DETAILED ACTION
Claims 1-4, 7-11, 14-18 and 21-26 are presented for examination. Claims 5-6, 12-13 and 19-20 are cancelled. Claims 1, 7-8 and 14-15 are amended. Claims 21-26 are new. This office action is response to the submission on 2/22/2026.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
With respect to 35 U.S.C. §101 Rejection:
Applicant’s arguments, see pages 9-10 of applicant response filed 2/22/2026, with respect to claim 1 have been fully considered and are not persuasive. Applicant argues that because the steps recited in the claim are tied to a specialized computing infrastructure and quantum processing unit, they can’t be performed in the human mind. Examiner disagrees, the device including at least one quantum processing unit is recited at a high level of generality and is therefore a generic computer element and evaluating constraints and incorporating them into an optimization model may be done using pen and paper. The device including a quantum processing unit being used to perform these actions is recited at a high level of generality and is merely a tool being used conventionally used to automate a process that may be done by a person with pen and paper.
Applicant’s arguments, see pages 10-11 of applicant response filed 2/22/2026, with respect to claim 1 have been fully considered and are not persuasive. Applicant argues that the use of a quantum processing unit to compute optimized schedules is a non-conventional computing technique that improves the field of process control. Examiner disagrees, applicant has not provided support as to how using a quantum processing unit represents an improvement in process scheduling. Using a processor to determine the optimal schedule given constraints is using a processor in a conventional way. There is no description in applicant’s arguments or in the specification of how using the quantum processor is an improvement in the technology, instead the applicant sets forth the improvement in a conclusory manner.
Applicant’s arguments, see page 11 of applicant response filed 2/22/2026, with respect to claim 1 have been fully considered and are not persuasive. Applicant argues that the newly added limitation of the transmitted solution values when used to generate the schedule cause the facility to yield output products by target times is a specific and practical application of the computation to industrial control and is therefore not similar to an abstract idea. Examiner disagrees. The claim limitations require that the solution values be transmitted to a scheduling device which is configured to generate a schedule and that the solution values, when used to generate the schedule, cause the facility to yield quantities of products by delivery times. This is merely stating the scheduling device’s capabilities when generating the schedule based on the solution values and does not require that the scheduling device actually implement the process schedule. Examiner recommends actively reciting the claim limitations rather than simply stating a device’s capabilities. (e.g. a limitation such as “transmitting the set of solution values to a scheduling device which generates a process schedule using the solution values… and implementing the process schedule, causing the industrial facility to yield one or more quantities of output products by one or more target delivery times calculated by the scheduling device, controlling operations executed by the one or more processing components." would require generating the process schedule and cause the schedule to be implemented by the processing components.
Applicant’s arguments, see pages 11-12 of applicant response filed 2/22/2026, with respect to claim 1 have been fully considered and are not persuasive. Applicant argues that the limitation of “the solution values, when used to generate the process schedule, are configured to cause the industrial facility to yield one or more quantities of output products by one or more target delivery times.” Establishes a link between the optimization performed by the processing unit and production output of the facility, integrating the optimization into a real-world process. Examiner disagrees. The claim limitations require that the solution values be transmitted to a scheduling device which is configured to generate a schedule and that the solution values, when used to generate the schedule, cause the facility to yield quantities of products by delivery times. This is merely stating the scheduling device’s capabilities when generating the schedule based on the solution values and does not require that the scheduling device actually implement the process schedule. Examiner recommends actively reciting the claim limitations rather than simply stating a device’s capabilities. (e.g. a limitation such as “transmitting the set of solution values to a scheduling device which generates a process schedule using the solution values… and implementing the process schedule, causing the industrial facility to yield one or more quantities of output products by one or more target delivery times calculated by the scheduling device, controlling operations executed by the one or more processing components." would require generating the process schedule and cause the schedule to be implemented by the processing components.
Applicant’s arguments, see pages 12-13 of applicant response filed 2/22/2026, with respect to claim 1 have been fully considered and are not persuasive. Applicant argues that conventional systems depend on fixed scheduling models which are incapable of responding in real-time and other limitations that the applicant’s instant invention overcomes by leveraging quantum computing. Examiner disagrees. Examiner reminds applicant that although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. There is no mention of millions of operation sequences in the claims or that the instructions are immediately executable. Applicant claims that a traditional scheduling system (without a quantum processor) would not be capable of performing the functions of claim 1 but provides no evidence of such. Applicant's argument fails to comply with 37 CFR 1.111(b) because they amount to a summary of Applicant’s invention without specifically pointing out how the language of claim 1 integrates the patent-ineligible concept into a practical application. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. Thus, Applicant relies on the Specification [0002], [0029]-[0034], [0043]-[0052], [0065], [0070], and [0071] for support of practical application is not persuasive.
Applicant’s arguments, see pages 14-15 of applicant response filed 2/22/2026, with respect to claim 1 have been fully considered and are not persuasive. Applicant argues that the claimed subject matter provides an execution driven control mechanism establishes a direct relationship between the optimization results and physical production outcomes and that by leveraging quantum based optimization to compute start times and durations under constraints, the claimed method improves speed and quality of scheduling decisions. Examiner disagrees. The claim limitations require that the solution values be transmitted to a scheduling device which is configured to generate a schedule and that the solution values, when used to generate the schedule, cause the facility to yield quantities of products by delivery times. This is merely stating the scheduling device’s capabilities when generating the schedule based on the solution values and does not require that the scheduling device actually implement the process schedule. Examiner recommends actively reciting the claim limitations rather than simply stating a device’s capabilities. (e.g. a limitation such as “transmitting the set of solution values to a scheduling device which generates a process schedule using the solution values… and implementing the process schedule, causing the industrial facility to yield one or more quantities of output products by one or more target delivery times calculated by the scheduling device, controlling operations executed by the one or more processing components." would require generating the process schedule and cause the schedule to be implemented by the processing components. Additionally, the calculation of optimized start times and durations via a device including quantum processor is merely using the processor as a tool to automate the optimization in a conventional way.
With respect to 35 U.S.C. §103 Rejection:
Applicant’s arguments, see pages 15-16 of applicant response filed 2/22/2026, with respect to claim 1 have been fully considered and are not persuasive. Applicant argues that Ghosh does not describe an optimization model defining variables corresponding to operations executed by processing components of the facility and that each variable includes a start time and duration for each operation. Additionally, applicant argues that Ghosh fails to describe variables representing execution start times and durations of processing components and rather describes production slots. Examiner disagrees, Ghosh’s assigning of a production slot inherently includes a start and end time i.e. duration as the production slots are assigned with a start and end slot (see Ghosh [0086]). The assigning of each order to a production line slot is equivalent to the defining of variables corresponding to operations executed by processing components (See Ghosh [0088]).
Applicant’s arguments, see pages 16-17 of applicant response filed 2/22/2026, with respect to claim 1 have been fully considered and are not persuasive. Applicant argues that Ghosh fails to teach the operational constraints specifically including processing capacity limits, processing speed limits, and storage limits and that Ghosh’s constraints are rules rather than operational constraints tied to the physical processing limits, etc. Examiner disagrees. Examiner reminds applicant that although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. Ghosh describes constraints of a maximum number of orders producible in a day i.e. processing capacity limits and a maximum number of orders producible in a slot i.e. processing speed limits (see Ghosh [0078-0079]). That the limits are soft constraints that incur a penalty if exceeded is moot, the claim limitation as written merely requires a processing capacity limit and processing speed limit, it does not require that it be tied to the physical processing limit as described in applicant’s argument. Ghosh fails to teach a storage limit, however Kocis does teach this claim limitation (see Kocis [0056]).
Applicant’s arguments, see page 17 of applicant response filed 2/22/2026, with respect to claim 1 have been fully considered and are not persuasive. Applicant argues that Ghosh fails to teach an optimized start time and duration of operations of the facility based on constraints and requirements of the facility because the orders being mapped to slots lack the flexibility/specificity of a start time and duration tailored to operations. Examiner disagrees. That Ghosh is limited to selections of production slots for each order is moot as the production slots inherently include a start and end time, as required by the claim limitations (see Ghosh [0086]).
Applicant’s arguments, see pages 17-19 of applicant response filed 2/22/2026, with respect to claim 1 have been fully considered and are not persuasive. Applicant argues that Kocis does not describe generating a process schedule that operates processing components based on constraints and optimized timing values and that it doesn’t describe causing a facility to yield specific quantities of products by delivery times. This argument is moot as Ghosh is relied upon to teach this claim limitation (see Ghosh [0105]). In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 21, 23 and 25 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claims contain subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claim 21 includes a limitation of “dynamically recalculating the set of solution values in response to a detected change in at least one of the plurality of operational constraints of the industrial facility,” In the applicant response filed 2/22/2026, page 9 states that paragraphs [0029]-[0031] which describes facilities use scheduling applications and that applicant's invention can optimize operations on a frequent e.g. hourly basis, [0034] which teaches the scheduling device on a computer system and scheduling devices may be associated with a facility, [0044] which describes setting a time for blending a product using a specific blender and that conventional schedulers may result in financial loss, [0048] which states that constraints may be max input resources, max speed of processing, max storage, max power to operate equipment, max temp of facility, etc., [0053] which states that solution values can be ingested by scheduler to generate process schedules for facilities using data/solution values received from optimization system 620, [0054] which states that methods of optimizing scheduling using quantum processing is described and that components can execute instructions below, steps can be added, omitted, rearranged, and [0071] which states that Schedule made by quantum processing can lead to more efficient resource use and financial performance improvement provide support for this limitation.
