DETAILED ACTION
Claims 1-7, 10-17, and 20 are presented for examination.
Claims 8-9 and 18-19 have been previously canceled.
This Office Action is in response to submission of Office Action Response on December 8, 2025.
Rejection of claims 1-7, 10-17, and 20 under 35 U.S.C. 101 for being directed to abstract ideas without significant additional elements have been maintained.
Rejection of claims 1-7, 10-17, and 20 under 35 U.S.C. 112(b) as being indefinite.
Rejection of claims 1-7, 10-17, and 20 under 35 U.S.C. 103 as being obvious over Piggott in view of Fukami and Frangos is withdrawn.
New rejection of claims 1-7, 10-17, and 20 under 35 U.S.C. 103 as being obvious over Piggott in view of Fukami and Song.
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
Regarding rejections under 35 U.S.C. 101:
Applicant asserts that “[t]he further amendments submitted herewith clarify how the combination of recited claims features” provide an improvement because “a new resolution is determined that is higher than the first resolution and lower than the second resolution.” Response at pg. 8. However, Examiner is not persuaded. The independent claims merely recite “the first resolution is determined such that a combined processing time for conducting the operational simulation based on the proposed design at the first resolution and providing the calculated performance result to the machine learning model to generate the predicted performance result of the operational simulation based on the proposed design at the second resolution is less than a processing time for the operational simulation based on the proposed design at the second resolution.” However, the claim does not recite how the first resolution is selected to ensure that resulting accuracy is acceptable and that the processing time is less other than indicating that those are results of selecting a particular first resolution.
Applicant asserts that the present amendments recite a “technical improvement” and therefore are directed to patentable subject matter. Specifically, the Office Action states that the amended limitation of “a combined processing time for conducting the operational simulation based on the proposed design at the first resolution and providing the calculated performance result to the machine learning model to generate the predicted performance result of the operational simulation based on the proposed design at the second resolution is less than a processing time for the operational simulation based on the proposed design at the second resolution" is a technical improvement.
As indicated in previous Office Actions, the limitation is directed to using a reduced-order model to perform a simulation, which is well-understood, routine, and conventional technology to employ to improve the functioning of a computer. By using a lower resolution simulation, it is well-understood that the simulation will be computationally less complex and therefore will execute in a reduced time frame. The claim does not purport to claim, for example, an improvement in the functioning of reduced-order model simulations, inverse design processes, and/or other known processes. Accordingly, the added limitation does not integrate the judicial exception into a practical application nor recite, with specificity, how a technology is improved.
Applicant further asserts that claims 10 and 20 provide additional improvements to technology. However, the claims recite “transmitting the proposed design to a fabrication system to cause the fabrication system to fabricate the physical device,” which is an additional element that includes mere instructions to apply the judicial exception. See 2106.05(f). For example, “caus[ing] the fabrication system to fabricate the physical device” is an idea of a solution without specificity as to how the solution is accomplished. See MPEP 2106.05(f)(1). Additionally, transmitting data is an additional element that does not add significantly more to the judicial exception (MPEP 2106.05(d), Section II).
Applicant analogizes claims 10 and 20 to Claim 2 of Example 46 of the Subject Matter Eligibility Examples. However, as an initial distinction, Claim 2 is a system claim which includes “a feed dispenser” (i.e., the recipient of the transmitted data) as a component of the system. In present claims 1 and 11, which are a non-transitory computer readable medium and a method, the “fabrication system” is not a component of what is being claimed. As indicated in the Subject Matter Eligibility Example analysis of Example 46, “[i]n this case, when the wherein clause is considered in view of the specification, it is clear that the wherein clause has patentable weight, in that the claim requires the presence of the feed dispenser, and that the monitoring component is further configured for performing limitation (d). Also, because claim 2 is a system claim, its BRI requires the structure for performing the function of limitation (d) to be present, even though that function (sending a control signal) only needs to occur if a condition precedent is met (i.e., when the analysis results for the animal indicate that the animal is exhibiting an aberrant behavioral pattern indicative of grass tetany). Accordingly, Examiner is not persuaded by the analogy.
Regarding rejection of the claims under 35 U.S.C. 103,
Examiner agrees that all of the limitations of the amended claims are not taught by the asserted references. Particularly, the asserted references do not teach a simulation that can be executed at different resolutions such that the resulting design of an iteration of the claimed process is utilized in an operational simulation at a second resolution to generate a new design. Accordingly, rejection of claims 1 and 11 under 35 U.S.C. 103 as being obvious over Piggott in view of Fukami and Frangos is withdrawn.
