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 .
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.
Claim(s) 1-6,20 is/are rejected under 35 U.S.C. 103 as being unpatentable over by US 20210042570 A1 (Iskandar) in view of US 20200057689 A1 (Farahat) and US 20150363699 A1 (Nikovski).
Regarding claim 1, Iskandar teaches
A computer-based modeling method for predicting semiconductor processing equipment failure and taking corrective action,(par 4 – generally describes processing manufacturing equipment trace data to identify features to input into a machine learning model, which outputs predictive data about failures, and then the system performs a corrective action associated with the manufacturing equipment based on the predictive data.) comprising:
training, by a computer, a machine learning model (MLM) based on input data and a selected training algorithm to generate a trained MLM, (fig 4A:408; par 93– teaches training a machine learning model using historical trace data and product data as training data in order to generate a trained machine learning model.), wherein the selected training algorithm includes a fault detection and classification algorithm;(fig 6A-C; par 19-22– teaches an FDC limit (FDC stands for “Fault Detection and Classification”(par 14)), which helps classifies a feature point as normal, abnormal, or something inbetween. Par 14 – teaches how you can use these FDC charts to determine abnormalities, faults and their causes.)
receiving, into the trained MLM, a multiplicity(par 25– teaches sending in sensor values from multiple sensors ) of time-series traces obtained from a corresponding multiplicity of equipment sensors during steps of a semiconductor process; (fig 4C:440; par 28, 67,107 – teaches manufacturing equipment 124 which produces semiconductor products, such as wafers, and sensors which provide sensor data associated with the manufacturing equipment.);
defining a plurality of stable windows and a plurality of transition windows, wherein each stable window is a time-based region where values of the traces are consistent and/or the rates of change of the values of the traces is the same,(par 102, 53, 72,89 – teaches that the trace data is segmented into windows using a sliding window to create a boundary between steady-state and transient segments, defining portions/segments of historical trace data as steady state and other segments as not steady state.) and wherein each transition window is a time- based region wherein the values of the traces are changing rapidly and/or the rates of change are inconsistent;(par 102,103, 53, 89 – teaches looking at the change of slope greater than the threshold change in slope in classifying a segment as not steady/transient state segments.)
detecting, by the trained MLM, an anomalous pattern of the traces in one of the plurality of stable windows at a first time-based location;(Par 102 “At block 422, the processing logic segments (e.g., windows), via a sliding window, the historical trace data to generate a boundary between steady state and transient segments (e.g., to generate segmented trace data).”)
identifying, by the trained MLM, an anomaly window while sliding around the first time-based location and containing the first anomalous pattern; (Par 102 - teaches that the processing logic segments/windows historical trace data via a sliding window to generate a boundary between steady state and transient segments. par 149 - teaches that sensor data values and/or feature patterns may be used to generate a hyperplane limit and/or FDC limit regions, including normal, abnormal, and gray regions. par 72 - teaches that the data set generator can discretize/segment data input or target output, including segmentation via a sliding window, for use in classification algorithms; par 97 - teaches that the trained MLM may provide multivariate limit optimization via sliding window, incremental learning, filtering, etc.)
determining, by the trained MLM, a plurality of key features for portions of the multiplicity of time-series traces within the anomaly window(par 53 – teaches determining key features from the time-series traces because it teaches generating historical and current features from trace data, where the features include trace patterns from sensor values.);
analyzing a distribution for each of the plurality of key features;(par 53 – teaches determining key features from the time-series traces because it teaches generating historical and current features from trace data, where the features include trace patterns from sensor values. par 59,60 – teaches providing the current features to the machine learning model, which then determines confidence data based on distribution of historical data. ) and
comparing, by the trained MLM, the plurality of determined key features with prior key features associated with prior anomalous patterns;(par 34,48 – teaches using the machine learning model to compare current features 154 with historical features 148 and historical product data 158. )
determining that the anomalous pattern matches one of the prior anomalous patterns;(par 59 – teaches providing the current features to the machine learning model, which then determines confidence data on if a corrective action associated with the manufacturing equipment is needed to be triggered based on the predictive data.)
