DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This final office action is in response to the amendment filed 17 August 2026.
Claims 1-20 are pending. Claims 1, 11, and 16 are independent claims.
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:
According to Step 1 of the two Step analysis, claims 1-10 are directed toward a method (process). Claims 11-15 are directed toward a system (machine). Claims 16-20 are directed toward a non-transitory computer-readable medium (manufacture). Therefore, each of these claims falls within one of the four statutory categories.
Claim 1:
Step 2A, Prong 1:
The claim recites in part:
extracting design features of training integrated circuit designs, from machine-readable design files of the training IC designs (mental process; As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses observing design features of training IC designs from a machine-readable design files)
select a subset of the extracted design features based on an extent to which the extracted design features correlated to computational resource requirements for performing a computational evaluation of the training (mental process; As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses performing a judgement to select a subset of extracted design features based on correlation to computation resource requirements for performing an evaluation of the training)
correlate the subset of the extracted design features with the computational resource requirements for performing the computation evaluation of the training IC designs (mental process; As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses a judgement to correlate the subset of extracted design features with metrics to perform an evaluation of the integrated circuit design)
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional element of training a machine learning model. Training the model is recited at a high-level of generality 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 recites the additional element:
provide a trained ML model to predict computational resource requirements for performing the computational evaluation of a test IC design based on design features of the test IC design
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 of a trained model for use (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.
The claim recites the additional element of training a machine learning model. Training the model is recited at a high-level of generality 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 recites the additional element:
provide a trained ML model to predict computational resource requirements for performing the computational evaluation of a test IC design based on design features of the test IC design
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 of a trained model for use (see MPEP 2106.05(g)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 2:
With respect to claim 2, the claim depends upon claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites in part:
wherein the computational resource requirements comprise one or more of memory requirement and a runtime metric (mental process; As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses performing a judgement to select one or more extracted design features based on how these design features correlate to a metric of a processing resource, wherein the metric of the processing resource comprises a memory metric and/or a runtime metric)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 3:
With respect to claim 3, the claim depends upon claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites in part:
select a subset of auxiliary features of the training IC designs, from an auxiliary machine-readable file, based on an extent to which the auxiliary features correlate to the computational resource requirements for performing the computational evaluation of the training IC designs, wherein the auxiliary features comprise one or more of operation information of the training IC designs and design constraints (mental process; As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses performing a judgement to select auxiliary features of the IC design based on how these design features correlate to a metric of a processing resource)
correlate the subset of the auxiliary features and the subset of the extracted design features with the computational resource requirements for performing the computational evaluation of the respective training IC designs (mental process; As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses performing a judgement to correlate selected features of the design and auxiliary features of the design with the resource requirements of the IC designs)
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional element of training a machine learning model and the using comprises using the trained model. Training the model and using the trained model are recited at a high-level of generality 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 claim recites the additional element of training a machine learning model and the using comprises using the trained model. Training the model and using the trained model are recited at a high-level of generality 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.
Claim 4:
With respect to claim 4, the claim depends upon claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites in part:
extracting the subset of design features from a machine-readable design file on the test IC design (mental process; As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses performing an observation to identify and extract a subset of design features from a machine-readable design file)
predicting the computational resource requirements for performing the computational evaluation of the test IC design… based on the subset of the design features of the test IC design (mental process; As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses performing a judgement to predict computational resource requirements based on the subset of design features)
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional element of training a machine learning model and the using comprises using the trained model. Training the model and using the trained model are recited at a high-level of generality 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 claim recites the additional element of training a machine learning model and the using comprises using the trained model. Training the model and using the trained model are recited at a high-level of generality 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.
Claim 5:
With respect to claim 5, the claim depends upon claim 4. The analysis of claim 4 is incorporated herein by reference.
Step 2A, Prong 1:
The claim is directed toward the same abstract idea identified with respect to claim 4.
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional element:
selecting one or more of multiple computing platforms on which to perform the computation evaluation of the test IC design based on the predicted computational resource requirements of the test IC design and specification of the computing platforms by a platform selector
The additional element of selecting by a platform selector, which is recited at a high level of generality, 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").