However, newly asserted reference Song discloses a multi-fidelity simulation that can be executed at varying resolutions. It would have been obvious to a person having ordinary skill in the art to substitute the simulation of Piggott with the multi-fidelity simulation of Song and vary the resolution between executions of the simulation to result in a system whereby a new resolution is selected for a subsequent execution of the inverse design process to reduce computational time in designing a device. Accordingly, claims 1-7, 10-17, and 20 are rejected under 35 U.S.C. 103 as being obvious over Piggott in view of Fukami and Song.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-7, 10-17, and 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1 and 11 recite “wherein the first resolution is determined such that a combined processing time for conducting the operational simulation based on the proposed design at the first resolution and providing the calculated performance result to the machine learning model to generate the predicted performance result.” As amended, the claims do not recite an initial step of “providing the calculated performance result,” thus the claims lack antecedent basis. Examiner suggests amending to recite “and processing the calculated performance result using the machine learning model….” Appropriate correction is required.
Claims 1 and 11 recite “repeating, by the computing system, at least the conducting of the operational simulation, the processing using the machine learning model, and the updating of the proposed design actions….” However, the claims, as amended, recite two instances each of “conducting of the operational simulation,” “processing using the machine learning model,” and “updating of the proposed design actions.” Thus, the claims are indefinite because it is unclear which is the conducting, processing, and updating steps are repeated and/or whether the “repeating” is directed to all six steps.
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 1-7, 10-17, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite the abstract ideas of mental processes and mathematical concepts. This judicial exception is not integrated into a practical application because additional elements are nothing more than extra-solution activities and recitation of generic computer components. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 1
Step 1: As amended, claim 1 is directed to an article of manufacture, one of the four statutory categories.
Claim 1
Mapping Under Step 2A Prong 1
1. A non-transitory computer-readable medium having logic stored thereon that, in response to execution by one or more processors of a computing system, cause the computing system to perform actions for creating a design for a physical device using an inverse design process, the actions comprising:
receiving, by the computing system, a proposed design;
determining, by the computing system, a first resolution for an operational simulation;
conducting, by the computing system, an operational simulation based on the proposed design at a first resolution to generate a calculated performance result;
processing, by the computing system, the calculated performance result using a machine learning model to generate a predicted performance result of the operational simulation based on the proposed design at a second resolution, wherein the second resolution is higher than the first resolution,
and wherein the first resolution is determined such that a combined processing time for conducting the operational simulation based on the proposed design at the first resolution and providing the calculated performance result to the machine learning model to generate the predicted performance result of the operational simulation based on the proposed design at the second resolution is less than a processing time for the operational simulation based on the proposed design at the second resolution;
updating, by the computing system, the proposed design based on the predicted performance result;
determining, by the computing system, a new first resolution, wherein the new first resolution is higher than the first resolution and lower than the second resolution;
conducting, by the computing system, the operational simulation based on the updated proposed design at the new first resolution to generate a new calculated performance result;
processing, by the computing system, the new calculated performance result using the machine learning model to generate a new predicted performance result of the operational simulation based on the updated proposed design at the second resolution; and
repeating, by the computing system, at least the conducting of the operational simulation,
The claim is not directed to any specific computer implemented tool to perform the inverse design process.
Abstract Idea: Mental Process
Determining a resolution is a mental process that can be performed in the human mind. The process requires observation, evaluation, opinion, and judgment to determine an appropriate value for the resolution based on knowledge of the operational simulation. See MPEP 2106.04(a)(2), Subsection III.
Abstract Idea: Mathematical Concepts
The operational simulation is a mathematical calculation that is comprised of a number of functions. See MPEP § 2106.04(a)(2), Subsection I.
Alternatively,
Abstract Idea: Mental Process
The claim does not indicate how the operational simulation is conducted. In addition to being a mathematical concept, “conducting” a simulation can include one or more mental processes that can be performed in the human mind, or by a human using a pen and paper. Further, courts do not distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. See MPEP § 2106.04(a)(2), Subsection III.
Abstract Idea: Mental Process
Predicting a result (or generating a predicted result) is a mental process that can be performed by a human. For example, a human can observe the result of a simulation and, based on opinion and judgment, predict how the results will change when a second resolution is selected. See MPEP 2106.04(a)(2), Subsection III.
See previous analysis of “determining…a first resolution,” above.
