DETAILED ACTION
Status of the Application
In response filed on April 23, 2026, the Applicant amended claims 1-3, 7-9, and 13-15. Claims 1-18 are pending and currently under consideration for patentability.
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 .
Response to Amendments and Arguments
v Applicant has amended the claims to correct informalities identified in the previous action. These objections have been withdrawn accordingly. Examiner notes, however, that the amended claim language has introduced new limitations that are objected to. Please see the new claim objections below.
v With respect to the rejection of claims 1-18 under 35 U.S.C. §112 (b), Applicant has appropriately amended the claims. The claims have been amended such that they no longer recite that which was identified as being indefinite. These rejections of claims 1-18 under 35 U.S.C. §112 (b) have been withdrawn. Examiner notes, however, that the amended claim language has introduced new limitations that are indefinite for failing to particularly and distinctly claim the subject matter which the application regards as the invention. Please see the new rejections under 35 U.S.C. §112 (b) below.
v Applicant’s arguments, with respect to the rejection of claims 1-18 under 35 U.S.C. 101 have been fully considered and are not persuasive. The rejections of claims 1-18 under 35 U.S.C. 101 have been maintained accordingly.
Applicant specifically argues that
1) “Applicant respectfully disagrees with the Examiner's contentions, and humbly submits that amended claims 1-18 cannot be construed as directed to a mental process…Referring to Example 39”
Examiner respectfully disagrees with Applicant’s first argument.
Although the Examiner agrees that the claims no longer recite subject matter falling within the “mental processes” subject matter grouping of abstract ideas (e.g., due to the recitation of “training, via the one or more hardware processors, a pretrained tree-based ensemble classifier comprising a Light Gradient Boosted Machine (LGBM) model using the same final feature set derived from a training data and validated using a testing data”, which is a step not practically performed in the human mind), the Examiner disagrees with Applicant’s conclusory statement that “the amended claim is not directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) due to personalized promotion offer generation by understanding customer buying intent to maximize Return On Investment (ROI)”. Applicant’s lengthy characterization of the claimed invention on pages 16-18 demonstrate that the claims recite a process amounting to a commercial or legal interactions (specifically, an advertising, marketing or sales activity or behavior; business relations). For example, Applicant confirms that the process “enables optimized offers to allocate over high number of offer variants with differing offer tiers or promotion bins, category, and customer segments to customers so that invested budget results in maximizing yield and keeping the loss margin less than the budget”. This is an advertising/marketing/sales activity or behavior. The claims therefore recite limitations falling within the “certain methods of organizing human activity” subject matter grouping of abstract ideas.
Applicant specifically argues that
2) “Applicant asserts that integration of judicial exception into the practical application is achieved in terms of an improvement to computing technology and/or improving the functionality of the computer… with the capability of, calculating the promotion percentage from the data as continuous float numbers and predefined bin ranges to perform binning of the floats to those integers… with the capability of, a pretrained tree-based ensemble classifier predicts promotion offer redemption probability for each customer segment. Further, a linear optimization model assigns an optimal number of promotion offers… to attain an increasing profit margin (maximum yield), thereby generating offers as per relevance (customer buying interest). A budget constraint is applied so that maximum revenue can be attained. The final per customer allocation of the offer is based on different rules of personalization as required from the industry. The customer benefit is the difference between the actual price and the promotion price in accordance with existing promotion offers for each of the plurality of transacted products…Ex parte Desjardins…in similar lines”
Examiner respectfully disagrees with Applicant’s second argument.
It appears, from Applicant’s multiple assertions that the claims are directed an improvement to computing technology and/or improving the functionality of the computer, that the alleged improvement is to the underlying process for generating the promotion offers. Applicant has not identified any technical way in which the computers themselves are improved. An improvement to the underlying process (e.g., over conventional or existing ways of generating promotion offers) is not enough. Nor is it enough that the underlying process is useful. Even if the steps/formulas provide a useful business outcome, that is not enough for eligibility. See Univ. of Fla. Research Found., Inc. v.. Gen. Elec. Co., 916 F.3d 1363, 1367 (Fed. Cir. 2019 - the automation of data synthesis technology and device drivers for different bedside machines did not render the claims any less abstract even if the automation resulted in “life altering consequences”); See In re Mohapatra, 842 F. App’x at 638 (“[T]he fact that an abstract idea may have beneficial uses does not mean that claims embodying the abstract idea are rendered patent eligible.”); See In re Elbaum, No. 2023-1418, 2023 WL 8794636, at *2 (Fed. Cir. Dec. 20, 2023 - holding that the usefulness and tax benefits of the abstract idea were insufficient to confer patent eligibility on the claims).See In re Mohapatra, 842 F. App’x 635, 638 (Fed. Cir. 2021 - A claim does not “cease to be abstract for section 101 purposes simply because the claim confines the abstract idea to a particular technological environment in order to effectuate a real-world benefit.”). See In re Smith, No. 2022-1310, 2022 WL 4112730, *3 (Fed. Cir. Sept. 9, 2022 – “But utility is not the test for patent eligibility under the Supreme Court’s cases.”); SAP, 898 F.3d at 1163 (“We may assume that the techniques claimed are ‘[g]roundbreaking, innovative, or even brilliant,’ but that is not enough for eligibility.”) (citation omitted).
