeted ads than to untargeted ads.”; ¶ 98 – “The advertising management module 518 includes an ad server 520 and a web server 522. The advertising management module 518 further includes a learning model 524. The learning model 524 may perform and behave in a manner similar to learning model 202. The ad server 520 can be a server process or dedicated machine that is responsible for serving ads to publisher web properties and for tracking various information related to the ad placement (e.g., cookies, user URLs, page content, geographic information). The web server 522 (e.g., Apache web page server) serves web pages to advertisers and publishers and provides a means for advertisers and publishers to specify a target cost-per-action for use by the learning model 524 to dynamically compute or adjust an advertiser's click-based bid (e.g., Max CPC bid) or other performance metric.”).
[Claim 9] Sharma discloses setting the bid to guarantee that the user is eligible for the product if an expected value satisfies a content provider target (¶ 77 – “ As shown, the process 300 begins with obtaining a target CPA bid and conversion rate (302). The target CPA bid may be a maximum CPA bid specified by the advertisers. Alternatively, a recommended CPA bid may be provided by the advertising management system 104 and used as the target CPA bid. In some implementations, the target CPA bid may be an average CPA bid specified by the advertisers. In another implementation, the target CPA bid may be a minimum CPA bid specified by the advertisers.”; ¶ 89 – “Process 400 then proceeds with predicting a conversion rate using the conversion data (410). In some implementations, the conversion data may include data associated with a default CPC bid and a target CPA bid specified by either the advertisers or the advertising management system. In these implementations, the conversion rate can be estimated by dividing a default CPC bid by a target CPA bid. Alternatively, the default maximum CPC bid can be used as default instead of predicting a conversion rate.”).
[Claim 10] Sharma discloses setting the bid to guarantee that a value of the bid does not exceed an amount the content provider will pay for a click (¶ 35 – “Each ad or ad group may include individual price information (e.g., cost, average cost, or maximum cost (per impression, per selection, per conversion, etc.)). For example, the advertiser 102 may specify a maximum monetary value with the advertising management system 104 as to how much the advertiser 102 is willing to pay per user click, impression or conversion per ad or ad group.”).
[Claim 11] Sharma discloses determining that the user is eligible for the product based upon the bid exceeding a floor price (¶ 77 – “ As shown, the process 300 begins with obtaining a target CPA bid and conversion rate (302). The target CPA bid may be a maximum CPA bid specified by the advertisers. Alternatively, a recommended CPA bid may be provided by the advertising management system 104 and used as the target CPA bid. In some implementations, the target CPA bid may be an average CPA bid specified by the advertisers. In another implementation, the target CPA bid may be a minimum CPA bid specified by the advertisers.”; The ads may be correlated with products (¶ 34). By nature of the association of the products with the ads, a ranking of the ads and conversion rates may be seen as also reflecting a ranking of the advertised products.).
[Claim 12] Claim 12 recites limitation already addressed by the rejection of claim 1 above; therefore, the same rejection applies.
Furthermore, Sharma discloses a non-transitory machine readable medium having stored thereon processor-executable instructions that when executed cause performance of the disclosed operations (¶¶ 92-103).
[Claim 13] Sharma discloses bounding the model by a set number of products selected to include products having higher amounts of conversions than other products and that exceed a predefined minimum number of conversions (¶ 32 – “The ad ranking model may be used as part of a function to determine what ads to provide to users 218 when the users are accessing the documents. To facilitate data generation for use by the ad ranking model, information concerning a user and documents accessed by the user can be collected. As discussed above, information concerning a user may include IP address, cookie information, languages, geographical information and document information may include information relating to the documents accessed by the user (e.g., a URL of a web site visited by the user). Ads stored in the ad repository 210 may then be ranked based, at least in part, on the data stored by the learning model 202. The rank of an ad may, in some cases, correspond to the probability that a user will select the ad when accessing a particular document. Each ad can then be served to the user based on a respective rank. For example, the top one or more ads may be served to the users 218. Alternatively, ads that have ranks above a predetermined threshold may be served to the users 218.”; ¶ 34 – “The advertiser 102 may define an "ad campaign" which can include one or more ad groups each including one or more ads. An ad group may define, for example, a product type (e.g., Hats or Pants), and a creative may include an ad textually or graphically defining the product type Each ad group or ad may include a start date, an end date, budget information, geographical targeting information, and syndication information.”; ¶ 38 – “As another example, auction factors such as clickthrough rates (CTRs) and conversion rates (CVRs) can be ranked from largest to smallest.” The ads may be correlated with products. By nature of the association of the products with the ads, a ranking of the ads and conversion rates may be seen as also reflecting a ranking of the advertised products. A minimum threshold is an example of a lower bound and a largest/highest ranking is an example of an upper bound.).
