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 .
This Office Action is in response to claims filed on 05/05/2022
Claims 1-20 are pending.
Claim Objections
Claims 14 and 19 are objected to because of the following informalities: Claims 14 and 19 recites the limitation "correlated ROM parameter", it should end with a “.”. Appropriate correction is required.
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 1-2 and 4-20 are rejected under 35 U.S.C. 103 as being unpatentable over Tetiker, US 2018/0314148 A1, Published Nov. 1, 2018, (hereafter Tetiker), in views of Luis Javier Segura, NPL “Gaussian process tensor responses emulation for droplet solidification in freeze nano 3D printing of energy products, Published June 10-14, 2019 (hereafter Segura).
Regarding claim 1. Tetiker teaches a method of calibrating a high fidelity (HF) model of molten droplet coalescence (Par 208, calibration pattern, optimized EPM model run for that calibration structure), comprising:
obtaining experimental data that describes behavior of a droplet ejected from a 3D (Fig 3, measure an experimental result)(Fig 3, calculate an error metric, difference between experimental and computed);
selecting initial HF parameter values for HF parameters of an HF model (Fig 4, select set of parameters); and
iteratively refining the HF parameter values until the HF model converges with the experimental data (Fig 4, on a loop, iteratively changes parameters until it converges), wherein each iteration comprises:
applying the HF parameter values to the HF model and running a plurality of simulations using the HF model to generate the simulated numerical data for each simulation (Fig 4, generate and save theoretical etch profile);
for each simulation, fitting a Reduced Order Model (ROM) to the simulated numerical data generated by the simulation to generate ROM parameter values for ROM parameters of the ROM (Fig 15, estimate quantity characteristic, using reduced order model); and
identifying, by a processing device, correlations between the ROM parameters and the HF parameters and narrowing a search space to be searched in a next iteration based on the correlations (Par 202, search in the parameter space for improved calibration optimization)(Par 215, search initially to narrow down relevant entries)(Par 240, construct an efficiently searchable LUT).
Tetiker does not teach a 3D printer.
Segura teaches a 3D printer (Par 4, Fig 2, 3D printer).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Tetiker to incorporate the teachings of Segura to simulate a 3D printer because it helps in real time process optimization (Segura, Page 1, Abstract)
Regarding claim 2. Tetiker and Segura teach the method of claim 1, wherein the HF model is used to generate simulated numerical data that describes a simulated droplet aspect ratio over time (Tetiker, Par 270, aspect ratio)(Tetiker, Par 291, vary over the course of the etch process).
Regarding claim 4. Tetiker and Segura teach the method of claim 1, wherein identifying correlations between the ROM parameters and the HF parameters comprises, for a selected ROM parameter and a selected HF parameter (Tetiker, fig 3, model parameters selected),
generating a scatter plot of the ROM parameter values and the HF parameter values (Segura, Page 6, Fig 6, solidification time plot, how its distributed), and
identifying a correlation between the selected ROM parameter and the selected HF parameter based on groupings and curve fittings between the ROM parameter values and the HF parameter values (Segura, Page 6, Fig 6c, error difference, comparison between simulated and predicted).
Regarding claim 5. Tetiker and Segura teach the method of claim 1, wherein identifying correlations between the ROM parameters and the HF parameters comprises computing a correlation strength for selected combinations of the ROM parameters and the HF parameters (Segura, Page 6, Fig 6c, error, bar ranges from -5 to 5, from blue to yellow),
wherein the method further comprises ranking the correlations based on the correlation strength (Segura, Page 6, Fig 6c, error, bar ranges from -5 to 5, from blue to yellow, ranking how different the comparison).
Regarding claim 6. Tetiker and Segura teach the method of claim 5, wherein narrowing the search space to be searched in a next iteration comprises selecting a specified number of top ranked correlations (Tetiker, Par 202, search in that parameter space)(Tetiker, Par 215, searched based on edge shape indicator, thus providing a priority) (Segura, Page 6, Fig 6c, error, bar ranges from -5 to 5, from blue to yellow, having a feature), and
identifying the HF model parameters for those correlations as target HF parameters to be adjusted for the next iteration (Tetiker, Par 202, search in that parameter space)(Tetiker, Par 215, narrow down relevant entries before detailed search)(Tetiker, Fig 4A, any more parameters, for the next iteration).
