DETAILED ACTION
This Non-Final Office Action is in response to the application filed on 02/21/2024, the Amendment & Remark filed on 11/20/2025 and the Request for Continued Examination filed on 12/22/2025.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/22/2025 has been entered.
Status of Claims
Claims 1 and 11 are amended.
Claims 8 and 18 are canceled.
Claims 1, 3, 7, 11, 13 and 17 are pending.
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, 7, 11, 13 and 17 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.
As an initial matter, the claims as a whole are to a method and a manufacture, which falls within one or more statutory categories. (Step 1: YES) The recitation of the claimed invention is then further analyzed as follow, in which the abstract elements are boldfaced.
Claim 1 recites:
A method, comprising:
generating, by a computer program executed on a graphical processing unit, random values for a plurality of pricing model parameters for a pricing model comprises a Monte Carlo model;
receiving, by the computer program, a selection of a plurality of anchors;
predicting, by a variance-reduction neural network, a function h for a Schoenmakers-Heemink (SH) variance-reduction algorithm based on the pricing model parameters;
calculating, by the computer program, simulated prices for the plurality of anchors using the pricing model;
initializing, by the computer program, encoder parameters to random values;
training, by the computer program, the encoder parameters for an encoder with the simulated prices to predict updated values for the pricing model parameters until the pricing model parameters converge;
predicting, by the pricing model using the updated values for the pricing model parameters, predicted prices for the anchors;
computing, by the computer program using an error module, a difference between the simulated prices and the predicted prices;
updating, by the computer program, the encoder parameters to minimize the difference between the simulated prices and the predicted prices and to modify the encoder parameters in the direction of a gradient of a loss function with respect to the encoder parameters;
applying the SH variance-reduction algorithm by adjusting a drift term using the function h. wherein applying the SH variance-reduction algorithm reduces variance of terminal distribution while maintaining an expected value of a computational cost of the graphical processing unit;
iterating, by the computer program, the calculating step, the training step, the predicting step, the computing step and the updating step until the difference is within a tolerance range; and
deploying, by the computer program, the pricing model to a production environment in response to the loss being within a threshold range.
Claim 11 recites:
A non-transitory computer readable storage medium, including instructions stored thereon, which when read and executed by one or more computer processors of a graphical processing unit, cause the one or more computer processors to perform steps comprising: generating random values for a plurality of pricing model parameters for a pricing model comprises a Monte Carlo model; receiving a selection of a plurality of anchors; predicting, by a variance-reduction neural network, a function h for a Schoenmakers-Heemink (SH) variance-reduction algorithm based on the pricing model parameters; calculating simulated prices for the plurality of anchors using the pricing model; applying the SH variance-reduction algorithm by adjusting a drift term using the function h. wherein applying the SH variance-reduction algorithm reduces variance of terminal distribution while maintaining an expected value of a computational cost of the graphical processing unit; initializing encoder parameters to random values; training the encoder parameters for an encoder with the simulated prices to predict updated values for the pricing model parameters until the pricing model parameters converge; predicting, using the updated values for the pricing model parameters, predicted prices for the anchors; computing a loss between the simulated prices and the predicted prices; updating the encoder parameters to minimize the difference between the simulated prices and the predicted prices and to modify the encoder parameters in the direction of a gradient of a loss function with respect to the encoder parameters; iterating the calculating step, the training step, the predicting step, the computing step and the updating step until the difference is within a tolerance range; and deploying the pricing model to a production environment in response to the loss being within a threshold range.
Claims 3 and 13 recite:
wherein a type of the pricing model is selected by a user.
Claims 7 and 17 recite:
wherein the plurality of anchors comprise securities that are liquid.
