DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 5/27/2026 has been entered.
Claim Objections
Claim objected to because of the following informalities:
Claim 2, line 2, “EPE metric” should be changed to -- edge placement error (EPE) metric
Appropriate correction is required.
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 – 5 and 8 - 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
As to claim 1,
Step 2A, Prong One
The claim recites in part:
predict, using the machine learning prediction model, multi-dimensional output substrate geometry based on the input information, wherein the multi-dimensional output substrate geometry comprises a representation of pattern probability in a plurality of dimensions that indicates a probability that a given geometry occupies a given location on a substrate.
+For example, a human can mentally estimate the probability of a geometry occupying locations on a substrate and predicts the most likely output geometry based on that evaluation. Humans have been making educated predictions before computers were ever invented.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
receive input information including geometry information and/or process information for a pattern
which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
The claim further recites non-transitory computer-readable media and one or more processors which are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The recitation of multi-dimensional output substrate geometry amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)). As such, the claim does not integrate the judicial exception into a practical application.
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
receive input information including geometry information and/or process information for a pattern
are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
The claim further recites non-transitory computer-readable media and one or more processors which are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The recitation of multi-dimensional output substrate geometry amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)). As such, the claim does not integrate the judicial exception into a practical application.
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 2,
Step 2A, Prong One
The claim recites in part:
wherein the prediction further comprises an EPE metric is symmetric or asymmetric for the one or more features of the pattern
For example, a human can mentally estimate the probability of a geometry being either symmetric or asymmetric. Humans have been making educated predictions before computers were ever invented.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claim 3,
Step 2A, Prong One
The claim does not recite an abstract idea or any other judicial exception and therefore passes Step 2A, Prong of the Alice/Mayo analysis.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the machine learning prediction model is trained using asymmetrically distributed training data such that weights and/or parameters of the trained machine learning prediction model facilitate determination of the symmetric or asymmetric EPE metric
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The recitation of asymmetrically distributed training data amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)). As such, the claim does not integrate the judicial exception into a practical application.
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. The limitations:
wherein the machine learning prediction model is trained using asymmetrically distributed training data such that weights and/or parameters of the trained machine learning prediction model facilitate determination of the symmetric or asymmetric EPE metric
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The recitation of asymmetrically distributed training data amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)). As such, the claim does not integrate the judicial exception into a practical application.
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception
As to claim 4, the limitations “wherein the asymmetrically distributed training data comprises asymmetrically distributed EPE metrics determined from multi-dimensional probability images associated with asymmetrically distributed critical dimension (CD) values” amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
As to claim 5, the limitations “wherein the multi-dimensional output substrate geometry indicates variability in shapes of features of the pattern” amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
As to claim 8,
Step 2A, Prong One
The claim recites in part:
wherein the instructions are further configured to cause the one or more processors to predict, with the machine learning prediction model, one-or-both-of (1) a symmetric or asymmetric stochastic edge placement error band and/or (2) a stochastic failure rate, based on the mulli-dimensional output substrate geometry
Under the broadest reasonable interpretation, these limitations are process steps that cover a mathematical relationship, mathematical formula, or algorithm, which is identified as an abstract idea. Specifically, the recited tokenizing recites a data processing step involving organization and manipulation of data.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
Further the claim does not include additional elements that integrate this abstract idea into a practical application. “Predicting” is performed using generic computer components performing their typical functions and does not provide a meaningful technological improvement.
The recitation of stochastic edge placement error band and stochastic failure rate amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)). As such, the claim does not integrate the judicial exception into a practical application.
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
Nothing in the claim adds “significantly more” beyond generic computing.
The recitation of stochastic edge placement error band and stochastic failure rate amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)). As such, the claim does not integrate the judicial exception into a practical application.
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 9,
Step 2A, Prong One
The claim does not recite an abstract idea or any other judicial exception and therefore passes Step 2A, Prong of the Alice/Mayo analysis.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the instructions are further configured to cause the one or more processors io tune the machine learning prediction model such that the multi-dimensional output substrate geometry matches a measured stochastic edge placement error band or measured failure rate, or matches a mean contour prediction from an optical proximity correction model or a lithography manufacturability check model
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The recitation of optical proximity correction model and lithography manufacturability check model amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)). As such, the claim does not integrate the judicial exception into a practical application.
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. The limitations:
wherein the instructions are further configured to cause the one or more processors io tune the machine learning prediction model such that the multi-dimensional output substrate geometry matches a measured stochastic edge placement error band or measured failure rate, or matches a mean contour prediction from an optical proximity correction model or a lithography manufacturability check model
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The recitation of lithography manufacturability check model amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)). As such, the claim does not integrate the judicial exception into a practical application.
