DETAILED ACTION
Non-Final Rejection
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 .
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 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Each of claims1-11 falls within one of the four statutory categories. See MPEP § 2106.03. Each of claims 1-10 fall within category of process; Each of claim 11-20 fall within category of machine, i.e., a “concrete thing, consisting of parts, or of certain devices and combination of devices.” Digitech, 758 F.3d at 1348–49, 111 USPQ2d at 1719 (quoting Burr v. Duryee, 68 U.S. 531, 570, 17 L. Ed. 650, 657 (1863));
Regarding Claims 1-10
Step 2A – Prong 1
Exemplary claim 1 is directed to an abstract idea of predicted structure of the grating.
The abstract idea is set forth or described by the following italicized limitations:
1. A computer-implemented method, in a processing device of an Optical Critical Dimension (OCD) metrology system, comprising:
receiving grating parameters as input to a neural network;
generating, by the neural network, an output comprising a predicted optical response of a grating based on the grating parameters;
responsive to determining that a difference between the predicted optical response and a measured optical response of the grating is within a specified threshold, outputting the grating parameters as a predicted structure of the grating; and
responsive to determining that the difference is greater than the specified threshold, iteratively updating the grating parameters received as input to the neural network until the predicted optical response and the measured optical response converge..
The italicized limitations above represent a mathematical concept (i.e., a process that can be performed by mathematical relationships or rules or idea). Therefore, the italicized limitations fall within the subject matter groupings of abstract ideas enumerated in Section I of the 2019 Revised Patent Subject Matter Eligibility Guidance.
For example, the limitations “generating [..]a predicted optical response [..]; [..]determining that a difference[..]within a specified threshold[..]a predicted structure[..];[..]determining that the difference is greater than the specified threshold[..] measured optical response converge” is mathematical concept (i.e., a process that can be performed by mathematical relationships or rules or idea), see 2106.04(a)(2).
Step 2A – Prong 2
Claims 1 does not include additional elements (when considered individually, as an ordered combination, and/or within the claim as a whole) that are sufficient to integrate the abstract idea into a practical application.
For example, first additional first element is “ receiving grating parameters as input to a neural network” to be performed, at least in-part, these additional elements appear to only add insignificant extra-solution activity (e.g., data gathering) and only generally link the abstract idea to a particular field. Therefore, this element individually or as a whole does not provide a practical application. See MPEP 2106.05(g).
For example, 2nd additional first element is “in a processing device of an Optical Critical Dimension (OCD) metrology system ”. This element amounts to mere use of a generic device with generic computer component, which is well understood routine and conventional (see background of current discloser and IDS and PTO 892) and this element individually does not provide a practical application. In view of the above, the “additional element” individually or combine does not provide a practical application of the abstract idea. see MPEP 2106.05(d).
For Examples, 2nd additional first element is “by the neural network”: This element amounts to mere use of a generic computer components with high level of generality (apply it) , which is well understood routine and conventional (see background of current discloser and IDS and PTO 892) and this element individually does not provide a practical application. In view of the above, the “additional element” individually or combine does not provide a practical application of the abstract idea. see MPEP 2106.05(d).
In view of the above, the three “additional elements” individually do not provide a practical application of the abstract idea.
Step 2B
Claims1 does not include additional elements, when considered individually and as an ordered combination, that are sufficient to amount to significantly more than the abstract idea. For example, the limitation of Claim 1 contains additional elements that are, i.e. n a processing device of an Optical Critical Dimension (OCD) metrology system”, generic metrology devices, which are well understood, routine and conventional (see background of current discloser and IDS and PTO 892) and MPEP 2106.05(d))The reasons for reaching this conclusion are substantially the same as the reasons given above in § Step 2A – Prong 2. For brevity only, those reasons are not repeated in this section. See MPEP §§ 2106.05(g) and MPEP §§2106.05(II).
