DETAILED ACTION
Status of Claims
• The following is an office action in response to the communication filed 07/08/2024.
• Claims 1-20 are currently pending and have been examined.
Priority
The applicant’s claim for benefit of Provisional Patent Application Serial No. 63/525,795 filed 07/10/2023 has been received and acknowledged.
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 .
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 a judicial exception without significantly more. The claims recite an abstract idea. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
First, it is determined whether the claims are directed to a statutory category of invention. See MPEP 2106.03(II). In the instant case, claims 1-12 are directed to a process, claims 13-16 are directed to a machine, and claims 17-20 are directed to a manufacture. Therefore, claims 1-20 are directed to statutory subject matter under Step 1 of the Alice/Mayo test (Step 1: YES).
The claims are then analyzed to determine if the claims are directed to a judicial exception. See MPEP 2106.04. In determining whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception (Prong 1 of Step 2A), as well as analyzed to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of the judicial exception (Prong 2 of Step 2A). See MPEP 2106.04.
Taking claim 1 as representative, claim 1 recites at least the following limitations that are believed to recite an abstract idea:
access a win-rate model based on historical auction data, wherein the win-rate model expresses a probability of winning an auction as a function of a bid-price;
access a KPI model based on historical auction data, wherein the KPI the model expresses an impact of winning an auction on a specific KPI;
generate an inventory forecast as a prediction of a joint histogram over the outputs of the win-rate and KPI models;
execute a search process to identify a bidding strategy that determines which auctions to bid on and at what bid prices to bid to optimize the KPI; and
deploy the identified bidding strategy, wherein the bidding strategy includes a bid price function that determines the bid price of an auction based on the outputs of the win-rate and KPI models, a worth-to-volume ratio of the auction, and a worth-to-volume ratio threshold that determines whether to submit a bid or abstain from bidding.
The above limitations recite the concept of generating and using a bidding strategy. These limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in the MPEP, in that they recite commercial or legal interactions such as advertising, marketing, or sales activities or behaviors. Specifically, the deployment of a bidding strategy pertains to sales. . Further, these limitations, under their broadest reasonable interpretation, fall within the “Mental Processes” grouping of abstract ideas, enumerated in the MPEP, in that they recite concepts performed in the human mind, including observations, evaluations, judgments, and opinions. Specifically, the analysis of data and generation of models to deploy a strategy are observations, evaluations, and judgements. Claims 13 and 17 recite the same abstract ideas as claim 1 and accordingly fall within the same grouping of abstract ideas. Accordingly, under Prong One of Step 2A of the MPEP, claims 1, 13, and 17 recite an abstract idea (Step 2A, Prong One: YES).
Under Prong Two of Step 2A of the MPEP, claims 13, and 17 recite additional elements, such as a system, comprising: one or more electronic processors configured to execute a set of computer-executable instructions; and one or more non-transitory electronic data storage media containing the set of computer-executable instructions, wherein when executed, the instructions cause the one or more electronic processors to; and one or more non-transitory computer-readable media comprising a set of computer-executable instructions that when executed by one or more programmed electronic processors, cause the processors to. Independent claim 1 is silent regarding any additional elements. In fact, claim 1 is completely silent regarding any implementation via a computer. Accordingly, claim 1 does not integrate the abstract idea into a practical application. With respect to the recited additional elements, these additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration. As such, these computer-related limitations are not found to be sufficient to integrate the abstract idea into a practical application. Although these additional computer-related elements are recited, claims 13 and 17 merely invoke such additional elements as a tool to perform the abstract idea. Implementing an abstract idea on a generic computer is not indicative of integration into a practical application. Similar to the limitations of Alice, claims 13, and 17 merely recite a commonplace business method (i.e., deploying a bidding strategy) being applied on a general purpose computer. See MPEP 2106.05(f). Furthermore, claims 13 and 17 generally link the use of the abstract idea to a particular technological environment or field of use. The courts have identified various examples of limitations as merely indicating a field of use/technological environment in which to apply the abstract idea, such as specifying that the abstract idea of monitoring audit log data relates to transactions or activities that are executed in a computer environment, because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer (see FairWarning v. Iatric Sys.). Likewise, claims 13 and 17 specifying that the abstract idea of deploying a bidding strategy is executed in a computer environment merely indicates a field of use in which to apply the abstract idea because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer. As such, under Prong Two of Step 2A of the MPEP, when considered both individually and as a whole, the limitations of claims 1, 13, and 17 are not indicative of integration into a practical application (Step 2A, Prong Two: NO).