Both the MPEP and the Desjardins memo describe the types of improvement that may establish eligibility, rather than elaborating on the specific improvements that do no establish eligibility. Rather than elaborating on the specific improvements that do no establish eligibility, these sources reiterate that improvements in an abstract idea itself is not an improvement in technology (i.e., is not the type of improvement that establishes eligibility). This is the case for the instant claims.
The MPEP explains that “the ‘improvements’ analysis in Step 2A determines whether the claim pertains to an improvement to the functioning of a computer or to another technology”. MPEP 2106.05(a) explains that “in computer-related technologies, the examiner should determine whether the claim purports to improve computer capabilities or, instead, invokes computers merely as a tool…the court did not distinguish between the types of technology when determining the invention improved technology. However, 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… To show that the involvement of a computer assists in improving the technology, the claims must recite the details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient. Thus, the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology.”
The Desjarins memo explains that the invention in Desjarins was found to be directed to an improvement in the functioning of a computer, and specifically, that the specific way of training the machine learning model results in reduced storage, reduced system complexity and streamlining, and preserves the performance attributes with earlier tasks during subsequent computational tasks as part of the machine learning training process to overcome the problem of “catastrophic forgetting” encountered in continual learning systems (i.e., an improvement as to how the machine learning model itself operates).
Although Applicant’s process may have several advantages, none of the alleged advantages/improvements are technological. At most, the ordered combination of claim elements is directed to a non-technical improvement to an abstract idea itself (e.g., an improved process for generating personalized offers/promotions, such that revenue is maximized etc.).
Claim Objections
v Claims 1, 7, and 13 are objected to because of the following informalities: “exacting, via the one or more hardware processors, a first set of promotion associated features by processing processed transactions, category wise average prices, customers and their corresponding segments to generate all possible category, customer segment[[s]], and associated [[the]] one or more promotion bin[[s]] combinations to generate required features” should be inserted to replace “exacting, via the one or more hardware processors, a first set of promotion associated features by processing processed transactions, category wise average prices, customer and their corresponding segments to generate all possible categories, customer segments, the one or more promotion bins combination to generate required features” to ensure the claim language conforms with standard grammatical construction. Appropriate correction is required.
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.
v Claim(s) 1-18 is/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.
Step 1:
Claim(s) 1-6 is/are drawn to methods (i.e., a process), claim(s) 7-12 is/are drawn to systems (i.e., a machine/manufacture), and claim(s) 13-18 is/are drawn to non-transitory machine-readable information storage mediums (i.e., a machine/manufacture). As such, claims 1-18 is/are drawn to one of the statutory categories of invention (Step 1: YES).
Step 2A - Prong One:
In prong one of step 2A, the claim(s) is/are analyzed to evaluate whether it/they recite(s) a judicial exception.
Claim 7 (representative of independent claim(s) 1 and 13) recites/describes the following steps;
generate merged data from a product catalog and transaction data of an entity, wherein the product catalogue comprises a product id, a category, a subcategory, and an actual price of each of a plurality of products, and the transaction data comprises a customer id, the product id, the actual price, a promotion price, and an order id, and wherein a promotion redemption flag for each of a plurality of transacted products bought by a plurality of customers from among the plurality of products is appended to the merged data;
obtain promotion percentages based on corresponding customer benefits received by one or more customers among the plurality of customers for one or more transacted products if promotion redemption flags are set for product ids associated with customer ids corresponding to the one or more customers, wherein the customer benefit is the difference between the actual price and the promotion price in accordance with existing promotion offers for each of the plurality of transacted products;
generate a plurality of customer segments within the plurality of customers based on one of Recency, Monetary, Frequency (RFM) and Average Order Value (AOV) score derived for each of the plurality of customers by processing the transaction data;
bin a plurality of promotion offers associated with the transaction data into a plurality of promotion bins by grouping the promotion percentage based on predefined bin ranges, wherein one or more promotion bins are associated with each customer segment within each category, and wherein an average actual price per category for each of a plurality of categories is determined from the product catalog;
wherein the promotion percentages are continuous float numbers and the predefined bin ranges perform binning of the floats to those integers, wherein the floats are converted to the nearest bin values;
exact, a first set of promotion associated features by processing processed transactions, category wise average prices, customer and their corresponding segments to generate all possible categories, customer segments, the one or more promotion bins combination to generate required features, and further the generated required features are processed to obtain the first set of promotion associated features as a final feature set, wherein calculated additional features including promotion price, number of promo purchases in a segment, segment discount purchase, maximum promo purchase are appended to the final feature set;
train a pretrained tree-based ensemble classifier comprising…using the same final feature set derived from a training data and validated using a testing data