Abstract Idea: Mathematical Concepts
Updating the proposed design is a mathematical concept that includes performing one or more mathematical operations to generate a new design. MPEP § 2106.04(a)(2), Subsection I.
Alternatively,
Abstract Idea: Mental Process
The claim does not indicate how the design is updated. In addition to being a mathematical concept, “updating” can include one or more mental processes that can be performed in the human mind, or by a human using a pen and paper. Further, courts do not distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. See MPEP § 2106.04(a)(2), Subsection III.
Abstract Idea: Mental Process
Determining a resolution is a mental process that can be performed in the human mind. The process requires observation, evaluation, opinion, and judgment to determine an appropriate value for the resolution based on knowledge of the operational simulation. See MPEP 2106.04(a)(2), Subsection III.
Abstract Idea: Mathematical Concepts
The operational simulation is a mathematical calculation that is comprised of a number of functions. See MPEP § 2106.04(a)(2), Subsection I.
Alternatively,
Abstract Idea: Mental Process
The claim does not indicate how the operational simulation is conducted. In addition to being a mathematical concept, “conducting” a simulation can include one or more mental processes that can be performed in the human mind, or by a human using a pen and paper. Further, courts do not distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. See MPEP § 2106.04(a)(2), Subsection III.
Abstract Idea: Mental Process
Predicting a result (or generating a predicted result) is a mental process that can be performed by a human. For example, a human can observe the result of a simulation and, based on opinion and judgment, predict how the results will change when a second resolution is selected. See MPEP 2106.04(a)(2), Subsection III.
Step 2A, Prong 2: In accordance with this step, the judicial exception is not integrated into a practical application. The claim does not recite any additional elements other than generic computer components and extra-solution activities (additional elements are bolded).
Claim 1
Mapping Under Step 2A Prong 2
1. A non-transitory computer-readable medium having logic stored thereon that, in response to execution by one or more processors of a computing system, cause the computing system to perform actions for creating a design for a physical device using an inverse design process, the actions comprising:
receiving, by the computing system, a proposed design;
determining, by the computing system, a first resolution for an operational simulation;
conducting, by the computing system, an operational simulation based on the proposed design at a first resolution to generate a calculated performance result;
processing, by the computing system, the calculated performance result using a machine learning model to generate a predicted performance result of the operational simulation based on the proposed design at a second resolution, wherein the second resolution is higher than the first resolution,
and wherein the first resolution is determined such that a combined processing time for conducting the operational simulation based on the proposed design at the first resolution and providing the calculated performance result to the machine learning model to generate the predicted performance result of the operational simulation based on the proposed design at the second resolution is less than a processing time for the operational simulation based on the proposed design at the second resolution;
updating, by the computing system, the proposed design based on the predicted performance result;
determining, by the computing system, a new first resolution, wherein the new first resolution is higher than the first resolution and lower than the second resolution;
conducting, by the computing system, the operational simulation based on the updated proposed design at the new first resolution to generate a new calculated performance result;
processing, by the computing system, the new calculated performance result using the machine learning model to generate a new predicted performance result of the operational simulation based on the updated proposed design at the second resolution; and
repeating, by the computing system, at least the conducting of the operational simulation,the processing using the machine learning model, and the updating of the proposed design actions until the predicted performance of the updated proposed design reaches a threshold performance value.
Under MPEP 2106.05(f), use of a computer or other machinery in its ordinary capacity or a general purpose computer or computer components after the fact to an abstract idea (e.g., a mathematical equation) does not integrate a judicial exception into a practical application. The claim merely recites generic computer components (i.e., “by the computing system”) and amounts to no more than reciting to apply the recited abstract idea using a general purpose computer.
Under MPEP 2106.05(g), receiving, by the computing system, a proposed design is an extra-solution activity. The claim does not specify how the proposed design is received and therefore is mere data gathering, which does not integrate the judicial exception into a practical application.
Using a machine learning model to perform an abstract idea is mere instructions to apply the exception. See MPEP 2106.05(f). As “generating a predicted performance result…” has been identified as an abstract idea, application of the abstract idea by a machine learning model does not integrate the judicial exception into a practical application.
Using a machine learning model to perform an abstract idea is mere instructions to apply the exception. See MPEP 2106.05(f). As “generating a predicted performance result…” has been identified as an abstract idea, application of the abstract idea by a machine learning model does not integrate the judicial exception into a practical application.