Examiner does not see support for recalculating solution values in response to a detected change in these paragraphs or anywhere in the specification. Specification paragraph [0045] states that “The operations data may be manually entered by facility personnel, automatically detected by scheduling applications 610 based on sensor data, retrieved from a remote database, or any combination of the foregoing.” However, this does not provide support for recalculating solution values in response to a detected change either, it merely states that operations data may be detected based on sensor data, not that the solution values are calculated in response to a detected change. As such, claim 21 (and claims 23 and 25 which are substantially the same) are rejected under 112(a) as failing to comply with the written description requirement.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-4, 7-11, 14-18 and 21-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent Claims 1, 8, and 15:
Claim 1 is drawn to a computer-implemented method, claim 8 is drawn to a computer-readable storage medium storing instructions, and claim 15 is drawn to a system. Therefore claims 1, 8, and 15 fall under one of the four categories of statutory subject matter (process/method, machines/products/apparatus, manufactures, and compositions of matter).
Step 2A: Is the claim directed to a law of nature, a natural phenomenon (product of nature), or an abstract idea?
It is an abstract idea.
Step 2A-Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes.
MPEP 2106.04(a) - “Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion).”
Claims 1, 8, and 15 are directed to a judicially recognized exception of an abstract idea without significantly more. Each of claims 1, 8, and 15 recites functions below that under the limitation’s broadest reasonable interpretation, enumerates mental concepts. Other than reciting generic computer elements “one or more processors operatively connected to the one or more memories”, “one or more quantum processing units”, “one or more memories storing instructions”, and “a scheduling device configured to generate a process schedule” (as recited in claim 15), nothing in the claims preclude the functions from the mental concept.
The mere nominal recitation of generic processors, quantum processing units, memories, and a scheduling device to perform the mental concept does not take the claim limitations out of the abstract idea (See MPEP 2106.04(a)(2)(III)).
“generating, an optimization model based on the set of operations data, wherein the optimization model defines a plurality of variables corresponding to operations executed by one or more processing components of the industrial facility and wherein the plurality of variables includes one or more time-based variables comprising a start time and a duration for each of the operations;” Generating an optimization model based on the operations data is a judgement based on an observation of the data. The newly added limitations of the variables including a start time and duration of each operation doesn’t prevent the limitation from being performed by a human with pen and paper.
“calculating, by the device using the at least one quantum processing unit and the optimization model, a set of solution values, wherein each of the set of solution values corresponds to one or more of the plurality of variables and wherein the set of solution values includes one or more combinations of optimized start time and optimized duration data of at least one of the operations of the industrial facility based on the plurality of operational constraints and one or more requirements of the industrial facility;” Calculating a set of solution values corresponding to the variables and operational constraints is a judgement. The solution values including an optimized start time and duration data of at least one operations of the facility based on requirements is further defining the solution values.
Step 2A-Prong 2: Does the claim recite additional element that integrate the judicial exception into a practical application?
No.
2106.05(f) Mere Instructions To Apply An Exception
The use of the “one or more processors operatively connected to the one or more memories”, “one or more quantum processing units”, “one or more memories storing instructions”, and “a scheduling device configured to generate a process schedule” (as recited in claim 15) are recited at a high level of generality i.e. as generic processors performing generic functions of obtaining data, generating an optimization model, calculating solution values, generating a process schedule. This generic recitation of the processors and scheduling device is not more than mere instructions to apply the exception using a generic component. The platform that performs the steps merely automates steps which may be done mentally or manually. Thus the additional elements don’t integrate the abstract idea into a practical application as it merely amounts to instructions to apply it. The claim does not set forth improvements to the functioning of a computer or another technological field and uses the generic elements as tools in a conventional way to perform the steps in the claims.
“As explained by the Supreme Court, in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do "‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’". Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984 (warning against a § 101 analysis that turns on "the draftsman’s art").”
2106.05(g) Insignificant Extra-Solution Activity
The term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent. An example of post-solution activity is an element that is not integrated into the claim as a whole, e.g., a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent.
The following is pre-solution activity (mere data gathering):
“comprising: obtaining, by a device including at least one quantum processing unit, a set of operations data for an industrial facility, the set of operations data including (1) input data associated with operations in the industrial facility, (2) output data for the industrial facility, and (3) a plurality of operational constraints for the industrial facility, wherein the plurality of operational constraints comprises of processing capacity limits, processing speed limits, and storage limits of the industrial facility;” (as recited in claim 1).
The following is post-solution activity:
“and transmitting the set of solution values to a scheduling device configured to generate a process schedule for the industrial facility, wherein the process schedule comprises the optimized start time and the optimized duration data for operating one or more processing components in the industrial facility,” (as recited in claim 1).
MPEP 2106.05(h) – Field of Use
“Another consideration when determining whether a claim integrates the judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.”
“A computer-implemented method of process control,” is directed towards implementing a method to a field of use.
“by the device using the at least one quantum processing unit and the optimization model,” is directed toward implementing a quantum processing unit to a field of use.
“generating, by the device, an optimization model” is directed toward implementing the device to a field of use.
“wherein the process schedule comprises the optimized start time and the optimized duration data for operating one or more processing components in the industrial facility.” (emphasis added) is directed toward implementing the process schedule toward the field of use of processing components.
“and wherein the set of solution values, when used to generate the process schedule, are configured to cause the industrial facility to yield one or more quantities of output products by one or more target delivery times.” (emphasis added) is directed towards implementing the process schedule to the field of use of adjusting the industrial facility’s product output to fulfill target delivery times.
The examiner has considered the limitations together as a single abstract idea for Step 2A Prong Two rather than as a plurality of separate ideas to be analyzed individually.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No.
The additional elements and functions that are a form of insignificant extra-solution activities and generic computer elements, do not amount to significantly more than an abstract idea because the court decisions have determined that these additional steps are well-understood, routine, and conventional when claimed in a merely generic manner for data storing, collecting and transmitting. This generic recitation of the processors and memory is not more than mere instructions to apply the exception using a generic component. The claim does not set forth improvements to the functioning of a computer or another technological field and uses the generic elements as tools in a conventional way to perform the steps in the claims. The insignificant pre and post-solution activity do not integrate the abstract idea into a practical application because they don’t include meaningful limits on practicing the abstract idea.
(See MPEP § 2106.05(d)(II)(i: 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)) or (iv: Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)), See further Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016))).
“Courts have held computer‐implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amounts to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog (i.e., by hand or by merely thinking). On the other hand, courts have held computer-implemented processes to be significantly more than an abstract idea (and thus eligible), where generic computer components are able in combination to perform functions that are not merely generic.” DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1257-59, 113 USPQ2d 1097, 1105-07 (Fed. Cir. 2014). ”Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display” Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016).
MPEP 2106.05(d)(II)(i) provides support that receiving or transmitting data over a network is well understood, routine, and conventional.
As such, claims 1, 8 and 15 are not patent eligible.
Dependent Claims 2-4, 7, 9-11, 14, 16-18, and 21-26:
Claim 2: “The computer-implemented method of claim 1, wherein the set of operations data for the industrial facility is obtained from the scheduling device.” Obtaining the operations data from the scheduling device is extra-solution activity 2106.05(g).
Claim 3: “The computer-implemented method of claim 1, wherein the input data includes one or more quantities of input resources and one or more times of arrival to the industrial facility for the input resources; and wherein the output data includes one or more quantities of output products and one or more target delivery times for the output products.” The input data including quantities and arrival times of input resources is further defining the extra-solution activity 2106.05(g), the output data including quantities and target delivery times is further defining the extra-solution activity 2106.05(g).
Claim 4: “The computer-implemented method of claim 3, wherein the operational constraints include constraints corresponding to one or more of a maximum quantity of input resources that the industrial facility is capable of processing, a maximum speed of processing input resources by the industrial facility, and a maximum quantity of output products that the industrial facility is capable of storing.” The operational constraints including a maximum quantity of resources the facility is capable of processing, a maximum speed of processing resources, or a maximum quantity of products the facility is capable of storing is further defining the extra-solution activity 2106.05(g).
Claim 7: “The computer-implemented method of claim 1, wherein the industrial facility is an oil refinery, the input resources include crude oil, and the output products include refined oil products.” The industrial facility being an oil refinery and input resources and output products including specific embodiments is merely further defining the operations data which is further defining the extra-solution activity 2106.05(g).
