DETAILED ACTION
Status of the Application
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 Final Action on the merits in response to the application filed on 05/29/2026.
Claims 1, 10, and 19 have been amended.
Claims 1-20 remain pending in this application.
Response to Amendment
Applicant’s amendments are acknowledged.
The 35 U.S.C. 103 rejections of claims in the previous office action are withdrawn in light of applicant’s amendments.
The 35 U.S.C. 101 rejections of claims 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, claims 10-18 are directed towards a method, and claims 19 and 20 are directed towards a computer readable medium all of which are among the statutory categories of invention.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claims are directed to a judicial exception without significantly more.
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, the claim is to a process, which is one of the statutory categories of invention. (Step 1: YES).
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
With respect to claims 1-20, the independent claims (claims 1, 10, and 19) are directed to managing of supply chain data, In independent claim 1, the bolded limitations emphasized below correspond to the abstract ideas of the claimed invention:
generate a supply chain lead time variability for an item at the fulfillment node based on the historical data, by grouping the items into clusters of items that are likely to be delivered together to the fulfillment node and share a lead time error,
generate a consumer demand variability for the item at the fulfillment node based on the historical data and the supply chain lead time variability by filtering historical sales data to remove anomalous sale records, and generating a second probability distribution representing the consumer demand variability for the item over a period including a lead time period and a review period,
these steps fall within and recite an abstract ideas because they are directed to a method of organizing human activity which includes commercial interaction includes business relations; managing personal behavior such as social activities and following rules or instructions (See MPEP 2106.04(a)(2), subsection II), as well as, Certain Methods of Organizing Human Activities” as recited, described or set forth above, could be argued as implementable through computer-aided mental processes, when tested per MPEP 2106.04(a) ¶3, 3), and MPEP 2106.04(a)(2) III C, such as by computer-aided evaluation, judgement and observation.
If a claim limitation, under its broadest reasonable interpretation, covers commercial interaction and managing personal behavior, then it falls within the “method of organizing human activity” grouping of abstract ideas. Therefore, If the identified limitation(s) falls within any of the groupings of abstract ideas enumerated in the 2019 PEG, 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 system, processor, memory, network, probability distribution, computing device, (Claim 10 network, probability distribution, computing device; Claim 19 computer readable medium, processor, probability distribution, computing device). The claims recite the steps are performed by the processor, memory, device, computer readable medium.
The limitations of
A system, comprising:
a processor; and
a non-transitory memory storing instructions, that when executed, cause the processor to:
obtain historical data of items at a fulfillment node in a network for a past time period,
and generating a first probability distribution representing the supply chain lead time variability for the item,
generate based on the supply chain lead time variability and the consumer demand variability, a recommended inventory level of the item in the fulfillment node for a future time period using a statistical model that applies a cost function balancing inventory holding cost and shortage cost based on the first probability distribution and the second probability distribution, and
transmit data indicating a value representing the recommended inventory level to a computing device associated with the fulfillment node for inventory arrangement in the future time period.
are mere data gathering and processing 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 system, processor, memory, network, probability distribution, computing device. The system, processor, memory, network, probability distribution, computing device are recited at a high level of generality. In limitation (a), system, processor, memory, network, probability distribution, computing device are used as a tool to perform the generic computer function of receiving data. See MPEP 2106.05(f). system, processor, memory, network, probability distribution, computing device are used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f).
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 system, processor, memory, network, probability distribution, 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 processing.
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
A system, comprising:
a processor; and
a non-transitory memory storing instructions, that when executed, cause the processor to:
obtain historical data of items at a fulfillment node in a network for a past time period,
and generating a first probability distribution representing the supply chain lead time variability for the item,
generate based on the supply chain lead time variability and the consumer demand variability, a recommended inventory level of the item in the fulfillment node for a future time period using a statistical model that applies a cost function balancing inventory holding cost and shortage cost based on the first probability distribution and the second probability distribution, and
transmit data indicating a value representing the recommended inventory level to a computing device associated with the fulfillment node for inventory arrangement in the future time period.
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 processor, memory, device, computer readable medium 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, 20 do not contain any new additional elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims. In this case, the claims are rejected for the same reasons at step 2a, prong one; step 2a, prong 2; and step 2b. Thus, the claim is not patent eligible.
Regarding the dependent claims, dependent claims 4, 7, 8, 12, 13, 16, 17 recite probability distribution; claim 20 recites a CRM to generate recommended inventory levels. The dependent claims 2-9, 11-18, 20 recite limitations that are not technological in nature and merely limits the abstract idea to a particular environment. Claims 2-9, 11-18, 20 recites system, processor, memory, network, probability distribution, computing device which are considered an insignificant extra-solution activities of collecting and analyzing data; see MPEP 2106.05(g). 2-9, 11-18, 20 recites system, processor, memory, network, probability distribution, computing device, 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, 20 recite steps that further narrow the abstract idea. No additional elements are disclosed in the dependent claims that were not considered in independent claims 1, 10, and 19. Therefore claims 2-9, 11-18, 20 do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself.
