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 .
DETAILED ACTION
This is a non-rejection. Claims 1-4, 6-11, 13-18, and 20 are pending.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 01/28/2026 has been entered.
Status of Claims
Applicant’s amendment date 1/28/2026. Amending claims 1, 8, and 15.
Response to Amendment
The previously pending rejection under 35 USC 101, will be maintained. The 101 is updated in light of the amendments.
The previously pending rejection under 35 USC 102/103, will be maintained. The 102/103 is updated in light of the amendments.
Response to Arguments
Applicant’s argument received 1/28/2026 have been fully considered, but they are not persuasive.
Response to Arguments under 35 USC 101:
Applicant argues (Pages 9-10 of the remarks): abstract idea
Applicants respectfully submit that Applicants' claims are not directed to "abstract ideas" such as mathematical concepts, certain methods of organizing human activity, mental processes, laws of nature or natural phenomena. Therefore, since Applicants' claims do not recite matter that falls within the enumerating groupings of abstract ideas, Applicants' claims should not be treated as reciting an abstract idea.
Examiner respectfully disagrees:
Independent Claims 1, similar steps likewise reflect in claims 8 and 15, the claims, when “taken as a whole,” are directed to the abstract idea and substantially recite the limitations: generate, by a user interface module, one or more assumption objects storing data related to one or more supply chain entities within a supply chain network wherein the one or more assumption objects are expressed as one or more machine-executable logical expressions, and wherein the one or more assumptions objects are in a hierarchy defined as explicit data objects;
bundle, by an assumption manager module, the one or more assumption objects into perspectives according to shared assumption object characteristics;
create, by a what-if engine, one or more hierarchical assumption variants based on the one or more assumption objects, and generate, by the what-if engine, one or more probability coefficients for each of the one or more hierarchical assumption variants;
model, by the what-if engine, an impact of the one or more hierarchical assumption variants according to one or more impact metrics;
generate, by a resolution recommendation engine, one or more mitigation options to reduce the impact of at least one of the one or more hierarchical assumption variants;
build, by the resolution recommendation engine, one or more response plans to respond to the one or more assumption variants using the one or more mitigation options; and
execute, by the computer, at least one of the one or more response plans wherein the at least one of the one or more response plans is executed in response to execution of the one or more machine-executable logical expressions; and
monitor and re-evaluate, by the computer, the one or more machine-executable logical expressions. The Applicant's Specification titled "ASSUMPTION-BASED PLANNING" emphasizes the business need for data analysis, "In summary, the present disclosure relates to methods and systems for generate a mitigation options to reduce an impact, build a response plans and execute the response plans" (Spec. [0002]). As the bolded claim limitations above demonstrate, independent claims 1, 8 and 15 recites the abstract idea of generate a mitigation options to reduce an impact, build a response plans and execute the response plans. The limitations recite generate a mitigation options to reduce an impact, build a response plans and execute the response plans. Which is “including agreements in the form of contract; legal obligations, advertising, marketing or sales activities or behaviors; business relations” and fundamental economic principles or practices (including hedging, insurance, mitigating risk). expressly categorized under a certain methods of organizing human activity. See MPEP §2106.04(a)(2)(II).
Applicant argues (Pages 10-12 of the remarks): prong two
even if Applicants' claims recites a judicial exception (a law of nature, a natural phenomenon, or the enumerating groupings of abstract ideas), which they do not, the Examiner must analyze Applicants' claims to determine whether any alleged judicial exception is integrated into a practical application of that exception. Applicants respectfully submit that Applicants' claims are not" directed to" a judicial exception, and thus are patent eligible, for at least the reason that Applicants' claims as a whole integrates any alleged judicial exception into a practical application of that exception. Appropriate clarification is respectfully requested. …. Applicants respectfully submit that the claims are directed to, among other things, data objects and machine-executable instructions for performing limitations in a software environment (see at least paragraph [0009]). Accordingly, the software embodiment of the Applicants' claimed limitations integrates any abstract idea into a practical application. Applicants thus respectfully submit that the claimed invention is not directed to an abstract idea of merely performing generic planning, but as a whole, because of the claimed additional elements, integrates any abstract idea into a practical application. MPEP 2106.04 II(A)(2).
Examiner respectfully disagrees:
In prong two of step 2A, an evaluation is made whether a claim recites any additional element, or combination of additional element, that integrate the exception into a practical application of that exception. An “additional element” is an element that is recited in the claim in addition to (beyond) the judicial exception (i.e., an element/limitation that sets forth an abstract idea is not an additional element). The phrase “integration into a practical application” is defined as requiring an additional element or a combination of additional elements in the claim to apply, rely on, or use exception, such that it is more than a drafting effort designed to monopolize the exception.