retrieving a root cause and corrective action associated with the matched prior anomalous pattern; (par 24 “A corrective action associated with the manufacturing equipment may be performed based on the predictive data.” Par 18 “The processing device may predict, based on the FDC limit, one or more causes of classification within the products (e.g., causes of abnormal wafers) so that a corrective action associated with the manufacturing equipment can be performed.”; par 59 – teaches providing the current features to the machine learning model, which then determines confidence data on if a corrective action associated with the manufacturing equipment is needed to be triggered based on the predictive data.); and
taking the corrective action in the semiconductor process to correct the root cause for the anomalous pattern.(par 59, 82– teaches using the output from the model to decide to perform corrective actions associated with the manufacturing equipment 124)
However, Iskandar does not specifically teach defining, by the trained MLM, an anomaly window around the first time-based location and containing the anomalous pattern; and assigning an anomaly index to each of the traces on the basis of the key features distribution; for each trace having an anomaly index exceeding a threshold:
On the other hand, Farahat teaches,
detecting, by the trained MLM, at least a first anomalous pattern of traces in the multiplicity of time-series traces at a first time-based location in a stable region or a transition region during operation of the semiconductor process;(fig 1(a):104; par 40– teaches extracting windows of sensor data and events from historical data which correspond to failure and normal cases. The extracted windows will be inspected by the failure prediction model for any signs of failures; fig 2(d):232; par 54 – teaches marking the timestamp of the sliding window as non-stable if the variance is greater than the threshold. par 56 - teaches extracting windows of data which contain pre-failure patterns. )
defining, by the trained MLM, an anomaly window around the first time-based location and containing the anomalous pattern; (fig 5(a); par 56 - teaches extracting windows of data which contain pre-failure patterns and using the features from the extracted windows in the failure prediction module.; par 57-61 – teach extracting other windows based on the failure instance, like the alert window, the evidence window, the before evidence window, the repair window and the normal operation windows. The alert window is before the failure time, and the repair window is after the failure time. )
determining, by the trained MLM, a plurality of key features for the anomalous pattern of traces contained within the anomaly window;(par 41– teaches extracting features from the anomaly window and providing the features to the failure prediction model.)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to further modify Iskandar to incorporate defining, by the trained MLM, an anomaly window around the first time-based location and containing the anomalous pattern of Farahat. One of ordinary skill in the art would have been motivated to remedy the shortcomings of Iskandar -- a need for how to implement predictive maintenance to prevent failures before they happen(Farahat par 6 “In related art implementations, one of the main objectives of predictive maintenance is to prevent failures before they happen. Related art implementations attempt to prevent failures by monitoring the equipment and searching for any pre-failure patterns.”). -- with Farahat providing a known method to solve a similar problem. Farahat provides “implementations described herein involve a system for maintenance recommendation based on data-driven failure prediction. The example implementations can involve estimating the probability of having a failure event in the near future given sensor measurements and events from the equipment, and then alerts the system user or maintenance staff if the probability of failure exceeds a certain threshold.”(Farahat par 8)
However, Iskandar and Farahat does not explicitly teach assigning an anomaly index to each of the traces on the basis of the key features distribution; for each trace having an anomaly index exceeding a threshold:
On the other hand, Nikovski teaches,
analyzing a distribution for each of the plurality of key features;(par 6,19,34 – teaches using probability distributions and probability density functions to analyze variables measured during operation and alert the system when the probability of the current measurements being normal operation falls below a threshold value.) and
assigning an anomaly index to each of the traces on the basis of the key features distribution;(par 19,34 – teaches an calculating an anomaly score for the real time series data based on the model.)
for each trace having an anomaly index exceeding a threshold:(par 6,19,34 – teaches using probability distributions and probability density functions to analyze variables measured during operation and calculate an anomaly score based on the probability of the current measurements being normal operation, and alerting the system when the anomaly score is greater than a threshold value.)
