DETAILED ACTION
This is in reference to communication received 18 February 2026. Cancellation of claims 4 – 5, 12 – 13 and 18 – 19 is acknowledged. Claims 1 – 3, 6 – 11, 14 – 17 and 20 are pending for examination. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 – 3, 6 – 11, 14 – 17 and 20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Independent claim 9, representative of claims 1 and 15, in part is directed toward a statutory category of invention, the claim appears to be directed toward a judicial exception namely an abstract idea. Claim 9 recites invention directed to
predicting a minimum winning price in a real time bidding environment. subsequent to receiving an auction attribute information associated with a current real time auction, based on historical bidding results, a minimum winning price bid is determined and used for making a bid as a response. When information is received that the placed bid was a winning bid or the bid was not the winning bid, procedure for determining future bids amount is updated accordingly.
These limitations describe marketing/sales/advertising activities. Acquiring historical auction information of different auctions, generating minimum bid amount as a response to received auction attribute information and placing a bid in the auction as a response, subsequent to which, when information is received whether the placed bid was a winning bid, results are logged/appended to the historical auction information for determining bid values for future auction opportunities, as claimed would be part of marketing research. Causing placing of a bid based on historical auction results would be the marketing team (or person) determining a bid amount and placing a bid to the received opportunity to target received associated auction attributes to promote their product and services.
In addition, a neural-network (e.g., artificial-intelligence) models, which are trained on historical auction results, are used to determine a minimum winning price bid based upon received auction attributes and provide the determined bid amount from placing a bid for the received attribute. When information is received that the placed bid was a winning bid or the bid was not the winning bid, said model is retrained (e.g updated) to incorporate current results for predicting future bid amounts in response to received attributes. When considered in combination, the additional elements are recited at a high level of generality and amount to using the words "apply it," and it can also reasonably be seen as the conventional application of well-known machine learning concepts to build and train a model to implement the abstract idea on a computer, and merely uses a computer as a tool to perform the abstract idea. See MPEP 2106.05(f). Therefore fail to amount to significantly more than the abstract idea.
Represented claims 1 and 15, which do recite statutory categories (machine, product of manufacture, for example), the same analysis as above applies to these claims since the method steps are the same. However, the judicial exception is not integrated into a practical application. These claims add the generic computer components (additional elements) of a system comprising one or more hardware processors and a memory (claim 1), and a non-transitory machine-readable medium comprising instructions that when executed by a processor of a machine cause the machine to perform the method addressed above (claim 15).
The processor, memory, and non-transitory machine-readable medium are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of the processor, memory, and non-transitory machine-readable medium amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
When taken as an ordered combination, nothing is added that is not already present when the elements are taken individually. When viewed as a whole, the marketing activities amount to instructions applied using generic computer components.
As for dependent claims 2 – 3, 6 – 8, 10 – 11, 14, 16 – 17 and 20, these claims recite limitations that further define the same abstract idea of determination of the relationship between the first-merchant and the second-merchant will be based upon the location characteristics and product characteristics associated with the first-merchant (preference of the customer) and the second-merchant, defining what customer data will be considered for generation of place-graph, and what information will be identified on the place-graph, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of organizing certain methods of human activity related to advertising, marketing or sales activities or behaviors but for the recitation of generic computer components. Accordingly, the claim recites an abstract idea.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1 – 3, 6 – 11, 14 – 17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Paliwal et al. US Publication 2022/0027959 in view of Nigam et al. published article “Consumer’s response to conditional promotion in retailing”.
Regarding claim 9 and represented claims 1 and 15, Paliwal teaches system and method for training an artificial neural network to predict a minimum winning price in a real time bidding network (Paliwal, a machine learning estimator arranged to receive historical data on the winning bids of previous auctions and also the losing bids of previous auctions and arranged to estimate the likely win price for a future auction; predictions that come from our model are learned from both win and lose bids. We could then learn a simpler uncensored model, which will be trained only on win bids, to weight predictions by winning rate to form the mixture model.) [Paliwal, 0005, 0092, 0044], the real time bidding network comprising a plurality of electronic devices and a demand side platform (DSP) server, where the DSP server is configured to negotiate bid in a real-time bidding environment (Paliwal, The DSPs negotiate on behalf of advertisers, with an RTB exchange) [Paliwal, 0024), the system and method comprising:
memory storing a neural network model and at least one instruction (Paliwal, based on the bid information from the database 22 and a selected model from a model builder 26. The model is updated by the model builder 26 as more bids and auctions occur, and the data in the database 22 is thus augmented. This is also described in more detail below) [Paliwal, 0040]; and