predict promotion offer redemption probability for each combination among a plurality of combinations of customer segment-promotion bin-category by applying the pretrained tree-based ensemble classifier on a first set of promotion associated features extracted by processing the plurality of customer segments, the plurality of promotion bins, and the plurality of categories;
extract a second set of promotion associated features derived from the predicted promotion offer redemption probability for each combination among the plurality of combinations;
generate an optimal number of promotion offers for each combination of the plurality of combinations of customer segment-promotion bin-category that that maximize yield of the entity by processing the second set of promotion associated features by a linear programming model, under a plurality of constraints; and
allocate the one or more promotion offers to each customer within each of the plurality of customer segments based on a (i) customer score computed for each customer using one of RFM or the AOV score, and (ii) a category score representing a number of unique product categories the customer has purchased, wherein customers with relatively lower RFM or AOV scores are allocated promotion offers from promotion bins of promotion offers having relatively higher promotion percentages
wherein performing the customer segmentation using the transaction data by using consumer habits including purchasing decision, promotion redemption habits, their brand/category affinity, the RFM score
These steps, under its broadest reasonable interpretation, describe or set-forth a process for analyzing product data, transaction data, and promotion data to generate an optimal number of promotion offers to allocate to different customer segments to maximize monetary yield. More specifically, this business process comprises generating merged data from a product catalog and transaction data of an entity (wherein the product catalogue comprises a product id, a category, a subcategory, and an actual price of each of a plurality of products, and the transaction data comprises a customer id, the product id, the actual price, a promotion price, an order id, and wherein a promotion redemption flag for each of a plurality of transacted products bought by a plurality of customers from among the plurality of products is appended to the merged data), obtaining a promotion percentage based on a customer benefit received by one or more customers among the plurality of customers for one or more transacted products if the promotion redemption flag is set for the product id associated with the customer id, wherein the customer benefit is difference between the actual price and the promotion price in accordance with existing promotion offers for each of the plurality of transacted products; generating a plurality of customer segments within the plurality of customers based on one of Recency, Monetary, Frequency (RFM) and Average Order Value (AOV) score derived for each of the plurality of customers by processing the transaction data; binning a plurality of promotion offers associated with the transaction data into a plurality of promotion bins by grouping the promotion percentage based on predefined bin ranges, wherein one or more promotion bins are associated with each customer segment within each category, and wherein an average actual price per category for each of a plurality of categories is determined from the product catalog; wherein the promotion percentages are continuous float numbers and the predefined bin ranges perform binning of the floats to those integers, wherein the floats are converted to the nearest bin values; exacting, a first set of promotion associated features by processing processed transactions, category wise average prices, customer and their corresponding segments to generate all possible categories, customer segments, the one or more promotion bins combination to generate required features, and further the generated required features are processed to obtain the first set of promotion associated features as a final feature set, wherein calculated additional features including promotion price, number of promo purchases in a segment, segment discount purchase, maximum promo purchase are appended to the final feature set; training a pretrained tree-based ensemble classifier comprising…using the same final feature set derived from a training data and validated using a testing data; predicting promotion offer redemption probability for each combination among a plurality of combinations of customer segment-promotion bin-category by applying the pretrained tree-based ensemble classifier on a first set of promotion associated features extracted by processing the plurality of customer segments, the plurality of promotion bins, and the plurality of categories; extracting a second set of promotion associated features derived from the predicted promotion offer redemption probability for each combination among the plurality of combinations; generating an optimal number of promotion offers for each combination of the plurality of combinations of customer segment-promotion bin-category that that maximize yield of the entity by processing the second set of promotion associated features by a linear programming model, under a plurality of constraints; and allocating the one or more promotion offers to each customer within each of the plurality of customer segments based on a (i) customer score computed for each customer using one of RFM or the AOV score, and (ii) a category score representing a number of unique product categories the customer has purchased, wherein customers with relatively lower RFM or AOV scores are allocated promotion offers from promotion bins of promotion offers having relatively higher promotion percentages; wherein performing the customer segmentation using the transaction data by using consumer habits including purchasing decision, promotion redemption habits, their brand/category affinity, the RFM score. This process amounts to a commercial or legal interactions (specifically, an advertising, marketing or sales activity or behavior; business relations). These limitations therefore fall within the “certain methods of organizing human activity” subject matter grouping of abstract ideas.
As such, the Examiner concludes that claim 7 recites an abstract idea (Step 2A – Prong One: YES).
Independent claim(s) 1 and 13 recite/describe nearly identical steps (and therefore also recite limitations that fall within this subject matter grouping of abstract ideas), and this/these claim(s) is/are therefore determined to recite an abstract idea under the same analysis.