Claims 9-11: The limitations of claims 9-11 are substantially the same as claims 2-4 respectively and they are rejected for the same reasons.
Claim 14: The limitations of claim 14 are substantially the same as claim 7 and it is rejected for the same reasons.
Claim 16-18: The limitations of claims 16-18 are substantially the same as claims 2-4 respectively and they are rejected for the same reasons.
Claim 21: “The computer-implemented method of claim 1, further comprising dynamically recalculating the set of solution values in response to a detected change in at least one of the plurality of operational constraints of the industrial facility, wherein the detected change corresponds to an updated processing capacity, processing speed, or storage availability.” Recalculating solution values in response to a detected change of constraints is a judgement 2106.04(a) and detecting a change is extra-solution activity 2106.05(g).
Claim 22: “The computer-implemented method of claim 1, wherein transmitting the set of solution values to the scheduling device causes the scheduling device to generate scheduling instructions for initiating and terminating individual operations of the one or more processing components of the industrial facility in accordance with optimized start times and optimized durations.” Generating scheduling instructions in response to receiving solution values is a judgement 2106.04(a) and the instructions being for initiating and terminating operations of the processing components is merely linking the instructions the field of use of processing components 2106.05(h).
Claims 23-24: The limitations of claims 23-24 are substantially the same as claims 21-22 respectively and they are rejected for the same reasons.
Claims 25-26: The limitations of claims 25-26 are substantially the same as claims 21-22 respectively and they are rejected for the same reasons.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4, 7-11 and 14-18 are rejected under 35 U.S.C. 103 as being unpatentable over Ghosh et al. (US20220122006A1), in view of Kocis et al. (US20120083914A1).
Claim 1:
Ghosh teaches “A computer-implemented method of process control, comprising: obtaining, by a device including at least one quantum processing unit,” (Ghosh [0032] "In one or more embodiments, the first optimization solver machine 108 may be implemented as a generalized quantum computing device on a cloud-based optimization system. The cloud-based optimization system may be implemented as one of a private cloud, a public cloud, or a hybrid cloud."),
“(2) output data for the industrial facility,” (Ghosh [0076] "The first input 402A may be associated with the set of orders to be produced at the production facility and may be received, for example, via the first UI element 302 of FIG. 3… The first input 402A may include one or more of attributes that may be associated with each of the set of orders, an order quantity, and a fulfilment schedule for the set of orders."),
“and (3) a plurality of operational constraints for the industrial facility, wherein the plurality of operational constraints comprises of processing capacity limits, processing speed limits, (Ghosh teaches a constraint of a maximum number of orders producible in a day i.e. processing capacity limits in Ghosh [0078-0079] "The third input 402C may be associated with a set of constraints associated with the production planning and may be received, for example, via the third UI element 306 of FIG. 3. The set of constraints may include hard constraints and soft constraints. The hard constraints must be enforced while solving the problem of production planning... In an embodiment, the soft constraints may include a first constraint, a second constraint, a third constraint, a fourth constraint, a fifth constraint, or a sixth constraint. The first constraint (MaxInDay) may determine a first maximum number of orders producible in a day without incurring a first penalty."Ghosh teaches a constraint of a maximum number of orders producible in a slot i.e. processing speed limits in Ghosh [0079] "The fifth constraint (NinM) may determine a maximum of a first number of orders (N) producible in a first number of sequential production slots (M) of a production line, without incurring a fifth penalty."),
“generating, by the device, an optimization model based on the set of operations data,” (Ghosh [0088-0089] "At 408, an objective function i.e. optimization model may be formulated. In an embodiment, the system 102 may formulate the objective function, which may have to be solved to find a schedule of production of the set of orders (O). The objective function may model the problem of the production planning as a constraint optimization problem. For instance, the objective function may be a cost function which may have to be minimized with respect to variables (such as xo,l,d,z) in the presence of a set of constraints on such variables. For example, the variable (xo,l,d,z for a given oϵO, lϵL, dϵD, and zϵFigure US20220122006A1-20220421-P00007) may determine a schedule which uniquely assigns each order (o) to a production slot (z) of a production line (l) on a specific day (d). The objective function may be formulated based on the extracted set of production-related datapoints i.e. the set of operations data (at 404) and the calculated first set of parameters (406). The formulated objective function may define an objective to minimize a total warning level (i.e. a cumulative penalty) associated with a violation of at least one of the set of constraints (specified in the third input 402C)."
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),
“wherein the optimization model defines a plurality of variables corresponding to operations executed by one or more processing components of the industrial facility” (Ghosh teaches the schedule assigns each order to a production slot of a production line i.e. the model defines operations to be executed by one or more processing components of a facility in Ghosh [0088] "At 408, an objective function may be formulated. In an embodiment, the system 102 may formulate the objective function, which may have to be solved to find a schedule of production of the set of orders (O). The objective function may model the problem of the production planning as a constraint optimization problem. For instance, the objective function may be a cost function which may have to be minimized with respect to variables (such as xo,l,d,z) in the presence of a set of constraints on such variables. For example, the variable (xo,l,d,z for a given oϵO, lϵL, dϵD, and zϵFigure US20220122006A1-20220421-P00007) may determine a schedule which uniquely assigns each order (o) to a production slot (z) of a production line (l) on a specific day (d)."),
“and wherein the plurality of variables includes one or more time-based variables comprising a start time and a duration for each of the operations;” (Ghosh teaches the schedule assigns each order to a production slot on a specific day i.e. the slot inherently includes a start time and end time or duration in Ghosh [0088] "At 408, an objective function may be formulated. In an embodiment, the system 102 may formulate the objective function, which may have to be solved to find a schedule of production of the set of orders (O). The objective function may model the problem of the production planning as a constraint optimization problem. For instance, the objective function may be a cost function which may have to be minimized with respect to variables (such as xo,l,d,z) in the presence of a set of constraints on such variables. For example, the variable (xo,l,d,z for a given oϵO, lϵL, dϵD, and zϵFigure US20220122006A1-20220421-P00007) may determine a schedule which uniquely assigns each order (o) to a production slot (z) of a production line (l) on a specific day (d)."),
“calculating, by the device using the at least one quantum processing unit and the optimization model, a set of solution values, wherein each of the set of solution values corresponds to one or more of the plurality of variables” (Ghosh [0102-0103] "At 414, the QUBO formulation may be submitted. In an embodiment, the system 102 may submit the generated QUBO formulation to the first optimization solver machine 108 via an application programming interface (API) call. In one or more embodiments, the system 102 may transform the QUBO formulation into an Ising formulation and may submit the Ising formulation to the second optimization solver machine 214. Details associated with the Ising formulation and an associated solution are provided in FIG. 5, for example. The first optimization solver machine 108 may solve the QUBO formulation by application of searching methods and/or meta-heuristic methods, such as quantum annealing, to obtain a first solution of the submitted QUBO formulation. Specifically, to search for the solution (i.e. values of the vector of binary decision variables (x)), the energy of the QUBO formulation may be minimized. The solution may be optimal (or near optimal) and may be searched from a discrete solution space."; Ghosh [0105] "At 418, a schedule determination operation may be performed. In the schedule determination operation, the system 102 may determine a schedule to be used for the production of the set of orders (O) on the set of production lines (L). The schedule may be determined based on the received first solution" Ghosh Fig. 4 [As shown above in claim 1] teaches the set of operations for optimizing production planning.),
“and wherein the set of solution values includes one or more combinations of optimized start time and optimized duration data of at least one of the operations of the industrial facility based on the plurality of operational constraints and one or more requirements of the industrial facility;” (Ghosh [0105] "At 418, a schedule determination operation may be performed. In the schedule determination operation, the system 102 may determine a schedule to be used for the production of the set of orders (O) on the set of production lines (L). The schedule may be determined based on the received first solution. For example, if vector (x) of binary decision variables is same as the binary variable (xo,l,d,z) of equation (4), then the first solution may include the value of the variable (xo,l,d,z). The variable (xo,l,d,z for a given oϵO, lϵL, dϵD, and zϵZ) may determine a schedule which uniquely assigns each order (o) to a production slot (z) of a production line (l) on a specific day (d). For example, the schedule may allocate each order to a production slot of a production line such the order is completed on or before a set delivery date (or completion date or earliest completion date)."; Ghosh teaches assigning a production slot of a production line for each order in order to minimize warnings associated with constraints i.e. based on requirements of the facility and operational constraints in Ghosh [0088-0089] "At 408, an objective function i.e. optimization model may be formulated. In an embodiment, the system 102 may formulate the objective function, which may have to be solved to find a schedule of production of the set of orders (O). The objective function may model the problem of the production planning as a constraint optimization problem. For instance, the objective function may be a cost function which may have to be minimized with respect to variables (such as xo,l,d,z) in the presence of a set of constraints on such variables. For example, the variable (xo,l,d,z for a given oϵO, lϵL, dϵD, and zϵFigure US20220122006A1-20220421-P00007) may determine a schedule which uniquely assigns each order (o) to a production slot (z) of a production line (l) on a specific day (d). The objective function may be formulated based on the extracted set of production-related datapoints i.e. the set of operations data (at 404) and the calculated first set of parameters (406). The formulated objective function may define an objective to minimize a total warning level (i.e. a cumulative penalty) associated with a violation of at least one of the set of constraints (specified in the third input 402C)."),
“and transmitting the set of solution values to a scheduling device configured to generate a process schedule for the industrial facility, wherein the process schedule comprises the optimized start time and the optimized duration data for operating one or more processing components in the industrial facility.” (Ghosh [0104] "At 416, the first solution to the submitted QUBO formulation may be received. In an embodiment, the system 102 may receive the first solution of the submitted QUBO formulation from the first optimization solver machine 108. "; [Ghosh 108] “processor 202 may receive …first input 402A…..and fulfilment schedule for the set of orders.”; Ghosh Fig. 1 teaches the optimization solver machine 108 communicating to the system 102 and user device 104.; Ghosh teaches that the schedule may assign a production slot of a production line i.e. a processing component in the industrial facility to each order in Ghosh [0105] "At 418, a schedule determination operation may be performed. In the schedule determination operation, the system 102 may determine a schedule to be used for the production of the set of orders (O) on the set of production lines (L). The schedule may be determined based on the received first solution. For example, if vector (x) of binary decision variables is same as the binary variable (xo,l,d,z) of equation (4), then the first solution may include the value of the variable (xo,l,d,z). The variable (xo,l,d,z for a given oϵO, lϵL, dϵD, and zϵZ) may determine a schedule which uniquely assigns each order (o) to a production slot (z) of a production line (l) on a specific day (d). For example, the schedule may allocate each order to a production slot of a production line such the order is completed on or before a set delivery date (or completion date or earliest completion date)."
