DETAILED ACTION
Status of the Application
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/02/2026 has been entered.
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 .
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 03/02/2026.
Claims 1 and 10 have been amended.
Claims 1-18 remain pending in this application.
Response to Amendment
Applicant’s amendments are acknowledged.
The 35 U.S.C. 101 rejections of claims 1-18 in the previous office action have been maintained.
The 35 U.S.C. 103 rejections of claims 1-18 in the previous office action have been maintained.
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 1-9 are directed towards a system and claims 10-18 are directed towards a method, both 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 applying an algorithm to a dataset. 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 1-18, the independent claims (claims 1 and 10) are directed to managing inventory information, In independent claim 1, the bolded limitations emphasized below correspond to the abstract ideas of the claimed invention:
Claim 1, A system, comprising:
receive at least a first input dataset associated with at least a lead time of a first raw material;
receive at least a second input dataset associated with a demand for a plurality of final products, the plurality of final products corresponding to the first raw material;
perform a non-parametric bootstrap using at least the first input dataset and at least the second input dataset to generate a probability density function of lead time demand for the first raw material, wherein the non-parametric bootstrap is performed by using a plurality of lead-time size random draws of demand with replacement to construct an empirical distribution over a plurality of variable lead times;
determine a safety stock estimate for the first raw material based at least in part on the probability density function of lead time demand; and
reorder the first raw material based at least in part on the safety stock estimate.
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 sales activities and business relations. (See MPEP 2106.04(a)(2), subsection II). Additionally, the steps can be directed to mathematical concepts such as mathematical calculations, which is supported by the following elements non-parametric bootstrap and a probability density function and at claim 10;
performing, via the at least one computing device, a non-parametric bootstrap using at least the first input dataset and at least the second input dataset to generate a probability density function of lead time demand for the first raw material, wherein performing the non-parametric bootstrap further comprises:
randomly drawing a plurality of lead-time size random draws of demand with replacement;
calculating a safety stock for individual ones of the plurality of lead- time size random draws of demand with replacement; and
constructing an empirical distribution over a plurality of variable lead times based at least in part on the safety stock calculated for individual ones of the plurality of lead-time size random draws of demand;
If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior, then it falls within the “method of organizing human activity” or mathematical calculations, then it falls within the mathematical concepts 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 computing device. The claims recite the steps are performed by the computing device.
The limitations of
at least one computing device;
and at least one application executable in the at least one computing device, wherein when executed the at least one application causes the at least one computing device to at least:
are mere data gathering 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 computing device. The computing device is recited at a high level of generality. In limitation the computing device is used as a tool to perform the generic computer function of receiving and outputting data. See MPEP 2106.05(f). The computing device is 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).
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 computing device. 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 gathering and outputting.
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
at least one computing device;
and at least one application executable in the at least one computing device, wherein when executed the at least one application causes the at least one computing device to at least:
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 computing device 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 2-9, 11-18 are not directed to any additional claim 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 2 recite computing device to receive data and perform a non-parametric bootstrap; claim 3, 4, 13, recite computing device using user interface to upload, analyze, and output on a client device; claim 5, 6 recite input and reports on structured output file; claim 9 recite computing device to receive and report data; claim 13, 14 recite input and reports on comma separated file. The dependent claims 2-9, 11-18 recite limitations that are not technological in nature and merely limits the abstract idea to a particular environment. Claims 2-9, 11-18 recites computing device, non-parametric bootstrap, probability density function, user interface, client device, structured output file, comma separated values file which are considered an insignificant extra-solution activities of collecting and analyzing data; see MPEP 2106.05(g). Claims 2-9, 11-18 recites servers and communication, which merely recites an instruction to apply the abstract idea using a generic computer component; MPEP 2106.05(f). Additionally, claims 2-9, 11-18 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 and 10. Therefore claims 2-9, 11-18 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.
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 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.
Claims 1-4, 9-13, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Publication US 20090150208, Rhodes, et al. to hereinafter Rhodes in view of United States Patent Number US 6205431, Willemain, et al.
Referring to Claim 1, Rhodes teaches a system, comprising:
at least one computing device; at least one application executable in the at least one computing device, wherein when executed the at least one application causes the at least one computing device (
Rhodes: Claim. 15, A computer program product comprising:
a computer usable medium having computer readable program code embodied therein configured to perform inventory planning, said computer program product comprising:
computer readable code configured to cause a computer to calculate demand;
computer readable code configured to cause a computer to calculate requirements; and
computer readable code configured to cause a computer to determine a schedule of operation for a factory system producing large buildings for transportation to a substantially permanent building site, wherein the schedule is based at least in part on a first calculation of the computer readable code configured to cause a computer to calculate demand and a second calculation of the computer readable code configured to cause a computer to calculate requirements.)
to at least:
receive at least a first input dataset associated with at least a lead time of a first raw material (
Rhodes: Sec. 0031, Preferably, the system learns from experience such as past errors between projections and actual occurrences for manufacturing waste amount, lead times, customer orders in response to promotions and/or pricing, theft of materials, other loss or any other suitable source of error; however, learning is not required. As a result, the system can provide better projections and planning when presented with similar input in the future. In one embodiment, a learning unit includes a neural network which makes projections based on one or more sets of input data and can be trained based on actual results using any suitable neural network training techniques including, but not limited to, those techniques which inject a noise component into the training data
Rhodes: Sec. 0051, Inputs can include consensus forecasts, demand priorities, material availability, capacity availability, allocation rules, promising rules (e.g., rules used to determine how and when to promise delivery of an item), orders, or any other suitable information.);
Rhodes describe the input of data into a learning model with includes materials and lead times.
receive at least a second input dataset associated with a demand for a plurality of final products, the plurality of final products corresponding to the first raw material (
Rhodes: Sec. 0055, Together, the demand calculation unit 1102 together with the requirements calculation unit 1104 calculate a demand for finished products, subcomponents needed to assemble the finished products or other subcomponents, and raw materials needed to assemble the finished product or subcomponents. The scheduling unit 1106 determines an assembly/manufacturing schedule for a factory to follow to make each subcomponent and finished product.