The claims recites the additional limitation of a system, computer, a processor, memory, a user interface module, an assumption manager module, what-if engine, and a resolution recommendation engine, a non-transitory, and computer-readable medium embodied with software are recited in a high level of generality and recited as performing generic computer functions routinely used in computer applications. Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp. 134 S. Ct, at 2360,110 USPQ2d at 1984 (see MPEP 2106.05(f).
The use of generic computer component to “generate a mitigation options to reduce an impact” does not impose any meaningful limit on the computer implementation of the abstract idea. Thus, taken alone, the additional elements do not amount to significantly more than the above identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claim(s) is/are directed to an abstract idea (step 2A-prong two: NO).
The Alice framework, we turn to step 2B (Part 2 of Mayo) to determine if the claim is sufficient to ensure that the claim amounts to “significantly more” than the abstract idea itself. These additional elements recite conventional computer components and conventional functions of:
Claims 1, 8 and 15 does not include my limitations amounting to significantly more than the abstract idea, along. Claims 1, 8 and 15 includes various elements that are not directed to the abstract idea. These elements include a system, computer, a processor, memory, a user interface module, an assumption manager module, what-if engine, and a resolution recommendation engine, a non-transitory, and computer-readable medium embodied with software.
Examiner asserts that the additional elements in the claims are a generic computing element performing generic computing functions.
Therefore, the claims at issue do not require any nonconventional computer, network, or display components, or even a “non-conventional and non-generic arrangement of know, conventional pieces,” but merely call for performance of the claimed on a set of generic computer components” and display devices.
Response to Arguments under 35 USC 103:
Applicant argues (Pages 14-15 of the remarks):
Applicants respectfully submit that merely quoting large sections of Najmi and Tilly verbatim fails to comply with MPEP 707(c)(2) because the Examiner's verbatim quoting directly from the references fails to provide any explanation, much less a clear explanation, as required, of how the references are being applied to Applicants' claim limitations. Applicants respectfully request the Examiner provide an explanation, beyond merely quoting large sections of Najmi and Tilly, of how these references are being applied to Applicants' claims.
Applicants respectfully submit the merely quoting large sections of Najmi and Tilly, is improper examination practice because, as discussed in MPEP 707, it fails to provide clarity of record and also fails to inform Applicants of how the references are being applied to Applicants' claimed invention.
Examiner respectfully disagrees:
Applicant’s amendment and/or arguments, with respect to the claim rejection
under 35 USC 103 as set forth in the Office Action of 10/28/2025 have been fully
considered and are not persuasive. Initially, the Examiner notes that applicant did not
present any arguments against the claim interpretation under 35 USC 103 in the
response. However, the Examiner has considered the amendments in an effort to
expedite prosecution of the application. As such the claim rejection of the claims under
USC 103 is maintained. Examiner underline in the non-final rejection where it needed to clarify the citation under art rejection. Examiner recommend the applicant to discuss the references applied against the claims, explain how the claims (limitation(s)) avoids the references or distinguish from them.
Applicant should submit an argument under the heading “Remarks” pointing out disagreements with the examiner’s contentions. Applicant must also discuss the references applied against the claims, explaining how the claims avoid the references or distinguish from them.
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 1-4, 6-11, 13-18, and 20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter, specifically an abstract idea without a practical application or significantly more than the abstract idea.
Under the 35 U.S.C. §101 subject matter eligibility two-part analysis, Step 1 addresses whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. See MPEP §2106.03. If the claim does fall within one of the statutory categories, it must then be determined in Step 2A [prong 1] whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea). See MPEP §2106.04. If the claim is directed toward a judicial exception, it must then be determined in Step 2A [prong 2] whether the judicial exception is integrated into a practical application. See MPEP §2106.04(d). Finally, if the judicial exception is not integrated into a practical application, it must additionally be determined in Step 2B whether the claim recites "significantly more" than the abstract idea. See MPEP §2106.05.
Examiner note: The Office's 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG) is currently found in the Ninth Edition, Revision 10.2019 (revised June 2020) of the Manual of Patent Examination Procedure (MPEP), specifically incorporated in MPEP §2106.03 through MPEP §2106.07(c).
Regarding Step 1
Claims 1-4, 6-7 are directed toward a system (machine). Claims 8-11, 13-14 are directed toward a method (process). Claims 15-18, and 20 are directed to a non-transitory computer-readable medium (machine). Thus, all claims fall within one of the four statutory categories as required by Step 1.
