Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Notice to Applicant
The following is a Final Office action to Application Serial Number 18/754,644, filed on June 26, 2024. In response to Examiner’s Non-Final Office Action of October 20, 2025, Applicant, on April 6, 2026, amended claims 1- 2, 4-5 and 7-8. Claims 1-9 are pending in this application and have been rejected below.
Response to Amendment
Applicant’s amendments are acknowledged.
Regarding 35 U.S.C. § 101 rejection, the amendment has been considered but is
insufficient to overcome the rejection. Please refer to the 35 U.S.C. § 101 rejection for further explanation and rationale.
The 35 U.S.C. § 103 rejections are hereby amended pursuant to applicants
amendments. Updated 35 U.S.C. § 103 rejections have been applied to amended
claims. Please refer to the § 103 rejection for further explanation and rationale.
Response to Arguments
Applicant’s arguments filed January 20, 2026 have been fully considered but they are not persuasive and/or are moot in view of the revised rejections. Applicant’s arguments will be addressed herein below in the order in which they appear in the response filed January 20, 2026.
On Pg. 5-7 of the Remarks regarding the 35 U.S.C. 101, Applicant states the Office Action alleges the claim covers “mental processes” without interpreting the claims in view of the specification. In response, Examiner asserts when performing the § 101 analysis, Examiner did consider each claim and every limitation, both individually and in combination as according to the PTO's guidelines for § 101 eligibility. In response, the claims primarily recite the additional element of using computer components to perform each step. The “processor”, “memory”, “computer”, “system”, and “computer readable medium”, which are recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a computer component. See MPEP 2106.05(f). Examiner respectfully reminds Applicant, regardless of the complexity and/or granularity of the type of data, computational data analysis without meaningful limitations within the claims that amount to significantly more than the abstract idea itself is a judicial exception (i.e. abstract idea). Applicant has also not identified a use case of the data analysis which would integrate the claimed invention into a practical application.
On Pg. 7-8 of the Remarks regarding the 35 U.S.C. 101, Applicant states the claimed features represent an improvement rooted in computer technology advance rooted in computer technology to solve the problem of, for a global optimization process, “spending significant computational resources in terms of, for example, time and memory space, on the less complex but numerous nature of the top-level production processes, while a pure heuristic approach may not provide an overall good solution in the presence of more complex production processes at the bottom levels of the supply chain.”. Applicant further states the claims improve computer capabilities or to improve an existing technology integrates the alleged judicial exception into a practical application or provide significantly more In response, Examiner respectfully disagrees. Heuristic optimization operations are included in the abstract idea grouping of Mathematical Concepts. The partitioning the modelled supply chain into heuristic levels comprising the one or more top levels and optimization levels comprising the complementary set of component products at the one or more bottom levels; performing a top-down heuristic operation on the modelled supply chain to determine demands and supplies at the one or more bottom levels by starting at the demands of the one or more finished or semi-finished goods at the one or more top levels and computing down to determine the demands at the one or more bottom levels that achieve the demands at the one or more top level; formulating an optimized model for the one or more bottom levels based on the determined demands and supplies, wherein the optimized model comprises decision variables, constraints, and an objective function that reduces, or minimizes, a sum of weighted costs; and solving the optimized model to generate optimized results; and performing a bottom-up heuristic operation on the optimized results to determine information associated with demands and supplies of the one or more top levels are included in the abstract idea grouping. The present claims amount to no more than utilizing computer components as tools to analyze the data.
On Pgs. 16-21 regarding of the Remarks regarding the 35 U.S.C. 101, Applicant states the claims recite an improvement in functioning of a computer or an improvement to other technology or technical similar to Amdocs . In response, Examiner finds the present claims do not demonstrate any functional advancement to any technology or technological field, in order for the claim elements to be considered significantly more than the abstract idea itself, such as receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); and storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93 (See MPEP 2106.05(d)(II). The limitations disclosed in Amdocs demonstrates improvements to a specific technological process (i.e., distributed network-based filtering and aggregating platform), thus improving distributed networking platform. In contrast, the present claims contain improvements to the data analysis of an existing supply chain process and not one of a technology or technological field. Examiner respectfully reminds Applicant, regardless of the complexity and/or granularity of the type of data, computational data analysis without meaningful limitations within the claims that amount to significantly more than the abstract idea itself is a judicial exception (i.e. abstract idea).
