DETAILED ACTION
Acknowledgement
This non-final office action is in response to claims filed on 12/30/2024.
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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/30/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention, “Systems and Methods for Optimizing Product Revenue”, is directed to an abstract idea, specifically Mental Processes and Certain Methods of Organizing Human Activity, without significantly more. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements individually or in combination provide mere instructions to implement the abstract idea on a computer.
Step 1: Claims 1-18 are directed to a statutory category, namely a process (claims 1-6), a machine (claims 7-12), and a manufacture (claims 13-18).
Step 2A (1): Independent claims 1, 7, and 13 are directed to an abstract idea of Mental Processes and Certain Methods of Organizing Human Activity, based on the following claim limitations: “receiving,…, a product family further comprising one or more of product models, from a user, wherein each product model amongst the one or more product models comprises a plurality of feature families, wherein each feature family amongst the plurality of feature families comprises a plurality of feature variants; generating,…, one or more configurations for each of the one or more product models, from (i) the plurality of feature variants, (ii) one or more supply constraints, and (iii) one or more usage policies, wherein each of the one or more configurations pertains to a unique bundle of the plurality of feature variants; extracting,…, a historical sales data pertaining to the one or more configurations of each of the one or more product models; predicting, by using one or more forecasting models…, a best-selling product configuration mix from the one or more configurations, across the one or more product models and a plurality of feature families based on the historical sales data, wherein the best-selling configuration mix comprises a plurality of product-feature variant bundles; creating,…, one or more scenarios for the best-selling product configuration mix,… further comprising of a plurality of options, wherein the plurality of options comprises a scenario objective, one or more scenario dimensions, and one or more scenario levels; obtaining,…, one or more datasets pertaining to each of the one or more scenarios, wherein the one or more datasets are created for each scenario configuration, using (i) the plurality of product-feature variant bundles of the best-selling product configuration mix, (ii) one or more supply constraints, and (iii) the one or more usage policies, and wherein the one or more datasets created for each scenario comprises one or more lane-costs, the one or more supply constraints, and the one or more usage policies; optimizing, by using the one or more created datasets…, the one or more scenarios selected based on a selected business scenario type by using an objective function, and generating a plurality of optimization values, wherein the plurality of optimization values comprises an optimal value, a range of optimality, and a shadow price, for the selected business scenario type; estimating,…, a final target value for the one or more product models based on a first set of inputs and a second set of inputs, wherein the first set of inputs comprise a historic target value pertaining to the one or more scenarios of the plurality of product models, and wherein the second set of inputs comprises at least one of a price, and an interest rate associated with one or more products; and selecting,…, at least one scenario amongst the one or more scenarios as a focal scenario, based on a comparison between an associated scenario objective value and the final target value.”. These claim limitations describes a process of analyzing product data, sales data, and product mix scenarios in order to predict a best-selling product mix and estimate a target value, which can practically be performed in the human mind with pen and paper. The claim limitations also reflect marketing and sales activities and behaviors. Dependent claims 2-6, 8-12, and 14-18 further describe the product mix features, scenario objectives, and scenario analysis to estimate a target value. Therefore, these limitations, under the broadest reasonable interpretation, fall within the abstract groupings of Mental Processes which include concepts performed in the human mind such as observations, evaluations, judgments, and opinions and Certain Methods of Organizing Human Activity which encompasses commercial interactions including subject matter relating to marketing or sales activities or behaviors, and business relations. Mental Processes include claims directed to collecting information, analyzing it, and displaying certain results of the collection and analysis even if they are claimed as being performed on a computer. Certain Methods of Organizing Human Activity can encompass the activity of a single person (e.g. a person following a set of instructions), activity that involve multiple people (e.g. a commercial interaction), and certain activity between a person and a computer (e.g. a method of anonymous loan shopping). Therefore, claims 1-18 are directed to an abstract idea and are not patent eligible.
