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-final, first office action on the merits.
Claims 1-20 are pending.
Information Disclosure Statement (IDS)
The information disclosure statement(s) filed on 11/08/2024 comply with the provisions 37 CFR 1.97, 1.98, and MPEP 609 and is considered by the Examiner.
Continuation
This application is a continuation of U.S. application 17/824,717 (filed 05/25/2020). See MPEP §201.08. In accordance with MPEP §609.02 A. 2 and MPEP §2001.06(b) (last paragraph), the Examiner has reviewed and considered the prior art cited in the Parent Applications. Also in accordance with MPEP §2001.06(b) (last paragraph), all documents cited or considered ‘of record’ in the Parent Applications are now considered cited or ‘of record’ in this application. Additionally, Applicant(s) are reminded that a listing of the information cited or ‘of record’ in the Parent Application need not be resubmitted in this application unless Applicants desire the information to be printed on a patent issuing from this application. See MPEP §609.02 A. 2. Finally, Applicants are reminded that the prosecution history of the Parent Application is relevant in this application. See e.g., Microsoft Corp. v. Multi-Tech Sys., Inc., 357 F.3d 1340, 1350, 69 USPQ2d 1815, 1823 (Fed. Cir. 2004) (holding that statements made in prosecution of one patent are relevant to the scope of all sibling patents).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claim 1 are provisionally rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1 of Application No. 17/824,717 (filed 05/25/2022). Also, Claim 1 are provisionally rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1 of Application No. 7/824,717 (filed 05/25/2022). Although the conflicting claims are not identical, they are not patentably distinct from each other because claims in each application recite substantially similar limitations directed to a process for scheduling a patient appointment.
Although the conflicting claims are not identical, they are not patentably distinct from each other because claim 1 in the referenced patents and claim 1 recited substantially similar limitation. however, claim 1 in the referenced patent is more narrow. The breadth of claim 1 of the instant application would read on the more narrow claim 1 of the referenced patents. Thus claim 1 in the instant application is an obvious variant of claim 1 in the reference applications.
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 1-20 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 1-7 are directed toward a system (machine). Claims 8-14 are directed toward a method (process). Claims 15-20 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 1-20 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 for executing one or more mitigation strategies based on one or more generated assumptions, comprising:
a computer, comprising a processor and memory, and configured to:
receive one or more assumptions via a graphical user interface;
bundle the one or more received assumptions into one or more perspectives;
create one or more assumption variants;
model a scope and impact of each of the one or more assumption variants;
generate one or more mitigation options for each of the one or more assumption variants;
build one or more response plans with one or more recommendations;
display the one or more assumptions and associated assumptions data; and
execute automatically the one or more response plans in response to one or more triggers for action defined in response plan data.
The Applicant's Specification titled "USER INTERFACE TOOL FOR GENERATING AND ANALYZING SCENARIOS FOR SUPPLY CHAIN " 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. [0002-0003]). 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) generate a hierarchical scenario structure of multiple assumption variants.
claim 3 (similarly claims 10, and 17) generate the one or more mitigation options to address anticipated impacts.
claim 4 (similarly claims 11 and 18) one or more perspectives, one or more assumption variants, one or more hierarchical scenario structures, one or more scopes, one or more impacts, one or more mitigation options, and one or more response plans.
claim 5 (similarly claims 12 and 19) one or more risks combined into a pessimistic perspective and one or more opportunities into an optimistic perspective.
claim 6 (similarly claims 13 and 20) generate one or more probability coefficients for each of the one or more assumption variants, wherein the one or more probability coefficients each specify an estimated likelihood of occurrence of each of the one or more assumption variants.
claim 7 (similarly claim 14) an assumption type, a confidence level, a scope, an expected timeframe, an impact and a mitigation.
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 1-20 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 a computer, a processor, a memory, a graphical user interface
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, 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 [0031] 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 2-7, 9-14, and 16-20 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 1-20 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, 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- 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.
REJECTIONS BASED ON PRIOR ART
Examiner Note: Some rejections will be followed/begin by an “EN” that will denote an examiner note. This will be place to further explain a rejection.
Claim Rejections - 35 USC § 102
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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-5, 7-12, and 14-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Najmi US 2016/0217406 (hereinafter Najmi).
Regarding Claim 1:
A system for executing one or more mitigation strategies based on one or more generated assumptions, comprising:
a computer, comprising a processor and memory, and configured to: receive one or more assumptions via a graphical user interface; (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 the one or more received assumptions into one or more perspectives; (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 one or more assumption variants; model a scope and impact of each of the one or more assumption variants; (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”.)
generate one or more mitigation options for each of the one or more 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”)
build one or more response plans with one or more recommendations; display the one or more assumptions and associated assumptions data; and (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])
execute automatically the one or more response plans in response to one or more triggers for action defined in response plan data. (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])
Regarding Claim 2:
Najmi disclose the system of Claim 1,
Najmi further teach wherein the computer is further configured to:
generate a hierarchical scenario structure of multiple assumption variants. (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 3:
Najmi disclose the system of Claim 1,
Najmi further teach wherein the computer is further configured to:
generate the one or more mitigation options to address anticipated impacts. (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 4:
Najmi disclose the system of Claim 1,
Najmi further teach wherein the displayed one or more assumptions and the associated assumption data comprise one or more of:
one or more perspectives, one or more assumption variants, one or more hierarchical scenario structures, one or more scopes, one or more impacts, one or more mitigation options, and one or more response plans. (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:
Najmi disclose the system of Claim 1,
Najmi further teach wherein the one or more perspectives comprise one or more of:
one or more risks combined into a pessimistic perspective and one or more opportunities into an optimistic perspective. (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 7:
Najmi disclose the system of Claim 1,
Najmi further teach wherein the associated assumption data comprises one or more of:
(EN: referring back to applicant specification, in ¶[0041], “an assumption type (e.g., risk, opportunity, strategy, etc.), confidence level, scope (e.g., what products, regions, customer, network nodes, etc. are impacted), expected timeframe, impact (e.g., what metrics or figures are impacted and by how much), and mitigation (e.g., action plan to resolve constraints or undesirable outcomes)”)
an assumption type, (Najmi [0079], “detection …of a risk and capitalizes on opportunities … a risk is identified”. Najmi [0036-0038] & [0046], “risks and assumptions repository … supply chain plan”.) a confidence level, a scope, (Najmi [0058], “determines whether or not satisfactory inventory levels are being achieved by displaying to the planner the levels of the items”. [0077], “various levels of reasons are diagramed … product … resource”. [0087], “level or interval”.) an expected timeframe, (Najmi fig. 9 [0037-0037], [0041], [0072], and [0077], “lead time”.)an impact and a mitigation. (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 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:
Claim 12 is the method claim corresponding to the system claim 5 rejected above. Therefore, Claim 12 is rejected under the same rational as claim 5.
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:
Claim 19 is the non-transitory claim corresponding to the system claim 5 rejected above. Therefore, Claim 19 is rejected under the same rational as claim 5.
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 6, 13, 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 6:
Najmi disclose the system of Claim 1 but, does not specifically teach or disclose, however, Tilly, in the same field of endeavor teaches wherein the computer is further configured to:
generate one or more probability coefficients for each of the one or more assumption variants, wherein the one or more probability coefficients each specify an estimated likelihood of occurrence of each of the one or more 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 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 20:
Claim 20 is the non-transitory claim corresponding to the system claim 6 rejected above. Therefore, Claim 20 is rejected under the same rational as claim 6.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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/HAMZEH OBAID/Primary Examiner, Art Unit 3624