On Pgs. 22-23 regarding of the Remarks regarding the 35 U.S.C. 103, Applicant states prior art fails to disclose amended claim language. In response, new ground(s) of rejection is made necessitated by amendment see MPEP 706.07a where Kamath is now applied for Claims 1, 4 and 7. Regarding the 35 U.S.C. § 103 rejection, Applicant’s arguments with respect to claims has been considered but are moot in view of the new grounds of rejection.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1- 9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-9 are directed to supply chain optimization.
Claim 1 recites a method for supply chain optimization, Claim 4 recites a system for supply chain optimization and Claim 7 recites an article of manufacture for supply chain optimization, which include receiving a modelled supply chain having one or more finished or semi-finished goods at one or more top levels and, for each of the one or more finished or semi-finished goods, a complementary set of component products at one or more bottom levels; partitioning the modelled supply chain into heuristic levels comprising the one or more top levels and optimization levels comprising the complementary set of component products at the one or more bottom levels; performing a top-down heuristic operation on the modelled supply chain to determine demands and supplies at the one or more bottom levels by starting at the demands of the one or more finished or semi-finished goods at the one or more top levels and computing down to determine the demands at the one or more bottom levels that achieve the demands at the one or more top level; formulating an optimized model for the one or more bottom levels based on the determined demands and supplies, wherein the optimized model comprises decision variables, constraints, and an objective function that reduces, or minimizes, a sum of weighted costs; and solving the optimized model to generate optimized results; and performing a bottom-up heuristic operation on the optimized results to determine information associated with demands and supplies of the one or more top levels.
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea grouping of “Mental Processes” – evaluation and “Mathematical Concepts”. The recitation of “processor”, “memory”, “computer”, “system”, and “computer readable medium”, provide nothing in the claim elements to preclude the step from being “Mental Processes”- evaluation and “Mathematical Concepts”- relationships. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claims primarily recite the additional element of using computer components to perform each step. The “processor”, “memory”, “computer”, “system”, and “computer readable medium” is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a computer component. See MPEP 2106.05(f).
Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims also fail to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, and/or an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See 84 Fed. Reg. 55. In particular, there is a lack of improvement to a computer or technical field in supply chain analysis.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “processor”, “memory”, “computer”, “system”, and “computer readable medium” is insufficient to amount to significantly more. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. With regards to receiving data and step 2B, it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information) and Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Examiner concludes that the additional elements in combination fail to amount to significantly more than the abstract idea based on findings that each element merely performs the same function(s) in combination as each element performs separately. The claim is not patent eligible. 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.
Dependent Claims 2-3, 5-7 and 8-9 recite wherein performing the bottom-up heuristic operation on the optimized results determines information associated with demands and supplies of the one or more top levels; wherein the information associated with the demands of the one or more top levels is the satisfaction date, or supply allocations, or both, for the demands and supplies at the one or more top levels; and further narrowing the abstract idea. These recited limitations in the dependent claims do not amount to significantly more than the above-identified judicial exceptions in Claims 1, 4 and 7. Regarding Claims, 2, 5, and 8 and the additional elements of “processor”, “memory” and “system” - it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).
Claim Rejections - 35 USC § 103
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 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 1-9 are rejected under 35 U.S.C. 103 as being unpatentable over Ouyang et al., US Publication No. 20220318710A1, [hereinafter Ouyang], in view of Kamath et al., US Publication No. 20170364847A1, [hereinafter Kamath], and in further view of Sumant, US Publication No. 20230245234A1, [hereinafter Sumant].
Regarding Claim 1,
Ouyang teaches
A computer-implemented method comprising: receiving, by a processor, a modelled supply chain having one or more finished or semi-finished goods at one or more top levels and, for each of the one or more finished or semi-finished goods, a complementary set of component products at one or more bottom levels; (Ouyang Par. 55; Par. 72-86-“ he computing system may be programmed to utilize a breadth-first searching method for determining path solutions, by beginning at the starting sites in the supply chain network (e.g., the suppliers) and increasing the possible path solutions level by level until a full path is determined once the path has reached the an ending site (e.g., a customer zone).; Par. 98-103”)
performing, by the processor, a … heuristic operation on the modelled supply chain to determine demands at the one or more bottom levels … (Ouyang Par. 81-“ To detect one or more loops in a supply chain network, the computing system may take a top-down approach, starting with the source sites at the beginning the of the supply chain network (e.g., suppliers, vendors, etc.) and proceeding forward. The computing system will proceed forward from the starting sites and search downstream sites for any return path fragments that may comprise a loop. Upon determining a path fragment leads to an upstream site that has already been visited (e.g., DC 510), the computing system may mark the path fragment as a “return path fragment” and determine a loop is present for a particular site.”)