Each of the depending claims likewise recite/describe these steps (by incorporation - and therefore also recite limitations that fall within this subject matter grouping of abstract ideas), and this/these claim(s) is/are therefore determined to recite an abstract idea under the same analysis. Any element(s) recited in a dependent claim that are not specifically identified/addressed by the Examiner under step 2A (prong two) or step 2B of this analysis shall be understood to be an additional part of the abstract idea recited by that particular claim. The same reasoning is similarly applicable to the limitations in the remaining dependent claims, and their respective limitations are not reproduced here for the sake of brevity.
Step 2A - Prong Two:
In prong two of step 2A, an evaluation is made whether a claim recites any additional element, or combination of additional elements, that integrate the exception into a practical application of that exception. An “addition element” is an element that is recited in the claim in addition to (beyond) the judicial exception (i.e., an element/limitation that sets forth an abstract idea is not an additional element). The phrase “integration into a practical application” is defined as requiring an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception.
The claim(s) recite the additional elements/limitations of
“processor implemented…via one or more hardware processors… via one or more hardware processors… via one or more hardware processors… via one or more hardware processors… via one or more hardware processors… via one or more hardware processors… via one or more hardware processors… via one or more hardware processors…” (claim 1)
“a system…comprising: a memory storing instructions; one or more Input/Output (1/O) interfaces; and one or more hardware processors coupled to the memory via the one or more I/O interfaces, wherein the one or more hardware processors are configured by the instructions to” (claim 7)
“one or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause” (claim 13)
“training…a pretrained tree-based ensemble classifier comprising a Light Gradient Boosted Machine (LGBM) model… by applying the pretrained tree-based ensemble classifier” (claims 1, 7, and 13)
The requirement to execute the claimed steps/functions using a method that is “processor implemented…via one or more hardware processors… via one or more hardware processors… via one or more hardware processors… via one or more hardware processors… via one or more hardware processors… via one or more hardware processors… via one or more hardware processors… via one or more hardware processors…” (claim 1) or by “a system…comprising: a memory storing instructions; one or more Input/Output (1/O) interfaces; and one or more hardware processors coupled to the memory via the one or more I/O interfaces, wherein the one or more hardware processors are configured by the instructions to” (claim 7) or by “one or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause” (claim 13) is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. Applicant’s own disclosure explains that these “additional” elements may be embodied as a general-purpose computer (e.g., the published specification at paragraphs [0051]-[0060] “the system 100…in accordance with some embodiments…processor(s)…I/O interface(s)…memory…the system 100 can be implemented in a variety of computing systems including laptop computers, notebooks, hand-held devices such as mobile phones, workstations, mainframe computers, servers, and the like…”, see also [0172]-[0173] “implementation of one or more steps of the method, when the program runs on a server or mobile device or any suitable programmable device. The hardware device can be any kind of device which can be programmed including e.g. any kind of computer like a server or a personal computer, or the like, or any combination thereof”). In other words, the claims invoke general-purpose computing elements merely as tools to execute the abstract idea. This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
The recitation of “training…a pretrained tree-based ensemble classifier comprising a Light Gradient Boosted Machine (LGBM) model… by applying the pretrained tree-based ensemble classifier” (claims 1, 7, and 13) provides nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f) and the July 2024 Subject Matter Eligibility Examples and corresponding analysis. MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. The tree-based ensemble classifier comprising a Light Gradient Boosted Machine (LGBM) model is used to generally apply the abstract idea without placing any limits on how the tree-based ensemble classifier comprising a Light Gradient Boosted Machine (LGBM) model functions. Rather, these limitations only recite the outcome of “predicting, via one or more hardware processors, promotion offer redemption probability for each combination among a plurality of combinations of customer segment-promotion bin-category” based on a set of inputs and do not include any details about how the “predicting” is accomplished. See MPEP 2106.05(f) and the July 2024 Subject Matter Eligibility Examples and corresponding analysis. This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
The recitation of “training…a pretrained tree-based ensemble classifier comprising a Light Gradient Boosted Machine (LGBM) model… by applying the pretrained tree-based ensemble classifier” (claims 1, 7, and 13) also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “pretrained tree-based ensemble classifier comprising a Light Gradient Boosted Machine (LGBM) model… by applying the pretrained tree-based ensemble classifier” limits the identified judicial exceptions to making the prediction using a “tree-based ensemble classifier comprising a Light Gradient Boosted Machine (LGBM) model”, this type of limitation merely confines the use of the abstract idea to a particular technological environment (tree-based ensemble classifier comprising a Light Gradient Boosted Machine (LGBM) model) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h) and the July 2024 Subject Matter Eligibility Examples and corresponding analysis. This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(g)).
