Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Claims 2, 7-8, 13, and 18-19 have been canceled. Claims 1, 3-6, 9-12, 14-17, and 20 are pending. Claims 1, 3-6, 9-12, 14-17, and 20 have been examined and rejected.
Response to Arguments
Applicant's arguments filed 3/7/2025 regarding the 35 USC 101 rejections of claims 1, 3-6, 9-12, 14-17, and 20 have been fully considered but they are not persuasive. The amended claims 1, 12, and 20 and other respective dependent claims are rejected under 35 USC 101 as indicated in the 35 USC 101 rejection section below.
Applicant’s arguments with respect to the 35 USC 103 rejections of claims 1, 3-6, 9-12, 14-17, and 20, see p. 16 last paragraph – p. 17, have been considered but are moot because the new ground of rejections rely on an additional reference to previous references applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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, 3-6, 9-12, 14-17, and 20 are rejected under 35 USC 101 for being directed to abstract ideas.
Claim 1 is a method claim and recites:
A method of implementing design cycles for developing a hardware component comprising:
receiving sets of experimental data, each set of experimental data resulting from an application of a set of variables to the hardware component during a common design cycle or a different design cycle of the hardware component, each variable representing an aspect of the hardware component or an aspect of a fabrication process applied to the hardware component; (insignificant extra-solution activity of data gathering)
making a determination that all sets of experimental data have not been received; (mental processes)
requesting, based on the determination, and receiving additional sets of experimental data; (insignificant extra-solution activity, data gathering MPEP 2106.05(g))
determining, using an unsupervised clustering model, discretized classes of one or more quality metrics, wherein the discretized classes of the quality metrics are determined independently from a determination of discretized classes of a different quality metric of the one or more quality metrics, wherein the discretized classes of the quality metrics are a classification of the quality metrics in an order of values of the experimental data; (math concepts; discretizing data into classes using classification of quality metrics is a math process, so this limitation is math concept)
obtaining statistical measurements of the variables; (math concepts)
generating a dataset based upon the statistical measurements of the variables; (math concepts)
applying a rank-correlated mining algorithm to the dataset, wherein the rank-correlated mining algorithm comprises using the discretized classes of the quality metrics to determine at least one probability trend; (math concepts)
determining, using the at least one probability trend, correlations between the discretized classes of the quality metrics and the statistical measurements of the variables to reduce a number of design cycles implemented on the hardware component during the developing of the hardware component; (math concepts)
implementing design cycles for developing the hardware component based on the at least one probability trend, the correlations, and the patterns to obtain updated parameters, wherein: (insignificant extra-solution activity, mere instructions to apply an exception, MPEP 2106.05(f))
the hardware component is a semiconductor; (insignificant extra-solution activity, data gathering MPEP 2106.05(g))
the updated parameters include an updated thickness of the semiconductor and an updated composition of the semiconductor; and (mental processes that can be done by pen and paper)
fabricating a final hardware component by implementing the updated parameters. (insignificant extra-solution activity, mere instructions to apply an exception, MPEP 2106.05(f))
Step 2A, prong 1: limitations are grouped into abstract ideas as indicated above.
Step 2A, prong 2: the claim does not recite any limitation that integrates a practical application into abstract ideas.
Step 2B: the claim does not recite any additional elements. Limitation “receiving sets of experimental data … applied to the hardware component” is insignificant extra-solution activity of data gathering, MPEP 2106.05(g).
Claim 3 is a method claim depending on claim 1 and recites:
The method of claim 2, wherein the unsupervised clustering model is a Gaussian Mixture Model (GMM). (math concepts)
Step 2A, prong 1: limitations are grouped into abstract ideas as indicated above.
Step 2A, prong 2: the claim does not recite any limitation that integrates a practical application into abstract ideas.
Step 2B: the claim does not recite any additional elements.
Claim 4 is a method claim depending on claim 1 and recites:
The method of claim 1, wherein the statistical measurements are based on characteristics of a distribution of the experimental data for each variable. (math concepts)
Step 2A, prong 1: limitations are grouped into abstract ideas as indicated above.
