DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
2. The following NON-FINAL office action is in response to Applicant communication filed on 12/29/2025 regarding application 17/665,417. Claims 1-2, 5, 9-10, 13, 17-18, 21, 30, 36 and 42 have been canceled. Claims 43-45 have been added as new Independent Claims. Thus, Claims 3-4, 6-8, 11-12, 14-16, 19-20, 22-29, 31-35, 37-41 and 43-45 are pending and have been rejected. EXAMINER NOTE: Please note the consideration of the Patent Trial & Appeal Board (PTAB) Decision filed/submitted on 10/23/2025 in where the Examiner was affirmed on the 35 U.S.C. § 101 rejection.
Response to Amendments
3. Applicant’s amendment filed on 12/29/2025 necessitated new grounds of rejection in this office action.
Continued Examination under 37 CFR 1.114
4. 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/29/2025 has been entered.
Priority
5. The Examiner has noted the Applicants claiming Priority from Provisional (PRO) Application 63/145,949 filed on 02/04/2021. Therefore, the effective filing date examined for this application is reflected of 02/04/2021.
IDS Statements
6. The 3 Information Disclosure Statements (IDS’s) filed on 06/25/2024, 11/29/2024 and 01/03/2025 complies with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609 and is considered by the Examiner.
Response to 35 U.S.C. § 101 Arguments
7. Applicant’s 35 U.S.C. § 101 arguments, filed with respect to Claims 3-4, 6-8, 11-12, 14-16, 19-20, 22-29, 31-35, 37-41 and 43-45 have been fully considered, but they are found not persuasive (see Applicant Remarks, Pages 13-14, dated 12/29/2025). Examiner respectfully disagrees.
Argument #1:
(A). Applicant argues that Claims 3-4, 6-8, 11-12, 14-16, 19-20, 22-29, 31-35, 37-41 and 43-45 do not recite an abstract idea, law of nature of natural phenomenon under revised step 2a prong one of the 35 U.S.C. § 101 analysis (see Applicant Remarks, Pages 13-14, dated 12/29/2025). Examiner respectfully disagrees.
Specifically, Applicant argues that the amended claim limitations of Independent Claims 43-45 cannot be properly rejected under 35 U.S.C. § 101. The new Independent Claims 43-45 include operations that cannot be practically performed mentally as well as rooted in and otherwise improve computer technology under step 2a prong 1 of the 35 U.S.C. § 101 analysis (see Applicant Remarks, Page 13 of 15, dated 12/29/2025). Examiner respectfully disagrees.
In response to Applicant’s remarks for step 2a prong 1, Examiner points out that a human with expertise in operations research and graph theory can sit down and, given enough time and paper, perform every single logical step outlined in these claims: They can draw the "graph representation" of the facility equipment. They can write down the "mixed integer quadratic program (MIQP)" equations. They can manually "remove one or more terms" (e.g., set certain variables to zero or assume certain constraints) to create the "relaxed optimization problem”. They can manually "perform at least optimization" (using established algorithms like branch and bound, even if tedious) to derive a "feasible production schedule”. The fact that a computer does it faster (in "real-time") is a speed advantage, which is a consideration for Step 2B (inventive concept) but does not change the fundamental nature of the underlying process in Step 2A, Prong 1.
Moreover, Independent Claims 43-45 are directed to the abstract idea of optimizing and managing industrial production schedules through mathematical modeling. These claims explicitly name a pure mathematical construct (MIQP) and relies on graph theory and optimization algorithms, all classic judicial exceptions. Mental Processes: Every step is a cognitive function: obtaining data (reading), generating a problem (formulating a plan), removing terms (evaluating and simplifying), and performing optimization (calculation). Organizing Human Activity: The output, a "production schedule," is the very definition of a method of organizing human activity. The automation merely changes the tool used to schedule, not the abstract idea of scheduling itself.
Regarding Applicant assertion of “automatically scheduling operations which are rooted in computer technology”, Examiner discloses that the use of "one or more processors," "data network," and "database server" are universally conventional generic computer components. They are used as mere conduits for the abstract idea. These claims do not recite an improvement to the computer technology itself (e.g., a new networking protocol or a novel memory architecture). It is a pure application of abstract ideas using generic technology. The "equipment" limitation is just a field-of-use restriction.
With respect to “Mathematical Concepts” category, Examiner refers Applicant to MPEP § 2106.04 (a) (2) (I) (C): “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping.” “It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). See, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018) (holding that claims to a ‘‘series of mathematical calculations based on selected information’’ are directed to abstract ideas); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014) (holding that claims to a ‘‘process of organizing information through mathematical correlations’’ are directed to an abstract idea).” Furthermore, see MPEP § 2106.05 (c): “For data, mere "manipulation of basic mathematical constructs [i.e.,] the paradigmatic ‘abstract idea,’" has not been deemed a transformation. CyberSource v. Retail Decisions, 654 F.3d 1366, 1372 n.2, 99 USPQ2d 1690, 1695 n.2 (Fed. Cir. 2011) (quoting In re Warmerdam, 33 F.3d 1354, 1355, 1360, 31 USPQ2d 1754, 1755, 1759 (Fed. Cir. 1994)).”
According to the BRI of the claims (e.g., Independent Claims 43-45) in view of the specification, that the underlying claimed invention is described as concepts that are performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer (e.g., see at least Applicant’s Specification ¶ [0030]: “Each user device 102 a-102 d represents any suitable device or system used by at least one user to provide or receive information, such as a desktop computer, a laptop computer, a smartphone, and a tablet computer. However, any other or additional types of user devices may be used in the system 100.” See also Applicant’s Specification ¶ [0038]: “The processing device 202 includes any suitable number(s) and type(s) of processors or other processing devices in any suitable arrangement. Example types of processing devices 202 include one or more microprocessors, microcontrollers, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or discrete circuitry.”), 2) in a computer environment (e.g., see Applicant’s Specification ¶ [0036]: “Also, these components may be located in any suitable locations and might be distributed over a large area. In addition, while FIG. 1 illustrates one example operational environment in which production scheduling may be used, this functionality may be used in any other suitable system.”), or 3) is merely using a computer as a tool to perform the concept." Thus, based on these 3 factors, Examiner maintains that the claims still recite a mental process under step 2a prong 1. The argument for ineligibility rests on the principle that the core logic of these claims are a series of abstract, mental evaluations and calculations that a human can conceptually perform, however inefficiently. The use of a computer to execute this logic does not change the abstract nature of the claim itself, maintaining its status as a judicial exception under 35 U.S.C. § 101 Step 2A, Prong 1. Therefore, in conclusion, Examiner maintains that Claims 3-4, 6-8, 11-12, 14-16, 19-20, 22-29, 31-35, 37-41 and 43-45 are directed to abstract ideas under “Mental Processes” or “Mathematical Concepts” Groupings under 35 U.S.C. § 101 Step 2A Prong 1.
Argument #2:
(B). Applicant argues that Claims 3-4, 6-8, 11-12, 14-16, 19-20, 22-29, 31-35, 37-41 and 43-45 recites additional elements that integrate the judicial exception into a practical application under revised step 2a prong two of the 35 U.S.C. § 101 analysis (see Applicant Remarks, Pages 13-14, dated 12/29/2025). Examiner respectfully disagrees.
Specifically, Applicant argues that new Independent Claims 43-45 are rooted in and otherwise improve computer technology. The claims recite technical aspects of a manufacturing planning process and automatically scheduling operations which are rooted in computer technology and therefore are patent-eligible subject matter (see Applicant Remarks, Page 13 of 15, dated 12/29/2024). Examiner respectfully disagrees.
In response to Applicant arguments here for step 2a prong 2, Examiner notes that when analyzing the additional elements in view of the claim limitations for Independent Claims 43-45 when considered individually and as a whole, these claims do not integrate the judicial exception due to the following reasons. First, automation of a Manual/Mental Process is Not a Technical Improvement. It remains well-established that "simply using a computer as a tool to automate conventional activity" does not constitute an improvement to computer technology. The steps of "obtaining data," "generating an optimization problem," and "performing optimization" are the digital equivalent of a human operations researcher gathering data, formulating equations, and calculating a solution.
Automating these steps does not make the processor faster, the data network more efficient, or the database server more secure; it merely uses them for their standard, intended purposes. "Relaxation" as a Mathematical Shortcut, is not a Technical Solution: The step of "removing one or more terms to generate a relaxed optimization problem" is a mathematical technique used to simplify complex equations. A technical solution must solve a technical problem (e.g., hardware latency, memory bottlenecks). If the "relaxation" is performed solely to reach a mathematical result faster, it is a mathematical improvement, not a technological one. In 2026, an improvement to the abstract idea itself (the math) does not integrate that idea into a practical application. Generic Computer Components are "Insignificant Extra-Solution Activity": The use of a "data network," "database server," and "one or more processors" are high-level, generic recitations of computer hardware. These components are used as a mere "conduit" to execute the abstract idea of production scheduling. Under current guidance, "mere instructions to apply an exception using a generic computer component" do not impose meaningful limits on the exception. Field-of-Use Limitation Does Not Create Eligibility: Tying the abstract idea to a "processing facility" and "equipment" is a nominal limitation. Limiting the application of a mathematical algorithm to a particular technological environment (manufacturing) does not transform the abstract idea into a patent-eligible practical application. The "automatic scheduling" of operations is simply the final output of the mental process, which is a method of organizing human activity. Because the additional elements—individually and as an ordered combination—consist of generic computer functions performing data gathering and mathematical simplification, they do not provide a technological solution to a technological problem.
Claims 3-4, 6-8, 11-12, 14-16, 19-20, 22-29, 31-35, 37-41 and 43-45 are maintained as "directed to" the abstract idea of mathematical scheduling and is therefore patent-ineligible under 35 U.S.C. § 101 under step 2a prong 2 as these additional elements do not integrate the judicial exception into a practical application.
Argument #3:
(C). Applicant argues that Claims 3-4, 6-8, 11-12, 14-16, 19-20, 22-29, 31-35, 37-41 and 43-45 recites additional elements that integrate the judicial exception into a practical application under revised step 2a prong two of the 35 U.S.C. § 101 analysis (see Applicant Remarks, Pages 13-14, dated 12/29/2025). Examiner respectfully disagrees.
Specifically, Applicant argues that new Independent Claims 43-45 are rooted in and otherwise improve computer technology. The claims recite technical aspects of a manufacturing planning process and automatically scheduling operations which are rooted in computer technology and therefore are patent-eligible subject matter (see Applicant Remarks, Page 13 of 15, dated 12/29/2024). Examiner respectfully disagrees.
Examiner points out that for Independent Claims 43-45 certain additional elements in these claims consist of generic computer functions that do not provide "significantly more". "One or more processors" and "Processing device": These are the basic building blocks of any computing system. Using a processor to execute mathematical algorithms is a "purely functional" recitation of a computer's intended purpose. "Data network" and "Database server": The transmission and retrieval of data over standard networks are considered conventional "input" activities. Using a network to obtain data for an abstract model is "insignificant extra-solution activity." "Automatically schedule operations": Automation of a manual process (scheduling) using generic computer instructions does not constitute an inventive concept. Reason #2: The Ordered Combination Fails to Solve a Technological Problem. While an ordered combination of conventional elements can sometimes be inventive, this combination follows a standard, logical workflow for data processing: Logical Workflow: The sequence of "Obtain data → Model problem → Simplify problem → Calculate solution → Output result" is the fundamental flow of operations research. There is no non-conventional interaction between the processors and the database that improves the computer's own functionality. Mathematical Relaxation: The technique of "removing terms" to create a "relaxed optimization problem" is a well-known mathematical shortcut used to reduce computational load. Utilizing a known mathematical simplification to make a calculation "feasible" on a generic processor is not a technological invention; it is a mathematical one. Reason #3: Lack of a "Technological" Improvement. To satisfy Step 2B, the claim must improve a technological process or the computer itself. No Improvement to Computer Functionality: The claim does not improve the speed of the processor, the capacity of the database, or the security of the network. It merely uses these tools to perform the abstract idea of MIQP optimization. Field-of-Use Restriction: Tying the output to a "processing facility" and "equipment" is a mere field-of-use limitation. Under Alice Corp. v. CLS Bank, limiting an abstract idea to a particular technological environment does not provide the "significantly more" required for eligibility. These claims essentially recite the abstract idea of mathematical optimization and adds the instruction to "apply it" using a computer. These claims are maintained as patent-ineligible under 35 U.S.C. § 101 step 2B because it fails to provide an inventive concept that is "significantly more" than the recited judicial exceptions.