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception
As to claim 10,
Step 2A, Prong One
The claim does not recite an abstract idea or any other judicial exception and therefore passes Step 2A, Prong of the Alice/Mayo analysis.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the representation of pattern probability comprises a pattern probability image, and wherein the instructions are further configured to cause the one or more processors to use the pattern probability image for a lithography manufacturability check and/or pattern fidelity metrology in a semiconductor device manufacturing process
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The claim further recites semiconductor device which are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The recitation of lithography manufacturability check and pattern fidelity metrology amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)). As such, the claim does not integrate the judicial exception into a practical application.
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. The limitations:
wherein the representation of pattern probability comprises a pattern probability image, and wherein the instructions are further configured to cause the one or more processors to use the pattern probability image for a lithography manufacturability check and/or pattern fidelity metrology in a semiconductor device manufacturing process
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The recitation of semiconductor device which are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The recitation of lithography manufacturability check and pattern fidelity metrology amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)). As such, the claim does not integrate the judicial exception into a practical application.
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception
As to claim 11, the limitations “wherein the input information comprises one or more selected from: a simulated aerial image, a simulated resist image, target substrate dimensions, or data from a lithography apparatus scanner associated with semiconductor device manufacturing, for a semiconductor device” amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
As to claim 12, the limitations “wherein the input information comprises a plurality of aerial images, and individual aerial images of the plurality of aerial images correspond to different heights in resist layers associated with a patterning process” amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
As to claim 13,
Step 2A, Prong One
The claim does not recite an abstract idea or any other judicial exception and therefore passes Step 2A, Prong of the Alice/Mayo analysis.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the instructions are further configured to cause the one or more processors to train the machine learning prediction model with training information comprising one or more selected from: aerial images, target pattern geometry, or patterning process parameters, and corresponding physical substrate measurements and/or predictions from a different non-machine learning prediction model
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The recitation of aerial images, target pattern geometry, or patterning process parameters, and corresponding physical substrate measurements and predictions from a different non-machine learning prediction model amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)). As such, the claim does not integrate the judicial exception into a practical application.
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. The limitations:
wherein the instructions are further configured to cause the one or more processors to train the machine learning prediction model with training information comprising one or more selected from: aerial images, target pattern geometry, or patterning process parameters, and corresponding physical substrate measurements and/or predictions from a different non-machine learning prediction model
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The recitation of aerial images, target pattern geometry, or patterning process parameters, and corresponding physical substrate measurements and predictions from a different non-machine learning prediction model amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)). As such, the claim does not integrate the judicial exception into a practical application.
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception
As to claim 14,
Step 2A, Prong One
The claim recites in part:
wherein the instructions are further configured to cause the one or more processors to determine an adjustment for a semiconductor device manufacturing apparatus based on the predicted multi-dimensional output substrate geometry
Under the broadest reasonable interpretation, these limitations are process steps that cover a mathematical relationship, mathematical formula, or algorithm, which is identified as an abstract idea. Specifically, the recited tokenizing recites a data processing step involving organization and manipulation of data.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
Further the claim does not include additional elements that integrate this abstract idea into a practical application. “Determining” is performed using generic computer components performing their typical functions and does not provide a meaningful technological improvement.
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
Nothing in the claim adds “significantly more” beyond generic computing.
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 15,
Step 2A, Prong One
The claim does not recite an abstract idea or any other judicial exception and therefore passes Step 2A, Prong of the Alice/Mayo analysis.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the instructions are further configured to cause the one or more processors to calibrate the machine learning prediction model based on one or both of after development inspection dimensions and after etch inspection dimensions associated with a semiconductor device manufacturing process
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. The limitations:
wherein the instructions are further configured to cause the one or more processors to calibrate the machine learning prediction model based on one or both of after development inspection dimensions and after etch inspection dimensions associated with a semiconductor device manufacturing process
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception
As to claim 16,
Step 2A, Prong One
The claim recites in part:
predict, using the machine learning prediction model, output device geometry based on the input information, wherein the output device geometry comprises a representation of pattern probability in a plurality of dimensions that indicates a probability that a given geometry occupies a given location on a substrate the output
For example, a human can mentally estimate the probability of a geometry occupying locations on a substrate and predicts the most likely output geometry based on that evaluation. Humans have been making educated predictions before computers were ever invented.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
receive input information including geometry information and/or patterning
process information for a device manufacturing process;
which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
The claim further recites non-transitory computer-readable media and processors which are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
In addition, the recitation of edge placement error (EPE) metric amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)). As such, the claim does not integrate the judicial exception into a practical application.
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
receive input information including geometry information and/or patterning
process information for a device manufacturing process;
are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
The non-transitory computer-readable media and processors are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
In addition, the recitation of edge placement error (EPE) metric amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)). As such, the claim does not integrate the judicial exception into a practical application.