Rhodes: Sec. 0034, if the inventory and planning system projects a surplus of finished products above a threshold level, marketing decision makers are automatically notified in any suitable manner (e.g., an e-mail, a report, a memo, an agenda item automatically inserted into a regularly scheduled meeting agenda, etc.). In one embodiment, the notice includes possibilities for reducing the surplus, such as pricing adjustments, promotional efforts, or changes to the finished product (e.g., inclusion of an indoor sauna or appliance) using projections based on previous system experience.);
perform a non-parametric bootstrap (See Willemain) using at least the first input dataset and at least the second input dataset to generate a probability density function (See Willemain) of lead time demand for the first raw material (
Rhodes: Sec. 0031, Preferably, the system learns from experience such as past errors between projections and actual occurrences for manufacturing waste amount, lead times, customer orders in response to promotions and/or pricing, theft of materials, other loss or any other suitable source of error; however, learning is not required. As a result, the system can provide better projections and planning when presented with similar input in the future. In one embodiment, a learning unit includes a neural network which makes projections based on one or more sets of input data and can be trained based on actual results using any suitable neural network training techniques including, but not limited to, those techniques which inject a noise component into the training data
Rhodes: Sec. 0051, Inputs can include consensus forecasts, demand priorities, material availability, capacity availability, allocation rules, promising rules (e.g., rules used to determine how and when to promise delivery of an item), orders, or any other suitable information.
Rhodes: Sec. 0055, Together, the demand calculation unit 1102 together with the requirements calculation unit 1104 calculate a demand for finished products, subcomponents needed to assemble the finished products or other subcomponents, and raw materials needed to assemble the finished product or subcomponents. The scheduling unit 1106 determines an assembly/manufacturing schedule for a factory to follow to make each subcomponent and finished product.
Rhodes: Sec. 0034, if the inventory and planning system projects a surplus of finished products above a threshold level, marketing decision makers are automatically notified in any suitable manner (e.g., an e-mail, a report, a memo, an agenda item automatically inserted into a regularly scheduled meeting agenda, etc.). In one embodiment, the notice includes possibilities for reducing the surplus, such as pricing adjustments, promotional efforts, or changes to the finished product (e.g., inclusion of an indoor sauna or appliance) using projections based on previous system experience.);
determine a safety stock estimate for the first raw material based at least in part on the probability density function of lead time demand (
Rhodes: Sec. 0040, forecast accuracy resulting in lower safety stock levels being necessary and lower production costs through more accurate forward visibility and production planning as well as reduced expediting costs.
Rhodes: Sec. 0045, The system 700 includes an integrated inventory planning solution to establish safety stock levels that is directly linked to the forecast system and allows for what-if analysis around service and inventory levels. ).
reorder (See Willemain) the first raw material based at least in part on the safety stock estimate (
Rhodes: Sec. 0032, one or more suppliers of components and/or raw materials are provided advanced notice of expected future orders. Preferably, the notice is provided automatically, based on projected needs calculated from the above described plans, orders, expectations and experiences; however, notice can be provided in any suitable manner. Preferably, the notice is not binding upon the orderer and merely provides the component and/or material provider the ability to ensure sufficient quantities will be on hand if the order is made; however, the notice can have any suitable nature and/or effect.
Rhodes: Sec. 0044, The system 600 compares the unconstrained consensus forecasts at the product family level against plant specific capabilities and high level materials issues.
Rhodes: Sec. 0045, FIG. 7 illustrates the inventory planning information flow in accordance with one embodiment. The system 700 includes an integrated inventory planning solution to establish safety stock levels that is directly linked to the forecast system and allows for what-if analysis around service and inventory levels. The safety stock targets are input to the master plan with exception report performance (e.g., expected variances) against the safety stock targets. The system 700 takes into account supply and demand variability as well as targeted service levels to set targeted safety stock levels throughout the supply chain. The system 700 has real-time visibility into actual inventory. The system 700 also functions with a master planning system that provides horizontal inventory plan visibility and exception reporting against safety stock targets).
Rhodes describes the ordering of materials based on projected safety stock levels, wherein materials include raw materials.
Rhodes does not explicitly teach perform a non-parametric bootstrap, a probability density function, reorder.
However, Willemain teaches perform a non-parametric bootstrap (
Willemain: Col. 11 Ln. 35-52, Three possible approaches are considered, one nonparametric and two parametric. The simple nonparametric version of this approach does not provide a probability for every possible integer value. The parametric versions provide a complete set of probabilities by utilizing discretized normal or lognormal distributions to fit the observed nonzero sums. The resulting forecasts have been found to be more accurate than those of exponential smoothing and Croston's method. In addition, because of the relative simplicity, subseries forecasts can be computed faster than the Smart Bootstrap forecast.)
a probability density function (
Willemain: Col. 12 Ln. 35-67, the nonparametric estimate F{circumflex over ( )}(X), the parametric estimates G{circumflex over ( )}(X) have probability increments at every possible value of LTD. The parameters of the normal and lognormal models are estimated by matching the sample mean and sample variance calculated from those subseries sums with nonzero values. A normal probability density function)
reorder (
Willemain: Col. 3 Ln. 35-50, The invention also comprises an inventory control system that can receive the lead time demand distribution in a discrete integer format and generate (1) reorder information and (2) performance information. The inventory control system can also generate a “warning” that informs the user of the likely efficacy of the generated information.
Willemain: Col. 4 Ln. 30-55, The continuous review model determines two quantities for each item, a reorder point and an order quantity. When on-hand inventory reaches the reorder point, one orders an amount equal to the order quantity to replenish stock. Calculating the reorder point requires forecasts of the entire distribution of demand over the lead time, defined as the time between the generation of a replenishment order and its arrival in inventory.)
wherein the non-parametric bootstrap is performed by using a plurality of lead-time size random draws of demand with replacement to construct an empirical distribution over a plurality of variable lead times (
Willemain: Col. 5 Ln. 25-50, bootstrap methodology, random samples are taken from the historical data and assigned to months 13-16 to form a series of lead time demand values. This is then repeated or replicated N times to yield N series, each having a cumulative lead time demand value or LTD sum indicated in Table 2 as LTD(n). Table 2 provides an LTD distribution with five replications, i.e., N=5.
Willemain: Col. 7 Ln. 45-62, describe inputting a lead time 54 that is fixed, the term lead time 54 may also encompass a random lead time. Specifically, the random lead time could comprise a distribution of possible lead times. In this case, each replicate (or series of lead time data) would be of varying size, depending upon a random selection from the inputted distribution of lead times.