formulating, by the processor, an optimized model for the one or more bottom levels based on the determined demands…(Ouyang Par. 38- “Embodiments disclosed herein provide optimal path solutions to fulfill an order for a quantity of one or more finished goods through a supply chain network using real-time or near real-time solutions. These techniques allow for solutions for finished goods and raw materials flow and sourcing optimization problems associated with one or more metrics of a supply chain network. In an embodiment, a computing system is programmed to receive input data comprising an order to provide a quantity of one or more finished goods to a site through an acyclic supply chain network, the one or more finished goods each comprising one or more raw materials. The computing system is programmed to access information that defines an architecture of the acyclic supply chain network comprising a plurality of sites (e.g., a CZ or distribution center). The sites may be connected by a plurality of path fragments defining a movement of a sub-quantity of the one or more finished goods or the one or more raw materials between the two sites. Using this data, the computing system is programmed to generate one or more path solutions along the supply chain network to fulfill the order that indicate the sites where particular quantities of goods should be manufactured, as well as sites where particular quantities of raw materials should be purchased from in order to obtain an optimal result for the consumer. The optimal result may include for example, one or more path solutions along the supply chain network that optimize one or more metrics of a supply chain network, for example reducing the cost or time associated with fulfillment. In some embodiments a heuristic method may be used to select high priority paths to handle the exponential growth of paths in a complex supply chain network. In particular embodiments the computing system may transmit signals to computing devices associated with one or more sites in the supply chain network to rapidly initiate the fulfillment of an order of finished goods or raw materials. In this manner, a consumer can quickly and efficiently fulfill multiple orders with a supply chain networks in an optimal or near optimal fashion while meeting the needs of their customers with a real-time response to changing market conditions.”; Par. 76-“ the network optimization model may output any number of revised paths solutions and any number of revised path fragments comprising sub-quantities of finished goods and raw materials necessary to fulfill the order.; Par. 85”);
solving, by the processor, the optimized model to generate optimized results.; (Ouyang Par. 76-“ the network optimization model may output any number of revised paths solutions and any number of revised path fragments comprising sub-quantities of finished goods and raw materials necessary to fulfill the order.; Par. 85”)
and performing, by the processor, a … heuristic operation on the optimized results to determine information associated with demands and supplies of the one or more top levels (Ouyang Par. 37-38- The optimal result may include for example, one or more path solutions along the supply chain network that optimize one or more metrics of a supply chain network, for example reducing the cost or time associated with fulfillment. In some embodiments a heuristic method may be used to select high priority paths to handle the exponential growth of paths in a complex supply chain network.”).