Step 2A, prong 2: the claim does not recite any limitation that integrates a practical application into abstract ideas.
Step 2B: the claim does not recite any additional elements.
Claim 5 is a method claim depending on claim 1 and recites:
The method of claim 1, wherein the statistical measurements include mean, median, variance, or percentile. (math concepts)
Step 2A, prong 1: limitations are grouped into abstract ideas as indicated above.
Step 2A, prong 2: the claim does not recite any limitation that integrates a practical application into abstract ideas.
Step 2B: the claim does not recite any additional elements.
Claim 6 is a method claim depending on claim 1 and recites:
The method of claim 1, further comprising:
performing the statistics measurements of the variables for each of the classes of the quality metrics. (math concepts)
Step 2A, prong 1: limitations are grouped into abstract ideas as indicated above.
Step 2A, prong 2: the claim does not recite any limitation that integrates a practical application into abstract ideas.
Step 2B: the claim does not recite any additional elements.
Claim 9 is a method claim depending on claim 1 and recites:
The method of claim 1, further comprising:
selecting one or more rank correlation patterns based on a frequency in the dataset. (mental processes)
Step 2A, prong 1: limitations are grouped into abstract ideas as indicated above.
Step 2A, prong 2: the claim does not recite any limitation that integrates a practical application into abstract ideas.
Step 2B: the claim does not recite any additional elements.
Claim 10 is a method claim depending on claim 9 and recites:
The method of claim 9, wherein the order of the rank correlation patterns is arranged from most relevant to less relevant so as to be used in further analysis of the hardware fabrication or design process. (mental processes)
Step 2A, prong 1: limitations are grouped into abstract ideas as indicated above.
Step 2A, prong 2: the claim does not recite any limitation that integrates a practical application into abstract ideas.
Step 2B: the claim does not recite any additional elements.
Claim 11 is a method claim depending on claim 9 and recites:
The method of claim 9, wherein a rank correlation pattern of the one or more rank correlation patterns is based on an increase or a decrease of one or more of the statistical measurements corresponding to an increase or a decrease of an order of value of one or more quality metrics. (mental processes)
Step 2A, prong 1: limitations are grouped into abstract ideas as indicated above.
Step 2A, prong 2: the claim does not recite any limitation that integrates a practical application into abstract ideas.
Step 2B: the claim does not recite any additional elements.
Claim 12 is a system claim and recites:
A system, comprising:
a processor; and
memory comprising instructions which, when executed by the processor, perform a method, the method comprising:
receiving sets of experimental data, each set of experimental data resulting from an application of a set of variables to the hardware component during a common design cycle or a different design cycle of the hardware component, each variable representing an aspect of the hardware component or an aspect of a fabrication process applied to the hardware component; (insignificant extra-solution activity of data gathering)
making a determination that all sets of experimental data have not been received; (mental processes)
requesting, based on the determination, and receiving additional sets of experimental data; (insignificant extra-solution activity of data gathering)
determining, using an unsupervised clustering model, discretized classes of one or more quality metrics, wherein the discretized classes of the quality metrics are determined independently from a determination of discretized classes of a different quality metric of the one or more quality metrics, wherein the discretized classes of the quality metrics are a classification of the quality metrics in an order of values of the experimental data; (math concepts; discretizing data into classes using classification of quality metrics is a math process, so this limitation is math concept)
obtaining statistical measurements of the variables; (math concepts)
generating a dataset based upon the statistical measurements of the variables; (math concepts)
applying a rank-correlated mining algorithm to the dataset, wherein the rank-correlated mining algorithm comprises using the discretized classes of the quality metrics to determine at least one probability trend; (math concepts)
determining, using the at least one probability trend, correlations between the discretized classes of the quality metrics and the statistical measurements of the variables to reduce a number of design cycles implemented on the hardware component during the developing of the hardware component; (math concepts)
implementing design cycles for developing the hardware component based on the at least one probability trend, the correlations, and the patterns to obtain updated parameters, wherein: (insignificant extra-solution activity, mere instructions to apply an exception, MPEP 2106.05(f))
the hardware component is a semiconductor; (insignificant extra-solution activity, data gathering MPEP 2106.05(g))
the updated parameters include an updated thickness of the semiconductor and an updated composition of the semiconductor; and (mental processes that can be done by pen and paper)
fabricating a final hardware component by implementing the updated parameters. (insignificant extra-solution activity, mere instructions to apply an exception, MPEP 2106.05(f))
Step 2A, prong 1: limitations are grouped into abstract ideas as indicated above.