Dependent Claims 2-10
Dependent claims 2-6 fail to cure this deficiency of independent claim 1 (set forth above) and are rejected accordingly. Particularly, claims 2-10 recite limitations that represent (in addition to the limitations already noted above) either the abstract idea or an additional element that is merely extra-solution activity, mere use of instructions and/or generic computer component(s) as a tool to implement the abstract idea, and/or merely limits the abstract idea to a particular technological environment.
For Examples, claim 2-5: imitations above represent a mathematical concept (i.e., a process that can be performed by mathematical relationships or rules or idea).
For Examples, claim 6-8 and 10: only add insignificant extra-solution activity (e.g., data gathering).
For Examples, claim 9: This claim, limitation amounts to mere use of a generic computer components with high level of generality (apply it) , which is well understood routine and conventional (see background of current discloser and IDS and PTO 892) and this element individually does not provide a practical application. In view of the above, the “additional element” individually or combine does not provide a practical application of the abstract idea. see MPEP 2106.05(d).
Regarding Claims 11-20
Claims 11-20 contains language similar to claims 1-10 as discussed in the preceding paragraphs, and for reasons similar to those discussed above, claims 11-20 are also rejected under 35 U.S.C. § 101(abstract idea). Furthermore, claim11 contain the additional elements “A processing device comprising: a processor; and a prediction module implemented at the processor, the prediction module implementing a neural network and configured by the processor to”. This element amounts to mere use of a generic device with computer components with high level of generality, which is well understood routine and conventional (see background of current discloser and IDS and PTO 892) and this element individually does not provide a practical application. In view of the above, the “additional element” individually or combine does not provide a practical application of the abstract idea. see MPEP 2106.05(d).
And claim 20 contain the additional elements “A near-eye display system comprising: an image source to project light comprising an image; at least one lens element; and a waveguide including at least one grating having a structure verified by a process”. This element amounts to mere use of a generic device with computer components with high level of generality, which is well understood routine and conventional (see background of current discloser and IDS and PTO 892) and this element individually does not provide a practical application. In view of the above, the “additional element” individually or combine does not provide a practical application of the abstract idea. see MPEP 2106.05(d).
Claim Rejections - 35 USC § 103
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.
Claim(s)1-5,7,9-16 and 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hench (US 2010/0017351) in view of Hadler et al. (US 20250355136).
Regarding Claims 1 and 11. Hench teaches a computer-implemented method, in a processing device of an Optical Critical Dimension (OCD) metrology system, comprising([0029]; fig. 2A):
(A processing device comprising: a processor; and
a prediction module implemented at the processor, the prediction module implementing a neural network and configured by the processor to(as cited in claim 11)(902: fig. 9; 230: fig. 2A)
receiving grating parameters (220: fig. 2A) as input to a neural network(230: fig. 2A);
generating, by the neural network, an output comprising a predicted optical response of a grating based on the grating parameters(240: fig. 2A);
responsive to determining that a difference (250: fig. 2A) between the predicted optical response (240: fig. 2A) and a measured optical response of the grating (205: fig. 2A)is within a specified threshold(best fit: [0028]; The Examiner considers “best fit” to be “specified threshold”, which is “specified threshold”=close to zero line ), outputting the grating parameters as a predicted structure of the grating(255: fig. 2A; [0028]); and
Hench silent about responsive to determining that the difference is greater than the specified threshold, iteratively updating the grating parameters received as input to the neural network until the predicted optical response and the measured optical response converge.
However, Hadler teaches responsive to determining that the difference is greater than the specified threshold, iteratively updating the grating parameters received as input to the neural network until the predicted optical response and the measured optical response converge (The neural network 200 takes a pixelated image 202 representing the metasurface topology of a metasurface device as input and produces one or more figures of merit characterizing the resulting performance 204 of the device as output. Relevant figures of merit generally depend on the type of metasurface device. For example, figures of merit that may be of interest in characterizing the performance of optical meta-gratings include a scattering efficiency, scattered power, or similar scattering metric, e.g., associated with a specific grating order: [0026]; In this iterative method, the gradient of the error with respect to the weights of the network is calculated, proceeding backward through the network, to determine adjustments to the weight during each training iteration. The training process ends when the error converges and/or falls below a specified threshold, corresponding to a desired accuracy of the predictions made by the neural network: [0028]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to the invention of Hench, responsive to determining that the difference is greater than the specified threshold, iteratively updating the grating parameters received as input to the neural network until the predicted optical response and the measured optical response converge, as taught by Hadler, so as to design parameters, as are used to update the topology in each iteration and predict the performance of a metasurface device for any arbitrary pattern on the device with high accuracy.