Step 2A (2): This judicial exception is not integrated into a practical application. In particular, claims 1, 7, and 13 recite additional elements of “a processor implemented method (claim 1); …via the one or more hardware processors; a configurator interface, …by using an autoregressive neural network model via the one or more hardware processors (claims 1, 7, and 13); a system, comprising: a memory storing instructions; one or more communication interfaces; and one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to (claim 7); and 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)”. The Examiner evaluated the claims in light of the Applicant’s specification and determined that the additional elements do not integrate the abstract idea into a practical application because the claims do not recite (a) an improvement to another technology or technical field and (b) an improvement to the functioning of the computer itself and (c) implementing the abstract idea with or by use of a particular machine, (d) effecting a particular transformation or reduction of an article, or (e) applying the judicial exception in some other meaningful way beyond generally linking the use of an abstract idea to a particular technological environment. These additional elements evaluated individually and in combination are viewed as computing and display devices that are used to perform the abstract process identified in Step 2A(1). Limitations that recite mere instructions to implement an abstract idea on a computer or merely uses a computer as a tool to perform an abstract idea are not indicative of integration into a practical application (see MPEP 2106.05(f)). Therefore, claims 1-18 do not include individual or a combination of additional elements that integrate the judicial exception into a practical application and thus are not patent eligible.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claims 1, 7, and 13 recite additional elements of “a processor implemented method (claim 1); …via the one or more hardware processors; a configurator interface, …by using an autoregressive neural network model via the one or more hardware processors (claims 1, 7, and 13); a system, comprising: a memory storing instructions; one or more communication interfaces; and one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to (claim 7); and 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)”. These additional elements evaluated individually and in combination are viewed as mere instructions to apply or implement the abstract idea on a computer. Applying an abstract idea on a computer does not integrate a judicial exception into a practical application or provide an inventive concept (see MPEP 2106.05(f)). Therefore, claims 1-18 do not include individual or a combination of additional elements that are sufficient to amount to significantly more than the judicial exception and thus are not patent eligible.
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.
Claims 1-18 are rejected under 35 U.S.C. 103 as being unpatentable over Ettl et al. (US 2009/0164262 A1) in view of Anderson (US 2022/0012756 A1) and in further view of Li et al. (US 2022/0261828 A1).
As per claims 1, 7, and 13, Ettl teaches a processor implemented method, comprising; A system, comprising: a memory storing instructions; one or more communication interfaces; and one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to; and 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 (Ettl e.g. Described herein is an exemplary method for planning under uncertainty, the method comprising: processing a stochastic programming formulation based on forecast values of at least one of product and service configurations, and determining a resource requirements plan for one or more planning periods in a non-deterministic bill of resources of at least two levels [0020]. There is disclosed a computer-readable medium tangibly embodying a program of machine-readable instructions executable by a digital processing apparatus to perform a method of planning under uncertainty, the method comprising processing a stochastic programming formulation based on forecast values of product and service configurations, and determining a resource requirements plan for one or more planning periods in a non-deterministic bill of resources of at least two levels (Figs. 3-6 and [0038]). FIG. 5 illustrates a typical hardware configuration of an information handling/computer system in accordance with the invention and which preferably has at least one processor or central processing unit (CPU) 511 [0138].):
Ettl teaches receiving/receive, via one or more hardware processors, a product family further comprising one or more of product models, from a user, wherein each product model amongst the one or more product models comprises a plurality of feature families, wherein each feature family amongst the plurality of feature families comprises a plurality of feature variants; (Ettl e.g. FIG. 2 illustrates a product hierarchy of an automotive product which includes family 210, engine/transmission/body style 221-224, CPOS (Customer Preferred Option Selection) 231-235, options sales code 241-243, and part numbers 250 [0050]. An objective of the stochastic capacity optimization is to determine the capacity and flex position of each CPOS package and options sales code so as to meet established sales targets and maximize expected (discounted) profit over the planning horizon [0060]. Data inputs and notation are used to formulate the stochastic capacity optimization problem. The data inputs comprise of top-level sales forecasts, sales code attach rates, contribution margins, shortage costs, and capacity investment costs [0062]. An attach rate is defined for a component-product pair, and represents the quantity of a component that is used to configure the corresponding product [0007]. Sales code attach rates explode the sales forecasts at price class/body style level all the way down to the sales code of the end vehicle [0073]. The product structure is provided in the form of a bill-of-materials structure for the selected set of Price Class/Body Style combinations and includes CPOS package and sales code information [0068]. The exemplary implementation's application code for solving the stochastic implosion problem has the following inputs [0119]: A specification of the product structure, including primary relationships between products and sales codes and their attach rates [0120]. A specification of the flexible relationships between products and sales codes: “Optional”, “Mandatory”, and “Selective Sets” [0121].)
Ettl teaches generating/generate, via the one or more hardware processors, one or more configurations for each of the one or more product models, from (i) the plurality of feature variants, (ii) one or more supply constraints, and (iii) one or more usage policies, wherein each of the one or more configurations pertains to a unique bundle of the plurality of feature variants; (Ettl e.g. The framework aims at developing risk-adjusted capacity recommendations at all relevant levels of the product bill of materials and evaluating the overall performance of the supply chain against a range demand forecast for products [0047]. FIG. 1 depicts a vehicle code of a pickup truck configuration [0008]. The components required for the various configurations constitute a bill of resources [0010]. The product and service configurations comprise one or more of an optional feature which is not required in a final product configuration, a mandatory feature which is required in a final product configuration, and a selective set of features, where exactly one feature of said selective set is required in a final product configuration and comprises two or more levels in the bill of resources [0031]. Supplier capacity limitations or other procurement considerations may lead to cases where the supply volumes that suppliers are able to commit will deviate from the optimal capacity plan determined by the stochastic explosion model [0052]. Therefore, a thorough review of the capacity and top-level demand plan is preferably done to achieve alignment of demand and supply over the planning horizon. This alignment problem is captured by a stochastic implosion model [0052]. FIG. 2 illustrates a product hierarchy of an automotive product which includes family 210, engine/transmission/body style 221-224, CPOS (Customer Preferred Option Selection) 231-235, options sales code 241-243, and part numbers 250 [0050]. Additional inputs are product configuration rules that define existing relationships between Sales Codes within a Sales Code Class (substitution rules) as well as attach rate properties that may apply to a given Sales Code Class [0069].)
