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
This office action is in response to communication filed 4/2/2024. Claims 1-12 are currently pending and claims 1 and 7 are the independent claims.
Specification
The disclosure is objected to because of the following informalities:
The specification recites “G-measurement value” and “G-measure” without first defining what “G-measurement”/“G-measure” /the “G” stands for and as such it is unclear what “G-measurement”/ “G-measure” means.
Appropriate correction is required.
Claim Objections
Claims 4 and 10 are objected to because of the following informalities:
As per claims 4 and 10, they recite “…a parameter extraction step of extracting parameters in the normalization, the feature selection, and the class imbalance learning and hyperparameters of the decision tree model by executing the cost-sensitive decision tree based on the harmony search with training data…”. For clarity/grammar the examiner would like to recommend the wording/phrasing “…a parameter extraction step of extracting parameters in the normalization, the feature selection, and the class imbalance learning, and extracting hyperparameters of the decision tree model by…”.
Appropriate correction is required.
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 1-12 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.
As per claims 1 and 7, they recite “A performance enhancement method…the performance enhancement method comprising…a parameter optimization step of simultaneously optimizing at least one parameter in a software defect prediction process by using an optimization algorithm to enhance performance of the software defect prediction model, wherein a preprocessing step and a classification model generation step are simultaneously performed for a search space of the optimization algorithm.” The examiner is unclear as to what is meant by “enhancement”, “optimization”, “optimizing”, and “enhance” as different persons of ordinary skill in the art may have different opinions as to what would be considered “optimum” or would make something better/enhance something (ex: speed, accuracy, power consumption , resource consumption, etc.). Additionally, while the claim recites that a preprocessing step and classical model generation step are simultaneously performed, it does not clarify what the “optimizing at least one parameter in a software defect prediction process” is being performed at the same time as/simultaneously with/etc., and as such the examiner is unclear as to what is meant by “simultaneously optimizing”. For the purpose of examination, the examiner will consider these limitations to be “A performance modification method…comprising…a step of modifying at least one parameter in a software defect prediction process by using an algorithm to modify performance of the software defect prediction model, wherein a preprocessing step and a classification model generation step are simultaneously performed for a search space of the algorithm.”
As per dependent claims 2-6 and 8-12, they incorporate the deficiencies of claims 1 and 7 upon which they, respectively, depend, and fail to correct the deficiencies of claims 1 and 7, respectively. Therefore claims 2-6 and 8-12 are rejected for similar reasoning as claims 1 and 7, above.
As per claims 5 and 11, they recite “wherein the evaluation of the performance of the software defect prediction model is performed by calculating probability of detection, probability of false alarm, G-measure, and file inspection reduction (FIR), using validation data and by calculating an average value of the calculated probability of detection, probability of false alarm, G-measure and FIR.” The examiner is unclear as to what is meant by “G-measure” as the claims do not clarify what the “G” stands for. For the purpose of examination, the examiner will consider “G-measure” to mean “geometric mean” and these limitations to be “wherein the evaluation of the performance of the software defect prediction model is performed by calculating a probability of detection, probability of false alarm, geometric mean (G-measure), and file inspection reduction (FIR), using validation data; and by calculating an average value of the calculated probability of detection, probability of false alarm, G-measure and FIR.”
As per dependent claims 6 and 12, they incorporate the deficiencies of claims 5 and 11 upon which they, respectively, depend, and fail to correct the deficiencies of claims 5 and 11, respectively. Therefore claims 6 and 12 are rejected for similar reasoning as claims 5 and 11, above.
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 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
As per claim 1, it recites “A performance enhancement method, performed by one or more computer processors, for a software defect prediction model, the performance enhancement method comprising: a software defect prediction model providing step of providing the software defect prediction model that identifies a module in which a software defect occurs; and a parameter optimization step of simultaneously optimizing at least one parameter in a software defect prediction process by using an optimization algorithm to enhance performance of the software defect prediction model, wherein a preprocessing step and a classification model generation step are simultaneously performed for a search space of the optimization algorithm.” The limitations “identifies a module in which a software defect occurs” and “a preprocessing step and a classification model generation step are simultaneously performed for a search space of the optimization algorithm” as drafted, is a function that, under its broadest reasonable interpretation, recite the abstract idea of a mental process. The limitations encompass a human mind carrying out the function through observation, evaluation, judgment, and /or opinion, or even with the aid of pen and paper. For example, a human may mentally/with pen and paper/etc. judge/decide/observe/identify a module in which a software defect occurs, and may mentally/with pen and paper/etc. perform preprocessing/analyzing/judging/evaluation/etc. and may mentally/with pen and paper determine/decide/write/generate a classification model. Therefore, these limitations recite and falls within the “Mental Processes” grouping of abstract ideas
This judicial exception is not integrated into a practical application. The claim recites the additional element/limitations “A performance enhancement method, performed by one or more computer processors, for a software defect prediction model, the performance enhancement method comprising”, “a software defect prediction model providing step of providing the software defect prediction model that”, and “a parameter optimization step of simultaneously optimizing at least one parameter in a software defect prediction process by using an optimization algorithm to enhance performance of the software defect prediction model”. The elements/limitations “A performance enhancement method, performed by one or more computer processors, for a software defect prediction model, the performance enhancement method comprising”, “a software defect prediction model providing step of providing the software defect prediction model that”, and “by using an optimization algorithm” recite that high level/generic computer/computer components/computer processors/software/software defect prediction models/algorithm/etc. are used to implement/perform the abstract idea/mental process and as such amounts to no more than mere instructions to apply the exception using generic computer, and/or mere computer components. Further, the additional element “a parameter optimization step of simultaneously optimizing at least one parameter in a software defect prediction process…to enhance performance of the software defect prediction model” does nothing more than add insignificant extra solution activity to the judicial exception of merely updating/modifying/optimizing/etc. data/information/parameters/etc., and the courts have identified functions such as gathering, displaying, updating, transmitting and storing data as well-understood, routine, conventional activity, (see MPEP 2106.05(d).). Accordingly, the additional elements do not integrate the recited judicial exception into a practical application and the claim is therefore directed to the judicial exception. See MPEP 210605(f), 2106.05(g), etc..