Regarding Step 2A [prong 1]
Claims 1-4, 6-11, 13-18, and 20 are directed toward the judicial exception of an abstract idea. Independent claims 8 and 15 recites essentially the same abstract features as claim 1, thus are abstract for the same reasons as claim 1.
Regarding independent claim 1, the bolded limitations emphasized below correspond to the abstract ideas of the claimed invention:
Claim 1. A system comprising a computer, comprising a processor and memory, and configured to:
generate, by a user interface module, one or more assumption objects storing data related to one or more supply chain entities within a supply chain network wherein the one or more assumption objects are expressed as one or more machine-executable logical expressions, and wherein the one or more assumptions objects are defined in a hierarchy as explicit data objects;
bundle, by an assumption manager module, the one or more assumption objects into perspectives according to shared assumption object characteristics;
create, by a what-if engine, one or more hierarchical assumption variants based on the one or more assumption objects and generate, by the what-if engine, one or more probability coefficients for each of the one or more hierarchical assumption variants;
model, by the what-if engine, an impact of the one or more hierarchical assumption variants according to one or more impact metrics;
generate, by a resolution recommendation engine, one or more mitigation options to reduce the impact of at least one of the one or more hierarchical assumption variants;
build, by the resolution recommendation engine, one or more response plans to respond to the one or more assumption variants using the one or more mitigation options; and
execute, by the computer, at least one of the one or more response plans wherein the at least one of the one or more response plans is executed in response to execution of the one or more machine-executable logical expressions; and
monitor and re-evaluate, by the computer, the one or more machine-executable logical expressions.
The Applicant's Specification titled "ASSUMPTION-BASED PLANNING" emphasizes the business need for data analysis, "In summary, the present disclosure relates to methods and systems for generate a mitigation options to reduce an impact, build a response plans and execute the response plans" (Spec. [0002]). As the bolded claim limitations above demonstrate, independent claims 1, 8 and 15 recites the abstract idea of generate a mitigation options to reduce an impact, build a response plans and execute the response plans.
The limitations recite generate a mitigation options to reduce an impact, build a response plans and execute the response plans. Which is “including agreements in the form of contract; legal obligations, advertising, marketing or sales activities or behaviors; business relations” and fundamental economic principles or practices (including hedging, insurance, mitigating risk). expressly categorized under a certain methods of organizing human activity. See MPEP §2106.04(a)(2)(II).
Dependent claims 2-7, 9-14, and 16-20 further reiterate the same abstract ideas with further embellishments (the bolded limitations), such as
claim 2 (similarly claims 9 and 16) wherein the one or more assumption objects persist across two or more separate supply chain planning cycles.
claim 3 (similarly claims 10, and 17) generates two or more what-if scenarios to simulate two or more assumption event outcomes.
claim 4 (similarly claims 11 and 18) wherein the computer is further configured to execute at least one of the one or more response plans by taking one or more actions selected a group consisting of: pushing execution instructions to one or more supply chain entities; transmitting the one or more response plans to one or more assigned persons; activating one or more parked assumption objects; altering one or more data values associated with an assumption object condition; creating a new supply chain planning scenario; and applying a mitigation response to one or more sets of planning data.
claim 5 (similarly claims 12 and 19) Cancelled
claim 6 (similarly claim 13) wherein the one or more assumption objects are generated from knowledge base data stored during carrying out of actions within a supply chain network.
claim 7 (similarly claims 14 and 20) wherein the one or more assumption objects represent one or more business strategies, one or more contractual agreements, one or more risks or one or more opportunities.
which are nonetheless directed towards fundamentally the same abstract ideas as indicated for independent claims 1, 8, and 15.
Regarding Step 2A [prong 2]
Claims 1-4, 6-11, 13-18, and 20 fail to integrate the abstract idea into a practical application. Independent claim 1 (similarly claims 8 and 15) include the following bolded additional elements which do not amount to a practical application:
Claim 1. A system comprising a computer, comprising a processor and memory, and configured to: by a user interface module, by an assumption manager module, by a what-if engine, a resolution recommendation engine,
The bolded limitations recited above in independent claim 1 (similarly claims 8 and 15) pertain to additional elements which merely provide an abstract-idea-based-solution implemented with computer hardware and software components, including the additional elements of a system, computer, a processor, memory, a user interface module, an assumption manager module, what-if engine, and a resolution recommendation engine, a non-transitory, and computer-readable medium embodied with software. which fail to integrate the abstract idea into a practical application because there are (1) no actual improvements to the functioning of a computer, (2) nor to any other technology or technical field, (3) nor do the claims apply the judicial exception with, or by use of, a particular machine, (4) nor do the claims provide a transformation or reduction of a particular article to a different state or thing, (5) nor provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment, in view of MPEP §2106.04(d)(1) and §2106.05 (a-c & e-h), (6) nor do the claims apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, in view of MPEP §2106.04(d)(2). The Specification provides a high level of generality regarding the additional elements claimed without sufficient detail or specific implementation structure so as to limit the abstract idea, for instance, the computing platform includes generic processors, memories, and communication interfaces.