Ettl teaches extracting/extract, via the one or more hardware processors, … sales data pertaining to the one or more configurations of each of the one or more product models; (Ettl e.g. An attach rate is defined for a component-product pair, and represents the quantity of a component that is used to configure the corresponding product. Attach rates may be derived qualitatively (e.g., using rules of thumb or judgement based on experience with similar products or services), or quantitatively (e.g., using historical averages or driven by sales targets) [0007]. FIG. 2 illustrates a product hierarchy of an automotive product which includes family 210, engine/transmission/body style 221-224, CPOS (Customer Preferred Option Selection) 231-235, options sales code 241-243, and part numbers 250 [0050]. Top-level forecasts are utilized to convert attach rates into forecast ranges of demand volumes (i.e., range demand forecasts) at the CPOS, sales code, and part number levels of the hierarchy [0050].)
Ettl teaches predicting/predict, by using one or more forecasting models via the one or more hardware processors, a best-selling product configuration mix from the one or more configurations, across the one or more product models and a plurality of feature families based on the…sales data, wherein the best-selling configuration mix comprises a plurality of product-feature variant bundles; (Ettl e.g. It is an exemplary feature of the present invention to provide an analytical model for capacity procurement that incorporates the uncertainty in demand forecasts and configuration preferences, with the objective of maximizing one or more business objectives (e.g., minimizing shortage/overage costs, maximizing services levels, maximize revenue/profit) [0048]. The model employs advanced forecasting and stochastic optimization techniques to find profit-optimal flex positions that balance excess capacity with product shortage risk, mitigating the cost of underutilized assets and missed revenue [0055]. It takes into account factors including demand uncertainty, profitability, service level objectives, capacity costs, and liability costs, and it allows the analysis of flex positions against demand range forecasts and confidence limits [0055]. The output of the optimization is an optimized capacity plan for all sales codes pertaining to the selected set of vehicle programs [0085].)
Ettl teaches creating/create, via the one or more hardware processors, one or more scenarios for the best-selling product configuration mix, through a configurator interface further comprising of a plurality of options, wherein the plurality of options comprises a scenario objective, one or more scenario dimensions, and one or more scenario levels; (Ettl e.g. The framework aims at developing risk-adjusted capacity recommendations at all relevant levels of the product bill of materials and evaluating the overall performance of the supply chain against a range demand forecast for products. The range demand forecast captures the variable nature of the market by considering demand scenarios that may deviate from a projected average demand scenario [0047]. The demand scenarios are generated automatically by applying statistical sampling techniques to the range demand forecasts [0048]. The optimization model utilizes the information in scenario-based forecast estimates and generates an efficient capacity plan over the multi-period planning horizon [0059]. The first stage variables are the amounts of capacity procured for each sales code, and the second stage variables determine the allocation of different sales codes to customer demand (expressed as demand at the price class/body style level) in each demand scenario [0059]. An additional set of outputs is the expected number of configured saleable vehicles that can be produced in each vehicle program under the recommended capacity plan. This information is useful for subsequent scenario analyses where the recommended capacity plan can be modified and re-evaluated, or for conducting sensitivity analyses for different values of the cost or profit parameters [0087]. The SAA method approximates the expected objective function of the stochastic program with a sample average estimation based on a number of randomly generated sample scenarios. It solves a number of deterministic equivalent instances of the stochastic explosion problem and seeks to optimize the average of the objective (expected discounted profit) across the sampled scenarios [0098]. FIG. 4 illustrates the process [0099]. The exemplary Application Programming Interface (API) is a well-documented C programming interface that provides the solution designer the capability to describe the exemplary model and solution technique [0116].)
