DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 11/4/2025 has been entered.
Status of Claims
This is in reply to the claim amendments and remarks of the RCE filed 11/4/2025.
Claims 1, 11, and 20 have been amended.
Claims 1, 3-5, 7-11, 13-15, and 17-24 are currently pending and have been examined.
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
Applicant’s amendments have been fully considered, but do not overcome the previously pending 35 USC 103 and 35 USC 101 rejections.
Response to Arguments
Applicant's arguments have been fully considered but they are not persuasive.
With regard to the limitations of claims 1, 3-5, 7-11, 13-15, and 17-24, Applicant argues that the claims are allowable over 35 USC 103 because the claim amendments overcome the current art rejection. The Examiner respectfully disagrees. Please see the updated rejection below since amendments by Applicant require additional reference to the Examiner’s art rejection.
The prioritization amendments have been addressed by the Examiner by citing a new prior art reference necessitated by amendment. Please see below.
Applicant argues the cited prior art does not disclose the generic machine learning recited by the claims. The Examiner respectfully disagrees. The Examiner asserts that Sarin et al. teach input at least portions of the historical product data to at least one machine learning model and generate predicted values; compare the predicted values with corresponding expected values to generate comparison values; input the comparison values to the at least one machine learning model to generate at least one trained machine learning model; input the plurality of market inputs to the at least one trained machine learning model (See Paragraph 0023 – “self-educating/self-learning system to calculate optimal markdown price based on factors such as, store specific product sales and product inventory levels (perpetual inventory), and historic success rate of selling reduced items to ensure marked down items achieve the best possible optimum selling price”, Paragraph 0037 – “The Bayesian Network algorithm may be based at least in part on historical values”, Paragraph 0065, Paragraph 0070, Paragraph 0073 – “During the training period the system is provided different values of markdown percentages for different days of the week and for different occurrences of holidays or seasons among others. The corresponding impact on rate of sale for an item is then observed and provided to the system. This training exercise is carried over a period of time, and makes up the initial data set to calculate the initial conditional probabilities. Addition of new data to the system over the period of time helps achieve steady state, that is the expected rate of sale for a given markdown percentage becomes nearly constant”, and Paragraph 0074), where the limitations are cited basically word for word. The Applicant copy and pastes the entire independent claim alleging it is not taught. The Examiner recommends taking a closer look at the Sarin et al. reference for specific details covering the claimed machine learning. Applicant’s arguments are not persuasive.
With regard to the limitations of claims 1, 3-5, 7-11, 13-15, and 17-24, Applicant argues that the claims are patent eligible under 35 USC 101 because the pending claims are not directed toward an abstract idea. The Examiner respectfully disagrees. The Examiner has already set forth a prima facie case under 35 USC 101. Applicant merely copy and pastes the entire independent claim and alleges it is eligible. The Examiner has clearly pointed out the limitations directed towards the abstract idea, what the additional elements are and why they do not integrate the abstract idea into a practical application, and why the additional elements and remaining limitations do not amount to significantly more than the abstract idea. Applicant’s arguments are not persuasive.
Applicant argues the claims are related to example 39. The Examiner respectfully disagrees. The Examiner asserts that the Example 39 does not recite an abstract idea because the neural network is claimed at such a high level of generality that it merely adds the words apply it with the judicial exception (See MPEP 2106), where the Examiner agrees that the claimed machine learning is so broad that it is not abstract as shown in the rejection. The Examiner asserts the machine learning model claimed is also claimed at such a high level of generality that it merely adds the words apply it with the judicial exception, but the other limitations do recite an abstract idea (as shown). The Examiner further asserts the running a machine learning model on a general purpose computer does not make the claims eligible. Applicant’s arguments are not persuasive.
The Examiner asserts that analyzing data to determine markdown values for products is a commercial activity, which is an abstract idea (Organizing Human Activity). The Examiner asserts the MPEP provides a non-exhaustive list of examples of abstract ideas. Applicant’s arguments are not persuasive.
Applicant argues the claims recite an improvement to the technology. The Examiner respectfully disagrees. The Examiner asserts that automating tasks using a general purpose computer does not improve the technology, but rather merely adds the words apply it with the judicial exception (See MPEP 2106). Applicant’s claims are merely using the general purpose computer to implement the abstract idea. The Examiner further asserts the machine learning model claimed is also claimed at such a high level of generality that it merely adds the words apply it with the judicial exception (See MPEP 2106). Applicant’s arguments are not persuasive.