The ordered combination of elements in the Dependent Claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself. Thus, Claims 3-4, 6-8, 11-12, 14-16, 19-20, 22-29, 31-35, 37-41 and 43-45 are ineligible with respect to the 35 U.S.C. § 101 analysis.
Argument #4:
(D). Applicant argues that Dependent Claims 3-4, 6-8, 11-12, 14-16, 19-20, 22-29, 31-35, 37-41 are allowable under the 35 U.S.C. § 101 analysis (see Applicant Remarks, Pages 13-14, dated 12/29/2025). Examiner respectfully disagrees.
In response to Applicant’s remarks here for the Dependent Claims 3-4, 6-8, 11-12, 14-16, 19-20, 22-29, 31-35, 37-41, Examiner notes that these claims are patent ineligible due to similar reasons and rationale explained above for Independent Claims 43-45 and are applied here as well for the 35 U.S.C. § 101 analysis. Examiner respectfully submits that Dependent Claims 3-4, 6-8, 11-12, 14-16, 19-20, 22-29, 31-35, 37-41 recite abstract ideas under step 2a prong 1 under “Mental Processes” or “Mathematical Concepts”. Additionally, Dependent Claims 3-4, 6-8, 11-12, 14-16, 19-20, 22-29, 31-35, 37-41 does not recite additional elements that integrate the judicial exception into a practical application under step 2a prong 2 nor do they recite additional elements that are significantly more than the recited judicial exceptions. Therefore, Claims 3-4, 6-8, 11-12, 14-16, 19-20, 22-29, 31-35, 37-41 and 43-45 are ineligible with respect to the 35 U.S.C. § 101 analysis.
Claim Rejections - 35 USC § 101
8. 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.
9. Claims 3-4, 6-8, 11-12, 14-16, 19-20, 22-29, 31-35, 37-41 and 43-45 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 3-4, 6-8, 11-12, 14-16, 19-20, 22-29, 31-35, 37-41 and 43-45 are each focused to a statutory category namely a “method” or a “process” (Claims 43, 3-4, 6-8 and 25-29), an “apparatus” or a “system” (Claims 44, 11-12, 14-16 and 31-35), and a “non-transitory computer-readable medium” or “an article of manufacture” (Claims 45, 19-20, 22-24 and 37-41).
Step 2A Prong One: Independent Claims 43-45 recite limitations that set forth the abstract idea(s), namely (see in bold except where strikethrough):
“” (see Independent Claim 44);
“obtain, , a graph representation indicating one or more pieces of equipment in a processing facility” (see Independent Claims 43-45);
“ generate an optimization problem associated with the processing facility, the optimization problem comprising a
“ remove one or more terms from the optimization problem to generate a relaxed optimization problem” (see Independent Claims 43-45);
“ perform at least optimization of the relaxed optimization problem to generate a feasible production schedule of the processing facility” (see Independent Claims 43-45);
“cause t use the feasible production schedule to schedule operations to be performed by the one or more pieces of equipment in the processing facility” (see Independent Claims 43-45).
Here, for Independent Claims 43-45, the claim limitations steps are directed to the abstract idea of optimizing a production schedule using mathematical optimization and scheduling—specifically, formulating a Mixed-Integer Quadratic Program (MIQP), simplifying it by removing terms (relaxation), solving it mathematically, and using the solution to build a schedule.
Mathematical Concepts: The claim explicitly recites a "mixed integer quadratic program (MIQP)," "removing one or more terms," and performing "optimization of the relaxed optimization problem." These are mathematical relationships, formulas, and algorithmic calculations. Mental Processes: The core mathematical manipulation—formulating an optimization problem, removing terms to relax it, and solving it—represents steps that can be performed conceptually via human mental processes or with the aid of pen and paper. See the following 35 U.S.C. § 101 analysis below: “one or more processors to; use a data network to obtain, from a database server, a graph representation indicating one or more pieces of equipment in a processing facility” -> This limitation recites generic computer components performing standard data gathering functions. A "graph representation" is a mathematical data structure used to model relationships. Collecting structural data about equipment falls into the gathering of data category, which often supports or recites an abstract idea. “cause at least one processing device to generate an optimization problem associated with the processing facility, the optimization problem comprising a mixed integer quadratic program (MIQP)” -> This explicitly recites a Mathematical Concept. A Mixed-Integer Quadratic Program (MIQP) is a specific mathematical optimization problem involving quadratic objective functions and integer variables. The generation of a mathematical program is a purely mathematical activity. “cause the at least one processing device to remove one or more terms from the optimization problem to generate a relaxed optimization problem” -> This recites a Mathematical Concept and a Mental Process. "Relaxing" an optimization problem by removing terms or constraints is a well-known mathematical simplification technique. This is a mathematical manipulation that can be performed mentally or with pen and paper. “cause the at least one processing device to perform at least optimization of the relaxed optimization problem to generate a feasible production schedule of the processing facility” -> This recites a Mathematical Concept. Performing optimization algorithms to find a feasible solution to a relaxed mathematical model is a mathematical operation. Generating a "schedule" based on data manipulation also aligns with a method of organizing human activity or a mental process. “cause the at least one processing device to use the feasible production schedule to automatically schedule operations to be performed by the one or more pieces of equipment in the processing facility” -> This limitation applies the mathematical result to a generic post-solution activity (scheduling operations). While it mentions physical entities ("pieces of equipment"), the core action is the organization/sequencing of data (scheduling), which falls into the realm of organizing activities.
Furthermore, these claims explicitly recite mathematical structures and relationships: "Graph representation": A recognized mathematical structure (nodes and edges) used to model equipment relationships. "Mixed integer quadratic program (MIQP)": A specific mathematical formula and optimization relationship. "Relaxed optimization problem": A mathematical technique involving the manipulation and simplification of equations.
Alternatively, several steps describe activities that can be practically performed in the human mind (even if more efficient on a computer). For example; "Obtaining" and "indicating": These are functions of data gathering and observation. "Generate an optimization problem": Formulating a problem based on a set of parameters is a mental act of judgment. "Remove one or more terms": Simplifying a problem or deciding which variables to ignore is an evaluation performed in the mind. Alternatively, these claims recite a fundamental business practice such as creating a schedule for industrial operations is a sub-category of managing commercial or industrial interactions and personal/equipment behavior.
Therefore, these abstract idea limitations (as identified above in bold), under their broadest reasonable interpretation of the claims as a whole, cover performance of their limitations as “Mental Processes” which pertains to (1) concepts performed in the human mind (including observations or evaluations or judgments) or (2) using pen and paper as a physical aid, in order to help perform these mental steps does not negate the mental nature of these limitations. The use of “physical aids” in implementing the abstract mental process, does not preclude the claims from reciting an abstract idea. See MPEP § 2106.04 (a) III C.
Alternatively, these abstract idea limitations (as identified above in bold), under their broadest reasonable interpretation of the claims as a whole, cover performance of their limitations as “Mathematical Concepts” which pertains to (3) mathematical calculations or (4) mathematical relationships.
That is, other than reciting (e.g., “at least one processing device” & “data network” & “database server” & “mixed integer quadratic program (MIQP)” & “one or more processors”, etc…), nothing in the claim elements precludes the steps from being performed as “Mental Processes” which pertains to (1) concepts performed in the human mind (including observations or evaluations or judgments) or (2) using pen and paper as a physical aid and additionally or alternatively as “Mathematical Concepts” which pertains to (3) mathematical calculations or (4) mathematical relationships.
Therefore, at step 2a prong 1, Yes, Claims 3-4, 6-8, 11-12, 14-16, 19-20, 22-29, 31-35, 37-41 and 43-45 recite an abstract idea. We proceed onto analyzing the claims at step 2a prong 2.
Step 2A Prong Two: With respect to Step 2A Prong Two of the eligibility inquiry (as explained in MPEP § 2106.04(d)), the judicial exception is not integrated into a practical application. Independent Claim 44 recites additional elements directed to: (e.g., “at least one processing device” & “data network” & “database server” & “one or more processors”). Independent Claims 43 and 45 recites additional elements directed to: (e.g., “at least one processing device” & “data network” & “database server”). These additional elements have been considered individually and in combination, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment. See MPEP § 2106.05(f) and MPEP § 2106.05(h). Furthermore, the step of (e.g., such as “using a data network to obtain, from a database server, a graph representation indicating one or more pieces of equipment in a processing facility”) is characterized as mere data gathering or "input" activity. Collecting data to be used in an abstract mathematical model is considered insignificant extra-solution activity (see MPEP § 2106.05 (g)) that does not move the claims into a practical application. Examiner notes that simply using a computer to perform mathematical calculations more quickly than a human does not constitute a "practical application.
Also, for Independent Claims 43-45, "Generate an optimization problem": Translating real-world data into a mathematical format (MIQP) is part of the abstract idea itself and does not provide a technological solution to a technical problem. With respect to the additional elements of a (e.g., “automatically” & “mixed integer quadratic program (MIQP)”) as recited in Independent Claims 43-45, these additional elements both individually and as a whole do not provide additional elements to integrate the judicial exception into a practical application under step 2a prong 2 due to: (1) reciting mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions (see MPEP § 2106.05(f)) or (2) the claims as a whole are limited to a particular field of use or technological environment pertaining to monitoring and analyzing post-processing heuristics of the one or more proposed schedules to identify a feasible production schedule for processing units, controlling the one or more processing units using the feasible production schedule and removing one or more terms from the optimized problem according to removing one or more terms from the objective function associated with transition costs in the production manufacturing or production scheduling environments (see MPEP § 2106.05 (h)).
The step of obtaining a graph representation from a database over a network is merely generic data gathering to feed the mathematical model.
The steps of generating the MIQP, removing terms to relax it, and optimizing it are the judicial exceptions themselves (mathematical manipulations).
The final step of "automatically scheduling operations to be performed by the one or more pieces of equipment" is a generic computer application/post-solution activity.
Independent Claims 43-45 do not improve the actual operations of the physical equipment itself (e.g., making a machine run cooler, faster, or with less wear on its physical joints). Instead, it improves the efficiency of the mathematical calculation (by relaxing the MIQP to make it easier to solve) and uses that mathematical efficiency to organize data (a schedule). Under USPTO guidance (e.g., cases like Electric Power Group or SAP America), using mathematical formulas to generate an optimized operational schedule or plan on generic computers does not constitute an integration into a practical application because it doesn't solve a technical problem in the computer's functioning or a physical engineering process. It merely automates the logic of scheduling.
Examiner notes that while these claims mention a "processing facility" and "equipment," these serve as a limiting field of use. Under Bilski and Alice, simply limiting the application of an abstract idea to a particular industry (industrial processing) do not make these claims patent-eligible. The "scheduling of operations" is a fundamental business practice; applying that practice to "equipment" is a nominal limitation that does not change the nature of the underlying abstract process.
The step of "removing one or more terms... to generate a relaxed optimization problem" is a mathematical shortcut. The specification does not explain how this "relaxation" improves the physical performance of the equipment itself (e.g., preventing a specific mechanical failure or improving a chemical reaction), then the improvement is merely a mathematical improvement (faster calculation). An improvement to the logic of an abstract idea or a faster way to do math on a computer is not a "technological improvement" to the processing facility's hardware. The final step of "automatically schedule operations" is a "do it on a computer" version of the abstract idea of scheduling (see MPEP § 2106.05 (f)). Because these claims do not recite how the hardware is physically controlled (e.g., specific voltage changes or mechanical valve adjustments), it remains a high-level instruction to "use the results of the math to perform the task."
In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception. Therefore, at step 2a prong 2, Claims 3-4, 6-8, 11-12, 14-16, 19-20, 22-29, 31-35, 37-41 and 43-45 are directed to the abstract idea and do not recite additional elements that integrate into a practical application.
Step 2B: (As explained in MPEP § 2106.05), it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Independent Claim 44 recites additional elements directed to: (e.g., “at least one processing device” & “data network” & “database server” & “one or more processors”). Independent Claims 43 and 45 recites additional elements directed to: (e.g., “at least one processing device” & “data network” & “database server”). These elements have been considered individually and in combination, but fail to add significantly more to the claims because they amount to using computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (computing environment) and does not amount to significantly more than the abstract idea itself. Notably, Applicant’s Specification suggests that the claimed invention relies on nothing more than a general-purpose computer executing the instructions to implement the invention (see at least Applicant’s Specification ¶ [0030]: “Each user device 102 a-102 d represents any suitable device or system used by at least one user to provide or receive information, such as a desktop computer, a laptop computer, a smartphone, and a tablet computer. However, any other or additional types of user devices may be used in the system 100.” See also Applicant’s Specification ¶ [0038]: “The processing device 202 includes any suitable number(s) and type(s) of processors or other processing devices in any suitable arrangement. Example types of processing devices 202 include one or more microprocessors, microcontrollers, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or discrete circuitry.” Additionally, see Applicant’s Specification ¶ [0131]: “This allows at least one approximate optimal solution for the full optimization problem to be obtained by solving the relaxed formulation of the optimization problem. Note that various approaches for performing constrained optimization are known in the art, and other approaches are sure to be developed in the future. Any suitable technique for performing constrained optimization of an optimization problem may be used here, such as techniques for performing nonlinear constrained optimization.”).