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 claim recites the additional element:
selecting one or more of multiple computing platforms on which to perform the computation evaluation of the test IC design based on the predicted computational resource requirements of the test IC design and specification of the computing platforms by a platform selector
The additional element of selecting by a platform selector, which is recited at a high level of generality, 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").
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 6:
With respect to claim 6, the claim depends upon claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites in part:
extract the subset of design features of the sub-blocks from the machine-readable design file of the test IC design (mental process; As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses performing an observation to identify and extract a subset of design features from a machine-readable design file)
predicting computational resource requirements for performing the computational evaluation of the sub-blocks… based on the subset of the extracted design features of the respective sub-blocks (mental process; As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses performing a judgement to predict computational resource requirements based on the subset of design features)
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional element of training a machine learning model and the using comprises using the trained model. Training the model and using the trained model are recited at a high-level of generality 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 claim recites the additional element of training a machine learning model and the using comprises using the trained model. Training the model and using the trained model are recited at a high-level of generality 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.
Claim 7:
With respect to claim 7, the claim depends upon claim 6. The analysis of claim 6 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites in part:
selecting one of multiple computing platforms on which to perform the computational evaluation, for each of the sub-blocks, on the predicted computational resource requirements of the sub-blocks and specification of the computing platform (mental process; As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses performing a judgement to select one or more computing platforms on which to evaluate the sub-blocks of the design)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 8:
With respect to claim 8, the claim depends upon claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites in part:
wherein the computational evaluation comprises static verification (mental process; As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses a evaluation as a static verification)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 9:
With respect to claim 9, the claim depends upon claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites in part:
wherein the extracting comprises extracting information related to one or more of: pins, ports, nets, a number of hierarchies, a number of libraries, macro cells, pad cells, and/or power management cells designs (mental process; As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses performing a judgement to extract information)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 10:
With respect to claim 10, the claim depends upon claim 1 and the analysis is incorporated herein by reference.
Step 2A, Prong 1:
The claim is directed toward the same abstract idea identified with respect to claim 1.
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional element of multiple linear regression based supervised training. Training the model is recited at a high-level of generality 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 claim recites the additional element of multiple linear regression based supervised training. Training the model is recited at a high-level of generality 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.
Claim 11:
Step 2A, Prong 1:
The claim recites in part:
extracting design features of training integrated circuit designs, from machine-readable design files of the training IC designs (mental process; As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses observing design features of training IC designs from a machine-readable design files)
select a subset of the extracted design features based on an extent to which the extracted design features correlated to computational resource requirements for performing a computational evaluation of the training (mental process; As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses performing a judgement to select a subset of extracted design features based on correlation to computation resource requirements for performing an evaluation of the training)
select a subset of auxiliary features of the training IC designs, from an auxiliary machine-readable file, based on an extent to which the auxiliary features correlate to the computational resource requirements for performing the computational evaluation of the training IC designs (mental process; As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses performing a judgement to select auxiliary features of the IC design based on how these design features correlate to a metric of a processing resource)
correlate the subset of the extracted design features with the computational resource requirements for performing the computation evaluation of the training IC designs (mental process; As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses a judgement to correlate the subset of extracted design features with metrics to perform an evaluation of the integrated circuit design)
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional elements of a processor and a memory comprising instructions that, when executed by the process, the processor to perform functions. The additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The claim recites the additional element of training a machine learning model. Training the model is recited at a high-level of generality 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 recites the additional element:
provide a trained ML model to predict computational resource requirements for performing the computational evaluation of a test IC design based on design features of the test IC design
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 of a trained model for use (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.
The claim recites the additional elements of a processor and a memory comprising instructions that, when executed by the process, the processor to perform functions. The additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The claim recites the additional element of training a machine learning model. Training the model is recited at a high-level of generality 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 recites the additional element:
provide a trained ML model to predict computational resource requirements for performing the computational evaluation of a test IC design based on design features of the test IC design
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 of a trained model for use (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.
