Notice of Pre-AIA or AIA Status
1. 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
2. This Non-Final Office action is in response to the application filed on November 17th, 2023 and in response to Applicant’s Arguments/Remarks filed on January 27th, 2026. Claims 1-4, 7-14, and 17-20 are pending.
Priority
3. Application 18/513,426 was filed on November 17th, 2023 and claims priority to provisional application 63/384,596 filed on November 21st, 2022.
Examiner Request
4. The Applicant is requested to indicate where in the specification there is support for amendments to claims should Applicant amend. The purpose of this is to reduce potential 35 U.S.C. §112(a) or §112 1st paragraph issues that can arise when claims are amended without support in the specification. The Examiner thanks the Applicant in advance.
Continued Examination Under 37 CFR 1.114
5. 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 January 27th, 2026 has been entered.
Response to Arguments
6. Applicant argues that the newly amended claims directly improve computational performance of the computer system by improving the search function of the system, thereby expending less computational energy in order to hone in on a feature vector as compared against older/traditional search functions. For such complex systems, this improvement in feature vector selection may significantly impact the kinds of computer systems needed to operate the disclosed methods or embody the disclosed systems. Further, rather than the process taking days of operation, the presently disclosed systems and methods may provide near real-time optimizations and results. This directly impacts the usability of the system, and its utility to a supply chain manager. Examiner notes that Applicant’s arguments have been fully considered but are not persuasive. Although amended claims 1 and 11 now recite improving computational performance of a computer system by selecting feature vectors “by following along contours of a fixed service level or using [a] gradient based search algorithm to rapidly identify input feature vectors near a local optima,” the recited improvement remains an improvement to the mathematical analysis used in performing the claimed supply-chain optimization rather than an improvement to computer technology itself. Specifically, following contours of a fixed service level and using a gradient-based search algorithm to identify feature vectors near a local optimum constitute techniques for mathematically searching for and selecting input feature vectors used in the optimization. The fact that such techniques may permit the mathematical search to be performed more rapidly or using fewer computational resources does not, without more, establish an improvement to the functioning of the computer itself. Applicant further argues that the claimed approach may require fewer virtual machine nodes or servers, may avoid use of a supercomputer, and may provide near real-time optimization results. However, claims 1 and 11 do not recite any particular reduction in computing resources, number or configuration of servers, processing time, or computational energy. Nor do the claims recite a technological modification to the computer system that produces such results. Rather, the asserted benefits flow from the particular mathematical technique used to search for and select feature vectors for the hypothetical supply-chain optimization. Thus, the alleged improvement concerns the efficiency with which the abstract mathematical optimization is performed, rather than an improvement to computer functionality or another technology. Accordingly, the newly added limitations do not integrate the identified mathematical concepts and supply-chain optimization into a practical application. Considered individually and as an ordered combination with the remaining limitations, the claims continue to use generic computing components to perform the identified abstract idea and do not amount to significantly more than the judicial exception. Therefore, Applicant’s arguments do not overcome the rejection under 35 U.S.C. § 101.
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.
7. Claims 1-4, 7-14, and 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1-4, 7-14, and 17-20 are directed to a system, method, or product which are/is one of the statutory categories of invention. (Step 1: YES).
Claims 1 and 11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites hypothetical testing of supply chain optimizations. Claims 1 and 11 the limitations of (Claim 1 being representative) recites:
receiving a hypothetical optimization query;
generating variable definitions responsive to the query;
generating scope definitions responsive to the query;
generating presentation definitions;
generating an optimization parameter set using the variable definitions and the scope definitions, wherein the optimization parameter set includes feature vectors and is defined by a minimum service level target for each given product being greater or equal to a cost series summation times a weight for each cost, and wherein the cost series includes the cost of discards, the cost of changeovers, the cost of shipping, the cost of warehousing, the cost of inventory, the cost of freshness, the cost of stockouts and the cost of cardon emissions;
improve computational performance […] by selecting feature vectors by following along contours of a fixed service level or using gradient based search algorithm to rapidly identify input feature vectors near a local optima; and
optimizing a hypothetical supply chain responsive to the optimization parameter set […].
