CTNF 18/184,416 CTNF 90029 DETAILED ACTION Status of the Application Claims 1-20 have been examined in this application. This communication is the first action on the merits. The information disclosure statement (IDS) submitted on 03/15/2023; was filed with this application. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status This action is a Non-Final Action on the merits in response to the application filed on 03/15/2023. Claims 1-20 remain pending in this application. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1-8 are directed towards a method, claims 9-16 are directed towards a system. and claims 17-20 are directed towards a computer-readable medium, all of which are among the statutory categories of invention. Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claim recites at least one step or act, including using optimization models. Thus, the claim is to a process, which is one of the statutory categories of invention. (Step 1: YES). Step 2A, Prong One : This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim. With respect to claims 1-20, the independent claims (claims 1, 9, and 17) are directed to managing broadcasting stream user accounts, In independent claim 1, the bolded limitations emphasized below correspond to the abstract ideas of the claimed invention: Claim 1, A computer-implemented method comprising: identifying, for an omnichannel retailer, and based on input quantitative data and input qualitative data, a collection of business constraints and a collection of business goals; determining, using a machine learning model with the collection of business constraints and collection of business goals, numerical parameters for use in an executable optimization model ; outputting the available-to-promise value to an e-commerce system for presentation to potential purchasers. these steps fall within and recite an abstract ideas because they are directed to a method of organizing human activity which includes commercial interaction such as business relations (See MPEP 2106.04(a)(2), subsection II). If a claim limitation, under its broadest reasonable interpretation, covers commercial interaction , then it falls within the “method of organizing human activity” grouping of abstract ideas. Therefore, If the identified limitation(s) falls within any of the groupings of abstract ideas enumerated in the MPEP 2106, the analysis should proceed to Prong Two. (Step 2A, Prong One: YES). Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). The claim recites the additional elements of machine learning model, optimization model, (Claim 9 recites memory, processor; claim 17 recite computer readable storage medium, processing circuit). The claims recite the steps are performed by the machine learning model, optimization model. The limitations of building the executable optimization model using at least one goal of the collection of goals as a respective at least one objective in the optimization model , at least one constraint of the collection of constraints as a respective at least one constraint in the optimization model , and at least one of the determined numerical parameters as at least one additional parameter in the executable optimization model ; executing the executable optimization model and generating, based on the executing, an available-to-promise value, the available-to-promise value comprising an indication of how much inventory of the retailer should be made available to promise to prospective online orders; and outputting the available-to-promise value to an e-commerce system for presentation to potential purchasers are mere data processing and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05. Further, the limitations are recited as being performed by machine learning model, optimization model. The machine learning model, optimization model are recited at a high level of generality. In limitation (a), the machine learning model, optimization model are used as a tool to perform the generic computer function of receiving data. See MPEP 2106.05(f). The machine learning model, optimization model are used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). Additionally, claim 1 recites machine learning model. The general use of a machine learning technique does not provide a meaningful limitation to transform the abstract idea into a practical application. Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES). Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05. As explained with respect to Step 2A, Prong Two, the additional elements are the machine learning model, optimization model. The additional elements were found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data processing and outputting. Then, the machine learning techniques recited in the claim are disclosed at a high-level of generality (see at least Specification [0049 “ using a machine learning model with the collection of business constraints and collection of business goals ”]) and does not amount to significantly more than the abstract idea. However, a conclusion that an additional element is insignificant extra solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g). As discussed in Step 2A, Prong Two above, the recitations of building the executable optimization model using at least one goal of the collection of goals as a respective at least one objective in the optimization model , at least one constraint of the collection of constraints as a respective at least one constraint in the optimization model , and at least one of the determined numerical parameters as at least one additional parameter in the executable optimization model ; executing the executable optimization model and generating, based on the executing, an available-to-promise value, the available-to-promise value comprising an indication of how much inventory of the retailer should be made available to promise to prospective online orders; and outputting the available-to-promise value to an e-commerce system for presentation to potential purchasers are recited at a high level of generality. These elements amount to transmitting data and are well understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. 