DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment filed on August 20, 2025 cancelled claims 3-4 and 10-11. Claims 1, 6, 9, 12-14, and 17 were amended an no new claims were added. Thus, the currently pending claims addressed below are claims 1-2, 5-9, and 12-17.
Claim Objections
The amendment filed on August 20, 2025 has corrected the issues of claims 1-13 and 17 which were objected to in the Office Action dated July 14, 2025. Thus, the objections are hereby withdrawn.
The amendment filed on August 20, 2025 has corrected the issues of claims 14-17 which were objected to in the Office Action dated July 14, 2025. Thus, the objections are hereby withdrawn.
Double Patenting
The Terminal Disclaimer filed on February 6, 2026 has overcome the nonstatutory double patenting rejections of claims 1-17 which were detailed in the Office Action dated July 14, 2025. Thus, the rejections are hereby withdrawn.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
The amendments filed on August 20, 2025 have corrected the 35 U.S.C. 112(b) issues of claims 6-8 which were detailed in the Office Action dated July 14, 2025. Thus, the rejections are hereby withdrawn.
The amendments filed on August 20, 2025 have corrected the 35 U.S.C. 112(b) issues of claim 17 which were detailed in the Office Action dated July 14, 2025. Thus, the rejections are hereby withdrawn.
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 1-2, 5-9 and 12-13 are directed to an apparatus and a method which would be classified under one of the listed statutory classifications (i.e., 2019 Revised Patent Subject Matter Eligibility Guidance (hereinafter “PEG”) “PEG” Step 1=Yes).
However, claims 1-2, 5-9 and 12-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim(s) 1, 9, and 12 recite(s) the following abstract idea:
receive, by an estimator algorithm, historical data on the winning bids of previous auctions and also the losing bids of previous auctions;
create a set of training data from the historical data on the winning bids and the losing bids, to calculate an overall loss function based on the set and to calculate first and second order derivatives of the overall loss function to generate a win price model;
estimate, by the estimator algorithm, the likely win price for a future auction;
receive data on a maximum bid for winning a future auction and the estimated win price from the estimator algorithm;
receive a profit margin goal which is used to adjust the estimated win price to ensure that auctions are only won with a sufficient profit margin;
determine whether to enter the future auction;
cause the entering of the future auction only when a budget is not less than the estimated win price.
The limitations as detailed above, as drafted, falls within the “Certain Method of Organizing Human Activity” grouping of abstract ideas namely commercial or legal interactions because they recite advertising, marketing and sales activities or behaviors. Accordingly, the claim recites an abstract idea (i.e. “PEG” Revised Step 2A Prong One=Yes).
This judicial exception is not integrated into a practical application because the claim only recites the additional elements of:
a computer/hardware resource executing software (e.g., bid determinator and estimator/machine learning estimator), and
a gradient boosting machine learning model.
The following limitations, if removed from the abstract idea and considered additional elements, merely perform generic computer function of processing, storing, communicating (e.g., transmitting and receiving), and displaying data and, as such, are insignificant extra-solution activities (see MPEP 2016.05(d)(II) and MPEP 2106.05(g)):
receive historical data on the winning bids of previous auctions and also the losing bids of previous auctions (receiving data);
receive data on a maximum bid for winning a future auction and the estimated win price from the estimator algorithm (receiving data);
receive a profit margin goal which is used to adjust the estimated win price to ensure that auctions are only won with a sufficient profit margin (receiving data); and
cause the entering of the future auction only when the budget is not less than the estimated win price (broad enough to be considered transmitting a bid if..).
The additional technical elements above are recited at a high-level of generality (i.e. as a generic processor performing a generic computer function of processing, communicating and displaying) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional technical elements above do not integrate the abstract idea/judicial exception into a practical application because it does not impose any meaningful limits on practicing the abstract idea. More specifically, the additional elements fail to include (1) improvements to the functioning of a computer or to any other technology or technical field (see MPEP 2106.05(a)), (2) applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition (see Vanda memo), (3) applying the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)), (4) effecting a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05(c)), or (5) applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (see MPEP 2106.05(e) and Vanda memo).