Regarding claim 7. Tetiker and Segura teach the method of claim 1, further comprising fitting the ROM to the experimental data to identify target values of the ROM parameters that cause the ROM to approximate the experimental data (Tetiker, Par 208, mapping is referred as reduced order model, for target calibrated structures).
Regarding claim 8. Tetiker and Segura teach the method of claim 7, wherein narrowing the search space to be searched in a next iteration comprises:
identifying, for a specific HF parameters of the HF parameters, a correlated ROM parameter (Tetiker, Par 215, searched based on edge shape indicator, thus providing a priority) (Segura, Page 6, Fig 6c, error, bar ranges from -5 to 5, from blue to yellow, having a feature); and
identifying a range of values for the specific HF parameter that produces similar simulation results compared to the target value of the correlated ROM parameter (Tetiker, Par 208, targeted calibration structures exhibit a range, likely to be seen in a real design layout).
Regarding claim 9. Tetiker and Segura teach the method of claim 1, further comprising, after the HF model converges with the experimental data (Tetiker, Fig 4A, difference between theoretical and experimental, converge),
storing the HF parameter values as final HF parameter values for a calibrated HF model (Tetiker, Fig 4A, converge Yes, done),
wherein the calibrated HF model is used to generate digital simulations of a 3D object created by a virtual 3D printer based on a digital model of the 3D object (Tetiker, Par 202, target calibration pattern, 3D patterns)( Tetiker, Par 247, run simulations for varying geometries) (Segura, Page 4, Fig 2, 3D printer).
Regarding claim 10. Tetiker and Segura teach the method of claim 9, wherein one or more settings of an actual 3D printer are adjusted based on the digital simulations (Segura, Page 6, Table 1, parameters) and
used for printing the 3D object on the actual 3D printer (Segura, Page 5, Fig 4, Proper waiting).
Regarding claim 11. Tetiker teaches an apparatus for calibrating a high fidelity (HF) model of molten droplet coalescence for a 3D print simulator (Par 208, calibration pattern, optimized EPM model run for that calibration structure), the apparatus comprising:
a memory to store experimental data that describes behavior of a droplet ejected from a 3D (Fig 3, measure an experimental result)(Fig 3, calculate an error metric, difference between experimental and computed);
a processing device operatively coupled to the memory, wherein the processing device is to:
select initial HF parameter values for HF parameters of an HF model (Fig 4, select set of parameters); and
iteratively refine the HF parameter values until the HF model converges with the experimental data (Fig 4, on a loop, iteratively changes parameters until it converges),
wherein at each iteration the processing device is to:
apply the HF parameter values to the HF model and run a plurality of simulations using the HF model to generate the simulated numerical data for each simulation (Fig 4, generate and save theoretical etch profile);
for each simulation, fit a Reduced Order Model (ROM) to the simulated numerical data generated by the simulation to generate ROM parameter values for ROM parameters of the ROM (Fig 15, estimate quantity characteristic, using reduced order model); and
identify correlations between the ROM parameters and the HF parameters and narrow a search space to be searched in a next iteration based on the correlations (Par 202, search in the parameter space for improved calibration optimization)(Par 215, search initially to narrow down relevant entries)(Par 240, construct an efficiently searchable LUT).
Tetiker does not teach a 3D printer.
Segura teaches a 3D printer (Par 4, Fig 2, 3D printer).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Tetiker to incorporate the teachings of Segura to simulate a 3D printer because it helps in real time process optimization (Segura, Page 1, Abstract)
Regarding claim 12. Tetiker and Segura teach the apparatus of claim 11, wherein the HF model is used to generate simulated numerical data that describes a simulated droplet aspect ratio over time (Tetiker, Par 270, aspect ratio)(Tetiker, Par 291, vary over the course of the etch process).