Step 2A, prong 2: the claim does not recite any limitation that integrates a practical application into abstract ideas.
Step 2B: the claim recites a processor and memory, which are components of a computer, at generic level. They are used for performing the recited generic functions. They do not amount significantly more to abstract ideas. Limitation “receiving sets of experimental data … applied to the hardware component” is insignificant extra-solution activity of data gathering, MPEP 2106.05(g).
Claims 14-17 are system claims depending on claim 12, respectively, and recite limitations analogous to claims 2-6, respectively. They are, hence, rejected for the same reasons.
Claim 20 is a system claim and recites limitations analogous to claim 12. It is, hence, rejected for the same reasons.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 4-6, 12, 14-17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Shimazu (2021/0056444) in view of Fatula (US 2005/0144283), Calders et al. (Mining Rank-Correlated Sets of Numerical Attributes, KDD, ACM Aug. 2006), and Robert et al. (WO 2019/182952).
Claim interpretation: Claim 1 recites “obtaining statistical measurements of the variables to determine correlations between the discretized classes of the quality metrics and the statistical measurements of the variables to reduce the number of design cycles implemented on the hardware component during the developing of the hardware component.” In this limitation the portion “to reduce the number of design cycles implemented on the hardware component during the developing of the hardware component” is interpreted as intended use of “obtaining statistical measurements of the variables to determine correlations between the discretized classes of the quality metrics and the statistical measurements of the variables.” Hence, a prior art does not have to teach “to reduce the number of design cycles implemented on the hardware component during the developing of the hardware component.”
As per claim 1, Shimazu teaches a method of implementing design cycles for developing a hardware component comprising:
receiving sets of experimental data, each set of experimental data resulting from an application of a set of variables to the hardware component during a common design cycle or a different design cycle of the hardware component, each variable representing an aspect of the hardware component or an aspect of a fabrication process applied to the hardware component; (¶ 0002-0003, 0097-0100; Shimazu teaches obtaining sets of experimental data of attributes, corresponding to a set of variables, in a fabrication process applied to a hardware component; the data should apparently be of a common or a different design cycles)
determining discretized classes of one or more quality metrics, wherein the discretized classes of the quality metrics are determined independently from a determination of discretized classes of a different quality metric of the one or more quality metrics, wherein the discretized classes of the quality metrics are a classification of the quality metrics in an order of values of the experimental data; and (¶ 0003, 0006, 0032; Shimazu teaches using a classification algorithm to divide data into groups, corresponding classes, of at least one quality features; a quality feature is a quality metric; groups of data based on a classification algorithm correspond to discretized classes as recited in this limitation)
obtaining statistical measurements of the variables; (¶ 0140-0145, 0148, 0150; Shimazu teaches calculating statistical values comprising average, median, sum, variance, etc. of sets of data)
generating a dataset based upon the statistical measurements of the variables (¶ 0038, 0140, 0150; Shimazu teaches generating a reduced dataset relying on statistical functions comprising, mean value, sum, variance; this teaching reads onto this limitation);
applying a rank algorithm to the data set (¶ 0312; Shimazu teaches using ranking influence of each feature);
determining, using the at least one probability trend, correlations, and patterns between the discretized classes of the quality metrics and the statistical measurements of the variables to reduce a number of design cycles implemented on the hardware component during the developing of the hardware component (¶ 0181-0182; Shimazu teaches using classification algorithm to classify dataset into discrete groups corresponding to classes as discussed into limitation above and also correlate feature data with label data; this teaching reads onto this limitation); and
implementing design cycles for developing the hardware component based on the at least one improve one or more labels, determination of importance or impact of features of the electronic items, based on the correlation of feature data with label data, and improvement of a manufacturing line or improve design/conception of an electronic item, see ¶ 0302, which are cycles for developing the hardware component; these teachings read onto this limitation);
the hardware component is a semiconductor (¶ 0091; Shimazu teaches electronic chips (dies, wafers), which are semiconductor);
fabricating a final hardware component by implementing the updated parameters (¶ 0091; Shimazu teaches manufacturing process of electronic chips).
