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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 05/04/2026 has been entered.
Response to Arguments
Applicant's arguments filed 05/04/2026 have been fully considered but they are not persuasive.
Applicant argues that the claims are no longer directed to the judicial exception because there is pruning that “yields a percent gain in a computational time during
training with respect to absence of pruning and pruning the subset of the mixture components improves performance of the trained GMDN model:” The Examiner disagrees. It appears Applicant is saying that the model is improved by the concept of pruning, but not actually pruning?? It is not really clear what Applicant wishes to convey with this claim amendment.
Further, Applicant asserts that the pruning, is an”… integration into practical application requirement is achieved in terms of (1) concrete pruning pipeline (sorting discarding lowest-valued mixture components renormalizing), (2) ties it to a specific performance improvement to the computer's operation (reduced component evaluations/computational time in training & inference), and (3) math achieve a computer-performance gain during model training/inference-rather than math "as such". As stated in paragraph [0077] of Applicant's published application, "Mixture components.” The Examiner disagrees. Applicant’s claims recites a series of abstract steps seemingly anchored by a model being trained. In this instance Applicant is using the model as a tool to perform the abstract steps.
Applicant further argues that the ordered combination is critical and non-conventional even if individual elements such as "sorting" or "normalizing" were known in isolation, the ordered combination recited in the claims is not conventional. Specifically, (a) convolution is first permitted to generate a full mixture distribution. (b) mixture coefficients are then evaluated as indicators of computational significance (c) low-impact components are selectively removed, (d) remaining components are renormalized to preserve probabilistic correctness, (e) CDF evaluation is constrained to the reduced mixture. This ordered combination transforms an otherwise exponentially scaling model into a bounded-execution model. The Examiner disagrees.
Applicant’s invention does not solve a technical problem by the invention of pruning or the way in which pruning corresponds with the invention, albeit confusing. Applicant is using the pruning in the furtherance of the training of a computational model. This again is not a technical solution to a problem rooted in computer technology. According to Applicant’s specification, the problem being solved is “…prediction of the customer’s choice on a new bundled offer necessitates estimating willingness-to-pay (WTP) distributions of individual products from the historical data. However, this becomes particularly challenging when the historical data contains bundled offers as the observed buy and no-buy decisions are determined by the aggregated effect of the individual WTPs while predictions require the disaggregated WTP distributions of the individual products.” As demonstrated by the cited portion above, the claimed invention is not providing a technical solution to a problem rooted in computer technology. Accordingly, Applicant’s arguments are not persuasive and the rejections are maintained.
Applicant argues that the claims of the instant invention share a commonality with the claims of Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision).
The Examiner disagrees with any attempt to draw a comparison between Desjardins and the claims of the instant invention. There is no similarity as the claims of Desjardins do not recite any steps relating to a business methods nor do the two sets of claims share a similar fact pattern, Accordingly, Applicant’s arguments are not persuasive and the rejections are maintained.
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 non-statutory subject matter 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.
