DETAILED ACTION
Status of Claims
• The following is an office action in response to the communication filed 06/16/2026.
• Claims 1-20 have been amended.
• 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 Interpretation
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitations use a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: “success-probability model…” (Claims 1 and 13), “outcome-impact model…” (Claims 1 and 13), “process or element…” (Claim 12) with the functional language “configured to,” which are not preceded by a structural modifier.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. The claim limitations are interpreted to be software operating on hardware, consistent with Specification paragraphs [0083] and [0092-0094].
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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:
express a probability of obtaining an opportunity as a function of a controllable resource level;
express an expected impact of obtaining the opportunity on an objective;
generating a forecasted distribution of future opportunities over outputs;
executing a search process over candidate decision policies to identify a decision policy that optimizes the objective across the forecasted distribution; and
deploying the identified decision policy, wherein the decision policy determines, for a received opportunity, whether to act or abstain and, if acting, determines the controllable resource level based on the outputs and an objective-to-resource threshold.
The above limitations recite the concept of generating and deploying a decision 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 managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions). Specifically, the deployment of a decision policy based on a data analysis represents following rules or instructions. 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 1, 13, and 17 recite additional elements, such as a success-probability model, an outcome-impact model, 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. 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 1, 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 1, 13, and 17 merely recite a commonplace business method (i.e., deploying a decision making strategy) being applied on a general purpose computer. See MPEP 2106.05(f). Furthermore, claims 1, 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 1, 13 and 17 specifying that the abstract idea of deploying a decision making 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 1, 13 and 17, these claims recite additional elements, such as a success-probability model, an outcome-impact model, 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. 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 1, 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 1, 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 1, 13 and 17 specifying that the abstract idea of generating and using a decision making 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 18-20 further fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in the MPEP, in that they recite managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions). 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 previously cited Kim et al. (US 20220301017 A1), hereinafter Kim, in view of newly cited Lokhande et al. (US 20230410201 A1), hereinafter Lokhande.
In regards to claim 1, Kim discloses a method of generating a sequential decision policy for repeated resource-allocation opportunities (Kim: [0002]; [0015]; [0060]; [0102]):
accessing a success-probability model configured to express a probability of obtaining an opportunity as a function of a controllable resource level (Kim: [0008] – “the first neural network model being trained to predict a first winning probability distribution for a bidding price based on advertisement history data of an advertiser”; the examiner notes winning an auction is obtaining an opportunity and price is a controllable resource level);
accessing an outcome-impact model configured to express an expected impact of obtaining the opportunity on an objective (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”; the examiner notes predicted user response is an expected impact);
generating a forecasted future opportunities over outputs of the success- probability model and the outcome-impact model (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 a forecasted of future opportunities is interpreted to be a prediction about a set of auctions, consistent with Specification [0040]);
executing a search process over candidate decision policies to identify a decision policy that optimizes the objective across the forecasted (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
deploying the identified decision policy, wherein the decision policy determines, for a received opportunity, whether to act or abstain and, if acting, determines the controllable resource level based on the outputs of the success-probability and outcome-impact models and an objective-to-resource threshold (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, which must be within range of a maximum amount, may be payment per click or payment per impression, interpreted to be an objective to resource threshold).
Kim further discloses a forecasting pertaining to an analysis (Kim: [0102]), yet Kim does not explicitly disclose generating a forecasted distribution of future opportunities, where the forecasted is the forecasted distribution.
However, Lokhande teaches a similar bidding system (Lokhande: [abstract]), including
a forecasted distribution of future opportunities, where the forecasted is the forecasted distribution (Lokhande: [0005] – “estimating, via the one or more hardware processors, a two-dimensional (2D) distribution of price-volume for the plurality of delivery slots in the initialized optimization window based on the forecasted generation and demand”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the distribution of Lokhande in the system of Kim because Kim already discloses an analysis and Lokhande is merely demonstrating how this analysis may occur. Additionally, it would have been obvious to have included a forecasted distribution of future opportunities, where the forecasted is the forecasted distribution as taught by Lokhande because distributions are well-known and the use of it in an auction setting would have generated optimal bids (Lokhande: [0013]).
In regards to claim 2, Kim/Lokhande teaches the method of claim 1. Kim further discloses wherein the decision policy applies the objective-to-resource threshold to an objective-to-resource ratio for the received opportunity, and wherein the objective-to-resource ratio is based on the expected impact of obtaining the opportunity relative to the controllable resource level (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 the objective-to-resource ratio).