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“and wherein the set of solution values, when used to generate the process schedule, are configured to cause the industrial facility to yield one or more quantities of output products by one or more target delivery times.” (Ghosh teaches that the schedule is allocated such that the order is completed before a set delivery date in Ghosh [0105] "At 418, a schedule determination operation may be performed. In the schedule determination operation, the system 102 may determine a schedule to be used for the production of the set of orders (O) on the set of production lines (L). The schedule may be determined based on the received first solution. For example, if vector (x) of binary decision variables is same as the binary variable (xo,l,d,z) of equation (4), then the first solution may include the value of the variable (xo,l,d,z). The variable (xo,l,d,z for a given oϵO, lϵL, dϵD, and zϵZ) may determine a schedule which uniquely assigns each order (o) to a production slot (z) of a production line (l) on a specific day (d). For example, the schedule may allocate each order to a production slot of a production line such the order is completed on or before a set delivery date (or completion date or earliest completion date).").
Ghosh does not appear to explicitly teach “a set of operations data for an industrial facility, the set of operations data including (1) input data for the industrial facility,” or “and (3) a plurality of operational constraints for the industrial facility, wherein the plurality of operational constraints comprises of However, Kocis does teach these claim limitations.
Kocis teaches “a set of operations data for an industrial facility, the set of operations data including (1) input data for the industrial facility,” (Kocis [0061] "In making a production schedule, the inventory of raw materials, intermediate materials, or finished products and changes thereof may be considered. Other constraints and limitations may be considered within the scheduling process. Additional elements of production scheduling that may be included in the optimization problem are stream line-ups, unit operating signals, unit marginal capacity economics, tank inventories, product shipment timing, blend recipes, timing of blocked operations, operating instructions, material delivery, arrival timing, and onshore inventories."), and
“and (3) a plurality of operational constraints for the industrial facility, wherein the plurality of operational constraints comprises of (Kocis teaches tank submodels which include decision variables relating to the quantities of materials and that inventory limits i.e. storage limits of the industrial facility may be one of the constraints in Kocis [0056] "Each production facility also includes one or more means (e.g. tanks) for storing materials being handled by the production facility. These storage tanks are connected, either upstream or downstream, to other production entities. The tank handles the interface between the production entities and may be subject to asynchronous process schedules of the adjacent production entities. The tanks can also handle inventory management in the production facility. The modeling tool can use any suitable model for the tanks, including those conventionally used to model tanks in a period-average planning model. The tank(s) may be represented as one or more submodels within the overall model of the problem. Such tank submodels typically include decision variables relating to the quantities of materials, the composition of materials (e.g. properties or qualities), or the in/out flow of materials, as well constraints for material balances and composition balances (including inventory limits or bounds), and other variables and equations needed to represent the tank behavior. The tank entities in the model may correspond to physical tanks in the production facility or may be virtual tanks which are used by the model to represent any holding or storage of materials in the production facility (e.g. between other production entities).").
Ghosh and Kocis are analogous art because they are from the same field of endeavor of creating optimized schedules for facilities operations. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having teachings of Ghosh and Kocis before him/her, to modify the teachings of production planning using optimization solver machines of Ghosh to include the teachings of material delivery and arrival timing affecting the production schedule of Kocis because adding the optimizing the operation of refineries and the related supply chains of Kocis would reduce penalties for failing to satisfy inventory management criteria as described by Kocis [0062] "Whereas conventional planning models may ignore these inventory dynamics in the tanks, the optimization tool of the present invention may consider the need for inventory management and changes in inventory levels in the tanks for determining an optimized production schedule. Inventory levels may enter into the economic calculations made by the model through inventory holding costs and cost penalties for failing to satisfy inventory management criteria. The model may also have constraints relating to inventory levels. For example, the model may include minimum and maximum inventory limits, which can be defined as hard limits (which should not be exceeded), or as soft limits which can be exceeded at a certain cost or penalty, or both. Thus, with these constraints and cost factors, the optimization tool can be used to minimize overall capital costs (including inventory costs and inventory level penalties) while maintaining inventory levels within a desired range (e.g. minimum and maximum limits on inventory) and maintaining other production constraints. In addition to production scheduling, inventory feasibility may also need to be considered in other activities performed by the optimization tool, including transportation scheduling."
Claim 2:
Ghosh in view of Kocis also teaches “The computer-implemented method of claim 1, wherein the set of operations data for the industrial facility is obtained from the scheduling device.” (Ghosh [0055] "FIG. 3 is a diagram that illustrates an example electronic user interface (UI) for providing input(s) for production planning using optimization solver machines, according to at least one embodiment described in the present disclosure. FIG. 3 is explained in conjunction with elements from FIG. 1 and FIG. 2. With reference to FIG. 3, there is shown an electronic UI 300, which may be an example implementation of the electronic UI 106 of FIG. 1.”; Ghosh [0057] “The order textbox 302A may be a textbox where the user 112 may add information about the set of orders in a defined format. The set of orders may have to be produced at the production facility at a future time. The order information in the order textbox 302A may include, for example, an order identifier and order description.”
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Claim 3:
Ghosh in view of Kocis also teaches “The computer-implemented method of claim 1, wherein the input data includes one or more quantities of input resources and one or more times of arrival to the industrial facility for the input resources;” (Kocis teaches the inventory of raw materials and material delivery i.e. input resources and arrival timing i.e. times of arrival for the input resources affecting the schedule in Kocis [0061] "In making a production schedule, the inventory of raw materials, intermediate materials, or finished products and changes thereof may be considered. Other constraints and limitations may be considered within the scheduling process. Additional elements of production scheduling that may be included in the optimization problem are stream line-ups, unit operating signals, unit marginal capacity economics, tank inventories, product shipment timing, blend recipes, timing of blocked operations, operating instructions, material delivery, arrival timing, and onshore inventories."),
“and wherein the output data includes one or more quantities of output products” (Ghosh [0076] " The first input 402A may be associated with the set of orders to be produced at the production facility and may be received, for example, via the first UI element 302 of FIG. 3. Examples of the production facility may include, but are not limited to, a manufacturing plant, an assembly plant, a production factory, a fabrication unit (such as a chip fab), or a product sorting unit. The first input 402A may include one or more of attributes that may be associated with each of the set of orders, an order quantity, and a fulfilment schedule for the set of orders."), and
“and one or more target delivery times for the output products.” (Ghosh [0076] "The first input 402A may be associated with the set of orders to be produced at the production facility and may be received, for example, via the first UI element 302 of FIG. 3. Examples of the production facility may include, but are not limited to, a manufacturing plant, an assembly plant, a production factory, a fabrication unit (such as a chip fab), or a product sorting unit. The first input 402A may include one or more of attributes that may be associated with each of the set of orders, an order quantity, and a fulfilment schedule for the set of orders.").