Since claims 1, 13, and 17 recite an abstract idea and fail to integrate the abstract idea into a practical application, claims 1, 13, and 17 are “directed to” an abstract idea (Step 2A: YES).
Next, under Step 2B, the claims are analyzed to determine if there are additional claim limitations that individually, or as an ordered combination, ensure that the claim amounts to significantly more than the abstract idea. See MPEP 2106.05. The instant claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for at least the following reasons.
Returning to independent claims 13 and 17, these claims recite additional elements, such as a system, comprising: one or more electronic processors configured to execute a set of computer-executable instructions; and one or more non-transitory electronic data storage media containing the set of computer-executable instructions, wherein when executed, the instructions cause the one or more electronic processors to; and one or more non-transitory computer-readable media comprising a set of computer-executable instructions that when executed by one or more programmed electronic processors, cause the processors to. Independent claim 1 is silent regarding any additional elements. In fact, as noted above, claim 1 is completely silent regarding any implementation via a computer. Accordingly, claim 1 does not provide significantly more. As discussed above with respect to Prong Two of Step 2A, although additional computer-related elements are recited, the claims merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f). Moreover, the limitations of claims 13 and 17 are manual processes, e.g., receiving information, sending information, etc. The courts have indicated that mere automation of manual processes is not sufficient to show an improvement in computer-functionality (see MPEP 2106.05(a)(I)). Furthermore, as discussed above with respect to Prong Two of Step 2A, claims 13 and 17 merely recite the additional elements in order to further define the field of use of the abstract idea, therein attempting to generally link the use of the abstract idea to a particular technological environment, such as the Internet or computing networks (see Ultramercial, Inc. v. Hulu, LLC. (Fed. Cir. 2014); Bilski v. Kappos (2010); MPEP 2106.05(h)). Similar to FairWarning v. Iatric Sys., claims 13 and 17 specifying that the abstract idea of generating and using a bidding strategy is executed in a computer environment merely indicates a field of use in which to apply the abstract idea because this requirement merely limits the claim to the computer field, i.e., to execution on a generic computer.
Even when considered as an ordered combination, the additional elements do not add anything that is not already present when they are considered individually. In Alice Corp., the Court considered the additional elements “as an ordered combination,” and determined that “the computer components…‘[a]dd nothing…that is not already present when the steps are considered separately’ and simply recite intermediated settlement as performed by a generic computer.” Id. (citing Mayo, 566 U.S. at 79, 101 USPQ2d at 1972). Similarly, viewed as a whole, claims 1, 13, and 17 simply convey the abstract idea itself facilitated by generic computing components. Therefore, under Step 2B of the Alice/Mayo test, there are no meaningful limitations in claims 1, 13, and 17 that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself (Step 2B: NO).
Dependent claims 2-12, 14-16, and 18-20, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because they do not add “significantly more” to the abstract idea. Dependent claims 2-12, 14-16, and 18-20further fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in the MPEP, in that they recite commercial or legal interactions such as advertising, marketing, or sales activities or behaviors. Dependent claims 2-12, 14-16, and 18-20 additionally fall within the “Mental Processes” grouping of abstract ideas, in that they recite concepts performed in the human mind, including observations, evaluations, judgments, and opinions. Dependent claims 2-3, 6-8, 10-11, 14, 16, 18, and 20 fail to identify additional elements and as such, are not indicative of integration into a practical application. Dependent claims 4-5, 9, 12, 15, and 19 further recite the additional elements of a machine learning algorithm; online; and retraining. Similar to the discussion above under Prong Two of Step 2A, although these additional computer-related elements are recited, claims 2-12, 14-16, and 18-20 merely invoke such additional elements as a tool to perform the abstract idea. As such, under Step 2A, dependent claims 2-12, 14-16, and 18-20 are “directed to” an abstract idea and are not integrated into a practical application. Similar to the discussion above with respect to claims 1, 13, and 17, dependent claims 2-12, 14-16, and 18-20, analyzed individually and as an ordered combination, merely further define the commonplace business method being applied on a general purpose computer and, therefore, do not amount to significantly more than the abstract idea itself. See MPEP 2106.05(f)(2). Further, these limitations generally link the use of the abstract idea to a particular technological environment or field of use. Accordingly, under the Alice/Mayo test, claims 1-20 are ineligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-9, 11, 13-15, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. (US 20220301017 A1), hereinafter Kim, in view of Peretz et al. (US 8255285 B1), hereinafter Peretz.