In regards to claim 3, Kim/Lokhande teaches the method of claim 1. Kim further discloses wherein the search process includes an outer process that searches over possible candidate decision policies, and an inner process that evaluates a performance of a candidate decision policy (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/Lokhande teaches the method of claim 1. Kim further discloses wherein the success probability model is developed by applying a machine learning algorithm to historical 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/Lokhande teaches the method of claim 1. Kim further discloses wherein the outcome impact model is developed by applying a machine learning algorithm to historical 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/Lokhande teaches the method of claim 5. Kim further discloses wherein the outcome impact model is developed using a probability, magnitude, or expected value of a downstream outcome associated with obtaining the opportunity (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/Lokhande teaches the method of claim 1. Kim further discloses wherein the forecasted is a prediction about the distribution of success probability and outcome impact model outputs that is expected to be observed for future opportunities based on historical opportunity data and the respective outputs of the success-probability and outcome-impact 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”).
Kim further discloses a forecasting pertaining to an analysis (Kim: [0102]), yet Kim does not explicitly disclose where the forecasted is the forecasted distribution.
However, Lokhande teaches a similar bidding system (Lokhande: [abstract]), including
where the forecasted is the forecasted distribution (Lokhande: [0005] – “estimating, via the one or more hardware processors, a two-dimensional (2D) distribution of price-volume for the plurality of delivery slots in the initialized optimization window based on the forecasted generation and demand”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed inventions to combine Lokhande with Kim for the reasons identified above with respect to claim 1.
In regards to claim 8, Kim/Lokhande teaches the method of claim 1. Kim further discloses wherein the forecasted is over the success probability and outcome impact models and is used to simulate the performance of parametrically defined decision policies, 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”).
Kim further discloses a forecasting pertaining to an analysis (Kim: [0102]), yet Kim does not explicitly disclose where the forecasted distribution is a joint histogram.
However, Lokhande teaches a similar bidding system (Lokhande: [abstract]), including
where the forecasted distribution is a joint histogram (Lokhande: [0005] – “estimating, via the one or more hardware processors, a two-dimensional (2D) distribution of price-volume for the plurality of delivery slots in the initialized optimization window based on the forecasted generation and demand”; [0119] – “This baseline helps the system and method of the present disclosure to understand the efficacy of modeling the market dynamics (through the joint price-volume histograms) in the optimal bid design”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed inventions to combine Lokhande with Kim for the reasons identified above with respect to claim 1.
In regards to claim 9, Kim/Lokhande teaches the method of claim 8. Kim further discloses wherein a strategy is deployed using the selected parameters to determine a resource level and whether to apply the resource level for an opportunity or not to apply the resource level, 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/Lokhande teaches the method of claim 1. Kim further discloses wherein the resource allocation opportunities comprise a situation where a system decides whether to act and how much resource to commit under uncertainty and resource constraints (Kim: [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”; [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”; [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/Lokhande 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/Lokhande teaches the system of claim 13. Kim further discloses wherein the search process includes an outer process that searches over possible candidate decision policies and an inner process that evaluates a performance of a candidate decision policy, the success probability model is developed by applying a machine learning algorithm to historical data, the outcome impact model is developed by applying a machine learning algorithm to historical data, and the outcome impact model is developed using a probability, magnitude, or expected value of a downstream outcome associated with obtaining the opportunity (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/Lokhande 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 Lokhande, in view of previously cited Hagen (US 7318008 B2), hereinafter Hagen.
In regards to claim 10, Kim/Lokhande teaches the method of claim 1. Kim further discloses wherein the success probability model based on historical data expresses a probability of success as a function of the resource level (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 (λ), where k represents a shape parameter and λ 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 (λ), where k represents a shape parameter and λ 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/Lokhande because Kim/Lokhande 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 (λ), where k represents a shape parameter and λ 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 Lokhande, in view of previously cited 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 success probability model and outcome impact 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 forecasted distribution.
However, Lokhande teaches a similar bidding system (Lokhande: [abstract]), including
where the forecasted is the forecasted distribution (Lokhande: [0005] – “estimating, via the one or more hardware processors, a two-dimensional (2D) distribution of price-volume for the plurality of delivery slots in the initialized optimization window based on the forecasted generation and demand”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed inventions to combine Lokhande with Kim for the reasons identified above with respect to claim 1.