Regarding claim 13. Tetiker and Segura teach the apparatus of claim 11, wherein: to identify the correlations, the processing device is to compute a correlation strength for selected combinations of the ROM parameters and the HF parameters (Tetiker, fig 3, model parameters selected); and
to narrow the search space, the processing device is to rank the correlations based on the correlation strength to select a specified number of top ranked correlations (Tetiker, Par 202, search in that parameter space)(Tetiker, Par 215, searched based on edge shape indicator, thus providing a priority) (Segura, Page 6, Fig 6c, error, bar ranges from -5 to 5, from blue to yellow, having a feature), and
identify the HF model parameters for those correlations as target HF parameters to be adjusted for the next iteration (Tetiker, Par 202, search in that parameter space)(Tetiker, Par 215, narrow down relevant entries before detailed search)(Tetiker, Fig 4A, any more parameters, for the next iteration).
Regarding claim 14. Tetiker and Segura teach the apparatus of claim 11, wherein the processing device is further to: fit the ROM to the experimental data to identify target values of the ROM parameters that cause the ROM to approximate the experimental data (Tetiker, Par 208, mapping is referred as reduced order model, for target calibrated structures); and
to narrow the search space, identifying, for a specific HF parameters of the HF parameters, a correlated ROM parameter (Tetiker, Par 215, searched based on edge shape indicator, thus providing a priority) (Segura, Page 6, Fig 6c, error, bar ranges from -5 to 5, from blue to yellow, having a feature), and
identify a range of values for the specific HF parameter that produces similar simulation results compared to the target value of the correlated ROM parameter (Tetiker, Par 208, targeted calibration structures exhibit a range, likely to be seen in a real design layout)
Regarding claim 15. Tetiker and Segura teach the apparatus of claim 11, wherein the processing device is further to: after the HF model converges with the experimental data (Tetiker, Fig 4A, difference between theoretical and experimental, converge), store the HF parameter values as final HF parameter values for a calibrated HF model (Tetiker, Fig 4A, converge Yes, done);
generate digital simulations of a 3D object created by a virtual 3D printer based on the calibrated HF model and a digital model of the 3D object (Tetiker, Par 202, target calibration pattern, 3D patterns)( Tetiker, Par 247, run simulations for varying geometries) (Segura, Page 4, Fig 2, 3D printer);
adjust one or more settings of an actual 3D printer based on the digital simulations (Segura, Page 6, Table 1, parameters).
Regarding claim 16. Tetiker teaches a non-transitory computer-readable storage medium having instructions stored thereon that (Par 13, computer systems), when executed by a processing device for calibrating a high fidelity (HF) model (Par 208, calibration pattern, optimized EPM model run for that calibration structure), cause the processing device to:
obtain experimental data that describes behavior of a droplet ejected from a 3D (Fig 3, measure an experimental result)(Fig 3, calculate an error metric, difference between experimental and computed), wherein the experimental data describes a measured droplet aspect ratio over time (Par 270, aspect ratio)(Par 291, vary over the course of the etch process);
select initial HF parameter values for HF parameters of an HF model (Fig 4, select set of parameters); and
iteratively refine the HF parameter values until the HF model converges with the experimental data (Fig 4, on a loop, iteratively changes parameters until it converges),
wherein at each iteration the processing device is to:
apply the HF parameter values to the HF model and run a plurality of simulations using the HF model to generate the simulated numerical data (Fig 4, generate and save theoretical etch profile);
for each simulation, fit a Reduced Order Model (ROM) to the simulated numerical data generated by the simulation to generate ROM parameter values for ROM parameters of the ROM (Fig 15, estimate quantity characteristic, using reduced order model); and
identify correlations between the ROM parameters and the HF parameters and narrow a search space to be searched in a next iteration based on the correlations (Par 202, search in the parameter space for improved calibration optimization)(Par 215, search initially to narrow down relevant entries)(Par 240, construct an efficiently searchable LUT).
Tetiker does not teach a 3D printer.
Segura teaches a 3D printer (Par 4, Fig 2, 3D printer).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Tetiker to incorporate the teachings of Segura to simulate a 3D printer because it helps in real time process optimization (Segura, Page 1, Abstract)
Regarding claim 17. Tetiker and Segura teach the non-transitory computer-readable storage medium of claim 16, wherein the HF model is used to generate simulated numerical data that describes a simulated droplet aspect ratio over time (Tetiker, Par 270, aspect ratio)(Tetiker, Par 291, vary over the course of the etch process).