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 17,
Step 2A, Prong One
The claim does not recite an abstract idea or any other judicial exception and therefore passes Step 2A, Prong of the Alice/Mayo analysis.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
receive new input information determined based on an adjustment to the device manufacturing process, the adjustment determined based on the output device geometry
which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
The claim further recites:
predict, using the machine learning model, updated output device geometry based on the new input information, including determination of an updated EPE metric based on the new input information and/or the updated output device geometry.
these elements are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)).
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
receive new input information determined based on an adjustment to the device manufacturing process, the adjustment determined based on the output device geometry
are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. The limitations:
predict, using the machine learning model, updated output device geometry based on the new input information, including determination of an updated EPE metric based on the new input information and/or the updated output device geometry.
are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
The recitation of question-answer pair, language model, and similarity score amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 18, the limitations “wherein the representation of pattern probability comprises a pattern probability image that comprises predicted two-dimensional substrate geometry for one or more features of the pattern” amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
As to claim 19, the limitations “wherein the representation of pattern probability comprises predicted two-dimensional geometry of one or more vias in a semiconductor device” amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
As to claim 20,
Step 2A, Prong One
The claim recites in part:
wherein the instructions are further configured to cause the one or more processors to predict, with the machine learning prediction model, an asymmetric stochastic edge placement error band
Under the broadest reasonable interpretation, these limitations are process steps that cover a mathematical relationship, mathematical formula, or algorithm, which is identified as an abstract idea. Specifically, the recited tokenizing recites a data processing step involving organization and manipulation of data.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
Further the claim does not include additional elements that integrate this abstract idea into a practical application. “Predicting” is performed using generic computer components performing their typical functions and does not provide a meaningful technological improvement.
The recitation of stochastic edge placement error band and stochastic failure rate amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)). As such, the claim does not integrate the judicial exception into a practical application.
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
Nothing in the claim adds “significantly more” beyond generic computing.
The recitation of stochastic edge placement error band and stochastic failure rate amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)). As such, the claim does not integrate the judicial exception into a practical application.
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
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.
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.
Claim(s) 1, 5, and 10 - 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over VELLANKI et al (US 2018/0031981) in view of ROGERS et al (US 2015/0380355).
As to claim 1, VELLANKI et al teaches one or more non-transitory, computer-readable media storing a machine learning prediction model (paragraph [0007]…a computer-implemented defect prediction method for a device manufacturing process involving processing one or more patterns onto a substrate) and instructions that, when executed by one or more processors (paragraph [0050]…computer system 100 includes a bus 102 or other communication mechanism for communicating information, and a processor 104 (or multiple processors 104 and 105) coupled with bus 102 for processing information), are configured to cause the one or more processors to at least:
receive input information including geometry information and/or process
information for a pattern (paragraph [0036]…FIG. 2 shows a flow chart of an embodiment of this method. In step 291, values of one or more processing parameters under which one or more patterns are processed (e.g., imaged or etched onto a substrate) are determined. In an embodiment, the one or more patterns may be selected exclusively among the PWLPs. In an embodiment, the one or more patterns may be selected among all the patterns. The one or more processing parameters may be local—dependent on the location of the one or more patterns, the die, or both. The one or more processing parameters may be global—independent of the location of the one or more patterns and the dies) (Examiner’s Note: “values of one or more processing parameters under which one or more patterns are processed (e.g., imaged or etched onto a substrate) are determined” reads on “receive input information including geometry information and/or process
information for a pattern”); and
predict, using the machine learning prediction model, output
substrate geometry based on the input information, wherein the output substrate geometry comprises a representation of pattern probability that indicates a probability that a given geometry occupies a given location on a substrate (paragraph [0036]… the values of the one or more processing parameters may be compiled into a map—one or more lithographic parameters, or process conditions, as a function of location. Of course, values of one or more other processing parameters may be represented as function of location, i.e., a map. In an embodiment, the values of the processing parameters may be determined before, or desirably immediately before (e.g., no other patterns processed after determining the one or more processing parameters and before the one or more patterns are processed), or during, processing the one or more patterns. ; paragraph [0037]…In step 292, existence, probability of existence, a characteristic, and/or a combination selected from the foregoing, of a defect the one or more patterns produce is determined or predicted using the values of the one or more processing parameters under which the one or more patterns are processed. In an embodiment, one or more characteristics of the one or more patterns can also be used in the determination or prediction. In an embodiment, the determination or prediction is done without using any characteristic of the one or more patterns ; (Examiner’s Note: “probability of existence, a characteristic, and/or a combination selected from the foregoing, of a defect the one or more patterns produce is determined or predicted using the values of the one or more processing parameters under which the one or more patterns are processed” reads on “predict, using the machine learning prediction model, multi-dimensional output substrate geometry based on the input information” ; “the values of the one or more processing parameters may be compiled into a map—one or more lithographic parameters, or process conditions, as a function of location” reads on “wherein the output substrate geometry comprises a representation of pattern probability that indicates a probability that a given geometry occupies a given location on a substrate”).