In regards to claim 1, Kim discloses a method of generating a bidding strategy for a repeated first price auction, comprising (Kim: [0059] – “auction server 230 may select a winner through a first price auction”; [0002]):
access a win-rate model based on historical auction data, wherein the win-rate model expresses a probability of winning an auction as a function of a bid-price (Kim: [0008] – “predict a first winning probability distribution for a bidding price based on advertisement history data of an advertiser”);
access a KPI model based on historical auction data, wherein the KPI the model expresses an impact of winning an auction on a specific KPI (Kim [0008] – “obtain response probability data of a user based on user information included in the bidding request, the response probability data indicating a probability of the user, when exposed to the advertisement, responding to the advertisement posted in the advertisement area”; [0014] – user information included in the bidding request may include…whether the user clicks the advertisement… a preference of the user with respect to the advertisement area”; [0128] – “the current state S.sub.t may include the auction-related data of the advertiser 10 (e.g., the rate of remaining budget, the rate (or numbers) of remaining auctions, etc.) and the response probability data of the user 20 (e.g., predicted click through rate P.sub.ar, etc.) based on the time at which the corresponding auction was progressed (e.g., the ratio of the remaining budget, the rate of remaining auctions, etc.) and the response probability data (e.g., advertisement impression) of the user 20”; [0013] – “advertisement history data may include, based on the advertiser posting a previous advertisement in a previous advertisement area according to a result of a past bidding, information about a click rate indicating a number of clicks of the previous advertisement with respect to a number of impression of the previous advertisement”);
generate an inventory forecast as a prediction over the outputs of the win-rate and KPI models (Kim: [0130] – “meaning that the reinforcement learning maximizes a target reward may mean maximizing the sum of the rewards according to each action performed at various times during the bidding period, instead of maximizing reward R.sub.t according to the single action A.sub.t the current time t”; [0102] – “the winning probability distribution for a bidding price may refer to a function in which x axis represents a bidding price (or ratio of a bidding price) and an y axis represents probability (or winning probability), as shown in FIG. 3. The minimum price is 0 (e.g., 0 won, 0 dollar, etc.), and the maximum bidding price may correspond to the amount of the remaining budget of the advertiser 10. That is, if the amount of the remaining budget of the advertiser 10 is reduced due to the winning bid of the advertiser 10 as the auction proceeds, the maximum bidding price may also be reduced. There may be a particular point 310 having the highest probability Y among the plurality of points included in the winning probability distribution of FIG. 3, which may indicate that the bidding price should be determined by the amount X of the particular point 310 in the corresponding bidding to maximize the advertising effect at the entire auctions (or remaining auctions).”; [0129] – “The reward R.sub.t may indicate the probability data that the user 20 exposed to the advertisement content of the winner of the auction may respond”; the examiner notes an inventory forecast is interpreted to be a prediction about a set of auctions, consistent with Specification [0040]);
execute a search process to identify a bidding strategy that determines which auctions to bid on and at what bid prices to bid to optimize the KPI (Kim: [0010] – “identify a bidding price that has a highest winning probability among winning probabilities for respective bidding prices based on the first and second winning probability distributions obtained from the first and second neural network models”; [0102] – “There may be a particular point 310 having the highest probability Y among the plurality of points included in the winning probability distribution of FIG. 3, which may indicate that the bidding price should be determined by the amount X of the particular point 310 in the corresponding bidding to maximize the advertising effect at the entire auctions (or remaining auctions)”); and
deploy the identified bidding strategy, wherein the bidding strategy includes a bid price function that determines the bid price of an auction based on the outputs of the win-rate and KPI models, a worth-to-volume ratio of the auction, and a worth-to-volume ratio threshold that determines whether to submit a bid or abstain from bidding (Kim: [0010] – “identify a bidding price that has a highest winning probability among winning probabilities for respective bidding prices based on the first and second winning probability distributions obtained from the first and second neural network models… control the communication interface to transmit the identified bidding price to the external server”; [0158-0160] – “the processor 130 may obtain response probability data of a user for the advertisement area based on the user information included in the bidding request, input the auction-related data of the advertiser 10 and the response probability data of the user to the first neural network model 133 to obtain a first winning probability distribution…among a plurality of points included in the first winning probability distribution 410 and the second winning probability distribution 420 in FIG. 4, a point 425 of the highest probability value in the total range of the price may have a probability value of 0.75 at a price of 200 won (X2), and a point 430 of the highest probability value equal to or below the maximum payment amount of 150 won (13) may have a probability value of 0.4 at a price of 120 won (X3). In this case, the processor 130 may identify a 120 won, which is the price (X3) of the point 430 having the highest winning probability value, from among the first and second winning probability distributions obtained from the first and second neural network models 133 and 135, as a bidding price in the range within the maximum payment amount (13)”; [0165] and Fig. 9 – “UI 900 provided to the terminal device of the advertiser 10 may include various information such as an item for setting a bidding of the advertiser 10 and an item for notifying a bidding status. For example, the UI 900 may include… an item for setting a bidding price”; the examiner notes that, as seen in Fig. 9, the bidding price may be payment per click or payment per impression, interpreted to be a worth-to-volume ratio).