It would have been obvious to one of ordinary skill in the art prior to the effective filing date to combine the fault detection and response system of Iskandar and Farahat with Nikovski’s fault detection techniques of - assigning an anomaly score to each of the traces on the basis of the key features distribution; and further analyzing for each trace having an anomaly score exceeding a threshold,.
One of ordinary skill in the art prior to the effective filing date would have been motivated to make the combination because Iskandar and Farahat are directed to analyzing equipment/process sensor trace data to detect abnormal conditions, predict equipment failure, and perform corrective action, and Nikovski teaches a known technique for improving anomaly detection in multivariate time-series sensor data from industrial machines(Nikovski par 2,18-19). The combination would have involved applying a known technique taught by Nikovski to the similar system of Iskandar and Farahat to improve a similar system in a predictable way.
Regarding claim 2, Iskandar, Farahat, and Nikovski teaches
The computer-based modeling method of claim 1,
Iskandar further teaches,
the step of determining key features comprising calculating a plurality of statistical indicators for the anomalous pattern of traces contained within the anomaly window.(Par 136 – teaches how the processing logic analyzes the trace data, describing various statistical methods for analyzing features generated from the trace data; Par 102– teaches analyzing trace data to determine which segments are considered steady state and which sections are transient segments.)
Regarding claim 3, Iskandar, Farahat, and Nikovski teaches
The computer-based modeling method of claim 2,
Iskandar further teaches,
the step of determining key features further comprising identifying a plurality of wafer attributes for the semiconductor process.(par 140 – teaches using observed features to determine if the product is normal or abnormal. Par 14 – teaches that the manufactured products may be wafers. Par 18 – teaches classifying wafers based on features. Par 97 – teaches feature ranking to rate which features are most important and assigning a percent causation or probability percentage for each feature.)
Regarding claim 4, Iskandar, Farahat, and Nikovski teaches
The computer-based modeling method of claim 1,
Iskandar further teaches,
further comprising performing feature engineering to select a subset of the key features determined to be important to detecting and identifying the anomalous pattern.(par 135 – teaches building a model that classifies based on a set of features and also showing which features affect the model.; fig 4C:446 par 110 – teaches obtaining, from the machine learning model, outputs indicative of predictive data associated with the model. fig 6A-B; par 112 – teaches plots that are generated by the processing logic using the predictive data. These plots show the important features and how they affect the classification. Par 97 – teaches feature ranking to rate which features are most important and assigning a percent causation or probability percentage for each feature. )
Regarding claim 5, Iskandar, Farahat, and Nikovski teaches
The computer-based modeling method of claim 1,
Iskandar further teaches,
the step of detecting an anomalous pattern further comprising detecting a rapid change in values of the traces in any of the stable windows (par 53, 89,97, 102 – teaches identifying anomalous patterns by monitoring data and various patterns in the data, such as slope or a combination of sensor values from the trace data. Slope in time series data is a measure of change in value over time. A larger slope means a more rapid change in values.)
Regarding claim 6, Iskandar, Farahat, and Nikovski teaches
The computer-based modeling method of claim 1,
Iskandar further teaches,
the step of detecting an anomalous pattern further comprising detecting a rapid change in the rate of change for values of the traces in any of the stable windows of process operation.(par 53, 89, 97, 102 – teaches identifying anomalous patterns by monitoring data and various patterns in the data, such as slope or a combination of sensor values from the trace data. Slope in time series data is a measure of change in value over time. A larger slope means a more rapid change in values.)