VELLANKI et al fails to explicitly show/teach a multi-dimensional output substrate geometry comprises a representation of pattern probability in a plurality of dimensions.
However, ROGERS et al teaches a multi-dimensional output substrate geometry comprises a representation of pattern probability in a plurality of dimensions (paragraph [0021]…the two-dimensional spatial geometry is an iterative two-dimensional geometry, such as a spatial geometry comprising a repeating pattern of elements having the same or similar shape (e.g., characterized by the unit cell shape). In an embodiment, for example, sequence of electrically conductive elements has a serial configuration or a branched configuration. Two-dimensional spatial geometries useful in the present systems may have a deterministic two-dimensional shape or a random two-dimensional shape. Optionally, for some embodiments, the two-dimensional spatial geometry has a spring-within-a-spring geometry. For example, in embodiments, the spring-within-in-spring geometry comprises a series of primary spring structures each independently having the primary unit cell shape connected in a serial or a branched configuration to form one or more secondary spring structures each independently having the secondary shape. Optionally, the primary spring structures, the secondary spring structures or both comprise a compression spring structure or coiled spring structure. In some embodiments, the spring-in-a-spring geometry is characterized by two-dimensional geometry comprising at least two spring configurations, wherein upon deformation a first spring configuration undergoes stretching to some degree prior to stretching of a second spring configurationv)
Therefore, it would have been obvious for one having ordinary skills in the art, at the time the invention was made, for VELLANKI et al to have multi-dimensional output substrate geometry comprises a representation of pattern probability in a plurality of dimensions, as in ROGERS et al, for the purpose of having processable component materials and flexible substrates to enable fabrication by continuous, high speed, printing techniques capable of generating electronic devices over large substrate areas at low cost.
As to claim 5, ROGERS et al teaches wherein the multi-dimensional output substrate geometry indicates variability in shapes of features of the pattern (paragraph
[0022]…the two-dimensional spatial geometry is a self-similar two-dimensional geometry. For example, in embodiments, the self-similar two-dimensional geometry is characterized by the secondary shape being similar to the primary unit cell shape but having a different length scale. In some embodiments, the self-similar two-dimensional geometry is characterized by a length scale of the secondary shape at least 2 times larger than a length scale of the primary unit cell shape, optionally at least 3 times larger than a length scale of the primary unit cell shape. For example, in embodiments, a length scale of the secondary shape is larger than a length scale of the primary unit cell shape by a factor selected over the range of 2 to 20, and optionally selected over the range of 3 to 10. In an embodiment, a self-similar geometry is characterized by a primary unit cell having a unit cell shape and one or more higher order patterns made up of the primary unit cell and having a similar overall spatial configuration as the primary unit cell shape but with a different length scale. In some embodiments, for example, the length scale of the unit cell shape and the length scales of high order patterns are multiples of each other (e.g., related by a constant), thereby giving rise to an overall two-dimensional geometry characterized by a plurality of spatial frequencies).
It would have been obvious for the multi-dimensional output substrate geometry indicates variability in shapes of features of the pattern for the same reasons as above.
As to claim 10, VELLANKI et al teaches one or more non-transitory, computer-readable media, the representation of pattern probability comprises a pattern probability image, and wherein the instructions are further configured to cause the one or more processors to use the pattern probability image for a lithography manufacturability check and/or pattern fidelity metrology in a semiconductor device manufacturing process (paragraph [0042]…FIG. 5 shows an exemplary flow that uses the method of FIG. 2. In step 510, step 291 is carried out on one or more patterns produced onto a portion, or an entirety, of a substrate, where, in this example, the values of the one or more processing parameters are determined by using one or more metrology tools on the patterning device and/or the substrate, and by obtaining (e.g., measuring) values of one or more parameters of the device manufacturing process or equipment (e.g., a lithographic apparatus))(Examiner’s Note: “the values of the one or more processing parameters are determined by using one or more metrology tools on the patterning device and/or the substrate, and by obtaining (e.g., measuring) values of one or more parameters of the device manufacturing process or equipment (e.g., a lithographic apparatus)” reads on “the representation of pattern probability comprises a pattern probability image, and wherein the instructions are further configured to cause the one or more processors to use the pattern probability image for a lithography manufacturability check and/or pattern fidelity metrology in a semiconductor device manufacturing process”).