a processor electronically connected to the memory and controlling the electronic apparatus (Paliwal, In accordance with a first aspect of the invention, there is provided apparatus for reducing usage of computer hardware resource in an automated real-time auction) [Paliwal, 0005, claim 16], wherein the processor is configured to, by executing the at least one instruction,
Therefore, Paliwal teaches system and method for:
determine, based on a trained neural network model, a minimum winning price bid, subsequent to receiving an auction attribute information associated with a current real time auction from the DSP server, the auction attribute information being received automatically from the DSP server based on a user accessing a publisher's site (Paliwal, The DSP usually has several campaigns which are competing against each other to be able to show their ad on a specific bid request. In such cases, the DSP computes separately an optimal bid price for each campaign ad, the importance of each ad to be shown and its influence on the campaign.) [Paliwal, 0007];
automatically transmit the minimum winning price bid to the DSP server (Paliwal, If it is decided to enter the auction, a bid is made to the RTB exchange 4 (step 56).) [Paliwal, 0046]; and
update the trained neural network model based on information from the DSP server indicating whether the minimum winning price bid won the current real time auction (Paliwal, estimating process is continuous as new auctions continue to operate. This is the process where the loss function and derivatives are used to build a predictive model. This model is regularly updated to use the most recent data. The process is typically executed in a DSP and creates updated training data sets from all historic bid wins and losses, and builds an updated winning bid price prediction model. Thus in step 42, an updated training set is created, and an updated winning price estimate is created and tuned (steps 44 and 46). The model is then deployed in an auction (step 48)) [Paliwal, 0044],
wherein training the trained neural network model to obtain the trained neural network model comprises:
acquiring a plurality of auction histories comprising auction histories of a first auction type with winning bid, auction histories of the first auction type with losing bid, auction histories of a second auction type with winning bid, and auction histories of the second auction type with losing bid (Paliwal, a machine learning estimator arranged to receive historical data on the winning bids of previous auctions and also the losing bids of previous auctions and arranged to estimate the likely win price for a future auction) [Paliwal, 0005, 0044];
generating a first bid winning probability distribution based on the auction histories of the second auction type with winning bid, the auction histories of the second auction type with losing bid, and the auction histories of the first auction type with losing bid (Paliwal, a bid determinator arranged to receive data on a maximum bid price for the future auction and the estimated winning bid from the estimator, and arranged to cause the hardware resource (DSP) to be employed in entering the future auction when the budget is not less than the estimated win price; in a second price auction, the RTB exchange 4 queries DSP 2-1 and DSP 2-2 for advertisers' bids. The DSP 2-1 bids $1.00, and the DSP 2-2 bids $1.10. Thus DSP 2-2 wins the auction, paying the second highest price, i.e. $1.00. DSP 2-2 receives this as a win notification 12.) [Paliwal, 0005];
updating the auction histories of the second auction type with winning bid based on the first bid winning probability distribution (Paliwal, estimating process is continuous as new auctions continue to operate. This is the process where the loss function and derivatives are used to build a predictive model. This model is regularly updated to use the most recent data. The process is typically executed in a DSP and creates updated training data sets from all historic bid wins and losses, and builds an updated winning bid price prediction model. Thus in step 42, an updated training set is created, and an updated winning price estimate is created and tuned (steps 44 and 46). The model is then deployed in an auction (step 48)) [Paliwal, 0044];
generating a second bid winning probability distribution based on the updated auction histories of the second auction type with winning bid (Paliwal, The full winning price distribution consists of the winning prices of all win bids plus the winning prices of all lose bids.) [Paliwal, 0035];
generating a minimum winning price probability distribution for all of the plurality of auction histories based on a difference between the first bid winning probability distribution and the second bid winning probability distribution being less than a threshold;
Paliwal does not teach generating conditional minimum winning price. However, Paliwal teaches The DSP usually has several campaigns which are competing against each other to be able to show their ad on a specific bid request. In such cases, the DSP computes separately an optimal bid price for each campaign ad, the importance of each ad to be shown and its influence on the campaign.) [Paliwal, 0007]. Nigam teaches conditional Both offline and online retailers use conditional promotions (CPs) to increase the value of a customer’s shopping cart (e.g., bid modification to win the advertising slot to make the conditional promotion presented to the customer). At times, the value of the cart falls short of the threshold order value to avail a CP. If a customer wants to avail the CP, he or she needs to add more products to the cart to reach the threshold order value. Nigam further recites, “A distinct feature of conditional discounts is that the deal is always coupled with an eligibility requirement, mostly in the form of a minimum purchase quantity or a minimum spending amount, that customers must meet or exceed to receive the discount” (Amornpetchkul et al., 2018, p. 1455). Some common examples of CP include buy X get Y free, buy X get Y% off on the Nth, buy for X and get Y% discount on the total cart value, etc. (Lee, 2016; Shukla & Banerjee, 2014; Sokolova & Li, 2021) [Nigam, page 1].
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Paliwal by adopting teachings of Nigam to Help the Managers plan for such CP by making it easier for the manager to predict a customer’s behavior and balance between increased sales and customer satisfaction by assigning additional weight to increase bid for a subsequent activity if the preceding conditional activity has already happened.