Rather, the limitations merely add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), or generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)).
Thus, the claim is “directed to” an abstract idea (i.e. “PEG” Revised Step 2A Prong Two=Yes)
When considering Step 2B of the Alice/Mayo test, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims do not amount to significantly more than the abstract idea.
More specifically, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a computer/hardware resource executing software and a gradient boosting machine learning model to perform the claimed functions amounts to no more than mere instructions to apply the exception using a generic computer component.
“Generic computer implementation” is insufficient to transform a patent-ineligible abstract idea into a patent-eligible invention (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Alice, 134 S. Ct. at 2352, 2357) and more generally, “simply appending conventional steps specified at a high level of generality” to an abstract idea does not make that idea patentable (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Mayo, 132 S. Ct. at 1300). Moreover, “the use of generic computer elements like a microprocessor or user interface do not alone transform an otherwise abstract idea into patent-eligible subject matter (See FairWarning, 120 U.S.P.Q.2d. 1293, citing DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1256 (Fed. Cir. 2014)). As such, the additional elements of the claim do not add a meaningful limitation to the abstract idea because they would be generic computer functions in any computer implementation. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of the computer or improves any other technology. Their collective functions merely provide generic computer implementation.
The Examiner notes simply implementing an abstract concept on a computer, without meaningful limitations to that concept, does not transform a patent-ineligible claim into a patent-eligible one (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Bancorp, 687 F.3d at 1280), limiting the application of an abstract idea to one field of use does not necessarily guard against preempting all uses of the abstract idea (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Bilski, 130 S. Ct. at 3231), and further the prohibition against patenting an abstract principle “cannot be circumvented by attempting to limit the use of the [principle] to a particular technological environment” (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Flook, 437 U.S. at 584), and finally merely limiting the field of use of the abstract idea to a particular existing technological environment does not render the claims any less abstract (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Alice, 134 S. Ct. at 2358; Mayo, 132 S. Ct. at 1294; Bilski v. Kappos, 561 U.S. 593, 612 (2010); Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat’l Ass’n, 776 F.3d 1343, 1348 (Fed. Cir. 2014); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014).
Applicant herein only requires a general purpose computer (as evidenced from figures 1, and 1a and page 1, lines 14-17 which demonstrate that the computer/hardware resource is no more specific than a general purpose computer, and Natekin et al., Gradient boosting machines, a tutorial, December 4, 2013, Frontiers in Neurorobotics, pages 1-21 which disclose in at least the abstract, page 2, column 1, lines 41-45, and page 4, column 1, lines 1-11 that gradient boosting machine learning models were old and well known by at least 2013); therefore, there does not appear to be any alteration or modification to the generic activities indicated, and they are also therefore recognized as insignificant activity with respect to eligibility. Finally, the following limitations, if removed from the abstract idea and considered additional elements, would be considered insignificant extra solution activity as they are directed to merely receiving, displaying, storing, and/or transmitting data (see MPEP 2016.05(d)(II) and MPEP 2106.05(g)):
receive historical data on the winning bids of previous auctions and also the losing bids of previous auctions (receiving data);
receive data on a maximum bid for winning a future auction and the estimated win price from the estimator algorithm (receiving data);
receive a profit margin goal which is used to adjust the estimated win price to ensure that auctions are only won with a sufficient profit margin (receiving daa); and
cause the entering of the future auction only when the budget is not less than the estimated win price (broad enough to be considered transmitting a bid if..).
Thus, taken individually and in combination, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea) (i.e. “PEG” Step 2B=No).