Reasons for Removing the Prior Art Rejection
The rejections under 35 U.S.C. 103 as to claims 1-20 are removed in light of Applicant's claims and remarks of 05/29/2026, which are deemed persuasive as to independent claims 1, 10, and 19. The reasons for withdrawal of the rejections under 35 U.S.C. 103 can be found at the following claim limitations of 05/29/2026 at claim 1 as follows:
Claim 1
A system, comprising:
a processor; and
a non-transitory memory storing instructions, that when executed, cause the processor to:
obtain historical data of items at a fulfillment node in a network for a past time period,
generate a supply chain lead time variability for an item at the fulfillment node based on the historical data, by grouping the items into clusters of items that are likely to be delivered together to the fulfillment node and share a lead time error, and generating a first probability distribution representing the supply chain lead time variability for the item,
generate a consumer demand variability for the item at the fulfillment node based on the historical data and the supply chain lead time variability by filtering historical sales data to remove anomalous sale records, and generating a second probability distribution representing the consumer demand variability for the item over a period including a lead time period and a review period,
generate based on the supply chain lead time variability and the consumer demand variability, a recommended inventory level of the item in the fulfillment node for a future time period using a statistical model that applies a cost function balancing inventory holding cost and shortage cost based on the first probability distribution and the second probability distribution, and
transmit data indicating a value representing the recommended inventory level to a computing device associated with the fulfillment node for inventory arrangement in the future time period.
Applicant’s Remarks of 05/29/2026 at pg. 20-23 as follows:
Pg. 20, “Dvorak does not disclose or suggest this element of independent claim 1. In particular, the Office Action (page 8) states that Dvorak discloses grouping items or locations. However, Dvorak's grouping relates to grouping selling locations to develop location-level sales profiles and shares, not to grouping items by their likelihood of being physically co-delivered so that they share a common lead time error. In other words, Dvorak's grouping is a sales forecasting technique applied at the location level for purposes of smoothing random selling noise (see Dvorak, col. 17, lines 55-59), which is entirely different from the co-delivery clustering recited in claim 1. However, Dvorak provides no disclosure or suggestion that items are clustered based on their likelihood of co-delivery to the same fulfillment node, or that such clusters are used as the basis for computing a lead time error probability distribution.
Huang does not remedy this deficiency of Dvorak. The Office Action relies (see pages 15- 16) on col. 60 lines 35-60 of Huang for the concept of lead time distributions. While Huang discloses fitting distributions to lead time parameters as part of its multi-echelon supply chain planning framework, Huang does not disclose or suggest grouping items into clusters based on co- delivery likelihood and shared lead time errors as the mechanism for generating a lead time variability probability distribution. To be sure, Huang's lead time modeling operates on individual products or product families within its production planning framework, but Huang does not disclose or suggest the cluster-based, co-delivery-driven lead time error computation as recited in Applicant's claim 1.
As discussed above, neither Dvorak nor Huang discloses or suggests "grouping the items into clusters of items that are likely to be delivered together to the fulfillment node and share a lead time error" as recited in independent claim 1 as now amended. Accordingly, the proposed combination of Dvorak and Huang fails to render claim 1 (or claims 10 and 19) obvious for at least this reason.”
Pg. 21, “Dvorak does not disclose a systematic, automated process for detecting and removing anomalous sale records from historical data before generating a consumer demand probability distribution. Nor does Dvorak provide any disclosure or suggestion of using anomaly- filtered data as the input for computing a consumer demand variability distribution.”;
“neither Dvorak nor Huang discloses or suggests the generation of a probability distribution representing the consumer demand variability for the item over a period including a lead time period and a review period as recited in independent claim 1 as now amended.”
“Dvorak's hurdle rate is not a statistical cost function that directly operates on probability distributions of lead time variability and consumer demand variability as inputs. In other words, Dvorak's does not disclose or suggest generating a shortage cost function and a holding cost function based on probability distributions and minimizing the combined cost function to determine an inventory level. Dvorak's safety stock and open-to-buy analyses are discussed at a high level of generality and do not disclose the specific statistical cost-function model recited in amended claim 1.”