10 As discussed in Step 2A, Prong Two above, the recitation of a machine learning model, optimization model to perform limitations amounts to no more than mere instructions to apply the exception using a generic computer component. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. (Step 2B: NO). Dependent claims 2-8, 10-16, 18-20 do not contain any new additional elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims. In this case, the claims are rejected for the same reasons at step 2a, prong one; step 2a, prong 2; and step 2b. Thus, the claim is not patent eligible. Regarding the dependent claims, dependent claims 3-5, 8, 11-13, 16, 19 recite machine learning model, predictive model; claim 7, 15, 20 recite software.. The dependent claims 2-8, 10-16, 18-20 recite limitations that are not technological in nature and merely limits the abstract idea to a particular environment. Claims 2-8, 10-16, 18-20 recites machine learning model, optimization model which are considered an insignificant extra-solution activities of processing and analyzing data; see MPEP 2106.05(g). Claims 2-8, 10-16, 18-20 recites machine learning model, optimization model, which merely recites an instruction to apply the abstract idea using a generic computer component; MPEP 2106.05(f). Additionally, claims 2-8, 10-16, 18-20 recite steps that further narrow the abstract idea. No additional elements are disclosed in the dependent claims that were not considered in independent claims 1, 9, and 17. Therefore claims 2-8, 10-16, 18-20 do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. While the Specification at paragraph 0017 explicitly states that the computer readable medium is not to be construed as storage in the form of transitory signals per se, Claims 17 through 20 are directed to a computer readable medium. The USPTO recognizes that applicants may have claims directed to computer readable media that cover signals per se, which the USPTO must reject under35 U.S.C. § 101 as covering both non-statutory subject matter and statutory subject matter. In an effort to assist the patent community in overcoming a rejection or potential rejection under35 US.C. §101 in this situation, the USPTO suggests the following approach. A claim drawn to such a computer readable storage medium that covers both transitory and non-transitory embodiments may be amended to narrow the claim to cover only statutory embodiments to avoid a rejection under 35 USC. § 101 by adding the limitation "non-transitory" to the claim. Claim Rejections - 35 USC § 103 07-20-aia AIA 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 of this title, 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. 07-21-aia AIA Claim s 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Publication US 20140372231, Karmon, et al. to hereinafter Karmon in view of United States Patent Publication US 20220270128, Padmanabhan, et al. to hereinafter Padmanabhan in view of United States Patent Publication US 20080065407, Klett, et al . Referring to Claim 1, Karmon teaches a computer-implemented method comprising: identifying, for an omnichannel retailer, and based on input quantitative data and input qualitative data, a collection of business constraints and a collection of business goals ( Karmon: Sec. 0005, “Online selling mechanism creation is described, for example, whereby a retailer with business goals and constraints benefits from automatic generation and execution of an automated selling mechanism so as to meet the business goals within the constraints.” Karmon: Sec. 0018, “The configuration and customization component 100 receives any one or more of: business goals and constraints 102, data from a retailer inventory 108, data from a retailer sales log 110, data from external sources 118 .” Karmon: Claim. 1, “1. A computer-implemented method comprising: receiving at a processor data comprising any one or more of: a business goal, a business constraint, retailer inventory data, retailer sales log data ; configuring and customizing at least one selling mechanism…” ); Karmon teaches shows business goals/constraints and quantitative inventory/sales data. determining, using a machine learning model ( See Padmanabhan ) with the collection of business constraints and collection of business goals, numerical parameters for use in an executable optimization model ( See Padmanabhan ) ( Karmon: Sec. 0017, “The selling mechanisms are in the form of software implementing algorithms, rules and/or criteria which may be used to control the generation of sales offers in the form of online offers.” Karmon: Sec. 0017, “Examples of selling mechanisms at the database 112 include but are not limited to: price optimization algorithms, auction rules and algorithms, product substitution