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 12:
With respect to claim 12, the claim depends upon claim 11. The analysis of claim 11 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites in part:
extract the subset of the selected design features for a test IC design from a machine-readable design file of the test IC design (mental process; As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses observing design features of a training set of integrated circuit designs to extract a subset of selected design features)
predict the computational resource requirements for performing the computational evaluation of the test IC design based on the subset of the design features of the test IC design (mental process; As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses performing a evaluation to predict the computational resource requirements for the IC design based on the selected features and the selected auxiliary features)
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional element of using comprises using the trained model. Training the model and using the trained model are recited at a high-level of generality 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 claim recites the additional element of using comprises using the trained model. Training the model and using the trained model are recited at a high-level of generality 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.
Claim 13:
With respect to claim 13, the claim depends upon claim 11. The analysis of claim 11 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites in part:
wherein the computational resource requirements for performing the computational evaluation comprises one or more of a memory metric and a runtime metric (mental process; As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses performing a judgement to select one or more extracted design features based on how these design features correlate to a resource requirement of a processing resource, wherein the resource requirement of the processing resource comprises a memory metric and/or a runtime metric)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 14:
With respect to claim 14, the claim depends upon claim 11. The analysis of claim 11 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites in part:
select one or more of multiple computing platforms on which to perform the computational evaluation of the test IC design based on the predicted computational resource requirements of the test IC design and specification of the computing platforms (mental process; As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses performing a judgement to select one or more computing platforms on which to evaluate the design)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 15:
With respect to claim 15, the claim depends upon claim 11. The analysis of claim 11 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites in part:
segment the test IC design into sub-blocks (mental process; As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses a judgement to divide a test design into sub-blocks)
predict the computational resource requirements for performing the computational evaluation for the sub-blocks based on the subset of the design features of the test IC design (mental process; As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses a judgement to predict the computational resource requirements for the sub-blocks of the new IC design)
selecting one of multiple computing platforms on which to perform the computational evaluation of the sub-blocks based on the predicted computational resource requirements of the sub-block and specifications of the computing platform (mental process; As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses performing a judgement to select one or more computing platforms on which to evaluate the sub-blocks of the design)
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional element of training a machine learning model. Training the model is recited at a high-level of generality 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 claim recites the additional element of training a machine learning model. Training the model is recited at a high-level of generality 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.
Claim 16:
Step 2A, Prong 1:
The claim recites in part:
extract design features of an IC design from a machine-readable design file (mental process; As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses observing design features of integrated circuit designs)
predict computational resource requirements for performing a computational evaluation of the IC design based on the extracted design features (mental process; As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses performing a evaluation to predict computational resource requirements for performing evaluation of the IC design based on design features)
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional elements of:
a non-transitory computer readable medium comprising instructions, which when executed by a processing device, cause the processing device
The additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
The claim recites the additional element of train a machine learning (ML) model and using the trained model. Training the model and using the model is recited at a high-level of generality 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 claim recites the additional elements of:
a non-transitory computer readable medium comprising instructions, which when executed by a processing device, cause the processing device
The additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
The claim recites the additional element of train a machine learning (ML) model and using the trained model. Training the model and using the model is recited at a high-level of generality 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.
Claim 17:
With respect to claim 17, the claim depends upon claim 16. The analysis of claim 16 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites in part:
wherein the computational resource requirements comprise one or more of a memory metric a runtime metric (mental process; As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses performing a judgement to select one or more extracted design features based on how these design features correlate to a computational resource requirement of a processing resource, wherein the metric of the processing resource comprises a memory metric and/or a runtime metric)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 18:
With respect to claim 18, the claim depends upon claim 16. The analysis of claim 16 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites in part:
selecting one or more of multiple computing platforms on which to perform the computational evaluation of the IC design based on the predicted computational resource requirements and specification of the computing platforms (mental process; As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses performing a judgement to select one or more computing platforms on which to evaluate the design)
Step 2A, Prong 2:
There are no additional elements considered under Step 2A, Prong 2.
Step 2B:
There are no additional elements considered under Step 2B.