Independent Claims 43-45: The additional elements in these claims consist of generic computer components and "extra-solution" activities in the field of industrial automation. For example; "One or more processors" and "Processing device": These are the most basic building blocks of a computer. Using a processor to perform calculations is the definition of a generic computer function and adds nothing "significantly more" to the mathematical exception. Use a data network to obtain... from a database server": The transmission of data over a network and the retrieval of data from a server are considered standard technological functions. They represent "mere data gathering" and do not constitute an inventive concept. "Automatically schedule operations": The word "automatically" simply replaces a human performing the abstract idea with a computer. Automating a human scheduling task using computational processing logic is a computer function. It does not alter how the processor works or solve a technical bottleneck in computing networks.
Examiner notes that the ordered combination is analyzed to see if it provides a non-conventional technological solution. The combination fails for Independent Claims 43-45 due to the sequence of "Obtaining Data → Formulating a Problem → Simplifying the Problem (Relaxation) → Calculating a Result → Implementing the Result" is the standard logical workflow of any optimization task. No Technological Improvement: The ordered combination does not improve the functioning of the computer itself (e.g., it doesn't make the processor faster or the network more secure). Nor does it improve the physical equipment of the facility in a non-conventional way; it merely dictates a schedule based on the math. "Removing terms" from a Mixed Integer Quadratic Program (MIQP) is a well-known mathematical technique called "relaxation." Using a known mathematical shortcut to make a calculation feasible on a generic computer is a "drafting effort designed to monopolize the mathematical concept" rather than a technological invention. These claims simply appear to be in specific field whereby it limits the math to "processing facilities," which is a mere field-of-use limitation. Does Not Solve a Technical Problem with a Technical Solution: It solves a mathematical problem (the complexity of MIQP) with a mathematical solution (relaxation), then instructs a generic computer to output the result. These claims amount to nothing more than a command to "apply the abstract idea of MIQP relaxation on a generic computer system to manage a production schedule."
Looked at as an ordered combination, the steps describe a classic mathematical modeling workflow: gather data -> formulate the exact model (MIQP) -> realize it's too hard to solve, so relax the model -> solve the relaxed model -> output the schedule. This is a sequence of operations performed by data scientists and industrial engineers when dealing with intractable quadratic optimization problems. The computer is merely used as a tool to execute these mathematical steps faster than a human could.
With respect to the additional elements of a (e.g., “automatically” & “mixed integer quadratic program (MIQP)”) as recited in Independent Claims 43-45, when considered individually and as an ordered combination (as a whole), these additional elements do not amount to significantly more than the judicial exceptions under step 2B due to the following: (1) reciting mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions (see MPEP § 2106.05(f)) or (2) the claims as a whole are limited to a particular field of use or technological environment pertaining to monitoring and analyzing post-processing heuristics of the one or more proposed schedules to identify a feasible production schedule for processing units, controlling the one or more processing units using the feasible production schedule and removing one or more terms from the optimized problem according to removing one or more terms from the objective function associated with transition costs in the production manufacturing or production scheduling environments (see MPEP § 2106.05 (h)). Examiner notes that while these claims mention a "processing facility" and "equipment," these serve as a limiting field of use. Under Bilski and Alice, simply limiting the application of an abstract idea to a particular industry (industrial processing) do not make these claims patent-eligible. The "scheduling of operations" is a fundamental business practice; applying that practice to "equipment" is a nominal limitation that does not change the nature of the underlying abstract process.
The additional element of “mixed integer quadratic program (MIQP)” in Independent Claims 43-45 does not amount to significantly more than the judicial exception under step 2B due to being expressly recognized as WURC (well-understood, routine and conventional) in the art.
For example; see US PG Pub (US 2022/0414518 A1) – “Quantum Computing Based Hybrid Solution Strategies for Large-Scale Discrete-Continuous Optimization”, hereinafter You, et. al. See You at ¶ [0041]: “The optimization problem can be any suitable mixed-integer linear programing (MILP) or mixed-integer nonlinear programing (MINP), such as mixed-integer quadratic programming (MIQP) or integer quadratic fractional programming (IQFP). The optimization problem to be solved may be from any suitable field, such as chemical engineering, industrial processes, traffic control, electric power generation, flight path optimization, artificial intelligence, and protein folding.” See You at ¶ [0084]: “In the MIQP model addressed in the method 900, the operational requirements for each part are known beforehand.” See You at ¶ [0114]: “The relaxed problem 1308 is solved on the classical subsystem 1310 by any classical solver like Gurobi, CPLEX, CBC, and others on any classical computer 1312.” See You at ¶ [0115]: “A system 1400 for solving manufacturing cell formation problems in cellular manufacturing system design using the hybrid QC-MIQP stepwise decomposition method is shown. The problem and control parameters are initialized in block 1402 using the plant data 1404 acquired from the manufacturing plant 1406. The dual problem 1408 is solved using any classical solver like Gurobi, CPLEX, CBC, and others on any classical computer 1410.”
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
Claims 3-4, 6-8, 11-12, 14-16, 19-20, 22-29, 31-35 and 37-41 recite additional elements directed to: (e.g., “auxiliary processing units”, “at least one processing device” & “processing units” & “multiple processing units”), in conjunction with the limitations recite the same abstract idea(s) as shown in Independent Claims 43-45 along with further steps/details that (1) could be performed in the human mind (including observations or evaluations or judgments) or (2) using pen to paper as a “physical aid”, which therefore fall under the “Mental Processes” Grouping and additionally or alternatively as “Mathematical Concepts” which pertains to (3) mathematical calculations or (4) mathematical relationships.
With respect to the additional elements of (e.g., “auxiliary processing units”, “at least one processing device” & “processing units” & “multiple processing units”) as recited in Dependent Claims 3-4, 6-8, 11-12, 14-16, 19-20, 22-29, 31-35 and 37-41, these additional elements both individually and as a whole do not integrate the abstract idea into a practical application under step 2a prong 2 and also do not amount to significantly more than the judicial exceptions under step 2B due to: (1) reciting mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions (see MPEP § 2106.05(f)) or (2) the claims as a whole are limited to a particular field of use or technological environment pertaining to monitoring and analyzing post-processing heuristics of the one or more proposed schedules to identify a feasible production schedule for processing units, controlling the one or more processing units using the feasible production schedule and removing one or more terms from the optimized problem according to removing one or more terms from the objective function associated with transition costs in the production manufacturing or production scheduling environments (see MPEP § 2106.05 (h)).
Furthermore, for Dependent Claims 28-29, 34-35 and 40-41, even if the steps of “transmitting information” or “transmitting data” are evaluated as additional elements, these activities at most amount to insignificant extra-solution activities, which has been recognized as Well-Understood, Routine and Conventional (WURC), and thus insufficient to add significantly more to the abstract idea. See MPEP § 2106.05(d) ii - 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); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
The ordered combination of elements in the Dependent Claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself. Therefore, under Step 2B, Claims 3-4, 6-8, 11-12, 14-16, 19-20, 22-29, 31-35, 37-41 and 43-45 do not include additional elements that are sufficient to amount to significantly more than the recited judicial exceptions. Thus, Claims 3-4, 6-8, 11-12, 14-16, 19-20, 22-29, 31-35, 37-41 and 43-45 are ineligible with respect to the 35 U.S.C. § 101 analysis.
Claim Rejections - 35 USC § 103
10. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
11. 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 (i.e., changing from AIA to pre-AIA ) 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.
12. 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.
13. Claims 29, 35, 41 and 43-45 are rejected under 35 U.S.C. 103 as being unpatentable over US PG Pub (US 2019/0338622 A1) hereinafter Alhasan, et. al., and in view of US PG Pub (US 2022/0414518 A1) hereinafter You, et. al.
Regarding Independent Claim 43, Alhasan method for constrained optimization and post-processing heuristics for optimal production scheduling for process manufacturing teaches the following:
- using a data network (see at least Alhasan: Fig. 12 & ¶ [0220]. Alhasan notes that examples of communication networks include a local area network (LAN), a radio access network (RAN), a metropolitan area network (MAN), a wide area network (WAN), Worldwide Interoperability for Microwave Access (WIMAX), a wireless local area network (WLAN) using, for example, 802.11a/b/g/n or 802.20 (or a combination of 802.11x and 802.20 or other protocols consistent with the present disclosure), all or a portion of the Internet, another communication network, or a combination of communication networks.) to obtain, from a database server (see at least Alhasan: Fig. 12 & ¶ [0170]. Alhasan notes that the illustrated Computer 1202 is intended to encompass any computing device such as a server.), a graph representation indicating one or more pieces of equipment in a processing facility (see at least Alhasan: ¶ [0046-0048] & Fig. 13. Alhasan notes that FIG. 13 illustrates a flow chart as an STN directed graph 1300, according to an implementation of the present disclosure. STN forms a directed graph with two types of distinctive nodes: 1) state nodes 1302 representing raw materials, intermediate, and final products and 2) task nodes 1304 representing physical or chemical operations. For each GOSP, a process is modeled (for example, as in FIG. 13) using the STN approach. The assignment of equipment is dealt implicitly in the model. With such representation, both options of one task one equipment (OTOE) and variable task equipment (VTE) can be assigned and considered. STN can be extended to a resource task network (RTN) framework which considers the entire characterizations of resources including materials, equipment, storage, and utilities. Each task transforms a set of resources to another set of resources.).
Moreover, Alhasan method for constrained optimization and post-processing heuristics for optimal production scheduling for process manufacturing does not explicitly disclose, but You does disclose the following:
- cause at least one processing device (see at least You: ¶ [0038] & Fig. 2. You teaches that the embodiments may also be implemented as instructions carried by or stored on one or more transitory or non-transitory machine-readable (e.g., computer-readable) storage medium, which may be read and executed by one or more processors. See also You at Fig. 2 item 202 noting processors.) to generate an optimization problem associated with the processing facility (see at least You: ¶ [0062-0064] & Figs. 8-14. See also You at ¶ [0014]: The optimization problem is a job-shop scheduling or planning problem for enterprise resource planning (ERP) or manufacturing execution system (MES) or scheduling over time with a limited scope. A physical system comprises a plurality of tasks, a plurality of resources wherein each resources comprises at least one relevant costs and related tasks, and at least one measure for the performance of the physical system, wherein the physical system is but not limited to an enterprise system, a manufacturing system, a supply chain system, a production system. See also You at ¶ [0208]: The optimization problem is to determine a molecular conformation, a schedule, a manufacturing cell formation, a vehicle routing, a production plan or schedule, a production schedule, a drug design, a protein folding structure, or a transportation system plan. See also You at Fig. 14 item #1406 manufacturing plant.), the optimization problem comprising a mixed integer quadratic program (see at least You: ¶ [0041] & [0063] & FIGS. 9-10. You teaches that in Figs. 9-10 are a simplified flow diagram of at least one embodiment of a method for solving a mixed-integer quadratic program (MIQP) optimization problem that may be executed by the system of FIG. 1. See also You at ¶ [0041]: The optimization problem to be solved can be any suitable optimization problem. For example, the optimization problem can be any suitable mixed-integer linear programing (MILP) or mixed-integer nonlinear programing (MINP), such as mixed-integer quadratic programming (MIQP) or integer quadratic fractional programming (IQFP). See also You at ¶ [0063].)
- cause the at least one processing device (see at least You: ¶ [0038] & Fig. 2. You teaches that the embodiments may also be implemented as instructions carried by or stored on one or more transitory or non-transitory machine-readable (e.g., computer-readable) storage medium, which may be read and executed by one or more processors. See also You at Fig. 2 item 202 noting processors.) to remove one or more terms from the optimization problem to generate a relaxed optimization problem (see at least You: ¶ [0075-0078] & ¶ [0080-0081] & Figs. 8-11. You teaches that the classical computer 102 checks whether the quantum computer 104 has found a feasible solution. A feasible solution at this step indicates that the optimal solution has been determined, and a lack of a feasible solution indicates that integer cuts must be added for machines that could not be scheduled successfully. If a feasible solution has not been found, the method proceeds to block 816, in which the classical computer 102 adds integer cuts to the relaxed MILP problem to exclude any conflicting assignments. For each machine with an infeasible schedule, the integer cut formulation is Σi∈S′xim≤|S′|−1, where S′ is the set of jobs assigned to machine m. It should be appreciated that the integer cuts are cumulative, which the integer cuts added in block 816 being in addition to any integer cuts already made. The method 800 then loops back to block 804 to re-solve the relaxed MILP with the additional integer cuts. The variable assignments are used to determine the integer cuts but are not directly used in the relaxed MILP problem. As a result, in some embodiments, the quantum computer 104 may determine appropriate integer cuts based on the QUBO solution and may send the result of the determined integer cuts to the classical computer 102 without sending the variable values to the classical computer 102. See also You at ¶ [0019] & ¶ [0075-0078] noting a relaxed MILP problem and a QUBO problem. See also You at Fig. 6 item #606 noting determining MIQP optimization problem.)