Ettl teaches obtaining/obtain, via the one or more hardware processors, one or more datasets pertaining to each of the one or more scenarios, wherein the one or more datasets are created for each scenario configuration, using (i) the plurality of product-feature variant bundles of the best-selling product configuration mix, (ii) one or more supply constraints, and (iii) the one or more usage policies, and wherein the one or more datasets created for each scenario comprises one or more lane-costs, the one or more supply constraints, and the one or more usage policies; (Ettl e.g. The optimization model utilizes the information in scenario-based forecast estimates and generates an efficient capacity plan over the multi-period planning horizon [0059]. The first stage variables are the amounts of capacity procured for each sales code, and the second stage variables determine the allocation of different sales codes to customer demand (expressed as demand at the price class/body style level) in each demand scenario [0059].To assess the expected profitability of a sourcing strategy, information on the profitability of each saleable vehicle configuration is needed. This includes CPOS and Sales Code profit margins and costs, or alternatively, Sales Code revenue and costs [0076]. The cost of a CPOS or Sales Code is the estimated cost of purchasing a given CPOS or Sales Code from a supplier (i.e. lane-costs) [0076]. The output of the optimization is an optimized capacity plan for all sales codes pertaining to the selected set of vehicle programs [0085]. Capacity recommendations are provided in the form of a base capacity plus upside flexibility for each sales code so that the expected (discounted) profit of the firm is maximized over all possible demand and attach rates scenarios [0085]. The stochastic capacity optimization problem can be expressed in the form of a profit maximization problem as follows: Stochastic Capacity Optimization Problem: equation (6) subject to the constraints equation (7), where the second term in the objective function represents maximum total expected profit over all realizations of demand given the capacity decision X ([0090]-[0091]). The term Q(X, D(φ), a(φ)) is the maximal total profit under a single demand scenario φ for a recommended capacity plan X. It can be obtained by solving the following sub-problem: equation (9) subject to constraints equations (10) and (11) [0093]. Constraints (10) indicate that the total number of vehicles that can be allocated to a vehicle program is less than or equal to the top-level demand Dpt(φ). Constraints (11) indicate that the total number of sales codes than can be allocated to all vehicle programs in each scenario is limited by the available capacity of that sales code [0094].)
Ettl teaches optimizing/optimize, by using the one or more created datasets via the one or more hardware processors, the one or more scenarios selected based on a selected business scenario type by using an objective function, and generating a plurality of optimization values, wherein the plurality of optimization values comprises an optimal value, a range of optimality, and a shadow price, for the selected business scenario type; (Ettl e.g. The desired financial or operational objective of the stochastic capacity optimization problem is formulated as the following [0053]: Profit optimization: Find a capacity plan that maximizes overall profitability (e.g., Revenue-Cost of Goods Sold-Cost of capacity investment-Loss of customer goodwill due to product shortages). The goal is to capture the trade-offs between the cost of capacity expansion to account for shortage risk and the cost of unused capacity to account for overage risk [0054]. The model employs advanced forecasting and stochastic optimization techniques to find profit-optimal flex positions that balance excess capacity with product shortage risk, mitigating the cost of underutilized assets and missed revenue [0055]. It takes into account factors including demand uncertainty, profitability, service level objectives, capacity costs, and liability costs, and it allows the analysis of flex positions against demand range forecasts and confidence limits [0055]. To assess the expected profitability of a sourcing strategy, information on the profitability of each saleable vehicle configuration is needed. This includes CPOS and Sales Code profit margins and costs, or alternatively, Sales Code revenue and costs [0076]. The cost of a CPOS or Sales Code is the estimated cost of purchasing a given CPOS or Sales Code from a supplier [0076]. The SAA method approximates the expected objective function of the stochastic program with a sample average estimation based on a number of randomly generated sample scenarios. It solves a number of deterministic equivalent instances of the stochastic explosion problem and seeks to optimize the average of the objective (expected discounted profit) across the sampled scenarios [0098]. The SAA method approximates the true objective function with a sample average objective function that consists of only a subset of all possible scenarios [0104].)
Ettl teaches estimating/estimate, by using a… model via the one or more hardware processors, a final target value for the one or more product models based on a first set of inputs and a second set of inputs, wherein the first set of inputs comprise a…target value pertaining to the one or more scenarios of the plurality of product models…, (Ettl e.g. The planning under uncertainty comprises determining sales targets for products or services in one or more planning periods [0029]. An exemplary feature of this invention is to position capacity and inventory in such a manner as to maximize the revenue generated from those assets [0047]. Subject matter experts can provide a top-level demand forecast—either in the form of a point forecast or a range forecast—for one or more vehicle programs at the level of price class/body style. These top-level sales targets will be established by a demand planning team in the long-term demand planning phase [0060]. An objective of the stochastic capacity optimization is to determine the capacity and flex position of each CPOS package and options sales code so as to meet established sales targets and maximize expected (discounted) profit over the planning horizon [0060]. Sales forecasts ensure that the recommended capacity plan can support the demand volume established by subject matter experts [0066].)