Shimazu does not teach:
making a determination that all sets of experimental data have not been received;
requesting, based on the determination, and receiving additional sets of experimental data;
determining, using an unsupervised clustering model, discretized classes of one or more quality metrics;
determining, using the at least one probability trend, correlations, and patterns between the discretized classes;
applying a rank-correlated mining algorithm to the dataset, wherein the rank-correlated mining algorithm comprises using the discretized classes of the quality metrics to determine at least one probability trend;
implementing design cycles for developing the hardware component based on the at least one probability trend and the patterns; and
the updated parameters include an updated thickness of the semiconductor and an updated composition of the semiconductor.
However, Fatula teaches:
making a determination that all sets of experimental data have not been received (¶ 0100-0101; Fatula teaches a manager determining if additional data are needed and if all data are received);
requesting, based on the determination, and receiving additional sets of experimental data (¶ 0100-0104; Fatula teaches a manager determining if additional data are needed and if all data are received; if data are missing and needed, requests would be sent and received).
Shimazu and Fatula are analogous art because they both deal with data transmission comprising requests and receipts protocol. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Shimazu and Fatula. One of ordinary skill in the art would have been motivated to make such a combination because Fatula’s teachings would have provided a method to retrieve complete and sufficient data for a client (Fatula, ¶ 0100, 0103).
Shimazu and Fatula do not teach:
determining, using an unsupervised clustering model, discretized classes of one or more quality metrics;
determining, using the at least one probability trend, correlations, and patterns between the discretized classes;
applying a rank-correlated mining algorithm to the dataset, wherein the rank-correlated mining algorithm comprises using the discretized classes of the quality metrics to determine at least one probability trend;
implementing design cycles for developing the hardware component based on the at least one probability trend and the patterns; and
the updated parameters include an updated thickness of the semiconductor and an updated composition of the semiconductor.
However, Calders teaches:
determining, using the at least one probability trend, correlations, and patterns between the discretized classes (p. 96 Abstract, right col. ¶ 2 – bullet 1., p. 97 left col. section 2.1 Rank Correlation Measures – right col. paragraph before section 2.2 Support For Numerical Attributes).
applying a rank-correlated mining algorithm to the dataset, wherein the rank-correlated mining algorithm comprises using the discretized classes of the quality metrics to determine at least one probability trend (p. 96 Abstract, right col. ¶ 2, bullet number 1, p. 97 left col. section 2.1 Rank Correlation Measures – right col. ¶ 3; Calders teaches using a rank-correlated mining algorithm to a data set comprising discretized classes and comparison of probability to determine a probability trend); and
determining the at least one probability trend and the patterns of the data set (p. 96 Abstract).
Shimazu, Fatula, and Calders are analogous art because they both deal with data analyses. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Shimazu, Fatula, and Calders to determine performance of a data set based on a probability trend and its patterns in addition to its correlation between groups of the quality metrics for implementing design cycles for developing the hardware component based on the at least one probability trend, the correlations, and the patterns. One of ordinary skill in the art would have been motivated to make such a combination because Calders’ teachings would have given efficient algorithms to find all frequent patterns for the proposed support measures and evaluate their performance on real-life datasets (Calders, p. 96 Abstract).
