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 .
Status of the Claims
Claims 1, 8, and 15 are amended. Claims 3 and 10 are canceled. Claims 1, 2, 4-9, and 11-15 are pending.
Response to Arguments
Applicant's arguments filed 11/12/2025 regarding 35 U.S.C. 101 have been fully considered but they are not persuasive.
Applicant provides arguments that the amended claims reflect an improvement in the functioning of a computer by saving valuable computing resources and facilitating the pricing engine to come up with optimal prices more efficiently by avoiding redundant and irrelevant computation. Examiner disagrees. Evaluating the current reward with respect to average reward obtained across the last iteration to stop further exploration at best describes an improvement in the judicial exception itself (business processes related to commercial interactions), but does not reflect an improvement in computer functionality or operations. Avoiding redundant and/or irrelevant computation may be convenient for the business retailer or end-user, but does not improve computer functionality. It is important to keep in mind that an improvement in the judicial exception itself (e.g., a recited fundamental economic concept) is not an improvement in technology (emphasis added). For example, in Trading Technologies Int’l v. IBG LLC, the court determined that the claim simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology. Similarly, the Applicant’s claim recitations are an improvement in the judicial exception, not an improvement in technology. "We have also held that improving a user's experience while using a computer application is not, without more, sufficient to render the claims directed to an improvement in computer functionality. For example, in Trading Techs. I, we held patent ineligible claims directed to a computer-based method for facilitating the placement of a trader's order. Trading Techs. Int'l, Inc. v. IBG LLC, 921 F.3d 1084, 1092-93 (Fed. Cir. 2019) (Trading Techs. I). Although the claimed display purportedly 'assist[ed] traders in processing information more quickly,' we held that this purported improvement in user experience did not 'improve the functioning of the computer, make it operate more efficiently, or solve any technological problem. Id.; see also Trading Techs. Int'l, Inc. v. IBG LLC, 921 F.3d 1378, 1381, 1384-85 (Fed. Cir. 2019) (Trading Techs. II) (holding that claims 'focused on providing information to traders in a way that helps them process information more quickly' did not constitute a patent-eligible improvement to computer functionality)." Customedia Technologies v. Dish Network, 951 F.3d 1359, 1365 (Fed. Cir. 2020). Additionally, there is no evidence that any of the machine learning algorithms utilized are improved. "[P]atents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101." Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025) (slip op. at 18). "The requirements that the machine learning model be 'iteratively trained' or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement." Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025), slip op. at 12.
Further, regarding applicant’s argument above on avoiding redundant and/or irrelevant computation along with applicant’s argument that a computer is improved due to saving valuable computing resources and reducing complexity, the argument is not persuasive. The judicial exception is not integrated into a practical application simply because the claims recite the additional elements of: one or more hardware processors, memory (claim 8), one or more communication interfaces (claim 8), and one or more non-transitory machine-readable storage mediums (claim 15). The additional elements are computer components recited at a high-level of generality performing the above-mentioned limitations. The combination of the additional elements are no more than mere instructions to apply the judicial exception using a generic computer. Computing overhead is merely a combination of excess computation time, usage, or memory required to perform the specific task, which further indicates that the alleged improvement is an improvement in the business process (being performed via computer) rather than an improvement in the actual computer itself. Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015); see also MPEP 2106.05(f). Also, as stated in Recentive Analytics "Finally, the claimed methods are not rendered patent eligible by the fact that (using existing machine learning technology) they perform a task previously undertaken by humans with greater speed and efficiency than could previously be achieved." Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025), slip op. at 15. Applicant’s statement that the subject matter cannot be performed in the human mind is invalid. Claims can recite a mental process even if they are claimed as being performed on a computer. If the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept, the claim is considered to recite a mental process (MPEP §2106.04(a)(2)). This is the case in the applicant’s invention.
Applicant further argues that the claims recite additional elements that amount to significantly more than the judicial exceptions, and details the use of Thompson sampling claiming that the revenue and sales unit improvements are obtained using Thompson sampling and improvements are observed to be higher compared with baseline regression models. Applicant’s arguments are unpersuasive. The use of Thompson sampling is not considered an additional element, but instead directly corresponds to the judicial exception grouping of mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) being that the limitations provide much detail on the various algorithms and computations (e.g., using Thompson sampling) to derive a plurality of price elasticity values based on ensemble technique(s) applied to the plurality of parameters associated with the plurality of price distributions. 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 elements (listed in the previous section) amount to no more than mere instructions to apply the exception using a generic computer. Mere instructions to apply an exception using a generic computer cannot provide an inventive concept. Thus, when viewed as an ordered combination, nothing in the claims add significantly more (i.e. an inventive concept) to the abstract idea. The claims are not patent eligible.