MPEP 2106 Step 2A-Prong 1
The claims recite:
receiving, a historical sales dataset comprising a plurality of input samples pertaining to a bundle, wherein the bundle comprises one or more products; and training, with each of the plurality of input samples in the historical sales data set by:
feeding a feature vector of each of the one or more products of the bundle in the input sample, to a corresponding feed forward layer to generate an intermediate representation for each of the one or more products;
passing the intermediate representation of each of the one or more products to a subsequent to learn a gamma mixture comprising a plurality of gamma mixture parameters for each of the one or more products;
obtaining a willingness-to-pay (WTP) distribution corresponding to each of the product of the one or more products from the obtained gamma mixture of the corresponding product;
estimating a bundle WTP distribution composed of the one or more products by convolving WTP distributions of the one or more products based on a bundle composition, wherein the input samples provide mixture components in the estimated bundled WTP distribution;
pruning a subset of the mixture components of the estimated bundle WTP distribution includes:(i) sorting the mixture components based on corresponding coefficient values in an ascending order,(ii) discarding a percentage c of lowest-valued components upon sorting the mixture components to reduce a total from Kn to K (< Kn) and reduces the mixture components for evaluations of Cumulative Density Function (CDF) value speeds up the training, and
(iii) normalizing remaining coefficients to sum to one, and reducing number of component evaluations required for subsequent computations, wherein pruning yields a percent gain in a computational time during training with respect to absence of pruning and pruning the subset of the mixture components improves performance of the trained GMDN model;
calculating a weighted CDF value, using (i) the bundle WTP distribution, and (ii) an offered price of the bundle composition, wherein pruning the subset of mixture components for evaluations of the CDF values results in speeding up the training of the GMDN model and the pruning of the subset of the mixture components reduces computational time;
predicting a class score of the bundle composition using the weighted CDF value;
computing a loss function using the predicted class score and an annotated binary customer’s choice; and
updating based on the computed loss function
wherein the WTP distributions as a mixture of gamma distributions and learns the WTP distributions from bundled and unbundled sales data, and predicts customer's choice of buy or no-buy decision for a sample of the product and estimates revenue optimal prices and revenues of the products and the bundles.
The claims falls into the abstract idea groupings of (a) mathematical concepts-**mathematical relationships mathematical formulas or equations mathematical calculations** (b) Certain Methods Of Organizing Human Activity ** fundamental economic principles or practices (including hedging, insurance, mitigating risk) commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations) managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)**
The limitations under their broadest reasonable interpretation, covers performance of business relations and mathematical relationships but for the recitation of generic computer components. That is, other than recited, “non-transitory computer readable medium, memory communication interfaces, one or more hardware processors, a Gamma Mixture Density Network (GMDN) model”, nothing in the claim element precludes the step from practically being mathematical concept and certain methods of organizing human activity. Accordingly, the claims recite an abstract idea.
MPEP 2106 Step 2A-Prong 2
The recited limitations are not indicative of integration into a practical application. In particular, the claims only recite the following additional elements, one or more hardware processors, a Gamma Mixture Density Network (GMDN) model. These additional elements are recited at a high-level of generality such that in conjunction with the abstract limitations, they amount to no more than:
Adding 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);
- (non-transitory computer readable medium, memory communication interfaces, one or more hardware processors, a Gamma Mixture Density Network (GMDN) model)
iv. Generally linking the use of the judicial exception to a particular technological environment or field of use, -(products, vector)
The claims do not include additional elements individually or in an ordered combination that are sufficient to amount to significantly more than the judicial exception. Integration into a practical application requires the additional element(s) to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. This is not the case in the instant application. Further, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than: mere instructions to apply the exception using a generic computer component;
MPEP 2106 Step 2B
Eligibility requires that the claim recites additional elements that amount to an inventive concept (aka “significantly more”) than the recited judicial exception. As discussed above, this is where the instant application falls short. The claims do not include additional elements individually or in an ordered combination that are sufficient to amount to significantly more than the judicial exception
Dependent Claims Step 2A:
The limitations of the dependent claims but for those addressed below merely set forth further refinements of the abstract idea without changing the analysis already
presented (that is, they further limit the organizing of human activities at step 2A —
Prong One without adding any new additional elements other than those already
analyzed above with respect to the independent claims at 2A — Prong Two; While claims 2, 9 and 16 describe a GMDN model, 3, 10,17 describes products, 4-5, 11-12,18-19 vectors, these additional elements do not remedy the deficiencies.
Dependent Claims Step 2B:
The dependent claims merely use the same general technological environment
and instructions to implement the abstract idea as the independent claims without
adding any new additional elements. Accordingly, they are not directed to significantly
more than the exception itself, and are not eligible subject matter under § 101.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TONYA S JOSEPH whose telephone number is (571)270-1361. The examiner can normally be reached M-F 6:30-2:30, First Fridays Off.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shannon Campbell can be reached at (571) 272-5587. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TONYA JOSEPH/Primary Examiner, Art Unit 3628
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