“Conducting” and “updating” have been previously identified as being either
mathematical calculations or mental processes.
Further, the step of "processing" has already
been identified as extra-solution activity.
Repetitively performing one or more judicial
exceptions and/or extra-solution activities is
itself an extra-solution activity that does not
integrate the judicial exceptions into a practical
application because it is tangentially related to
the judicial exception. See MPEP 2106.05(g).
Step 2B: In view of MPEP 2106.05(f), use of a computer or other machinery in its ordinary capacity or a general purpose computer or computer components after the fact to an abstract idea (e.g., a mathematical equation) does not integrate a judicial exception does not amount to significantly more. Because the claim merely recites generic computer components, the recitation of “a non-transitory computer readable medium” and “a computing system” do not amount to significantly more than the abstract idea. Further, in view of MPEP 2106.05(g), “receiving” data amounts to the extra-solution activity of data gathering in conjunction with an abstract idea. See also Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). Since receiving data does not amount to significantly more, the additional element of “receiving, by the computing system, a proposed design” is not significantly more than the judicial exception.
Further, the step of “repeating” one or more judicial exceptions and/or extra solution activities does not amount to significantly more. See MPEP 2106.05(d); Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) (“The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.”).
Further, with regards to the clause stating that “a combined processing time for conducting the operational simulation based on the proposed design at the first resolution and providing the calculated performance result to the machine learning model to generate the predicted performance result of the operational simulation based on the proposed design at the second resolution is less than a processing time for the operational simulation based on the proposed design at the second resolution,” the limitation is a well-understood, routine, and conventional result of using a reduced-order model to perform a simulation. For example, “Methods to reduce computational cost of solving of a statistical inverse problem can be classed broadly in three groups. First, there are many ways to reduce the cost of a posterior evaluation (or more specifically, the cost of a forward simulation) through surrogate models, reduced-order models, multigrid and multiscale approaches, and stochastic spectral approaches. Second, the dimension of the input space can be reduced, through truncated Karhunen-Loeve expansions, coarse grids, and parameter-space reductions. The third set of approaches targets a reduction in the number of forward simulations required to compute estimators of interest, i.e., more efficient sampling. In the Bayesian setting, these methods include a wide range of adaptive and multi-stage Markov chain Monte Carlo (MCMC) schemes.” Frangos at Introduction.
Accordingly, claim 1 is considered to be patent ineligible.
Claims 2-5 merely recite types of resolutions that may be utilized in performing the inverse design process recited in claim 1. As previously stated, the recited “resolutions” are inputs and outputs of a machine learning model that performs one or more mathematical calculations and are therefore directed to part of an abstract idea. See MPEP § 2106.04(a)(2), Subsection I. Accordingly, claims 2-5 merely specify types of mathematical elements that are utilized to implement an abstract idea and are directed to a judicial exception without adding significantly more and are therefore considered to be patent ineligible.
Claims 6 and 7 merely recite types of predicted performance results that are calculated by the machine learning model. Because, as previously indicated, the machine learning model is one or more mathematical calculations, specifying the type of output of the model is similarly related to the same abstract idea. Accordingly, claims 6 and 7 merely specify types of mathematical elements that are utilized to implement an abstract idea and are directed to a judicial exception without adding significantly more and are therefore considered to be patent ineligible.
Claim 10 recites transmitting the proposed design to a fabrication system to cause the fabrication system to fabricate the physical device. Transmitting data has been found to be extra-solution activity that does not amount to significantly more than a recited judicial exception. See MPEP 2106.05(g). Instead, courts have found data transmission to be well-understood, routine, and conventional. See Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). Accordingly, claim 10 is directed to patent ineligible subject matter. Further, “caus[ing] the fabrication system to fabricate the physical device” is an idea of a solution that is not recited with specific details as to how the fabrication is accomplished. See MPEP 2106.05(f)(1).
Claim 11 recites a computer-implemented method of creating a design for a physical device using an inverse design process that includes the steps that were claimed as residing on a computer readable medium in claim 1. Regarding Step 1 of the analysis under 35 U.S.C. 101, claim 11 is directed to a process, which is one of the four statutory categories of invention. The steps of the method are substantially similar to the steps of claim 1. Accordingly, for at least the same reasons as provided regarding claim 1, claim 11 is patent ineligible.
Regarding claim 12-17 and 20, each claim corresponds to one of claims 2-7 and 10 and includes substantially similar language as its corresponding claim. Accordingly, claims 2-7 and 10 are rejected for at least the same reasons as previously provided for rejection of claims 12-17 and 20.