The 35 U.S.C. 101 rejection is maintained.
Applicant’s arguments, see pg. 15, filed 11/12/2025, with respect to 35 U.S.C. 102 have been fully considered and are persuasive. The 35 U.S.C. 102 rejection has been 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 11-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
Claims 1, 2, and 4-7 recite a method (i.e. process), claims 8, 9, and 11-14 recite a system (i.e. machine), and claim 15 recites a non-transitory machine-readable storage medium (i.e. machine or article of manufacture). Therefore claims 1, 2, 5-9, and 11-15 fall within one of the four statutory categories of invention.
Independent claims 1, 8, and 15 recite the limitations: receiving at least one of: (i) a transaction data, and (ii) an attribute data associated with a plurality of products from a user, as an input data; processing the input data to obtain pre-processed data, wherein the pre-processed data comprises a plurality of model selection parameters; determining a plurality of selected models based on the plurality of model selection parameters; receiving a flow of the determined plurality of selected models in a sequential price elasticity computation using Thompson sampling for calculation of a plurality of priors; computing, via the one or more hardware processors, the plurality of priors at different groups of data based on the received flow of the plurality of selected models, wherein the plurality of priors is computed based on at least one of: (a) a SKU level with a high variance, (b) group of similar products, (c) a merchandise hierarchy level, (d) similar selling characteristics of group of products, (e) selling range of group of products, and (f) combination thereof; iteratively determining a plurality of parameters associated with a plurality of price elasticity distributions through at least one of unsupervised reinforcement learning model based on the plurality of priors and the plurality of likelihoods, and wherein the at least one of unsupervised reinforcement learning model corresponds to a plurality of component approaches, and wherein the plurality of component approaches corresponds to at least one of (a) a first component approach, or (b) a second component approach, or (c) a third component approach, or (d) a fourth component approach, and combination thereof; updating the plurality of price elasticity distributions in each iteration with demand corresponding to new price points captured through one or more price elasticity distributions which are fit on historical sales data of selected styles in previous season using the Thompson sampling, wherein during each iteration, one or more price points and corresponding demand along with inventory is sent to an optimization engine, which returns a price probability for each of the one or more price points based on an objective and constraint; checking for changes in a reward calculated in last 'n' iterations, wherein when there is a change in terms of the reward obtained further exploration is stopped, and the parameters associated with the one or more price elasticity distributions are fixed, wherein for a price chosen, a demand is obtained from historical sales data and when a reward objective is to maximize sales units, the demand is directly taken as a reward and when the reward objective is revenue maximization, the reward is taken as price multiplied by sales units; and deriving a plurality of price elasticity values based on at least one ensemble technique applied to the plurality of parameters associated with the plurality of price elasticity distributions. The invention is drawn towards a pricing system for computing price elasticity of products, and the claims recite limitations that directly correspond to certain methods of organizing human activity (managing personal interactions or behaviors; commercial interactions, business relations), as shown by limitations detailing the computation of price elasticity for a retailer to use in inventory planning, allocation, re-distribution utilizing transaction data and attribute data associated with products from a user. The claims also recite limitations that directly correspond to mental processes (observation, evaluation, judgment, opinion), as shown by limitations detailing observing or analyzing data, and making a determination (judgment/opinion) based on the analyzed or observed data such as determining a plurality of selected models based on the plurality of model selection parameters that are obtained from pre-processed data of transaction and attribute product data. The claims also directly correspond to mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) as evidenced by the claim limitations providing much detail on various algorithms and computations (e.g., using Thompson sampling) to derive a plurality of price elasticity values based on ensemble technique(s) applied to the plurality of parameters associated with the plurality of price distributions. The claims recite an abstract idea.
The judicial exception is not integrated into a practical application simply because the claims recite the additional elements of: one or more hardware processors, memory (claim 8), one or more communication interfaces (claim 8), and one or more non-transitory machine-readable storage mediums (claim 15). The additional elements are computer components recited at a high-level of generality performing the above-mentioned limitations. The combination of the additional elements are no more than mere instructions to apply the judicial exception using a generic computer. Accordingly, in combination, 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 elements amount to no more than mere instructions to apply the exception using a generic computer. Mere instructions to apply an exception using a generic computer cannot provide an inventive concept. Thus, when viewed as an ordered combination, nothing in the claims add significantly more (i.e. an inventive concept) to the abstract idea. The claims are not patent eligible.