Ouyang teaches supply chain analysis and the feature is expounded by Kamath:
partitioning, by the processor, the modelled supply chain into heuristic levels comprising the one or more top levels and optimization levels comprising the complementary set of component products at the one or more bottom levels (Kamath Par. 35-37-At step 410 supply chain planner 110 sorts the buffers in the model from downstream to upstream and assigns the buffers levels based on their position in supply chain network 100. For example, in the case of finished goods buffers at the same level, supply chain planner 110 distinguishes these buffers based on the priority of the demand they serve. At step 420, supply chain planner 110 takes the LP solution as a base and calculates the bucket-wise cumulative consumption and cumulative production profiles for each buffer. In one embodiment, supply chain planner 110 starts with the most prioritized downstream buffer and recursively moves upstream.; Fig. 4) ;
performing, by the processor, a … heuristic operation on the modelled supply chain to determine demands at the one or more bottom levels ‘by starting at the demands of the one or more finished or semi-finished goods at the one or more top levels and computing down to determine the demands at the one or more bottom levels that achieve the demands at the one or more top level’; (Kamath Par. 35-39-At step 440, supply chain planner 110 checks for material availability from, for example, upstream to downstream for each buffer. In one embodiment, if material shortage is encountered, supply chain planner 110 moves the consumption to later buckets, matching the cumulative production profile to cumulative consumption profile in each bucket. In addition, set demand lateness, safety stock requirement, shelf life and the like are also taken into consideration at this point. At the end of this step, supply chain)
formulating, by the processor, an optimized model for the one or more bottom levels based on the determined demands ‘wherein the optimized model comprises decision variables, constraints, and an objective function that reduces, or minimizes, a sum of weighted costs’ (Kamath Par. 17-In one embodiment, supply chain planner 110 considers various discrete decision variables, discrete lot-sizing rules, production constraints and operational/business objectives of one or more supply chain entities 120 when determining an optimized global hierarchical solution. As described below in more detail, these various discrete decision variables, discrete lot-sizing rules, production constraints and operational/business objectives may prevent one or more supply chain entities 120 from satisfying supply chain demand, and may delay supply chain demand from being satisfied during a particular planning horizon. ;Par. 24; Par. 58-59)
Ouyang teaches supply chain analysis and the feature is expounded upon by Sumant:
…top-down heuristic operation… . (Sumant Par. 13- “Reconciliation and Integration Algorithms By designing reconciliation algorithms to match data across line item and projection hierarchy using top-down and bottom-up approach.”; Par. 56-“ FIGS. 2 a and 2 b are diagrams 200 a and 200 b that illustrate usage and benefits of the proposed solution, in accordance with an exemplary embodiment of the disclosure. In an exemplary embodiment, the product usage 202 may include one or more of, but without any limitations, cash projections, revenue, and profit projections, working capital management, financing decisions, investing decisions, pricing and product mixing, marketing spend strategy, supply chain budgeting,”; Par. 173)
… a bottom-up heuristic operation… (Sumant Par. 13- “Reconciliation and Integration Algorithms By designing reconciliation algorithms to match data across line item and projection hierarchy using top-down and bottom-up approach.”; Par. 56-“ FIGS. 2 a and 2 b are diagrams 200 a and 200 b that illustrate usage and benefits of the proposed solution, in accordance with an exemplary embodiment of the disclosure. In an exemplary embodiment, the product usage 202 may include one or more of, but without any limitations, cash projections, revenue, and profit projections, working capital management, financing decisions, investing decisions, pricing and product mixing, marketing spend strategy, supply chain budgeting,”; Par. 173)
Ouyang, Kamath and Sumant are directed to supply chain analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Ouyang in view of Kamath, as taught by Sumant, by utilizing additional data analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Ouyang with the motivation of optimizing processing of several types of cash related decisions (Sumant Par. 2).
Regarding Claim 2, Claim 5 and Claim 8 Ouyang in view of Kamath in further view of Sumant teaches The computer-implemented method according to claim 1, further comprising…, The system according to claim 4, further comprising the memory storing instructions that, when executed by the processor, configure the system to…, and The non-transitory computer-readable medium according to claim 7, further including instructions that, when executed by the computer, cause the computer to…
Ouyang in view of Kamath teaches supply chain analysis and the feature is expounded upon by Sumant:
wherein performing. by the processor, the bottom-up heuristic operation on the optimized results determines information associated with demands and supplies of the one or more top levels. (Sumant Par. 13- “Reconciliation and Integration Algorithms By designing reconciliation algorithms to match data across line item and projection hierarchy using top-down and bottom-up approach.”; Par. 56-“ FIGS. 2 a and 2 b are diagrams 200 a and 200 b that illustrate usage and benefits of the proposed solution, in accordance with an exemplary embodiment of the disclosure. In an exemplary embodiment, the product usage 202 may include one or more of, but without any limitations, cash projections, revenue, and profit projections, working capital management, financing decisions, investing decisions, pricing and product mixing, marketing spend strategy, supply chain budgeting,”; Par. 80-88; Par. 173)
Ouyang, Kamath and Sumant are directed to supply chain analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Ouyang in view of Kamath, as taught by Sumant, by utilizing additional data analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Ouyang in view of Kamath with the motivation of optimizing processing of several types of cash related decisions (Sumant Par. 2).