Regarding Claims 2 and 12. Hench silent about responsive to determining that the predicted structure of the grating deviates from a design specification for the grating by more than a specified threshold, failing the grating; and
responsive to determining that the predicted structure of the grating is within a specified threshold of the design specification for the grating, passing the grating.
However, Hadler teaches responsive to determining that the predicted structure of the grating deviates from a design specification for the grating by more than a specified threshold, failing the grating(322: fig. 3(design the meta-grating); [0033]); and
responsive to determining that the predicted structure of the grating is within a specified threshold of the design specification for the grating, passing the grating(324: fig. 3(design the meta-grating); [0033]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to the invention of Hench, responsive to determining that the predicted structure of the grating deviates from a design specification for the grating by more than a specified threshold, failing the grating; and responsive to determining that the predicted structure of the grating is within a specified threshold of the design specification for the grating, passing the grating, as taught by Hadler, so as to design parameters, as are used to update the topology in each iteration and predict the performance of a metasurface device for any arbitrary pattern on the device with high accuracy.
Regarding Claims 3 and 13. Hadler further teaches responsive to determining that the predicted structure of the grating deviates from a design specification for the grating by more than a specified threshold, updating one or more fabrication parameters associated with the grating(322: fig. 3(design the meta-grating); [0033]).
Regarding Claims 4 and 14. Hadler further teaches each iteration of the iteratively updating the grating parameters comprises([0028]):
computing a loss value by applying a loss function to the predicted optical response and the measured optical response using a loss function(error: [0028]);
performing a backpropagation process to compute a gradient of the loss function for each of the grating parameters based on the loss value(backpropagation: [0028]); and
adjusting a value of each of the grating parameters to reduce the loss value by subtracting a fraction of the gradient calculated for the grating parameter, wherein adjusting the value of each of the grating parameters generates updated grating parameters(to determine adjustments to the weight: [0028]).
Regarding Claims 5 and 15. Hadler further teaches receiving the updated grating parameters as input to the neural network(iteratively adjusting parameters: [0028]);
generating, by the neural network, an output comprising a different predicted optical response of the grating based on the updated grating parameters(figure(s) of merit output by the neural network: [0028]);
responsive to determining that the different predicted optical response and the measured optical response of the grating converge, outputting the updated grating parameters as the predicted structure of the grating(converges and/or falls below a specified threshold, corresponding to a desired accuracy of the predictions made by the neural network: [0028]); and
responsive to determining that the different predicted optical response and the measured optical response of the grating do not converge, performing backpropagation and an optimization process to further update the grating parameters(Neural networks can be trained by backpropagation of errors using gradient descent, a learning algorithm: [0028]).
Regarding Claims 7 and 16. Hench further teaches the predicted structure of the grating comprises one or more of grating period pitch, grating width, grating height or depth, grating sidewall angle, grating shape, or grating material properties(255: fig. 2A; [0029]).
Regarding Claims 9 and 18. Hench further teaches selecting a neural network architectural configuration from a plurality of neural network architectural configurations based on one or more aspects of the grating(z=ƒ(p) as y=NN(p) :[0032], [0050]); and
implementing the neural network based on the selected neural network architectural configuration (z=ƒ(p) as y=NN(p): [0032], [0050] )
Regarding Claims 10 and 19. Hench further teaches obtaining optical response data for a plurality of grating constructional parameters and a plurality of illumination conditions([0029]-[0032]); and
training the neural network such that the neural network learns how to map each of the grating constructional parameters of the plurality of grating constructional parameters and each illumination condition of the plurality of illumination conditions to the optical response data([0031]-[0032]).