Shimazu, Fatula, and Calders do not teach:
determining, using an unsupervised clustering model, discretized classes of one or more quality metrics.
the updated parameters include an updated thickness of the semiconductor and an updated composition of the semiconductor.
However, Robert teaches:
determining, using an unsupervised clustering model, discretized classes of one or more quality metrics (¶ 000207, 000361; Robert teaches using unsupervised learning and clustering including K-cluster algorithm for implementing the pattern recognition; clustering in unsupervised learning is inherently unsupervised clustering; K-cluster algorithm is an unsupervised clustering algorithm).
the updated parameters include an updated thickness of the semiconductor and an updated composition of the semiconductor (¶ 000278-000279; Robert teaches making adjustments to parameters, attributes including thickness and composition).
Shimazu, Fatula, Calders, and Robert are analogous art because they are in the same field of machine learning and dimension reduction by clustering. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Shimazu, Fatula, Calders, and Robert. One of ordinary skill in the art would have been motivated to make such a combination because Robert’s teachings would have utilized cost-benefit analysis to determine a set of trade-off scenarios in which production of specific output proceeds without output for specific, highly costly tool systems (Robert, ¶ 000392)
As per claim 4, Shimazu, Fatula, Calders, and Robert in combination teach the method of claim 1, Shimazu further teaches wherein the statistical measurements are based on characteristics of a distribution of the experimental data for each variable. (¶ 0140, 0284-0285; Shimazu teaches statistical values calculated using statistical functions such as average, median, variance, etc. based on statistical distribution such as binomial, normal, gamma, etc.)
As per claim 5, Shimazu, Fatula, Calders, and Robert in combination teach the method of claim 1, Shimazu further teaches wherein the statistical measurements include mean, median, variance, (¶ 0140) or
As per claim 6, Shimazu, Fatula, Calders, and Robert in combination teach the method of claim 1, Shimazu further teaches further comprising:
performing the statistics measurements of the variables for each of the classes of the quality metrics. (¶ 0140-0143, 0149-0150; Shimazu teaches calculating statistics values, corresponding to statistics measurements of the variables for each group of the quality features)
As per claim 12, Shimazu teaches a system, comprising:
a processor (¶ 0006, 0079); and
memory comprising instructions which, when executed by the processor, perform a method (¶ 0006, 0079), the method comprising: (Shimazu, Fatula, Calders, and Robert in combination teach the below limitations have already been discussed in claim 1; they are, hence, rejected for the same reasons)
receiving sets of experimental data, each set of experimental data resulting from an application of a set of variables to a hardware component during a common design cycle or a different design cycle of the hardware component, each variable representing an aspect of the hardware component or an aspect of a fabrication process applied to the hardware component;
making a determination that all sets of experimental data have not been received;
requesting, based on the determination, and receiving additional sets of experimental data;
determining, using an unsupervised clustering model, discretized classes of one or more quality metrics, wherein the discretized classes of the quality metrics are determined independently from a determination of discretized classes of a different quality metric of the one or more quality metrics, wherein the discretized classes of the quality metrics are a classification of the quality metrics in an order of values of the experimental data;
obtaining statistical measurements of the variables;
generating a dataset based upon the statistical measurements of the variables;
apply a rank-correlated mining algorithm to the dataset, wherein the rank-correlated mining algorithm comprises using the discretized classes of the quality metrics to determine at least one probability trend;
determining, using the at least one probability trend, correlations and patterns between the discretized classes of the quality metrics and the statistical measurements of the variables to reduce a number of design cycles implemented on the hardware component during the developing of the hardware component; and
implementing design cycles for developing the hardware component based on the at least one probability trend, the correlations, and the patterns to obtain updated parameters, wherein:
the hardware component is a semiconductor;
the updated parameters include an updated thickness of the semiconductor and an updated composition of the semiconductor; and
fabricating a final hardware component by implementing the updated parameters.
As per claim 14, these limitations have already been discussed in claim 3. They are, hence, rejected for the same reasons.