Regarding Claim 3, Claim 6 and Claim 9 Ouyang in view of Kamath in further view of Sumant teaches The computer-implemented method according to claim 2, …, The system according to claim 5,…, and The non-transitory computer-readable medium according to claim 7,…
Ouyang in view of Kamath teach supply chain analysis and the feature is expounded upon by Sumant:
wherein the information associated with the demands of the one or more top levels is the satisfaction date, or supply allocations, or both, for the demands and supplies at the one or more top levels. (Sumant Par. 161-162 “the line items may include net terms, customer, and invoice, the starting attributes may include cash in (negative for customer credits), invoice date, due date, IsRecurring, invoice amount, dilution or credit, expected payment date, IsOverdue etc., and the primary cash network KPIs may include days to collect, overdue, dilution, and total collections. Further, in this case, under the manual or algorithm override types (i.e., value or % adjustment), days to collect (DSO) and amount to collect (dilution) may be changed. Further, the prescriptive algorithm goals may aim to minimize DSO and minimize dilution.”)
Ouyang, Kamath and Sumant are directed to supply chain analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Ouyang in view of Kamath, as taught by Sumant, by utilizing additional data analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Ouyang in view of Kamath with the motivation of optimizing processing of several types of cash related decisions (Sumant Par. 2).
Regarding Claim 4,
Ouyang teaches
A system comprising: a processor; and a memory storing instructions that, when executed by the processor, configure the system to: receive, by the processor, a modelled supply chain having one or more finished or semi-finished goods at one or more top levels and, for each of the one or more finished or semi-finished goods, a complementary set of component products at one or more bottom levels; (Ouyang Par. 55; Par. 72-86-“ he computing system may be programmed to utilize a breadth-first searching method for determining path solutions, by beginning at the starting sites in the supply chain network (e.g., the suppliers) and increasing the possible path solutions level by level until a full path is determined once the path has reached the an ending site (e.g., a customer zone).”; Par. 98-103)
perform, by the processor, a … heuristic operation on the modelled supply chain to determine demands and supplies at the one or more bottom levels; (Ouyang Par. 81-“ To detect one or more loops in a supply chain network, the computing system may take a top-down approach, starting with the source sites at the beginning the of the supply chain network (e.g., suppliers, vendors, etc.) and proceeding forward. The computing system will proceed forward from the starting sites and search downstream sites for any return path fragments that may comprise a loop. Upon determining a path fragment leads to an upstream site that has already been visited (e.g., DC 510), the computing system may mark the path fragment as a “return path fragment” and determine a loop is present for a particular site.”)
formulate, by the processor, an optimized model for the one or more bottom levels based on the determined demands and supplies (Ouyang Par. 38- “Embodiments disclosed herein provide optimal path solutions to fulfill an order for a quantity of one or more finished goods through a supply chain network using real-time or near real-time solutions. These techniques allow for solutions for finished goods and raw materials flow and sourcing optimization problems associated with one or more metrics of a supply chain network. In an embodiment, a computing system is programmed to receive input data comprising an order to provide a quantity of one or more finished goods to a site through an acyclic supply chain network, the one or more finished goods each comprising one or more raw materials. The computing system is programmed to access information that defines an architecture of the acyclic supply chain network comprising a plurality of sites (e.g., a CZ or distribution center). The sites may be connected by a plurality of path fragments defining a movement of a sub-quantity of the one or more finished goods or the one or more raw materials between the two sites. Using this data, the computing system is programmed to generate one or more path solutions along the supply chain network to fulfill the order that indicate the sites where particular quantities of goods should be manufactured, as well as sites where particular quantities of raw materials should be purchased from in order to obtain an optimal result for the consumer. The optimal result may include for example, one or more path solutions along the supply chain network that optimize one or more metrics of a supply chain network, for example reducing the cost or time associated with fulfillment. In some embodiments a heuristic method may be used to select high priority paths to handle the exponential growth of paths in a complex supply chain network. In particular embodiments the computing system may transmit signals to computing devices associated with one or more sites in the supply chain network to rapidly initiate the fulfillment of an order of finished goods or raw materials. In this manner, a consumer can quickly and efficiently fulfill multiple orders with a supply chain networks in an optimal or near optimal fashion while meeting the needs of their customers with a real-time response to changing market conditions.”; Par. 76-“ the network optimization model may output any number of revised paths solutions and any number of revised path fragments comprising sub-quantities of finished goods and raw materials necessary to fulfill the order.; Par. 85”);
solve, by the processor, the optimized model to generate optimized results. (Ouyang Par. 76-“ the network optimization model may output any number of revised paths solutions and any number of revised path fragments comprising sub-quantities of finished goods and raw materials necessary to fulfill the order.; Par. 85”)
and perform, by the processor, a … heuristic operation on the optimized results to determine information associated with demands and supplies of the one or more top levels (Ouyang Par. 37-38- The optimal result may include for example, one or more path solutions along the supply chain network that optimize one or more metrics of a supply chain network, for example reducing the cost or time associated with fulfillment. In some embodiments a heuristic method may be used to select high priority paths to handle the exponential growth of paths in a complex supply chain network.”).