Pg. 22, Huang does not disclose or suggest the specific cost function model as recited in independent claim 1, where a shortage cost function and a holding cost function are separately generated based on the first and second probability distributions and then combined and minimized to yield a recommended inventory level for a fulfillment node. The structural specificity of the cost function recited in claim 1, including the use of both probability distributions as inputs and the minimization of the combined cost function to yield the inventory recommendation, is not taught or suggested by Huang. Even assuming that Huang teaches fitting distributions to demand or lead time parameters, Huang does not teach conditioning demand distributions on a probabilistic lead-time-and-review (LTR) period derived from clustered delivery behavior, nor using those conditioned distributions as direct inputs to a shortage/holding cost minimization.
Because neither Dvorak nor Huang, alone or in combination, discloses or suggests the specific cost-function-based inventory level generation limitation of claim 1 as amended, the proposed combination fails to render claim 1 obvious for at least these additional reasons.
This applies to independent claims 10 and 19 as these claims includes the same feature of claim 1.
Page 9 of 13
Page 9 of 13
Response to Arguments
Applicant’s arguments filed 05/29/2026 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 05/29/2026.
Regarding the 35 U.S.C. 101 rejection, at pg. 12-19 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:
a processor; and
a non-transitory memory storing instructions, that when executed, cause the processor to:
obtain historical data of items at a fulfillment node in a network for a past time period,
generate a supply chain lead time variability for an item at the fulfillment node based on the historical data, by grouping the items into clusters of items that are likely to be delivered together to the fulfillment node and share a lead time error,
and generating a first probability distribution representing the supply chain lead time variability for the item,
generate a consumer demand variability for the item at the fulfillment node based on the historical data and the supply chain lead time variability by filtering historical sales data to remove anomalous sale records, and generating a second probability distribution representing the consumer demand variability for the item over a period including a lead time period and a review period,
generate based on the supply chain lead time variability and the consumer demand variability, a recommended inventory level of the item in the fulfillment node for a future time period using a statistical model that applies a cost function balancing inventory holding cost and shortage cost based on the first probability distribution and the second probability distribution, and
transmit data indicating a value representing the recommended inventory level to a computing device associated with the fulfillment node for inventory arrangement in the future time period.
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 of supply chain data, which constitutes methods related to commercial interaction includes business relations; managing personal behavior such as social activities and following rules or instructions which are still considered an abstract idea under the 2019 PEG. The processors 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 at pg. 13, 15-18 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:
“(it is specifically configured to execute a defined sequence of computational operations: (i) clustering items that are likely co-delivered to the fulfillment node so as to capture correlated lead time errors within a cluster, thereby generating a first probability distribution representing supply chain lead time variability; (ii) filtering historical sales data to remove anomalous records and then generating a second probability distribution representing consumer demand variability over a combined lead time and review period; and (iii) applying a statistical model that balances holding cost against shortage cost, using the first and second probability distributions as inputs, to determine a recommended inventory level and transmit that recommendation to a device at the fulfillment node.”
and arguments at pg. 13 seems to describe a “particular way” managing of supply chain data are part of the abstract idea. “ The Applicant is basically relying on processors and probability distributions as integrating the abstract idea into a practical application but those elements aren't really utilized in any particular manner.
Next, the regarding the following arguments:
Pg. 13 “The ordered pipeline of operations as recited in claim 1 reflects a technical transformation of raw historical delivery and sales records into a structured probabilistic model that produces a specific, optimized inventory recommendation.”
Examiner finds Applicant has failed to adequately point out what technological process in the claimed invention has improved. Using an probability distributions/ probabilistic model to produce recommendation, are considered an improvement to an existing business process and not an improvement to the functioning of a computer, or any other technology or technological field.
Then, regarding the following arguments
Pg. 14 “In other words, these operations require computer-implemented probabilistic modeling and numerical optimization over large historical datasets that are infeasible without computerized processing resources. In addition, while the output of Applicant's claimed system relates to inventory decisions, the claimed features relate to computationally modeling lead time uncertainty and demand variability, not to business rules or managerial policy. Accordingly, the claimed system does not recite a mental process for at least these reasons.”
Pg. 17 “The improvement lies not in a business rule or organizational concept, but in the way the computing system acquires, conditions, and processes historical operational data to generate a technically superior and more accurate inventory recommendation.”
The Examiner did not recite that claimed system as being directed to a mental process. However, Certain Methods of Organizing Human Activities” as recited, described or set forth above, could be argued as implementable through computer-aided mental processes, when tested per MPEP 2106.04(a) ¶3, 3), and MPEP 2106.04(a)(2) III C, such as by computer-aided evaluation, judgement and observation.