Kim further discloses a prediction pertaining to an analysis (Kim: [0102]), yet Kim does not explicitly disclose a prediction of a joint histogram.
However, Peretz teaches a similar auction system (Peretz: Col. 9, Ln. 15-20), including
a prediction of a joint histogram (Peretz: Col. 7, Ln. 1-10 and Fig. 2 – “a graph 200 of an example histogram that is a mathematical representation of a mapping of recorded winning bids for a day. Here, the graph 200 contains a vertical axis 202, a horizontal axis 204, a line 210 of values, and a polygon 212. The vertical axis 202 contains a scale for measuring the values for potential bids on a thousand impressions. The horizontal axis 204 indicates the total percent of advertisements won given a CPM bid for a day. The bids can be organized according to amount, such as from the lowest to the highest”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the histogram of Peretz in the system of Kim because Kim already discloses an analysis and Peretz is merely demonstrating how this analysis may occur. Additionally, it would have been obvious to have included a prediction of a joint histogram as taught by Peretz because histograms are well-known and the use of it in an auction setting would have improved bidding (Peretz: Col. 2, Ln. 28-34).
In regards to claim 2, Kim/Peretz teaches the method of claim 1. Kim further discloses wherein the worth-to-volume ratio of the auction is a ratio of the KPI to the bid price for the auction (Kim: [0165] and Fig. 9 – “UI 900 provided to the terminal device of the advertiser 10 may include various information such as an item for setting a bidding of the advertiser 10 and an item for notifying a bidding status. For example, the UI 900 may include… an item for setting a bidding price”; the examiner notes that, as seen in Fig. 9, the bidding price may be payment per click or payment per impression, interpreted to be a worth-to-volume ratio).
In regards to claim 3, Kim/Peretz teaches the method of claim 1. Kim further discloses wherein the search includes an outer process that searches over possible bid functions, and an inner process that evaluates a performance of each bid function (Kim: [0010] – “identify a bidding price that has a highest winning probability among winning probabilities for respective bidding prices based on the first and second winning probability distributions obtained from the first and second neural network models”; [0102] – “There may be a particular point 310 having the highest probability Y among the plurality of points included in the winning probability distribution of FIG. 3, which may indicate that the bidding price should be determined by the amount X of the particular point 310 in the corresponding bidding to maximize the advertising effect at the entire auctions (or remaining auctions)”; [0130] – “meaning that the reinforcement learning maximizes a target reward may mean maximizing the sum of the rewards according to each action performed at various times during the bidding period, instead of maximizing reward R.sub.t according to the single action A.sub.t the current time t”).
In regards to claim 4, Kim/Peretz teaches the method of claim 1. Kim further discloses wherein the win-rate model is developed by applying a machine learning algorithm to at least a portion of the historical auction data (Kim: [0089] – “the first neural network model 133 may be an artificial intelligence model trained to predict a winning probability distribution for a bidding price based on the advertisement history data of the advertiser 10. The first neural network model 133 may be trained by a reinforcement learning method”).