Even if considered to be an “additional” element for the purpose of the eligibility analysis (which the Examiner maintains, they are not), the recitation “by a linear programming model” (claims 1, 7, and 13) provides nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f) and the July 2024 Subject Matter Eligibility Examples and corresponding analysis. MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. The recited “linear programming model” (claims 1, 7, and 13) are used to generally apply the abstract idea without placing any limits on how the linear programming model function. Rather, these limitations only recite the outcomes of and “generating an optimal number of promotion offers for each combination of the plurality of combinations of customer segment-promotion bin-category that maximizes yield of the entity by processing the second set of promotion associated features…under a plurality of constraints” (describing the output of the linear programming model and high-level inputs) and do not include any details about how the actual “generating…” functions are accomplished. See MPEP 2106.05(f) and the July 2024 Subject Matter Eligibility Examples and corresponding analysis. This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
Even if considered to be an “additional” element for the purpose of the eligibility analysis (which the Examiner maintains, they are not), the recitation of “by a linear programming model” (claims 1, 7, and 13) also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the recitation of “by a linear programming model” (claims 1, 7, and 13) limits the identified judicial exceptions to generating an optimal number of promotion offers “by a linear programming model” (claims 1, 7, and 13), these types of limitation merely confines the use of the abstract idea to a particular technological environment (linear programming models) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h) and the July 2024 Subject Matter Eligibility Examples and corresponding analysis. This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(g)).
Furthermore, although the claims recite a specific sequence of computer-implemented functions, and although the specification suggests certain functions may be advantageous for various reasons (e.g., business reasons), the Examiner has determined that the ordered combination of claim elements (i.e., the claims as a whole) are not directed to an improvement to computer functionality/capabilities, an improvement to a computer-related technology or technological environment, and do not amount to a technology-based solution to a technology-based problem. For example, Applicant’s published specification suggests that it is advantageous to implement the claimed business process because doing so can increase customer satisfaction (e.g., personalized/relevant offers they are likely to use) while helping an entity (e.g., retailer offering the promotions) to optimize their ROI or profit margin (see, for example, Applicant’s published disclosure at paragraphs [0004]-[0005] & [0048]-[0049] & [0135] & [0170). These are non-technical business advantages/improvements. At most, the ordered combination of claim elements is directed to a non-technical improvement to an abstract idea itself (e.g., an improved process for generating promotion offers to allocate to customers).
Dependent claims 2-7, 8-12, and 14-18 fail to include any additional elements. In other words, each of the limitations/elements recited in respective dependent claims 2-7, 8-12, and 14-18 is/are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e. they are part of the abstract idea recited in each respective claim). For example, claim 2 recites “wherein allocating the one or more offers is based on one or more rules set by the entity, wherein the rules comprise (i) allocating a single promotion offer is to a customer from a collection of multiple eligible offers, wherein the single promotion offer is selected based on a highest inventory, and (ii) allocating one or more promotion offer based on a brand of the product the customer has transacted using category-based offers”. This is an additional abstract limitation which further sets forth the abstract idea encompassed by claim 2. For example, this limitation describes one of the steps of the business process (allocation of the offers), which is also a description of one of the mental steps/processes. This limitation is not an “additional element”, and therefore it is not subject to further analysis under Step 2A- Prong Two or Step 2B. The same logic applies to each of the other dependent claims, whose limitations are not being repeated here for the sake of brevity and clarity. With respect to the other dependent claims not specifically listed here - each of the limitations/elements recited in these dependent claims other than those identified as being “additional” elements above (at the beginning of the Prong One analysis), are further part of the abstract idea encompassed by each respective dependent claim (i.e. it should be understood that these limitations are part of the abstract idea recited in each respective claim).
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claim(s) is/are directed to an abstract idea (Step 2A – Prong two: NO).
Step 2B:
In step 2B, the claims are analyzed to determine whether any additional element, or combination of additional elements, is/are sufficient to ensure that the claims amount to significantly more than the judicial exception. This analysis is also termed a search for an "inventive concept." An "inventive concept" is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim as a whole amounts to significantly more than the judicial exception itself. Alice Corp., 134 S. Ct. at 2355, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966)
As discussed above in “Step 2A – Prong 2”, the requirement to execute the claimed steps/functions using a method that is “processor implemented…via one or more hardware processors… via one or more hardware processors… via one or more hardware processors… via one or more hardware processors… via one or more hardware processors… via one or more hardware processors… via one or more hardware processors… via one or more hardware processors…” (claim 1) or by “a system…comprising: a memory storing instructions; one or more Input/Output (1/O) interfaces; and one or more hardware processors coupled to the memory via the one or more I/O interfaces, wherein the one or more hardware processors are configured by the instructions to” (claim 7) or by “one or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause” (claim 13) is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. These limitations therefore do not qualify as “significantly more” (see MPEP 2106.05(f)).