The Examiner notes that PTAB decisions are not controlling for the claims at hand and are unrelated. The Examiner has done a full eligibility analysis in accordance with the MPEP as shown below. Applicant’s arguments are not persuasive.
Applicant argues the Memorandum shows the claims are eligible, but does not recite any details from the claims as to how the claims are eligible. The generating (e.g. the how) of the markdown plan is the abstract idea. Applicant does not properly identify the additional elements. Applicant’s arguments are not persuasive.
Applicant argues analyzing historical data makes the claims amount to significantly more because they are not taught by the cited prior art. The Examiner respectfully disagrees. The Examiner further points to MPEP 2106.05 which states “the search for an inventive concept should not be confused with a novelty or non-obviousness determination. See Mayo, 566 U.S. at 91, 101 USPQ2d at 1973 (rejecting "the Government’s invitation to substitute §§ 102, 103, and 112 inquiries for the better established inquiry under § 101 "). As made clear by the courts, the "‘novelty’ of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter”, where a narrow abstract idea is still an abstract idea. Applicant’s arguments are not persuasive.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-5, 7-11, 13-15, and 17-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter;
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself.
In the instant case (Step 1), claims 1, 3-5, 7-11, 13-15, and 17-24 are directed toward a process, a product, and a system; which are statutory categories of invention.
Additionally (Step 2A Prong One), the independent claims are directed toward a system, comprising: a database storing historical product data associated with a plurality of products; and a computing device comprising a transceiver and at least one processor in communication with the database and the transceiver, the computing device being configured to: receive, via the transceiver and over a network, the historical product data from the database; input at least portions of the historical product data to at least one machine learning model and generate predicted values; compare the predicted values with corresponding expected values to generate comparison values; input the comparison values to the at least one machine learning model to generate at least one trained machine learning model; identify, based on the historical product data, one or more products from the plurality of products for implementation of a markdown; receive, from a user interface, a plurality of market inputs associated with the one or more products, the plurality of market inputs identifying item-store attributes and inventory levels of the one or more products; input the plurality of market inputs to the at least one trained machine learning model and generate an optimal time period and optimal markdown value associated with the markdown each of the one or more products, wherein the optimal time period identifies an amount of time before the optimal markdown value is to be implemented; generate a prioritization indication for each of the one or more products based on the optimal time period and the optimal markdown value, wherein the prioritization indication comprises a priority value in a range of priority values; generate a markdown plan associated with the one or more products based on the prioritization indication of each the one or more products, wherein the markdown plan comprises a markdown amount, a markdown spend, and a projected sell-through lift for each of the one or more products; display the markdown plan to a user via the user interface; receive, from the user interface, one or more constraints; update the markdown plan based on the one or more constraints; and transmit, via the transceiver and over the network, the adjusted markdown plan to a user device for implementation (Organizing Human Activity), which are considered to be abstract ideas (See MPEP 2106). The steps/functions disclosed above and in the independent claims are directed toward the abstract idea of Organizing Human Activity because the claimed limitations are analyzing historical product data against market inputs from a human user to determine an optimal time period to markdown/discount products based on prioritization to generate markdown plans for humans to implement, which is a commercial interaction.
Dependent claims 3-5, 7-10, 13-15, 17-19, and 21-24 further narrow the abstract idea identified in the independent claims, where any additional elements introduced are discussed below.
Step 2A Prong Two: In this application, even if not directed toward the abstract idea, the independent claims additionally recite “a system, comprising: a database; and a computing device comprising a transceiver and at least one processor in communication with the database and the transceiver, the computing device being configured to: via the transceiver and over a network, from the database; to at least one machine learning model; input the comparison values to the at least one machine learning model, from a user interface, to a user via the user interface, via the transceiver and over the network (claim 1)”; “computer-implemented method; via a transceiver and over a network; a database; to at least one machine learning model; inputting the comparison values to the at least one machine learning model; to a user interface, via the transceiver and over the network (claim 11)”; “non-transitory computer readable medium having instructions stored thereon, wherein the instructions, when executed by at least one processor, cause at least one device to perform operations comprising; via the transceiver and over a network, from the database; to at least one machine learning model; inputting at least portions of the historical product data to at least one machine learning model, from a user interface, to a user via the user interface, via the transceiver and over the network (claim 20)”, which are additional elements that do not integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See MPEP 2106) and are recited at such a high level of generality. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. Even when viewed in combination, the additional elements in the claims do no more than use the computer components as a tool. There is no change to the computer or other technology that is recited in the claim, and thus the claims do not improve computer functionality or other technology.