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 1-7, 10-17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Piggott, “Automated Design of Photonic Devices,” in view of Fukami, et al., (“Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows,” hereinafter “Fukami”), and Song, et al., (“Optimizing Photonic Nanostructures via Multi-fidelity Gaussian Processes,” hereinafter “Song”).
Claim 1
Regarding claim 1, Piggott discloses:
[a] non-transitory computer-readable medium having logic stored thereon that, in response to execution by one or more processors of a computing system, cause the computing system to perform actions for creating a design for a physical device using an inverse design process, the actions comprising:
The device was designed in approximately 60 hours on a single computer with an Intel Core i7-5820K processor, 64GB of RAM, and three Nvidia Titan Z graphics cards. Piggott at pg. 65.
receiving, by the computing system, a proposed design
To design the broadband 1 × 3 power splitter, we chose to use 500 nm wide input and output waveguides, and a design area of 3.8 × 2.5 µm. We constrained the mininum radius of curvature to be 100 nm, well within the typical design rules of a silicon photonics process, and enforced bilateral symmetry. We specified that power in the fundamental traverse-electric (TE) mode of the input waveguide should be equally split into the fundamental TE mode of the three output waveguides, with at least 95% efficiency. Broadband performance was achieved by simultaneously optimizing at 6 equally spaced wavelengths from 1400 − 1700 nm. Piggott at pg. 61.
See also Figure 6.1, illustrating the initial design (Iteration 0).
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conducting, by the computing system, the operational simulation based on the proposed design at the first resolution to generate a calculated performance result
The optimization process is illustrated in figure 6.1. Starting with a star-shaped geometry, the optimization process converged in 18 iterations. Each iteration required two electromagnetic simulations per design frequency (see supplementary information), resulting in a total of 216 simulations. Piggott at pg. 61.
conducting, by the computing system, the operational simulation based on the updated proposed design
The optimization process is illustrated in figure 6.1. Starting with a star-shaped geometry, the optimization process converged in 18 iterations. Each iteration required two electromagnetic simulations per design frequency (see supplementary information), resulting in a total of 216 simulations. Piggott at pg. 61.
The same process may be utilized for subsequent executions of the operational simulation to generate results based on an inputted design.
updating, by the computing system, the proposed design based on the predicted performance result
See Figure 6.1, illustrating the updated design at Iterations 10 and 18.
repeating, by the computing system, at least the conducting of the operational simulation,
We specified that power in the fundamental traverse-electric (TE) mode of the input waveguide should be equally split into the fundamental TE mode of the three output waveguides, with at least 95% efficiency. Broadband performance was achieved by simultaneously optimizing at 6 equally spaced wavelengths from 1400 − 1700 nm. Piggott at pg. 61.
…the optimization process converged in 18 iterations… Piggott at pg. 61.
Piggott does not appear to disclose:
determining, by the computing system, a first resolution for an operational simulation;
processing, by the computing system, the calculated performance result using a machine learning model to generate a predicted performance result of the operational simulation based on the proposed design at a second resolution, wherein the second resolution is higher than the first resolution,
and wherein the first resolution is determined such that a combined processing time for conducting the operational simulation based on the proposed design at the first resolution and providing the calculated performance result to the machine learning model to generate the predicted performance result of the operational simulation based on the proposed design at the second resolution is less than a processing time for the operational simulation based on the proposed design at the second resolution;
determining, by the computing system, a new first resolution, wherein the new first resolution is higher than the first resolution and lower than the second resolution;
conducting, by the computing system, the operational simulation
processing, by the computing system, the new calculated performance result using the machine learning model to generate a new predicted performance result of the operational simulation based on the updated proposed design at the second resolution;
repeating, by the computing system,
Fukami, which is analogous art to the claimed invention, discloses:
processing, by the computing system, the calculated performance result using a machine learning model to generate a predicted performance result of the operational simulation based on the proposed design at a second resolution, wherein the second resolution is higher than the first resolution,
In the present study, we perform a machine-learning-based spatio-temporal SR analysis inspired by the aforementioned spatial SR and temporal inbetweening techniques to reconstruct high-resolution turbulent flow data from extremely low-resolution flow data both in space and time. pg. 2, paragraph 5-pg. 3, paragraph 1.