As discussed above in “Step 2A – Prong 2”, the recitation of “training…a pretrained tree-based ensemble classifier comprising a Light Gradient Boosted Machine (LGBM) model… by applying the pretrained tree-based ensemble classifier” (claims 1, 7, and 13) is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. These limitations therefore do not qualify as “significantly more” (see MPEP 2106.05(f)).
As discussed above in “Step 2A – Prong 2”, the recitation of “training…a pretrained tree-based ensemble classifier comprising a Light Gradient Boosted Machine (LGBM) model… by applying the pretrained tree-based ensemble classifier” (claims 1, 7, and 13) also serves merely to generally link the use of the judicial exception to a particular technological environment or field of use. These limitations therefore do not qualify as “significantly more” (see MPEP 2106.05(g)).
As discussed above in “Step 2A – Prong 2”, even if considered to be an “additional” element for the purpose of the eligibility analysis (which the Examiner maintains, they are not), the recitation of “by a linear programming model” (claims 1, 7, and 13) is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. These limitations therefore do not qualify as “significantly more” (see MPEP 2106.05(f)).
As discussed above in “Step 2A – Prong 2”, even if considered to be an “additional” element for the purpose of the eligibility analysis (which the Examiner maintains, they are not), the recitation of “by a linear programming model” (claims 1, 7, and 13) also serves merely to generally link the use of the judicial exception to a particular technological environment or field of use. These limitations therefore do not qualify as “significantly more” (see MPEP 2106.05(g)).
Viewing the additional limitations in combination also shows that they fail to ensure the claims amount to significantly more than the abstract idea. When considered as an ordered combination, the additional components of the claims add nothing that is not already present when considered separately, and thus simply append the abstract idea with words equivalent to “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer, and generally link the abstract idea to a particular technological environment or field of use.
Dependent claims 2-7, 8-12, and 14-18 fail to include any additional elements. In other words, each of the limitations/elements recited in respective dependent claims 2-7, 8-12, and 14-18 is/are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e. they are part of the abstract idea identified by the Examiner to which each respective claim is directed).
The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claim(s) amount to significantly more than the abstract idea identified above (Step 2B: NO).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(B) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
v Claims 1, 7, and 13 recite "wherein the promotion percentages are continuous float numbers and the predefined bin ranges perform binning of the floats to those integers, wherein the floats are converted to the nearest bin values." There is insufficient antecedent basis for the underlined limitations in the claims. Furthermore, the metes and bounds of “the predefined bin ranges perform binning” is unclear, as it is unclear how predefined bin ranges can perform the action/function of binning. For the purpose of examination, the phrase “wherein the promotion percentages are continuous float numbers and the predefined bin ranges perform binning of the floats to those integers, wherein the floats are converted to the nearest bin values” will be interpreted as being “wherein the promotion percentages are continuous float numbers, [[and]] the predefined bin ranges are suitable for binning [[of]] the continuous float[[s]] numbers when converted to nearest integers, and wherein the continuous float[[s]] numbers are converted to [[the]] nearest integers for binning in a respective predefined bin range bin .”
Each of the dependent claims are similarly rejected by virtue of their dependency on one of these claims.
v Claims 1, 7, and 13 recite “training…a pretrained tree-based ensemble classifier comprising a Light Gradient Boosted Machine (LGBM) model… by applying the pretrained tree-based ensemble classifier” (claims 1, 7, and 13), which is vague and indefinite. It is unclear how one would train a pretrained classifier/model (i.e., one that is already trained). A PHOSITA would understand that either a classifier/model is trained, or a pretrained classifier/model is retrained. For the purpose of examination, the phrase “training…a pretrained tree-based ensemble classifier comprising a Light Gradient Boosted Machine (LGBM) model” will be interpreted as being “training…a comprising a Light Gradient Boosted Machine (LGBM) model.”, and all subsequent references to the “the pretrained tree-based ensemble classifier” will be interpreted as “the trained tree-based ensemble classifier”
Each of the dependent claims are similarly rejected by virtue of their dependency on one of these claims.
v Claims 1 recites "wherein the; extracting, via one or more hardware processors….". There is insufficient antecedent basis for the underlined limitations in the claims. There is no previous mention of an extracting step. For the purpose of examination, the phrase “wherein the; extracting, via one or more hardware processors…” will be interpreted as being “”.
Each of dependent claims 2-6 are similarly rejected by virtue of their dependency on this claim.
Indication of Novel and Non-Obvious Subject Matter
Independent claims 1, 7, and 13 recite novel and non-obvious subject matter. Each of the dependent claims similarly recite novel and non-obvious subject matter by virtue of their dependency on one of these claims.