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 forecasted (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 forecasted 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]).
Response to Arguments
Applicant’s arguments, filed 06/16/2026, have been fully considered.
35 U.S.C. § 101
Applicant argues the claims are patent eligible because the claims “are directed at providing a solution to a resource allocation problem…a specific computational approach for generating and deploying bidding strategies…a significant problem that arises in the field of automated actions” (Remarks pages 10-12). The examiner disagrees. The MPEP provides guidance on how to evaluate whether claims recite an improvement in the functioning of a computer or an improvement to other technology or technical field. For example, the MPEP states “the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement.” The MPEP further states that “[t]he specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art,” and that, “conversely, if the specification explicitly sets forth an improvement but in a conclusory manner…the examiner should not determine the claim improves technology” (see MPEP 2106.04). That is, the claim includes the components or steps of the invention that provide the improvement described in the specification. Looking to the specification is a standard that the courts have employed when analyzing claims as it relates to improvements in technology. For example, in Enfish, the specification provided teaching that the claimed invention achieves benefits over conventional databases, such as increased flexibility, faster search times, and smaller memory requirements. Enfish LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36 (Fed. Cir. 2016). Additionally, in Core Wireless the specification noted deficiencies in prior art interfaces relating to efficient functioning of the computer. Core Wireless Licensing v. LG Elecs. Inc., 880 F.3d 1356 (Fed Cir. 2018). With respect to McRO, the claimed improvement, as confirmed by the originally filed specification, was “…allowing computers to produce ‘accurate and realistic lip synchronization and facial expressions in animated characters…’” and it was “…the incorporation of the claimed rules, not the use of the computer, that “improved [the] existing technological process” by allowing the automation of further tasks”. McRO, Inc. v. Bandai Namco Games America Inc., 837 F.3d 1299, (Fed. Cir. 2016).
While the examiner acknowledges that improvements to the functioning of a computer or to any other technology or technical field may constitute integration into a practical application (see MPEP 2106.05(a)), the instant claims do not provide a technical improvement. Rather, the claims provide an improvement to the abstract idea of determining a decision making strategy. While the Examiner acknowledges Applicant’s arguments regarding the strategy, the Examiner notes that determining a strategy is merely activity encompassed by the abstract idea and does not represent a technical improvement but an improvement to the abstract idea.
Although the claims include computer technology such as a success-probability model, an outcome-impact model, 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, such elements are merely peripherally incorporated in order to implement the abstract idea. Put another way, these additional elements are merely used to apply the abstract idea of determining a decision making strategy in a technological environment without effectuating any improvement or change to the functioning of the additional elements or other technology. This is unlike the improvements recognized by the courts in cases such as Enfish, Core Wireless, and McRO. Unlike precedential cases, neither the specification nor the claims of the instant invention identify such a specific improvement to computer capabilities. The instant claims are not directed to technological improvements but are directed to improving the business method of determining a strategy. The claimed process, while arguably resulting in a better process for determining a strategy, is not providing any improvement to another technology or technical field as the claimed process is not, for example, improving the server and/or computer components that operate the system. Rather, the claimed process is utilizing data sets related to determinations while still employing the same server and/or computer components used in conventional systems to improve determining a strategy, e.g. a business method, and therefore is merely applying the abstract idea using generic computing components. As such, the claims are not integrated into practical application.
Applicant argues the claims are patent eligible because the claims do not preempt the abstract idea (Remarks page 11). The examiner disagrees. The examiner notes that questions of preemption are inherent in the two-part framework from Alice Corp. and Mayo (incorporated in the MPEP 2106), and are resolved by using this framework to distinguish between preemptive claims, and “those that integrate the building blocks into something more...the latter pose no comparable risk of pre-emption, and therefore remain eligible.” It should be kept in mind, however, that while a preemptive claim may be ineligible, the absence of complete preemption does not guarantee that a claim is eligible (see MPEP 2106.04). Therefore, while preemption may signal patent ineligible subject matter, the absence of complete preemption does not demonstrate patent eligibility. Where a patent's claims are deemed only to disclose patent ineligible subject matter under the Alice/Mayo framework, as they are in this case, preemption concerns are fully addressed and made moot (see MPEP 2106.04).
