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 non-final office action is in response to the application filed 29 August 2022.
Claims 1-20 are pending. Claims 1, 11, and 16 are independent claims.
Drawings
The examiner accepts the drawings filed 29 August 2022.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3-7 and 11-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 3 recites the limitation "the metrics" in lines 3 and 5-6. The examiner notes that the claim further recites “the metric” which has antecedent basis to “selecting… design features correlate to a metric of a processing resource… (claim 1, lines 3-5).” For at least this reason, “the metric” and “the metrics” appear to have differing antecedent bases. Therefore, there is insufficient antecedent basis for this limitation in the claim.
Claim 3 recites the limitation "the using" in line 7. There is insufficient antecedent basis for this limitation in the claim.
Claim 4 depends upon claim 3 and fails to cure the deficiencies of claim 3. Claim 4 is rejected under similar rationale.
Claim 5 recites the limitation "the new IC design" in lines 2-3. There is insufficient antecedent basis for this limitation in the claim.
Claim 6 recites the limitation "the using" in line 2. There is insufficient antecedent basis for this limitation in the claim.
Claim 6 recites the limitation "the new IC design" in line 2. There is insufficient antecedent basis for this limitation in the claim.
Claim 7 recites the limitation "the new IC design" in line 3. There is insufficient antecedent basis for this limitation in the claim.
Claim 7 recites the limitation "the new IC design" in line 3. There is insufficient antecedent basis for this limitation in the claim.
Claim 11 recites the limitation "the new IC design" in line 13. There is insufficient antecedent basis for this limitation in the claim.
Claim 14 recites the limitation "the new IC design" in line 2. There is insufficient antecedent basis for this limitation in the claim.
Claim 14 recites the limitation "the predicted metric" in line 3. There is insufficient antecedent basis for this limitation in the claim.
Claim 15 recites the limitation "the new IC design" in line 3. There is insufficient antecedent basis for this limitation in the claim.
Claim 15 recites the limitation "the predicted metric" in line 5. There is insufficient antecedent basis for this limitation in the claim.
Claim 15 recites the limitation "the computing platforms" in line 5. There is insufficient antecedent basis for this limitation in the claim.
Claim 12-15 depends upon claim 11 and fails to cure the deficiencies of claim 11. Claims 12-15 are rejected under similar rationale.
Claim 16 recites the limitation "the extracted features" in line 4. The examiner notes that the claim recites both “the extracted features (line 4)” and “the extracted design features (lines 4-5). The term “the extracted design features” appears to reference “extract design features… (line 3).” For at least this reason, “the extracted features” and “the extracted design features” appear to have differing antecedent bases. Therefore, there is insufficient antecedent basis for this limitation in the claim.
Claim 18 recites the limitation "the computing platform" in lines 1-2. There is insufficient antecedent basis for this limitation in the claim.
Claim 19 recites the limitation "the computing platform" in lines 1-2. There is insufficient antecedent basis for this limitation in the claim.
Claim 17-20 depends upon claim 16 and fails to cure the deficiencies of claim 16. Claims 17-20 are rejected under similar rationale.
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 a training set of integrated circuit 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 a training set of integrated circuit designs)
selecting a one or more of the extracted design features based on an extent to which the extracted design features correlated to a metric of a processing resource utilized to evaluate the 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 select one or more extracted design features based on how these design features correlate to a metric of a processing resource)
correlate the selected design features of the IC designs with the metric of the processing resource utilized to evaluate the 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 correlate 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)).
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 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 metric comprises a memory metric and/or 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:
the selecting comprises selecting one or more auxiliary features of the IC designs based on an extent to which the auxiliary features correlate to the metrics of the 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 a combination of the selected design features of the IC designs and the selected auxiliary features of the IC designs with the metrics of the 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 metrics of the IC designs)
predict the metric for the new IC design based on the combination of the selected design features of the new IC design and the selected auxiliary features of the new 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 metric 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 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:
wherein the auxiliary features comprise: design constraints of the IC designs; and/or power consumption information of the 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, wherein the auxiliary features include design constraints and/or power consumption 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 5:
With respect to claim 5, 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:
selecting one or more of multiple computing platforms on which to evaluate the new IC design based on the predicted metric 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 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:
predict the metric for the sub-blocks of the new 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 metric for the sub-blocks of the new IC 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 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 evaluate one of the sub-blocks of the new IC design based on the predicted metric 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:
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:
correlate the selected design features of the IC designs with the metric of a processing resource utilized to perform a static validation of the 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 evaluation to validate the IC designs based upon the correlation of design features and metrics of a processing resource)
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 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 design features relate to: a number of instances, 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 correlate selected features 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 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:
extract design features of a training set of integrated circuit (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 a training set of integrated circuit designs)
select a one or more of the extracted design features based on an extent to which the extracted design features correlate to a metric of a processing resource utilized to evaluate the 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 select one or more extracted design features based on how these design features correlate to a metric of a processing resource)
select one or more auxiliary features of the IC design based on an extent to which the auxiliary features correlate to the metric of the processing resource utilized to evaluate the 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 a combination of the selected design features of the IC design and the selected auxiliary features of the new IC design with the metric of the processing resource utilized to evaluate the 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 correlate 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 memory
a processing device coupled to the memory, the processing device configured
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 an artificial intelligence/machine learning (AI/ML) 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 elements of:
a memory
a processing device coupled to the memory, the processing device configured
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 an artificial intelligence/machine learning (AI/ML) 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 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 selected design features from a new 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)
predict the metric for the new IC design based on the selected design features extracted from the new IC design and the selected auxiliary features of the new 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 metric 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 metric comprises a memory metric and/or 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 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:
selecting one or more of multiple computing platforms on which to evaluate the new IC design based on the predicted metric 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:
predict the metric for the sub-blocks of the new 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 metric for the sub-blocks of the new IC design)
selecting one of multiple computing platforms on which to evaluate one of the sub-blocks of the new IC design based on the predicted metric 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 a training set of integrated circuit (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 a training set of integrated circuit designs)
select a one or more of the extracted design features based on an extent to which the extracted design features correlate to a metric of a processing resource utilized to evaluate the 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 select one or more extracted design features based on how these design features correlate to a metric of a processing resource)
select one or more auxiliary features of the IC design based on an extent to which the auxiliary features correlate to the metric of the processing resource utilized to evaluate the 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 a combination of the selected design features of the IC design and the selected auxiliary features of the new IC design with the metric of the processing resource utilized to evaluate the 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 correlate design features with metrics to perform an evaluation of the integrated circuit design)
extract the selected design features from a new 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)
predict the metric for the new IC design based on the selected design features extracted from the new IC design and the selected auxiliary features of the new 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 metric 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 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 metric comprises a memory metric and/or 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 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 evaluate the new IC design based on the predicted metric 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:
predict the metric for the sub-blocks of the new 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 metric for the sub-blocks of the new IC design)
selecting one of multiple computing platforms on which to evaluate one of the sub-blocks of the new IC design based on the predicted metric 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 20:
With respect to claim 20, the claim depends upon claim 16. The analysis of claim 16 is incorporated herein by reference.