In regards to claim 5, Kim/Peretz teaches the method of claim 1. Kim further discloses wherein the KPI model is developed by applying a machine learning algorithm to at least a portion of the historical auction data (Kim [0008] – “obtain response probability data of a user based on user information included in the bidding request, the response probability data indicating a probability of the user, when exposed to the advertisement, responding to the advertisement posted in the advertisement area”; [0014] – user information included in the bidding request may include…whether the user clicks the advertisement… a preference of the user with respect to the advertisement area”; [0126] – “The learning data used for learning of the reinforcement learning method may include data related to each auction that was performed in the past and response probability data of the user 20”).
In regards to claim 6, Kim/Peretz teaches the method of claim 5. Kim further discloses wherein the KPI model id developed using a probability of conversion as the KPI (Kim [0008] – “obtain response probability data of a user based on user information included in the bidding request, the response probability data indicating a probability of the user, when exposed to the advertisement, responding to the advertisement posted in the advertisement area”; [0014] – user information included in the bidding request may include…whether the user clicks the advertisement… a preference of the user with respect to the advertisement area”; [0128] – “the current state S.sub.t may include the auction-related data of the advertiser 10 (e.g., the rate of remaining budget, the rate (or numbers) of remaining auctions, etc.) and the response probability data of the user 20 (e.g., predicted click through rate P.sub.ar, etc.) based on the time at which the corresponding auction was progressed (e.g., the ratio of the remaining budget, the rate of remaining auctions, etc.) and the response probability data (e.g., advertisement impression) of the user 20”; [0013] – “advertisement history data may include, based on the advertiser posting a previous advertisement in a previous advertisement area according to a result of a past bidding, information about a click rate indicating a number of clicks of the previous advertisement with respect to a number of impression of the previous advertisement”).
In regards to claim 7, Kim/Peretz teaches the method of claim 1. Kim further discloses wherein the inventory forecast is a prediction about the distribution of win-rate and KPI-model outputs that is expected to be observed on future auctions based on the historical auction data (Kim: [0130] – “meaning that the reinforcement learning maximizes a target reward may mean maximizing the sum of the rewards according to each action performed at various times during the bidding period, instead of maximizing reward R.sub.t according to the single action A.sub.t the current time t”; [0102] – “the winning probability distribution for a bidding price may refer to a function in which x axis represents a bidding price (or ratio of a bidding price) and an y axis represents probability (or winning probability), as shown in FIG. 3. The minimum price is 0 (e.g., 0 won, 0 dollar, etc.), and the maximum bidding price may correspond to the amount of the remaining budget of the advertiser 10. That is, if the amount of the remaining budget of the advertiser 10 is reduced due to the winning bid of the advertiser 10 as the auction proceeds, the maximum bidding price may also be reduced. There may be a particular point 310 having the highest probability Y among the plurality of points included in the winning probability distribution of FIG. 3, which may indicate that the bidding price should be determined by the amount X of the particular point 310 in the corresponding bidding to maximize the advertising effect at the entire auctions (or remaining auctions).”; [0129] – “The reward R.sub.t may indicate the probability data that the user 20 exposed to the advertisement content of the winner of the auction may respond”).
In regards to claim 8, Kim/Peretz teaches the method of claim 1. Kim further discloses wherein the inventory forecast is used to simulate the performance of parametrically defined bidding strategies, and a set of parameters that lead to a best simulated performance are selected (Kim: [0130] – “meaning that the reinforcement learning maximizes a target reward may mean maximizing the sum of the rewards according to each action performed at various times during the bidding period, instead of maximizing reward R.sub.t according to the single action A.sub.t the current time t”; [0102] – “the winning probability distribution for a bidding price may refer to a function in which x axis represents a bidding price (or ratio of a bidding price) and an y axis represents probability (or winning probability), as shown in FIG. 3. The minimum price is 0 (e.g., 0 won, 0 dollar, etc.), and the maximum bidding price may correspond to the amount of the remaining budget of the advertiser 10. That is, if the amount of the remaining budget of the advertiser 10 is reduced due to the winning bid of the advertiser 10 as the auction proceeds, the maximum bidding price may also be reduced. There may be a particular point 310 having the highest probability Y among the plurality of points included in the winning probability distribution of FIG. 3, which may indicate that the bidding price should be determined by the amount X of the particular point 310 in the corresponding bidding to maximize the advertising effect at the entire auctions (or remaining auctions).”; [0129] – “The reward R.sub.t may indicate the probability data that the user 20 exposed to the advertisement content of the winner of the auction may respond”).