Applicant argues that the claims do not recite an abstract idea because the claims are not directed to Certain Methods of Organizing Human Activity or Mental Processes and “the claimed steps or operations are such that it would not be feasible or practical for a person to perform them in their mind”. Remarks pages 10-15. The examiner disagrees. As shown in the rejection of claims under 35 U.S.C. 101 above, the limitations directed to the abstract idea are directly quoted and concepts within the identified as belonging to the Certain Methods of Organizing Human Activity and Mental Processes groupings of abstract ideas. With respect to the instant claims, a success-probability model, an outcome-impact model, 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 have been analyzed as additional elements and accordingly are not analyzed under Step 2A, Prong 1. The claims further recite determining a decision policy using output models and an analysis. These claims fall into the Methods of Organizing Human Activity grouping, which includes activity that falls within the enumerated sub-grouping of managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions). Specifically, the deployment of a decision policy based on a data analysis represents following rules or instructions.. These claims further fall within the Mental Processes grouping of abstract ideas. Specifically, expressing a probability and expected outcome, generating a forecasted distribution, executing a search, and deploying a decision policy are observations, evaluations, and judgements that can be performed in the human mind or by a human using pen and paper. Accordingly, these claims recite an abstract idea.
35 U.S.C. § 103
Applicant argues the claims are allowable because the Kim/Peretz does not teach or disclose “a success-probability model configured to express a probability of obtaining an opportunity as a function of a controllable resource level” (Remarks page 15). The examiner disagrees. Kim discloses this limitation in this claim. Kim discloses in [0008] a first neural network model being trained to predict a first winning probability distribution for a bidding price based on advertisement history data of an advertiser. It is noted that winning an auction is obtaining an opportunity and price is a controllable resource level. Thus, Kim discloses this limitation.
Applicant argues the claims are allowable because the Kim/Peretz does not teach or disclose “an outcome-impact model configured to express an expected impact of obtaining the opportunity on an objective” (Remarks page 15). The examiner disagrees. Kim discloses this limitation. Kim discloses in [0008] and [0014] that response probability data of a user is determined based on user information such as whether the user clicks the advertisement and a preference of the user with respect to the advertisement area. Where the response probability data indicates a probability of the user, when exposed to the advertisement, responding to the advertisement posted in the advertisement area. These outcomes with respect to user actions are interpreted to be an expected impact, and they are the expected impact of winning an advertising bid (see also [0128]). Thus, Kim discloses this limitation in this claim.
Applicant argues the claims are allowable because the Kim/Peretz does not teach or disclose “a forecasted distribution of future opportunities over outputs of the success- probability model and the outcome-impact model” (Remarks page 16). The examiner disagrees. The amendments have necessitated a new grounds of rejection and a new reference has been cited to teach a forecasted distribution. Furthermore, Kim discloses generating a forecasted future opportunities over outputs of the success- probability model and the outcome-impact model. Kim discloses in [0130] maximizing a target reward by 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. Kim additionally discloses in [0102] that 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). 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 is reduced due to the winning bid of the advertiser as the auction proceeds, the maximum bidding price may also be reduced. There may be a particular point having the highest probability Y among the plurality of points included in the winning probability distribution, which may indicate that the bidding price should be determined by the amount X of the particular point in the corresponding bidding to maximize the advertising effect at the entire auctions (or remaining auctions), where see at least [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. It is noted that a forecasted of future opportunities is interpreted to be a prediction about a set of auctions, consistent with Specification [0040]. Thus, the cited art teaches this limitation.
Applicant argues the claims are allowable because the Kim/Peretz does not teach or disclose “executing a search process over candidate decision policies to identify a decision policy that optimizes the objective across the forecasted distribution” (Remarks page 17). The examiner disagrees. Kim discloses this limitation in [0010] and [0102], disclosing identifying 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. Where there may be a particular point having the highest probability Y among the plurality of points included in the winning probability distribution, which may indicate that the bidding price should be determined by the amount X of the particular point in the corresponding bidding to maximize the advertising effect at the entire auctions (or remaining auctions). Thus, Kim discloses this limitation in this claim.
Conclusion
NPL Reference U, initially cited in the Office action dated 03/16/2026, 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.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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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/ANNA MAE MITROS/Examiner, Art Unit 3689
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