Dependent claims 5 and 12 recite the limitations: (a) computing the plurality of priors from a plurality of historical sales data, wherein at least one prior is generated as a Gaussian distribution with mean as a value of price elasticity, wherein standard error as variance for at least one independent product, and wherein covariance matrix for at least one dependent product; (b) determining the plurality of likelihoods as a Gaussian distribution with mean and variance of at least one monetary objective chosen based on the input data for a corresponding selling duration of the plurality of products from the plurality of historical sales data; and (c) forecasting a demand based on at least one [machine learning model] trained on in-season transaction data and the historical sales data corresponding to the plurality of products, and the sampling posterior price elasticity distribution based on the plurality of priors and the plurality of likelihoods. The claim limitations are further directed to the abstract ideas analyzed above, and additionally recites limitations that correspond to mathematical concepts (mathematical relationships, mathematical calculations, mathematical formulas or equations). The claims also recite the additional elements of one or more hardware processors and at least one machine learning model. The additional elements amount to “apply it” or merely using a computer as a tool to implement the judicial exception. Additionally, the machine learning model amounts to generally linking the judicial exception to a particular field of use. Accordingly, in combination, 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. Further, when viewed as an ordered combination, nothing in the claims add significantly more (i.e. an inventive concept) to the abstract idea. The claims are not patent eligible.
Dependent claims 6 and 13 recite the limitations: (a) computing the plurality of priors from a plurality of historical sales data, wherein at least one prior is generated as a Gaussian distribution with mean as a value of price elasticity, wherein standard error as variance for at least one independent product, and wherein covariance matrix for at least one dependent product; (b) determining the plurality of likelihoods as a Gaussian distribution with mean and variance of at least one monetary objective chosen based on the input data for a corresponding selling duration of the plurality of products from the plurality of historical sales data; and (c) forecasting a demand based on at least one deep learning model trained on in-season transaction data and the plurality of historical sales data corresponding to the plurality of products, and the sampling posterior price elasticity distribution based on the plurality of priors and the plurality of likelihoods. The claim limitations are further directed to the abstract ideas analyzed above, and additionally recites limitations that correspond to mathematical concepts (mathematical relationships, mathematical calculations, mathematical formulas or equations). The claims also recite the additional elements of one or more hardware processors. The additional element amounts to “apply it” or merely using a computer as a tool to implement the judicial exception. Accordingly, in combination, the additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Further, when viewed as an ordered combination, nothing in the claims add significantly more (i.e. an inventive concept) to the abstract idea. The claims are not patent eligible.
Dependent claims 2, 4, 7, 9, 11 and 14 recite additional limitations that are further directed to the abstract idea analyzed in the rejected claims above. The claims also recite additional elements that have been analyzed in the rejected claims above. Thus, claims 2, 4, 7, 9, 11 and 14 are also rejected under 35 U.S.C. 101.
Allowable Subject Matter
Claims 1, 2, 4-9, and 11-15would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action.
The closest patent or patent application prior art reference found that is relevant to the applicant’s invention includes Keng (2021/0110429) which discloses a system for generating an output analytic for a promotion that includes using an optimization machine learning model trained with an optimization training set, at least one determined parameter for the promotion which optimizes at least one of received input parameters, the optimization training set comprising received historical data; forecasting, using a promotion forecasting machine learning model trained with an forecasting training set, at least one output analytic of the promotion, the prediction training set comprising the received historical data, the at least one received input parameter and the at least one determined parameter; and outputting the at least one output analytic to the user. Also, Ivanov (2003/0177103) which discloses calculating price elasticity utilizing a number of demand models and demand data describing a number of items. Neither prior art reference, individually nor in combination, discloses the detailed amended limitations of applicant’s claims. The claims overcome the prior art.
The closest non-patent literature prior art reference found that is relevant to the applicant’s invention includes the publication “Elasticity Based Demand Forecasting and Price Optimization for Online Retail” (Liu, Sustik; 2021) which explores the problem associated with online retailers that observes the unit sales of a product, and dynamically changes the retail price, in order to maximize the expected revenue. The system investigates the relationship between retail price and demand and estimate the demand function, predicts demand and revenue at a given retail price, and formulates a revenue maximization problem over a discrete time horizon with discrete retail price. This solves the optimal pricing policy based on predicted demand the revenue values. The prior art reference does not disclose the detailed amended limitations of applicant’s claims. The claims overcome the prior art.
Conclusion
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 DIONE N SIMPSON whose telephone number is (571)272-5513. The examiner can normally be reached M-F; 7:30 a.m.-4:30 p.m..
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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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DIONE N. SIMPSON
Primary Examiner
Art Unit 3628
/DIONE N. SIMPSON/ Primary Examiner, Art Unit 3628