Then, regarding the following arguments
Pg. 14 “The specification further explains that the co-delivery clustering step captures that "all frozen food items are grouped together, as they are likely to be delivered on the same truck and share the same lead time error" (see par. [0093]), and that the anomaly-filtering step addresses the documented problem that "if the sales of an item for a particular day are much higher than the forecast for that day and much higher than the sales for the days immediately preceding or succeeding that day, by a difference beyond some threshold, this day becomes a marked outlier," which, if unaddressed, causes the system to "maintain an unreasonably high amount of safety stock, even if those circumstances will almost certainly not be repeated" (see par. [0103]). These are technical problems rooted in data processing and supply chain computation, not abstract concepts.”
Pg. 15 “The present application confirms that the claimed processor operations produce a technical improvement over conventional approaches, describing how "the operating characteristic curve (i.e. the tradeoff curve between customer service and inventory levels) is improved," represented by a shift from curve 1010 to the superior curve 1020 in FIG. 10, such that "the system can achieve a better tradeoff: a higher customer service availability given a same inventory level, and a lower inventory level given a same customer service availability" (see par. [0112]). This technical improvement in the performance of the computing system itself, not merely a business outcome, confirms that the claimed processor operations transform the way inventory optimization is computationally performed.”
that claims do not show or break down how this is actually executed, at this point it’s just an aspirational statement, 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”.
Then, regarding the following arguments
Pg. 16 “Similarly, the filtering of historical sales data to remove anomalous records before generating the second probability distribution is a specific technical step that directly addresses the problem of corrupted or unrepresentative historical data skewing demand forecasts. These elements are not recited at a high level of generality.”
the Applicant admission that the application is directed to improving the user’s experience and not the computer itself.
Then, regarding the following arguments
Pg. 17 “The improvement lies not in a business rule or organizational concept, but in the way the computing system acquires, conditions, and processes historical operational data to generate a technically superior and more accurate inventory recommendation. Thus, the claim satisfies MPEP § 2106.05(a) by improving the technical field of automated inventory management and supply chain optimization.”
When determining whether a computer-implemented functional claim would have been obvious, examiners should note that broadly claiming an automated means to replace a manual function to accomplish the same result does not distinguish over the prior art. See Leapfrog Enters., Inc. v. Fisher-Price, Inc., 485 F.3d 1157, 1161, 82 USPQ2d 1687, 1691 (Fed. Cir. 2007) (“Accommodating a prior art mechanical device that accomplishes [a desired] goal to modern electronics would have been reasonably obvious to one of ordinary skill in designing children’s learning devices. Applying modern electronics to older mechanical devices has been commonplace in recent years.”); In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958); see also MPEP § 2144.04. Furthermore, implementing a known function on a computer has been deemed obvious to one of ordinary skill in the art if the automation of the known function on a general purpose computer is nothing more than the predictable use of prior art elements according to their established functions. KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 417, 82 USPQ2d 1385, 1396 (2007); see also MPEP § 2143, Exemplary Rationales D and F. Likewise, it has been found to be obvious to adapt an existing process to incorporate Internet and Web browser technologies for communicating and displaying information because these technologies had become commonplace for those functions. Muniauction, Inc. v. Thomson Corp., 532 F.3d 1318, 1326-27, 87 USPQ2d 1350, 1357 (Fed. Cir. 2008).
Then, regarding the following arguments
Pg. 17 “The claim therefore does not risk preemption of an abstract idea, but instead confines its scope to a particular, machine-implemented technological solution with specific computational constraints. And as discussed in more detail below, neither Dvorak nor Huang treats correlated lead-time error as a random variable derived from co-delivered item clusters.”
In response, Examiner respectfully disagrees with this analysis. A claim cannot avoid this preemption concern by limiting itself to a particular technological environment. See Alice, 134 S. Ct. at 2357-58 (limiting an abstract idea to computer environment does not mitigate preemption concerns). The Supreme Court has warned that a “draftsman’s art” should not trump the prohibitions against patenting abstract ideas. See Alice Corp., 132 S.Ct. at 2359 (citing Mayo, 132 S.Ct. at 1294 (quoting Parker v. Flook, 437 U.S. 584, 593 (1978))).
Additionally, the Examiner would like to point the Applicant to the 2019 PEG, in which managing of supply chain data 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)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hubbs., W.O. Pub. 2020086214, (discussing the use of machine learning for managing logistics and inventory ).
Dogan et al., U.S. Pub. 20040230474, (discussing the use of probability distribution for managing supply chain data.).
Ponte et al., Holism Versus Reductionism In Supply Chain Management: An Economic analysis, https://orca.cardiff.ac.uk/id/eprint/98144/, or http://dx.doi.org/10.1016/j.dss.2016.03.010, Decision Support Systems., 2016 (discussing the integration of supply chain from a holistic approach).
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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