In addition, dependent claims 3-5, 7-10, 13-15, 17-19, and 21-24 further narrow the abstract idea and dependent claims 3-4, 8-9, 13-14, and 19 additionally recite “one or more retailers (claims 3, 8, 13, and 18); an inventory replenishment system (claims 4 and 14); user input via the user interface (claims 9 and 19)” which do not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See MPEP 2106.05(f)).
Step 2B: When analyzing the additional element(s) and/or combination of elements in the claim(s) other than the abstract idea per se the claim limitations amount(s) to no more than: a general link of the use of an abstract idea to a particular technological environment and merely amounts to the application or instructions to apply the abstract idea on a computer (See MPEP 2106). Further, method; System; and Product Independent claims 1, 11, and 20 recite “a system, comprising: a database; and a computing device comprising a transceiver and at least one processor in communication with the database and the transceiver, the computing device being configured to: via the transceiver and over a network, from the database; to at least one machine learning model; input the comparison values to the at least one machine learning model, from a user interface, to a user via the user interface, via the transceiver and over the network (claim 1)”; “computer-implemented method; via a transceiver and over a network; a database; to at least one machine learning model; inputting the comparison values to the at least one machine learning model; to a user interface, via the transceiver and over the network (claim 11)”; “non-transitory computer readable medium having instructions stored thereon, wherein the instructions, when executed by at least one processor, cause at least one device to perform operations comprising; via the transceiver and over a network, from the database; to at least one machine learning model; inputting at least portions of the historical product data to at least one machine learning model, from a user interface, to a user via the user interface, via the transceiver and over the network (claim 20)”; however, these elements merely facilitate the claimed functions at a high level of generality and they perform conventional functions and are considered to be general purpose computer components which is supported by Applicant’s specification in Paragraphs 0109-0110 and Figures 1-2. The Applicant’s claimed additional elements are mere instructions to implement the abstract idea on a general purpose computer and generally link of the use of an abstract idea to a particular technological environment. When viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
In addition, claims 3-5, 7-10, 13-15, 17-19, and 21-24 further narrow the abstract idea identified in the independent claims. The Examiner notes that the dependent claims merely further define the data being analyzed and how the data is being analyzed. Similarly, claims 3-4, 8-9, 13-14, and 19 additionally recite “one or more retailers (claims 3, 8, 13, and 18); an inventory replenishment system (claims 4 and 14); user input via the user interface (claims 9 and 19)” which do not account for additional elements that amount to significantly more than the abstract idea because the claimed structure merely amounts to the application or instructions to apply the abstract idea on a computer and does not move beyond a general link of the use of an abstract idea to a particular technological environment (See MPEP 2106.05). The additional limitations of the independent and dependent claim(s) when considered individually and as an ordered combination do not amount to significantly more than the abstract idea. The examiner has considered the dependent claims in a full analysis including the additional limitations individually and in combination as analyzed in the independent claim(s). Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 3-5, 7-11, 13-15, and 17-24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Forcada Margarit et al. (US 2024/0169387 A1) in view of Sarin et al. (US 2017/0116631 A1) and further in view of Starostenko (US 2022/0083954 A1).