As to claim 11, VELLANKI et al teachesone or more non-transitory computer-readable media, wherein the input information comprises one or more selected from: a
simulated aerial image, a simulated resist image, target substrate dimensions, or data
from a lithography apparatus associated with semiconductor device manufacturing, for a
semiconductor device (paragraph [0026]… In a lithographic apparatus, a source provides illumination (i.e. radiation) to a patterning device; projection optics direct and shapes the illumination, via the patterning device, onto a substrate. The term “projection optics” is broadly defined here to include any optical component that may alter the wavefront of the radiation beam. For example, projection optics may include at least some of the components 14, 16a, 16b and 16c. An aerial image (AI) is the radiation intensity distribution on the substrate ; paragraph [0042]…FIG. 5 shows an exemplary flow that uses the method of FIG. 2. In step 510, step 291 is carried out on one or more patterns produced onto a portion, or an entirety, of a substrate, where, in this example, the values of the one or more processing parameters are determined by using one or more metrology tools on the patterning device and/or the substrate, and by obtaining (e.g., measuring) values of one or more parameters of the device manufacturing process or equipment (e.g., a lithographic apparatus))(Examiner’s Note: “the values of the one or more processing parameters are determined by using one or more metrology tools on the patterning device and/or the substrate, and by obtaining (e.g., measuring) values of one or more parameters of the device manufacturing process or equipment (e.g., a lithographic apparatus)” reads on “the input information comprises data from a lithography apparatus associated with semiconductor device manufacturing, for a semiconductor device”).
As to claim 12, VELLANKI et al teachesone or more non-transitory computer-readable media, wherein the input information comprises a plurality of aerial images, and individual aerial images of the plurality of aerial images correspond to different heights in resist layers associated with a patterning process (paragraph [0026]…In a lithographic apparatus, a source provides illumination (i.e. radiation) to a patterning device; projection optics direct and shapes the illumination, via the patterning device, onto a substrate. The term “projection optics” is broadly defined here to include any optical component that may alter the wavefront of the radiation beam. For example, projection optics may include at least some of the components 14, 16a, 16b and 16c. An aerial image (AI) is the radiation intensity distribution on the substrate. A resist layer on the substrate is exposed and the aerial image is transferred to the resist layer as a latent “resist image” (RI) therein. The resist image (RI) can be defined as a spatial distribution of solubility of the resist in the resist layer. A resist model can be used to calculate the resist image from the aerial image, an example of which can be found in commonly assigned U.S. Patent Application Publication No. US 2009-0157360,the disclosure of which is hereby incorporated by reference in its entirety. The resist model is related only to properties of the resist layer (e.g., effects of chemical processes which occur during exposure, PEB and development)).
As to claim 13, VELLANKI et al teaches one or more non-transitory computer-readable media, wherein the instructions are further configured to cause the one or more processors to train the machine learning prediction model with training information
comprising one or more selected from: aerial images, target pattern geometry, or patterning process parameters, and corresponding physical substrate measurements and/or predictions from a different non-machine learning prediction model (paragraph [0041]… the term “classifier” or “classification model” sometimes also refers to a mathematical function, implemented by a classification algorithm, which maps input data to a category. In machine learning and statistics, classification is the problem of identifying to which of a set of categories 440 (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known. The individual observations are analyzed into a set of quantifiable properties, known as various explanatory variables, features, etc. These properties may variously be categorical (e.g. “good”—a lithographic process that does not produce a defect or “bad”—a lithographic process that produces a defect; “type 1”, “type 2”, . . . “type n”—different types of defects). Classification is considered an instance of supervised learning, i.e. learning where a training set of correctly identified observations is available. Examples of classification models are logistic regression, multinomial logit, probit regression, the perceptron algorithm, support vector machine, import vector machine, and/or linear discriminant analysis).
As to claim 15, VELLANKI et al teaches one or more non-transitory computer-readable media, wherein the instructions are further configured to cause the one or more processors to calibrate the machine learning prediction model based on one or both of after development inspection dimensions and after etch inspection dimensions
associated with a semiconductor device manufacturing process (paragraph [0036]… [0036] FIG. 2 shows a flow chart of an embodiment of this method. In step 291, values of one or more processing parameters under which one or more patterns are processed (e.g., imaged or etched onto a substrate) are determined).
As to claim 15, Rodgers et al teaches wherein the instructions are further configured to cause the one or more processors to calibrate the machine learning prediction model based on one or both of after development inspection dimensions and after etch inspection dimensions associated with a semiconductor device manufacturing process (paragraph [0016]…the two-dimensional spatial geometry of the metallic or semiconducting device component(s) allows for significant deformation without substantial degradation of electronic performance or failure, for example, via compression, expansion, twisting and/or bending deformations. In an embodiment, for example, the two-dimensional spatial geometry allows the metallic or semiconducting device component to undergo elastic deformation. In an embodiment, for example, the two-dimensional spatial geometry allows the metallic or semiconducting device component to undergo biaxial deformation, radial deformation or both. In an embodiment, for example, the two-dimensional spatial geometry allows the metallic or semiconducting device component to undergo in-plane deformation, out-of-plane deformation or both).