Paragraph [0019] of the specification disclose each of the one or more computers 160 may be a work station, personal computer (PC), network computer, notebook computer, tablet, personal digital assistant (PDA), cell phone, telephone, smartphone, wireless data port, augmented or virtual reality headset, or any other suitable computing device. Nothing in the Specification describes the specific operations recited in claims 1, 8, and 15 as particularly invoking any inventive programming, or requiring any specialized computer hardware or other inventive computer components, i.e., a particular machine, or that the claimed invention is somehow implemented using any specialized element other than all-purpose computer components to perform recited computer functions. The claimed invention is merely directed to utilizing computer technology as a tool for solving a business problem of data analytics. Nowhere in the Specification does the Applicant emphasize additional hardware and/or software elements which provide an actual improvement in computer functionality, or to a technology or technical field, other than using these elements as a computational tool to automate and perform the abstract idea. See MPEP §2106.05(a & e).
The relevant question under Step 2A [prong 2] is not whether the claimed invention itself is a practical application, instead, the question is whether the claimed invention includes additional elements beyond the judicial exception that integrate the judicial exception into a practical application by imposing a meaningful limit on the judicial exception. This is not the case with Applicant's claimed invention which merely pertains to steps for generate a mitigation options to reduce an impact, build a response plans and execute the response plans and merely linking the use of the abstract idea to a particular technological environment. See MPEP §2106.04 and §21062106.05(f-h). Alternatively, the Office has long considered data gathering, analysis and data output to be insignificant extra-solution activity, and these additional elements do not impose any meaningful limits on practicing the abstract idea. See MPEP §2106.04 and §2106.05(g). Thus, the additional elements recited above fail to provide an actual improvement in computer functionality, or to a technology or technical field. See MPEP §2106.04(d)(1) and §2106§2106.05 (a & e).
Instead, the recited additional elements above, merely limit the invention to a technological environment in which the abstract concept identified above is implemented utilizing the computational tools provided by the additional elements to automate and perform the abstract idea, which is insufficient to provide a practical application since the additional elements do no more than generally link the use of the abstract idea to a particular technological environment. See MPEP §2106.04. Automating the recited claimed features as a combination of computer instructions implemented by computer hardware and/or software elements as recited above does not qualify an otherwise unpatentable abstract idea as patent eligible. Alternatively, the Office has long considered data gathering and data processing as well as data output recruitment information on a social network to be insignificant extra-solution activity, and these additional elements used to gather and output recruitment information on a social network are insignificant extra-solution limitations that do not impose any meaningful limits on practicing the abstract idea. See MPEP §2106.05(g). The current invention generate a mitigation options to reduce an impact, build a response plans and execute the response plans. When considered in combination, the claims do not amount to improvements of the functioning of a computer, or to any technology or technical field. Applicant's limitations as recited above do nothing more than supplement the abstract idea using additional hardware/software computer components as a tool to perform the abstract idea and generally link the use of the abstract idea to a technological environment, which is not sufficient to integrate the judicial exception into a practical application since they do not impose any meaningful limits.
Dependent claims 2-4, 6-7, 9-11, 13-14, 16-18, and 20 merely incorporate the additional elements recited above, no additional elements are disclosed in the dependent claims that were not considered in independent claims 1, 8 and 15 Therefore, the additional elements recited in the claimed invention individually, and in combination fail to integrate the recited judicial exception into any practical application.