The dependent claims 2, 5-8; and 13 appear to merely further limit the abstract idea by adding an additional step of receiving a profit margin goal which is used to adjust the estimated win price which is considered part of the abstract idea (Claim 2); adding the additional step of generalizing the win price model which is considered part of the abstract idea and further limiting the additional element of a machine learning model to be a gradient boost machine learning model which has already been addressed above (Claims 5 and 13); adding the additional steps of tuning the win price model, calculating the win price model, creating an updated training data set, and calculating an updated win price model which are all considered part of the abstract idea (Claims 6-8), and therefore only further limit the abstract idea (i.e. “PEG” Revised Step 2A Prong One=Yes), does/do not include any new additional elements that are sufficient to amount to significantly more than the judicial exception, and as such are “directed to” said abstract idea (i.e. “PEG” Step 2A Prong Two=Yes); and do not add significantly more than the idea (i.e. “PEG” Step 2B=No)..
Thus, based on the detailed analysis above, claims 1-2, 5-9 and 12-13 are not patent eligible.
Claims 14-17 are directed to a method and a computer program product which would be classified under one of the listed statutory classifications (i.e., 2019 Revised Patent Subject Matter Eligibility Guidance (hereinafter “PEG”) “PEG” Step 1=Yes).
However, claims 14-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) 14 and 17 recite(s) the following abstract idea:
receiving historical data on the winning bids of previous auctions and also the losing bids of previous auctions and estimating the likely win price for a future auction;
receiving data on a maximum bid for winning a future auction and the estimated win price from an estimator algorithm;
receiving a profit margin goal which is used to adjust the estimated win price to ensure that auctions are only won with a sufficient profit margin;
determining whether to enter the future auction;
entering the future auction only when the budget is not less than the estimated win price;
creating a set of training data from the historical data on the winning bids and the losing bids;
calculating an overall loss function based on the set, a likelihood function of historical wins, and a likelihood function of historical of losses, wherein the likelihood function of historical wins is calculated and the likelihood function of historical losses is calculated;
calculating first and second order derivatives of the overall loss function to generate a win price model;
using the data that is most recent to create an updated training data set;
tuning the win price model using a number M of boosting iterations of training;
calculating the win price model;
creating the updated training data set;
calculating the updated win price model;
using the updated training data set to generate an updated win price model;
calculating a likelihood function of historical wins and a likelihood function of historical losses and combining these functions to calculate the overall loss function,
wherein the step of creating the set of training data from the historical data on the winning bids and the losing bids is performed after the step of receiving the historical data;
calculating an overall loss function based on the set, and
calculating first and second order derivatives of the overall loss function to generate a win price model.
The limitations as detailed above, as drafted, falls within the “Certain Method of Organizing Human Activity” grouping of abstract ideas namely commercial or legal interactions because they recite advertising, marketing and sales activities or behaviors. Accordingly, the claim recites an abstract idea (i.e. “PEG” Revised Step 2A Prong One=Yes).
This judicial exception is not integrated into a practical application because the claim only recites the additional elements of:
a computer/computer hardware resource with a memory storing instructions (e.g., an estimator) (Examiner note: A requirement that the win price model be a machine learning model or a gradient boost machine learning model is not recited in any of claims 14-17. As such, the examiner has not included a gradient boost machine learning model as an “additional element” in claims 14-17. This results in the only “additional element” recited in claims 14-17 being a computer/computer hardware resource with a memory storing instructions. If the applicant somehow believes that the win price model inherently is a gradient boost machine learning model, said gradient boost machine learning model would not have overcome the rejection for the same reasons detailed above with regards to the 35 USC 101 of claims 1-2, 5-9 and 12-13).