Claim 19:
With respect to claim 19, the claim depends upon claim 16. The analysis of claim 16 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites in part:
segment the IC design into sub-blocks (mental process; As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses a judgement to divide a test design into sub-blocks)
predict the computational resource requirements for performing the computational evaluation, for each of the sub-blocks… based on the extracted design features of the sub-block (mental process; As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses a judgement to predict the computational resource requirements for performing the computational evaluation for the sub-blocks of the new IC design)
selecting one of multiple computing platforms on which to perform the computational evaluation, for each of the sub-blocks, based on the predicted computational resource requirements of the sub-blocks and specifications of the computing platform (mental process; As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses performing a judgement to select one or more computing platforms on which to evaluate the sub-blocks of the design)
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional element of training a machine learning model. Training the model is recited at a high-level of generality 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 claim recites the additional element of training a machine learning model. Training the model is recited at a high-level of generality 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.
Claim 20:
With respect to claim 20, the claim depends upon claim 19. The analysis of claim 16 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
predict the computational resource requirements of the sub-blocks… based further on auxiliary features that comprise one or more of operational information of the IC design and design constraints (mental process; As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses performing a judgement to predict computational resource requirements based on auxiliary features that comprise operational information of the IC design and design constraints)
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional element of train the ML model with a multiple linear regression technique. Training the model is recited at a high-level of generality 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 claim recites the additional element of train the ML model with a multiple linear regression technique. Training the model is recited at a high-level of generality 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.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-4, 6-7 and 11-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Chen (US 2020/0349236, published 5 November 2020).
As per independent claim 1, Chen discloses a method, comprising:
extracting design features of training integrated circuit (IC) designs (Figure 16; paragraphs 0136-0139: Here, an electronic design automation tool uses machine learning modules to facilitate integrate circuit design. This includes extracting design features (parameters) including power, performance, and/or area estimates for an integrated circuit described by the integrated circuit design data structure) from machine-readable design files of the training IC designs (paragraph 0132: Here, the design data structure is based on the register-transfer level data structure, the software development kit, the physical design data structure, and the test results. The design data structure may be a collection of files)
training a machine learning (ML) model (Figure 16; paragraphs 0136-0139: Here, the machine learning module may include a neural network that has been trained to identify adjustments to the features (parameters) and how these adjustments cause changes to the integrated circuit) to:
select a subset of the extracted design features based on an extent to which the extracted design features correlate to computational resource requirements for performing a computational evaluation of the training the IC designs (Figure 16; paragraphs 0136-0139: Here, the machine learning module may include a neural network that has been trained to identify adjustments to the features (selecting a subset of design features) and how these adjustments cause changes (performing a computational evaluation) to the integrated circuit design)
correlate the subset of the extracted design features with the computational resource requirements for performing the computational evaluation of the training IC designs to provide a trained ML model to predict computational resource requirements for performing the computational evaluation of a test IC design based on design features of the test IC design (Figure 16; paragraphs 0136-0139: Here, the machine learning module may include a neural network that has been trained to identify adjustments to the features (parameters) and how these adjustments correlate to the changes to the integrated circuit)
As per dependent claim 2, Chen discloses wherein the computational resource requirements comprise one or more of a memory requirement and a runtime metric (paragraphs 0136-0139: Here, the parameters (features) correlate to estimates of power and/or performance of the integrated circuit. These performance parameter correlates to runtime metric of the integrated circuit).