The following is an examiner’s statement of reasons for indication of novel and non-obvious subject matter:
The closest prior art of record is Bangad et al. (U.S. PG Pub No. 2024/0362676 -October 31, 2024); Qi et al. (U.S. PG Pub No. 2021/0118004 - April 22, 2021); Klein et al. (U.S. PG Pub No. 2008/0065464 – March 13, 2008); Yang et al. (U.S. PG Pub No. 2020/0357013 November 12, 2020); Chang et al. (U.S. PG Pub No. 2020/0027119 January 23, 2020); Sewak et al. (U.S. PG Pub No. 2017/0372338 – December 28, 2017); Thayaparan et al. (U.S. PG Pub No. 2023/0096633 – March 30, 2023); Bibelnicks et al. (U.S. PG Pub No. 2008/0015936, January 17, 2008); Corke. (U.S. PG Pub No. 2010/0094693 April 15, 2010); Reed (U.S. Patent No. 7,707,059 – April 27, 2010); and “Supply Chain Demand Forecasting and Price Optimization Models with Substitution Effect” (Hee Lee, Keun; Abdollahian, Mali; Schreider, Sergei; and Taheri, Sona - Mathematics 2023, 11, 2502. https://doi.org/10.3390/math11112502)
Bangad teaches a system for optimizing offer/incentive generation and delivery. Discloses analyzing historical product data and historical transaction data of an entity, generation of a plurality of customer groups (segments) based on quantitative analysis of the historical data, using derived statistical models (e.g., a tree-based model such as a decision tree) to forecast redemption rates for hypothetical offers/incentives for different customer segments, and use of a linear programming model (under a plurality of constraints) to determine an optimal offer/incentive generation and distribution plan based at least in part on combinations of customer segments and product category groupings. The optimization goal may be revenue maximization or incremental sales gain. Further discloses use of gift cards as a discount mechanism.
Qi teaches a system for personalizing offers/promotions, and optimizing offer/incentive generation and delivery. Discloses analyzing historical product data and historical transaction data of an entity, extracting various features (e.g., customer features, product features) and using these extracted features to determine predicted redemption/sales rates for hypothetical offers/incentives for customers by applying a pretrained tree-based ensemble classifier (e.g., gradient boosted decision trees). The optimization goal may be revenue maximization
Klein teaches a system for optimizing offer/incentive generation and delivery. Discloses analyzing historical product data and historical transaction data of an entity, using the historic transaction data to generate a plurality of customer segments and binning data associated with various offer/incentive features as well as the customer segment data in order to compile various statistics for each of the bins, generating customer RFM scores, and using a linear optimization model (under a plurality of constraints) to determine an optimal combination of offer/incentive variables based on this statistical data that maximizes revenue.
Yang teaches a system for optimizing offer/incentive generation and delivery. Discloses analyzing historic transaction data to generate a plurality of customer segments, determining predicted redemption/sales rates for hypothetical offers/incentives and estimated ROI by applying a pretrained tree-based ensemble classifier (e.g., gradient boosted decision tree, XGBoost model) to the data. The optimization goal may be ROI maximization.
Chang teaches a system for optimizing offer/incentive generation and delivery. Discloses analyzing historic transaction data and offer data to generate a model that predicts redemption/sales rates for hypothetical offers/incentives for various customer segments (or other types of segments, such as temporal segments, product segments, etc.), and further discloses extracting promotion features from the predicted promotion offer redemption probabilities.
Sewak teaches deriving customer RFM scores to generating customer segments, and binning data associated with various offer/incentive features as well as data associated with these customer segments data in order to compile various statistics for each of the offer/segment bins.
Thayaparan teaches analyzing historic transaction data and product price data using a combination of a pretrained tree-based ensemble classifier (e.g., XGBoost model, random forest) and linear programming model to generate an optimal product pricing strategy that optimizes revenue/profit.
Bibelnicks teaches a system for optimizing offer/incentive generation and delivery. Discloses analyzing historic transaction data to derive customer RFM score, generating customer segments based at least in part on the RFM scores, and applying a linear programming model (under a plurality of constraints) to determine an optimal offer/incentive generation and distribution plan based at least in part on combinations of customer segments. The optimization goal may be ROI/revenue maximization. The linear programming model can be used to determine the optimal number of promotion offers to send to each customer segment.
Corke teaches a system for optimizing offer/incentive generation and delivery. Discloses analyzing historic transaction data, determining predicted redemption/sales rates for hypothetical offers/incentives by applying a pretrained tree-based ensemble classifier (e.g., random forest) to the data, and subsequent use of a linear programming model (under a plurality of constraints) to determine an optimal offer/incentive generation and distribution plan.
Reed teaches a system for optimizing offer/incentive generation and delivery. Discloses analyzing historic transaction data, determining AOV scores for customers, segmenting customer based at least in part on the AOV scores, use of ML models to determining predicted redemption/sales rates for hypothetical offers/incentives for different customer segments, and optimizing campaign parameters using this forecasted information.
“Supply Chain Demand Forecasting and Price Optimization Models with Substitution Effect” discloses use of pretrained tree-based ensemble classifiers to analyze historic transaction data associated with customer segments to generate optimal product pricing plans/strategies.