Paliwal in view of Nigam teaches system and method for training an artificial intelligence model
based on the minimum winning price probability distribution for all of the plurality of auction histories, generating a conditional minimum winning price probability distribution for each of the plurality of auction histories (Nigam, Further, the quantum of the additional expenditure will depend on the relative distance from the threshold order value required for a CP as well as on the minimum price of the available items that can be added (Chen et al., 2021; Nigam & Dewani, 2022). In addition, such unplanned buying mainly consists of hedonic items, but it can also be for utilitarian items (Sohn & Ko, 2021; Suher & Hoyer, 2020; Stern, 1962)) [Nigam, page 752] ; and
training the neural network model, over more than one iteration, by using a respective auction attribute information for each of the plurality of auction histories as an independent variable and a respective conditional minimum winning price probability distribution for each of the plurality of auction histories as a dependent variable (Paliwal, a machine learning estimator arranged to receive historical data on the winning bids of previous auctions and also the losing bids of previous auctions and arranged to estimate the likely win price for a future auction; predictions that come from our model are learned from both win and lose bids. We could then learn a simpler uncensored model, which will be trained only on win bids, to weight predictions by winning rate to form the mixture model.) [Paliwal, 0005, 0092, 0044].
Regarding claim 10 and represented claims 2 and 16, as combined and under the same rationale as above, Paliwal in view of Nigam teaches system and method for training an artificial intelligence model, wherein the first auction type is a first-price auction, and the second auction type is a second-price auction (Paliwal, a machine learning estimator arranged to receive historical data on the winning bids of previous auctions and also the losing bids of previous auctions and arranged to estimate the likely win price for a future auction) [Paliwal, 0005].
Regarding claim 11 and represented claims 3 and 17, as combined and under the same rationale as above, Paliwal in view of Nigam teaches system and method for training an artificial intelligence model, wherein the information on the plurality of auction histories comprises: at least one of the auction attribute information, information on a bid price, information on whether respective auctions were won, or information on a winning price, and the auction attribute information comprises: at least one of information on auction types, information on advertisements, or information on users (Paliwal, a machine learning estimator arranged to receive historical data on the winning bids of previous auctions and also the losing bids of previous auctions and arranged to estimate the likely win price for a future auction) [Paliwal, 0005].
Regarding claim 14 and represented claims 6 and 20, as combined and under the same rationale as above, Paliwal in view of Nigam teaches system and method for training an artificial intelligence model, wherein the generating the conditional minimum winning price probability distribution for each of the plurality of auction histories comprises:
based on a first auction history among the plurality of auction histories being an auction history with a winning bid in the first auction type [Paliwal, 0040], acquiring a first conditional minimum winning price probability distribution for the first auction history by using a probability value for a price higher than or equal to the winning bid as 0 in the minimum winning price probability distribution for entire of the plurality of auction histories (Nigam, We expect that the likelihood to avail a CP should be more when the customer is paying via a non-transparent means, is nearer to the threshold order value, and has initially purchased hedonic products. Given that our study involves three independent variables with two levels each, we will have eight scenarios. We first fix the initial cart, and then based on the combination of the other two independent variables; we predict the customer behavior (Chen & Wang, 2016; Chen & Zhang, 2015; Hull, 1932; Kivetz et al., 2006; Pisani & Atalay, 2018)) [Nigam, page 753].
Regarding claim 7, as combined and under the same rationale as above, Paliwal in view of Nigam teaches system and method for training an artificial intelligence model, wherein the processor is configured to:
based on a second auction history among the plurality of auction histories being an auction history with a winning bid in the second auction type [Paliwal, 0040], acquire a second conditional minimum winning price probability distribution for the second auction history by using a probability value for the winning bid as 1 in the minimum winning price probability distribution for all of the plurality of auction histories [Nigam, page 753].
Regarding claim 8, as combined and under the same rationale as above, Paliwal in view of Nigam teaches system and method for training an artificial intelligence model, wherein the processor is configured to:
based on a third auction history among the plurality of auction histories being an auction history with a losing bid in the first auction type or the second auction type [Paliwal, 0040], acquire a third conditional minimum winning price probability distribution for the third auction history by using a probability value for a price lower than or equal to the losing bid as 0 in the minimum winning price probability distribution for all of the plurality of auction histories [Nigam, page 753].
Response to Arguments
Applicant's argument that pending claimed amended invention is eligible for patent under 35 USC 101 because the claims related to a specific and technical solution to the technical problem and integrates practical ideas into a practical application, provides technical solution in an inherently technical field is acknowledged and considered.
However, upon further consideration, it is deemed that the claimed invention is not eligible for patent under 35 USC 101, and have been responded to in Rejection under 35 USC 101 section.
Applicant's argument that pending claimed amended invention is eligible for patent because cited prior art does not teach the added limitations is acknowledged and considered.
However, applicant’s arguments are moot under new grounds of rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Han et al. US Publication 2005/0262009 which teaches system and method for bidding in on-line auction.
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 Naresh Vig whose telephone number is (571)272-6810. The examiner can normally be reached Mon-Fri 06:30a - 04:00p.
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/NARESH VIG/Primary Examiner, Art Unit 3622
May 27, 2026
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