[Claim 16] Claim 16 recites limitation already addressed by the rejection of claim 1 above; therefore, the same rejection applies.
Furthermore, Sharma discloses a computing device comprising:
a processor (¶¶ 92-103); and
memory comprising processor-executable instructions that when executed by the processor cause performance of the disclosed operations (¶¶ 92-103).
[Claim 17] Claim 17 recites limitation already addressed by the rejection of claim 13 above; therefore, the same rejection applies.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Sharma et al. (US 2008/0275757), as applied to claim 1 above, in view of Fernandez-Ruiz (US 2014/0372415).
[Claim 6] Sharma discloses wherein the model comprises a page section corresponding to a webpage identifier (Sharma: ¶ 33 – “The position of the ads within a document accessed by the users 218 also may be based, at least in part, on the ranks of the ads. For example, higher ranking ads may be positioned at more prominent or visually-recognized locations then lower ranking ads. Ads presented to the user, whether the user selected or did not select the ad(s), and the document(s) that the user accessed when presented with the ad(s) may be forwarded to the ad ranking model to create an empirical model so as to improve ad ranking.”; ¶ 43 – “Based on the ranking (and any adjustment thereof as a result of the weighted scores) determined during the auction, the identified ads can be selected for presentation. For example, if the identified ads are to be presented in a web page that has four ad slots each slot displaying one ad, the four highest ranked ads can be selected for presentation. Further, the ranking established during the auction can be used to determine a display order. For example, the highest ranked ad can be assigned to the most prominent display position.”; ¶ 70 – “The bidding period "t" for which these parameters are measured can be empirically determined, or based on historical data.”; ¶ 40 – “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 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). Many other definitions of what constitutes a conversion are possible. For example, a conversion also may include signing up to be a member of a web site, filling out an online form, making a purchase upon contacting an advertiser via online creatives and the like.” Looking at historical conversion activities implies that the conversion activities are related to user interactions with a web page.).
Sharma does not explicitly disclose wherein the model comprises a page section corresponding to a webpage identifier where a user viewed a content item. Sharma uses machine learning to update the models related to ads, auctions, etc. and the learning may utilize historical data. Ads may be viewed on a website. As seen above, Sharma also acknowledges that there is optimal ad placement on a website. Sharma simply does not explicitly explain that the model itself takes into account where a user viewed a content item (e.g., to address the page section aspects of the model). Fernandez-Ruiz explicitly describes how a model may be trained and optimized based on specific user engagement metrics, including a clickthrough rate and an advertisement conversion rate and the engagement analysis is used in conjunction with a page layout optimizer (Fernandez-Ruiz: ¶ 62). The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Sharma wherein the model comprises a page section corresponding to a webpage identifier where a user viewed a content item in order to allow for continuous updates of the learning model in light of actual user activities so that the model can remain as accurate as possible over time, especially as it pertains to ad placement.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Sharma et al. (US 2008/0275757), as applied to claim 1 above, in view of Lo et al. (US 2018/0321980).
[Claim 7] Sharma discloses adjusting, during training of the model, predictions generated by the model based upon the model under-predicting (¶ 61 – “ In some instances, these factors may cause the pCVR to over-predict or under-predict a conversion rate. The inaccuracy can lead to a deviation between the revenues generated by the publishers (i.e., eCPC). For example, if the pCVR is over-predicted (e.g., from 0.1% to 0.2%), then a publisher may be credited at a higher cost per click than an actual cost for each displayed ad. As another example, if the pCVR is under-predicted (e.g., from 0.1% to 0.05%), the publisher may be compensated with a lower amount than that would have received under the designated pricing model.”). Sharma does not explicitly disclose that the model under-predicting is due to the model being trained on an additional negative event for each positive event. However, Lo describes problems that can occur when there are more negative errors that positive errors:
[0161] At 104, execution time of the plurality of program tasks on one or more computing cores is estimated. Each program feature is mapped to an execution time estimate on a selected computing core. Predicting execution time of the program features involves training and fitting a mathematical model. In some embodiments, a linear model trained with offline training data sets can achieve this purpose. A linear model has the advantage of being simple to train and fast to evaluate at runtime. The most common way to fit a linear model is to use linear least squares regression, aiming to minimize the sum of the absolute errors in the prediction. However, negative and positive errors should be weighed differently for the purpose of meeting response-time requirements. Negative errors (under-prediction) lead to deadline misses that would impact user experience, while positive errors (over-prediction) result in an overly conservative frequency setting which does not save as much energy as possible. To maintain a good user experience, the training process should place greater weight on avoiding under-prediction as opposed to over-prediction.