Ettl teaches selecting/select, via the one or more hardware processors, at least one scenario amongst the one or more scenarios as a focal scenario, based on a comparison between an associated scenario objective value and the final target value. (Ettl e.g. An optimal sales target is selected from an independent sample set of demand scenarios [0036]. It is an exemplary feature of the present invention to provide an analytical model for capacity procurement that incorporates the uncertainty in demand forecasts and configuration preferences, with the objective of maximizing one or more business objectives (e.g., minimizing shortage/overage costs, maximizing services levels, maximize revenue/profit) [0048]. The possible outputs of the invention include one or more of the following: recommendations for capacity procurement, material procurement, material production, and sales targets for each period in the planning horizon [0048]. The decision faced in the long-term supply planning phase is represented by a stochastic explosion model. The stochastic explosion model differs from a traditional Material Requirements Planning (MRP) explosion model in that it explicitly accounts for configuration uncertainty, as represented by the range forecast of attach rates, inherent in the automotive supply chain. It also accounts for component dependencies and commonality of sales codes or part numbers across vehicle models or programs [0051]. The solution to the stochastic explosion model determines a risk-adjusted, profit maximizing capacity plan corresponding to the top-level volume targets set in the long-term demand planning stage [0051]. The model employs advanced forecasting and stochastic optimization techniques to find profit-optimal flex positions that balance excess capacity with product shortage risk, mitigating the cost of underutilized assets and missed revenue [0055]. It takes into account factors including demand uncertainty, profitability, service level objectives, capacity costs, and liability costs, and it allows the analysis of flex positions against demand range forecasts and confidence limits [0055]. In conjunction with the range forecast, the capacity optimization model enables planners to estimate the financial performance of a product portfolio as function of flex. An exemplary objective is to maximize risk-adjusted portfolio return by optimizing flex alternatives based on excess and overage exposure, liabilities, and lost sales risk [0055]. An objective of the stochastic capacity optimization is to determine the capacity and flex position of each CPOS package and options sales code so as to meet established sales targets and maximize expected (discounted) profit over the planning horizon [0060]. To assess the expected profitability of a sourcing strategy, information on the profitability of each saleable vehicle configuration is needed. This includes CPOS and Sales Code profit margins and costs, or alternatively, Sales Code revenue and costs [0076]. The cost of a CPOS or Sales Code is the estimated cost of purchasing a given CPOS or Sales Code from a supplier [0076]. The gross profit captured from the sale of a given end vehicle can be estimated as the gross profit of the base configuration, combined with the gross profit of all CPOS and Sales Codes selected for the vehicle [0076].)
Ettl does not explicitly teach, however, Anderson teaches the following:
Anderson teaches extracting historical sales data pertaining to one or more configuration of each of the one or more product models (Anderson e.g. System and methods for providing a target price for a target vehicle with a target mileage are provided (Abstract). FIG. 1 shows a system 100 consistent with embodiments of the present invention. In various embodiments, system 100 may be configured to collect and process vehicle data, receive inquiries about the vehicle data from prospective buyers, and communicate data for the requested inquiries to the prospective buyers [0030]. System 100 may further include sales data (i.e., data related to sales of various previously sold vehicles) 171. Sales data 171 may include car dealer data 172 obtained from car dealers/car auctions or the like, as well as data 173 from financial institutions. Sales data 171 may also include government (e.g., Department of Motor Vehicles—DMV) data 174, external data 175, and vehicle history data 176 for previously sold vehicles 155 [0031]. Vehicle data system 105 may obtain by gathering (or receiving) sales data 171 and vehicle history data 176. This data may include sales and historical data for a variety of vehicle configurations [0040].)
Anderson teaches estimating, by using a neural network model, a final target value for the one or more product models wherein the first set of inputs and a second set of inputs, wherein the first set of inputs comprise a historic target value…and wherein the second set of inputs comprises at least one of a price and an interest rate associated with one or more product (Anderson e.g. A system for providing a target price for a target vehicle with a target mileage is provided. The system may comprise a database configured to store attributes for sold vehicles, and store mileages for the sold vehicles. The system may further comprise at least one memory storing instructions, and at least one processor executing the instructions to perform operations that may include receiving attributes for the target vehicle, and receiving prices for sold vehicles having attributes corresponding to the target vehicle attributes [0009]. FIG. 1 shows a system 100 consistent with embodiments of the present invention. In various embodiments, system 100 may be configured to collect and process vehicle data, receive inquiries about the vehicle data from prospective buyers, and communicate data for the requested inquiries to the prospective buyers [0030]. System 105 may receive external data 175 related to sales data 171 for various vehicles 155. External data 175 may be obtained from various other information sources, online or otherwise, which may provide other types of desired data, such as data regarding location of vehicles, demographics at vehicle locations, current economic conditions, fuel prices, interest rates, and vehicle insurance rates that may influence current and future vehicle prices [0038]. FIG. 2 shows an example process 200 of generating a target price for target vehicle 103 for prospective buyer 102 [0045]. At a step 202, data processing module 140 of vehicle system 105 may analyze data for sold vehicles, and at a step 203 module 140 may generate a target price for prospective buyer 102. A statistical approach for analyzing data for sold vehicles and the use of computer-based models for determining the target price are described below [0045]. A linear regression model may be used to obtain a linear regression plot 502 shown in FIG. 5. For a given target vehicle with a target mileage 520 a target price 522 may be determined using the linear regression plot 502 as shown in FIG. 5 [0049]. In various embodiments, data points 501 for sold vehicles 155 are selected to match target vehicle 103 attributes. In an example embodiment, the year, make, and model of sold vehicles 155 may be the same as the year, make, and model of target vehicle 103 [0050]. In an example embodiment, outward appearance may be an important factor determining a target price of target vehicle 103, and effect of outward appearance on the target price may be analyzed by analyzing outward appearances of sold vehicles 155 [0053]. In an example embodiment, a computer-based model may be used to evaluate a set of images for sold vehicle 155 and determine if sold vehicle 155 matches the outward appearance of target vehicle 103. For example, the computer-based model may return a match score determining the degree to which the outward appearance of sold vehicle 155 matches the outward appearance of target vehicle 103 [0055]. FIG. 8 shows an exemplary process of obtaining a match score 830 relating sold vehicle 155 to target vehicle 103 using a model 820 for image data 810A and 810B. In various embodiments, match score 830 may be calculated using model 820 that may include machine-learning models, such as neural networks, decision trees, and models based on ensemble methods, such as random forests [0056].)