In regards to claim 9, Kim/Peretz teaches the method of claim 8. Kim further discloses wherein a strategy is deployed using the selected parameters to determine a bid price and whether to submit a bid or not to submit a bid on an auction, in an online and substantially real-time fashion (Kim: [0010] – “identify a bidding price that has a highest winning probability among winning probabilities for respective bidding prices based on the first and second winning probability distributions obtained from the first and second neural network models… control the communication interface to transmit the identified bidding price to the external server”; [0158-0160] – “the processor 130 may obtain response probability data of a user for the advertisement area based on the user information included in the bidding request, input the auction-related data of the advertiser 10 and the response probability data of the user to the first neural network model 133 to obtain a first winning probability distribution…among a plurality of points included in the first winning probability distribution 410 and the second winning probability distribution 420 in FIG. 4, a point 425 of the highest probability value in the total range of the price may have a probability value of 0.75 at a price of 200 won (X2), and a point 430 of the highest probability value equal to or below the maximum payment amount of 150 won (13) may have a probability value of 0.4 at a price of 120 won (X3). In this case, the processor 130 may identify a 120 won, which is the price (X3) of the point 430 having the highest winning probability value, from among the first and second winning probability distributions obtained from the first and second neural network models 133 and 135, as a bidding price in the range within the maximum payment amount (13)”; [0165] and Fig. 9 – “UI 900 provided to the terminal device of the advertiser 10 may include various information such as an item for setting a bidding of the advertiser 10 and an item for notifying a bidding status. For example, the UI 900 may include… an item for setting a bidding price”).
In regards to claim 11, Kim/Peretz teaches the method of claim 1. Kim further discloses wherein the specific KPI is a combination of more than a single KPI, and the KPI model is used to generate a value of winning an auction for each of the KPIs in the combination (Kim [0008] – “obtain response probability data of a user based on user information included in the bidding request, the response probability data indicating a probability of the user, when exposed to the advertisement, responding to the advertisement posted in the advertisement area”; [0014] – user information included in the bidding request may include…whether the user clicks the advertisement… a preference of the user with respect to the advertisement area”; [0128] – “the current state S.sub.t may include the auction-related data of the advertiser 10 (e.g., the rate of remaining budget, the rate (or numbers) of remaining auctions, etc.) and the response probability data of the user 20 (e.g., predicted click through rate P.sub.ar, etc.) based on the time at which the corresponding auction was progressed (e.g., the ratio of the remaining budget, the rate of remaining auctions, etc.) and the response probability data (e.g., advertisement impression) of the user 20”; [0013] – “advertisement history data may include, based on the advertiser posting a previous advertisement in a previous advertisement area according to a result of a past bidding, information about a click rate indicating a number of clicks of the previous advertisement with respect to a number of impression of the previous advertisement”).
In regards to claim 13, claim 13 is directed to a system. Claim 13 recites limitations that are substantially parallel in nature to those addressed above for claim 1 which is directed towards a method. Kim/Peretz teaches the limitations of claim 1 as noted above. Kim further discloses a system, comprising: one or more electronic processors configured to execute a set of computer-executable instructions; and one or more non-transitory electronic data storage media containing the set of computer-executable instructions, wherein when executed, the instructions cause the one or more electronic processors to (Kim: [0172-0173]). Claim 13 is therefore rejected for the reasons set forth above in claim 1 and in this paragraph.
In regards to claim 14, all the limitations in system claim 14 are closely parallel to the limitations of method claim 2 analyzed above and rejected on the same bases.