Regarding Claim 1: Forcada Margarit et al. teach a system, comprising (See Figure 1):
a database storing historical product data associated with a plurality of products (See Paragraph 0028 – “The first data structure is a date store SKU (Table 1) that contains information about the products (both historical and markdown scope)”, Paragraph 0031, and claim 1);
and a computing device comprising a transceiver and at least one processor in communication with the database and the transceiver, the computing device being configured to (See Figure 1 and Figures 9-10):
receive, via the transceiver and over a network, the historical product data from the database (See Paragraph 0028 – “The first data structure is a date store SKU (Table 1) that contains information about the products (both historical and markdown scope)” and claim 1);
identify, based on the historical product data, one or more products from the plurality of products for implementation of a markdown (See Paragraph 0024 – “The data structures 15 include fields that have data product identifiers defined herein as product class and one or more fields for product metric. The markdown engine 12 provides an optimized allocation of recommended discounts, i.e., recommended markdowns across plural stock keeping units and, optionally stock keeping units and stores”, Paragraph 0028 – “The first data structure is a date store SKU (Table 1) that contains information about the products (both historical and markdown scope)”, Paragraph 0032 – “The markdown process 40 also includes picklist selection 49 to determine which products to include”, and claim 1);
receive, from a user interface, a plurality of market inputs associated with the one or more products, the plurality of market inputs identifying item-store attributes and inventory levels of the one or more products (See Figure 2, Figure 7, Figure 8, Paragraph 0024 – “The data processing system 10 includes a markdown engine 12 and an input data store 14 … The input data store 14 has data that define “products” as product class and product metrics (e.g., product parameters)”, Paragraph 0027, Paragraph 0091, and claim 1);
and generate an optimal time period and optimal markdown value associated with the markdown each of the one or more products, wherein the optimal time period identifies an amount of time before the optimal markdown value is to be implemented (See Figure 2, Figure 7, Paragraph 0012 – “provide a markdown and lifecycle management tool that enables merchandisers to maximize margins by determining the right time and discount for every product”, Paragraph 0024 – “The markdown engine 12 provides an optimized allocation of recommended discounts, i.e., recommended markdowns across plural stock keeping units and, optionally stock keeping units and stores”, Paragraph 0025 – “The optimizer engine 24 produces from the demand forecast, the received goals 25a and the received constraints 25b, an optimized discount path”, Paragraph 0031, Paragraph 0051, Paragraph 0053 – “a prediction of a sales volume forecast over time per discount”, and claim 1);
generate a prioritization indication for each of the one or more products based on the optimal time period and the optimal markdown value; generate a markdown plan associated with the one or more products based on the prioritization indication of each the one or more products, wherein the markdown plan comprises a markdown amount, a markdown spend, and a projected sell-through lift for each of the one or more products; display the markdown plan to a user via the user interface (See Figure 2, Figure 7, Paragraph 0024 – “data that define “products” as product class and product metrics (e.g., product parameters) … The markdown engine 12 provides an optimized allocation of recommended discounts, i.e., recommended markdowns across plural stock keeping units and, optionally stock keeping units and stores”, Tables 1-6, Paragraph 0032 – “category and other attributes and performance such as selling speed, coverage weeks (stock/average sales)”, Paragraph 0051, Paragraph 0053, Paragraph 0075 – “price to be below cost … Minimum and maximum sell through”, Paragraph 0077 – “The updated demand forecast is fed to optimizer 54′ that optimizes the updated demand forecast according to received goals 55a and constraints 55b, e.g., an available budget and outputs an updated set of recommendations 62”, and claim 1);
receive, from the user interface, one or more constraints; update the markdown plan based on the one or more constraints; and transmit, via the transceiver and over the network, the adjusted markdown plan to a user device for implementation (See Figure 2, Figure 7, Paragraph 0024 – “The markdown engine 12 provides an optimized allocation of recommended discounts, i.e., recommended markdowns across plural stock keeping units and, optionally stock keeping units and stores”, Paragraph 0051, Paragraph 0077 – “The updated demand forecast is fed to optimizer 54′ that optimizes the updated demand forecast according to received goals 55a and constraints 55b, e.g., an available budget and outputs an updated set of recommendations 62”, Paragraph 0080 – “determine that the received goals 55a and constraints are not satisfied, the react process 58 re-optimizes 58i by re-executing the optimize process 54 for the discount path for the set of SKU's by determining a new, updated discount path for the set of SKU's”, and claim 1).
Forcada Margarit et al. do not specifically disclose input at least portions of the historical product data to at least one machine learning model and generate predicted values; compare the predicted values with corresponding expected values to generate comparison values; input the comparison values to the at least one machine learning model to generate at least one trained machine learning model; input the plurality of market inputs to the at least one trained machine learning model; wherein the prioritization indication comprises a priority value in a range of priority values. However, Sarin et al. further teach:
input at least portions of the historical product data to at least one machine learning model and generate predicted values; compare the predicted values with corresponding expected values to generate comparison values; input the comparison values to the at least one machine learning model to generate at least one trained machine learning model; input the plurality of market inputs to the at least one trained machine learning model (See Paragraph 0023 – “self-educating/self-learning system to calculate optimal markdown price based on factors such as, store specific product sales and product inventory levels (perpetual inventory), and historic success rate of selling reduced items to ensure marked down items achieve the best possible optimum selling price”, Paragraph 0037 – “The Bayesian Network algorithm may be based at least in part on historical values”, Paragraph 0065, Paragraph 0070, Paragraph 0073 – “During the training period the system is provided different values of markdown percentages for different days of the week and for different occurrences of holidays or seasons among others. The corresponding impact on rate of sale for an item is then observed and provided to the system. This training exercise is carried over a period of time, and makes up the initial data set to calculate the initial conditional probabilities. Addition of new data to the system over the period of time helps achieve steady state, that is the expected rate of sale for a given markdown percentage becomes nearly constant”, and Paragraph 0074).