Willemain: Col. 8 Ln. 45-62, The size of the LTD series is equal to the number of months or other time units that make up the lead time, and will generally be user defined or could be random, as explained above. For example, if a user wanted to know how many widgets would be required over the next four time periods, e.g., months, the LTD series size would equal four. Once the LTD series is complete, the lead time values making up this series are summed 40 to generate a first lead time demand sum, e.g., LTD(1). Next 42, the entire process is replicated N times to create N LTD sums, LTD(1), LTD(2) . . . LTD(N).
Willemain: Col. 6 Ln. 25-30, randomly changed to “neighboring” values in order);
Willemain: Col. 14 Ln. 65-Col. 15 Ln. 5, Our response was to divide the range of possible LTD values into random-width bins defined by the empirical distribution of subseries sums. To illustrate, consider again the example cited earlier in which the observed subseries values were: 0 (fourteen times), 1, 2, 5 (twice), 6 (three times), 7, 10 (twice), 12, and 54. In this case, likelihoods were computed for the following events: 0, 1, 2, 3-5, 6, 7, 8-10, 11-12, 13-54 and 55+);
Rhodes and Willemain are both directed to the analysis of inventory (See Rhodes at 0028, 0036, 0045; Willemain at Col. 5 Ln. 1-25, Col. 13 Ln. 27-50,). Rhodes discloses that additional element, such as the use of computer-maintained Material Requirements Plan model can be considered (See Rhodes at 0028). 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 Rhodes, which teaches detecting and repairing inventory information technology problems in view of Willemain, to efficiently apply analysis of inventory to enhancing the capability to perform various analytical tools for processing inventory data. (See Willemain at Col. 5 Ln. 1-25, Col. 7 Ln. 1-20).
Referring to Claim 2, Rhodes teaches the system of claim 1, wherein, when executed, the at least one application further causes the at least one computing device to at least:
receive a third input dataset relating the first raw material to a consumption rate of the plurality of final products (
Rhodes: Sec. 0034, if the inventory and planning system projects a surplus of finished products above a threshold level, marketing decision makers are automatically notified in any suitable manner (e.g., an e-mail, a report, a memo, an agenda item automatically inserted into a regularly scheduled meeting agenda, etc.).
Rhodes: Sec. 0045, The safety stock targets are input to the master plan with exception report performance (e.g., expected variances) against the safety stock targets.
Rhodes: Sec. 0055, Together, the demand calculation unit 1102 together with the requirements calculation unit 1104 calculate a demand for finished products, subcomponents needed to assemble the finished products or other subcomponents, and raw materials needed to assemble the finished product or subcomponents. The scheduling unit 1106 determines an assembly/manufacturing schedule for a factory to follow to make each subcomponent and finished product.);
the non-parametric bootstrap (See Willemain) is performed using at least the first input dataset, at least the second input dataset, and the third input dataset to generate the probability density function (See Willemain) of lead time demand for the first raw material (
Rhodes: Sec. 0031, Preferably, the system learns from experience such as past errors between projections and actual occurrences for manufacturing waste amount, lead times, customer orders in response to promotions and/or pricing, theft of materials, other loss or any other suitable source of error; however, learning is not required. As a result, the system can provide better projections and planning when presented with similar input in the future. In one embodiment, a learning unit includes a neural network which makes projections based on one or more sets of input data and can be trained based on actual results using any suitable neural network training techniques including, but not limited to, those techniques which inject a noise component into the training data
Rhodes: Sec. 0051, Inputs can include consensus forecasts, demand priorities, material availability, capacity availability, allocation rules, promising rules (e.g., rules used to determine how and when to promise delivery of an item), orders, or any other suitable information.
Rhodes: Sec. 0055, Together, the demand calculation unit 1102 together with the requirements calculation unit 1104 calculate a demand for finished products, subcomponents needed to assemble the finished products or other subcomponents, and raw materials needed to assemble the finished product or subcomponents. The scheduling unit 1106 determines an assembly/manufacturing schedule for a factory to follow to make each subcomponent and finished product.
Rhodes: Sec. 0034, if the inventory and planning system projects a surplus of finished products above a threshold level, marketing decision makers are automatically notified in any suitable manner (e.g., an e-mail, a report, a memo, an agenda item automatically inserted into a regularly scheduled meeting agenda, etc.). In one embodiment, the notice includes possibilities for reducing the surplus, such as pricing adjustments, promotional efforts, or changes to the finished product (e.g., inclusion of an indoor sauna or appliance) using projections based on previous system experience.).
Rhodes does not explicitly teach the non-parametric bootstrap; generate the probability density function.
However, Willemain teaches the non-parametric bootstrap (
Willemain: Col. 11 Ln. 35-52, Three possible approaches are considered, one nonparametric and two parametric. The simple nonparametric version of this approach does not provide a probability for every possible integer value. The parametric versions provide a complete set of probabilities by utilizing discretized normal or lognormal distributions to fit the observed nonzero sums. The resulting forecasts have been found to be more accurate than those of exponential smoothing and Croston's method. In addition, because of the relative simplicity, subseries forecasts can be computed faster than the Smart Bootstrap forecast.)
generate the probability density function (
Willemain: Col. 12 Ln. 35-67, the nonparametric estimate F{circumflex over ( )}(X), the parametric estimates G{circumflex over ( )}(X) have probability increments at every possible value of LTD. The parameters of the normal and lognormal models are estimated by matching the sample mean and sample variance calculated from those subseries sums with nonzero values. A normal probability density function)
Rhodes and Willemain are both directed to the analysis of inventory (See Rhodes at 0028, 0036, 0045; Willemain at Col. 5 Ln. 1-25, Col. 13 Ln. 27-50,). Rhodes discloses that additional element, such as the use of computer-maintained Material Requirements Plan model can be considered (See Rhodes at 0028). 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 Rhodes, which teaches detecting and repairing inventory information technology problems in view of Willemain, to efficiently apply analysis of inventory to enhancing the capability to perform various analytical tools for processing inventory data. (See Willemain at Col. 5 Ln. 1-25, Col. 7 Ln. 1-20).
Referring to Claim 3, Rhodes teaches the system of claim 2, wherein, when executed, the at least one application further causes the at least one computing device to at least:
generate a user interface comprising an upload-data component and a set cycle service level component, the upload-data component configured to, upon selection, receive a plurality of input datasets, and the set cycle service level component configured to receive a cycle service level input corresponding to the raw material; cause the user interface to be rendered on a client device (
Rhodes: Sec. 0028, using the MPS as a starting point, it is possible to combine it with the data on lead times and BOMs to derive a schedule of component and/or raw materials requirements to as fine a level of assembly and production detail as is desired. In another embodiment, the MPS, itself, includes the finer level details. In one embodiment, the schedule accounts for such factors as work-in-progress, current inventory of and pending orders for materials and components, and direct demand for components as service items. From the schedule of requirements, a material replenishment strategy that satisfies these requirements can be determined. In one embodiment, one or more of a wide variety of ordering rules and/or heuristics are incorporated into a computer-maintained Material Requirements Plan (“MRP”) model.