In regards to claim 15, Kim/Peretz teaches the system of claim 13. Kim further discloses wherein the search includes an outer process that searches over possible bid functions and an inner process that evaluates a performance of each bid function, the win-rate model is developed by applying a machine learning algorithm to at least a portion of the historical auction data, the KPI model is developed by applying a machine learning algorithm to at least a portion of the historical auction data, and the KPI model is developed using a probability of conversion as the KPI (Kim [0008] – “obtain response probability data of a user based on user information included in the bidding request, the response probability data indicating a probability of the user, when exposed to the advertisement, responding to the advertisement posted in the advertisement area”; [0014] – user information included in the bidding request may include…whether the user clicks the advertisement… a preference of the user with respect to the advertisement area”; [0128] – “the current state S.sub.t may include the auction-related data of the advertiser 10 (e.g., the rate of remaining budget, the rate (or numbers) of remaining auctions, etc.) and the response probability data of the user 20 (e.g., predicted click through rate P.sub.ar, etc.) based on the time at which the corresponding auction was progressed (e.g., the ratio of the remaining budget, the rate of remaining auctions, etc.) and the response probability data (e.g., advertisement impression) of the user 20”; [0013] – “advertisement history data may include, based on the advertiser posting a previous advertisement in a previous advertisement area according to a result of a past bidding, information about a click rate indicating a number of clicks of the previous advertisement with respect to a number of impression of the previous advertisement”; [0102] – “the winning probability distribution for a bidding price may refer to a function in which x axis represents a bidding price (or ratio of a bidding price) and an y axis represents probability (or winning probability), as shown in FIG. 3. The minimum price is 0 (e.g., 0 won, 0 dollar, etc.), and the maximum bidding price may correspond to the amount of the remaining budget of the advertiser 10. That is, if the amount of the remaining budget of the advertiser 10 is reduced due to the winning bid of the advertiser 10 as the auction proceeds, the maximum bidding price may also be reduced. There may be a particular point 310 having the highest probability Y among the plurality of points included in the winning probability distribution of FIG. 3, which may indicate that the bidding price should be determined by the amount X of the particular point 310 in the corresponding bidding to maximize the advertising effect at the entire auctions (or remaining auctions).”; [0129] – “The reward R.sub.t may indicate the probability data that the user 20 exposed to the advertisement content of the winner of the auction may respond”; [0010] – “identify a bidding price that has a highest winning probability among winning probabilities for respective bidding prices based on the first and second winning probability distributions obtained from the first and second neural network models”; [0102] – “There may be a particular point 310 having the highest probability Y among the plurality of points included in the winning probability distribution of FIG. 3, which may indicate that the bidding price should be determined by the amount X of the particular point 310 in the corresponding bidding to maximize the advertising effect at the entire auctions (or remaining auctions)”; [0130] – “meaning that the reinforcement learning maximizes a target reward may mean maximizing the sum of the rewards according to each action performed at various times during the bidding period, instead of maximizing reward R.sub.t according to the single action A.sub.t the current time t”; [0089] – “the first neural network model 133 may be an artificial intelligence model trained to predict a winning probability distribution for a bidding price based on the advertisement history data of the advertiser 10. The first neural network model 133 may be trained by a reinforcement learning method”).).
In regards to claim 17, claim 17 is directed to a medium. Claim 17 recites limitations that are substantially parallel in nature to those addressed above for claim 1 which is directed towards a method. Kim/Peretz teaches the limitations of claim 1 as noted above. Kim further discloses one or more non-transitory computer-readable media comprising a set of computer-executable instructions that when executed by one or more programmed electronic processors, cause the processors to (Kim: [0172-0173]). Claim 17 is therefore rejected for the reasons set forth above in claim 1 and in this paragraph.
In regards to claims 18-19, all the limitations in medium claims 18-19 are closely parallel to the limitations of method claim 2 and system claim 15, respectively, analyzed above and rejected on the same bases.
Claims 10, 16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kim, in view of Peretz, in view of Hagen (US 7318008 B2), hereinafter Hagen.
In regards to claim 10, Kim/Peretz teaches the method of claim 1. Kim further discloses wherein the win-rate model based on historical auction expresses a probability of winning an auction as a function of the bid-price (Kim: [0102] – “the winning probability distribution for a bidding price may refer to a function in which x axis represents a bidding price (or ratio of a bidding price) and an y axis represents probability (or winning probability), as shown in FIG. 3. The minimum price is 0 (e.g., 0 won, 0 dollar, etc.), and the maximum bidding price may correspond to the amount of the remaining budget of the advertiser 10. That is, if the amount of the remaining budget of the advertiser 10 is reduced due to the winning bid of the advertiser 10 as the auction proceeds, the maximum bidding price may also be reduced. There may be a particular point 310 having the highest probability Y among the plurality of points included in the winning probability distribution of FIG. 3, which may indicate that the bidding price should be determined by the amount X of the particular point 310 in the corresponding bidding to maximize the advertising effect at the entire auctions (or remaining auctions)”).