- cause the at least one processing device (see at least You: ¶ [0038] & Fig. 2. You teaches that the embodiments may also be implemented as instructions carried by or stored on one or more transitory or non-transitory machine-readable (e.g., computer-readable) storage medium, which may be read and executed by one or more processors. See also You at Fig. 2 item 202 noting processors.) to perform at least optimization of the relaxed optimization problem (see at least You: ¶ [0019] & ¶ [0075-0078] & ¶ [0080-0081] & Figs. 8-11. You notes that the number of constraints in the second sub-problem or the rest of sub-problems is more than the number of constraints in the original optimization problem. The number of constraints in the second sub-problem or the rest of sub-problems is less than the number of constraints in the original optimization problem, such that the second sub-problem is a relaxed optimization problem of the original optimization problem.) to generate a feasible production schedule of the processing facility (see at least You: ¶ [0079-0082] & ¶ [0208] & Figs. 8-11. You teaches that the quantum computer 104 uses the partial optimal solutions from the classical computer 102 to solve the Hamiltonian using the quantum processor 302 in order to locate a feasible schedule in the integer space. The result of the QUBO is then sent to the classical computer 102. Referring back to block 814, if a feasible schedule is found, the method 800 proceeds to block 818, in which an optimal solution is returned. If a feasible solution exists for the problem in the second phase of the hybrid algorithm, the algorithm will converge to a global optimum. Thus, it should be noted that the hybrid QC-MILP decomposition method converges to an optimal solution or proves infeasibility in finite number of iterations. See also You at ¶ [0014]: The optimization problem is a job-shop scheduling or planning problem for enterprise resource planning (ERP) or manufacturing execution system (MES) or scheduling over time with a limited scope. A physical system comprises a plurality of tasks, a plurality of resources wherein each resources comprises at least one relevant costs and related tasks, and at least one measure for the performance of the physical system, wherein the physical system is but not limited to an enterprise system, a manufacturing system, a supply chain system, a production system. See also You at ¶ [0208]: The optimization problem is to determine a molecular conformation, a schedule, a manufacturing cell formation, a vehicle routing, a production plan or schedule, a production schedule, a drug design, a protein folding structure, or a transportation system plan. See also You at Fig. 14 item #1406 manufacturing plant.)
- cause the at least one processing device (see at least You: ¶ [0038] & Fig. 2. You teaches that the embodiments may also be implemented as instructions carried by or stored on one or more transitory or non-transitory machine-readable (e.g., computer-readable) storage medium, which may be read and executed by one or more processors. See also You at Fig. 2 item 202 noting processors.) to use the feasible production schedule (see at least You: ¶ [0079-0082] & ¶ [0208] & Figs. 8-11. You teaches that the quantum computer 104 uses the partial optimal solutions from the classical computer 102 to solve the Hamiltonian using the quantum processor 302 in order to locate a feasible schedule in the integer space. The result of the QUBO is then sent to the classical computer 102. Referring back to block 814, if a feasible schedule is found, the method 800 proceeds to block 818, in which an optimal solution is returned. If a feasible solution exists for the problem in the second phase of the hybrid algorithm, the algorithm will converge to a global optimum. Thus, it should be noted that the hybrid QC-MILP decomposition method converges to an optimal solution or proves infeasibility in finite number of iterations. See also You at ¶ [0014]: The optimization problem is a job-shop scheduling or planning problem for enterprise resource planning (ERP) or manufacturing execution system (MES) or scheduling over time with a limited scope. A physical system comprises a plurality of tasks, a plurality of resources wherein each resources comprises at least one relevant costs and related tasks, and at least one measure for the performance of the physical system, wherein the physical system is but not limited to an enterprise system, a manufacturing system, a supply chain system, a production system. See also You at ¶ [0208]: The optimization problem is to determine a molecular conformation, a schedule, a manufacturing cell formation, a vehicle routing, a production plan or schedule, a production schedule, a drug design, a protein folding structure, or a transportation system plan.) to automatically schedule operations to be performed by the one or more pieces of equipment in the processing facility (see at least You: Figs. 8-14 & ¶ [0021] & ¶ [0083] & ¶ [0114]. You notes that the computation results are sent back to the user's local classical computer. The computation results are sent to one or more target devices or equipment. The computation results are formulated as instructions to control the operations of one or more target devices or equipment. See also You at ¶ [0083]: Referring now to FIG. 9, in use, the system 100 may execute a method 900 for solving a cellular manufacturing problem in the form of a MIQP. Cellular manufacturing is an important application of group technology and is being widely applied in manufacturing industries. In this manufacturing approach, the equipment is arranged to facilitate continuous flow production, resulting in increased workflow, reduced response and production times, and increased profits. In cellular manufacturing, the manufacturing system is divided into several cells. Similar parts are processed in the same cell, such that the interactions of machines and parts within a cell are maximized to improve efficiency. See also You at ¶ [0114]: The relaxed problem 1308 is solved on the classical subsystem 1310 by any classical solver like Gurobi, CPLEX, CBC, and others on any classical computer 1312. The partial optimal solution 1314 produced by the classical subsystem is used by the quantum subsystem 1316 to determine optimal schedule. The problem in the quantum computing step is solved using quantum solver 1318 assisted by any suitable quantum computer 1320. Problems in the quantum computing step can be solved locally or remotely depending on the location of quantum computer hardware. Remote quantum computing operation is facilitated by cloud infrastructure 1322 that is set up to promote quantum computing's applicability. Based on solutions obtained through the quantum subsystem 1316, integer cuts are added to the relaxed problem at block 1324 and is re-solved by the classical subsystem 1310. The optimal schedule 1326 is used by the machines 1306 to process the jobs 1304/orders 1302 such that minimum cost is incurred.).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Alhasan method for constrained optimization and post-processing heuristics for optimal production scheduling for process manufacturing with the aforementioned teachings of: causing at least one processing device to generate an optimization problem associated with the processing facility, the optimization problem comprising a mixed integer quadratic program (MIQP); causing the at least one processing device to remove one or more terms from the optimization problem to generate a relaxed optimization problem; causing the at least one processing device to perform at least optimization of the relaxed optimization problem to generate a feasible production schedule of the processing facility and causing the at least one processing device to use the feasible production schedule to automatically schedule operations to be performed by the one or more pieces of equipment in the processing facility, and in view of You, whereby an optimization problem may correspond to any suitable real-world problem, such as a scheduling problem, a cellular manufacturing problem, a vehicle routing problem, etc. The classical computer 102 may determine a MILP optimization problem in block 604, such as a scheduling problem. The classical computer may determine a MIQP optimization problem in block 606, such as a cellular manufacturing problem (see at least You: ¶ [0063]). System 100 may execute a method 900 for solving a cellular manufacturing problem in the form of a MIQP. Cellular manufacturing is an important application of group technology and is being widely applied in manufacturing industries. In this manufacturing approach, the equipment is arranged to facilitate continuous flow production, resulting in increased workflow, reduced response and production times, and increased profits. In cellular manufacturing, the manufacturing system is divided into several cells. Similar parts are processed in the same cell, such that the interactions of machines and parts within a cell are maximized to improve efficiency (see at least You: ¶ [0083]). Furthermore, several variants of the manufacturing cell formation model exist in literature. Factors like resource and operational costs, intra-cellular movement costs, resource utilization costs, grouping efficiency, and others are considered in the manufacturing cell formation model formulation (see at least You: ¶ [0084]).
Further, the claimed invention is merely a combination of old elements in a similar field for a constrained optimization and post-processing heuristics for optimal production scheduling for process manufacturing system, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by You, the results of the combination were predictable.
Regarding Independent Claims 44-45, Alhasan apparatus / non-transitory computer-readable medium for constrained optimization and post-processing heuristics for optimal production scheduling for process manufacturing teaches the following:
- one or more processors to (see at least Alhasan: ¶ [0176] & Fig. 12.);
- storing computer-readable program code that (see at least Alhasan: ¶ [0175-0176] & Fig. 12.), when executed, causes one or more processors to (see at least Alhasan: ¶ [0176] & Fig. 12.):
- using a data network (see at least Alhasan: Fig. 12 & ¶ [0220]. Alhasan notes that examples of communication networks include a local area network (LAN), a radio access network (RAN), a metropolitan area network (MAN), a wide area network (WAN), Worldwide Interoperability for Microwave Access (WIMAX), a wireless local area network (WLAN) using, for example, 802.11a/b/g/n or 802.20 (or a combination of 802.11x and 802.20 or other protocols consistent with the present disclosure), all or a portion of the Internet, another communication network, or a combination of communication networks.) to obtain, from a database server (see at least Alhasan: Fig. 12 & ¶ [0170]. Alhasan notes that the illustrated Computer 1202 is intended to encompass any computing device such as a server.), a graph representation indicating one or more pieces of equipment in a processing facility (see at least Alhasan: ¶ [0046-0048] & Fig. 13. Alhasan notes that FIG. 13 illustrates a flow chart as an STN directed graph 1300, according to an implementation of the present disclosure. STN forms a directed graph with two types of distinctive nodes: 1) state nodes 1302 representing raw materials, intermediate, and final products and 2) task nodes 1304 representing physical or chemical operations. For each GOSP, a process is modeled (for example, as in FIG. 13) using the STN approach. The assignment of equipment is dealt implicitly in the model. With such representation, both options of one task one equipment (OTOE) and variable task equipment (VTE) can be assigned and considered. STN can be extended to a resource task network (RTN) framework which considers the entire characterizations of resources including materials, equipment, storage, and utilities. Each task transforms a set of resources to another set of resources.).
Moreover, Alhasan apparatus / non-transitory computer-readable medium for constrained optimization and post-processing heuristics for optimal production scheduling for process manufacturing does not explicitly disclose, but You does disclose the following:
- cause at least one processing device (see at least You: ¶ [0038] & Fig. 2. You teaches that the embodiments may also be implemented as instructions carried by or stored on one or more transitory or non-transitory machine-readable (e.g., computer-readable) storage medium, which may be read and executed by one or more processors. See also You at Fig. 2 item 202 noting processors.) to generate an optimization problem associated with the processing facility (see at least You: ¶ [0062-0064] & Figs. 8-14. See also You at ¶ [0014]: The optimization problem is a job-shop scheduling or planning problem for enterprise resource planning (ERP) or manufacturing execution system (MES) or scheduling over time with a limited scope. A physical system comprises a plurality of tasks, a plurality of resources wherein each resources comprises at least one relevant costs and related tasks, and at least one measure for the performance of the physical system, wherein the physical system is but not limited to an enterprise system, a manufacturing system, a supply chain system, a production system. See also You at ¶ [0208]: The optimization problem is to determine a molecular conformation, a schedule, a manufacturing cell formation, a vehicle routing, a production plan or schedule, a production schedule, a drug design, a protein folding structure, or a transportation system plan. See also You at Fig. 14 item #1406 manufacturing plant.), the optimization problem comprising a mixed integer quadratic program (see at least You: ¶ [0041] & [0063] & FIGS. 9-10. You teaches that in Figs. 9-10 are a simplified flow diagram of at least one embodiment of a method for solving a mixed-integer quadratic program (MIQP) optimization problem that may be executed by the system of FIG. 1. See also You at ¶ [0041]: The optimization problem to be solved can be any suitable optimization problem. For example, the optimization problem can be any suitable mixed-integer linear programing (MILP) or mixed-integer nonlinear programing (MINP), such as mixed-integer quadratic programming (MIQP) or integer quadratic fractional programming (IQFP). See also You at ¶ [0063].)