Claim 4:
Ghosh in view of Kocis also teaches “The computer-implemented method of claim 3, wherein the operational constraints include constraints corresponding to one or more of (Ghosh [0111] "The set of constraints may include one or more of a first constraint which determines a first maximum number of orders producible in a day without incurring a first penalty"), and
“and a maximum quantity of output products that the industrial facility is capable of storing.” (Kocis [0062] "During the operation of a production facility, the inventory levels in the tanks can vary over time. Whereas conventional planning models may ignore these inventory dynamics in the tanks, the optimization tool of the present invention may consider the need for inventory management and changes in inventory levels in the tanks for determining an optimized production schedule. Inventory levels may enter into the economic calculations made by the model through inventory holding costs and cost penalties for failing to satisfy inventory management criteria. The model may also have constraints relating to inventory levels. For example, the model may include minimum and maximum inventory limits, which can be defined as hard limits (which should not be exceeded), or as soft limits which can be exceeded at a certain cost or penalty, or both.").
Claim 7:
Ghosh in view of Kocis also teaches “The computer-implemented method of claim 1, wherein the industrial facility is an oil refinery,” (Kocis [0046] "At a production plant, feed materials (including raw materials, such as crude oil, and intermediate materials) are fed into production equipment as steps in the production of desired products. The modeling tool of the present invention can be used to assist in planning the operations for the production plant, including determining the process operations and the scheduling of these process operations."; Kocis [0129] "FIG. 13 shows how the present invention can be used to solve a problem encompassing transportation scheduling of feed material into a refinery, operation of the process units in the refinery, and transportation scheduling of products out of the refinery and using asynchronous continuous-time modeling to integrate the schedules of the various activities."
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“the input resources include crude oil, and the output products include refined oil products.” (Kocis [0129] "FIG. 13 shows how the present invention can be used to solve a problem encompassing transportation scheduling of feed material into a refinery, operation of the process units in the refinery, and transportation scheduling of products out of the refinery and using asynchronous continuous-time modeling to integrate the schedules of the various activities."; Kocis [0140] "FIG. 16 shows a flowsheet to illustrate a production processing facility composed of the following 7 process units which are displayed as rectangles: CDU, CCU, CRU, SCN, ASP, HF# 1, and HF# 3. Inside of each rectangle, a number is shown in parenthesis, for example “CDU (6),” to denote the number of internal time intervals for each process unit... Each product group may have one or more product grades, for example, regular unleaded gasoline (RUL) and premium unleaded gasoline (PUL) in the mogas blender. Each blender product grade will have a given number of time intervals, similar to those defined for the process units."; Kocis Fig. 16 teaches an oil refinery receiving crude oil and outputting asphalt and kerosene i.e. refined oil product.
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Claim 8:
Ghosh teaches “A non-transitory computer-readable storage medium storing program instructions that are computer-executable to implement operations comprising:” (Ghosh [0044] "By way of example, and not limitation, such computer-readable storage media may include tangible or non-transitory computer-readable storage media including Random Access Memory (RAM), Read-Only Memory (ROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Compact Disc Read-Only Memory (CD-ROM) or other optical disk storage, magnetic disk storage or other magnetic storage devices, flash memory devices (e.g., solid state memory devices), or any other storage medium which may be used to carry or store particular program code in the form of computer-executable instructions or data structures and which may be accessed by a general-purpose or special-purpose computer. Combinations of the above may also be included within the scope of computer-readable storage media. Computer-executable instructions may include, for example, instructions and data configured to cause the processor 202 to perform a certain operation or group of operations associated with the system 102."),
“obtaining, by a device including at least one quantum processing unit,” (Ghosh [0032] "In one or more embodiments, the first optimization solver machine 108 may be implemented as a generalized quantum computing device on a cloud-based optimization system. The cloud-based optimization system may be implemented as one of a private cloud, a public cloud, or a hybrid cloud."),
“(2) output data for the industrial facility,” (Ghosh [0076] "The first input 402A may be associated with the set of orders to be produced at the production facility and may be received, for example, via the first UI element 302 of FIG. 3… The first input 402A may include one or more of attributes that may be associated with each of the set of orders, an order quantity, and a fulfilment schedule for the set of orders."),
“and (3) a plurality of operational constraints for the industrial facility, wherein the plurality of operational constraints comprises of processing capacity limits, processing speed limits, (Ghosh teaches a constraint of a maximum number of orders producible in a day i.e. processing capacity limits in Ghosh [0078-0079] "The third input 402C may be associated with a set of constraints associated with the production planning and may be received, for example, via the third UI element 306 of FIG. 3. The set of constraints may include hard constraints and soft constraints. The hard constraints must be enforced while solving the problem of production planning... In an embodiment, the soft constraints may include a first constraint, a second constraint, a third constraint, a fourth constraint, a fifth constraint, or a sixth constraint. The first constraint (MaxInDay) may determine a first maximum number of orders producible in a day without incurring a first penalty."Ghosh teaches a constraint of a maximum number of orders producible in a slot i.e. processing speed limits in Ghosh [0079] "The fifth constraint (NinM) may determine a maximum of a first number of orders (N) producible in a first number of sequential production slots (M) of a production line, without incurring a fifth penalty."),
“generating, by the device, an optimization model based on the set of operations data,” (Ghosh [0088-0089] "At 408, an objective function i.e. optimization model may be formulated. In an embodiment, the system 102 may formulate the objective function, which may have to be solved to find a schedule of production of the set of orders (O). The objective function may model the problem of the production planning as a constraint optimization problem. For instance, the objective function may be a cost function which may have to be minimized with respect to variables (such as xo,l,d,z) in the presence of a set of constraints on such variables. For example, the variable (xo,l,d,z for a given oϵO, lϵL, dϵD, and zϵFigure US20220122006A1-20220421-P00007) may determine a schedule which uniquely assigns each order (o) to a production slot (z) of a production line (l) on a specific day (d). The objective function may be formulated based on the extracted set of production-related datapoints i.e. the set of operations data (at 404) and the calculated first set of parameters (406). The formulated objective function may define an objective to minimize a total warning level (i.e. a cumulative penalty) associated with a violation of at least one of the set of constraints (specified in the third input 402C)."
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“wherein the optimization model defines a plurality of variables corresponding to operations executed by one or more processing components of the industrial facility” (Ghosh teaches the schedule assigns each order to a production slot of a production line i.e. the model defines operations to be executed by one or more processing components of a facility in Ghosh [0088] "At 408, an objective function may be formulated. In an embodiment, the system 102 may formulate the objective function, which may have to be solved to find a schedule of production of the set of orders (O). The objective function may model the problem of the production planning as a constraint optimization problem. For instance, the objective function may be a cost function which may have to be minimized with respect to variables (such as xo,l,d,z) in the presence of a set of constraints on such variables. For example, the variable (xo,l,d,z for a given oϵO, lϵL, dϵD, and zϵFigure US20220122006A1-20220421-P00007) may determine a schedule which uniquely assigns each order (o) to a production slot (z) of a production line (l) on a specific day (d)."),
“and wherein the plurality of variables includes one or more time-based variables comprising a start time and a duration for each of the operations;” (Ghosh teaches the schedule assigns each order to a production slot on a specific day i.e. the slot inherently includes a start time and end time or duration in Ghosh [0088] "At 408, an objective function may be formulated. In an embodiment, the system 102 may formulate the objective function, which may have to be solved to find a schedule of production of the set of orders (O). The objective function may model the problem of the production planning as a constraint optimization problem. For instance, the objective function may be a cost function which may have to be minimized with respect to variables (such as xo,l,d,z) in the presence of a set of constraints on such variables. For example, the variable (xo,l,d,z for a given oϵO, lϵL, dϵD, and zϵFigure US20220122006A1-20220421-P00007) may determine a schedule which uniquely assigns each order (o) to a production slot (z) of a production line (l) on a specific day (d)."),
“calculating, by the device using the at least one quantum processing unit and the optimization model, a set of solution values, wherein each of the set of solution values corresponds to one or more of the plurality of variables” (Ghosh [0102-0103] "At 414, the QUBO formulation may be submitted. In an embodiment, the system 102 may submit the generated QUBO formulation to the first optimization solver machine 108 via an application programming interface (API) call. In one or more embodiments, the system 102 may transform the QUBO formulation into an Ising formulation and may submit the Ising formulation to the second optimization solver machine 214. Details associated with the Ising formulation and an associated solution are provided in FIG. 5, for example. The first optimization solver machine 108 may solve the QUBO formulation by application of searching methods and/or meta-heuristic methods, such as quantum annealing, to obtain a first solution of the submitted QUBO formulation. Specifically, to search for the solution (i.e. values of the vector of binary decision variables (x)), the energy of the QUBO formulation may be minimized. The solution may be optimal (or near optimal) and may be searched from a discrete solution space."; Ghosh [0105] "At 418, a schedule determination operation may be performed. In the schedule determination operation, the system 102 may determine a schedule to be used for the production of the set of orders (O) on the set of production lines (L). The schedule may be determined based on the received first solution" Ghosh Fig. 4 [As shown above in claim 1] teaches the set of operations for optimizing production planning.),