Yet Kim does not explicitly disclose expression as a Weibull distribution parameterized by k and lambda (A), where k represents a shape parameter and A represents a scale parameter of the distribution.
However, Hagen teaches a similar price estimation method (Hagen: [abstract]), including
expression as a Weibull distribution parameterized by k and lambda (A), where k represents a shape parameter and A represents a scale parameter of the distribution (Hagen: Col. 7, Ln. 28-40 – “the life data is Weibull life data and the failure distribution analysis is based on a Weibull model. The Weibull life data can include beta, eta and gamma, which can be input under columns 122, 124 and 126 of spare parts list 100 into spare part rows 110, 112, 114, 116, 118 and 120, wherein each row corresponds to a specific spare part population. Any or all of the Weibull life data can be used in the failure distribution analysis, which produces a failure distribution for each spare part population as a function of time. Beta refers to a shape parameter for defining the shape of the distribution. Eta is the scale parameter for defining where the bulk of the distribution lies”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the Weibull distribution of Hagen in the method of Kim/Peretz because Kim/Peretz already discloses an analysis and Hagen is merely demonstrating how this analysis may occur. Additionally, it would have been obvious to have included expression as a Weibull distribution parameterized by k and lambda (A), where k represents a shape parameter and A represents a scale parameter of the distribution as taught by Hagen because Weibull distributions are well-known and the use of it in an auction setting would have improved accuracy of estimates (Hagen: Col. 1, Ln. 45-46).
In regards to claim 16, all the limitations in system claim 16 are closely parallel to the limitations of method claim 10 analyzed above and rejected on the same bases.
In regards to claim 20, all the limitations in medium claim 20 are closely parallel to the limitations of method claim 10 analyzed above and rejected on the same bases.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Kim, in view of Peretz, in view of Yan et al. (US 20190303980 A1), hereinafter Yan.
In regards to claim 12, Kim/Peretz teaches the method of claim 1. Kim further discloses that the models are the win-rate model and KPI model (Kim: [0008] – “predict a first winning probability distribution for a bidding price based on advertisement history data of an advertiser; [0008] – “obtain response probability data of a user based on user information included in the bidding request, the response probability data indicating a probability of the user, when exposed to the advertisement, responding to the advertisement posted in the advertisement area”; [0014] – user information included in the bidding request may include…whether the user clicks the advertisement… a preference of the user with respect to the advertisement area”).
Yet Kim does not explicitly disclose monitoring performance of one or more of the model and model, wherein if the performance is acceptable the determined strategy is deployed, and wherein if the performance is not acceptable, then control is passed to a process or element that is configured and operates to control the retraining of one or more of the models or to control the generation of an updated inventory forecast.
However, Yan teaches a similar auction method (Yan: [0046]), including
monitoring performance of one or more of the model and model, wherein if the performance is acceptable the determined strategy is deployed, and wherein if the performance is not acceptable, then control is passed to a process or element that is configured and operates to control the retraining of one or more of the models or to control the generation of an updated inventory forecast (Yan: [0110] – “ensemble performance modeling system 102 can determine whether the parent performance learning model needs to be re-trained based on several factors….the ensemble performance modeling system 102 can determine that the parent performance learning model needs to be re-trained in response to determining that there has been a statistically significant change in the parent-level performance metric (e.g., indicating a spike or dip in performance associated with the parent bidding parameter and/or its child bidding parameters)”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the retraining of Yan in the method of Kim/Peretz because Kim/Peretz already discloses machine learning and Yan is merely demonstrating that these models may be retrained. Additionally, it would have been obvious to have included monitoring performance of one or more of the model and model, wherein if the performance is acceptable the determined strategy is deployed, and wherein if the performance is not acceptable, then control is passed to a process or element that is configured and operates to control the retraining of one or more of the models or to control the generation of an updated inventory forecast as taught by Yan because retraining is well-known and the use of it in an auction setting would have improved efficiency, stability, and flexibility (Yan: [0005]).
Conclusion
NPL Reference U teaches a system of ad bidding using machine learning. The long term expected reward is maximized. KPIs are tracked and the results of an auction are predicted.
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/ANNA MAE MITROS/Examiner, Art Unit 3689