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hench (US 2010/0017351) in view of Hadler et al. (US 20250355136), further in view of Gawhary et al. (US 20120243004).
Regarding Claim 6. The modified Hench does not explicitly teach computing an uncertainty measure for one or more parameters of the predicted structure of the grating; and outputting the uncertainty measure with the predicted structure of the grating.
However, Gawhary teaches computing an uncertainty measure for one or more parameters of the predicted structure of the grating([0083], [0100]-[0106]); and
outputting the uncertainty measure with the predicted structure of the grating([0096], [0100]-[0106], [0128]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to the modified invention of Hench, computing an uncertainty measure for one or more parameters of the predicted structure of the grating; and outputting the uncertainty measure with the predicted structure of the grating, as taught by Gawhary, so as to design parameters, as are used to estimate target structure parameters more accurately.
Claim(s) 8 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hench (US 2010/0017351) in view of Hadler et al. (US 20250355136), further in view of Johnson et al. (US 20020038196).
Regarding Claims 8 and 17. The modified Hench does not explicitly teach the predicted optical response includes one or more of ellipsometric data or Mueller matrices.
However, Johnson teaches the predicted optical response includes one or more of ellipsometric data (ellipsometric :[0054], [0103])or Mueller matrices(Mueller matrices:[0101], [0103]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to the modified invention of Hench, c the predicted optical response includes one or more of ellipsometric data or Mueller matrices, as taught by Johnson, so as to avoid the trade off between measurement resolution and accuracy..
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hadler et al. (US 20250355136) in view of Hench (US 2010/0017351).
Regarding Claim 20. Hadler teaches a near-eye display system comprising(fig. 2; Metasurfaces device (wearable or augmented-reality (AR) and/or virtual-reality (VR)): [0003])
an image source to project light comprising an image(optical device design, as their unique sub-wavelength features make it possible to engineer very precisely the way light interacts with the surface: [0003]; imaged: 104: fig. 1);
at least one lens element(optical devices, including a meta-lens: [0003], [0026]); and
a waveguide including at least one grating having a structure (meta surface) verified by a process comprising(characterizing the performance of optical meta-gratings include a scattering efficiency, scattered power, or similar scattering metric, e.g., associated with a specific grating order, polarization, and/or wavelength or wavelength range: [0003], [0026]; geometric shape: [0018]):
receiving grating parameters (202: fig. 2; the performance of optical meta-gratings include a scattering efficiency, scattered power, or similar scattering metric, e.g., associated with a specific grating order, polarization, and/or wavelength or wavelength range, or an overall transmission or reflection efficiency: [0026]) as input to a neural network(200: fig. 2);
generating, by the neural network, an output comprising a predicted optical response of a grating based on the grating parameters (204: fig.2; desired accuracy of the predictions, a performance of optical meta-gratings :[0026], [0028]);
responsive to determining that the difference is greater than the specified threshold, iteratively updating the grating parameters received as input to the neural network until the predicted optical response and the measured optical response converge (The neural network 200 takes a pixelated image 202 representing the metasurface topology of a metasurface device as input and produces one or more figures of merit characterizing the resulting performance 204 of the device as output. Relevant figures of merit generally depend on the type of metasurface device. For example, figures of merit that may be of interest in characterizing the performance of optical meta-gratings include a scattering efficiency, scattered power, or similar scattering metric, e.g., associated with a specific grating order: [0026]; In this iterative method, the gradient of the error with respect to the weights of the network is calculated, proceeding backward through the network, to determine adjustments to the weight during each training iteration. The training process ends when the error converges and/or falls below a specified threshold, corresponding to a desired accuracy of the predictions made by the neural network: [0028]).