Rhodes: Sec. 0029, In addition to or instead of the material requirements, other useful data can be generated from the MPS, such as the projected inventory levels for any end product, the projected schedule for any assembly or production process, and the projected utilization of capacity for a particular production operation at any suitable point in time or during any suitable period. In one embodiment, any of the above information is utilized to evaluate current or potential materials replenishment strategies.
Rhodes: Sec. 0031, distribution planning 200 can be related to the master schedule 202, which is related to rough cut capacity information 204 and material planning 206. Material planning 206 is related to detail capacity 208, procurement 210 and production management 212. Typically the length of a planning cycle for an MRP is related to inventory and supply chain costs, with longer cycles increasing costs; however, the cycle length can have any relationship, including no relationship, with costs.
Rhodes: Sec. 0033, As a result, the system can provide better projections and planning when presented with similar input in the future. In one embodiment, a learning unit includes a neural network which makes projections based on one or more sets of input data and can be trained based on actual results using any suitable neural network training techniques including, but not limited to, those techniques which inject a noise component into the training data.
Rhodes: Sec. 0036, The system 300 also implements sales and operations planning by enabling what-if analysis around consensus forecasting and the supply/demand matching activities with the consensus output to be directly linked to the downstream planning processes. Further, the system implements inventory planning by taking into account the various facilities within the supply chain and allowing for optimization and what-if analysis around how much and where to keep strategic stocking levels. The system 300 also takes accurate cycle counts by separating store room inventory from floor stock and scrap.
Rhodes: Sec. 0040, The system 400 also provides flexibility and improved reaction cycle time, enables measurement of promotion effectiveness and production planning impact as well as collaboration with internal (e.g., sales, operations, finance, etc.) and external (e.g., key customers, all customers, etc.) entities.
Rhodes: Sec. 0052, implements a collaborative planning, forecasting and replenishment process with the manufacturer's supply base by communicating demand plans (e.g., forecasts) and any change events to proactively resolve demand and supply mismatches and sharing demand and inventory signals between buyers and suppliers to enable efficient replenishment and vendor managed inventory.).
Referring to Claim 4, Rhodes teaches the system of claim 3, wherein, when executed, the at least one application further causes the at least one computing device to at least:
generate a results report comprising at least the safety stock estimate and a corresponding confidence interval; modify the user interface to include the results report (
Rhodes: Sec. 0045, The safety stock targets are input to the master plan with exception report performance (e.g., expected variances) against the safety stock targets. The system 700 takes into account supply and demand variability as well as targeted service levels to set targeted safety stock levels throughout the supply chain. The system 700 has real-time visibility into actual inventory. The system 700 also functions with a master planning system that provides horizontal inventory plan visibility and exception reporting against safety stock targets.
Rhodes: Sec. 0046, decision support for trade-offs between service levels and inventory resulting in improved control over safety stock, synchronization of inventory levels throughout the supply chain reducing excess inventory, visibility into projected inventory and exception reporting on both excess and short inventory levels, reduced transportation expediting cost by efficiently moving inventory, and reduced manufacturing cost by avoiding unnecessary changeovers.).
Referring to Claim 9, Rhodes teaches the system of claim 4, wherein, when executed, the at least one application further causes the at least one computing device to at least:
receive diagnostic files, diagnostic files relating to data quality issues in the plurality of input datasets; cause the data quality issues to be included in the results report (
Rhodes: Sec. 0029, In one embodiment, any of the above information is utilized to evaluate current or potential materials replenishment strategies.
Rhodes: Sec. 0044, The system 600 compares the unconstrained consensus forecasts at the product family level against plant specific capabilities and high level materials issues.).
Claims 10-13 and 18 recite limitations that stand rejected via the art citations and rationale applied to claims 1-4 and 9. Regarding wherein performing the non-parametric bootstrap further comprises:
randomly drawing a plurality of lead-time size random draws of demand with replacement (
Willemain: Col. 5 Ln. 25-50, bootstrap methodology, random samples are taken from the historical data and assigned to months 13-16 to form a series of lead time demand values. This is then repeated or replicated N times to yield N series, each having a cumulative lead time demand value or LTD sum indicated in Table 2 as LTD(n). Table 2 provides an LTD distribution with five replications, i.e., N=5.
describe inputting a lead time 54 that is fixed, the term lead time 54 may also encompass a random lead time. Specifically, the random lead time could comprise a distribution of possible lead times. In this case, each replicate (or series of lead time data) would be of varying size, depending upon a random selection from the inputted distribution of lead times.
Willemain: Col. 8 Ln. 45-62, The size of the LTD series is equal to the number of months or other time units that make up the lead time, and will generally be user defined or could be random, as explained above. For example, if a user wanted to know how many widgets would be required over the next four time periods, e.g., months, the LTD series size would equal four. Once the LTD series is complete, the lead time values making up this series are summed 40 to generate a first lead time demand sum, e.g., LTD(1). Next 42, the entire process is replicated N times to create N LTD sums, LTD(1), LTD(2) . . . LTD(N).
Willemain: Col. 6 Ln. 25-30, randomly changed to “neighboring” values in order);
calculating a safety stock (See Rhodes) for individual ones of the plurality of lead- time size random draws of demand with replacement (
Willemain: Col. 6 Ln. 10-30, One of the preferred embodiments of the invention utilizes a variant of the traditional bootstrap method in order to simulate the distribution of demand over a fixed lead time. Traditional bootstrapping samples randomly from the historical time series. This preferred embodiment, referred to as the Smart Bootstrap, provides two improvements over traditional bootstrapping…randomly changed to “neighboring” values in order.
); and
constructing an empirical distribution over a plurality of variable lead times based at least in part on the safety stock (See Rhodes) calculated for individual ones of the plurality of lead-time size random draws of demand (
Willemain: Col. 8 Ln. 45-62, The size of the LTD series is equal to the number of months or other time units that make up the lead time, and will generally be user defined or could be random, as explained above. For example, if a user wanted to know how many widgets would be required over the next four time periods, e.g., months, the LTD series size would equal four. Once the LTD series is complete, the lead time values making up this series are summed 40 to generate a first lead time demand sum, e.g., LTD(1). Next 42, the entire process is replicated N times to create N LTD sums, LTD(1), LTD(2) . . . LTD(N).