- cause the at least one processing device (see at least You: ¶ [0038] & Fig. 2. You teaches that the embodiments may also be implemented as instructions carried by or stored on one or more transitory or non-transitory machine-readable (e.g., computer-readable) storage medium, which may be read and executed by one or more processors. See also You at Fig. 2 item 202 noting processors.) to remove one or more terms from the optimization problem to generate a relaxed optimization problem (see at least You: ¶ [0075-0078] & ¶ [0080-0081] & Figs. 8-11. You teaches that the classical computer 102 checks whether the quantum computer 104 has found a feasible solution. A feasible solution at this step indicates that the optimal solution has been determined, and a lack of a feasible solution indicates that integer cuts must be added for machines that could not be scheduled successfully. If a feasible solution has not been found, the method proceeds to block 816, in which the classical computer 102 adds integer cuts to the relaxed MILP problem to exclude any conflicting assignments. For each machine with an infeasible schedule, the integer cut formulation is Σi∈S′xim≤|S′|−1, where S′ is the set of jobs assigned to machine m. It should be appreciated that the integer cuts are cumulative, which the integer cuts added in block 816 being in addition to any integer cuts already made. The method 800 then loops back to block 804 to re-solve the relaxed MILP with the additional integer cuts. The variable assignments are used to determine the integer cuts but are not directly used in the relaxed MILP problem. As a result, in some embodiments, the quantum computer 104 may determine appropriate integer cuts based on the QUBO solution and may send the result of the determined integer cuts to the classical computer 102 without sending the variable values to the classical computer 102. See also You at ¶ [0019] & ¶ [0075-0078] noting a relaxed MILP problem and a QUBO problem. See also You at Fig. 6 item #606 noting determining MIQP optimization problem.)
- cause the at least one processing device (see at least You: ¶ [0038] & Fig. 2. You teaches that the embodiments may also be implemented as instructions carried by or stored on one or more transitory or non-transitory machine-readable (e.g., computer-readable) storage medium, which may be read and executed by one or more processors. See also You at Fig. 2 item 202 noting processors.) to perform at least optimization of the relaxed optimization problem (see at least You: ¶ [0019] & ¶ [0075-0078] & ¶ [0080-0081] & Figs. 8-11. You notes that the number of constraints in the second sub-problem or the rest of sub-problems is more than the number of constraints in the original optimization problem. The number of constraints in the second sub-problem or the rest of sub-problems is less than the number of constraints in the original optimization problem, such that the second sub-problem is a relaxed optimization problem of the original optimization problem.) to generate a feasible production schedule of the processing facility (see at least You: ¶ [0079-0082] & ¶ [0208] & Figs. 8-11. You teaches that the quantum computer 104 uses the partial optimal solutions from the classical computer 102 to solve the Hamiltonian using the quantum processor 302 in order to locate a feasible schedule in the integer space. The result of the QUBO is then sent to the classical computer 102. Referring back to block 814, if a feasible schedule is found, the method 800 proceeds to block 818, in which an optimal solution is returned. If a feasible solution exists for the problem in the second phase of the hybrid algorithm, the algorithm will converge to a global optimum. Thus, it should be noted that the hybrid QC-MILP decomposition method converges to an optimal solution or proves infeasibility in finite number of iterations. See also You at ¶ [0014]: The optimization problem is a job-shop scheduling or planning problem for enterprise resource planning (ERP) or manufacturing execution system (MES) or scheduling over time with a limited scope. A physical system comprises a plurality of tasks, a plurality of resources wherein each resources comprises at least one relevant costs and related tasks, and at least one measure for the performance of the physical system, wherein the physical system is but not limited to an enterprise system, a manufacturing system, a supply chain system, a production system. See also You at ¶ [0208]: The optimization problem is to determine a molecular conformation, a schedule, a manufacturing cell formation, a vehicle routing, a production plan or schedule, a production schedule, a drug design, a protein folding structure, or a transportation system plan. See also You at Fig. 14 item #1406 manufacturing plant.)
- cause the at least one processing device (see at least You: ¶ [0038] & Fig. 2. You teaches that the embodiments may also be implemented as instructions carried by or stored on one or more transitory or non-transitory machine-readable (e.g., computer-readable) storage medium, which may be read and executed by one or more processors. See also You at Fig. 2 item 202 noting processors.) to use the feasible production schedule (see at least You: ¶ [0079-0082] & ¶ [0208] & Figs. 8-11. You teaches that the quantum computer 104 uses the partial optimal solutions from the classical computer 102 to solve the Hamiltonian using the quantum processor 302 in order to locate a feasible schedule in the integer space. The result of the QUBO is then sent to the classical computer 102. Referring back to block 814, if a feasible schedule is found, the method 800 proceeds to block 818, in which an optimal solution is returned. If a feasible solution exists for the problem in the second phase of the hybrid algorithm, the algorithm will converge to a global optimum. Thus, it should be noted that the hybrid QC-MILP decomposition method converges to an optimal solution or proves infeasibility in finite number of iterations. See also You at ¶ [0014]: The optimization problem is a job-shop scheduling or planning problem for enterprise resource planning (ERP) or manufacturing execution system (MES) or scheduling over time with a limited scope. A physical system comprises a plurality of tasks, a plurality of resources wherein each resources comprises at least one relevant costs and related tasks, and at least one measure for the performance of the physical system, wherein the physical system is but not limited to an enterprise system, a manufacturing system, a supply chain system, a production system. See also You at ¶ [0208]: The optimization problem is to determine a molecular conformation, a schedule, a manufacturing cell formation, a vehicle routing, a production plan or schedule, a production schedule, a drug design, a protein folding structure, or a transportation system plan.) to automatically schedule operations to be performed by the one or more pieces of equipment in the processing facility (see at least You: Figs. 8-14 & ¶ [0021] & ¶ [0083] & ¶ [0114]. You notes that the computation results are sent back to the user's local classical computer. The computation results are sent to one or more target devices or equipment. The computation results are formulated as instructions to control the operations of one or more target devices or equipment. See also You at ¶ [0083]: Referring now to FIG. 9, in use, the system 100 may execute a method 900 for solving a cellular manufacturing problem in the form of a MIQP. Cellular manufacturing is an important application of group technology and is being widely applied in manufacturing industries. In this manufacturing approach, the equipment is arranged to facilitate continuous flow production, resulting in increased workflow, reduced response and production times, and increased profits. In cellular manufacturing, the manufacturing system is divided into several cells. Similar parts are processed in the same cell, such that the interactions of machines and parts within a cell are maximized to improve efficiency. See also You at ¶ [0114]: The relaxed problem 1308 is solved on the classical subsystem 1310 by any classical solver like Gurobi, CPLEX, CBC, and others on any classical computer 1312. The partial optimal solution 1314 produced by the classical subsystem is used by the quantum subsystem 1316 to determine optimal schedule. The problem in the quantum computing step is solved using quantum solver 1318 assisted by any suitable quantum computer 1320. Problems in the quantum computing step can be solved locally or remotely depending on the location of quantum computer hardware. Remote quantum computing operation is facilitated by cloud infrastructure 1322 that is set up to promote quantum computing's applicability. Based on solutions obtained through the quantum subsystem 1316, integer cuts are added to the relaxed problem at block 1324 and is re-solved by the classical subsystem 1310. The optimal schedule 1326 is used by the machines 1306 to process the jobs 1304/orders 1302 such that minimum cost is incurred.).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Alhasan apparatus / non-transitory computer-readable medium for constrained optimization and post-processing heuristics for optimal production scheduling for process manufacturing with the aforementioned teachings of: causing at least one processing device to generate an optimization problem associated with the processing facility, the optimization problem comprising a mixed integer quadratic program (MIQP); causing the at least one processing device to remove one or more terms from the optimization problem to generate a relaxed optimization problem; causing the at least one processing device to perform at least optimization of the relaxed optimization problem to generate a feasible production schedule of the processing facility and causing the at least one processing device to use the feasible production schedule to automatically schedule operations to be performed by the one or more pieces of equipment in the processing facility, and in view of You, whereby an optimization problem may correspond to any suitable real-world problem, such as a scheduling problem, a cellular manufacturing problem, a vehicle routing problem, etc. The classical computer 102 may determine a MILP optimization problem in block 604, such as a scheduling problem. The classical computer may determine a MIQP optimization problem in block 606, such as a cellular manufacturing problem (see at least You: ¶ [0063]). System 100 may execute a method 900 for solving a cellular manufacturing problem in the form of a MIQP. Cellular manufacturing is an important application of group technology and is being widely applied in manufacturing industries. In this manufacturing approach, the equipment is arranged to facilitate continuous flow production, resulting in increased workflow, reduced response and production times, and increased profits. In cellular manufacturing, the manufacturing system is divided into several cells. Similar parts are processed in the same cell, such that the interactions of machines and parts within a cell are maximized to improve efficiency (see at least You: ¶ [0083]). Furthermore, several variants of the manufacturing cell formation model exist in literature. Factors like resource and operational costs, intra-cellular movement costs, resource utilization costs, grouping efficiency, and others are considered in the manufacturing cell formation model formulation (see at least You: ¶ [0084]).
Further, the claimed invention is merely a combination of old elements in a similar field for a constrained optimization and post-processing heuristics for optimal production scheduling for process manufacturing system, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by You, the results of the combination were predictable.
Regarding Dependent Claims 29, 35 and 41, Alhasan / You method / apparatus / non-transitory computer readable medium for a constrained optimization and post-processing heuristics for optimal production scheduling for process manufacturing teaches the limitations of Independent Claims 43-45 above, and You further teaches the method / apparatus / non-transitory computer readable medium for a constrained optimization and post-processing heuristics for optimal production scheduling for process manufacturing comprising:
- wherein controlling one or more processing units (see at least You: ¶ [0021] & Figs. 2-3 & Figs. 13-14.) using the feasible production schedule (see at least You: ¶ [0079-0082] & ¶ [0208] & Figs. 8-11. You teaches that the quantum computer 104 uses the partial optimal solutions from the classical computer 102 to solve the Hamiltonian using the quantum processor 302 in order to locate a feasible schedule in the integer space. The result of the QUBO is then sent to the classical computer 102. Referring back to block 814, if a feasible schedule is found, the method 800 proceeds to block 818, in which an optimal solution is returned. If a feasible solution exists for the problem in the second phase of the hybrid algorithm, the algorithm will converge to a global optimum. Thus, it should be noted that the hybrid QC-MILP decomposition method converges to an optimal solution or proves infeasibility in finite number of iterations. See also You at ¶ [0014]: The optimization problem is a job-shop scheduling or planning problem for enterprise resource planning (ERP) or manufacturing execution system (MES) or scheduling over time with a limited scope. A physical system comprises a plurality of tasks, a plurality of resources wherein each resources comprises at least one relevant costs and related tasks, and at least one measure for the performance of the physical system, wherein the physical system is but not limited to an enterprise system, a manufacturing system, a supply chain system, a production system. See also You at ¶ [0208]: The optimization problem is to determine a molecular conformation, a schedule, a manufacturing cell formation, a vehicle routing, a production plan or schedule, a production schedule, a drug design, a protein folding structure, or a transportation system plan.) comprises: transmitting control instructions (see at least You: ¶ [0008] & ¶ [0014] & ¶ [0020-0021].) based on the feasible production schedule (see at least You: Figs. 8-14) to production equipment via a network (see at least You: ¶ [0021] & Figs. 2-3 & Figs. 13-14.).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Alhasan / You method / apparatus / non-transitory computer readable medium for constrained optimization and post-processing heuristics for optimal production scheduling for process manufacturing with the aforementioned teachings of: wherein controlling one or more processing units using the feasible production schedule comprises: transmitting control instructions based on the feasible production schedule to production equipment via a network, and in further view of You, whereby an optimization problem may correspond to any suitable real-world problem, such as a scheduling problem, a cellular manufacturing problem, a vehicle routing problem, etc. The classical computer 102 may determine a MILP optimization problem in block 604, such as a scheduling problem. The classical computer may determine a MIQP optimization problem in block 606, such as a cellular manufacturing problem (see at least You: ¶ [0063]). System 100 may execute a method 900 for solving a cellular manufacturing problem in the form of a MIQP. Cellular manufacturing is an important application of group technology and is being widely applied in manufacturing industries. In this manufacturing approach, the equipment is arranged to facilitate continuous flow production, resulting in increased workflow, reduced response and production times, and increased profits. In cellular manufacturing, the manufacturing system is divided into several cells. Similar parts are processed in the same cell, such that the interactions of machines and parts within a cell are maximized to improve efficiency (see at least You: ¶ [0083]). Furthermore, several variants of the manufacturing cell formation model exist in literature. Factors like resource and operational costs, intra-cellular movement costs, resource utilization costs, grouping efficiency, and others are considered in the manufacturing cell formation model formulation (see at least You: ¶ [0084]).
Further, the claimed invention is merely a combination of old elements in a similar field for a constrained optimization and post-processing heuristics for optimal production scheduling for process manufacturing system, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by You, the results of the combination were predictable.