algorithms, product bundling processes, group buying processes, raffle processes and bilateral negotiation rules and algorithms .; an auction may have properties that include but are not limited to: auction interaction protocol, bid expressiveness, closing rules, allocation rules, pricing rules, and parameters such as floor price, price increments and price decrements . ” Karmon: Sec. 0018, “The configuration and customization component 100 receives any one or more of: business goals and constraints 102, data from a retailer inventory 108, data from a retailer sales log 110 …” ); Karmon teaches using business goals/constraints and data to configure algorithms. building the executable optimization model ( See Padmanabhan ) using at least one goal of the collection of goals as a respective at least one objective in the optimization model ( See Padmanabhan ), at least one constraint of the collection of constraints as a respective at least one constraint in the optimization model ( See Padmanabhan ), and at least one of the determined numerical parameters as at least one additional parameter in the executable optimization model ( See Padmanabhan ) ( Karmon: Sec. 0036, “The configuration and customization component maps 302 the business goals and constraints to selling mechanism properties and in so doing identifies selling mechanisms which may be used.The identified selling mechanisms are configured 304 by instantiating an instance of the identified selling mechanism software from the generic software. The software instance is customized according to the constraints.” ); Karmon teaches showing mapping goals/constraints into mechanisms. executing the executable optimization model ( See Padmanabhan ) and generating, based on the executing, an available-to-promise value, the available-to-promise value ( See Klett ) comprising an indication of how much inventory of the retailer should be made available to promise ( See Klett ) to prospective online orders ( Karmon: Sec. 0016, “The configuration and customization component 100 … produces a bespoke online selling mechanism 114.” Karmon: Sec. 0032, “The commerce mechanism 202 is arranged to control the creation and output of online offers from the commerce server 200 according to the bespoke online selling mechanism 114.” Karmon: Claim 1, “1. A computer-implemented method comprising: … executing the configured and customized selling mechanism to control generation of offers by a commerce server. ” ); Karmon teaches execution controls how much inventory is exposed/available in offers. outputting the available-to-promise value ( See Klett ) to an e-commerce system for presentation to potential purchasers ( Karmon: Sec. 0032, “The commerce mechanism 202 is arranged to control the creation and output of online offers from the commerce server 200 according to the bespoke online selling mechanism 114.” Karmon: Sec. 0033, “The commerce server is arranged to send offers to an end user device 208 such as a smart phone, a personal computer, a laptop computer or any other end user computing device which is able to display offers.” ). Karmon teaches output to an e-commerce system and onward presentation. Karmon does not explicitly teach machine learning model; numerical parameters for use in an executable optimization model; at least one of the determined numerical parameters as at least one additional parameter in the executable optimization model. However, Padmanabhan teaches machine learning model; numerical parameters for use in an executable optimization model; at least one of the determined numerical parameters as at least one additional parameter in the executable optimization model ( Padmanabhan: Sec. 0007, “Disclosed herein is a method and system that combines machine-learning and optimization for solving constraint problems, where traditional linear programming is not possible due to mixed constraints (real, integer and Boolean)” Padmanabhan: Sec. 0008, “Disclosed herein is a method and system in which a demand for every item in a retailer's ‘universe’ (that is, every item that the retailer is planning to stock and sell) is forecasted , assuming that a given item will be sold at a regular price (without any special promotion), as well as sold with the application of one or more candidate promotions. These forecasts can be used to calculate the anticipated cost of every promotion and its overall effect on sales. This information is used to formulate an optimization problem that allows for a suggestion as to how, and when, to promote which items, in order to maximize overall sales.” Padmanabhan: Sec. 0012, “The methodology can accurately forecast a system response to small perturbations to subsets of variables (among many hundreds), which result in millions to billions of combinations when optimizing against multiple objectives/constraints.” Padmanabhan: Sec. 0017, “The computer-implemented method may also include an AI demand forecasting engine that uses a machine learning model selected from at least one of: Deep Learning, Gradient Boosted Trees, Random Forest, Kernel Density Estimators, Gaussian Processes, Isolation Forests, Generalized Additive Models, Representation Learning, Non-parametric techniques, Econometric Models, Bayesian Models, Time-Series Models, and Bayesian Additive Regression Trees ” ) Padmanabhan teaches a machine-learning model (AI demand forecasting engine) that produces numerical forecast parameters (demand/uplift) which are explicitly used to formulate and solve an optimization problem, satisfying the use of ML to determine numerical parameters for an executable optimization model. Karmon and Padmanabhan are both directed to the analysis of product management (See Karmon at 0017, 0019, 0023; Padmanabhan at 0054-0059). Karmon discloses that additional elements, such as the business goals and mechanisms can be considered (See Karmon at 0018, 0026). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Karmon, which teaches detecting and repairing business management problems in view of Padmanabhan, to efficiently apply analysis of product management to by using machine learning based demand forecasts and formal optimization (MILP, branch and cut) to better satisfy business goals and constraints in an online retail/supply chain environment.. (See Padmanabhan at 0036, 0040, 0062, 0085, ). Karmon in view of Padmanabhan does not explicitly teach an available-to-promise value, the available-to-promise value comprising an indication of how much inventory of the retailer should be made available to promise to prospective online orders. However, Klett teaches an available-to-promise value, the available-to-promise value comprising an indication of how much inventory of the retailer should be made available to promise to prospective online orders ( Klett: Sec. 0010, “The present invention provides a method and a system for determining a promise date for a demand whose supply depends on constraints in a business environment. According to the present invention, information related to the business environment is stored within a data storage means. The present invention provides a processor coupled to the data storage means from which it requests and retrieves data. The processor also determines a plan date for each supply in the supply structure. … After determining a plan date, the processor determines an available date for each supply in the supply structure. … Finally, the processor generates a promise date for the demand for the item based on each available date determined for a corresponding supply in the supply structure ” Klett: Sec. 0011, “The present invention is advantageous in that it generates promise dates based on information available in real-time on both the supply structure and the capacity constraints for a given item. ” Klett: Sec. 0030, “The planning engine 30 determines plan dates for each supply in the supply structure based on the availability of capacity constraints and existing and planned supplies required to fulfill a demand for a given item. The execution engine 35 determines available dates for each supply in the supply structure based on the availability of supplies, the availability of capacity constraints and the plan date of the supplies determined by the planning engine 30. Finally, the execution engine 35 generates a promise date for the demand based on the determined available dates. ” Klett: Sec. 0003, “The present invention relates to the field of committing to the completion of an activity or delivery of a demand in a business environment by providing a promised completion, or delivery date, for an item in a business environment.” ) Klett teaches generating an ATP-like value (promise date) based on real-time information about supply structure; capacity and demand constraints in a business environment. Karmon, Padmanabhan, and Klett are all directed to the analysis of product management (See Karmon at 0017, 0019, 0023; Padmanabhan at 0054-0059; Klett at 0033, 0057, 0071). Karmon discloses that additional elements, such as the business goals and mechanisms can be considered (See Karmon at 0018, 0026). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Karmon in view of Padmanabhan, which teaches detecting and repairing business management problems in view of Klett, to efficiently apply analysis of product management to improving the accuracy and responsiveness of online offers by incorporating real-time supply and capacity constraints so that commitments made to customers (e.g., dates and effective availability) reflect actual supply capabilities for promise dates based on constraints and supply structure (See Klett at 0011, 0037, 0038, 0042). Referring to Claim 2, Karmon teaches the method of claim 1, wherein the identifying the collection of business constraints and collection of business goals comprises identifying at least one selected from the group consisting of (i) one or more business constraint of the collection of business constraints, and (ii) one or more business goal of the collection of business goals, by applying automated content analysis against at least one selected from the group consisting of the quantitative data, and the qualitative data ( Karmon: Claim 18, “a software wizard 104 … arranged to receive input from a marketeer in response to a series of questions and to determine at least one business goal and at least one business constraint from the received input.” [file:79, p.11, claim 18] (Wizard-based; not “content analysis” of raw data.) Karmon: Claim 19, “an automated process which analyses a retailer inventory and/or a retailer sales log to obtain the received data.” [file:79, p.11, claim 19] (Analysis of structured data.) ). Karmon teaches you can chart this as partial support for automated content analysis of text; your system uses it for goals/constraints instead of promotions. Referring to Claim 3, Karmon teaches the method of claim 1, Karmon does not explicitly