As per Independent claims 1, 7, and 13, the closest prior art of record taken either individually or in combination with other prior art of record fails to teach or suggest the specific combination of limitations required by the claim language. Specifically, the closest prior art of record taken either individually or in combination fails to teach or suggest the specific combination of
“generate merged data from a product catalog and transaction data of an entity, wherein the product catalogue comprises a product id, a category, a subcategory, and an actual price of each of a plurality of products, and the transaction data comprises a customer id, the product id, the actual price, a promotion price, and an order id, and wherein a promotion redemption flag for each of a plurality of transacted products bought by a plurality of customers from among the plurality of products is appended to the merged data;
obtain promotion percentages based on corresponding customer benefits received by one or more customers among the plurality of customers for one or more transacted products if promotion redemption flags are set for product ids associated with customer ids corresponding to the one or more customers, wherein the customer benefit is the difference between the actual price and the promotion price in accordance with existing promotion offers for each of the plurality of transacted products;
generate a plurality of customer segments within the plurality of customers based on one of Recency, Monetary, Frequency (RFM) and Average Order Value (AOV) score derived for each of the plurality of customers by processing the transaction data;
bin a plurality of promotion offers associated with the transaction data into a plurality of promotion bins by grouping the promotion percentage based on predefined bin ranges, wherein one or more promotion bins are associated with each customer segment within each category, and wherein an average actual price per category for each of a plurality of categories is determined from the product catalog;
wherein the promotion percentages are continuous float numbers and the predefined bin ranges perform binning of the floats to those integers, wherein the floats are converted to the nearest bin values;
exact, a first set of promotion associated features by processing processed transactions, category wise average prices, customer and their corresponding segments to generate all possible categories, customer segments, the one or more promotion bins combination to generate required features, and further the generated required features are processed to obtain the first set of promotion associated features as a final feature set, wherein calculated additional features including promotion price, number of promo purchases in a segment, segment discount purchase, maximum promo purchase are appended to the final feature set;
train a pretrained tree-based ensemble classifier comprising a Light Gradient Boosted Machine (LGBM) model using the same final feature set derived from a training data an validated using a testing data;
predict promotion offer redemption probability for each combination among a plurality of combinations of customer segment-promotion bin-category by applying the pretrained tree-based ensemble classifier on a first set of promotion associated features extracted by processing the plurality of customer segments, the plurality of promotion bins, and the plurality of categories;
extract a second set of promotion associated features derived from the predicted promotion offer redemption probability for each combination among the plurality of combinations;
generate an optimal number of promotion offers for each combination of the plurality of combinations of customer segment-promotion bin-category that that maximize yield of the entity by processing the second set of promotion associated features by a linear programming model, under a plurality of constraints; and
allocate the one or more promotion offers to each customer within each of the plurality of customer segments based on a (i) customer score computed for each customer using one of RFM or the AOV score, and (ii) a category score representing a number of unique product categories the customer has purchased, wherein customers with relatively lower RFM or AOV scores are allocated promotion offers from promotion bins of promotion offers having relatively higher promotion percentages; wherein performing the customer segmentation using the transaction data by using consumer habits including purchasing decision, promotion redemption habits, their brand/category affinity, the RFM score”.
Various prior art references together disclose merging historic product data and transaction data, including the various pieces of product and transaction data required by the claim language. Various prior art references also disclose scoring customers with AOV scores (Reed) and RFM scores (Klein, Sewak, Bibelnicks) to derive customer segments, binning data associated with customer segments and historic promotions and product data such as product categories (Klein, Sewak), predicting promotion offer redemption by applying a pretrained tree-based ensemble classifier (Bangad, Qi, Yang, Thayaparan, Corke, “Supply Chain Demand Forecasting and Price Optimization Models with Substitution Effect”), extracting various features from predicted promotion redemption probability (Chang), use of a linear programming model to generate optimal promotion offer plans that maximize revenue/ROI for the entity (Bangad, Klein, Thayaparan, Bibelnicks, Corke), including the generation of an optimal number of promotion offers to provide for each customer segment (Bibelnicks), as well as allocating the optimal promotion offers to the customers. However, while each of the individual claim features may have been known per se, there is no teaching or suggestion absent Applicants’ own disclosure to combine each of these individual features to arrive at the specific combination of limitations required by the claim language other than with impermissible hindsight.
Claims 2-6, 8-12, and 14-18 depend upon claims 1, 7, or 13 and have all the limitations of claims 1, 7, or 13, and therefore similarly recite novel and non-obvious subject matter.
Conclusion
No claim is allowed
THIS ACTION IS MADE FINAL. 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 extension fee 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 JAMES M DETWEILER whose telephone number is (571)272-4704. The examiner can normally be reached on Monday-Friday from 8 AM to 5 PM ET.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Waseem Ashraf can be reached at telephone number (571)-270-3948. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JAMES M DETWEILER/Primary Examiner, Art Unit 3621
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