The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Sharma such that the model under-predicting is due to the model being trained on an additional negative event for each positive event so that Sharma can make model corrections accordingly to increase modeling accuracy, which is a goal of Sharma, as evidenced by its use of a correction factor to account for over-prediction or under-prediction (as seen in ¶¶ 61-63 of Sharma).
Claims 8 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Sharma et al. (US 2008/0275757), as applied to claims 1 and 16 above, in view of Chang et al. (US 2009/0254410).
[Claim 8] Sharma discloses calculating the target cost per action, wherein the products are part of retargeting impressions (¶ 10 – “An advertiser specifies a target bid (e.g., CPA target bid or other target) for a conversion event associated with an ad. A predicted conversion rate or value is determined (e.g., empirically) for potential impressions of the ad based on conversion data (e.g., historical conversion data) for the ad and impression context data (e.g., current impression context data). The predicted conversion rate and target bid can be used to estimate a click-based bid. Publishers can be compensated based on the estimated click-based bid, while advertisers can be debited using the target bid originally specified.” Considering that an ad may have multiple impressions, this implies that the ads may be retargeted impressions. The ads may be correlated with products (¶ 34). By nature of the association of the products with the ads, a ranking of the ads and conversion rates may be seen as also reflecting a ranking of the advertised products.; ¶ 27 – “In some implementations, when the user 218 clicks an ad served by the ad server 206, the user 218 is directed to a landing page on the web property (e.g., a web site) of the advertiser 214. The user 218 may then perform a conversion event at the website (e.g., make a purchase, register). The conversion event generates conversion data which is sent to the system 200 and stored in a repository (e.g., MySQL® database). In this manner, a conversion history can be accumulated and maintained for each ad or ad group in an advertiser's ad campaign.”).
Sharma does not explicitly disclose calculating the target cost per action by applying a factor to an average spend divided by a number of conversions of products of a content provider of the product. Chang explains that “the volume of clicks corresponds to the number of clicks seen in a core market, the conversion rate corresponds to an expected conversion rate for the key words that make up the core market description 200, and the average cost per acquisition (CPA) corresponds to the average amount an advertiser spends divided by a number of sales the advertiser makes. It may be necessary to accurately compute the conversion rate so as to minimize the risk to the sponsored search provider.” (Chang: ¶ 33) The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Sharma to perform the step of calculating the target cost per action by applying a factor to an average spend divided by a number of conversions of products of a content provider of the product is order to incorporate a more accurate computation of cost per action so that advertisers and people selling ad space can plan their bidding and pricing strategies in a more effective and efficient manner.
[Claim 20] Claim 20 recites limitation already addressed by the rejection of claim 8 above; therefore, the same rejection applies.
Claims 14-15 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Sharma et al. (US 2008/0275757), as applied to claims 12 and 16 above, in view of Liu et al. (US 2015/0339704).
[Claims 14-15, 18-19] Sharma does not explicitly disclose:
[Claims 14, 18] reducing the target cost per action to reduce a value of the bid and to decrease a pool of eligible users for the product;
[Claims 15, 19] increasing the target cost per action to increase the value of the bid and to increase a pool of eligible users for the product.
Liu explains:
[0091] AMS 106 generates auction scores for a predefined number of advertisements to develop 314 a pool of candidate advertisements for presentation to a publisher. Typically, the candidate advertisements are ranked according to auction score. Accordingly, because auction score is directly proportional to CPA price, the higher a price that advertiser 102 is willing to pay, the higher the auction score for the advertisement is likely to be. The methods described herein enable an advertiser 102 to value their advertisements in relation to their estimated lifetime revenues, relative to the specific consumers to whom the advertisements may be presented.
In other words, there is value attributed to advertisements that can increase lifetime revenues from users who engage in sales conversions in response to the selected ads. High cost per action is associated with higher value; however, higher value ads are expected to yield more lifetime revenue and vice-versa. The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Sharma to perform the steps of:
[Claims 14, 18] reducing the target cost per action to reduce a value of the bid and to decrease a pool of eligible users for the product;
[Claims 15, 19] increasing the target cost per action to increase the value of the bid and to increase a pool of eligible users for the product
in order to encourage higher value ad bids for ads that are more likely to increase lifetime revenues and to encourage lower value ad bids for ads that are less likely to yield revenue, thereby enabling advertisers to better strategize when and how to spend their marketing budget.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/SUSANNA M. DIAZ/
Primary Examiner
Art Unit 3625A