Willemain: Col. 14 Ln. 65-Col. 15 Ln. 5, Our response was to divide the range of possible LTD values into random-width bins defined by the empirical distribution of subseries sums. To illustrate, consider again the example cited earlier in which the observed subseries values were: 0 (fourteen times), 1, 2, 5 (twice), 6 (three times), 7, 10 (twice), 12, and 54. In this case, likelihoods were computed for the following events: 0, 1, 2, 3-5, 6, 7, 8-10, 11-12, 13-54 and 55+);
Claims 5-8, 16, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Publication US 20090150208, Rhodes, et al. to hereinafter Rhodes in view of United States Patent Number US 6205431, Willemain, et al. to hereinafter Willemain in view of United States Patent Publication US 20050209732, Audimoolam, et al.
Referring to Claim 5, Rhodes teaches the system of claim 1, wherein the plurality of input datasets are included in a structured output file (See Audimoolam) (
Rhodes: Sec. 0031, Preferably, the system learns from experience such as past errors between projections and actual occurrences for manufacturing waste amount, lead times, customer orders in response to promotions and/or pricing, theft of materials, other loss or any other suitable source of error; however, learning is not required. As a result, the system can provide better projections and planning when presented with similar input in the future. In one embodiment, a learning unit includes a neural network which makes projections based on one or more sets of input data and can be trained based on actual results using any suitable neural network training techniques including, but not limited to, those techniques which inject a noise component into the training data
Rhodes: Sec. 0051, Inputs can include consensus forecasts, demand priorities, material availability, capacity availability, allocation rules, promising rules (e.g., rules used to determine how and when to promise delivery of an item), orders, or any other suitable information.).
Rhodes in view of Willemain does not explicitly teach structured output file.
However, Audimoolam teaches (
Audimoolam: Sec. 0053, All backend systems 62 intercommunicate with each other and with the solution platform 50 through XML scripted application files communicated over a wide area network, such as the internet.)
Rhodes, Willemain, and Audimoolam are all directed to the analysis of inventory (See Rhodes at 0028, 0036, 0045; Willemain at Col. 5 Ln. 1-25, Col. 13 Ln. 27-50; Audimoolam at 0051, 0052, 0063). Rhodes discloses that additional element, such as the use of computer-maintained Material Requirements Plan model can be considered (See Rhodes at 0028). 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 Rhodes in view of Willemain, which teaches detecting and repairing inventory information technology problems in view of Audimoolam, to efficiently apply analysis of inventory to improving the processing of inventory data by using various evaluation reporting tools . (See Audimoolam at 0022, 0033, 0035, 0054, 0055).
Referring to Claim 6, Rhodes teaches the system of claim 4, wherein the results report are included in a structured output file (See Audimoolam) (
Rhodes: Sec. 0034, if the inventory and planning system projects a surplus of finished products above a threshold level, marketing decision makers are automatically notified in any suitable manner (e.g., an e-mail, a report, a memo, an agenda item automatically inserted into a regularly scheduled meeting agenda, etc.). In one embodiment, the notice includes possibilities for reducing the surplus, such as pricing adjustments, promotional efforts, or changes to the finished product (e.g., inclusion of an indoor sauna or appliance) using projections based on previous system experience.
Rhodes: Sec. 0045, The safety stock targets are input to the master plan with exception report performance (e.g., expected variances) against the safety stock targets. The system 700 takes into account supply and demand variability as well as targeted service levels to set targeted safety stock levels throughout the supply chain. The system 700 has real-time visibility into actual inventory. The system 700 also functions with a master planning system that provides horizontal inventory plan visibility and exception reporting against safety stock targets.
Rhodes: Sec. 0046, decision support for trade-offs between service levels and inventory resulting in improved control over safety stock, synchronization of inventory levels throughout the supply chain reducing excess inventory, visibility into projected inventory and exception reporting on both excess and short inventory levels, reduced transportation expediting cost by efficiently moving inventory, and reduced manufacturing cost by avoiding unnecessary changeovers.).
Rhodes in view of Willemain does not explicitly teach structured output file.
However, Audimoolam teaches structured output file (
Audimoolam: Sec. 0053, All backend systems 62 intercommunicate with each other and with the solution platform 50 through XML scripted application files communicated over a wide area network, such as the internet.)
Rhodes, Willemain, and Audimoolam are all directed to the analysis of inventory (See Rhodes at 0028, 0036, 0045; Willemain at Col. 5 Ln. 1-25, Col. 13 Ln. 27-50; Audimoolam at 0051, 0052, 0063). Rhodes discloses that additional element, such as the use of computer-maintained Material Requirements Plan model can be considered (See Rhodes at 0028). 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 Rhodes in view of Willemain, which teaches detecting and repairing inventory information technology problems in view of Audimoolam, to efficiently apply analysis of inventory to improving the processing of inventory data by using various evaluation reporting tools . (See Audimoolam at 0022, 0033, 0035, 0054, 0055).
Referring to Claim 7, Rhodes teaches the system of claim 1, wherein the first input dataset comprises stochastic (See Audimoolam) replenishment lead-time data for the first raw material (
Rhodes: Sec. 0008, the schedule accounts for such factors as work-in-progress, current inventory of and pending orders for materials and components, and direct demand for components as service items. From the schedule of requirements, a material replenishment strategy that satisfies these requirements can be determined. In one embodiment, one or more of a wide variety of ordering rules and/or heuristics are incorporated into a computer-maintained Material Requirements Plan (“MRP”) model.
Rhodes: Sec. 0009, In addition to or instead of the material requirements, other useful data can be generated from the MPS, such as the projected inventory levels for any end product, the projected schedule for any assembly or production process, and the projected utilization of capacity for a particular production operation at any suitable point in time or during any suitable period. In one embodiment, any of the above information is utilized to evaluate current or potential materials replenishment strategies.