Hadler silent about responsive to determining that a difference between the predicted optical response and a measured optical response of the grating is within a specified threshold, outputting the grating parameters as a predicted structure of the grating;
Hench teaches receiving grating parameters (220: fig. 2A) as input to a neural network(230: fig. 2A);
generating, by the neural network, an output comprising a predicted optical response of a grating based on the grating parameters(240: fig. 2A);
however, Hench also teaches responsive to determining that a difference (250: fig. 2A) between the predicted optical response (240: fig. 2A) and a measured optical response of the grating (205: fig. 2A)is within a specified threshold(best fit: [0028]; The Examiner considers “best fit” to be “specified threshold”, which is “specified threshold”=close to zero line ), outputting the grating parameters as a predicted structure of the grating(255: fig. 2A; [0028]);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to the modified invention of Hadler, responsive to determining that a difference between the predicted optical response and a measured optical response of the grating is within a specified threshold, outputting the grating parameters as a predicted structure of the grating, as taught by Hench, so as to the grating parameters such as critical dimension, sidewall angle and feature height of the workpiece can be estimated rapidly and accurately with high precision while reducing the function estimation errors.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
a) Dong et al. (US 20230325999) teaches the reconstruction model based on a sample color image and a sample spectrum image, and calculate a loss function based on the sample spectrum image and a predicted reconstructed spectrum image output by the reconstruction model. When the loss function converges and a value of the loss function is less than or equal to a threshold, a parameter of the reconstruction model that is used to generate the predicted reconstructed spectrum image output by the reconstruction model is determined as a parameter of the reconstruction model that is finally used to generate the reconstructed spectrum image based on the color image. The loss function is calculated based on the sample spectrum image and the predicted reconstructed spectrum image. After a plurality of iterations, when the loss function converges and a value of the loss function is less than or equal to a threshold, the parameter of the reconstruction model is acquired. When the loss function converges and a value of the loss function value is greater than a threshold, the parameter of the reconstruction model is adjusted, and the reconstruction model continues to be trained by using the foregoing method.
b) Cho et al. (US 20210181090) disclose a normal incidence ellipsometer and a method for measuring the optical properties of a sample by using same. The purpose of the present invention is to provide: a normal incidence ellipsometer in which a wavelength-dependent compensator is replaced with a wavelength-independent linear polarizer such that equipment calibration procedures are simplified while a measurement wavelength range expansion can be easily implemented; and a method for measuring the optical properties of a sample by using same.
c) Feng et al (US 20190311083) disclose the computational process calculates a predicted optical response, it may compute a reflectance spectrum or ellipsometric response by simulating reflection of electromagnetic radiation off of said computed etch profile. The reflectance spectrum or ellipsometric response may be generated using, for example, a Rigorous Coupled Wave Analysis (“RCWA”) simulation or a Finite Difference Time-Domain (“FDTD”) simulation.
d) Lai et al. (US 20210157228) disclosehe one or more cameras of the sensor 238 and lens 226 may be mounted, integrated, incorporated or arranged on an HMD to correspond to a left-eye view of a user or wearer of the HMD and a right-eye view of the user or wearer. For example, an HMD may include a first camera with a first lens mounted forward-facing on the left side of the HMD corresponding to or near the left eye of the wearer and a second camera with a second lens mounted forward-facing on the right-side of the HMD corresponding to or near the right eye of the wearer. The left camera and right camera may form a front-facing pair of cameras providing for stereographic image capturing. In some embodiments, the HMD may have one or more additional cameras, such as a third camera between the first and second cameras an offers towards the top of the HMD and forming a triangular shape between the first, second and third cameras. This third camera may be used for triangulation techniques in performing the depth buffer generations techniques of the present solution, as well as for object tracking.
e) Sarwer et al. (US 12481894) disclose An artificial reality system performs local user adaptation of machine learning models and global improvement of the machine learning models while ensuring data security and privacy.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMAD K ISLAM whose telephone number is (571)270-0328. The examiner can normally be reached M-F 9:00 a.m. - 5:00 p.m..
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shelby A Turner can be reached at 571-272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MOHAMMAD K ISLAM/ Primary Examiner, Art Unit 2857