The following limitations, if removed from the abstract idea and considered additional elements, merely perform generic computer function of processing, storing, communicating (e.g., transmitting and receiving), and displaying data and, as such, are insignificant extra-solution activities (see MPEP 2016.05(d)(II) and MPEP 2106.05(g)):
receiving historical data on the winning bids of previous auctions and also the losing bids of previous auctions and estimating the likely win price for a future auction (receiving data);
receiving data on a maximum bid for winning a future auction and the estimated win price from an estimator (receiving data);
receiving a profit margin goal which is used to adjust the estimated win price to ensure that auctions are only won with a sufficient profit margin (receiving data); and
entering the future auction only when the budget is not less than the estimated win price (broad enough to be considered transmitting a bid if…).
The additional technical elements above are recited at a high-level of generality (i.e. as a generic processor performing a generic computer function of processing, communicating and displaying) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional technical elements above do not integrate the abstract idea/judicial exception into a practical application because it does not impose any meaningful limits on practicing the abstract idea. More specifically, the additional elements fail to include (1) improvements to the functioning of a computer or to any other technology or technical field (see MPEP 2106.05(a)), (2) applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition (see Vanda memo), (3) applying the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)), (4) effecting a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05(c)), or (5) applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (see MPEP 2106.05(e) and Vanda memo).
Rather, the limitations merely add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), or generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)).
Thus, the claim is “directed to” an abstract idea (i.e. “PEG” Revised Step 2A Prong Two=Yes)
When considering Step 2B of the Alice/Mayo test, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims do not amount to significantly more than the abstract idea.
More specifically, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a computer/computer hardware resource and a memory storing instructions to perform the claimed functions amounts to no more than mere instructions to apply the exception using a generic computer component.
“Generic computer implementation” is insufficient to transform a patent-ineligible abstract idea into a patent-eligible invention (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Alice, 134 S. Ct. at 2352, 2357) and more generally, “simply appending conventional steps specified at a high level of generality” to an abstract idea does not make that idea patentable (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Mayo, 132 S. Ct. at 1300). Moreover, “the use of generic computer elements like a microprocessor or user interface do not alone transform an otherwise abstract idea into patent-eligible subject matter (See FairWarning, 120 U.S.P.Q.2d. 1293, citing DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1256 (Fed. Cir. 2014)). As such, the additional elements of the claim do not add a meaningful limitation to the abstract idea because they would be generic computer functions in any computer implementation. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of the computer or improves any other technology. Their collective functions merely provide generic computer implementation.
The Examiner notes simply implementing an abstract concept on a computer, without meaningful limitations to that concept, does not transform a patent-ineligible claim into a patent-eligible one (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Bancorp, 687 F.3d at 1280), limiting the application of an abstract idea to one field of use does not necessarily guard against preempting all uses of the abstract idea (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Bilski, 130 S. Ct. at 3231), and further the prohibition against patenting an abstract principle “cannot be circumvented by attempting to limit the use of the [principle] to a particular technological environment” (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Flook, 437 U.S. at 584), and finally merely limiting the field of use of the abstract idea to a particular existing technological environment does not render the claims any less abstract (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Alice, 134 S. Ct. at 2358; Mayo, 132 S. Ct. at 1294; Bilski v. Kappos, 561 U.S. 593, 612 (2010); Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat’l Ass’n, 776 F.3d 1343, 1348 (Fed. Cir. 2014); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014).
Applicant herein only requires a general-purpose computer (as evidenced from figures 1, and 1a and page 1, lines 14-17 which demonstrate that the computer/hardware resource with a memory storing instruction is no more specific than a general-purpose computer); therefore, there does not appear to be any alteration or modification to the generic activities indicated, and they are also therefore recognized as insignificant activity with respect to eligibility. Finally, the following limitations, if removed from the abstract idea and considered additional elements, would be considered insignificant extra solution activity as they are directed to merely receiving, displaying, storing, and/or transmitting data (see MPEP 2016.05(d)(II) and MPEP 2106.05(g)):
receiving historical data on the winning bids of previous auctions and also the losing bids of previous auctions and estimating the likely win price for a future auction (receiving data);
receiving data on a maximum bid for winning a future auction and the estimated win price from an estimator (receiving data);
receiving a profit margin goal which is used to adjust the estimated win price to ensure that auctions are only won with a sufficient profit margin (receiving data); and
entering the future auction only when the budget is not less than the estimated win price (broad enough to be considered transmitting a bid if…).