The Examiner submits that before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify Ettl’s resource planning system to include analyzing historical sales and pricing data of vehicles using neural network models to estimate a final target value/price of a vehicle as taught by Anderson in order to determine the value of a vehicle and whether the vehicle price is reasonable (Anderson e.g. [0003] and [0006]).
Ettl nor Anderson explicitly teach, however, Li teaches estimating, by using an autoregressive neural network model, a final target value…. (Li e.g. A computer-implemented method for determining auction prices of vehicles may include obtaining wholesale auction price data indicative of wholesale auction prices and vehicle attribute values of a plurality of vehicles (Abstract). FIG. 1 is a diagram depicting an example of a system environment 100 according to one or more embodiments of the present disclosure [0018]. The computer system 110 may have one or more processors configured to perform methods described in this disclosure. The computer system 110 may include one or more modules, models, or engines [0019]. The algorithm model 112 may be a plurality of algorithm models. The algorithm model 112 may include one or more regression models (e.g., trained machine learning models) [0020]. The determining the plurality of regression models may include training each of the plurality of regression models using a machine learning algorithm/model and using the respective vehicle group of the plurality of vehicle groups as a training set [0037]. The determining the estimated wholesale auction price may be based further on a time series model. The time series model may include an autoregressive process that may take into account time dependency of one or more vehicle attributes or economic indicators. The autoregressive process may indicate that the output variable depends linearly on its own previous values and on a stochastic term (an imperfectly predictable term). For each vehicle, an autoregressive process (e.g., an autoregressive model) may be fit on the time series of regression residuals [0042]. The regression models may accept the data identified in any steps described above as input data. Regression models may predict a number (e.g., an estimated post-repossession auction price). One or more regression models may be a machine learning model. The machine learning model may be of any suitable form, and may include, for example, a neural network, linear regression, logistic regression, tree-based methods like random forest or gradient boosting machines (GBM), support vector machines (SVM), or naive Bayes classifiers [0061].)
The Examiner submits that before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify Ettl in view of Anderson’s resource planning and vehicle valuation system to include estimating a final target value using an autoregressive neural network model as taught by Li in order to improve accuracy of the estimated wholesale auction price, especially for short-term estimation (Li e.g. [0042]).
As per claims 2, 8, and 14, Ettl in view of Anderson and Li teach the processor implemented method of claim 1, the system of claim 7, and the one or more non-transitory machine-readable information storage mediums of claim 13, Ettl teaches wherein two or more combinations of the plurality of feature variants are used to build the one or more configurations (Ettl e.g. FIG. 1 depicts a vehicle code of a pickup truck configuration. In an automobile, there may be within a single family platform 111 a variety of product lines 112, series 113, body styles 114, and trim levels 117, requiring different components or combinations of components. Some components, such as a vehicle platform, may be common to different configurations, and others may be unique to a particular configuration [0008]. The components required for the various configurations constitute a bill of resources [0010]. The product and service configurations comprise one or more of an optional feature which is not required in a final product configuration, a mandatory feature which is required in a final product configuration, and a selective set of features, where exactly one feature of said selective set is required in a final product configuration and comprises two or more levels in the bill of resources [0031]. FIG. 2 illustrates a product hierarchy of an automotive product which includes family 210, engine/transmission/body style 221-224, CPOS (Customer Preferred Option Selection) 231-235, options sales code 241-243, and part numbers 250 [0050]. The product structure is provided in the form of a bill-of-materials structure for the selected set of Price Class/Body Style combinations and includes CPOS package and sales code information [0068].).