Rhodes: Sec. 0028, using the MPS as a starting point, it is possible to combine it with the data on lead times and BOMs to derive a schedule of component and/or raw materials requirements to as fine a level of assembly and production detail as is desired. In another embodiment, the MPS, itself, includes the finer level details. In one embodiment, the schedule accounts for such factors as work-in-progress, current inventory of and pending orders for materials and components, and direct demand for components as service items. From the schedule of requirements, a material replenishment strategy that satisfies these requirements can be determined. In one embodiment, one or more of a wide variety of ordering rules and/or heuristics are incorporated into a computer-maintained Material Requirements Plan (“MRP”) model.).
Rhodes in view of Willemain does not explicitly teach Stochastic.
However, Audimoolam teaches Stochastic (
Audimoolam: Sec. 0012, the times at which customer orders are received form a non-stationary stochastic process.)
Rhodes, Willemain, and Audimoolam are all directed to the analysis of inventory (See Rhodes at 0028, 0036, 0045; Willemain at Col. 5 Ln. 1-25, Col. 13 Ln. 27-50; Audimoolam at 0051, 0052, 0063). Rhodes discloses that additional element, such as the use of computer-maintained Material Requirements Plan model can be considered (See Rhodes at 0028). 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 Rhodes in view of Willemain, which teaches detecting and repairing inventory information technology problems in view of Audimoolam, to efficiently apply analysis of inventory to improving the processing of inventory data by using various evaluation reporting tools . (See Audimoolam at 0022, 0033, 0035, 0054, 0055).
Referring to Claim 8, Rhodes teaches the system of claim 1, wherein the second input dataset comprises stochastic (See Audimoolam) demand data for the plurality of final products (
Rhodes: Sec. 0055, The system 1100 includes a demand calculation unit 1102, a requirements calculation unit 1104, a scheduling unit 1106 and a learning unit 1108. Together, the demand calculation unit 1102 together with the requirements calculation unit 1104 calculate a demand for finished products, subcomponents needed to assemble the finished products or other subcomponents, and raw materials needed to assemble the finished product or subcomponents. The scheduling unit 1106 determines an assembly/manufacturing schedule for a factory to follow to make each subcomponent and finished product.).
Rhodes in view of Willemain does not explicitly teach Stochastic.
However, Audimoolam teaches Stochastic (
Audimoolam: Sec. 0012, the times at which customer orders are received form a non-stationary stochastic process.)
Rhodes, Willemain, and Audimoolam are all directed to the analysis of inventory (See Rhodes at 0028, 0036, 0045; Willemain at Col. 5 Ln. 1-25, Col. 13 Ln. 27-50; Audimoolam at 0051, 0052, 0063). Rhodes discloses that additional element, such as the use of computer-maintained Material Requirements Plan model can be considered (See Rhodes at 0028). 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 Rhodes in view of Willemain, which teaches detecting and repairing inventory information technology problems in view of Audimoolam, to efficiently apply analysis of inventory to improving the processing of inventory data by using various evaluation reporting tools . (See Audimoolam at 0022, 0033, 0035, 0054, 0055).
Claims 16 and 17 recite limitations that stand rejected via the art citations and rationale applied to claims 7 and 8.
Claims 14 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Publication US 20090150208, Rhodes, et al. to hereinafter Rhodes in view of United States Patent Number US 6205431, Willemain, et al. to hereinafter Willemain in view of United States Patent Publication US 20090259527, Yang, et al.
Referring to Claim 14, Rhodes teaches the method of claim 10, Rhodes in view of Willemain in view of Audimoolam does not explicitly teach wherein the plurality of input datasets comprise comma separated value files.
However, Yang teaches wherein the plurality of input datasets comprise comma separated value files (
Yang: Sec. 0073, correlating input and output information among clients. The correlation is among the local and fragmented information that is different for each client. In the particular embodiment of FIG. 11, the CORRELATION PROCESSOR 98′-2 performs mapping and data integrity processing in connection with the supply chain management. In FIG. 11, the CLIENTs 91-1, 91-2, . . . , 91-C connect over INTERNET 99 to the CORRELATION PROCESSOR 98′-2. The CORRELATION PROCESSOR 98′-2 is part of the BUSINESS LOGIC 98-2 of FIG. 10. The MESSAGE FILE CONNECTOR 88-1 functions using conventional internet protocols (httpRobot, ftpRobot, ftpServer) for incoming and outgoing communications over the INTERNET 99. The FILE MONITOR 88-2 detects the file format and makes conventional conversion to comma separated values (for example, flat2csv, xls2csv). The CONVERTER 88-3 converts the csv values to an xml format as an input to the INPUT MAPPER 88-4.).
Rhodes, Willemain, Audimoolam, and Yang are all directed to the analysis of inventory (See Rhodes at 0028, 0036, 0045; Willemain at Col. 5 Ln. 1-25, Col. 13 Ln. 27-50; Audimoolam at 0051, 0052, 0063; Yang at 0004, 0019, 0182). Rhodes discloses that additional element, such as the use of computer-maintained Material Requirements Plan model can be considered (See Rhodes at 0028). 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 Rhodes in view of Willemain in view of Audimoolam, which teaches detecting and repairing inventory information technology problems in view of Yang, to efficiently apply analysis of inventory to refining the processing of inventory data by using various evaluation reporting tools to create unique output files . (See Yang at 0073, 0075, 0076, 0091).
Referring to Claim 15, Rhodes teaches the method of claim 13, Rhodes in view of Willemain in view of Audimoolam does not explicitly teach wherein the results report comprises a comma separated value file.
However, Yang teaches wherein the results report comprises a comma separated value file (
Yang: Sec. 0073, correlating input and output information among clients. The correlation is among the local and fragmented information that is different for each client. In the particular embodiment of FIG. 11, the CORRELATION PROCESSOR 98′-2 performs mapping and data integrity processing in connection with the supply chain management. In FIG. 11, the CLIENTs 91-1, 91-2, . . . , 91-C connect over INTERNET 99 to the CORRELATION PROCESSOR 98′-2. The CORRELATION PROCESSOR 98′-2 is part of the BUSINESS LOGIC 98-2 of FIG. 10. The MESSAGE FILE CONNECTOR 88-1 functions using conventional internet protocols (httpRobot, ftpRobot, ftpServer) for incoming and outgoing communications over the INTERNET 99. The FILE MONITOR 88-2 detects the file format and makes conventional conversion to comma separated values (for example, flat2csv, xls2csv). The CONVERTER 88-3 converts the csv values to an xml format as an input to the INPUT MAPPER 88-4.