“and wherein the set of solution values includes one or more combinations of optimized start time and optimized duration data of at least one of the operations of the industrial facility based on the plurality of operational constraints and one or more requirements of the industrial facility;” (Ghosh [0105] "At 418, a schedule determination operation may be performed. In the schedule determination operation, the system 102 may determine a schedule to be used for the production of the set of orders (O) on the set of production lines (L). The schedule may be determined based on the received first solution. For example, if vector (x) of binary decision variables is same as the binary variable (xo,l,d,z) of equation (4), then the first solution may include the value of the variable (xo,l,d,z). The variable (xo,l,d,z for a given oϵO, lϵL, dϵD, and zϵZ) may determine a schedule which uniquely assigns each order (o) to a production slot (z) of a production line (l) on a specific day (d). For example, the schedule may allocate each order to a production slot of a production line such the order is completed on or before a set delivery date (or completion date or earliest completion date)."; Ghosh teaches assigning a production slot of a production line for each order in order to minimize warnings associated with constraints i.e. based on requirements of the facility and operational constraints in Ghosh [0088-0089] "At 408, an objective function i.e. optimization model may be formulated. In an embodiment, the system 102 may formulate the objective function, which may have to be solved to find a schedule of production of the set of orders (O). The objective function may model the problem of the production planning as a constraint optimization problem. For instance, the objective function may be a cost function which may have to be minimized with respect to variables (such as xo,l,d,z) in the presence of a set of constraints on such variables. For example, the variable (xo,l,d,z for a given oϵO, lϵL, dϵD, and zϵFigure US20220122006A1-20220421-P00007) may determine a schedule which uniquely assigns each order (o) to a production slot (z) of a production line (l) on a specific day (d). The objective function may be formulated based on the extracted set of production-related datapoints i.e. the set of operations data (at 404) and the calculated first set of parameters (406). The formulated objective function may define an objective to minimize a total warning level (i.e. a cumulative penalty) associated with a violation of at least one of the set of constraints (specified in the third input 402C)."),
“and transmitting the set of solution values to a scheduling device configured to generate a process schedule for the industrial facility, wherein the process schedule comprises the optimized start time and the optimized duration data for operating one or more processing components in the industrial facility.” (Ghosh [0104] "At 416, the first solution to the submitted QUBO formulation may be received. In an embodiment, the system 102 may receive the first solution of the submitted QUBO formulation from the first optimization solver machine 108. "; [Ghosh 108] “processor 202 may receive …first input 402A…..and fulfilment schedule for the set of orders.”; Ghosh Fig. 1 teaches the optimization solver machine 108 communicating to the system 102 and user device 104.; Ghosh teaches that the schedule may assign a production slot of a production line i.e. a processing component in the industrial facility to each order in Ghosh [0105] "At 418, a schedule determination operation may be performed. In the schedule determination operation, the system 102 may determine a schedule to be used for the production of the set of orders (O) on the set of production lines (L). The schedule may be determined based on the received first solution. For example, if vector (x) of binary decision variables is same as the binary variable (xo,l,d,z) of equation (4), then the first solution may include the value of the variable (xo,l,d,z). The variable (xo,l,d,z for a given oϵO, lϵL, dϵD, and zϵZ) may determine a schedule which uniquely assigns each order (o) to a production slot (z) of a production line (l) on a specific day (d). For example, the schedule may allocate each order to a production slot of a production line such the order is completed on or before a set delivery date (or completion date or earliest completion date)."
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“and wherein the set of solution values, when used to generate the process schedule, are configured to cause the industrial facility to yield one or more quantities of output products by one or more target delivery times.” (Ghosh teaches that the schedule is allocated such that the order is completed before a set delivery date in Ghosh [0105] "At 418, a schedule determination operation may be performed. In the schedule determination operation, the system 102 may determine a schedule to be used for the production of the set of orders (O) on the set of production lines (L). The schedule may be determined based on the received first solution. For example, if vector (x) of binary decision variables is same as the binary variable (xo,l,d,z) of equation (4), then the first solution may include the value of the variable (xo,l,d,z). The variable (xo,l,d,z for a given oϵO, lϵL, dϵD, and zϵZ) may determine a schedule which uniquely assigns each order (o) to a production slot (z) of a production line (l) on a specific day (d). For example, the schedule may allocate each order to a production slot of a production line such the order is completed on or before a set delivery date (or completion date or earliest completion date).").
Ghosh does not appear to explicitly teach “obtaining… a set of operations data for an industrial facility, the set of operations data including (1) input data for the industrial facility,” or “and (3) a plurality of operational constraints for the industrial facility, wherein the plurality of operational constraints comprises of However, Kocis does teach these claim limitations.
Kocis teaches “obtaining… a set of operations data for an industrial facility, the set of operations data including (1) input data for the industrial facility,” (Kocis [0061] "In making a production schedule, the inventory of raw materials, intermediate materials, or finished products and changes thereof may be considered. Other constraints and limitations may be considered within the scheduling process. Additional elements of production scheduling that may be included in the optimization problem are stream line-ups, unit operating signals, unit marginal capacity economics, tank inventories, product shipment timing, blend recipes, timing of blocked operations, operating instructions, material delivery, arrival timing, and onshore inventories."), and
“and (3) a plurality of operational constraints for the industrial facility, wherein the plurality of operational constraints comprises of (Kocis teaches tank submodels which include decision variables relating to the quantities of materials and that inventory limits i.e. storage limits of the industrial facility may be one of the constraints in Kocis [0056] "Each production facility also includes one or more means (e.g. tanks) for storing materials being handled by the production facility. These storage tanks are connected, either upstream or downstream, to other production entities. The tank handles the interface between the production entities and may be subject to asynchronous process schedules of the adjacent production entities. The tanks can also handle inventory management in the production facility. The modeling tool can use any suitable model for the tanks, including those conventionally used to model tanks in a period-average planning model. The tank(s) may be represented as one or more submodels within the overall model of the problem. Such tank submodels typically include decision variables relating to the quantities of materials, the composition of materials (e.g. properties or qualities), or the in/out flow of materials, as well constraints for material balances and composition balances (including inventory limits or bounds), and other variables and equations needed to represent the tank behavior. The tank entities in the model may correspond to physical tanks in the production facility or may be virtual tanks which are used by the model to represent any holding or storage of materials in the production facility (e.g. between other production entities).").
Ghosh and Kocis are analogous art because they are from the same field of endeavor of creating optimized schedules for facilities operations. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having teachings of Ghosh and Kocis before him/her, to modify the teachings of production planning using optimization solver machines of Ghosh to include the teachings of material delivery and arrival timing affecting the production schedule of Kocis because adding the optimizing the operation of refineries and the related supply chains of Kocis would reduce penalties for failing to satisfy inventory management criteria as described by Kocis [0062] "Whereas conventional planning models may ignore these inventory dynamics in the tanks, the optimization tool of the present invention may consider the need for inventory management and changes in inventory levels in the tanks for determining an optimized production schedule. Inventory levels may enter into the economic calculations made by the model through inventory holding costs and cost penalties for failing to satisfy inventory management criteria. The model may also have constraints relating to inventory levels. For example, the model may include minimum and maximum inventory limits, which can be defined as hard limits (which should not be exceeded), or as soft limits which can be exceeded at a certain cost or penalty, or both. Thus, with these constraints and cost factors, the optimization tool can be used to minimize overall capital costs (including inventory costs and inventory level penalties) while maintaining inventory levels within a desired range (e.g. minimum and maximum limits on inventory) and maintaining other production constraints. In addition to production scheduling, inventory feasibility may also need to be considered in other activities performed by the optimization tool, including transportation scheduling."
Claims 9-11: The limitations of claims 9-11 are substantially the same as claims 2-4 respectively and they are rejected for the same reasons.
Claim 14: The limitations of claim 14 are substantially the same as claim 7 and it is rejected for the same reasons.
Claim 15:
Ghosh teaches “A system comprising: one or more quantum processing units;” (Ghosh [0032] "In one or more embodiments, the first optimization solver machine 108 may be implemented as a generalized quantum computing device on a cloud-based optimization system. The cloud-based optimization system may be implemented as one of a private cloud, a public cloud, or a hybrid cloud."),
“one or more memories storing instructions;” (Ghosh [0044] "By way of example, and not limitation, such computer-readable storage media may include tangible or non-transitory computer-readable storage media including Random Access Memory (RAM), Read-Only Memory (ROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Compact Disc Read-Only Memory (CD-ROM) or other optical disk storage, magnetic disk storage or other magnetic storage devices, flash memory devices (e.g., solid state memory devices), or any other storage medium which may be used to carry or store particular program code in the form of computer-executable instructions or data structures and which may be accessed by a general-purpose or special-purpose computer. Combinations of the above may also be included within the scope of computer-readable storage media. Computer-executable instructions may include, for example, instructions and data configured to cause the processor 202 to perform a certain operation or group of operations associated with the system 102."),
“and one or more processors operatively connected to the one or more memories,” (Ghosh [0020] "FIG. 2 is a block diagram of a system for production planning using optimization solver machines, according to at least one embodiment of the disclosure. FIG. 2 is explained in conjunction with elements from FIG. 1. With reference to FIG. 2, there is shown a block diagram 200 of the system 102. The system 102 may include a processor 202, a memory 204, and a persistent data storage 206."; Ghosh Fig. 2 teaches the processor 202 being operatively connected to the memory 204.
