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 1-2, 4-5, 10-11, and 13-14 are pending. Claims 1 and 10 are amended. Claims 6, 8, 15 and 17 are cancelled.
Response to Arguments
Applicant’s arguments, filed 02/20/2026, with respect to the 101 rejection have been considered but are not persuasive.
Applicant argues, on pages 8-9, that the claims are not directed to an abstract idea and that the claims have been amended to recite specific technical implementation details of the LSTM neural network architecture that go far beyond merely naming a technology. Applicant argues that this specific neural network architecture with its particular gate structure represents a concrete technical implementation, not an abstract concept.
Examiner respectfully disagrees. The claim limitations as drafted, recite a concept, that, under broadest reasonable interpretation, is a certain method of organizing human activity. The limitations are analogous to managing personal behavior or interactions between people (interactions between people), or a commercial or legal interaction (sales activity) such as using historical demand data to set an optimal price of a product. (See specification, Par. 0017-0018). The generic computer implementations (i.e. additional elements) do not change the character of the limitations. Therefore, the claims recite an abstract idea. The additional elements recited in the independent claims (memory, processor, XGBoost technique, LSTM neural network architecture) at a high-level of generality such that they amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. Accordingly, the additional elements, when viewed individually and in combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Furthermore, the same holds true for the dependent claims. While they recite further additional elements (Database, KNN clustering technique, XGBoost technique, Real-valued Genetic Algorithm), when viewed individually and in combination, they do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. With respect to the specific recited LSTM neural network architecture, this recitation is reciting generic architecture for a LSTM and does not provide an improvement to a technology or technical field and therefore does not integrate the judicial exception into a practical application and still is directed to an abstract idea.
Applicant argues, on pages 10-11, that the claims integrate into a practical application by reciting a specific, end-to-end pricing system that operates on real-world retail data and but instead requires a particular technical workflow. Applicant argues that the claims recite specific machine learning techniques and specific constraints using KNN clustering, LSTM neural network, XGBOOST and Genetic Algorithms which integrates the judicial exception into a practical application.
Examiner respectfully disagrees. As mentioned above, the additional elements do not integrate the judicial exception into a practical application. It is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface 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. Here, the alleged improvement to discount optimization and profit optimization is an improvement to a sales activity, and not to a technology or technical field. Therefore, the claims are not integrated into a practical application.
Applicant argues, on pages 11-13, that the claims amount to significantly more than the abstract idea. Applicant argues that the claims provide an inventive concept and provide a technical advantage which qualifies as significantly more than an abstract idea. Applicant argues that the claimed system incorporates specific technological features not found in generic sales activity or conventional forecasting tools.
Examiner respectfully disagrees. As discussed above, the additional elements, amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. The same analysis applies here in 2B. The additional elements, when considered separately and in combination, do not add significantly more to the exception. They are generally linking the use of a judicial exception to a particular technological environment or field of use and cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Therefore, the claims are ineligible.
Novelty/Non-Obviousness
The closest prior art of record is included in the previous office action mailed on 06/27/2025. The claims would be considered allowable if re-written or amended to overcome the rejections in this office action.
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, 4-5, 10-11, and 13-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claim 1-2, 4-5, and 7 are directed to a system with multiple components, and therefore is a machine.
Claims 10-11 and 13-14 are directed to a series of steps, and therefore is a process.
Independent Claims
Step 2A Prong One
The limitation of Claim 1 recites:
segment one or more products into a complementary product cluster or a competitive product cluster based on demand history data of the one or more products and attributes of the one or more products, wherein the segmentation of the one or more products into the complementary product cluster or the competitive product cluster is performed … ;
perform a demand forecast of the one or more products in the product cluster based on the demand history data of the one or more products and attributes of the one or more products, wherein the demand forecast of the one or more products in the product cluster yields a demand forecast for a plurality of Stock Keeping Units (SKUs) of the one or more products;
perform a causality analysis of the one or more products in the product cluster based on the demand forecast of the one or more products in the product cluster, wherein the causality analysis of the one or more products in the product cluster based on the demand forecast of the one or more products in the product cluster is performed … and comprises a quantification of inter-product effects of the one or more products in the product cluster to predict a conversion rate of the conversion of the demand for the one or more products into a sale of the one or more products, and wherein the quantification further comprises determining self-causal weights and cross-causal weights of the one or more products using at least one of calendar events and demographic parameters, the quantified causal weights being updated using real-time article data, transaction data, and visibility data; and
set an optimal price of the one or more products based on the causality analysis of the one or more products by a non-linear price optimization technique, wherein the optimal price of the one or more products set by the non- linear price optimization technique is determined … to globally maximize a revenue and a margin associated with the one or more products,
wherein the demand forecast of the one or more products in the product cluster is performed by … and comprises a demand sensing for the one or more products to consider a long-term demand for the one or more products and a short-term demand for the one or more products, … .