Regarding claim 20, Iskandar teaches
A method of using a machine learning model (MLM) for predicting and correcting semiconductor processing equipment failure,( par 4 – generally describes processing manufacturing equipment trace data to identify features to input into a machine learning model, which outputs predictive data about failures, and then the system performs a corrective action associated with the manufacturing equipment based on the predictive data.) comprising:
training, by a computer, the MLM based on input data and a selected training algorithm to generate a trained MLM, (fig 4A:408; par 93– teaches training a machine learning model using historical trace data and product data as training data in order to generate a trained machine learning model.), wherein the selected training algorithm includes a fault detection and classification algorithm;( fig 6A-C; par 19-22– teaches an FDC limit (FDC stands for “Fault Detection and Classification”(par 14)), which helps classifies a feature point as normal, abnormal, or something inbetween. Par 14 – teaches how you can use these FDC charts to determine abnormalities, faults and their causes.)
receiving, into the trained MLM, a multiplicity(par 25– teaches sending in sensor values from multiple sensors ) of time-series traces obtained from a corresponding multiplicity of semiconductor processing equipment sensors during a semiconductor process; (fig 4C:440; par 28, 67,107 – teaches manufacturing equipment 124 which produces semiconductor products, such as wafers, and sensors which provide sensor data associated with the manufacturing equipment.)
detecting one or more anomalous patterns of time-series traces in the multiplicity of time- series traces at a time-based location in a stable region of process operation using the trained MLM; (par 102, 53, 72,89 – teaches that the trace data is segmented into windows using a sliding window to create a boundary between steady-state and transient segments, defining portions/segments of historical trace data as steady state and other segments as not steady state.)
identifying, by the MLM, a window while sliding around the time-based location and containing at least a first anomalous pattern of the one or more anomalous patterns using the trained MLM; (Par 102 - teaches that the processing logic segments/windows historical trace data via a sliding window to generate a boundary between steady state and transient segments. par 149 - teaches that sensor data values and/or feature patterns may be used to generate a hyperplane limit and/or FDC limit regions, including normal, abnormal, and gray regions. par 72 - teaches that the data set generator can discretize/segment data input or target output, including segmentation via a sliding window, for use in classification algorithms; par 97 - teaches that the trained MLM may provide multivariate limit optimization via sliding window, incremental learning, filtering, etc.)
determining, by the trained MLM, a plurality of key features for the anomalous pattern of traces contained within the window; (par 53 – teaches determining key features from the time-series traces because it teaches generating historical and current features from trace data, where the features include trace patterns from sensor values.)
analyzing a distribution for each of the plurality of key features;(par 53 – teaches determining key features from the time-series traces because it teaches generating historical and current features from trace data, where the features include trace patterns from sensor values. par 59,60 – teaches providing the current features to the machine learning model, which then determines confidence data based on distribution of historical data. ); and
calculating a plurality of statistical indicators for the first anomalous pattern of traces contained within the window; (par 53 – teaches determining key features from the time-series traces because it teaches generating historical and current features from trace data, where the features include trace patterns from sensor values. par 59,60 – teaches providing the current features to the machine learning model, which then determines confidence data based on distribution of historical data.);
determining that the first anomalous pattern is a likely match with at least one prior anomalous pattern based on the statistical indicators using the MLM; (par 34,48 – teaches using the machine learning model to compare current features 154 with historical features 148 and historical product data 158. par 59 – teaches providing the current features to the machine learning model, which then determines confidence data on if a corrective action associated with the manufacturing equipment is needed to be triggered based on the predictive data.)
retrieving a root cause and a corrective action associated with the prior anomalous pattern; (par 24 “A corrective action associated with the manufacturing equipment may be performed based on the predictive data.” Par 18 “The processing device may predict, based on the FDC limit, one or more causes of classification within the products (e.g., causes of abnormal wafers) so that a corrective action associated with the manufacturing equipment can be performed.”; par 59 – teaches providing the current features to the machine learning model, which then determines confidence data on if a corrective action associated with the manufacturing equipment is needed to be triggered based on the predictive data.); and
taking the corrective action in the semiconductor process.( par 59, 82– teaches using the output from the model to decide to perform corrective actions associated with the manufacturing equipment 124)
However, Iskandar does not specifically teach defining, by the MLM, a window around the time-based location and containing at least a first anomalous pattern of the one or more anomalous patterns using the trained MLM.