Step 2A, Prong 1:
The claim is directed toward the same abstract idea identified with respect to claim 16.
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 1-7 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 a training set of 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)
selecting a one or more of the extracted design features based on an extent to which the extracted design features correlate to a metric of a processing resource utilized to evaluate 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 (parameters) and how these adjustments cause changes to the integrated circuit)
training a machine learning (ML) model to correlate the selected design features of the IC designs with the metric of the processing resources utilized to evaluate 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 (parameters) and how these adjustments cause changes to the integrated circuit)
As per dependent claim 2, Chen discloses wherein the metric comprises a memory metric and/or 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:
the selecting comprises selecting one or more auxiliary features of the IC designs based on an extent to which the auxiliary features correlate to the metrics of the IC designs (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)
the training comprises training the ML model to correlate a combination of the selected design features of the IC designs and the selected auxiliary features of the IC designs with the metrics of the IC designs (Figure 16; paragraphs 0136-0139: Here, 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)
the using comprises using the trained model to predict the metric for the new IC design based on the combination of the selected design features of the new IC design and the selected auxiliary features of the new 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 cause changes to the integrated circuit)
As per dependent claim 4, Chen discloses wherein the auxiliary features comprise:
design constraints of the ID design, and/or
power consumption information of the IC design (paragraphs 0136-0139)
As per dependent claim 5, Chen discloses selecting one or more of multiple computing platforms on which to evaluate the new IC design based on the predicted metric 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 dependent claim 6, Chen discloses segmenting the IC design into sub-blocks, wherein the using comprises using the trained model to predict for the sub-blocks of the new IC design (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).
As per dependent claim 7, Chen discloses selecting one or more of multiple computing platforms on which to evaluate one of the sub-blocks of the new IC design based on the predicted metric of the sub-block 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 memory (paragraph 0032)
a processing device coupled with the memory (paragraph 0032), the processing device configured to:
extract design features of a training set of 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)
select a one or more of the extracted design features based on an extent to which the extracted design features correlate to a metric of a processing resource utilized to evaluate 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 (parameters) and how these adjustments cause changes to the integrated circuit)
select one or more auxiliary features of the IC designs based on an extent to which the auxiliary features correlate to the metric of the processing resource utilized to evaluate the IC designs (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)
train an artificial intelligence/machine learning (AI/ML) model to correlate a combination of the selected design features of the IC design and the selected auxiliary features of the new IC design with the metric of the processing resource utilized to evaluate 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 (parameters) and how these adjustments cause changes to the integrated circuit)
As per dependent claim 12, Chen discloses wherein the processing device is further configure to:
extract the selected design feature from a new IC design (paragraphs 0136-0139)
use the trained model to predict the metric for the new IC design based on the selected design features extracted from the new IC design and the selected auxiliary features of the new 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.
With respect to claim 14, the claim recites the limitations substantially similar to those in claim 5. Claim 14 is rejected under similar rationale.
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 a training set of 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)
select a one or more of the extracted design features based on an extent to which the extracted design features correlate to a metric of a processing resource utilized to evaluate 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 (parameters) and how these adjustments cause changes to the integrated circuit)
select one or more auxiliary features of the IC designs based on an extent to which the auxiliary features correlate to the metric of the processing resource utilized to evaluate the IC designs (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)
train an artificial intelligence/machine learning (AI/ML) model to correlate a combination of the selected design features of the IC design and the selected auxiliary features of the new IC design with the metric of the processing resource utilized to evaluate 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 (parameters) and how these adjustments cause changes to the integrated circuit)
extract the selected design feature from a new IC design (paragraphs 0136-0139)
use the trained model to predict the metric for the new IC design based on the selected design features extracted from the new IC design and the selected auxiliary features of the new 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 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.
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 further discloses training the ML model to correlate the selected design features of the IC design with the metric of a processing resource (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).
Chen fails to specifically disclose performing a static validation of the IC designs.
However, Tadja, which is analogous to the claimed invention because it is directed toward integrated circuit design, discloses static validation of the IC designs (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 the design features relate to:
a number of instances
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 the design features relate to:
a number of instances
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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Bustany et al. (US 11645440): Discloses training a machine learning model for use in route placement and routing during integrated circuit design (Abstract)
Akkiraju et al. (US 2023/0064987): Discloses synthetic pattern training of machine learning based mask prediction for layout design of integrated circuits (paragraph 022)
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
Prosecution Insights