As per claims 3, 9, and 15, Ettl in view of Anderson and Li teach the processor implemented method of claim 1, the system of claim 7, and the one or more non-transitory machine-readable information storage mediums of claim 13, Ettl teaches wherein the step of creating the one or more scenarios comprises: selecting a scenario objective from a predefined scenario objective list (Ettl e.g. It is an exemplary feature of the present invention to provide an analytical model for capacity procurement that incorporates the uncertainty in demand forecasts and configuration preferences, with the objective of maximizing one or more business objectives (e.g., minimizing shortage/overage costs, maximizing services levels, maximize revenue/profit) [0048]. An exemplary objective is to maximize risk-adjusted portfolio return by optimizing flex alternatives based on excess and overage exposure, liabilities, and lost sales risk [0055]. An objective of the stochastic capacity optimization is to determine the capacity and flex position of each CPOS package and options sales code so as to meet established sales targets and maximize expected (discounted) profit over the planning horizon [0060].); selecting one or more scenario dimensions from a predefined scenario dimensions list; selecting one or more scenario levels from a predefined scenario levels list, wherein each of the one or more scenario dimensions comprises the one or more scenario levels (Ettl e.g. The optimization model utilizes the information in scenario-based forecast estimates and generates an efficient capacity plan over the multi-period planning horizon [0059]. The first stage variables are the amounts of capacity procured for each sales code, and the second stage variables determine the allocation of different sales codes to customer demand (expressed as demand at the price class/body style level) in each demand scenario [0059]. An optimal sales target is selected from an independent sample set of demand scenarios [0036].); and creating the one or more scenarios based on one or more combinations of the one or more scenario levels (Ettl e.g. The demand scenarios are generated automatically by applying statistical sampling techniques to the range demand forecasts [0048]. The output of the optimization is an optimized capacity plan for all sales codes pertaining to the selected set of vehicle programs [0085]. Capacity recommendations are provided in the form of a base capacity plus upside flexibility for each sales code so that the expected (discounted) profit of the firm is maximized over all possible demand and attach rates scenarios [0085]. In this problem, the uncertainty in vehicle demand and sales code attach rates is captured by the scenario space, where each scenario expresses one possible realization of demand and attach rates [0097].The SAA method approximates the expected objective function of the stochastic program with a sample average estimation based on a number of randomly generated sample scenarios. It solves a number of deterministic equivalent instances of the stochastic explosion problem and seeks to optimize the average of the objective (expected discounted profit) across the sampled scenarios [0098]. FIG. 4 illustrates the process [0099]. For use by the SAA method, the final set of range forecasts created in Module 1 will be stored in a form that enables the generation of scenarios of future demand [0107].)
As per claims 4, 10, and 16, Ettl in view of Anderson and Li teach the processor implemented method of claim 1, the system of claim 7, and the one or more non-transitory machine-readable information storage mediums of claim 13, Ettl teaches wherein the one or more lane-costs comprise a source representing the one or more product models, a destination representing an entity representing the one or more configurations, a category representing a product family, one or more decision variables for optimization of number of product variants to be built representing supply quantities for each source-destination-entity combination, and a coefficient representing a unit reserve used in defining an objective function (Ettl e.g. An attach rate is defined for a component-product pair, and represents the quantity of a component that is used to configure the corresponding product. Examples of components in a manufacturing supply chain may include supplier parts and sub-assemblies [0007]. Supplier capacity limitations or other procurement considerations may lead to cases where the supply volumes that suppliers are able to commit will deviate from the optimal capacity plan determined by the stochastic explosion model [0052]. Therefore, a thorough review of the capacity and top-level demand plan is preferably done to achieve alignment of demand and supply over the planning horizon. This alignment problem is captured by a stochastic implosion model [0052]. Its solution suggests how the achievable component supply—constrained by the capacities and flex positions maintained by suppliers—should exemplarily be adjusted so as to maximize expected profit. As in the explosion model, the implosion model accounts for uncertainty in the way finished products are configured. The final capacity plan produced by the model leverages any flexibility in supplier capacities to determine the quantities of all finished products that can be optimally supported by the supply chain over the planning horizon [0052]. To assess the expected profitability of a sourcing strategy, information on the profitability of each saleable vehicle configuration is needed. This includes CPOS and Sales Code profit margins and costs, or alternatively, Sales Code revenue and costs. The cost of a CPOS or Sales Code is the estimated cost of purchasing a given CPOS or Sales Code from a supplier. The gross profit captured from the sale of a given end vehicle can be estimated as the gross profit of the base configuration, combined with the gross profit of all CPOS and Sales Codes selected for the vehicle [0076]. qjt: Average unit profit (contribution margin) of sales j in time period t…[0077]. upt: Average unit profit for vehicle program p in time period t…[0078].)