Yang: Sec. 0074, Often suppliers can provide only one format for these reports to all of their buyers due to the constraints in their computer systems. To bridge this information gap, the supply chain management system uses a database schema which provides a master property table holding a super set of information for all the clients (buyers and suppliers) using the system. When the buyers and suppliers send their records, reports and inquiries to the supply chain management system, the data are mapped into the master database schema.).
Rhodes, Willemain, Audimoolam, and Yang are all directed to the analysis of inventory (See Rhodes at 0028, 0036, 0045; Willemain at Col. 5 Ln. 1-25, Col. 13 Ln. 27-50; Audimoolam at 0051, 0052, 0063; Yang at 0004, 0019, 0182). Rhodes discloses that additional element, such as the use of computer-maintained Material Requirements Plan model can be considered (See Rhodes at 0028). 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 Rhodes in view of Willemain in view of Audimoolam, which teaches detecting and repairing inventory information technology problems in view of Yang, to efficiently apply analysis of inventory to refining the processing of inventory data by using various evaluation reporting tools to create unique output files . (See Yang at 0073, 0075, 0076, 0091).
Response to Arguments
Applicant’s arguments filed 03/02/2026 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed hereinbelow in the order in which they appear in the response filed 03/02/2026.
Regarding the 35 U.S.C. 101 rejection, at pg. 8-18 Applicant argues with respect to claims at issue are not directed to an abstract idea
In response to the 35 USC § 101 claim rejection argument, the Examiner respectfully disagrees. The Examiner did consider each claim and every limitation both individually and as a whole, since the grounds of rejection clearly indicates that an abstract idea has been identified from elements recited in the claims. Using the two-part analysis, the Office has determined there are no elements, in the claim sufficient enough to ensure that the claims amounts to significantly more than the abstract idea itself. As recited, the claims are directed towards:
A system, comprising:
at least one computing device;
and at least one application executable in the at least one computing device, wherein when executed the at least one application causes the at least one computing device to at least:
receive at least a first input dataset associated with at least a lead time of a first raw material;
receive at least a second input dataset associated with a demand for a plurality of final products, the plurality of final products corresponding to the first raw material;
perform a non-parametric bootstrap using at least the first input dataset and at least the second input dataset to generate a probability density function of lead time demand for the first raw material, wherein the non-parametric bootstrap is performed by using a plurality of lead-time size random draws of demand with replacement to construct an empirical distribution over a plurality of variable lead times;
determine a safety stock estimate for the first raw material based at least in part on the probability density function of lead time demand; and
reorder the first raw material based at least in part on the safety stock estimate.
The claim(s) does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the computer as recited is a generic computer component that performs functions.
Examiner finds the claim recite concepts which are now described in the 2019 PEG as certain methods of organizing human activity. In particular the claims recites limitations for managing inventory information, which constitutes methods related to commercial interaction such as sales activities and business relations; mathematical concepts such as mathematical calculations which are still considered an abstract idea under the 2019 PEG. The computing device, user interface, client device, structured are comprised of generic computer elements to perform an existing business process. Examiner finds the claims recite mere instructions to implement the abstract idea on a computer and uses the computer as a tool to perform the abstract idea without reciting any improvements to a technology, technological process or computer-related technology.
Regarding, the steps that Applicant points to as practical application are merely narrowing the abstract idea to a particular technological environment, which has been found to be ineffective to render an abstract idea eligible. Furthermore, the Examiner respectfully disagrees because the steps of:
“[0002] Setting appropriate safety stock levels is an important decision for firms in many industries. For several years, global sourcing has continued to grow leaving firms to face long and variable lead times. Furthermore, lead time demands (LTD) that are highly variable can persist with domestic suppliers due to multiple sources of uncertainty, such as unpredictable manufacturing environments, potential disruptions to transportation and distribution infrastructure, and potentially permanent shifts in consumer demand patterns. In practice, final product demand is highly volatile and has high incidence of zero demand. The textbook approach to setting safety stocks assumes that LTD follows a known distribution (e.g., normal), but it is well documented that LTD can be long, highly variable, skewed or multi-modal. Costs of both inventory storage and service failures can be high, making the safety stock decision critical. Existing bootstrap approaches for industry management either operate directly on observed LTD or assume deterministic lead times, permitting direct application of the bootstrap approach for univariate quantile estimation. Given these characteristics, following a standard approach for setting component input safety stocks directly from aggregate demand and assuming that lead time demand follows a normal distribution, works poorly. ”
and arguments at pg. 15-16 seems to describe a “particular way” of managing inventory information. “ The Applicant is basically relying on the system elements for calculating and processing as integrating the abstract idea into a practical application but those system elements for calculating and processing aren't really utilized in any particular manner, and the specification indicates that at 0074 "non-parametric bootstrap application 216 and other various systems described herein may be embodied in software or code executed by general purpose hardware as discussed above, as an alternative the same may also be embodied in dedicated hardware or a combination of software/general purpose hardware and dedicated hardware", which indicates the lack of particularity in the application to the technological environment. Furthermore, at 0145 the Applicant recites that “ It should be emphasized that the above-described embodiments of the present disclosure are merely possible examples of implementations set forth for a clear understanding of the principles of the disclosure. Many variations and modifications can be made to the above-described embodiments without departing substantially from the spirit and principles of the disclosure. All such modifications and variations are intended to be included herein within the scope of this disclosure and protected by the following claims.” Which is further supported by the arguments at page 16:
please see paragraphs (9) and (10) of the "Subject Matter Eligibility Declaration of John Saldanha" submitted herewith (hereinafter "the Second Saldanha Declaration"), which describe the issues in the technical fields of inventory management systems, computer-implemented supply chain optimization, and safety stock computation under demand uncertainty at the time of filing and how Applicant's laims improve these technical fields, including improvements to a non-parametric bootstrap. Further, paragraph (11) of the Second Saldanha Declaration details a practical application of the present disclosure, including actual implementations. Accordingly, claim 1 as amended contains these technical improvements with language such as "perform a non-parametric bootstrap using at least the first input dataset and at least the second input dataset to generate a probability density function of lead time demand for the first raw material, wherein the non-parametric bootstrap is performed by using a plurality of lead-time size random draws of demand with replacement to construct an empirical distribution over a plurality of variable lead times,""determine a safety stock estimate for the first raw material based at least in part on the probability density function of lead time demand," and "reorder the first raw material based at least in part on the safety stock estimate."