14. Claims 3, 11 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over US PG Pub (US 2019/0338622 A1) hereinafter Alhasan, et. al., and in view of US PG Pub (US 2022/0414518 A1) hereinafter You, et. al., and in further view of US PG Pub (US 2023/0147377 A1) hereinafter Taniguchi, et. al.
Regarding Dependent Claims 3, 11 and 19, Alhasan / You method / apparatus / non-transitory computer-readable medium for constrained optimization and post-processing heuristics for optimal production scheduling for process manufacturing does not explicitly disclose, but Taniguchi does disclose the following:
- wherein removing the one or more terms from the optimization problem further comprises removing one or more terms from an objective function associated with changeover times, each changeover time associated with a switch from production of one product to production of another product (see at least Taniguchi: Fig. 9 & Fig. 12C & ¶ [0022-0025]. Taniguchi notes that a planning method has been known that solves combinatorial optimization problems such as a cutting stock problem or a bin packing problem. However, if a product input order is switched in order to reduce an excess amount of materials, the number of changeovers in the production line increases, and there is a possibility that an objective function reflecting the changeover is deteriorated. See also Taniguchi at [0033-0034]: Since the product type is changed four times for the raw material 1, the changeover is performed four times. If the number of changeovers increases, there is a possibility that an objective function such as a production completion time reflecting the changeovers is deteriorated. An information processing device, a work plan specifying method, and a work plan specifying program that can improve an objective function reflecting a changeover while suppressing an excess material amount will be described. See also Taniguchi at ¶ [0056]: It is possible to suppress the excess material amount by switching the input order of each product to the production line and performing optimization, and an objective function reflecting the changeover caused by switching the input order is optimized. As a result, it is possible to improve the objective function reflecting the changeover while suppressing the excess material amount. See also Taniguchi at ¶ [0060-0095]. Taniguchi notes that power consumption, and a cost when a changeover for switching a product type is performed. In the example in FIG. 12C, the processing time for switching each product type such as product type switching from a product type A to a product type B is set to “100”, the power consumption is set to “40”, and the cost is set to “30”. The power consumption in this case is power needed for a changeover for switching a product type. The cost in this case is a cost needed for a changeover for switching a product type. “The specific mathematical terms representing changeover times and any conditions under which terms are omitted are explicitly modeled in the section outlining the Objective Function formulation (typically around paragraphs [0060] through [0095]) and the processing flows describing how the optimization constraints are relaxed or simplified.”).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Alhasan / You method / apparatus / non-transitory computer-readable medium for constrained optimization and post-processing heuristics for optimal production scheduling for process manufacturing with the aforementioned teachings of: wherein removing the one or more terms from the optimization problem further comprises removing one or more terms from an objective function associated with changeover times, each changeover time associated with a switch from production of one product to production of another product, and in further view of Taniguchi, whereby the system of Taniguchi is able to suppress the excess material amount by switching the input order of each product to the production line and performing optimization, and an objective function reflecting the changeover caused by switching the input order is optimized. As a result, it is possible to improve the objective function reflecting the changeover while suppressing the excess material amount (see at least Taniguchi: ¶ [0056]).
Further, the claimed invention is merely a combination of old elements in a similar field for a constrained optimization and post-processing heuristics for optimal production scheduling for process manufacturing system, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Taniguchi, the results of the combination were predictable.
15. Claims 4, 12 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over US PG Pub (US 2019/0338622 A1) hereinafter Alhasan, et. al., and in view of US PG Pub (US 2022/0414518 A1) hereinafter You, et. al., and in further view of NPL Document: Solving large-scale production scheduling and planning in the process industries. Cham, Switzerland: Springer International Publishing, 2019., hereinafter Kopanos, et. al.
Regarding Dependent Claims 4, 12 and 20, Alhasan / You method / apparatus / non-transitory computer-readable medium for constrained optimization and post-processing heuristics for optimal production scheduling for process manufacturing does not explicitly disclose, but Kopanos, et., al. does disclose the following:
- wherein at least one of the one or more proposed schedules is infeasible in view of at least one or more constraints (see at least Kopanos: Page 200, under formula 9.9: Kopanos notes that constraints (9.9) relate the two binary variables for online cleaning tasks for the production units. These constraints are needed in order to model correctly the modified maximum operating levels of production units during the period that are under online cleaning. If online cleaning does not affect the maximum operating level of production units, then these constraints can be ignored and variables VPR ði;e;tÞ do not need to be defined. See also Kopanos at Page 219 noting At the end of the planning horizon of interest, there are two types of terminal constraints for the: (i) inventory levels of utility and product resources; and (ii) the performance level of the operating utility units. See also Kopanos at Page 15 noting “Second, suboptimal or even infeasible schedules may be generated because of the reduction of the domain of the timing decisions.” See also Kopanos at Page 28 noting “The schedule graph of the optimal schedule can be effectively generated by an appropriate search strategy, which enables early detection of infeasible schedules (Sanmartí et al. 1998, 2002).” See also Kopanos at Page 99 noting “Any change in the packing schedule might lead to an infeasible schedule in the upstream departments. For instance, packing lines may not run due to lack of intermediate products or unnecessary intermediate products being made in the process plant floor. See also Kopanos at Page 106 noting “Feasible Solution”? if not solve MIP (1)-(17).” See also Kopanos at Page 218 noting “A total of 633 units of utility resources need to be purchased throughout the planning horizon. If there is no option of acquiring utilities from externals sources, this would make the production plan infeasible in practice.”) associated with one or more processing units and one or more interconnections (see at least Kopanos: Fig. 2.1 & Fig. 9.1 on Page 192 & Page 192 & 2nd ¶ of Page 40. Kopanos notes that Gimenez et al. (2009a) presented a network-based MIP framework for the short-term scheduling of multipurpose batch processes. Their approach was based on five key concepts: (i) a new continuous-time representation is developed that does not require tasks to start (end) exactly at a time point; thus reducing the number of time points needed to represent a solution, (ii) processing units were modeled as being in different activity states to allow storage of input/output materials, (iii) time variables for “idle” and “storage” periods of a unit were introduced to enable the matching between tasks and time points without big-M constraints, (iv) material transfer variables were added to explicitly account for unit connectivity, and (iv) inventory variables for storage in processing units were incorporated to model nonsimultaneous and partial material transfers. In the RTN representation, circles represent not only states but also other resources required in the batch process such as processing units and vessels (see Fig. 2.1). A special feature of the RTN is that processing equipment in different conditions (e.g., “clean” or “dirty”) can be treated as different resources, with diverse activities (e.g., “processing” or “cleaning”) consuming and generating them, thus achieving a simpler representation of changeovers. See also Kopanos at Fig 9.1 representative layout for the interaction of production and utility systems on Page 192 noting “process industries typically follow a sequential approach for the optimization of the operational plan of their production and utility systems. The main drawback of this approach is that it provides suboptimal solutions (with respect to energy efficiency and costs) since the two interconnected systems are not optimized simultaneously.” See Fig. 2.1 of Kopanos screenshot below.)
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It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Alhasan / You method / apparatus / non-transitory computer-readable medium for constrained optimization and post-processing heuristics for optimal production scheduling for process manufacturing with the aforementioned teachings of: wherein at least one of the one or more proposed schedules is infeasible in view of at least one or more constraints associated with one or more processing units and one or more interconnections, and in further view of Kopanos, et., al., whereby the emergence of the STN representation formed the basis of many other works aiming to take into advantage of the representational capabilities of the formulation while enhancing its computational performance. More specifically, Sahinidis and Grossmann (1991) disaggregated the allocation constraints and exploited the embedded lot-sizing nature of the model where relatively small demands were distributed throughout the scheduling horizon. The computational performance of the model was improved, despite the larger nature of the disaggregated model. In addition, the formulation of Sahinidis and Grossmann (1991) had a much smaller integrality gap than the original STN model. Afterward, Shah et al. (1993a) modified the allocation constraints even further to generate the smallest possible integrality gap for this type of formulations. They also devised a tailored branch and-bound solution procedure that utilizes a much smaller LP relaxation and solution processing to improve integrality at each node (see at least Kopanos, et., al: 1st ¶ of Page 34).
Further, the claimed invention is merely a combination of old elements in a similar field for a constrained optimization and post-processing heuristics for optimal production scheduling for process manufacturing system, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Kopanos, et., al., the results of the combination were predictable.
16. Claims 6-8, 14-16 and 22-24 are rejected under 35 U.S.C. 103 as being unpatentable over US PG Pub (US 2019/0338622 A1) hereinafter Alhasan, et. al., and in view of US PG Pub (US 2022/0414518 A1) hereinafter You, et. al., and in further view of US PG Pub (US 2022/0027817 A1) to Hubbs.
Regarding Dependent Claim 6, 14 and 22, Alhasan / You method for constrained optimization and post-processing heuristics for optimal production scheduling for process manufacturing does not explicitly disclose, but Hubbs does disclose the following:
- wherein performing the post-processing of the one or more proposed schedules comprises, for each proposed schedule (see at least Hubbs: Fig. 9 & ¶ [0143]. Hubbs notes that agent 710 can provide schedules for some, if not all, of the multiple production lines. In some of these examples, agent 710 can provide schedules that take into account operating constraints related to multiple production lines such as, but not limited to: 1) some or all of the production lines can share a common unit operation, resources, and/or operating equipment that prevents these production lines from producing a common product on the same day, 2) some or all of the production lines can share a common utility which limits production on these production lines, and (3) some or all of the production lines can be geographically distributed.), at least one of:
- discretizing the proposed schedule into chunks and merging batches of common products for each chunk (see at least Hubbs: ¶ [0143-0145]. Hubbs teaches determining the production of each part and splitting it between production lines (e.g., chunks) in order to adjust the ratio of parts required for later production. See also Hubbs at ¶ [0149]. Hubbs provides an example of chunks, and Hubbs at ¶ [0146] wherein the production facility determines parts for both upstream and downstream processes for multiple products at the same time on multiple production lines (e.g., common parts being merged to a final product).)
- ordering production batches to minimize transition costs within each chunk (see at least Hubbs: ¶ [0140]. Hubbs provides an example of using a cost as a factor to plan minimization of changing costs (e.g., transition cost).)
- rounding production batches in the proposed schedule to integer multiples of a production unit (see at least Hubbs: ¶ [0047]. Hubbs teaches using mixed integer linear programming as a scheduler to only provide integer values for production determination.)
- introducing downtimes and transition times into the proposed schedule (see at least Hubbs: ¶ [0141]. Hubbs teaches scheduling type change (time (e.g., downtime) due to transition from one product to another.)
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Alhasan / You / Hubbs method for constrained optimization and post-processing heuristics for optimal production scheduling for process manufacturing with the aforementioned teachings of: wherein performing the post-processing of the one or more proposed schedules comprises, for each proposed schedule, at least one of: discretizing the proposed schedule into chunks and merging batches of common products for each chunk & ordering production batches to minimize transition costs within each chunk & rounding production batches in the proposed schedule to integer multiples of a production unit & introducing downtimes and transition times into the proposed schedule, and in further view of Hubbs, wherein a trained policy neural network and a trained value neural network can be utilized to generate a schedule of production actions at the production facility that satisfy the one or more product requests over an interval of time, the trained policy neural network associated with a policy function representing production actions to be scheduled at the production facility, and the trained value neural network associated with a value function representing benefits of products produced at the production facility based on the production actions, where the schedule of the production actions relates to penalties due to late production of the one or more requested products determined based on the one or more requested times and due to changes in production of the one or more products at the production facility (see Hubbs: ¶ [0010]).
Further, the claimed invention is merely a combination of old elements in a similar field for a constrained optimization and post-processing heuristics for optimal production scheduling for process manufacturing system, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Hubbs, the results of the combination were predictable.
Regarding Dependent Claim 7, Alhasan / You method for constrained optimization and post-processing heuristics for optimal production scheduling for process manufacturing does not explicitly disclose, but Hubbs does disclose the following:
- wherein performing the post-processing of the one or more proposed schedules further comprises, for each proposed schedule (see at least Hubbs: Fig. 9 & ¶ [0143]. Hubbs notes that agent 710 can provide schedules for some, if not all, of the multiple production lines. In some of these examples, agent 710 can provide schedules that take into account operating constraints related to multiple production lines such as, but not limited to: 1) some or all of the production lines can share a common unit operation, resources, and/or operating equipment that prevents these production lines from producing a common product on the same day, 2) some or all of the production lines can share a common utility which limits production on these production lines, and (3) some or all of the production lines can be geographically distributed.), at least one of:
- ensuring that production batches on auxiliary processing units align to production batches of their parent processing units (see at least Hubbs: ¶ [0143-0145]. Hubbs teaches a model that represents the production facility that has multiple production lines and make schedules taking into account operating constraints. See also Hubbs at ¶ [0146] wherein the production facility determines parts for both upstream and downstream processes for multiple products at the same time on multiple production lines (e.g., common parts being merged to a final product to align to production batch of their parent processing units)).