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“(2) output data for the industrial facility,” (Ghosh [0076] "The first input 402A may be associated with the set of orders to be produced at the production facility and may be received, for example, via the first UI element 302 of FIG. 3… The first input 402A may include one or more of attributes that may be associated with each of the set of orders, an order quantity, and a fulfilment schedule for the set of orders."),
“and (3) a plurality of operational constraints for the industrial facility, wherein the plurality of operational constraints comprises of processing capacity limits, processing speed limits, (Ghosh teaches a constraint of a maximum number of orders producible in a day i.e. processing capacity limits in Ghosh [0078-0079] "The third input 402C may be associated with a set of constraints associated with the production planning and may be received, for example, via the third UI element 306 of FIG. 3. The set of constraints may include hard constraints and soft constraints. The hard constraints must be enforced while solving the problem of production planning... In an embodiment, the soft constraints may include a first constraint, a second constraint, a third constraint, a fourth constraint, a fifth constraint, or a sixth constraint. The first constraint (MaxInDay) may determine a first maximum number of orders producible in a day without incurring a first penalty."Ghosh teaches a constraint of a maximum number of orders producible in a slot i.e. processing speed limits in Ghosh [0079] "The fifth constraint (NinM) may determine a maximum of a first number of orders (N) producible in a first number of sequential production slots (M) of a production line, without incurring a fifth penalty."),
“generate an optimization model based on the set of operations data,” (Ghosh [0088-0089] "At 408, an objective function i.e. optimization model may be formulated. In an embodiment, the system 102 may formulate the objective function, which may have to be solved to find a schedule of production of the set of orders (O). The objective function may model the problem of the production planning as a constraint optimization problem. For instance, the objective function may be a cost function which may have to be minimized with respect to variables (such as xo,l,d,z) in the presence of a set of constraints on such variables. For example, the variable (xo,l,d,z for a given oϵO, lϵL, dϵD, and zϵFigure US20220122006A1-20220421-P00007) may determine a schedule which uniquely assigns each order (o) to a production slot (z) of a production line (l) on a specific day (d). The objective function may be formulated based on the extracted set of production-related datapoints i.e. the set of operations data (at 404) and the calculated first set of parameters (406). The formulated objective function may define an objective to minimize a total warning level (i.e. a cumulative penalty) associated with a violation of at least one of the set of constraints (specified in the third input 402C)."
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“wherein the optimization model defines a plurality of variables corresponding to operations executed by one or more processing components of the industrial facility” (Ghosh teaches the schedule assigns each order to a production slot of a production line i.e. the model defines operations to be executed by one or more processing components of a facility in Ghosh [0088] "At 408, an objective function may be formulated. In an embodiment, the system 102 may formulate the objective function, which may have to be solved to find a schedule of production of the set of orders (O). The objective function may model the problem of the production planning as a constraint optimization problem. For instance, the objective function may be a cost function which may have to be minimized with respect to variables (such as xo,l,d,z) in the presence of a set of constraints on such variables. For example, the variable (xo,l,d,z for a given oϵO, lϵL, dϵD, and zϵFigure US20220122006A1-20220421-P00007) may determine a schedule which uniquely assigns each order (o) to a production slot (z) of a production line (l) on a specific day (d)."),
“and wherein the plurality of variables includes one or more time-based variables comprising a start time and a duration for each of the operations;” (Ghosh teaches the schedule assigns each order to a production slot on a specific day i.e. the slot inherently includes a start time and end time or duration in Ghosh [0088] "At 408, an objective function may be formulated. In an embodiment, the system 102 may formulate the objective function, which may have to be solved to find a schedule of production of the set of orders (O). The objective function may model the problem of the production planning as a constraint optimization problem. For instance, the objective function may be a cost function which may have to be minimized with respect to variables (such as xo,l,d,z) in the presence of a set of constraints on such variables. For example, the variable (xo,l,d,z for a given oϵO, lϵL, dϵD, and zϵFigure US20220122006A1-20220421-P00007) may determine a schedule which uniquely assigns each order (o) to a production slot (z) of a production line (l) on a specific day (d)."),
“cause the one or more quantum processing units to calculate, using the optimization model, a set of solution values, wherein each of the set of solution values corresponds to one or more of the plurality of variables” (Ghosh [0102-0103] "At 414, the QUBO formulation may be submitted. In an embodiment, the system 102 may submit the generated QUBO formulation to the first optimization solver machine 108 via an application programming interface (API) call. In one or more embodiments, the system 102 may transform the QUBO formulation into an Ising formulation and may submit the Ising formulation to the second optimization solver machine 214. Details associated with the Ising formulation and an associated solution are provided in FIG. 5, for example. The first optimization solver machine 108 may solve the QUBO formulation by application of searching methods and/or meta-heuristic methods, such as quantum annealing, to obtain a first solution of the submitted QUBO formulation. Specifically, to search for the solution (i.e. values of the vector of binary decision variables (x)), the energy of the QUBO formulation may be minimized. The solution may be optimal (or near optimal) and may be searched from a discrete solution space."; Ghosh [0105] "At 418, a schedule determination operation may be performed. In the schedule determination operation, the system 102 may determine a schedule to be used for the production of the set of orders (O) on the set of production lines (L). The schedule may be determined based on the received first solution" Ghosh Fig. 4 [As shown above in claim 1] teaches the set of operations for optimizing production planning.),
“and wherein the set of solution values includes one or more combinations of optimized start time and optimized duration data of at least one of the operations of the industrial facility based on the plurality of operational constraints and one or more requirements of the industrial facility;” (Ghosh [0105] "At 418, a schedule determination operation may be performed. In the schedule determination operation, the system 102 may determine a schedule to be used for the production of the set of orders (O) on the set of production lines (L). The schedule may be determined based on the received first solution. For example, if vector (x) of binary decision variables is same as the binary variable (xo,l,d,z) of equation (4), then the first solution may include the value of the variable (xo,l,d,z). The variable (xo,l,d,z for a given oϵO, lϵL, dϵD, and zϵZ) may determine a schedule which uniquely assigns each order (o) to a production slot (z) of a production line (l) on a specific day (d). For example, the schedule may allocate each order to a production slot of a production line such the order is completed on or before a set delivery date (or completion date or earliest completion date)."; Ghosh teaches assigning a production slot of a production line for each order in order to minimize warnings associated with constraints i.e. based on requirements of the facility and operational constraints in Ghosh [0088-0089] "At 408, an objective function i.e. optimization model may be formulated. In an embodiment, the system 102 may formulate the objective function, which may have to be solved to find a schedule of production of the set of orders (O). The objective function may model the problem of the production planning as a constraint optimization problem. For instance, the objective function may be a cost function which may have to be minimized with respect to variables (such as xo,l,d,z) in the presence of a set of constraints on such variables. For example, the variable (xo,l,d,z for a given oϵO, lϵL, dϵD, and zϵFigure US20220122006A1-20220421-P00007) may determine a schedule which uniquely assigns each order (o) to a production slot (z) of a production line (l) on a specific day (d). The objective function may be formulated based on the extracted set of production-related datapoints i.e. the set of operations data (at 404) and the calculated first set of parameters (406). The formulated objective function may define an objective to minimize a total warning level (i.e. a cumulative penalty) associated with a violation of at least one of the set of constraints (specified in the third input 402C)."),
“and transmit the set of solution values to a scheduling device configured to generate a process schedule for the industrial facility, wherein the process schedule comprises the optimized start time and the optimized duration data for operating one or more processing components in the industrial facility.” (Ghosh [0104] "At 416, the first solution to the submitted QUBO formulation may be received. In an embodiment, the system 102 may receive the first solution of the submitted QUBO formulation from the first optimization solver machine 108. "; [Ghosh 108] “processor 202 may receive …first input 402A…..and fulfilment schedule for the set of orders.”; Ghosh Fig. 1 teaches the optimization solver machine 108 communicating to the system 102 and user device 104.; Ghosh teaches that the schedule may assign a production slot of a production line i.e. a processing component in the industrial facility to each order in Ghosh [0105] "At 418, a schedule determination operation may be performed. In the schedule determination operation, the system 102 may determine a schedule to be used for the production of the set of orders (O) on the set of production lines (L). The schedule may be determined based on the received first solution. For example, if vector (x) of binary decision variables is same as the binary variable (xo,l,d,z) of equation (4), then the first solution may include the value of the variable (xo,l,d,z). The variable (xo,l,d,z for a given oϵO, lϵL, dϵD, and zϵZ) may determine a schedule which uniquely assigns each order (o) to a production slot (z) of a production line (l) on a specific day (d). For example, the schedule may allocate each order to a production slot of a production line such the order is completed on or before a set delivery date (or completion date or earliest completion date)."