The teachings of Forcada Margarit et al. and Sarin et al. are related because both are analyzing product data to make determinations about markdowns. Therefore it would have been obvious to one of ordinary skill in the art at the effective filing date of the claimed invention to have modified the product markdown analysis and optimization system of Forcada Margarit et al. to incorporate the use of machine learning of Sarin et al. in order to ensure the markdowns are optimized based on the companies needs.
Forcada Margarit et al. in view of Sarin et al. do not specifically disclose wherein the prioritization indication comprises a priority value in a range of priority values. However, Starostenko further teaches wherein the prioritization indication comprises a priority value in a range of priority values (See Paragraph 0010, Paragraph 0098 – “a prioritization weight”, and Paragraph 0122).
The teachings of Forcada Margarit et al., Sarin et al., and Starostenko are related because all are analyzing product data to make determinations about selling products. Therefore it would have been obvious to one of ordinary skill in the art at the effective filing date of the claimed invention to have modified the product markdown analysis and optimization system of Forcada Margarit et al. in view of Sarin et al. to incorporate the prioritization weight of Starostenko in order to give priority to certain aspects of products, which improves the results of the analysis based on user needs.
Regarding Claim 3: Forcada Margarit et al. in view of Sarin et al. and further in view of Starostenko teach the limitations of claim 1. Forcada Margarit et al. further teach wherein the computing device is further configured to: in response to the generation of the markdown plan, transmit the markdown plan to one or more retailers for implementation of the markdown of the one or more products (See Figure 2, Figure 7, Paragraph 0024 – “The markdown engine 12 provides an optimized allocation of recommended discounts, i.e., recommended markdowns across plural stock keeping units and, optionally stock keeping units and stores”, Paragraph 0051, Paragraph 0077 – “The updated demand forecast is fed to optimizer 54′ that optimizes the updated demand forecast according to received goals 55a and constraints 55b, e.g., an available budget and outputs an updated set of recommendations 62”, and claim 1).
Regarding Claim 4: Forcada Margarit et al. in view of Sarin et al. and further in view of Starostenko teach the limitations of claim 3. Forcada Margarit et al. further teach wherein the computing device is further configured to: in response transmitting the markdown plan, automatically update an inventory of the one or more products via an inventory replenishment system (See Figure 2, Figure 7, Figure 8, Paragraph 0031, Paragraph 0051 – “Forecast inventory at end of an end of season sale (EOSS) for all seasonal and non-seasonal articles if no intervention made (under the assumption that the articles remain in current discount cluster). Prioritize SKUs with high inventory and high expected responsiveness to discounts. Work with business to finalize SKUs and compare with control stores”, Paragraph 0079 – “The react process 58 has the data processing system 10 receive 58a current sales of a set of SKU's, current inventory levels for each SKU of the set of SKU's”, and claim 1).
Regarding Claim 5: Forcada Margarit et al. in view of Sarin et al. and further in view of Starostenko teach the limitations of claim 3. Forcada Margarit et al. further teach wherein the computing device is further configured to: in response transmitting the markdown plan, automatically update a price of the one or more products with an updated price based on the generated optimal markdown value (See Figure 2, Figure 7, Paragraph 0024 – “The markdown engine 12 provides an optimized allocation of recommended discounts, i.e., recommended markdowns across plural stock keeping units and, optionally stock keeping units and stores”, Paragraph 0025 – “The optimizer engine 24 produces from the demand forecast, the received goals 25a and the received constraints 25b, an optimized discount path 26”, Paragraph 0066 – “all permutations of discounts and pricing paths are assessed”, and claim 1).