- ensuring that the production batches on the auxiliary processing units align to raw materials available in inventory (see at least Hubbs: ¶ [0044]. Hubbs teaches production scheduling taking into account raw materials (e.g., inventory).)
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Alhasan / You method for a constrained optimization and post-processing heuristics for optimal production scheduling for process manufacturing system with the aforementioned teachings of: wherein performing the post-processing of the one or more proposed schedules comprises, for each proposed schedule, at least one of: ensuring that production batches on auxiliary processing units align to production batches of their parent processing units & ensuring that the production batches on the auxiliary processing units align to raw materials available in inventory, and in further view of Hubbs, wherein the policy and value ANNs can be executed by the one or more computing devices (if the ANNs were not moved) or by the server(s) (if the ANNs were moved) so that the ANNs can react in real-time to changes at production facility PF1. The policy and value ANNs can determine a schedule of production actions that can be carried out at production facility PF1 to produce one or more products based on one or more input (raw) materials. Production facility PF1 can implement the schedule of production actions through normal processes at PF1. Feedback about the implemented schedule can then be provided to the trained policy and value ANNs and/or the model of production facility PF1 to continue on-going updating and learning (see Hubbs: ¶ [0044]).
Further, the claimed invention is merely a combination of old elements in a similar field for a constrained optimization and post-processing heuristics for optimal production scheduling for process manufacturing system, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Hubbs, the results of the combination were predictable.
Regarding Dependent Claims 15 and 23, Alhasan / You / Hubbs apparatus / non-transitory computer readable medium for a constrained optimization and post-processing heuristics for optimal production scheduling for process manufacturing teaches the limitations of Claims 14, 22 and 44-45 above, and Hubbs further teaches the method / apparatus / non-transitory computer readable medium for a constrained optimization and post-processing heuristics for optimal production scheduling for process manufacturing comprising:
- wherein performing the post-processing of the one or more proposed schedules further comprises, for each proposed schedule (see at least Hubbs: Fig. 9 & ¶ [0143]. Hubbs notes that agent 710 can provide schedules for some, if not all, of the multiple production lines. In some of these examples, agent 710 can provide schedules that take into account operating constraints related to multiple production lines such as, but not limited to: 1) some or all of the production lines can share a common unit operation, resources, and/or operating equipment that prevents these production lines from producing a common product on the same day, 2) some or all of the production lines can share a common utility which limits production on these production lines, and (3) some or all of the production lines can be geographically distributed.), at least one of:
- ensuring that production batches on auxiliary processing units align to production batches of their parent processing units (see at least Hubbs: ¶ [0143-0145]. Hubbs teaches a model that represents the production facility that has multiple production lines and make schedules taking into account operating constraints. See also Hubbs at ¶ [0146] wherein the production facility determines parts for both upstream and downstream processes for multiple products at the same time on multiple production lines (e.g., common parts being merged to a final product to align to production batch of their parent processing units)).
- ensuring that the production batches on the auxiliary processing units align to raw materials available in inventory (see at least Hubbs: ¶ [0044]. Hubbs teaches production scheduling taking into account raw materials (e.g., inventory).)
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Alhasan / You / Hubbs apparatus / non-transitory computer readable medium for a constrained optimization and post-processing heuristics for optimal production scheduling for process manufacturing system with the aforementioned teachings of: wherein performing the post-processing of the one or more proposed schedules comprises, for each proposed schedule, at least one of: ensuring that production batches on auxiliary processing units align to production batches of their parent processing units & ensuring that the production batches on the auxiliary processing units align to raw materials available in inventory, and in further view of Hubbs, wherein the policy and value ANNs can be executed by the one or more computing devices (if the ANNs were not moved) or by the server(s) (if the ANNs were moved) so that the ANNs can react in real-time to changes at production facility PF1. The policy and value ANNs can determine a schedule of production actions that can be carried out at production facility PF1 to produce one or more products based on one or more input (raw) materials. Production facility PF1 can implement the schedule of production actions through normal processes at PF1. Feedback about the implemented schedule can then be provided to the trained policy and value ANNs and/or the model of production facility PF1 to continue on-going updating and learning (see Hubbs: ¶ [0044]).
Further, the claimed invention is merely a combination of old elements in a similar field for a constrained optimization and post-processing heuristics for optimal production scheduling for process manufacturing system, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Hubbs, the results of the combination were predictable.
Regarding Dependent Claim 8, 16 and 24, Alhasan / You / Hubbs method / apparatus / non-transitory computer readable medium for a constrained optimization and post-processing heuristics for optimal production scheduling for process manufacturing teaches the limitations of Claims 7, 14-15, 22-23 and 43-45 above, and Hubbs further teaches the method / apparatus / non-transitory computer readable medium for a constrained optimization and post-processing heuristics for optimal production scheduling for process manufacturing comprising:
- wherein performing the post-processing of the one or more proposed schedules further comprises, for each proposed schedule (see at least Hubbs: Fig. 9 & ¶ [0143]. Hubbs notes that agent 710 can provide schedules for some, if not all, of the multiple production lines. In some of these examples, agent 710 can provide schedules that take into account operating constraints related to multiple production lines such as, but not limited to: 1) some or all of the production lines can share a common unit operation, resources, and/or operating equipment that prevents these production lines from producing a common product on the same day, 2) some or all of the production lines can share a common utility which limits production on these production lines, and (3) some or all of the production lines can be geographically distributed.):
- searching for a locally-optimal schedule by merging of production batches in the proposed schedule (see at least Hubbs: ¶ [0143-0146]. Hubbs teaches running multiple lines with different products to complete downstream units. See also Hubbs at ¶ [0149] which provides example of merging different products.)
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Alhasan / You / Hubbs method / apparatus / non-transitory computer readable medium for a constrained optimization and post-processing heuristics for optimal production scheduling for process manufacturing system with the aforementioned teachings of: wherein performing the post-processing of the one or more proposed schedules comprises, for each proposed schedule: searching for a locally-optimal schedule by merging of production batches in the proposed schedule, and in further view of Hubbs, in order for the reinforcement learning agent can learn from the oracle which production actions to take when multiple production actions are feasible over time, thereby reducing (and eventually eliminating) reliance on the oracle. Another possibility for combining reinforcement learning and model-based optimization techniques is to use a reinforcement learning agent to restrict a search space of a stochastic programming algorithm. Once trained, the reinforcement learning agent could assign low probabilities of receiving a high reward to certain actions in order to remove those branches and accelerate the search of the optimization algorithm (see Hubbs: ¶ [0046]).
Further, the claimed invention is merely a combination of old elements in a similar field for a constrained optimization and post-processing heuristics for optimal production scheduling for process manufacturing system, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Hubbs, the results of the combination were predictable.
17. Claims 25-26, 28, 31-32, 34, 37-38 and 40 are rejected under 35 U.S.C. 103 as being unpatentable over US PG Pub (US 2019/0338622 A1) hereinafter Alhasan, et. al., and in view of US PG Pub (US 2022/0414518 A1) hereinafter You, et. al., and in further view of US Patent # (US 5,369,570 A) to Parad.
Regarding Dependent Claims 25, 31 and 37, Alhasan / You method / apparatus / non-transitory computer readable medium for a constrained optimization and post-processing heuristics for optimal production scheduling for process manufacturing teaches the limitations of Independent Claims 43-45 above, and You further teaches the method / apparatus / non-transitory computer readable medium for a constrained optimization and post-processing heuristics for optimal production scheduling for process manufacturing comprising:
- one or more processing units (see at least You: Figs. 2-3 & Figs. 13-14.) comprise chemical production equipment in a chemical manufacturing or processing facility (see at least You: ¶ [0021] & ¶ [0083] & Figs. 8-14. You teaches that the computation results are sent to one or more target devices or equipment. In some embodiments, the computation results are formulated as instructions to control the operations of one or more target devices or equipment. The instructions could be substantially real-time (such as for real-time optimization), in time series (such as for a scheduling problem), dynamic or static. In Fig. 9 the system 100 may execute a method 900 for solving a cellular manufacturing problem in the form of a MIQP. Cellular manufacturing is an important application of group technology and is being widely applied in manufacturing industries. In this manufacturing approach, the equipment is arranged to facilitate continuous flow production, resulting in increased workflow, reduced response and production times, and increased profits. See also You at ¶ [0041]: “The optimization problem to be solved may be from any suitable field, such as chemical engineering, industrial processes, traffic control, electric power generation, flight path optimization, artificial intelligence, and protein folding.” See also You at ¶ [0229]: Hybrid QC-CC system directly or indirectly, to initialize at least of portion of parameters for the computation problem (e.g. optimization problem), wherein the sensors are selected from a GPS sensor, a Inertial Measurement Units (IMU) sensor, an image sensor, a video sensor, a camera, an optical sensor, a radar, an acoustic sensor, a pressure sensor, a temperature sensor, a chemical sensor or any combination thereof.)
- the feasible production schedule (see at least You: ¶ [0078-0081] & Fig. 8.) satisfies one or more specified criteria comprising at least one of: minimizing energy usage and minimizing raw material usage during production of at least one chemical (see at least You: Figs. 8-14 & ¶ [0016]. You teaches that the optimization problem is used to determine an optimal measure of the molecule or the design of molecule such as the minimal potential energy, and the corresponding optimal structure of the molecule under the optimal measure (e.g. minimal potential energy) comprising the optimized atom elements or optimized functional group elements and the optimized bonds between or among two or more of the optimal atom elements or optimal functional group elements.)
- controlling the one or more processing units (see at least You: ¶ [0021] & Figs. 2-3 & Figs. 13-14.) using the feasible production schedule (see at least You: ¶ [0079-0082] & ¶ [0208] & Figs. 8-11. You teaches that the quantum computer 104 uses the partial optimal solutions from the classical computer 102 to solve the Hamiltonian using the quantum processor 302 in order to locate a feasible schedule in the integer space. The result of the QUBO is then sent to the classical computer 102. Referring back to block 814, if a feasible schedule is found, the method 800 proceeds to block 818, in which an optimal solution is returned. If a feasible solution exists for the problem in the second phase of the hybrid algorithm, the algorithm will converge to a global optimum. Thus, it should be noted that the hybrid QC-MILP decomposition method converges to an optimal solution or proves infeasibility in finite number of iterations. See also You at ¶ [0014]: The optimization problem is a job-shop scheduling or planning problem for enterprise resource planning (ERP) or manufacturing execution system (MES) or scheduling over time with a limited scope. A physical system comprises a plurality of tasks, a plurality of resources wherein each resources comprises at least one relevant costs and related tasks, and at least one measure for the performance of the physical system, wherein the physical system is but not limited to an enterprise system, a manufacturing system, a supply chain system, a production system. See also You at ¶ [0208]: The optimization problem is to determine a molecular conformation, a schedule, a manufacturing cell formation, a vehicle routing, a production plan or schedule, a production schedule, a drug design, a protein folding structure, or a transportation system plan.) comprises transmitting control instructions (see at least You: ¶ [0008] & ¶ [0014] & ¶ [0020-0021].) based on the feasible production schedule to the chemical production equipment via a network (see at least You: Figs. 8-14 & ¶ [0021] & ¶ [0083] & ¶ [0114]. You notes that the computation results are sent back to the user's local classical computer. The computation results are sent to one or more target devices or equipment. The computation results are formulated as instructions to control the operations of one or more target devices or equipment. See also You at ¶ [0083]: Referring now to FIG. 9, in use, the system 100 may execute a method 900 for solving a cellular manufacturing problem in the form of a MIQP. Cellular manufacturing is an important application of group technology and is being widely applied in manufacturing industries. In this manufacturing approach, the equipment is arranged to facilitate continuous flow production, resulting in increased workflow, reduced response and production times, and increased profits. In cellular manufacturing, the manufacturing system is divided into several cells. Similar parts are processed in the same cell, such that the interactions of machines and parts within a cell are maximized to improve efficiency. See also You at ¶ [0114]: The relaxed problem 1308 is solved on the classical subsystem 1310 by any classical solver like Gurobi, CPLEX, CBC, and others on any classical computer 1312. The partial optimal solution 1314 produced by the classical subsystem is used by the quantum subsystem 1316 to determine optimal schedule. The problem in the quantum computing step is solved using quantum solver 1318 assisted by any suitable quantum computer 1320. Problems in the quantum computing step can be solved locally or remotely depending on the location of quantum computer hardware. Remote quantum computing operation is facilitated by cloud infrastructure 1322 that is set up to promote quantum computing's applicability. Based on solutions obtained through the quantum subsystem 1316, integer cuts are added to the relaxed problem at block 1324 and is re-solved by the classical subsystem 1310. The optimal schedule 1326 is used by the machines 1306 to process the jobs 1304/orders 1302 such that minimum cost is incurred.).