Ouyang teaches supply chain analysis and the feature is expounded by Kamath:
partition, by the processor, the modelled supply chain into heuristic levels comprising the one or more top levels and optimization levels comprising the complementary set of component products at the one or more bottom levels (Kamath Par. 35-37-At step 410 supply chain planner 110 sorts the buffers in the model from downstream to upstream and assigns the buffers levels based on their position in supply chain network 100. For example, in the case of finished goods buffers at the same level, supply chain planner 110 distinguishes these buffers based on the priority of the demand they serve. At step 420, supply chain planner 110 takes the LP solution as a base and calculates the bucket-wise cumulative consumption and cumulative production profiles for each buffer. In one embodiment, supply chain planner 110 starts with the most prioritized downstream buffer and recursively moves upstream.; Fig. 4);
perform, by the processor, a … heuristic operation on the modelled supply chain to determine demands at the one or more bottom levels ‘by starting at the demands of the one or more finished or semi-finished goods at the one or more top levels and computing down to determine the demands at the one or more bottom levels that achieve the demands at the one or more top level’; (Kamath Par. 35-39-At step 440, supply chain planner 110 checks for material availability from, for example, upstream to downstream for each buffer. In one embodiment, if material shortage is encountered, supply chain planner 110 moves the consumption to later buckets, matching the cumulative production profile to cumulative consumption profile in each bucket. In addition, set demand lateness, safety stock requirement, shelf life and the like are also taken into consideration at this point. At the end of this step, supply chain)
formulate, by the processor, an optimized model for the one or more bottom levels based on the determined demands ‘wherein the optimized model comprises decision variables, constraints, and an objective function that reduces, or minimizes, a sum of weighted costs’ (Kamath Par. 17-In one embodiment, supply chain planner 110 considers various discrete decision variables, discrete lot-sizing rules, production constraints and operational/business objectives of one or more supply chain entities 120 when determining an optimized global hierarchical solution. As described below in more detail, these various discrete decision variables, discrete lot-sizing rules, production constraints and operational/business objectives may prevent one or more supply chain entities 120 from satisfying supply chain demand, and may delay supply chain demand from being satisfied during a particular planning horizon. ;Par. 24; Par. 58-59)
Ouyang teaches supply chain analysis and the feature is expounded upon by Sumant:
…top-down heuristic operation… . (Sumant Par. 13- “Reconciliation and Integration Algorithms By designing reconciliation algorithms to match data across line item and projection hierarchy using top-down and bottom-up approach.”; Par. 56-“ FIGS. 2 a and 2 b are diagrams 200 a and 200 b that illustrate usage and benefits of the proposed solution, in accordance with an exemplary embodiment of the disclosure. In an exemplary embodiment, the product usage 202 may include one or more of, but without any limitations, cash projections, revenue, and profit projections, working capital management, financing decisions, investing decisions, pricing and product mixing, marketing spend strategy, supply chain budgeting,”; Par. 173)
… a bottom-up heuristic operation… (Sumant Par. 13- “Reconciliation and Integration Algorithms By designing reconciliation algorithms to match data across line item and projection hierarchy using top-down and bottom-up approach.”; Par. 56-“ FIGS. 2 a and 2 b are diagrams 200 a and 200 b that illustrate usage and benefits of the proposed solution, in accordance with an exemplary embodiment of the disclosure. In an exemplary embodiment, the product usage 202 may include one or more of, but without any limitations, cash projections, revenue, and profit projections, working capital management, financing decisions, investing decisions, pricing and product mixing, marketing spend strategy, supply chain budgeting,”; Par. 173)
Ouyang, Kamath and Sumant are directed to supply chain analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Ouyang in view of Kamath, as taught by Sumant, by utilizing additional data analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Ouyang with the motivation of optimizing processing of several types of cash related decisions (Sumant Par. 2).