See also FIG. 6, showing a “Low” and “Medium” SR (spatio-temporal data), coupled with higher resolution results (a)-(f).
processing, by the computing system, the new calculated performance result using the machine learning model to generate a new predicted performance result of the operational simulation based on the updated proposed design at the second resolution;
In the present study, we perform a machine-learning-based spatio-temporal SR analysis inspired by the aforementioned spatial SR and temporal inbetweening techniques to reconstruct high-resolution turbulent flow data from extremely low-resolution flow data both in space and time. pg. 2, paragraph 5-pg. 3, paragraph 1.
See also FIG. 6, showing a “Low” and “Medium” SR (spatio-temporal data), coupled with higher resolution results (a)-(f).
The same process as utilized in the initial “processing” step can be utilized with new parameters (i.e., “new calculated performance result” and at a “second resolution”) using the same machine learning model.
repeating, by the computing system,
The claim recites providing data at a “first resolution,” receiving, as output, data at a “second resolution,” and “repeating” the providing. Thus, under broadest reasonable interpretation, the step requires that the “providing” be performed at least twice.
For spatio-temporal SR analysis of two-dimensional turbulence, we consider four cases (i.e., providing data at least twice) comprised of two spatial and two temporal coarseness levels as shown in figure 6. pg. 7, paragraph 3.
Fukami is analogous art because both are related to generating a solution to a physical phenomenon that is based on simulated data using a reduced order model. Fukami indicates that super-resolution may be utilized to improve resolution on a number of different physical phenomena. (See Fukami, pg. 2, Paragraph 2). A person of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the multiplexer design process disclosed by Piggott, could be substituted for the equations used to model fluid dynamics in Fukami. Furthermore, a person of ordinary skill in the art would have been able to carry out the substitution. Finally, the substitution achieves the predictable result of generating a physical device design using an inverse design process with reduced iterations and computing time.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to substitute the inverse design process of Piggott for the fluid dynamic parameters of Fukami according to known methods to yield the predictable result of generating an updated design for a physical device using fewer iterations than an inverse design simulation alone.
Fukami does not appear to disclose:
determining, by the computing system, a first resolution for an operational simulation;
and wherein the first resolution is determined such that a combined processing time for conducting the operational simulation based on the proposed design at the first resolution and providing the calculated performance result to the machine learning model to generate the predicted performance result of the operational simulation based on the proposed design at the second resolution is less than a processing time for the operational simulation based on the proposed design at the second resolution;
determining, by the computing system, a new first resolution, wherein the new first resolution is higher than the first resolution and lower than the second resolution;
conducting, by the computing system, the operational simulation
Song, which is analogous art, discloses:
determining, by the computing system, a first resolution for an operational simulation;
We experiment with three design tasks for filtering light with wavelengths of 550 nm, 650 nm and 750 nm. For each task, we vary the conformal mesh size and the time-domain solver total time duration of simulated physical processes to obtain two sets of multi-fidelity data, each with three fidelity levels on 4983 designs. Song at pg. 6, paragraph 3.
and wherein the first resolution is determined such that a combined processing time for conducting the operational simulation based on the proposed design at the first resolution and providing the calculated performance result to the machine learning model to generate the predicted performance result of the operational simulation based on the proposed design at the second resolution is less than a processing time for the operational simulation based on the proposed design at the second resolution;
We demonstrate the performance of various numerical optimization approaches on several precollected real-world datasets and show that by properly trading off expensive information sources with cheap simulations, one can more effectively optimize the transmission properties with a fixed budget. Song at Abstract.
determining, by the computing system, a new first resolution, wherein the new first resolution is higher than the first resolution and lower than the second resolution;
The first set of data is based on different conformal mesh sizes. The mesh size determines how accurate the final results are, with finer meshes lead to more accurate results. We generated the lowest fidelity data using a mesh size of 3nm × 3nm, the middle fidelity 2nm × 2nm and the target fidelity 1nm × 1nm. The costs, CPU time, are inverse proportional to the mesh size, so we use the following costs [1, 2.25, 9] for our three fidelity function evaluation, respectively. Song at pg. 6, paragraph 4.