teach further comprising building the machine learning model as a predictive model, wherein the using the machine learning model with the collection of business constraints and collection of business goals to determine the numerical parameters comprises using the predictive model to predict the estimated parameters as output of the predictive model. However, Padmanabhan teaches further comprising building the machine learning model as a predictive model, wherein the using the machine learning model with the collection of business constraints and collection of business goals to determine the numerical parameters comprises using the predictive model to predict the estimated parameters as output of the predictive model ( Padmanabhan: Sec. 0012, “The methodology can accurately forecast a system response to small perturbations to subsets of variables…” Padmanabhan: Sec. 0013, “In one aspect, a computer-implemented method for constraint-based optimization, the method includes: receiving… historical data…; generating… entities…; forecasting, by the AI demand forecasting engine, an objective associated with each entity …” Padmanabhan: Sec. 0017, “The computer-implemented method may also include an AI demand forecasting engine that uses a machine learning model selected from at least one of: Deep Learning, Gradient Boosted Trees, Random Forest, …, Time-Series Models, and Bayesian Additive Regression Trees. ” ). Padmanabhan teaches that these are direct word-for-word support for a ML predictive model producing numerical outputs used by optimization. Karmon and Padmanabhan are both directed to the analysis of product management (See Karmon at 0017, 0019, 0023; Padmanabhan at 0054-0059). Karmon discloses that additional elements, such as the business goals and mechanisms can be considered (See Karmon at 0018, 0026). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Karmon, which teaches detecting and repairing business management problems in view of Padmanabhan, to efficiently apply analysis of product management to by using machine learning based demand forecasts and formal optimization (MILP, branch and cut) to better satisfy business goals and constraints in an online retail/supply chain environment.. (See Padmanabhan at 0036, 0040, 0062, 0085, ). Referring to Claim 4, Karmon teaches the method of claim 3, Karmon does not explicitly teach wherein the building the predictive model comprises using at least one selected from the group of regression techniques consisting of: linear regression, random decision forest, and gradient-boosting. Karmon does not explicitly teach wherein the building the predictive model comprises using at least one selected from the group of regression techniques consisting of: linear regression, random decision forest, and gradient-boosting ( Padmanabhan: Sec. 0017, “The computer-implemented method may also include an AI demand forecasting engine that uses a machine learning model selected from at least one of: Deep Learning, Gradient Boosted Trees, Random Forest, Kernel Density Estimators, Gaussian Processes, Isolation Forests, Generalized Additive Models, Representation Learning, Non-parametric techniques, Econometric Models, Bayesian Models, Time-Series Models, and Bayesian Additive Regression Trees. ” ). Padmanabhan teaches Gradient Boosted Trees and Random Forest. Karmon and Padmanabhan are both directed to the analysis of product management (See Karmon at 0017, 0019, 0023; Padmanabhan at 0054-0059). Karmon discloses that additional elements, such as the business goals and mechanisms can be considered (See Karmon at 0018, 0026). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Karmon, which teaches detecting and repairing business management problems in view of Padmanabhan, to efficiently apply analysis of product management to by using machine learning based demand forecasts and formal optimization (MILP, branch and cut) to better satisfy business goals and constraints in an online retail/supply chain environment.. (See Padmanabhan at 0036, 0040, 0062, 0085, ). Referring to Claim 5, Karmon teaches the method of claim 4, Karmon does not explicitly teach wherein the building the predictive model further comprises using at least one time series analysis. However, Padmanabhan teaches wherein the building the predictive model further comprises using at least one time series analysis. ( Padmanabhan: Sec. 0017, “The computer-implemented method may also include an AI demand forecasting engine that uses a machine learning model selected from at least one of: Deep Learning, Gradient Boosted Trees, Random Forest, Kernel Density Estimators, Gaussian Processes, Isolation Forests, Generalized Additive Models, Representation Learning, Non-parametric techniques, Econometric Models, Bayesian Models, Time-Series Models, and Bayesian Additive Regression Trees. ” ). Karmon and Padmanabhan are both directed to the analysis of product management (See Karmon at 0017, 0019, 0023; Padmanabhan at 0054-0059). Karmon discloses that additional elements, such as the business goals and mechanisms can be considered (See Karmon at 0018, 0026). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Karmon, which teaches detecting and repairing business management problems in view of Padmanabhan, to efficiently apply analysis of product management to by using machine learning based demand forecasts and formal optimization (MILP, branch and cut) to better satisfy business goals and constraints in an online retail/supply chain environment.. (See Padmanabhan at 0036, 0040, 0062, 0085, ). Referring to Claim 6, Karmon teaches the method of claim 1, Karmon does not explicitly teach wherein the determined numerical parameters include at least one selected from the group consisting of (i) prediction of future demand and inventory, (ii) future order cancellation probability, and (iii) future reschedule probability. However, Padmanabhan teaches wherein the determined numerical parameters include at least one selected from the group consisting of (i) prediction of future demand and inventory, (ii) future order cancellation probability, and (iii) future reschedule probability ( Padmanabhan: Sec. 0008, “Disclosed herein is a method and system in which a demand for every item in a retailer's ‘universe’ … is forecasted , assuming that a given item will be sold at a regular price … as well as sold with the application of one or more candidate promotions. These forecasts can be used to calculate the anticipated cost of every promotion and its overall effect on sales. ” Padmanabhan teaches future demand. Karmon and Padmanabhan are both directed to the analysis of product management (See Karmon at 0017, 0019, 0023; Padmanabhan at 0054-0059). Karmon discloses that additional elements, such as the business goals and mechanisms can be considered (See Karmon at 0018, 0026). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Karmon, which teaches detecting and repairing business management problems in view of Padmanabhan, to efficiently apply analysis of product management to by using machine learning based demand forecasts and formal optimization (MILP, branch and cut) to better satisfy business goals and constraints in an online retail/supply chain environment.. (See Padmanabhan at 0036, 0040, 0062, 0085, ). Referring to Claim 7, Karmon teaches the method of claim 1, Karmon does not explicitly teach wherein the executing the executable optimization model is performed by solver software that runs the executable optimization model using one or more mathematical decision making techniques to solve for the available-to- promise value as a decision variable of the executable optimization model. However, Padmanabhan teaches wherein the executing the executable optimization model is performed by solver software that runs the executable optimization model using one or more mathematical decision making techniques to solve for the available-to- promise value as a decision variable of the executable optimization model ( Padmanabhan: Sec. 0004, l“One approach is to model this as a combined linear-programming and machine-learning problem ; while trivial for real-valued (continuous) constraints, objective-functions for this problem involve optimizations across real, integer, and Boolean variables, which is NP-complete …” Padmanabhan: Sec. 0005, “A subsequent approach is to consider a constraint-based optimization mixed with machine-learning from historical data based on a mixed-linear-programming approach (MILP). ” Padmanabhan: Sec. 0017, “The computer-implemented method may further include: … using, by the optimization engine, Boolean variables to generate the plurality of plans; and applying, by the optimization engine, a branch-and-cut approach to maximize the objective. ” Padmanabhan: Sec. 0065, “The optimization engine 408 then explores all possible plans. A particular plan is made up of a particular subset of entities. Each plan is checked against the list of constraints 404. Only a plan that satisfies all conditions is considered as a candidate plan for the optimal plan. The total uplift is computed for each of the candidate plans. The optimal plan is one with the highest total uplift ” ). Padmanabhan teaches branch-and-cut and Boolean variables are useful solver quotes. Karmon and Padmanabhan are both directed to the analysis of product management (See Karmon at 0017, 0019, 0023; Padmanabhan at 0054-0059). Karmon discloses that additional elements, such as the business goals and mechanisms can be considered (See Karmon at 0018, 0026). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Karmon, which teaches detecting and repairing business management problems in view of Padmanabhan, to efficiently apply analysis of product management to by using machine learning based demand forecasts and formal optimization (MILP, branch and cut) to better satisfy business goals and constraints in an online retail/supply chain environment.. (See Padmanabhan at 0036, 0040, 0062, 0085, ). Referring to Claim 8, Karmon teaches the method of claim 1, further comprising repeating the identifying, the using a machine learning model, the building an executable optimization model, the executing the executable optimization model, and the outputting, based on receiving orders and on changed at least one selected from the group consisting of: business constraints, and business goals of the retailer ( Karmon: Sec. 0005, “Online selling mechanism creation is described, for example, whereby a retailer with business goals and constraints benefits from automatic generation and execution of an automated selling mechanism so as to meet the business goals within the constraints. For example, as business goals and constraints change over time, bespoke selling mechanisms may be automatically updated. ” Karmon: Sec. 0036, “The configured and customized software may then be executed at the commerce mechanism in order to operate 308 the bespoke selling