These citations are a strong indicator that the technical application is NOT particular, and furthermore the claim invention does not “improves the functioning of a computer or improves another technology or technical field.” or “an improvement to another technology or technical field. As, the claims are clear steps for managing inventory information and not the improvement of the computing device, non-parametric, probability density function, user interface, client device, structured output file, comma separated values file, or even software. Furthermore, the Second Saldanha Declaration admits to the present application being directed to including reduced inventory investment, which is not improving any "technology or technical field."
Additionally, the Examiner did consider “the Kim Memo” and all examination falls in line with the memo.
Next, the Examiner would like to point the Applicant to the 2019 PEG, in which managing inventory information will fall under. The 2019 PEG which states:
Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)
Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)
Regarding the 35 U.S.C. 103 rejection, at pg. 19-24 Applicant argues “the Office Action (p.12) alleges that "Willemain teaches perform a non-parametric bootstrap," and "a probability density function." Applicant respectfully disagrees. As set forth in the accompanying declaration of John Saldanha under 37 C.F.R. § 1.132 submitted herewith (hereinafter "the Saldanha Declaration"), the technique disclosed in Willemain is not a true bootstrap, but rather a Monte-Carlo simulation. As explained by the inventor in the Saldanha Declaration, Willemain describes a method in which a single random draw of demands over the lead time is used to construct a single empirical distribution of demand over the lead time, rather than multiple lead-time size random draws of demand, which the present disclosure uses to construct the empirical distribution over variable lead times. A true bootstrap involves drawing repeated samples with replacement from the observed dataset, with the empirical distribution serving as the sampling distribution. In contrast, a Monte-Carlo simulation involves drawing random samples from a known or assumed theoretical distribution, with the process depending on the correctness of the assumed model. Accordingly, the source of the resampling and the inferential purpose are fundamentally distinct.
In response, the Examiner respectfully disagrees. The Applicant's argument that the references fail to show certain features of applicant’s invention, it is noted that the features upon which applicant relies (i.e., “A true bootstrap involves drawing repeated samples with replacement from the observed dataset, with the empirical distribution serving as the sampling distribution.”; “As shown in the Saldanha Declaration, the method of Willemain introduces model- based bias because it relies on a pre-specified distributional form. A bootstrap derives its inference directly from the observed data without imposing a model structure. Therefore, the methodology of Willemain cannot be considered a bootstrap. Instead, it is a Monte- Carlo simulation subject to distributional bias.” ) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Additionally, the Examiner will encourage to the Applicant to narrow the performing limitation as it includes the bootstrap feature that the Applicant is arguing with details that the Applicant did not include in the claims. Also, the Examiner did not mention or was motivated to substitute a Monte-Carlo process for a bootstrap method.
Lastly, regarding the Office Action failing to establish prima facie obviousness. The Examiner respectfully disagrees. Rhodes and Willemain are both directed towards analyzing the inventory management systems, which is what the Applicants current claims are directed to as well. Furthermore, Willemain teaches a bootstrap that involves drawing repeated samples with changing data from the observed dataset, and the empirical distribution at Willemain: Col. 5 Ln. 25-50; Col. 12 Ln. 1-30; Col. 14 Ln. 65-Col Ln.30, As such the Applicant’s arguments are not persuasive.
The Declaration under 37 CFR 1.132 filed 03/02/2026 is insufficient to overcome the rejection of claim 1-18 based upon
demonstrate the practical application of the presently claimed
non-parametric bootstrap, show technological improvement over prior safety stock
estimation methods, and provide evidence of real-world implementation and results;
These issues in the technical fields of inventory management systems, computer-
implemented supply chain optimization, and safety stock computation under demand
uncertainty are addressed by the present disclosure.
Accordingly, a skilled artisan would read the '661 Specification to understand the claimed improvement of a non-parametric, data-driven approach that leverages item-level demands to estimate safety stocks to meet a target service level that provides both accurate estimates as well as statistically valid confidence intervals, as further described herein.
as set forth in the last Office action these arguments are not persuasive.
The Examiner wants to make aware to the Applicant that the cited paras from the specification are not facts. Additionally, the Examiner would like to point the Applicant to MPEP 2106.04 "even newly discovered or novel judicial exceptions are still exceptions"; Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151, 120 USPQ2d 1473, 1483 (Fed. Cir. 2016) ("a new abstract idea is still an abstract idea"). As, stated above the non-parametric bootstrap is a part of abstract idea and is not improving any "technology or technical field." The non-parametric bootstrap is a statistical technique, which is mathematical calculations; wherein mathematical calculations, falls within the mathematical concepts grouping of abstract ideas.
Then regarding,
“The present application also includes real-world implementation results from an anonymous industry partner demonstrating operational improvements, including reduced inventory investment (e.g., inventory investment was reduced by $1.17 million) and improved service level when the bootstrap-based approach was deployed (see e.g., paras. [0064]-[0066], para. [0075], and paras. [0119]-[0121] of the '661 Specification). The implementation results provide concrete, quantitative outcomes that show the methods and systems of the present disclosure yield real technical benefits in a production environment.”;
“actual implementations, such as the implementation at "MakerCo" detailed in
the '661 Specification. The outputs of the present bootstrap methods can be integrated into
inventory control systems and used to generate purchase orders and adjust reorder points
in real time or batch processes. The resulting safety stock estimate of the present bootstrap
can directly control inventory levels, affect physical goods movement, and reduce stockouts
or overstocking.”
The Examiner wants to make aware to the Applicant that the cited paras from the specification are not facts. Additionally, the Examiner would like to point the Applicant to MPEP 2106.04 "even newly discovered or novel judicial exceptions are still exceptions"; Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151, 120 USPQ2d 1473, 1483 (Fed. Cir. 2016) ("a new abstract idea is still an abstract idea"). As, stated above the implementation of reducing inventory investment, is not improving any "technology or technical field." This is directed to a method of organizing human activity which includes commercial interaction such as sales activities and business relations. These remarks are directed to improving a business process/operation and not improvements to a technology or technological field.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Martinez et al., U.S. Pub. 20160063419, (discussing the managing of inventory by the use of various tools to optimize the management of inventory information.).
Bohnsack et al., W.O. Pub. 2017173380, (discussing the managing of inventory such as consumables and services.).
Arifoğlu et al., Inventory Management With Random Supply And Imperfect Information: A Hidden Markov Model, https://www.sciencedirect.com/science/article/pii/S0925527311002726, International Journal of Production Economics, 2011 (discussing the managing of inventory data and using various tools to analyze the data.).
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