Thus, taken individually and in combination, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea) (i.e. “PEG” Step 2B=No).
The dependent claims 15-16 appear to merely further limit the abstract idea by adding an additional step of tuning a learning rate which is considered part of the abstract idea (Claim 15); and adding an early stopping step which is considered part of the abstract idea (Claim 16), and therefore only further limit the abstract idea (i.e. “PEG” Revised Step 2A Prong One=Yes), does/do not include any new additional elements that are sufficient to amount to significantly more than the judicial exception, and as such are “directed to” said abstract idea (i.e. “PEG” Step 2A Prong Two=Yes); and do not add significantly more than the idea (i.e. “PEG” Step 2B=No)..
Thus, based on the detailed analysis above, claims 14-17 are not patent eligible.
Possible Allowable Subject Matter
Claims 1-2, 5-9, and 12-17 would be allowable if the applicant were to be able to overcome the 35 USC 101 rejections above.
The following is a statement of reasons for the indication of allowable subject matter: Li et al. (PGPUB: 2012/0084142), Bowman et al. (PGPUB: 2011/0246289) and Saberian et al ("TaylorBoost: First and second-order boosting algorithms with explicit margin control," 2011, Conference on Computer Vision and Pattern Recognition (CVPR) 2011, pp. 2929-2934) discloses computer program products comprising instructions which, when the program is executed by a computer, cause the computer to carry out steps of: :
receiving historical data on the winning bids of previous auctions and also the losing bids of previous auctions and
estimating the likely win price for a future auction,
receiving data on a maximum bid for winning a future auction and the estimated win price from the estimator, and
causing the hardware resource to be employed in entering the future auction only when a budget is not less than the estimated win price,
creating a set of training data from the historical data on the winning bids and the losing bids,
calculating an overall loss function based on the set, and
calculating first and second order derivatives of the overall loss function to generate a win price model,
using the data that is most recent to create an updated training data set,
tuning the updated win price model using a number M of boosting iterations of training, calculating the win price model, creating the updated training data set, and calculating the updated win price model and
using the updated training data set to generate an updated win price model.
receiving a profit margin goal which is used to adjust the estimated win price to ensure that auctions are only won with a sufficient profit margin
However, the likelihood functions of Li, Bowman, and Saberian are based on average bid value for all segments in the user segment list; maximum bid value for all segments in the user segment list; average bid value for key segments in the user segments list; and bid value of the principal key segment in the user segment list.
As such Li, Bowman and Saberian do not disclose:
calculate a likelihood function of historical wins, calculate a likelihood function of historical losses, and combine these functions to create the overall loss function; and/or
calculating an overall loss function based on the set of training data from the historical data on the winning bids and the losing bids
Modifying Li, Bowman, and Saberian to use the historical data on the winning bids and the losing bids when calculating the overall loss function instead of the ones disclosed would result in a significantly different invention and would not have been obvious to one of ordinary skill in the art. As such, claims 1-2, 5-9, and 12-17 contains subject matter that would be allowable over the prior art, if the applicant were to be able to overcome the 35 USC 101 rejections above.
Response to Arguments
Applicant's arguments filed August 20, 2026 have been fully considered but they are not persuasive.