On the other hand, Farahat teaches,
detecting one or more anomalous patterns of time-series traces in the multiplicity of time- series traces at a time-based location in a stable region of process operation using the trained MLM; (fig 1(a):104; par 40– teaches extracting windows of sensor data and events from historical data which correspond to failure and normal cases. The extracted windows will be inspected by the failure prediction model for any signs of failures; fig 2(d):232; par 54 – teaches marking the timestamp of the sliding window as non-stable if the variance is greater than the threshold. par 56 - teaches extracting windows of data which contain pre-failure patterns.)
defining, by the MLM, a window around the time-based location and containing at least a first anomalous pattern of the one or more anomalous patterns using the trained MLM; (fig 5(a); par 56 - teaches extracting windows of data which contain pre-failure patterns and using the features from the extracted windows in the failure prediction module.; par 57-61 – teach extracting other windows based on the failure instance, like the alert window, the evidence window, the before evidence window, the repair window and the normal operation windows. The alert window is before the failure time, and the repair window is after the failure time.)
calculating a plurality of statistical indicators for the first anomalous pattern of traces contained within the window; (par 41– teaches extracting features from the anomaly window and providing the features to the failure prediction model.)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to further modify Iskandar to incorporate defining, by the trained MLM, an anomaly window within the first window of Farahat. One of ordinary skill in the art would have been motivated to remedy the shortcomings of Iskandar -- a need for how to implement predictive maintenance to prevent failures before they happen(Farahat par 6 “In related art implementations, one of the main objectives of predictive maintenance is to prevent failures before they happen. Related art implementations attempt to prevent failures by monitoring the equipment and searching for any pre-failure patterns.”). -- with Farahat providing a known method to solve a similar problem. Farahat provides “implementations described herein involve a system for maintenance recommendation based on data-driven failure prediction. The example implementations can involve estimating the probability of having a failure event in the near future given sensor measurements and events from the equipment, and then alerts the system user or maintenance staff if the probability of failure exceeds a certain threshold.”(Farahat par 8)
However, Iskandar and Farahat does not explicitly teach assigning an anomaly index to each of the traces on the basis of the key features distribution; for each trace having an anomaly index exceeding a threshold:
On the other hand, Nikovski teaches,
analyzing a distribution for each of the plurality of key features; (par 6,19,34 – teaches using probability distributions and probability density functions to analyze variables measured during operation and alert the system when the probability of the current measurements being normal operation falls below a threshold value.) and
assigning an anomaly index to each of the traces on the basis of the key features distribution; (par 19,34 – teaches an calculating an anomaly score for the real time series data based on the model.)
for each trace having an anomaly index exceeding a threshold: (par 6,19,34 – teaches using probability distributions and probability density functions to analyze variables measured during operation and calculate an anomaly score based on the probability of the current measurements being normal operation, and alerting the system when the anomaly score is greater than a threshold value.)
It would have been obvious to one of ordinary skill in the art prior to the effective filing date to combine the fault detection and response system of Iskandar and Farahat with Nikovski’s fault detection techniques of - assigning an anomaly score to each of the traces on the basis of the key features distribution; and further analyzing for each trace having an anomaly score exceeding a threshold,.
One of ordinary skill in the art prior to the effective filing date would have been motivated to make the combination because Iskandar and Farahat are directed to analyzing equipment/process sensor trace data to detect abnormal conditions, predict equipment failure, and perform corrective action, and Nikovski teaches a known technique for improving anomaly detection in multivariate time-series sensor data from industrial machines(Nikovski par 2,18-19). The combination would have involved applying a known technique taught by Nikovski to the similar system of Iskandar and Farahat to improve a similar system in a predictable way.
Response to Arguments
Applicant’s arguments, see remarks pg 6-8, filed 04/06/2026, with respect to rejection under 35 U.S.C. 101 have been fully considered and are persuasive. The rejection under 35 U.S.C. 101 of 11/05/2025 has been withdrawn.