Regarding Step 2B
Claims 1-4, 6-11, 13-18, and 20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional element(s) as described above with respect to Step 2A Prong 2, the additional element of claims 1, 8, and 15 include a system, computer, a processor, memory, a user interface module, an assumption manager module, what-if engine, and a resolution recommendation engine, a non-transitory, and computer-readable medium embodied with software. The displaying interface and storing data merely amount to a general purpose computer used to apply the abstract idea(s) (MPEP 2106.05(f)) and/or performs insignificant extra-solution activity, e.g. data retrieval and storage, as described above (MPEP 2106.05(g)) which are further merely well-understood, routine, and conventional activit(ies) as evidenced by MPEP 2106.06(05)(d)(II) (describing conventional activities that include transmitting and receiving data over a network, electronic recordkeeping, storing and retrieving information from memory, electronically scanning or extracting data from a physical document, and a web browser’s back and forward button functionality). Therefore, similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that the claims amount to significantly more than the abstract idea directed to generate a mitigation options to reduce an impact, build a response plans and execute the response plans.
Claims 1-4, 6-11, 13-18, and 20 is accordingly rejected under 35 USC 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea(s)) without significantly more.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-4, 6-11, 13-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Najmi US 2016/0217406 (hereinafter Najmi) in view of Tilly US 2019/0295032 (hereinafter Tilly)
Regarding Claim 1:
A system comprising a computer, comprising a processor and memory, and configured to:
generate, by a user interface module, one or more assumption objects storing data related to one or more supply chain entities within a supply chain network, wherein the one or more assumption objects are expressed as one or more machine-executable logical expressions, and wherein the one or more assumption objects are defined in a hierarchy as explicit data objects; (Najmi [0023-0024], “provide input to supply to supply chain … one or more supply chain entities 120 represent one or more supply chain networks including one or more entities”. Najmi [0036], “to continuously capture institutional knowledge of one or more supply chain entities 120 and integrates that knowledge into future supply chain plans. In some embodiments, knowledge data layer 230 captures and integrates this institutional knowledge by, for example, utilizing at least one or more of the following databases and systems: risks and assumptions repository 232; business rules configuration manager 234; root cause diagnostics library 236; persistent problems repository 238; planning levers library 240; supply chain planning database 220; and integration interfaces to transaction systems 204. [0039], “business rules configuration manager provides for business configuration analysis by providing a user interface to compute, monitor, and change any one of business rules, model attribute, and/or optimization settings”. Also, see Fig. 3 [0071], “knowledge data layer”)
bundle, by an assumption manager module, the one or more assumption objects into perspectives according to shared assumption object characteristics; (Najmi [0037], “risks and assumptions repository 232 utilizes one or both of plan assumptions process control charts and early warning monitors 344”. Najmi [0084-00887], “multi-dimensional segmentation is used to stratify characteristics into segments. self-learning system 110 accounts for characteristics such as markets, customers, products, supply chain structures and other characteristics. In some embodiments, these segments constitute similar business preferences, similar constraint regimes and similar cost-benefit trade-offs as judged by supply chain managers”.)
create, by a what-if engine, one or more hierarchical assumption variants based on the one or more assumption objects, (Najmi [0045-0046], “levers effectiveness and optimization module 376 presents the levers 376 in a structured way such that the most effective, most used, or highest priority levers are easily distinguishable to a user of self-learning system 110 from the less effective, less used, or lower priority levers. Self-learning system 110 may rank levers 372 based on these or other factors. Similarly, in some embodiments, when a problem is encountered, self-learning system 110 assigns a score to a lever 372 based on the effectiveness, frequency of use, highest
priority, least disruptive, or other factor that may be useful in scoring a lever 372 to deal with a supply chain disruption of one or more supply chain entities 120. Self-learning system 110 then displays the levers to a user wherein the levers are
ranked by score”.) and generate, by the what-if engine, one or more [[probability coefficients]] for each of the one or more hierarchical assumption variants; (Najmi [0066], “Automated compliance monitoring 642 monitors any deviations from the supply chain plan. In one embodiment, automated compliance monitoring 642 indicates large deviations from the supply chain plan. In other embodiments, automated compliance monitoring 642 indicates when the supply chain plan was first deviated from. This indication of timing is important because timing aids in identifying what caused the supply chain plan to deviate and what actions may be effective in remedying the deviation now or in future iterations of the supply chain plan. Automated compliance monitoring 642 optionally monitors KPis, such as, a first alert that a deviation from a supply chain plan may be occurring”)