The applicant argues, with respect to step 2A, Prong 2 of the 35 USC 101 rejection, that the claims have been amended to recite more than one computing device, wherein each of the computing devices perform a significant step that results in an improvement that transforms the abstract idea into a practical application. The examiner disagrees. There is no indication in the applicant’s specification that the claimed bid determinator or the claimed estimator/machine learning estimator recited in claims 1 and 9, as well as, claims 14 and 17 recite the estimator but not the determinator, as currently amended, are required to be different physical devices. Instead, it appears based on the applicant’s disclosure that both the estimator and the determinator may merely be software executing on a DSP computer which is merely used to apply the abstract idea. According to figure 2 the determinator (20) and the estimator (24) are both within the DSP. Based on at least page 1, line 19 through page 2, line 9 the DSP is a computer. Additionally, originally filed claim 17 which is part of the applicant’s disclosure, as well as claim 17 as currently amended, is to a computer program product executing on a computer and the said computer program product, while not reciting the determinator, recites a program performing substantially the same steps as those performed in claims 1, 9 and/or 14 including the argued limitations performed by the claimed estimator and determinator. As such, the claimed bid determinator and the claimed estimator/machine learning estimator, based on the broadest reasonable interpretation of the terms, may be just software executing on the DSP computer which are merely being used as a tool to apply the abstract idea, which is insufficient to be considered an arrangement of devices capable of transforming the abstract idea into a practical application under Step 2a, Prong 2 and insufficient to be considered significantly more under step 2b. Instead, of reciting an arrangement of different computing devices each performing a significant step that results in an improvement that overcome the 35 USC 101 as recited in the claims of the BASCOM decision, the instant claim recite a single computer device executing software which is merely used as a tool to apply the abstract idea. As such, the instant claims have not been amended in the manner suggested by the examiner during the interview conducted on July 28, 2025. Any purported improvement obtained by practicing the claim of this instant application, as currently amended, is an improvement to an abstract idea which is merely being applied by a general-purpose computer with generic computer components. Improvements of this nature are improvement to an abstract idea which are improvements in ineligible subject matter (see SAP v. Investpic decision: Page 2, line 22 through Page 3, line 13 - Even assuming that the algorithms claimed are groundbreaking, innovative or even brilliant, the claims are ineligible because their innovation is an innovation in ineligible subject matter because they are nothing but a series of mathematical algorithms based on selected information and the presentation of the results of those algorithms. Thus, the advance lies entirely in the realm of abstract ideas, with no plausible alleged innovation in the non-abstract application realm. An advance of this nature is ineligible for patenting; and Page 10, lines 18-24 - Even if a process of collecting and analyzing information is limited to particular content, or a particular source, that limitations does not make the collection and analysis other than abstract.). As such, the applicant’s arguments are not convincing and the rejections have been maintained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Jordan et al. (PGPUB: 2014/0006171) discloses an advertiser may decide his/her bidding price based on prior behaviors of other participants, such as how much was the winning bid in a previous auction for similar online advertisement display. If the advertiser lost the previous auction, he/she may decide to raise his/her bid in the current auction more than the previous winning bid, or if doing so costs him/her too much, he/she may decide to quit the current auction.
Siegler (PGPUB: 2017/0091829) discloses determining a predicted winning bid amount by analyzing the ad impression request, the determined additional data, previous or historical auctions for similar or related ad slots, bidding trends from potential bidders, and the like to determine a predicted winning bid amount.
Wang et al. (PGPUB: 2018/0150886) discloses storing information about past auctions performed by each user or content provider, including identification of content providers that participated in an auction, candidate content items, a date and time the auction occurred, results of the auction, bids of auction participants, and any other data related to the auction; and using this information to determine a minimum value for a target user that a content provider needs to bid to win an auction. wherein the minimum value (also referred to as a “threshold value” or a “price floor”) is determined for each auction associated with each target user.
Watine et al. (PGPUB: 2018/0204250) discloses estimating bids in an advertisement bidding process according to a predicted attribution probability that is based on past auctions, the winning bids associated with them, and the corresponding attribution of those auctions.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN W VAN BRAMER whose telephone number is (571)272-8198. The examiner can normally be reached Monday-Thursday 5:30 am - 4 pm EST.
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/John Van Bramer/Primary Examiner, Art Unit 3622