Applicant’s arguments, see remarks pg 7-10, filed 9/17/2025, with respect to the rejection(s) of claim(s) 1 and 20 under 35 U.S.C. 103 as being unpatentable over by US 20210042570 A1 (Iskandar) in view of US 20200057689 A1 (Farahat) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of 35 U.S.C. 103 as being unpatentable over by US 20210042570 A1 (Iskandar) in view of US 20200057689 A1 (Farahat) and US 20150363699 A1 (Nikovski).
With respect to the independent claims, the applicant has argued that Farahat does not teach limitation “defining, by the trained MLM, an anomaly window around the first time-based location and containing the first anomalous pattern.”, explaining that Farahat’s extract “windows of data which are believed to contain pre-failure patterns” is not the same as “defining an anomaly window in an otherwise stable operating region”, and also explaining that Farahat’s pre-failure patterns are not the same as actual failure data since the pre-failure patterns happen before a failure happens. Examiner would like to point out that “defining an anomaly window in an otherwise stable operating region” is not specifically claimed. The two claim limitations most similar to that idea are “detecting, by the trained MLM, an anomalous pattern of the traces in one of the plurality of stable windows at a first time-based location;” and “defining, by the trained MLM, an anomaly window around the first time-based location and containing the anomalous pattern;”. Farahat teaches an anomaly window containing the anomalous pattern needed to predict failures in the cited (fig 5(a); par 56 - teaches extracting windows of data which contain pre-failure patterns and using the features from the extracted windows in the failure prediction module. par 57-61 – teach extracting other windows based on the failure instance, like the alert window, the evidence window, the before evidence window, the repair window and the normal operation windows. The alert window is before the failure time, and the repair window is after the failure time.) Farahat’s pre-failure patterns are anomalous patterns used by the failure prediction module to predict failures. The examiner interprets this as limitation “defining, by the trained MLM, an anomaly window around the first time-based location and containing the first anomalous pattern.”. The cited par 54, 56 also teaches determining where unstable windows are, and extracting windows of data which contain pre-failure patterns, covering limitation “detecting, by the trained MLM, an anomalous pattern of the traces in one of the plurality of stable windows at a first time-based location;”.
With respect to the independent claims, the applicant has argued that Iskandar and Farahat does not teach amended limitations (i) analyzing a distribution for each of the plurality of key features; and (ii) assigning an anomaly index to each of the traces on the basis of the key features distribution.
Regarding limitation (i), the newly cited Nikovski teaches in the cited (par 6,19,34 – teaches using probability distributions and probability density functions to analyze variables measured during operation and alert the system when the probability of the current measurements being normal operation falls below a threshold value.). Under the broadest reasonable interpretation, this teaches or at least suggests the claimed “analyzing a distribution for each of the plurality of key features;”.
Regarding limitation (ii), the newly cited Nikovski teaches in the cited (par 19,34 – teaches an calculating an anomaly score for the real time series data based on the model.). Under the broadest reasonable interpretation, this teaches or at least suggests the claimed “assigning an anomaly index to each of the traces on the basis of the key features distribution”.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 9952921 B2 - Kim – analyzes time series data in windows to predict failures
US 20180025483 A1 - Finlay - semiconductor manufacturing error prediction system
US 7676775 B2 - Chen - tests wafers after production to find defects and find root cause to improve manufacturing process. Matches fail patterns to find root cause and implements resolutions.
US 20230122653 A1 - Yoshida - error cause estimation when manufacturing computer parts. Looks at features and determines anomalies
US 20190379589 A1 - Ryan - analyzes time series data to find anomalous spikes within time windows.
US 20190324831 A1 - Gu - analyzes log data to create key feature error labels.
US 20160246662 A1 - Meng- diagnosis window and refined diagnosis window for troubleshooting in general. Also extracts features and discovers normal and abnormal "drifts"/changes
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 MICHAEL XU whose telephone number is (571)272-5688. The examiner can normally be reached Monday-Friday 8:00am - 5:00pm.
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/M.X./Examiner, Art Unit 2113 /BRYCE P BONZO/Supervisory Patent Examiner, Art Unit 2113
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