As per dependent claim 3, Chen discloses wherein:
select a subset of auxiliary features of the training IC designs, from an auxiliary machine-readable file, based on an extent to which the auxiliary features correlate to the computational resource requirements for performing the computational evaluation of the training IC designs, wherein the auxiliary features comprise one or more of operational information of the training IC designs and design constraints (Figure 16; paragraphs 0136-0139: Here, an auxiliary feature, such as power consumption, is used to evaluate and configured the parameters of the integrated circuit. Further, multiple machine learning modules are used (Figure 16, items 1650, 1660, and 1670). These modules may incorporate features, such as performance, and auxiliary features, such as power. Additionally, the design data structure is based on the register-transfer level data structure, the software development kit, the physical design data structure, and the test results. The design data structure may be a collection of files (paragraph 0132))
correlated the subset of the auxiliary features and the subset of the extracted design features with the computational resource requirements for performing the computational evaluation of the respective training IC designs (Figure 16; paragraphs 0136-0139: Here, the machine learning module may include a neural network that has been trained to identify adjustments to the features (parameters) and how these adjustments cause changes to the integrated circuit. This examiner interprets this as correlating the features with the computational resource requirements)
As per dependent claim 4, Chen discloses wherein the auxiliary features comprise:
extracting the subset of the design features from a machine-readable design file of the test IC design (paragraph 0132: Here, the design data structure is based on the register-transfer level data structure, the software development kit, the physical design data structure, and the test results. The design data structure may be a collection of files)
predicting the computational resource requirements for performing the computational evaluation of the test IC design, by the ML model, based on the subset of the design features of the test IC design (Figures 4-5; paragraphs 0076-0077: Here, a physical design data structure for the IC is determined. This includes determining power, performance, and area estimates (predictions) for the resulting IC design and providing these estimates to the user)
As per dependent claim 6, Chen discloses:
segmenting the test IC design into sub-blocks (Figures 16-17; paragraphs 0136-0144: Here, the integrated circuit design is divided into a plurality of design flowmodules (sub-blocks). These design flow includes a sequence of flowmodules and the trained machine learning module may provide feedback regarding parameters and ordering of tasks (flowmodules) in the design flow)
extracting the subset of design features of the sub-blocks from the machine-readable design file of the test IC design (paragraph 0132: Here, the design data structure is based on the register-transfer level data structure, the software development kit, the physical design data structure, and the test results. The design data structure may be a collection of files)
predicting computational resource requirements for performing the computational evaluation of the sub-blocks, by the ML model, based on the subset of the extracted design features of the respective sub-blocks (Figures 4-5; paragraphs 0076-0077: Here, a physical design data structure for the IC is determined. This includes determining power, performance, and area estimates (predictions) for the resulting IC design and providing these estimates to the user)
As per dependent claim 7, Chen discloses:
selecting one of multiple computing platforms on which to perform the computational evaluation, for each of the sub-blocks, based on the predicted computational resource requirements of the sub-blocks and specifications of the computing platforms (Figure 17; paragraph 0140: Here, the machine learning module evaluates the integrated circuit design by implementing and testing the integrated circuit design by a physical design service module, the register-transfer service module, and/or the verification service module. Each of these represents platforms to evaluate the new IC design).
As per independent claim 11, Chen discloses a system, comprising:
a processor (paragraph 0032)
memory (paragraph 0032) comprising instructions that, when executed by the processor, cause the processor to:
extracting design features of training integrated circuit (IC) designs (Figure 16; paragraphs 0136-0139: Here, an electronic design automation tool uses machine learning modules to facilitate integrate circuit design. This includes extracting design features (parameters) including power, performance, and/or area estimates for an integrated circuit described by the integrated circuit design data structure) from machine-readable design files of the training IC designs (paragraph 0132: Here, the design data structure is based on the register-transfer level data structure, the software development kit, the physical design data structure, and the test results. The design data structure may be a collection of files)
training a machine learning (ML) model (Figure 16; paragraphs 0136-0139: Here, the machine learning module may include a neural network that has been trained to identify adjustments to the features (parameters) and how these adjustments cause changes to the integrated circuit) to:
select a subset of the extracted design features based on an extent to which the extracted design features correlate to computational resource requirements for performing a computational evaluation of the training IC designs (Figure 16; paragraphs 0136-0139: Here, the machine learning module may include a neural network that has been trained to identify adjustments to the features (selecting a subset of design features) and how these adjustments cause changes (performing a computational evaluation) to the integrated circuit design)
As per dependent claim 12, Chen discloses wherein the processing device is further configure to:
extract the subset of the design features for a test IC design (paragraphs 0136-0139) from a machine-readable design file of the test IC design (paragraph 0132)
use the trained ML model to predict the computational resource requirements for performing the computational evaluation of test IC design based on the subset of the design features of the test IC design (Figure 16; paragraphs 0136-0139: Here, an auxiliary feature, such as power consumption, is used to evaluate and configured the parameters of the integrated circuit)
With respect to claim 13, the claim recites the limitations substantially similar to those in claim 2. Claim 13 is rejected under similar rationale.