Regarding claim 18. Tetiker and Segura teach the non-transitory computer-readable storage medium of claim 16, wherein: to identify the correlations, the processing device is to compute a correlation strength for selected combinations of the ROM parameters and the HF parameters (Segura, Page 6, Fig 6c, error, bar ranges from -5 to 5, from blue to yellow); and
to narrow the search space, the processing device is to rank the correlations based on the correlation strength to select a specified number of top ranked correlations (Tetiker, Par 202, search in that parameter space)(Tetiker, Par 215, searched based on edge shape indicator, thus providing a priority) (Segura, Page 6, Fig 6c, error, bar ranges from -5 to 5, from blue to yellow, having a feature), and
identify the HF model parameters for those correlations as target HF parameters to be adjusted for the next iteration (Tetiker, Par 202, search in that parameter space)(Tetiker, Par 215, narrow down relevant entries before detailed search)(Tetiker, Fig 4A, any more parameters, for the next iteration).
Regarding claim 19. Tetiker and Segura teach the non-transitory computer-readable storage medium of claim 16, wherein the processing device is further to: fit the ROM to the experimental data to identify target values of the ROM parameters that cause the ROM to approximate the experimental data (Tetiker, Par 208, mapping is referred as reduced order model, for target calibrated structures); and
to narrow the search space, identifying, for a specific HF parameters of the HF parameters, a correlated ROM parameter (Tetiker, Par 215, searched based on edge shape indicator, thus providing a priority) (Segura, Page 6, Fig 6c, error, bar ranges from -5 to 5, from blue to yellow, having a feature), and
identify a range of values for the specific HF parameter that produces similar simulation results compared to the target value of the correlated ROM parameter (Tetiker, Par 208, targeted calibration structures exhibit a range, likely to be seen in a real design layout)
Regarding claim 20. Tetiker and Segura teach the non-transitory computer-readable storage medium of claim 16, wherein the processing device is further to: after the HF model converges with the experimental data (Tetiker, Fig 4A, difference between theoretical and experimental, converge), store the HF parameter values as final HF parameter values for a calibrated HF model (Tetiker, Fig 4A, converge Yes, done);
generate digital simulations of a 3D object created by a virtual 3D printer based on the calibrated HF model and a digital model of the 3D object (Tetiker, Par 202, target calibration pattern, 3D patterns)( Tetiker, Par 247, run simulations for varying geometries) (Segura, Page 4, Fig 2, 3D printer); and
adjust one or more settings of an actual 3D printer based on the digital simulations (Segura, Page 6, Table 1, parameters).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Tetiker, US 2018/0314148 A1, Published Nov. 1, 2018, (hereafter Tetiker), in views of Luis Javier Segura, NPL “Gaussian process tensor responses emulation for droplet solidification in freeze nano 3D printing of energy products, Published June 10-14, 2019 (hereafter Segura), in further views of Theron James Price, NPL, “Experimental Investigation of Transverse Mode Nozzle”, University of Tennessee, Published 12-2017 (hereafter Price).
Regarding claim 3. Tetiker and Segura teach the method of claim 1, wherein the ROM is a three-parameter damped-harmonic oscillator model, and the ROM parameters comprise dumping coefficient F, angular frequency fl, and p describing time evolution of the angular frequency (Tetiker, Par 348, flow rate setting, radio frequency)(Tetiker, Par 214, ROM LUT, feature’s angular dependent)(Tetiker, Par 223, delineate the angular limits)(Tetiker, Par 188, frequency power generates an inductive current).
Tetiker and Segura do not teach damped-harmonic oscillator model.
Price teaches damped-harmonic oscillator model (Page 10, influence of damping on the oscillatory nature of the system).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Tetiker and Segura to incorporate the teachings of Price to model the motions using the damped harmonic oscillator model because a mathematical equation produces predictable results (Price, Page 9, sec 2.1.1, damped simple harmonic motion).
Conclusion
The prior art made of record, listed on PTO-892, and not relied upon is considered pertinent to applicant's disclosure.
Korneev et al. discloses additive manufacturing process to print product part and determining the differences between the printed part and the computer representation.
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/A.C./Examiner, Art Unit 2189
/REHANA PERVEEN/Supervisory Patent Examiner, Art Unit 2189