Regarding Dependent Claims 25, 31 and 37, Alhasan / You method / apparatus / non-transitory computer readable medium for a constrained optimization and post-processing heuristics for optimal production scheduling for process manufacturing system does not explicitly disclose, but Parad does disclose the following:
- the feasible production schedule comprises a multi-dimensional doubly-linked data structure having multiple pointers identifying relationships between batches of materials (see at least Parad: Col. 18, Ln. 51 – Col. 20, Ln. 23. Parad discloses a solution that involves recursive doubly linked list data and provides batches of materials that need to be manufactured, and contains many pointers that identify the link between the batches and materials.).
- the method further comprises modifying the feasible production schedule by inserting or removing one or more of the batches of materials and adjusting one or more of the pointers associated with the one or more inserted or removed batches of materials (see at least Parad: Fig. 15, Col. 23, Lns. (36-43) and Col. 19, Lines (48-56) of Parad teaches making changes to schedule (e.g., inserting and removing) dealing with batches of materials, and adjusting utilizing pointers to handle the materials for production).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Alhasan / You method / apparatus / non-transitory computer readable medium for a constrained optimization and post-processing heuristics for optimal production scheduling for process manufacturing system with the aforementioned teachings of: the feasible production schedule comprises a multi-dimensional doubly-linked data structure having multiple pointers identifying relationships between batches of materials & the method further comprises modifying the feasible production schedule by inserting or removing one or more of the batches of materials and adjusting one or more of the pointers associated with the one or more inserted or removed batches of materials, and in further view of Parad, wherein prospective scheduling methods solve a wide variety of business resource management problems. Resources may include raw materials, machine time, labor, space, power, or any other entity whose constraint affects the delivery of goods or services. In construction, the scheduled use of resources includes people with general or specialized skills, various single task or multi-purpose equipment, and materials requirements; in transportation, the efficient use of special or general-purpose vehicles to transport people, materials, and equipment between a multitude of locations; in health care, the scheduled use of beds, operating rooms, general or specialized staff, and fixed or mobile equipment (see Parad: Col. 1, Lns. 37-49).
Further, the claimed invention is merely a combination of old elements in a similar field for a constrained optimization and post-processing heuristics for optimal production scheduling for process manufacturing system, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Parad, the results of the combination were predictable.
Regarding Dependent Claim 26, 32 and 38, Alhasan / You method / apparatus / non-transitory computer readable medium for a constrained optimization and post-processing heuristics for optimal production scheduling for process manufacturing system as applied to Independent Claims 43-45 does not explicitly disclose, but Parad does disclose the following:
- the feasible production schedule comprises a multi-dimensional doubly-linked data structure having multiple pointers identifying relationships between batches of materials (see at least Parad: Col. 18, Ln. 51 – Col. 20, Ln. 23. Parad discloses a solution that involves recursive doubly linked list data and provides batches of materials that need to be manufactured, and contains many pointers that identify the link between the batches and materials.).
- the method further comprises modifying the feasible production schedule by inserting or removing one or more of the batches of materials and adjusting one or more of the pointers associated with the one or more inserted or removed batches of materials (see at least Parad: Fig. 15, Col. 23, Lns. (36-43) and Col. 19, Lines (48-56) of Parad teaches making changes to schedule (e.g., inserting and removing) dealing with batches of materials, and adjusting utilizing pointers to handle the materials for production).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Alhasan / You method / apparatus / non-transitory computer readable medium for a constrained optimization and post-processing heuristics for optimal production scheduling for process manufacturing system with the aforementioned teachings of: the feasible production schedule comprises a multi-dimensional doubly-linked data structure having multiple pointers identifying relationships between batches of materials & the method further comprises modifying the feasible production schedule by inserting or removing one or more of the batches of materials and adjusting one or more of the pointers associated with the one or more inserted or removed batches of materials, and in further view of Parad, wherein prospective scheduling methods solve a wide variety of business resource management problems. Resources may include raw materials, machine time, labor, space, power, or any other entity whose constraint affects the delivery of goods or services. In construction, the scheduled use of resources includes people with general or specialized skills, various single task or multi-purpose equipment, and materials requirements; in transportation, the efficient use of special or general-purpose vehicles to transport people, materials, and equipment between a multitude of locations; in health care, the scheduled use of beds, operating rooms, general or specialized staff, and fixed or mobile equipment (see Parad: Col. 1, Lns. 37-49).
Further, the claimed invention is merely a combination of old elements in a similar field for a constrained optimization and post-processing heuristics for optimal production scheduling for process manufacturing system, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Parad, the results of the combination were predictable.
Regarding Dependent Claim 28, 34 and 40, Alhasan / You / Parad method / apparatus / non-transitory computer readable medium for a constrained optimization and post-processing heuristics for optimal production scheduling for process manufacturing teaches the limitations of Claims 26, 32, 38 and 43-45 above, and You further teaches the method / apparatus / non-transitory computer readable medium for a constrained optimization and post-processing heuristics for optimal production scheduling for process manufacturing comprising:
- wherein controlling one or more processing units (see at least You: ¶ [0021] & Figs. 2-3 & Figs. 13-14.) using the feasible production schedule (see at least You: ¶ [0079-0082] & ¶ [0208] & Figs. 8-11. You teaches that the quantum computer 104 uses the partial optimal solutions from the classical computer 102 to solve the Hamiltonian using the quantum processor 302 in order to locate a feasible schedule in the integer space. The result of the QUBO is then sent to the classical computer 102. Referring back to block 814, if a feasible schedule is found, the method 800 proceeds to block 818, in which an optimal solution is returned. If a feasible solution exists for the problem in the second phase of the hybrid algorithm, the algorithm will converge to a global optimum. Thus, it should be noted that the hybrid QC-MILP decomposition method converges to an optimal solution or proves infeasibility in finite number of iterations. See also You at ¶ [0014]: The optimization problem is a job-shop scheduling or planning problem for enterprise resource planning (ERP) or manufacturing execution system (MES) or scheduling over time with a limited scope. A physical system comprises a plurality of tasks, a plurality of resources wherein each resources comprises at least one relevant costs and related tasks, and at least one measure for the performance of the physical system, wherein the physical system is but not limited to an enterprise system, a manufacturing system, a supply chain system, a production system. See also You at ¶ [0208]: The optimization problem is to determine a molecular conformation, a schedule, a manufacturing cell formation, a vehicle routing, a production plan or schedule, a production schedule, a drug design, a protein folding structure, or a transportation system plan.) comprises: transmitting control instructions (see at least You: ¶ [0008] & ¶ [0014] & ¶ [0020-0021].) based on the feasible production schedule (see at least You: Figs. 8-14) to the one or more processing units via a network (see at least You: ¶ [0021] & Figs. 2-3 & Figs. 13-14.).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Alhasan / You / Parad method / apparatus / non-transitory computer readable medium for constrained optimization and post-processing heuristics for optimal production scheduling for process manufacturing with the aforementioned teachings of: wherein controlling one or more processing units using the feasible production schedule comprises: transmitting control instructions based on the feasible production schedule to the one or more processing units via a network, and in further view of You, whereby an optimization problem may correspond to any suitable real-world problem, such as a scheduling problem, a cellular manufacturing problem, a vehicle routing problem, etc. The classical computer 102 may determine a MILP optimization problem in block 604, such as a scheduling problem. The classical computer may determine a MIQP optimization problem in block 606, such as a cellular manufacturing problem (see at least You: ¶ [0063]). System 100 may execute a method 900 for solving a cellular manufacturing problem in the form of a MIQP. Cellular manufacturing is an important application of group technology and is being widely applied in manufacturing industries. In this manufacturing approach, the equipment is arranged to facilitate continuous flow production, resulting in increased workflow, reduced response and production times, and increased profits. In cellular manufacturing, the manufacturing system is divided into several cells. Similar parts are processed in the same cell, such that the interactions of machines and parts within a cell are maximized to improve efficiency (see at least You: ¶ [0083]). Furthermore, several variants of the manufacturing cell formation model exist in literature. Factors like resource and operational costs, intra-cellular movement costs, resource utilization costs, grouping efficiency, and others are considered in the manufacturing cell formation model formulation (see at least You: ¶ [0084]).
Further, the claimed invention is merely a combination of old elements in a similar field for a constrained optimization and post-processing heuristics for optimal production scheduling for process manufacturing system, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by You, the results of the combination were predictable.
18. Claims 27, 33 and 39 are rejected under 35 U.S.C. 103 as being unpatentable over US PG Pub (US 2019/0338622 A1) hereinafter Alhasan, et. al., and in view of US PG Pub (US 2022/0414518 A1) hereinafter You, et. al., and in further view of US Patent # (US 5,369,570 A) to Parad, and in further view of US PG Pub (US 2022/0027817 A1) to Hubbs.
Regarding Dependent Claim 27, 33 and 39, Alhasan / You / Parad method / apparatus / non-transitory computer readable medium for a constrained optimization and post-processing heuristics for optimal production scheduling for process manufacturing system as applied to Claims 26, 32, 38 and 43-45 does not explicitly disclose, but Hubbs does disclose the following:
- wherein performing the post-processing of the one or more proposed schedules comprises, for each proposed schedule (see at least Hubbs: Fig. 9 & ¶ [0143]. Hubbs notes that agent 710 can provide schedules for some, if not all, of the multiple production lines. In some of these examples, agent 710 can provide schedules that take into account operating constraints related to multiple production lines such as, but not limited to: 1) some or all of the production lines can share a common unit operation, resources, and/or operating equipment that prevents these production lines from producing a common product on the same day, 2) some or all of the production lines can share a common utility which limits production on these production lines, and (3) some or all of the production lines can be geographically distributed.), at least one of:
- discretizing the proposed schedule into chunks and merging batches of common products for each chunk (see at least Hubbs: ¶ [0143-0145]. Hubbs teaches determining the production of each part and splitting it between production lines (e.g., chunks) in order to adjust the ratio of parts required for later production. See also Hubbs at ¶ [0149]. Hubbs provides an example of chunks, and Hubbs at ¶ [0146] wherein the production facility determines parts for both upstream and downstream processes for multiple products at the same time on multiple production lines (e.g., common parts being merged to a final product).)
- ordering production batches to minimize transition costs within each chunk (see at least Hubbs: ¶ [0140]. Hubbs provides an example of using a cost as a factor to plan minimization of changing costs (e.g., transition cost).)
- rounding production batches in the proposed schedule to integer multiples of a production unit (see at least Hubbs: ¶ [0047]. Hubbs teaches using mixed integer linear programming as a scheduler to only provide integer values for production determination.)
- introducing downtimes and transition times into the proposed schedule (see at least Hubbs: ¶ [0141]. Hubbs teaches scheduling type change (time (e.g., downtime) due to transition from one product to another.)
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Alhasan / You / Parad method / apparatus / non-transitory computer readable medium for a constrained optimization and post-processing heuristics for optimal production scheduling for process manufacturing system with the aforementioned teachings of: wherein performing the post-processing of the one or more proposed schedules comprises, for each proposed schedule, at least one of: discretizing the proposed schedule into chunks and merging batches of common products for each chunk & ordering production batches to minimize transition costs within each chunk & rounding production batches in the proposed schedule to integer multiples of a production unit & introducing downtimes and transition times into the proposed schedule, and in further view of Hubbs, wherein a trained policy neural network and a trained value neural network can be utilized to generate a schedule of production actions at the production facility that satisfy the one or more product requests over an interval of time, the trained policy neural network associated with a policy function representing production actions to be scheduled at the production facility, and the trained value neural network associated with a value function representing benefits of products produced at the production facility based on the production actions, where the schedule of the production actions relates to penalties due to late production of the one or more requested products determined based on the one or more requested times and due to changes in production of the one or more products at the production facility (see Hubbs: ¶ [0010]).
Further, the claimed invention is merely a combination of old elements in a similar field for a constrained optimization and post-processing heuristics for optimal production scheduling for process manufacturing system, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Hubbs, the results of the combination were predictable.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DERICK HOLZMACHER whose telephone number is (571) 270-7853. The examiner can normally be reached on Monday-Friday 9:00 AM – 6:30 PM EST.
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/DERICK J HOLZMACHER/ Patent Examiner, Art Unit 3625A /SARA GRACE BROWN/Primary Examiner, Art Unit 3625