Regarding Claim 7,
Ouyang teaches
A non-transitory computer-readable medium, the computer-readable medium including instructions that when executed by a computer, cause the computer to: receive, by a processor, a modelled supply chain having one or more finished or semi-finished goods at one or more top levels and, for each of the one or more finished or semi-finished goods, a complementary set of component products at one or more bottom levels; (Ouyang Par. 55; Par. 72-86-“ he computing system may be programmed to utilize a breadth-first searching method for determining path solutions, by beginning at the starting sites in the supply chain network (e.g., the suppliers) and increasing the possible path solutions level by level until a full path is determined once the path has reached the an ending site (e.g., a customer zone).”; Par. 98-104)
perform, by the processor, a … heuristic operation on the modelled supply chain to determine demands at the one or more bottom levels; (Ouyang Par. 81-“ To detect one or more loops in a supply chain network, the computing system may take a top-down approach, starting with the source sites at the beginning the of the supply chain network (e.g., suppliers, vendors, etc.) and proceeding forward. The computing system will proceed forward from the starting sites and search downstream sites for any return path fragments that may comprise a loop. Upon determining a path fragment leads to an upstream site that has already been visited (e.g., DC 510), the computing system may mark the path fragment as a “return path fragment” and determine a loop is present for a particular site.”)
formulate, by the processor, an optimized model for the one or more bottom levels based on the determined demands (Ouyang Par. 38- “Embodiments disclosed herein provide optimal path solutions to fulfill an order for a quantity of one or more finished goods through a supply chain network using real-time or near real-time solutions. These techniques allow for solutions for finished goods and raw materials flow and sourcing optimization problems associated with one or more metrics of a supply chain network. In an embodiment, a computing system is programmed to receive input data comprising an order to provide a quantity of one or more finished goods to a site through an acyclic supply chain network, the one or more finished goods each comprising one or more raw materials. The computing system is programmed to access information that defines an architecture of the acyclic supply chain network comprising a plurality of sites (e.g., a CZ or distribution center). The sites may be connected by a plurality of path fragments defining a movement of a sub-quantity of the one or more finished goods or the one or more raw materials between the two sites. Using this data, the computing system is programmed to generate one or more path solutions along the supply chain network to fulfill the order that indicate the sites where particular quantities of goods should be manufactured, as well as sites where particular quantities of raw materials should be purchased from in order to obtain an optimal result for the consumer. The optimal result may include for example, one or more path solutions along the supply chain network that optimize one or more metrics of a supply chain network, for example reducing the cost or time associated with fulfillment. In some embodiments a heuristic method may be used to select high priority paths to handle the exponential growth of paths in a complex supply chain network. In particular embodiments the computing system may transmit signals to computing devices associated with one or more sites in the supply chain network to rapidly initiate the fulfillment of an order of finished goods or raw materials. In this manner, a consumer can quickly and efficiently fulfill multiple orders with a supply chain networks in an optimal or near optimal fashion while meeting the needs of their customers with a real-time response to changing market conditions.”; Par. 76-“ the network optimization model may output any number of revised paths solutions and any number of revised path fragments comprising sub-quantities of finished goods and raw materials necessary to fulfill the order.; Par. 85”);
solve, by the processor, the optimized model to generate optimized results. (Ouyang Par. 76-“ the network optimization model may output any number of revised paths solutions and any number of revised path fragments comprising sub-quantities of finished goods and raw materials necessary to fulfill the order.; Par. 85”)
and perform, by the processor, a … heuristic operation on the optimized results to determine information associated with demands and supplies of the one or more top levels (Ouyang Par. 37-38- The optimal result may include for example, one or more path solutions along the supply chain network that optimize one or more metrics of a supply chain network, for example reducing the cost or time associated with fulfillment. In some embodiments a heuristic method may be used to select high priority paths to handle the exponential growth of paths in a complex supply chain network.”).