The “multi-fidelity” nature of the simulation allows the simulation to be executed with varying resolutions. Thus, the “lowest fidelity” is analogous to the “first resolution,” the “middle fidelity” is analogous to the “new first resolution,” and the “target fidelity” is analogous to the “second fidelity.”
conducting, by the computing system, the operational simulation
Figure 2 and Figure 3 show the results of this experiment. As usual, the x-axis is the cost and yaxis is Figure of Merit and smaller is better. After a small portion of the budget is used in initial exploration, MF-MI-Greedy (red) is able to arrive at a better final design compared with MF-GPUCB, GP-UCB and Particle Swarm. MF-MI-Greedy tends to have a worse figure of merit at the beginning because the initial explorations in the lower fidelity do not yield FOM scores on the target fidelity, so essentially it has a late start in all the plots because it starts querying the target fidelity late. However, the advantage of exploring lower fidelities becomes apparent once the exploitation phase starts in the target fidelity level, as seen by the rapid convergence to low FOM designs. Song at pg. 7, paragraph 5.
The same simulation can be utilized at different resolutions (i.e., different fidelities) to generate results.
Song is analogous to the claimed invention because both are directed to utilizing a simulation in the optimal design of photonic devices. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to combine the inverse design process of Piggott and the super-resolution machine learning model of Fukami with the multi-fidelity simulation of Song to result in a system that utilizes the same operational simulation at different resolutions during an inverse design process, whereby the results of the simulation are super-resolved to a higher resolution by a machine learning model and the resulting new proposed design is utilized in a subsequent simulation at a higher resolution until a threshold is reached. Motivation to combine includes improved speed and lower computational costs with increased accuracy by increasing resolution only as needed to achieve desired accuracy while simulating at a resolution below a full resolution.
Claim 2
Regarding claim 2, Piggott does not disclose wherein the first resolution is a first spatial resolution and the second resolution is a second spatial resolution.
Fukami, however, discloses wherein the first resolution is a first spatial resolution and the second resolution is a second spatial resolution:
Super resolution analysis can reconstruct spatially high-resolution data from spatially input data, as illustrated in figure 1(a)” pg. 3, paragraph 2.
For spatial SR analysis, we prepare two levels of spatial coarseness: medium- ( 16×16 ) and low-resolution ( 8×8 grids) data, analogous to our previous work… pg. 7, paragraph 3 and FIG. 6, showing a “Low” and “Medium” SR (spatio-temporal data).
Fukami indicates that super-resolution may be utilized to improve resolution on a number of different physical phenomena. (See Fukami, pg. 2, Paragraph 2). A person of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the spatial super-resolution process of Fukami could be applied to the multiplexer design process of Piggott. Motivation includes performing the simulation of Piggott at a lower resolution, thus resulting in an inverse design process with reduced iterations and computing time
Claim 3
Regarding claim 3, Piggott does not disclose wherein the first resolution is a first temporal resolution and the second resolution is a second temporal resolution.
Fukami, however, discloses wherein the first resolution is a first temporal resolution and the second resolution is a second temporal resolution (“For the temporal resolution set-up, we define a medium ( ΔT=1.0 ) and a wide time step (ΔT=4.0), where ΔT is the time step between the first and last frames of the inbetweening analysis.” pg. 8, paragraph 1).
A person of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the temporal resolution changes of Fukami could be applied to the simulation resolution of Piggott. Motivation includes performing the inverse design process of Piggott at a lower temporal resolution, thus resulting in an inverse design process with reduced iterations and computing time.
Claim 4
Regarding claim 4, Piggott does not appear to disclose wherein the first temporal resolution is a first time step length and wherein the second temporal resolution is a second time step length. Fukami, however, discloses wherein the first temporal resolution is a first time step length and wherein the second temporal resolution is a second time step length:
The time step for DNS is set to Δt=2.50×10−3 and yields a maximum Courant–Friedrichs–Lewy number of 0.3. pg. 6, paragraph 3.
For purposes of examination, Examiner interprets “time step length” as “a length of time between calculations.”
The value of Δt is a set value for a particular model and can vary based on technique used for a simulation and/or the spatial values of the simulation.1 Thus, a “first time step length” can be set for a first simulation and a “second time step length” can be set for a second simulation, with the Courant–Friedrichs–Lewy relating the values.
A person of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the time step length changes of Fukami could be applied to the simulation of Piggott because Piggott discloses, inter alia, using finite-difference time domain simulations. Piggott at pg. 33. A person of ordinary skill in the art, before the effective filing date of the claimed invention, would have been motivated to perform the simulation of Piggott with a greater time step, thus resulting in an inverse design process with reduced iterations and computing time.