As per claim 15, these limitations have already been discussed in claim 4. They are, hence, rejected for the same reasons.
As per claim 16, these limitations have already been discussed in claim 5. They are, hence, rejected for the same reasons.
As per claim 17, these limitations have already been discussed in claim 6. They are, hence, rejected for the same reasons.
As per claim 20, these limitations have already been discussed in claim 12. They are, hence, rejected for the same reasons.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Shimazu (2021/0056444) in view of Fatula (US 2005/0144283), Calders et al. (Mining Rank-Correlated Sets of Numerical Attributes, KDD, ACM Aug. 2006), Robet et al., and further in view of Yang et al. (Unsupervised Diemnsionality Reduction for Gaussian Mixture Model, ICONIP 2014, Part II, pp. 84-92),
As per claim 3, Shimazu, Fatula, Calders, and Robert in combination teach the method of claim 1,
Shimazu, Fatula, Calders, and Robert do not teach:
wherein the unsupervised clustering model is a Gaussian Mixture Model (GMM).
However, Yang teaches:
the unsupervised clustering model is a Gaussian Mixture Model (GMM). (p. 85 ¶ 3)
Shimazu, Fatula, Calders, Robert, and Yang are analogous art because they are in the same field of machine learning and dimension reduction by clustering. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Shimazu, Fatula, Calders, Robert, and Yang. One of ordinary skill in the art would have been motivated to make such a combination because Yang’s teachings would have optimized a dimensionality reduction together with the parameters of GMM (Yang, p. 84 Abstract).
Claims 9-11 are rejected under 35 U.S.C. 103 as being unpatentable over Shimazu in view of Fatula, Calders et al., Yang et al., and Robert et al. as applied to claim 1 above, and further in view of Lempesis et al. (Combining Learning-to-Rank with Clustering, Proceeding of the 10th International Conference on Web Information Systems and Technologies, Science and Technology Publication, pp. 266-294, 2014).
As per claim 9, Shimazu, Fatula, Calders, and Robert in combination teach the method of claim 1,
Shimazu, Fatula, Calders, and Robert do not teach:
selecting one or more rank correlation patterns based on a frequency in the dataset.
However, Lempesis teaches:
selecting one or more rank correlation patterns based on a frequency in the dataset. (p. 287 left col. ¶ 1 – right col. ¶ 1; Lempesis teaches ranking of clusters based on frequency of a feature occurring in a cluster to select a cluster)
Shimazu, Fatula, Calders, Robert, and Lempesis are analogous art because they are in the same field of machine learning and clustering. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Shimazu, Fatula, Calders, Robert, and Lempesis. One of ordinary skill in the art would have been motivated to make such a combination because Lempesis’ teachings would have improved better effectiveness and efficiency and gotten better overall quality according to the well-known evaluation metrics (Lempesis, p. 293 left col. last paragraph).
As per claim 10, Shimazu, Fatula, Calders, Robert, and Lempesis in combination teach the method of claim 9, Shimazu further teaches wherein the order of the rank correlation patterns is arranged from most relevant to less relevant so as to be used in further analysis of the hardware fabrication or
As per claim 11, Shimazu, Fatula, Calders, Robert, and Lempesis in combination teach the method of claim 9, Shimazu further teaches wherein a rank correlation pattern of the one or more rank correlation patterns is based on an increase or a decrease of one or more of the statistical measurements corresponding to an increase or a decrease of an order of value of one or more quality metrics (0304-0309, 0312; Shimazu teaches using a statistical measurement such as mean value to determine failure rate; Shimazu also teaches ranking influence of each feature on the label, e.g. failure rate; these teachings read onto this limitation).
Conclusion
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 Cuong Van Luu whose telephone number is 571-272-8572. The examiner can normally be reached on Monday - Friday from 8:30 to 5:00.
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/CUONG V LUU/Examiner, Art Unit 2189
/REHANA PERVEEN/Supervisory Patent Examiner, Art Unit 2189