mechanisms. As new business goals and/or constraints are identified the process may repeat to enable the bespoke selling mechanism to dynamically adjust. ” ). Karmon teaches that there word support for repetition of identification, configuration, and execution upon changes in goals/constraints Claims 9-16 recite limitations that stand rejected via the art citations and rationale applied to claims 1-8. Regarding a computer system comprising: a memory; a processor in communication with the memory ( Karmon: Sec. 0053, “Computer-readable media may include, for example, computer storage media such as memory 612 and communications media. Computer storage media, such as memory 612 , includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EPROM, EEPROM, flash memory or other memory technology” Karmon: Sec. 0052, “Computing-based device 600 comprises one or more processors 602 which may be microprocessors, controllers or any other suitable type of processors for processing computer executable instructions to control the operation of the device in order to dynamically generate software implementing bespoke online selling mechanisms. In some examples, for example where a system on a chip architecture is used, the processors 602 may include one or more fixed function blocks (also referred to as accelerators) which implement a part of the method of dynamically generating offers using retailer inventory data in hardware (rather than software or firmware).” ), Claims 17-20 recite limitations that stand rejected via the art citations and rationale applied to claims 1-3, and 7. Regarding, a computer program product comprising: a computer readable storage medium readable by a processing circuit and storing instructions for execution by the processing circuit ( Karmon: Sec. 0057, “methods described herein may be performed by software in machine readable form on a tangible storage medium e.g. in the form of a computer program comprising computer program code means adapted to perform all the steps of any of the methods described herein when the program is run on a computer and where the computer program may be embodied on a computer readable medium. Examples of tangible storage media include computer storage devices comprising computer-readable media such as disks, thumb drives, memory etc. and do not include propagated signals” Karmon: Sec. 0053, “computer executable instructions may be provided using any computer-readable media that is accessible by computing based device 600 . Computer-readable media may include, for example, computer storage media such as memory 612 and communications media. Computer storage media, such as memory 612 , includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EPROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information for access by a computing device. In contrast, communication media may embody computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave, or other transport mechanism. ” ) Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Menezes et al., U.S. Pub. 20180075379, (discussing providing a knowledge-management foundation to explain all relevant failure modes” and “representing and locating risks through a process ontology and a process map. The system uses a system’s ontology framework and knowledge-management framework to store and organize lifecycle risk knowledge, including failure modes and events; features (events, failure modes, KPIs, lifecycle stages) are associated with ontology-based representations.). Daitou et al., W.O. Pub. 2018042940, (discussing the software and system components for managing industrial equipment across multiple systems. One system tracks power state or operating state information for equipment or areas, while another system tracks maintenance history and maintenance status. The invention links these two kinds of records so the user can see which monitored device corresponds to which maintenance object. It then presents the linked information together on a display. The display may show monitoring data, maintenance data, alerts, and detailed diagnostics in coordinated windows. When a trigger occurs, such as abnormal power usage or abnormal operating behavior, the system identifies the related equipment and brings up its maintenance information. The user can also move in the opposite direction, starting from maintenance records and then viewing the associated monitoring data. The system supports automatic registration updates when new devices are added on either the monitoring side or the maintenance side with managing production.). Favi et al., A Design For Disassembly Approach To Analyze And Manage End-Of-Life Options For Industrial Products In The Early Design Phase, https://www.academia.edu/download/49186748/Chapter_15.pdf, Technology and Manufacturing Process Selection: The Product Life Cycle Perspective, 2013 (discussing the product disassembly in a manufacturing environment in which, product disassembly is an important phase of the product lifecycle to consider from the environmental and economic point of view.). Any inquiry concerning this communication or earlier communications from the examiner should be directed to UCHE BYRD whose telephone number is (571)272-3113. The examiner can normally be reached Mon.-Fri.. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /UCHE BYRD/Examiner, Art Unit 3624 Application/Control Number: 18/184,416 Page 1 Art Unit: 3624
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