The limitations of Claim 10 recites:
A method for price optimization, the method comprising:
segmenting, …, one or more products into a complementary product cluster or a competitive product cluster based on demand history data of the one or more products and attributes of the one or more products, wherein the segmentation of the one or more products into the complementary product cluster or the competitive product cluster is performed …;
performing, …, a demand forecast of the one or more products in the product cluster based on the demand history data of the one or more products and attributes of the one or more products, wherein the demand forecast of the one or more products in the product cluster yields a demand forecast for a plurality of Stock Keeping Units (SKUs) of the one or more products;
performing, …, a causality analysis of the one or more products in the product cluster based on the demand forecast of the one or more products in the product cluster, wherein the causality analysis of the one or more products in the product cluster based on the demand forecast of the one or more products in the product cluster is performed … and comprises a quantification of inter-product effects of the one or more products in the product cluster to predict a conversion rate of the conversion of the demand for the one or more products into a sale of the one or more products, and wherein the quantification further comprises determining self-causal weights and cross-causal weights of the one or more products using at least one of calendar events and demographic parameters, the quantified causal weights being updated using real-time article data, transaction data, and visibility data; and
setting, …, an optimal price of the one or more products based on the causality analysis of the one or more products by a non-linear price optimization technique, wherein the optimal price of the one or more products set by the non-linear price optimization technique is performed … to globally maximize a revenue and a margin associated with the one or more products,
wherein the demand forecast of the one or more products in the product cluster is performed … and comprises a demand sensing for the one or more products to consider a long-term demand for the one or more products and a short-term demand for the one or more products, … .
The claim limitations as drafted, recite a concept, that, under broadest reasonable interpretation, is a certain method of organizing human activity. The limitations are analogous to managing personal behavior or interactions between people (interactions between people), or a commercial or legal interaction (sales activity) such as using historical demand data to set an optimal price of a product. The generic computer implementations (see below) do not change the character of the limitations. Accordingly, the claims recite an abstract idea.
Step 2A Prong Two
The judicial exception is not integrated into a practical application. In particular, the claims recite the following additional elements:
Claim 1:
A system for price optimization, the system comprising:
a processor;
a memory coupled to the processor, wherein the memory comprises processor-executable instructions, which on execution, causes the processor to:
K-Nearest Neighbor (KNN) clustering technique
a stacked Long- and Short-Tern Memory (LSTM) neural network architecture
eXtreme Gradient Boosting (XGBoost) technique
using a real-valued Genetic Algorithm (GA)
LSTM neural network architecture includes a cell state and three gates comprising an input gate, an output gate, and a forget gate that selectively learn, unlearn, or retain information to enable hierarchical forecasting at a cluster level that is then disaggregated to SKU level using distribution weights derived from historic demand data.
Claim 10:
Processor
K-Nearest Neighbor (KNN) clustering technique
a stacked Long- and Short-Tern Memory (LSTM) neural network architecture
eXtreme Gradient Boosting (XGBoost) technique
using a real-valued Genetic Algorithm (GA)
LSTM neural network architecture includes a cell state and three gates comprising an input gate, an output gate, and a forget gate that selectively learn, unlearn, or retain information to enable hierarchical forecasting at a cluster level that is then disaggregated to SKU level using distribution weights derived from historic demand data.
These additional elements are recited at a high-level of generality such that they amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. Accordingly, the additional elements, when viewed individually and in combination, 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 do not amount to more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h))
Therefore, the claims recite an abstract idea.
Step 2B
As discussed above with respect to Step 2A Prong Two, the additional elements, amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. The same analysis applies here in 2B. The additional elements, when considered separately and in combination, do not add significantly more to the exception. They are generally linking the use of a judicial exception to a particular technological environment or field of use and cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. The claims are ineligible.
Dependent Claims
Dependent claims 2, 4-5, 11, 13-14 further narrow the same abstract ideas recited in Claim 1 and 10, respectively. Therefore, claims 2, 4-5, 11, 13-14 are directed to an abstract idea for the reasons given above.
Step 2A Prong Two
The judicial exception is not integrated into a practical application. In particular, the dependent claims recite the following additional elements:
Claim 2:
Database
Claim 11
Database
These additional elements are recited at a high-level of generality such that they amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. Accordingly, the additional elements, when viewed individually and in combination, 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 do not amount to more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). Therefore, the claims recite an abstract idea.
Step 2B
As discussed above with respect to Step 2A Prong Two, the additional elements, amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. The same analysis applies here in 2B. The additional elements, when considered separately and in combination, do not add significantly more to the exception. They are generally linking the use of a judicial exception to a particular technological environment or field of use and cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. The claims are ineligible.
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 ISMAIL A MANEJWALA whose telephone number is (571)272-8904. The examiner can normally be reached M-F 8-5.
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/ISMAIL A MANEJWALA/Examiner, Art Unit 3628
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