As per dependent claim 14, Chen discloses selecting one or more multiple computing platforms on which to perform a computational evaluation of the test IC design based on the predicted computational resource requirements of the test IC design specification of the computing platforms (Figure 17; paragraph 0140: Here, the machine learning module evaluates the integrated circuit design by implementing and testing the integrated circuit design by a physical design service module, the register-transfer service module, and/or the verification service module. Each of these represents platforms to evaluate the new IC design).
With respect to claim 15, the claim recites the limitations substantially similar to those in claims 6 and 7. Claim 13 is rejected under similar rationale.
As per independent claim 16, Chen discloses a non-transitory computer readable medium comprising instructions, which when executed by a processing device (paragraph 0032), cause the processing device to:
extract design features of an IC design from a machine-readable file (paragraph 0132)
perform a computational resource requirements for performing a computational evaluation of the IC design based on the extracted design features and a machine learning (ML) model (Figure 16; paragraphs 0136-0139: Here, an electronic design automation tool uses machine learning modules to facilitate integrate circuit design. This includes extracting design features (parameters) including power, performance, and/or area estimates for an integrated circuit described by the integrated circuit design data structure. An auxiliary feature, such as power consumption, is used to evaluate and configured the parameters of the integrated circuit)
With respect to claims 17-19, the claims recite the limitations substantially similar to those in claims 13-15, respectively. Claims 17-19 are rejected under similar rationale.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Chen and further in view of Demay (US 2016/0239376, published 18 August 2016).
As per dependent claim 5, Chen discloses selecting one or more multiple computing platforms on which to perform a computational evaluation of the test IC design based on the predicted computational resource requirements of the test IC design specification of the computing platforms (Figure 17; paragraph 0140: Here, the machine learning module evaluates the integrated circuit design by implementing and testing the integrated circuit design by a physical design service module, the register-transfer service module, and/or the verification service module. Each of these represents platforms to evaluate the new IC design).
Chen fails to specifically disclose selecting by a platform selector.
However, Demay, which is analogous to the claimed invention because it is directed toward an integrated circuit test system, discloses selecting by a platform selector (claim 1: Here, an integrated test system allows for selection of a test platform coupled to a host computer for testing an integrated circuit system). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Demay with Chen, with a reasonable expectation of success, as it would have allowed for specifying a test platform for testing an integrated circuit (Demay: claim 1).
Claims 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Chen and further in view of Tadja et al. (US 2014/0068532, published 6 March 2014, hereafter Tadja).
As per dependent claim 8, Chen discloses the limitations substantially similar to those in claim 1, and the same rejection is incorporated herein. Chen fails to specifically disclose wherein the computational evaluation comprises static verification.
However, Tadja, which is analogous to the claimed invention because it is directed toward integrated circuit design, discloses wherein the computational evaluation comprises static verification (Abstract; paragraph 0021: Here, a static analysis validation is performed to as a quality measure). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective fling date to have combined Tadja with Chen, with a reasonable expectation of success, as it would have allowed for evaluating an integrated circuit design based upon a quality measure (Tadja: paragraph 0021).
As per dependent claim 9, Chen discloses the limitations similar to those in claim 1, and the same rejection is incorporated herein. Chen fails to specifically disclose wherein extracting comprises extracting information related to one or more of:
pins
ports
nets
a number of hierarchies
a number of libraries
macro cells
pad cells and/or
power management cells
However, Tadja, which is analogous to the claimed invention because it is directed toward integrated circuit design, discloses wherein extracting comprises extracting information related to one or more of:
pins
ports
nets
a number of hierarchies
a number of libraries
macro cells
pad cells and/or
power management cells (paragraph 0001: Here, the integrated circuit includes a power grid for distributing power to the various internal components. The integrated circuit design considers the power management cells to improve matching power grids with the needs of the IC (paragraph 0008))
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Tadja with Chen, with a reasonable expectation of success, as it would have allowed for improving IC implementation by matching appropriate power management cells with the needs of the IC (Tadja: paragraph 0008).
Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Chen and further in view of Bach (US 2022/0284649, filed 7 June 2021).
As per dependent claim 10, Chen discloses the limitations similar to those in claim 1, and the same rejection is incorporated herein. Chen fails to disclose multiple linear regression based supervised training.
However, Bach, which is analogous to the claimed invention because it is directed toward training a model, discloses multiple linear regression based supervised training (paragraph 0067: Here, one or more machine learning models may use a supervised learning technique including linear regression). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Bach with Chen, with a reasonable expectation of success, as it would have allowed for training a model using supervised linear regression techniques (Bach: paragraph 0067).
With respect to claim 20, the claim recites the limitations substantially similar to those in claim 10. Claim 20 is rejected under similar rationale.
Response to Arguments
Applicant’s arguments with respect to the rejection of claims under 35 USC 112 have been fully considered and are persuasive in view of the amendments. The rejection has been withdrawn.
Applicant's arguments with respect to the rejection of claims under 35 USC 101 have been fully considered but they are not persuasive.
The applicant argues that the claims are not a mental process because the claims encompass AI in a way that cannot be practically performed in the human mind (pages 11-13).
The examiner agrees that artificial intelligence is not a mental process. However, the examiner does not allege that artificial intelligence is a mental process. Instead, the claimed “training a machine learning (ML) model” and use of a model is considered under Step 2A, Prong 2 and Step 2B as an additional element.
With respect to Step 2A, Prong 1, the examiner identifies the following limitations as mental processes:
extracting design features of training integrated circuit designs, from machine-readable design files of the training IC designs (mental process; As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses observing design features of training IC designs from a machine-readable design files)
select a subset of the extracted design features based on an extent to which the extracted design features correlated to computational resource requirements for performing a computational evaluation of the training (mental process; As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses performing a judgement to select a subset of extracted design features based on correlation to computation resource requirements for performing an evaluation of the training)
correlate the subset of the extracted design features with the computational resource requirements for performing the computation evaluation of the training IC designs (mental process; As drafted and under its broadest reasonable interpretation, this 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. For example, this limitation encompasses a judgement to correlate the subset of extracted design features with metrics to perform an evaluation of the integrated circuit design)
For this reason, this argument is not persuasive.
Applicant's arguments with respect to the rejection of claims under 35 USC 103 have been fully considered but they are not persuasive.
With respect to claims 1, 11, and 16, the applicant argues that Chen fails to teach or suggest, “training an ML model to provide a trained ML model that predicts computational resource requirements for performing a computation evaluation of an IC design, based on features of the IC design (page 13-14).” The examiner respectfully disagrees.
Chen discloses training a machine learning (ML) model (Figure 16; paragraphs 0136-0139: Here, the machine learning module may include a neural network that has been trained to identify adjustments to the features (parameters) and how these adjustments cause changes to the integrated circuit) to perform the computational evaluation of the training IC designs to provide a trained ML model to predict computational resource requirements for performing the computational evaluation of a test IC design based on design features of the test IC design (Figure 16; paragraphs 0136-0139: Here, the machine learning module may include a neural network that has been trained to identify adjustments to the features (parameters) and how these adjustments correlate to the changes to the integrated circuit). For this reason, this argument is not persuasive.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Chokhani et al. (US 11429776): Discloses testing a fabricated integrated circuit chip (claim 15)
Khurana et al. (US 11379644): Discloses testing an integrated circuit chip using a test engine executing on a computing platform (claim 17)
Cox et al. (US 2022/0067236): Discloses a parameter selector, test parameter generator, and a test platform for an integrated circuit (paragraph 0042)
Wen et al. (US 2006/0156268): Discloses a circuit design platform to test integrated circuit products of selected test service providers (claim 13)
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 KYLE R STORK whose telephone number is (571)272-4130. The examiner can normally be reached 8am - 2pm; 4pm - 6pm.
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 Rivas 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.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/KYLE R STORK/Primary Examiner, Art Unit 2128