It would have been obvious for the instructions are further configured to cause the one or more processors to calibrate the machine learning prediction model based on one or both of after development inspection dimensions and after etch inspection dimensions associated with a semiconductor device manufacturing process, for the same reasons as above.
Claim 16 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above.
Response to Arguments
Applicant's arguments filed 5/27/2026 have been fully considered but they are not persuasive.
Claim Rejections - 35 USC § 101
The 101 Rejection still has not been overcome. The claims are abstract and the steps in the claims can be completed with a mental process and/or generic computer components. Additionally, the steps in the claims do not describe an improvement of technology in any way.
The applicant argues:
The Claims Do Not Recite a Mental Process Under Step 2A, Prong One
The Office Action characterizes the claimed prediction as an "observation, evaluation, judgment or opinion" that could be performed in the human mind or with pencil and paper. Applicant respectfully disagrees.
The proper inquiry is not whether the claim uses words such as "predict" or "determine." The proper inquiry is whether the claim limitations, under their broadest reasonable interpretation in light of the specification, can practically be performed in the human mind. The MPEP expressly states that "[c]laims do not recite a mental process when they do not contain limitations that can practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations." MPEP § 2106.04(a)(2), subsection III.A. The MPEP further identifies as non-mental examples claims requiring manipulation of computer data structures, such as pixels of a digital image and a two-dimensional mask.
Claim 1 does not merely recite looking at information and forming an opinion.
Claim 1 requires, inter alia:
"predict, using the machine learning prediction model, multi-dimensional
output substrate geometry based on the input information,"
wherein:
"the multi-dimensional output substrate geometry comprises a representation of pattern probability in a plurality of dimensions that indicates a probability that a given geometry occupies a given location on a substrate."
These limitations cannot reasonably be reduced to an observation, evaluation,
judgment, or opinion. They require a machine learning prediction model to generate a specific technical output: multi-dimensional output substrate geometry, including a representation of pattern probability in multiple dimensions that indicates a probability that a given geometry occupies a given location on a substrate. A human mind is not practically equipped to generate such a multi-dimensional substrate-geometry prediction from semiconductor geometry/process information using a machine learning model.
The specification confirms that these claim terms are not generic labels for a subjective assessment. The specification [0134] explains that "a machine-learning based prediction model is configured to take simulated aerial image intensity distributions and/or other information as input, and predicts two-dimensional pattern probability images (PPI) and/or other information." The specification [0187] further explains that "the predicted probabilities of the locations of various features of a pattern in a substrate (e.g., represented by the gradient shading of features) in a pattern probability image may be used to determine whether a feature (e.g., a via), or a portion of a feature, will be present at a given location in a manufactured semiconductor device." Thus, the claimed prediction is not a human judgment about whether a pattern is acceptable; it is the generation of a technical geometry/probability representation of physical substrate geometry.
The examiner disagrees. The claims under their broadest reasonable interpretation, recite receiving information and predicting substrate geometry based on that information. Merely reciting that the prediction is performed using a machine learning model does not remove the claim from being a mental process. Humans have made predictions and evaluations based on available information long before computers or machine learning existed.
Further, the claims do not recited physically etching, forming, or modifying a substrate. Instead, they merely recite generating a predicted geometry or probability representation. The claimed output is simply information representing a prediction, not a physical transformation.
The arguments presented rely on limitations that are neither explicitly recited in the claims nor reasonably inferred from them. At no point in the pending claims does the applicant assert, describe, or even suggest the limitation of “a machine-learning based prediction model is configured to take simulated aerial image intensity distributions and/or other information as input, and predicts two-dimensional pattern probability images (PPI) and/or other information” Rather, the applicant appears to have introduced this language as part of the argument, but such a limitation cannot be read into the claims when it is not supported by the actual claim language. Without clear support in the claim language the examiner cannot give weight to arguments premised on these alleged limitations.
The applicant argues:
The Examiner's Mental-Process Characterization Overgeneralizes the Claims
Applicant respectfully submits that the rejection overgeneralizes the claims by characterizing the claimed invention as merely "organizing, analyzing, processing information." That characterization omits the specific limitations that make the claim non-mental: the claimed use of a machine learning prediction model to predict multi-dimensional output substrate geometry, including a representation of pattern probability in multiple dimensions that indicates a probability that a given geometry occupies a given location on a substrate.