model, by the what-if engine, an impact of the one or more hierarchical assumption variants according to one or more impact metrics; (Najmi [0046-0050], “planning livers library 240 comprises a conditional analysis planner 374 which is utilized by self-learning system 110 to evaluate feasibility and/or impact of utilizing a lever 376. In some embodiments, self-learning system 110 utilizes conditional analysis planner 374 to generate simulations of the utilization of one or more levers 3 72. The simulations compute and display the feasibility, impact, cost, or the like of implementing one or more levers 372 in resolution playbooks 308. In some embodiments, a levers effectiveness monitoring and optimization module 376 is utilized by self-learning system to generate reports in resolution playbook 308, which analyzes an effectiveness of one or more levers 376 and optimizes an association of one or more levers 376 with alerts 206. Levers effectiveness monitoring and optimization module 376 may comprise a list of levers 372 prioritized by a metric, e.g. feasibility, impact, cost, effectiveness, or the like. Planning”. Najmi [0072], “supply chain plans generated by the planning model stored in the risks and assumptions repository”. Also, see [0045] and [0056])
generate, by a resolution recommendation engine, one or more mitigation options to reduce the impact of at least one of the one or more hierarchical assumption variants; (Najmi [0043-0045], “levers comprise workflow that automate corrective action. For example and not by way of limitation, if the supply chain performance of one or more supply chain entities 120 is not aligned with the supply chain plan due to a late order, some potential resolutions include redirecting product from another order, splitting demand, and utilizing other workflows. Self-learning system 110 displays to a user one or more levers 372, which, when selected by a user, will enact one or more resolutions to the misalignment with the supply chain plan. In the example just mentioned comprising a misaligned supply chain plan due to a late order, the levers, when selected by a user, may redirect product from another order, split demand, and/or utilize an alternate workflow. Other corrective actions include, for example, expending material in transport, increasing the priority for a manufacturing lot, utilizing material from a first order to fulfill a second order, marking down products, expediting transportation, adding overtime to increase capacity, and offloading work to alternate resources. self-learning system 110 exercises a lever 3 72 predetermined to be effective to resolve the supply chain problem or, alternatively, presents to a user the option to select a lever that will be effective to resolve the supply chain problem”. Also, see [0034-0036], “one or more planning engines 212 which may generate supply chain plans based on input”.)
build, by the resolution recommendation engine, one or more response plans to respond to the one or more hierarchical assumption variants using the one or more mitigation options; and execute, by the computer, at least one of the one or more response plans, (Najmi [0044-0045], “resolution levers which require little or no input from a supply chain planner before execution and also other types and varieties of levers 372, which may allow for user customization prior to execution. Some levers 372 may be termed automatic because self-learning system 110 executes the lever 372 in response to a supply chain plan problem from one or more entities 120 without any user input. Anon-limiting example of a lever 372 used to resolve a supply chain plan problem is now given”. Najmi [0051], “generates plans which are then executed”. Also, see [0065-0066] and [0075])
wherein the at least one of the one or more response plans is executed in response to execution of the one or more machine-executable logical expressions; and (Najmi [0051], “generates plans which are then executed”. Najmi [0058], “self-learning supply chain system executes a process that redefines a supply chain management from generating optimal plans”. Najmi [0043], “workflows that automate corrective actions”. Also, see [0044])
monitor and re-evaluate, by the computer, the one or more machine-executable logical expressions. (Najmi [0034-0037], “selflearning system 110 may continuously adjust the supply chain plan to a state of feasibility and/or optimality due to disruptions in the supply chain by continually monitoring any type of data or KPis using KPI monitors 216 or alerts 206 in order to update a plan as soon as data or KPis received from supply chain entities 120 indicate that a disruption or plan problem has, will, or is likely to occur. Self-learning system 110 monitors data or KPIs by receiving such information from supply chain entities 120 and detecting out of range limits or patterns that indicate a supply chain plan problem using alerts 206 or KPI monitors 216”.)
Najmi disclose the above limitations but, specifically fails to disclose generate, by the what-if engine, one or more probability coefficients for each of the one or more hierarchical assumption variants;
However, Tilly teaches the following limitation:
generate, by the what-if engine, one or more probability coefficients for each of the one or more hierarchical assumption variants; (Tilly [0057-0058], “the sum of weighted sales quantities for N previous time periods from ~ up to, and including, ~-k, and where the weight wk is the probability (which may be represented as a percentage) for k periods before the nth period that the return takes from the sales quantity (when the weight is positive) or that the return gives to the sales quantity (where the weight is negative). For example, if a weight comprises a large positive value and corresponds to a time period of three weeks (i.e. k=3), then retailer 148 would estimate the number of returns will increase in three weeks' time. On the other hand, a weight with a negative value indicates that returns are less likely to occur during the week corresponding with the negative weight … after initializing the N+l filter coefficients with the historical transfer function (he). As new returns data is received, the N adaptive filter coefficients (v$ n) are updated with the vectorized recursive form”. Also, see [0077], “hierarchy of segments to aggregate or segment sales”)
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the system of Najmi self-learning supply chain system, to include the feature of probability coefficient, as taught by Tilly forecasting returns for retail demand planning, in order to determine a probability associated with assumption variant will occur based on different supply chain data (Tilly figure 1 and 4 [0057-0058]). Also, it will generate an accurate response plans.