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“and wherein the set of solution values, when used to generate the process schedule, are configured to cause the industrial facility to yield one or more quantities of output products by one or more target delivery times.” (Ghosh teaches that the schedule is allocated such that the order is completed before a set delivery date in Ghosh [0105] "At 418, a schedule determination operation may be performed. In the schedule determination operation, the system 102 may determine a schedule to be used for the production of the set of orders (O) on the set of production lines (L). The schedule may be determined based on the received first solution. For example, if vector (x) of binary decision variables is same as the binary variable (xo,l,d,z) of equation (4), then the first solution may include the value of the variable (xo,l,d,z). The variable (xo,l,d,z for a given oϵO, lϵL, dϵD, and zϵZ) may determine a schedule which uniquely assigns each order (o) to a production slot (z) of a production line (l) on a specific day (d). For example, the schedule may allocate each order to a production slot of a production line such the order is completed on or before a set delivery date (or completion date or earliest completion date).").
Ghosh does not appear to explicitly teach “the one or more processors configured to execute the instructions to: obtain a set of operations data for an industrial facility, the set of operations data including (1) input data for the industrial facility,” or “and (3) a plurality of operational constraints for the industrial facility, wherein the plurality of operational constraints comprises of However, Kocis does teach these claim limitations.
Kocis teaches “the one or more processors configured to execute the instructions to: obtain a set of operations data for an industrial facility, the set of operations data including (1) input data for the industrial facility,” (Kocis [0061] "In making a production schedule, the inventory of raw materials, intermediate materials, or finished products and changes thereof may be considered. Other constraints and limitations may be considered within the scheduling process. Additional elements of production scheduling that may be included in the optimization problem are stream line-ups, unit operating signals, unit marginal capacity economics, tank inventories, product shipment timing, blend recipes, timing of blocked operations, operating instructions, material delivery, arrival timing, and onshore inventories."), and
“and (3) a plurality of operational constraints for the industrial facility, wherein the plurality of operational constraints comprises of (Kocis teaches tank submodels which include decision variables relating to the quantities of materials and that inventory limits i.e. storage limits of the industrial facility may be one of the constraints in Kocis [0056] "Each production facility also includes one or more means (e.g. tanks) for storing materials being handled by the production facility. These storage tanks are connected, either upstream or downstream, to other production entities. The tank handles the interface between the production entities and may be subject to asynchronous process schedules of the adjacent production entities. The tanks can also handle inventory management in the production facility. The modeling tool can use any suitable model for the tanks, including those conventionally used to model tanks in a period-average planning model. The tank(s) may be represented as one or more submodels within the overall model of the problem. Such tank submodels typically include decision variables relating to the quantities of materials, the composition of materials (e.g. properties or qualities), or the in/out flow of materials, as well constraints for material balances and composition balances (including inventory limits or bounds), and other variables and equations needed to represent the tank behavior. The tank entities in the model may correspond to physical tanks in the production facility or may be virtual tanks which are used by the model to represent any holding or storage of materials in the production facility (e.g. between other production entities).").
Ghosh and Kocis are analogous art because they are from the same field of endeavor of creating optimized schedules for facilities operations. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having teachings of Ghosh and Kocis before him/her, to modify the teachings of production planning using optimization solver machines of Ghosh to include the teachings of material delivery and arrival timing affecting the production schedule of Kocis because adding the optimizing the operation of refineries and the related supply chains of Kocis would reduce penalties for failing to satisfy inventory management criteria as described by Kocis [0062] "Whereas conventional planning models may ignore these inventory dynamics in the tanks, the optimization tool of the present invention may consider the need for inventory management and changes in inventory levels in the tanks for determining an optimized production schedule. Inventory levels may enter into the economic calculations made by the model through inventory holding costs and cost penalties for failing to satisfy inventory management criteria. The model may also have constraints relating to inventory levels. For example, the model may include minimum and maximum inventory limits, which can be defined as hard limits (which should not be exceeded), or as soft limits which can be exceeded at a certain cost or penalty, or both. Thus, with these constraints and cost factors, the optimization tool can be used to minimize overall capital costs (including inventory costs and inventory level penalties) while maintaining inventory levels within a desired range (e.g. minimum and maximum limits on inventory) and maintaining other production constraints. In addition to production scheduling, inventory feasibility may also need to be considered in other activities performed by the optimization tool, including transportation scheduling."
Claim 16-18: The limitations of claims 16-18 are substantially the same as claims 2-4 respectively and they are rejected for the same reasons.
Claims 21, 23 and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Ghosh et al. (US20220122006A1), in view of Kocis et al. (US20120083914A1), further in view of Goshima (JP2009015597A) (citations to examiner provided translation).
Claim 21:
Ghosh in view of Kocis teaches “The computer-implemented method of claim 1,” as described above.
Neither Ghosh or Kocis appear to explicitly teach “further comprising dynamically recalculating the set of solution values in response to a detected change in at least one of the plurality of operational constraints of the industrial facility, wherein the detected change corresponds to an updated However, Goshima does teach this claim limitation (Goshima teaches that when a unit obtains info on change in processing time of equipment, it recalculate elements of matrices and calculates a modified matrix i.e. it recalculates solution values in response to a change in processing speed in Goshima [0036] "Furthermore, in the above method, when the calculation unit obtains information on the change in the processing time of the equipment in the discrete event system, it recalculates each element of the matrices P<sub>ｋ</sub>, F<sub>ｏ</sub>, F<sub>ｋ</sub>NER44_ based on this information on the change in processing time, and based on these elements, it calculates the modified matrix F<sup>〜</sup> of the matrix F<sub>ｋ</sub>NER46_.").
Ghosh, Kocis, and Goshima are analogous art because they are from the same field of endeavor of creating optimized schedules for facilities operations. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having teachings of Ghosh, Kocis, and Goshima before him/her, to modify the teachings of production planning using optimization solver machines of Ghosh modified to include the teachings of material delivery and arrival timing affecting the production schedule of Kocis to include the recalculation of a matrix for scheduling upon processing time of equipment changing of Goshima because adding the scheduling method of Goshima would make it possible to efficiently determine the earliest and latest start and end times for all equipment after a change as described by Goshima [0358] "Furthermore, in Example 2, even if the input time, processing time, processing start time, and processing end time change for any reason after the system has started operating, the system is configured to create a new schedule in the same way as in Example 1 described above. This makes it possible to accurately and efficiently determine the earliest start time, earliest end time, latest start time, and latest end time for all equipment after the change.”
Claims 23 and 25:
The limitations of claims 23 and 25 are substantially the same as claim 21 and they are rejected for the same reasons.
Claims 22, 24 and 26 are rejected under 35 U.S.C. 103 as being unpatentable over Ghosh et al. (US20220122006A1), in view of Kocis et al. (US20120083914A1), further in view of Varvarezos et al. (US20120296690A1).
Claim 22:
Ghosh in view of Kocis teaches “The computer-implemented method of claim 1,” as described above.
Neither Ghosh or Kocis appear to explicitly teach “wherein transmitting the set of solution values to the scheduling device causes the scheduling device to generate scheduling instructions for initiating and terminating individual operations of the one or more processing components of the industrial facility in accordance with optimized start times and optimized durations.” However, Varvarezos does teach this claim limitation (Varvarezos teaches transmitting the optimized schedule to plant applications to provide new value ranges to parameters/variables of the control system in Varvarezos [0117] "Output of the modeler 400 is an optimized schedule which can be presented in various forms. In one form, modeler 400 presents the optimized schedule for screen display (for example in a user interface or report generation). In another form, modeler 400 transmits the optimized schedule to other plant applications, such as blending control or plant process control systems. There, the optimized schedule (its output values) provide new value range (input value) to parameters and variables of the control system. This updates or reinitializes the control system's operations.").
Ghosh, Kocis, and Varvarezos are analogous art because they are from the same field of endeavor of creating optimized schedules for facilities operations. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having teachings of Ghosh, Kocis, and Varvarezos before him/her, to modify the teachings of production planning using optimization solver machines of Ghosh modified to include the teachings of material delivery and arrival timing affecting the production schedule of Kocis to include the execution of the optimized schedule by plant process control systems of Varvarezos because adding the Rundown blending optimization apparatus and method of Varvarezos would allow for more efficient utilization of components, reducing operation costs as described by Varvarezos [0053] "The benefits of modeling and optimizing the entire blending operation using the framework described herein are higher utilization of the lower-value components, such as butane, and lower utilization of high-value components, such as alkylate, reformate and aromatics, resulting in the utilization of the most valuable components in additional higher quality products or in direct sales, thus increasing the net profitability of the refinery. Even in case where there is no market for additional high-priced products and there is no opportunity for selling the high-value component, benefits can be still realized by reducing the operating cost of the refinery by lowering the demand on units that produce high-value components.”
Claims 24 and 26:
The limitations of claims 24 and 26 are substantially the same as claim 22 and they are rejected for the same reasons.
Conclusion
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/Z.A.C./Examiner, Art Unit 2116
/KENNETH M LO/Supervisory Patent Examiner, Art Unit 2116
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