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 .
DETAILED ACTION
This action is in response to the communication filed on 6/4/2026.
Claims 1 - 20 are examined.
Claims 1, 3, 5, 6, 9, 10, 12, 14, 15, 18-20 are rejected.
Claims 2, 4, 7, 8, 11, 13, 16, 17 are objected.
Allowable Subject Matter
Claims 2, 4, 7, 8, 11, 13, 16, 17 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Examiner notes that Reason for allowance will be described upon selection of claims.
Response to Arguments
Applicant arguments, dated 6/4/2026 have been fully considered.
Applicant argues the following : Combination of Lee - Murray fails to teach or suggest, ‘ .. in a state in which parameters of the forward simulation neural network are fixed .. ‘.
Examiner acknowledges the argument and submits the response below.
Examiner’s response as following: –
Examiner does not find argument persuasive, as combination of references teaches the following limitation(s) as described below –
Lee teaches in para 58 the following – ‘ .. forward propagation constructs a convolutional neural network in a direction in which the size of the input image generated by the numerical simulation is maintained, and performs a convolution calculation.. ‘
Examiner interprets Lee’s teaching as where the size of input or numerical consistency of data in forward propagation is maintained, similar to claimed function of state of parameters are fixed in forward simulation neural network.
Examiner further describes that ‘what constitutes as state’ is interpreted broadly in view of claim limitations as known in art as ‘state’ can cover – size, context of data, input source, output source or many other parameters in forward simulation of training data.
Examiner concludes that Lee - Murray teaches argued limitation as described above.
Examiner is open for phone call interview to discuss further with applicant’s representative for the purpose of compact prosecution.
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.
Claims 1, 3, 5, 6, 9, 10, 12, 14, 15, 18-20 are rejected under 35 U.S.C. 103 as being unpatentable by U.S. Publication 2024/0046629 to Lee et al. (hereinafter known as “Lee”) and U.S. Publication 2024/0070466 to Murray et al. (hereinafter known as "Murray”).
As per claim 1 Lee teaches, a processor-implemented method, the method comprising:
generating a first corrected result image of a first desired pattern image using a backward correction neural network based on the first desired pattern image, the backward correction neural network performing a backward correction of a first process (Lee para 57-58 and 69-70 Fig 3 and 5 teach CNN (convolutional neural network) with backward propagation processes on the generated convolution layer and activation function application unit, and an image matching output unit 360 that matches and outputs features extracted by repetitive machine learning of the CNN repetitive machine learning unit);
generating a first simulated result image by executing a forward simulation neural network based on the first corrected result image, the forward simulation neural network performing a forward simulation of a performance of the first process (Lee para 70-72, Fig 3, teaches the CNN repetitive machine learning unit 350 is configured to minimize the loss value of the cost function calculated using the mean square error method through repetitive forward propagation and backward propagation calculation processes); and
in a state in which parameters of the forward simulation neural network are fixed (Lee para 58 teaches forward propagation constructs a convolutional neural network in a direction in which the size of the input image generated by the numerical simulation is maintained), updating the first corrected result image so that an error between the first desired pattern image and the first simulated result image is reduced (Lee para 43-44 Fig 3 teaches back propagation corrects weight and bias by partially differentiating the convolution calculation factor in the reverse direction of the forward propagation calculation in order to minimize the loss).
Lee does not teach however Murray teaches Fig 3 para 48, 53-55 image pattern recognition, pixel enhancements and update of images based on machine learning CNN (neural network).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Lee-Murray before him or her, to combine Lee’s image analysis based on backward and forward correction neural network (Lee Fig 3) with Murray’s teaching of image pattern recognition, pixel enhancements and update of images based on machine learning CNN (Murray para 48, 53-55). The suggestion/motivation for doing so would have been to enhance image outputs by comparing the generated output with an expected output for the training input, and modifying weights and biases associated with the neurons with the aim of matching (or at least reducing the error between) the generated output and the expected output security of device access based on key-value storage that stores data on a key-value basis (Murray para 3).
As per claim 3 combination of Lee-Murray teaches, the method of claim 1, wherein the updating of the first corrected result image comprises:
in a state in which the parameters of the forward simulation neural network are fixed, adjusting parameters of the backward correction neural network so that the error between the first desired pattern image and the first simulated result image is reduced.
(Lee para 41 teaches convolution calculation of the input data and the filter in the convolution layer generation unit 330, zero padding is performed to solve the problem that the edge information of the input data disappears, and the result of the convolution calculation adjusts the size of the output data to the same size as the input value by setting the stride to 1 (stride:=1) when the filter moves within the input data Fig 6A 6B).
As per claim 5 combination of Lee-Murray teaches, the method of claim 1, wherein the updating of the first corrected result image comprises updating the first corrected result image based on gradient descent (Lee para 69, FIG. 6B illustrates a backward propagation calculation formula for CNN repetitive machine learning and also illustrates a process of calculating a gradient by taking a partial differentiation to the forward propagation calculation formula in order to correct the weight which is a convolution calculation factor).