Claim 5
Regarding claim 5, Piggott does not appear to disclose wherein the first temporal resolution is a first time step update interval and the second temporal resolution is a second time step update interval. Fukami, however, discloses wherein the first temporal resolution is a first time step update interval and the second temporal resolution is a second time step update interval:
For the temporal resolution set-up, we define a medium (Δ𝑇=1.0) and a wide time step (Δ𝑇=4.0), where Δ𝑇 is the time step between the first and last frames of the inbetweening analysis. pg. 7, paragraph 4.
For the purposes of examination, Examiner interprets a “time step update interval” as the sampling rate of “time step lengths.” Thus, as shown in Figure 6, Δ𝑇=1.0 selects every “time step length” and Δ𝑇=4.0 selects every fourth “time step length.”
A person of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the time step length changes of Fukami could be applied to the simulation of Piggott because Piggott discloses, inter alia, using finite-difference time domain simulations. Piggott at pg. 33. A person of ordinary skill in the art, before the effective filing date of the claimed invention, would have been motivated to perform the simulation of Piggott with a greater time step, thus resulting in an inverse design process with reduced iterations and computing time.
Claim 6
Piggott discloses:
wherein the predicted performance result is a predicted s-parameter of the physical device
The final design and simulated performance are illustrated in figure 5.5. The spatial mode multiplexer has an average insertion loss of 0.826 dB, and a contrast better than 16 dB over the design bandwidth of 1400 − 1700 nm. Piggott at pg. 56.
See also Figure 4.10 and accompanying description.
See also Figure 5.5, illustrating the performance of the final multiplexer and the plotted s-parameters of the device:
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Claim 7
Piggott discloses:
wherein the predicted performance result is a predicted scalar performance metric
See Figure 4.5: Simulated and experimentally measured splitting ratios for the coupler, defined as the ratio of power into the left and right waveguides. Around 1300 nm, light is predominantly coupled to the left grating, whereas around 1550 nm, light is predominantly coupled into the right grating. The experimentally measured splitting ratios are 17 ± 2 dB at 1310 nm and 12 ± 2 dB at 1540 nm. Piggott at pg. 39.
The “power ratio” is a “scalar performance metric” that is maximized by the simulation.
Claim 10
Piggott discloses:
wherein the actions further comprise transmitting the proposed design to a fabrication system to cause the fabrication system to fabricate the physical device.
The devices were fabricated by using electron beam lithography followed by plasma etching, as detailed in Appendix A.1. Scanning electron microscopy (SEM) images of a final fabricated device is shown in figure 4.9a. Piggott at pg. 44.
Claim 11
Claim 11 recites a computer-implemented method of creating a design for a physical device using an inverse design process to perform the same steps as previously recited in claim 1. Accordingly, for at least the same reasons as previously stated regarding the rejection of claim 1, claim 11 is rejected under 35 U.S.C. 103 as being obvious over Piggott in view of Fukami and Song.
Claim 12
Claim 12 is substantially similar to claim 2 and therefore is rejected under 35 U.S.C. 103 as being obvious over Piggott in view of Fukami and Song.
Claim 13
Claim 13 is substantially similar to claim 3 and therefore is rejected under 35 U.S.C. 103 as being obvious over Piggott in view of Fukami and Song.
Claim 14
Claim 14 is substantially similar to claim 4 and therefore is rejected under 35 U.S.C. 103 as being obvious over Piggott in view of Fukami and Song.
Claim 15
Claim 15 is substantially similar to claim 5 and therefore is rejected under 35 U.S.C. 103 as being obvious over Piggott in view of Fukami and Song.
Claim 16
Claim 16 is substantially similar to claim 6 and therefore is rejected under 35 U.S.C. 103 as being obvious over Piggott in view of Fukami and Song.
Claim 17
Claim 17 is substantially similar to claim 7 and therefore is rejected under 35 U.S.C. 103 as being obvious over Piggott in view of Fukami and Song.
Claim 20
Claim 20 is substantially similar to claim 10 and therefore is rejected under 35 U.S.C. 103 as being obvious over Piggott in view of Fukami and Song.
Conclusion
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JOSEPH MORRIS
Examiner
Art Unit 2188
/JOSEPH P MORRIS/Examiner, Art Unit 2188
/RYAN F PITARO/Supervisory Patent Examiner, Art Unit 2188
1 See Wikipedia for “Courant–Friedrichs–Lewy condition”: https://en.wikipedia.org/wiki/Courant%E2%80%93Friedrichs%E2%80%93Lewy_condition (retrieved March 10, 2025), which cites to original 1927 paper by Richard Courant, Kurt Friedrichs, and Hans Lewy.