Based on the limitations above, the claims describe a process that covers optimizing a pricing model. Pricing is considered to be a commercial interaction / a fundamental economic practice, which fall(s) within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas; while modeling is considered to be mathematical concept. As such, the claim(s) recite(s) one or more Judicial Exception. (Step 2A prong one: Yes)
This analysis then evaluates whether the claims as a whole integrates the recited Judicial Exception into a practical application of the exception. In particular, the claims recite the additional element(s) of “computer program executed on a GPU” and “a non-transitory computer readable storage medium, including instructions stored thereon, which when read and executed by one or more computer processors of a GPU, cause the one or more computer processors to perform” as a mere tool to perform the steps of the Judicial Exception, which encompasses no more than Mere Instruction to Apply.
For example, the limitation “generating, by a computer program executed on a graphical processing unit, random values for a plurality of pricing model parameters for a pricing model” encompasses no more than generically invoking a computer program to apply the Judicial Exception step of generating random values for the plurality of pricing model parameters;
the limitation “receiving, by the computer program, a selection of a plurality of anchors” encompasses no more than generically invoking a computer program to apply the Judicial Exception step of receiving selection of the plurality of anchors;
the limitation “predicting, by a variance-reduction neural network, a function h for a Schoenmakers-Heemink (SH) variance-reduction algorithm based on the pricing model parameters” encompasses no more than generically invoking a computer program to apply the Judicial Exception step of predicting a function for a algorithm;
the limitation “calculating, by the computer program, simulated prices for the plurality of anchors using the pricing model” encompasses no more than generically invoking a computer program to apply the Judicial Exception step of calculating simulated prices for the plurality of anchors using the pricing model;
the limitation “initializing, by the computer program, encoder parameters to random values” encompasses no more than generically invoking a computer program to apply the Judicial Exception step of initializing encoder parameters to random values;
the limitation “training, by the computer program, the encoder parameters for an encoder with the simulated prices to predict updated values for the pricing model parameters” encompasses no more than generically invoking a computer program to apply the Judicial Exception step of training encoder parameter to predict updated values for pricing model parameters;
the limitation “predicting, by the pricing model using the updated values for the pricing model parameters, predicted prices for the anchors” encompasses no more than generically invoking a computer program to apply the Judicial Exception step of predicting the predicted prices using the pricing model with updated parameters;
the limitation “computing, by the computer program using an error module, a loss between the simulated prices and the predicted prices” encompasses no more than generically invoking a computer program to apply the Judicial Exception step of computing the loss (difference) between the simulated prices and the predicted prices;
the limitation “updating, by the computer program, the encoder parameters to minimize the difference between the simulated prices and the predicted prices and to modify the encoder parameters in the direction of a gradient of a loss function with respect to the encoder parameters” encompasses no more than generically invoking a computer program to apply the Judicial Exception step of updating the encoder parameters to minimize the difference between the simulated prices and the predicted process and to modifying the encoder parameters in the direction of a gradient of a loss function;
the limitation “applying the SH variance-reduction algorithm by adjusting a drift term using the function h. wherein applying the SH variance-reduction algorithm reduces variance of terminal distribution while maintaining an expected value of a computational cost of the graphical processing unit;” encompasses no more than generically invoking a computer program to apply the Judicial Exception step of applying an variance;
the limitation “iterating, by the computer program, the calculating step, the training step, the predicting step, the computing step and the updating step until the difference is within a tolerance range” encompasses no more than generically invoking a computer program to apply the Judicial Exception steps iteratively until the difference is within a tolerance range;
the limitation “deploying, by the computer program, the pricing model to a production environment in response to the loss being within a threshold range” encompasses no more than generically invoking a computer program to apply the Judicial Exception step of deploying the pricing model to production in response the loss being with a threshold range.
Other than being generally linked to the steps of the Judicial Exception, the additional elements in the above step(s) is/are recited at a high-level of generality, without technological detail of how the particular steps are performed technologically. The additional element(s) of “memory” and/or “non-transitory storage medium” are generically recited to store data and/or instructions of the Judicial Exception.
The additional element(s) of “a variance-reduction neural network” is generically recited to perform the Judicial Exception step predicting function h for a Schoenmakers-Heemink (SH) variance-reduction algorithm.