Moreover, the instant amendments incorporate features of claims 6 and 7, which were improperly rejected under 101 for at least the following reasons. First, the rejection refers to "tokenizing" of data. Tokenization is not recited in Claim 6 or any other claim and therefore it is not even apparent that the instant rejections are drawn to the instant application. The reference to "tokenization" in alleged support of other 101 rejections of five dependent claims (6, 7, 8, 14, and 20) suggests that the instant claims were not properly considered under § 101. Furthermore, Claim 6's elements did not recite math itself; it recited a substrate-specific output representation tied to physical geometry/location information.
The MPEP cautions that examination must not expand the mental-process
grouping to cover limitations that cannot practically be performed in the human mind. The USPTO's August 4, 2025 memorandum reiterates that "a claim does not recite a mental process when it contains limitation(s) that cannot practically be performed in the human mind," and further instructs that "[t]he mental process grouping is not without limits" and that examiners should not expand that grouping to encompass claim limitations that cannot practically be performed mentally.
The claimed prediction here is not analogous to a person mentally comparing two values or making a subjective assessment. Rather, it is analogous to the MPEP's examples of non-mental computer-implemented image/data-structure operations. The MPEP cites Research Corp. Technologies as an example of a claim that did not recite a mental process because it required manipulation of "the pixels of a digital image and a two-dimensional array known as a mask" and output of a modified computer data structure. MPEP § 2106.04(a)(2), subsection III.A.
Here, the claimed machine learning prediction model similarly produces a
technical, multi-dimensional substrate-geometry representation. The claimed representation of pattern probability is not a mental label. It is a technical representation of predicted physical geometry in semiconductor manufacturing.
The examiner disagrees as the rejection does not overgeneralize the claims. Under their broadest reasonable interpretation, the clams recite receiving information and generating a prediction, Merely reciting a machine learning mode or a multi-dimensional probability representation does not remove the claim from being a mental process.
Further, the claims do not recited physically etching, forming, or modifying a substrate. Instead, they merely recite generating a predicted geometry or probability representation. The claimed output is simply information representing a prediction, not a physical transformation.
The applicant argues:
"Using a Machine Learning Prediction Model" Does Not Convert the Claim
Into a Mental Process
The Office Action appears to treat the recitation of a machine learning prediction model as merely using a generic computer to perform a mental process. Applicant respectfully submits that this analysis is backwards. The fact that a machine learning model is required is one reason the claimed prediction is not practically performable in the human mind.
The claim does not say merely "analyze information." It requires prediction using the machine learning prediction model of a specific semiconductor-manufacturing output: a representation of pattern probability in multiple dimensions that indicates a probability that a given geometry occupies a given location on a substrate. The human mind cannot practically apply trained model weights and parameters to semiconductor geometry/process information to generate a multi-dimensional substrate-geometry prediction.
The USPTO has recognized that Al-related limitations should not automatically be treated as abstract. The August 4, 2025 USPTO memorandum explains that" claim limitations that encompass AI in a way that cannot be practically performed in the human mind do not fall within" the mental-process grouping. The same memorandum also distinguishes between claims that merely involve mathematical concepts and claims that recite them, noting that a neural-network training limitation in USPTO Example 39 did not recite a judicial exception merely because it involved techniques that may rely on mathematics.
Likewise here, the claim may involve computations performed by a machine learning model, but the claim is not directed to a person mentally performing those computations. Nor does the claim recite a mathematical formula divorced from technological context. The claim recites a specific technical prediction in semiconductor manufacturing: a representation of pattern probability in multiple dimensions that indicates a probability that a given geometry occupies a given location on a substrate.
The examiner disagrees. Merely reciting that a prediction is performed using a machine learning model does not remove the claim from the mental process grouping. The claims are directed to receiving information and generating a prediction, which is an evaluation that humans have performed long before computer or machine learning existed.
Further, the claims do not recite training the machine learning model physically manufacturing, etching, or modifying a semiconductor substrate. Instead, they merely recite generating a predicted geometry or probability representation. Applicant’s reliance on AI guidance and the specification os unpersuasive because the eligibility analysis is based on the claim language.
As per MPEP 2106.04(a)(2)(III)(C)), a claim that requires a computer may still recite a mental process. In evaluating whether a claim that requires a computer recites a mental process, examiners should carefully consider the broadest reasonable interpretation of the claim in light of the specification. For instance, examiners should review the specification to determine if the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept. In these situations, the claim is considered to recite a mental process.
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The applicant argues:
The Dependent Claims Further Confirm That the Claimed Subject Matter Is
Not a Mental Process
The dependent claims further narrow the prediction to technical semiconductor- manufacturing outputs that are even less practically able to be performed in the human mind.