As per claims 5, 11, and 17, Ettl in view of Anderson and Li teach the processor implemented method of claim 1, the system of claim 7, and the one or more non-transitory machine-readable information storage mediums of claim 13, Ettl teaches wherein when a difference between the associated scenario objective value and the final target value is within a predefined tolerance, the focal scenario is recommended to enable one or more actions (Ettl e.g. The possible outputs of the invention include one or more of the following: recommendations for capacity procurement, material procurement, material production, and sales targets for each period in the planning horizon [0048]. An objective of the stochastic capacity optimization is to determine the capacity and flex position of each CPOS package and options sales code so as to meet established sales targets and maximize expected (discounted) profit over the planning horizon [0060]. Sales forecasts ensure that the recommended capacity plan can support the demand volume established by subject matter experts [0066]. To assess the expected profitability of a sourcing strategy, information on the profitability of each saleable vehicle configuration is needed. This includes CPOS and Sales Code profit margins and costs, or alternatively, Sales Code revenue and costs. The cost of a CPOS or Sales Code is the estimated cost of purchasing a given CPOS or Sales Code from a supplier. The gross profit captured from the sale of a given end vehicle can be estimated as the gross profit of the base configuration, combined with the gross profit of all CPOS and Sales Codes selected for the vehicle [0076]. Capacity recommendations are provided in the form of a base capacity plus upside flexibility for each sales code so that the expected (discounted) profit of the firm is maximized over all possible demand and attach rates scenarios [0085].).
As per claims 6, 12, and 18, Ettl in view of Anderson and Li teach the processor implemented method of claim 1, the system of claim 7, and the one or more non-transitory machine-readable information storage mediums of claim 13, Ettl teaches wherein when a difference between the associated scenario objective value and the final target value is exceeding a predefined tolerance, the method comprises: identifying one or more deviations, by comparing one or more selected plurality of options and one or more constraints of the focal scenario with the plurality of options and the constraints of a base scenario; and iteratively modifying the one or more constraints of the focal scenario, to improve the associated scenario objective value, till the one or more deviations of the associated scenario objective value in comparison to the estimated final target value is within the predefined tolerance. (Ettl e.g. An objective of the stochastic capacity optimization is to determine the capacity and flex position of each CPOS package and options sales code so as to meet established sales targets and maximize expected (discounted) profit over the planning horizon [0060]. The stochastic capacity optimization problem can be expressed in the form of a profit maximization problem as follows: Stochastic Capacity Optimization Problem: equation (6) subject to the constraints equation (7), where the second term in the objective function represents maximum total expected profit over all realizations of demand given the capacity decision X ([0090]-[0091]). The first term in the objective function (6) is the total investment cost for procuring capacity Xjt for all sales codes j=1,2, . . . , S over the planning horizon. The second term, Q(X,D,a), is the maximal total expected profit attainable under a recommended capacity plan X over all possible demand scenarios φ and is expressed as: equation (8) [0092]. The term Q(X, D(φ), a(φ)) is the maximal total profit under a single demand scenario φ for a recommended capacity plan X. It can be obtained by solving the following sub-problem: equation (9) subject to constraints equations (10) and (11) [0093]. Constraints (10) indicate that the total number of vehicles that can be allocated to a vehicle program is less than or equal to the top-level demand Dpt(φ). Constraints (11) indicate that the total number of sales codes than can be allocated to all vehicle programs in each scenario is limited by the available capacity of that sales code [0094]. For a given set of N demand scenarios ω=(ω1, . . . , ωN), the deterministic equivalent of the above stochastic program can be expressed as: equation (12) subject to constraints (13)-(15) [0095]. If N is the number of all possible demand scenarios, then the solution of the deterministic problem (12)-(15) would represent the capacity plan X that maximizes the expected total profit pertaining to the original stochastic capacity optimization problem (6)-(11) [0096]. The stochastic capacity optimization problem represented by constraints (6)-(11) is formulated as a two-stage stochastic program with recourse [0097]. In this problem, the uncertainty in vehicle demand and sales code attach rates is captured by the scenario space, where each scenario expresses one possible realization of demand and attach rates [0097]. The SAA method approximates the expected objective function of the stochastic program with a sample average estimation based on a number of randomly generated sample scenarios. It solves a number of deterministic equivalent instances of the stochastic explosion problem and seeks to optimize the average of the objective (expected discounted profit) across the sampled scenarios [0098]. FIG. 4 illustrates the process [0099]. The SAA method approximates the true objective function with a sample average objective function that consists of only a subset of all possible scenarios [0104].)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure include FOR: Jeisobers, T. (AU-2019200862-B2) “Method And System For Generating Customer Decision Tree Through Machine Learning: and NPL: N. Cassaigne and M. G. Singh, "Intelligent decision support for the pricing of products and services in competitive consumer markets," in IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 31, no. 1, pp. 96-106, Feb. 2001.
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/A.M./Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624