Ouyang teaches supply chain analysis and the feature is expounded by Kamath:
partition, by the processor, the modelled supply chain into heuristic levels comprising the one or more top levels and optimization levels comprising the complementary set of component products at the one or more bottom levels (Kamath Par. 35-37-At step 410 supply chain planner 110 sorts the buffers in the model from downstream to upstream and assigns the buffers levels based on their position in supply chain network 100. For example, in the case of finished goods buffers at the same level, supply chain planner 110 distinguishes these buffers based on the priority of the demand they serve. At step 420, supply chain planner 110 takes the LP solution as a base and calculates the bucket-wise cumulative consumption and cumulative production profiles for each buffer. In one embodiment, supply chain planner 110 starts with the most prioritized downstream buffer and recursively moves upstream.; Fig. 4) ;
perform, by the processor, a … heuristic operation on the modelled supply chain to determine demands at the one or more bottom levels ‘by starting at the demands of the one or more finished or semi-finished goods at the one or more top levels and computing down to determine the demands at the one or more bottom levels that achieve the demands at the one or more top level’; (Kamath Par. 35-39-At step 440, supply chain planner 110 checks for material availability from, for example, upstream to downstream for each buffer. In one embodiment, if material shortage is encountered, supply chain planner 110 moves the consumption to later buckets, matching the cumulative production profile to cumulative consumption profile in each bucket. In addition, set demand lateness, safety stock requirement, shelf life and the like are also taken into consideration at this point. At the end of this step, supply chain)
formulate, by the processor, an optimized model for the one or more bottom levels based on the determined demands ‘wherein the optimized model comprises decision variables, constraints, and an objective function that reduces, or minimizes, a sum of weighted costs’ (Kamath Par. 17-In one embodiment, supply chain planner 110 considers various discrete decision variables, discrete lot-sizing rules, production constraints and operational/business objectives of one or more supply chain entities 120 when determining an optimized global hierarchical solution. As described below in more detail, these various discrete decision variables, discrete lot-sizing rules, production constraints and operational/business objectives may prevent one or more supply chain entities 120 from satisfying supply chain demand, and may delay supply chain demand from being satisfied during a particular planning horizon. ;Par. 24; Par. 58-59)
Ouyang teaches supply chain analysis and the feature is expounded upon by Sumant:
…top-down heuristic operation… . (Sumant Par. 13- “Reconciliation and Integration Algorithms By designing reconciliation algorithms to match data across line item and projection hierarchy using top-down and bottom-up approach.”; Par. 56-“ FIGS. 2 a and 2 b are diagrams 200 a and 200 b that illustrate usage and benefits of the proposed solution, in accordance with an exemplary embodiment of the disclosure. In an exemplary embodiment, the product usage 202 may include one or more of, but without any limitations, cash projections, revenue, and profit projections, working capital management, financing decisions, investing decisions, pricing and product mixing, marketing spend strategy, supply chain budgeting,”; Par. 173)
… a bottom-up heuristic operation… (Sumant Par. 13- “Reconciliation and Integration Algorithms By designing reconciliation algorithms to match data across line item and projection hierarchy using top-down and bottom-up approach.”; Par. 56-“ FIGS. 2 a and 2 b are diagrams 200 a and 200 b that illustrate usage and benefits of the proposed solution, in accordance with an exemplary embodiment of the disclosure. In an exemplary embodiment, the product usage 202 may include one or more of, but without any limitations, cash projections, revenue, and profit projections, working capital management, financing decisions, investing decisions, pricing and product mixing, marketing spend strategy, supply chain budgeting,”; Par. 173)
Ouyang, Kamath and Sumant are directed to supply chain analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Ouyang in view of Kamath, as taught by Sumant, by utilizing additional data analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Ouyang with the motivation of optimizing processing of several types of cash related decisions (Sumant Par. 2).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: US Publication No. 20110173042 A1 to Riepshoff et al.- Abstract-“ The disclosure relates generally to methods and apparatus to optimize a supply plan through a hybrid meta-heuristic approach based on genetic algorithms to optimize inventory and generate a supply plan. The apparatuses include a supply chain planner that interacts with the processes of a supply chain network. To provide a complete optimization for the type of platform being deployed in theater a heuristic algorithm is devised to decompose the supply plan problem into a production center schedule and an asset routing problem, which will be tackled one after the other. The decomposed supply plan problem is solved with different heuristic algorithms. Namely, genetic algorithms are used to optimize the supply plans based on ever changing set of operational demands from in theater and the priority of those demands to the assigned depots, while efficient constructive heuristics are used to deal with footprint and timing constraints.”
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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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Sincerely,
/CHESIREE A WALTON/Examiner, Art Unit 3624
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