Regarding Claim 2:
Najmi in view of Tilly disclose the system of Claim 1,
Najmi further teach wherein the one or more assumption objects persist across two or more separate supply chain planning cycles. (Najmi [0041], “supply chain plan problems and across lifecycles and planning cycles of a supply chain plan” . Najmi [0051-0057], “KPis are identified 423 which are then incorporated into the performance analysis 415 which leads to updates 416 into subsequent cycles of the planning process 417. … comprising a learning cycle process 502 that augments an execution cycle process 501 according to embodiment. Self-learning system 110 enables continuous learning by closed loop-based … Learning cycle process 502 comprises updates, refinements, and reconfiguration of the assumptions, business rules, and planning models. Learning cycle process 502 replaces unknown unknowns with known unknowns, which may thereby incorporate contingencies into a supply chain plan”. Also, see [0034])
Regarding Claim 3:
Najmi in view of Tilly disclose the system of Claim 1,
Najmi further teach wherein the what-if engine generates two or more what-if scenarios to simulate two or more assumption event outcomes. (Najmi [0046], “utilized by self-learning system 110 to evaluate feasibility and/or impact of utilizing a lever 376. In some embodiments, self-learning system 110 utilizes conditional analysis planner 374 to generate simulations of the utilization of one or more levers 372. The simulations compute and display the feasibility, impact, cost, or the like of implementing one or more levers 372 in resolution playbooks 308. In some embodiments, a levers effectiveness monitoring and optimization module 376 is utilized by self-learning system to generate reports in resolution playbook 308, which analyzes an effectiveness of one or more levers 376 and optimizes an association of one or more levers 376 with alerts 206. Levers effectiveness monitoring and optimization module 376 may comprise a list of levers 372 prioritized by a metric, e.g. feasibility, impact, cost, effectiveness, or the like”. Also, see [0083])
Regarding Claim 4:
Najmi in view of Tilly disclose the system of Claim 1,
Najmi further teach wherein the computer is further configured to execute at least one of the one or more response plans by taking one or more actions selected a group consisting of: pushing execution instructions to one or more supply chain entities; transmitting the one or more response plans to one or more assigned persons; activating one or more parked assumption objects; altering one or more data values associated with an assumption object condition; creating a new supply chain planning scenario; and applying a mitigation response to one or more sets of planning data. (Najmi [0043-0045], “levers comprise workflow that automate corrective action. For example and not by way of limitation, if the supply chain performance of one or more supply chain entities 120 is not aligned with the supply chain plan due to a late order, some potential resolutions include redirecting product from another order, splitting demand, and utilizing other workflows. Self-learning system 110 displays to a user one or more levers 372, which, when selected by a user, will enact one or more resolutions to the misalignment with the supply chain plan. In the example just mentioned comprising a misaligned supply chain plan due to a late order, the levers, when selected by a user, may redirect product from another order, split demand, and/or utilize an alternate workflow. Other corrective actions include, for example, expending material in transport, increasing the priority for a manufacturing lot, utilizing material from a first order to fulfill a second order, marking down products, expediting transportation, adding overtime to increase capacity, and offloading work to alternate resources. self-learning system 110 exercises a lever 3 72 predetermined to be effective to resolve the supply chain problem or, alternatively, presents to a user the option to select a lever that will be effective to resolve the supply chain problem”. Also, see [0034-0036], “one or more planning engines 212 which may generate supply chain plans based on input”.)
Regarding Claim 5: Cancelled
Regarding Claim 6:
Najmi in view of Tilly disclose the system of Claim 1,
Najmi further teach wherein the one or more assumption objects are generated from knowledge base data stored during carrying out of actions within a supply chain network. (Najmi Figures 2-3A-B [0032] & [0036], “self-learning system 110 enables a learning paradigm by coupling knowledge data layer 230 Knowledge data layer 230 resides on one or more computers 202 and integrates with transaction systems 204, supply chain planning database 220 and planning models and engines 212 using communication links 320-340 to continuously capture institutional knowledge of one or more supply chain entities 120 and integrates that knowledge into future supply chain plans. In some embodiments, knowledge data layer 230 captures and integrates this institutional knowledge by, for example, utilizing at least one or more of the following databases and systems: risks and assumptions repository 232; business rules configuration manager 234; root cause diagnostics library 236; persistent problems repository 238; planning levers library 240; supply chain planning database 220; and integration interfaces to transaction systems 204. In one embodiment, institutional knowledge includes, but is not limited to, data that is generated, stored, or retrieved by knowledge data layer 230 ”. Namji [0053], “accumulates data in the knowledge data layer”.)