These limitations as drafted, recite a process that under broadest reasonable interpretation covers a mathematical concept that includes mathematical relationships, mathematical formulas or equations, and mathematical calculations (cost summations and weighting factors, following contours of a fixed service level, and using a gradient-based search algorithm to identify feature vectors near a local optimum), but for the recitation of generic computer component language, an artificial intelligence (AI) modeling platform (claims 1 and 11), and an interface including a network connection and a server including a processor (claim 11) (discussed at Step 2A2). See Specification, paragraphs [0011 and 0129]. That is, other than reciting the generic computer component language, the claim recites a procedure for generating definitions and parameters in response to a query and optimizing a hypothetical supply chain responsive to the optimization parameter set which encompasses a mathematical concept. The Examiner notes that the mathematical concept need not be expressed in mathematical symbols. MPEP § 2106.04(a)(2)(I). If a claim limitation, under its broadest reasonable interpretation, covers mathematical relationships and calculations but for the recitation of generic computer component language, then it falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, claims 1 and 11 recite an abstract idea (Step 2A- Prong 1: YES. The claims are abstract).
This judicial exception is not integrated into a practical application. Claims 1 and 11 recite the additional elements of a computer system, an artificial intelligence (AI) modeling platform (claims 1 and 11), and an interface and a server (claim 11). These additional elements are recited at a high level of generality (i.e., generic computers performing generic computer functions) such that they amount to no more than mere instructions to apply the exception using generic computer components. The limitation of improving computational performance by selecting features vectors along contours of a fixed service level or using a gradient-based search algorithm to identify feature vectors near a local optimum does not constitute an improvement to computer functionality. Rather, the limitation specifies the mathematical technique by which feature vectors used in the claimed optimization are searched for and selected. Any resulting reduction in processing time or computational resources results from more efficiently performing the mathematical optimization itself and does not reflect a technological improvement to the computer system. Accordingly, the identified judicial exception, considered in combination with the additional computer elements, is not integrated into a practical application because the additional elements do not impose any meaningful limits on practicing the abstract idea. Claims 1 and 11 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO: the additional claimed elements are not integrated into a practical application).
Claims 1 and 11 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a computer system, an artificial intelligence (AI) modeling platform (claims 1 and 11), and an interface and a server (claim 11) to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). Accordingly, even when considered separately and as an ordered combination, nothing in the claim adds significantly more (i.e. an inventive concept) to the abstract idea. Thus claims 1 and 11 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more).
Dependent Claims 2-4, 7-10, 12-14, and 17-20 are similarly rejected because they merely further narrow the same abstract idea of independent claims 1 and 11 as discussed above and hence are abstract for at least the reasons presented above. Claims 2-4, 7, 12-14 and 17 merely describe the variable definitions. Claims 8, 9, 18, and 19 merely describe the scope and presentation definitions. Claims 10 and 20 merely recite generating recommendations. Therefore claims 1-4, 7-14, and 17-20 are considered patent ineligible for the reasons given above.
Subject Matter Free of Prior Art
8. The cited prior art of record fails to expressly teach or suggest, either alone or in
combination, the features found within claims 1 and 11 (and depending claims 2-4, 7-10, 12-14, and 17-20).
Adulyasak discloses generating an inventory policy using any form of demand distributions, non-linear cost functions and/or multiple target measures of service levels utilizing weighted cost.
Bajaj discloses focus on service level and/or delivery performance to a customer. The raw data may be processed via the platform and subsequently displayed in the dashboard to indicate a service level.
In particular, the cited prior art of record fails to expressly teach or suggest all of the features within claims 1 and 11 and more specifically the limitations of: wherein the optimization parameter set includes feature vectors and is defined by a minimum service level target for each given product being greater or equal to a cost series summation times a weight for each cost, and wherein the cost series includes the cost of discards, the cost of changeovers, the cost of shipping, the cost of warehousing, the cost of inventory, the cost of freshness, the cost of stockouts and the cost of cardon emissions; selecting feature vectors by following the minimum service level to improve computational performance.
Conclusion
9. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Grichnik (US2007/0203810) discloses supply chain modeling as a tool to process and analyze data and determine various requirements of supply chain planning.
Mantryvong et al. (US2020/0012984) discloses a supply chain optimization method where input is collected and inferred, and a solution to the decision problem is output based on optimization parameters.
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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/JESSICA LEMIEUX/Supervisory Patent Examiner, Art Unit 3626
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