As per claim 6 combination of Lee-Murray teaches, the method of claim 1, further comprising:
finalizing the first corrected result image based on a first result of iteratively updating the first corrected result image; generating a second corrected result image of a second desired pattern image using the backward correction neural network provided a first input based on the second desired pattern image;
generating a second simulated result image using the forward simulation neural network provided a second input based on the second corrected result image; and
updating the second corrected result image so that an error between the second desired pattern image and the second simulated result image is reduced (Lee para 58-59 and 69-70 teaches the forward propagation constructs a convolutional neural network in a direction in which the size of the input image generated by the numerical simulation is maintained, and performs a convolution calculation. The forward propagation is a process for calculating the loss value after the result values generated through several convolution calculations are compared with the reference value (ground-truth) using the mean square error method.
[0059] And, the back propagation corrects weight and bias, which are the convolution factors, by performing the calculation in the reverse direction in order to reduce the calculated error values. The ideal weight and bias can be inferred by repeatedly performing forward propagation and back propagation processes several times, which covers the claimed limitation).
As per claim 9 combination of Lee-Murray teaches, the method of claim 1, wherein the first process comprises one of a develop process and an etch process (Lee para 5-6 teaches industrial process of develop and etch process (lithography)).
Claim 10,
Claim 10 is rejected in accordance with claim 1.
Claim 12,
Claim 12 is rejected in accordance with claim 3.
Claim 14,
Claim 14 is rejected in accordance with claim 5.
Claim 15,
Claim 15 is rejected in accordance with claim 6.
Claim 18,
Claim 18 is rejected in accordance with claim 9.
As per claim 19 Lee teaches, a processor-implemented method, the method comprising:
training a backward correction neural network based on input pattern images corresponding to a simulation input of a target process (Lee para 57-58 and 69-70 Fig 3 and 5 teach CNN (convolutional neural network) with backward propagation processes on the generated convolution layer and activation function application unit, and an image matching output unit 360 that matches and outputs features extracted by repetitive machine learning of the CNN repetitive machine learning unit);
training a forward simulation neural network based on output pattern images corresponding to a simulation output of the target process (Lee para 70-72, Fig 3, teaches the CNN repetitive machine learning unit 350 is configured to minimize the loss value of the cost function calculated using the mean square error method through repetitive forward propagation and backward propagation calculation processes);
generating, by the backward correction neural network, a corrected image based on a pattern image (Lee para 57-58 and 69-70 Fig 3 and 5);
generating, by the forward simulation neural network, a simulated result image based on the corrected image (Lee para 70-72, Fig 3); and
in a state in which parameters of the forward simulation neural network are fixed (Lee para 58 teaches forward propagation constructs a convolutional neural network in a direction in which the size of the input image generated by the numerical simulation is maintained),
adjusting parameters of the backward correction neural network according to an error between the simulated result image and the pattern image (Lee para 41 teaches convolution calculation of the input data and the filter in the convolution layer generation unit 330, zero padding is performed to solve the problem that the edge information of the input data disappears, and the result of the convolution calculation adjusts the size of the output data to the same size as the input value by setting the stride to 1 (stride:=1) when the filter moves within the input data Fig 6A 6B).
Lee does not teach however Murray teaches Fig 3 para 48, 53-55 image pattern recognition, pixel enhancements and update of images based on machine learning CNN (neural network).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Lee-Murray before him or her, to combine Lee’s image analysis based on backward and forward correction neural network (Lee Fig 3) with Murray’s teaching of image pattern recognition, pixel enhancements and update of images based on machine learning CNN (Murray para 48, 53-55). The suggestion/motivation for doing so would have been to enhance image outputs by comparing the generated output with an expected output for the training input, and modifying weights and biases associated with the neurons with the aim of matching (or at least reducing the error between) the generated output and the expected output security of device access based on key-value storage that stores data on a key-value basis (Murray para 3).
As per claim 20 combination of Lee-Murray teaches, the method of claim 19, wherein the adjusting of the parameters of the backward correction neural network comprises iteratively updating the parameters to reduce the error to a predetermined threshold (Lee para 63-64 Fig 5-7 teaches convolution calculation of the input data and the filter in the convolution layer generation unit 330, zero padding is performed to solve the problem that the edge information of the input data disappears, and the result of the convolution calculation adjusts the size of the output data to the same size as the input value).
Prior Art of Record
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Shen et al US Patent 12087042
Liu et al US Patent 11620427
Wang et al US Patent 11727569
Jiang et al US Publication 20220232232
Ding et al US Publication 20230186081
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VIRAL S LAKHIA whose telephone number is (571)270-3363. The examiner can normally be reached on 8 am - 6 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lynn Feild can be reached on 571-272-2092. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/VIRAL S LAKHIA/Primary Examiner, Art Unit 2431
Prosecution Insights