Regarding Claim 7: Forcada Margarit et al. in view of Sarin et al. and further in view of Starostenko teach the limitations of claim 1. Forcada Margarit et al. further teach wherein the computing device is further configured to: execute, via one or more models, a bi-level optimization model for generating the optimal time period and the optimal markdown value (See Figure 2, Figure 7, Paragraph 0012 – “provide a markdown and lifecycle management tool that enables merchandisers to maximize margins by determining the right time and discount for every product”, Paragraph 0024 – “The markdown engine 12 provides an optimized allocation of recommended discounts, i.e., recommended markdowns across plural stock keeping units and, optionally stock keeping units and stores”, Paragraph 0025 – “The optimizer engine 24 produces from the demand forecast, the received goals 25a and the received constraints 25b, an optimized discount path”, Paragraph 0031, Paragraph 0051, Paragraph 0064 – “Boost models … Uplift models”, Paragraph 0077 – “The updated demand forecast is fed to optimizer 54′ that optimizes the updated demand forecast according to received goals 55a and constraints 55b, e.g., an available budget and outputs an updated set of recommendations 62”, and claim 1 – “an artificial intelligence (AI) system”).
Regarding Claim 8: Forcada Margarit et al. in view of Sarin et al. and further in view of Starostenko teach the limitations of claim 1. Forcada Margarit et al. further teach wherein the computing device is further configured to: receive a markdown request from one or more retailers, the markdown request including desired metrics and generate the markdown plan based on the desired metrics (See Figure 2, Figure 7, Figure 8, Paragraph 0024 – “The data processing system 10 includes a markdown engine 12 and an input data store 14 … The input data store 14 has data that define “products” as product class and product metrics (e.g., product parameters)”, Paragraph 0025 – “receives a picklist selection”, Paragraph 0027, Paragraph 0077 – “The updated demand forecast is fed to optimizer 54′ that optimizes the updated demand forecast according to received goals 55a and constraints 55b, e.g., an available budget and outputs an updated set of recommendations 62”, Paragraph 0080 – “determine that the received goals 55a and constraints are not satisfied, the react process 58 re-optimizes 58i by re-executing the optimize process 54 for the discount path for the set of SKU's by determining a new, updated discount path for the set of SKU's”, Paragraph 0091, and claim 1).
Regarding Claim 9: Forcada Margarit et al. in view of Sarin et al. and further in view of Starostenko teach the limitations of claim 1. Forcada Margarit et al. further teach wherein the computing device is further configured to: generate a projected sell through at out-of-stock date associated with the one or more products; compare the projected sell through at out-of-stock date with a target sell through at out-of-stock date to generate a comparison, the target sell through at out-of-stock date being based on user input via the user interface; and based on the comparison, generate the optimal time period and the optimal markdown value (See Figure 2, Figure 7, Figure 8, Paragraphs 0024-0025, Paragraph 0031, Paragraph 0051 – “Forecast inventory at end of an end of season sale (EOSS) for all seasonal and non-seasonal articles if no intervention made (under the assumption that the articles remain in current discount cluster). Prioritize SKUs with high inventory and high expected responsiveness to discounts. Work with business to finalize SKUs and compare with control stores”, Paragraph 0056 – “stock-out dates based on user input in the stock-out table”, Paragraph 0079 – “The react process 58 has the data processing system 10 receive 58a current sales of a set of SKU's, current inventory levels for each SKU of the set of SKU's”, and claim 1).
Regarding Claim 10: Forcada Margarit et al. in view of Sarin et al. and further in view of Starostenko teach the limitations of claim 1. Forcada Margarit et al. further teach wherein the prioritization indication is based on one or more user specific constraints (See Figure 2, Figure 7, Paragraph 0024 – “The markdown engine 12 provides an optimized allocation of recommended discounts, i.e., recommended markdowns across plural stock keeping units and, optionally stock keeping units and stores”, Paragraph 0051, Paragraph 0077 – “The updated demand forecast is fed to optimizer 54′ that optimizes the updated demand forecast according to received goals 55a and constraints 55b, e.g., an available budget and outputs an updated set of recommendations 62”, and claim 1).
Regarding Claims 11, 13-15, and 17-24: Claims 11, 13-15, and 17-24 recite limitations already addressed by the rejections of claims 1, 3-5, and 7-10 above; therefore the same rejections apply.
Conclusion
The prior art made of record, but not relied upon is considered pertinent to Applicant's disclosure is listed on the attached PTO-892 and should be taken into account / considered by the Applicant upon reviewing this office action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW D HENRY whose telephone number is (571)270-0504. The examiner can normally be reached on Monday-Thursday 9AM-5PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, BRIAN EPSTEIN can be reached on (571)-270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MATTHEW D HENRY/Primary Examiner, Art Unit 3625