For example, dependent claim 8 requiring prediction of an asymmetric stochastic edge placement error band does not merely require a person to opine that an edge error may be larger on one side than another. The specification explains that an asymmetric EPE metric may have a non-Gaussian distribution and may be determined from multi-dimensional probability images associated with asymmetrically distributed critical dimension values. The claimed asymmetric stochastic band is thus a spatial/probabilistic characterization of stochastic edge-location variation in manufactured semiconductor geometry.
Similarly, dependent claim 10 recites that the representation of pattern probability includes a pattern probability image used for a lithography manufacturability check and/or pattern fidelity metrology in a semiconductor device manufacturing process. This is not a mental judgment. It is a practical use of the generated probability
The examiner disagrees. The additional limitations of the dependent claims do not alter the abstract nature of the independent claim. The recited asymmetric stochastic edge placement error band, pattern probability image, and lithography manufacturability check merely further define the content or intended use of the predicted information. The claims still recite generating and analyzing information rather than physically manufacturing or modifying semiconductor substrate.
The applicant argues:
The Rejection Should Be Withdrawn at Least Because the Pending Claims Cannot Practically Be Performed Mentally
Under the MPEP, a claim recites a mental process only when it contains limitations that can practically be performed in the human mind. MPEP § 2106.04(a)(2), subsection III. The claims here require prediction, using a machine learning prediction model, of a representation of pattern probability in multiple dimensions that indicates a probability that a given geometry occupies a given location on a substrate. Such prediction of substrate geometry is not an observation, evaluation, judgment, or opinion that a human can practically perform mentally or with pencil and paper.
The examiner disagrees. The claims under their broadest reasonable interpretation, recite receiving information and predicting substrate geometry based on that information. Merely reciting that the prediction is performed using a machine learning model does not remove the claim from being a mental process. Humans have made predictions and evaluations based on available information long before computers or machine learning existed.
Further, the claims do not recited physically etching, forming, or modifying a substrate. Instead, they merely recite generating a predicted geometry or probability representation. The claimed output is simply information representing a prediction, not a physical transformation.
The applicant argues:
In the Alternative, the Claims Integrate Any Alleged Abstract Idea Into a Practical Application
Even assuming, solely for argument, that the claims were found to recite an abstract idea, the claims integrate any such alleged abstract idea into a practical application. The MPEP provides that a claim integrates a judicial exception into a practical application when the claim improves "the functioning of a computer, or an improvement to other technology or technical field." MPEP § 2106.04(d); see also MPEP § 2106.05(a).
The claims improve the technical field of semiconductor manufacturing by
predicting physical substrate geometry affected by stochastic patterning effects. The specification [0133] explains that prior empirical stochastic models based on CD measurements "generally do not provide adequate information about two-dimensional pattern shapes" and [0131] "do[] not have predictive capabilities for any specified varying process parameters, or for new or different pattern types and shapes that were not used for model fitting or calibration." The specification [0132] further explains that Monte Carlo stochastic simulation "cannot handle large-scale or full-chip pattern layouts, due to runtime constraints."
The claimed machine-learning prediction model addresses these technical deficiencies by predicting a representation of pattern probability in multiple dimensions that indicates a probability that a given geometry occupies a given location on a substrate. The claims therefore do not merely collect and display data. They recite a particular machine-learning prediction technique for generating a semiconductor-manufacturing geometry output that can be used for lithography manufacturability checking, pattern fidelity metrology, hotspot analysis, and/or manufacturing process adjustment.
The MPEP requires consideration of the claim "as a whole" and cautions against evaluating additional limitations "in a vacuum." It further instructs that the analysis should consider how the claim limitations "interact and impact each other" when determining whether an exception is integrated into a practical application. Viewed as a whole, the pending claims are directed to a practical semiconductor-manufacturing application, not to a disembodied mental process.
The examiner disagrees. The claims do not integrate the abstract idea into a practical application. Although, the claims are directed to semiconductor manufacturing, they merely generate predicted information regarding substrate geometry. The claims do not recite using the prediction to physically manufacture, etch, modify, or control a semiconductor manufacturing process.
The applicant’s reliance on the specification is unpersuasive because the alleged technological improvements are note recited in the claims. Any use of the predicted information constitutes extra-solution activity and does not integrate the judicial exception into a practical application.
It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below. In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception. See MPEP § 2106.04(d) (discussing Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1303-04, 125 USPQ2d 1282, 1285-87 (Fed. Cir. 2018))
It is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology (MPEP 2106.05(a)(II).
Claim Rejections - 35 USC § 103
Applicant’s arguments with respect to claim(s) 1, 5, and 10 - 16 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRANDON S COLE whose telephone number is (571)270-5075. The examiner can normally be reached Mon - Fri 7:30pm - 5pm EST (Alternate Friday's Off).
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, Omar Fernandez can be reached at 571-272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRANDON S COLE/ Primary Examiner, Art Unit 2128
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