The additional element(s) of “encoder” is generically recited to perform the Judicial Exception step predicting updated values for the pricing model parameters.
Indeed, the instant claims (1) attempted to cover a solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result; (2) used of a computer or other machinery in its ordinary capacity for economic or other tasks or simply added a general purpose computer or computer components after the fact to the Judicial Exception and (3) generally applied the Judicial Exception to a generic computing environment without limitation indicative of practical application (See MPEP 2106.04(d)I).
The step of “training … encoder parameters …” is disclosed to be achieved by modifying the encoder parameters in the direction of a gradient of a loss function with respect to the encoder parameter, which is a machine learning technique – gradient descent algorithm similar to the step of training an Artificial Neural Network (ANN) in the ineligible claim 2 of Example 47 that is mathematical calculation. The training of the encoder parameters is also performed by the generically recited computer program in claim 1 or the generically recited processor in claim 11.
The step of “iterating, by the computer program, the calculating step, the training step, the predicting step. the computing step, and the updating step until the difference is within a tolerance range” describes basic machine learning characteristics like iterative training and dynamic adjustment of parameter. The examiner noted that the mere inclusion of machine learning model usage / training does not render an otherwise abstract claim patent eligible under 101. In Recentive Analytics, Inc. v. Fox Corp., the Federal Circuit specifically rejected the argument that requiring iterative training of a machine learning model creates patent eligibility, noting that "[i]terative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning." Id. at 12. The court further explained that "the requirements that the machine learning model be 'iteratively trained' or dynamically adjusted . . . do not represent a technological improvement" because these features are inherent to the applying of machine learning technology itself. Id.
Thus, the claims are no more than Mere Instruction to Apply the Judicial Exception (See MPEP 2106.05(f)) or adding insignificant extra-solution activity to the judicial exception (See MPEP 2106.05(g)), which do not integrate the cited Judicial Exception into practical application (Step 2A prong two: No) The claims are directed to a Judicial Exception.
The claim does 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 using a computer program / processor to optimize a pricing model amounts to no more than mere instructions to apply the exception using generic computer components. The recited ordered combination of additional elements includes invoking generic computer program or processor to apply the Judicial Exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Dependent claim 3, 7, 8, 13, 17 and 18 merely limit the abstract idea but do not recite any additional element beyond the cited abstract idea, thus, do not amount to significantly more. No additional element currently recited in the claims amount the claims to be significantly more than the cited abstract idea. (Step 2B: No)
Therefore, claims 1, 3, 7, 11, 13 and 17 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant's arguments filed 11/20/2025 have been fully considered but they are not persuasive.
Regarding the applicant’s argument that the amended claims integrate the cited Judicial Exception into practical application by providing improvement to computer functionality, the examiner respectfully disagrees. The examiner noted that the amended limitation of “using a variance-reduction neural network” to predict a function h (later applied to reduce variance of terminal distribution of the pricing model) is a mere invoking of the neural network to perform the desired result of variance reduction. The property of variance-reduction is nominally appended to the unspecified neural network to predict a function h that would reduce variance of the pricing model. No disclosure is given as to how the variance reduction could be technologically achieved by the neural network. Thus, the claims’ mere invoking of the neural network is a Mere Instruction to Apply drafting effort, which would not integrate the Judicial Exception into practical application. Moreover, the examiner noted that reduction in variance is applicable to the pricing model, not to the innate functionality of the computer. The computer’s functionality remains to be executing a pricing model, only that the pricing model has it variance reduction. The improvement to a Judicial Exception (pricing model variance reduction) that is applied to a particular technological environment does not equate an improvement to such technological environment. Thus, the applicant’s argument is not persuasive.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHO KWONG whose telephone number is (571)270-7955. The examiner can normally be reached 9am - 5pm EST M-F.
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/CHO YIU KWONG/Primary Examiner, Art Unit 3693