Regarding Claim 7:
Najmi in view of Tilly disclose the system of Claim 1,
Najmi further teach wherein the one or more assumption objects represent one or more business strategies, one or more contractual agreements, one or more risks or one or more opportunities. (Najmi [0071-0072], “The risks and assumptions validation 628 receives business objectives, rules and policies 626 and integrates the updated assumptions or assumption alerts 662 to generate updates 630 to planning process 632. … assuming that there is a two week lead time between the date that material is ordered and the date that it is received from the supplier (which may be a negotiated agreement); then plans are made which rely on having material available two weeks after ordering”. Najmi [0079], “self-learning system 110 enables early detection of sources, or suspected sources, of risk and capitalizes on opportunities to start proactively detecting the sources to maximize available reaction time. For each problem that may arise in execution of a supply chain plan, self-learning system 110 looks at the earliest possible detection of the problem and places one or more sensors to monitor the likely sources for the problem. In some embodiments, this increases lead time available to respond to a problem. In some embodiments, sensors detect the emergence of an identified risk to the supply chain plan. In this manner, contingency plans are implemented as quickly as possible”.)
Regarding Claim 8:
Claim 8 is the method claim corresponding to the system claim 1 rejected above. Therefore, Claim 8 is rejected under the same rational as claim 1.
Regarding Claim 9:
Claim 9 is the method claim corresponding to the system claim 2 rejected above. Therefore, Claim 9 is rejected under the same rational as claim 2.
Regarding Claim 10:
Claim 10 is the method claim corresponding to the system claim 3 rejected above. Therefore, Claim 10 is rejected under the same rational as claim 3.
Regarding Claim 11:
Claim 11 is the method claim corresponding to the system claim 4 rejected above. Therefore, Claim 11 is rejected under the same rational as claim 4.
Regarding Claim 12: Cancelled
Regarding Claim 13:
Claim 13 is the method claim corresponding to the system claim 6 rejected above. Therefore, Claim 13 is rejected under the same rational as claim 6.
Regarding Claim 14:
Claim 14 is the method claim corresponding to the system claim 7 rejected above. Therefore, Claim 14 is rejected under the same rational as claim 7.
Regarding Claim 15:
Claim 15 is the non-transitory claim corresponding to the system claim 1 rejected above. Therefore, Claim 15 is rejected under the same rational as claim 1.
Regarding Claim 16:
Claim 16 is the non-transitory claim corresponding to the system claim 2 rejected above. Therefore, Claim 16 is rejected under the same rational as claim 2.
Regarding Claim 17:
Claim 17 is the non-transitory claim corresponding to the system claim 3 rejected above. Therefore, Claim 17 is rejected under the same rational as claim 3.
Regarding Claim 18:
Claim 18 is the non-transitory claim corresponding to the system claim 4 rejected above. Therefore, Claim 18 is rejected under the same rational as claim 4.
Regarding Claim 19: Cancelled
Regarding Claim 20:
Claim 20 is the non-transitory claim corresponding to the system claim 7 rejected above. Therefore, Claim 20 is rejected under the same rational as cl aim 7.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Saarenvirta US 11,468,387: System and method for operating an enterprise on an autonomous basis.
Iwata US 2021/0248528: Information technology utilization evaluation device, information technology utilization evaluation system, and information technology information technology utilization evaluation method.
Bennice et al. US 2021/0192430: Forecasting inventory model system.
Koc et al. US 2021/0117896: Supply-chain simulation.
Gardner et al. US 2019/0295023: System, method, and computer program product for predicting the value of an idea based on crowd input.
Simon US 2017/0236086: Systems and/or methods for context-driven contribution ranking.
Connolly et al. US 2013/0080200: Analyzing and presenting supply, fabrication, and logistics data.
Najmi DE102004023265: Determine an order lead time for a supply chain using a probability distribution for expected order lead time.
Klibi W, Martel A. Scenario-based supply chain network risk modeling. European Journal of Operational Research. 2012 Dec 16;223(3):644-58.
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/HAMZEH OBAID/ Primary Examiner, Art Unit 3624