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception using generic computer, and/or mere computer components, which does not provide an inventive concept, and insignificant extra solution activity to the judicial exception of merely updating/modifying/optimizing/etc. data/information/parameters/etc., and the courts have identified functions such as gathering, displaying, updating, transmitting and storing data as well-understood, routine, conventional activity, thus do not amount to significantly more than the judicial exception (see MPEP 2106.05(d)). Accordingly, the claims are not patent eligible under 35 USC 101.
As per claim 2, it incorporates the deficiencies of claim 1, upon which it depends, and further recites “…wherein the optimization algorithm uses a cost-sensitive decision tree based on harmony search (HS-CSDT), and the cost-sensitive decision tree uses a harmony search algorithm (HS) that is a metaheuristic algorithm” which, conceptually, with broadest reasonable interpretation, provides further clarification as to the software/computer components/algorithm/etc. used which does not integrate the abstract idea/mental process into a practical application and is not significantly more than the abstract idea/mental process. As such, claim 2 fails to correct the deficiencies of claim 1 and is therefore rejected for similar reasoning as claim 1, above.
As per claim 3, it incorporates the deficiencies of claim 1, upon which it depends, and further recites “…wherein the preprocessing step includes normalization, feature selection, and class imbalance learning, and the classification model generation step includes a decision tree (DT) model” which, conceptually, with broadest reasonable interpretation, provides further clarification as to the abstract idea/mental process/etc. which does not integrate the abstract idea/mental process into a practical application and is not significantly more than the abstract idea/mental process. As such, claim 3 fails to correct the deficiencies of claim 1 and is therefore rejected for similar reasoning as claim 1, above.
As per claim 4, it incorporates the deficiencies of claim 1, upon which it depends, and further recites “…wherein the parameter optimization step includes: a parameter extraction step of extracting parameters in the normalization, the feature selection, and the class imbalance learning and hyperparameters of the decision tree model by executing the cost-sensitive decision tree based on the harmony search with training data; and a performance evaluation step of evaluating performance of the software defect prediction model by using the extracted parameters in the normalization, the feature selection, and the class imbalance learning, and the extracted hyperparameters of the decision tree model” which, conceptually, with broadest reasonable interpretation, provides further clarification as to the abstract idea/mental process/judging/observing/determining/extracting/analyzing/evaluating/etc. performed which does not integrate the abstract idea/mental process into a practical application and is not significantly more than the abstract idea/mental process. As such, claim 4 fails to correct the deficiencies of claim 1 and is therefore rejected for similar reasoning as claim 1, above.
As per claim 5, it incorporates the deficiencies of claim 1, upon which it depends, and further recites “…wherein the evaluation of the performance of the software defect prediction model is performed by calculating probability of detection, probability of false alarm, G-measure, and file inspection reduction (FIR), using validation data and by calculating an average value of the calculated probability of detection, probability of false alarm, G-measure and FIR” which, conceptually, with broadest reasonable interpretation, provides further clarification as to the abstract idea/mental process/judging/analyzing/evaluating/calculating/etc. performed which does not integrate the abstract idea/mental process into a practical application and is not significantly more than the abstract idea/mental process. As such, claim 5 fails to correct the deficiencies of claim 1 and is therefore rejected for similar reasoning as claim 1, above.
As per claim 6, it incorporates the deficiencies of claim 1, upon which it depends, and further recites “…wherein the parameter optimization step includes adjusting the parameters in the normalization, the feature selection, and the class imbalance learning and the hyperparameters of the decision tree model to increase the G-measure” which, conceptually, with broadest reasonable interpretation, provides further clarification as to the insignificant extra solution activity to the judicial exception of merely updating/modifying/adjusting/etc. data/information/parameters/hyperparameters/etc., which does not integrate the abstract idea/mental process into a practical application and the courts have identified functions such as gathering, displaying, updating, transmitting and storing data as well-understood, routine, conventional activity, thus do not amount to significantly more than the judicial exception (see MPEP 2106.05(d)). As such, claim 6 fails to correct the deficiencies of claim 1 and is therefore rejected for similar reasoning as claim 1, above.
As per claim 7, it recites a performance enhancement device having similar limitations as the performance enhancement method of claim 1, and therefore recites a similar abstract idea/mental process and has similar deficiencies as claim 1, above. Claim 7 recites further recites the additional elements/limitations “A performance enhancement device for a software defect prediction model, the performance enhancement device comprising: a software defect prediction model providing processor… and a parameter optimization processor that…” which, conceptually, with broadest reasonable interpretation, recites that high level/generic computer/computer components/processors/etc. are used to implement/perform the abstract idea/mental process, which does not integrate the abstract idea into a practical application and is not significantly more than the abstract idea/mental process. As such, the additional elements/limitations of claim 7 fail to correct the deficiencies of claim 1, and therefore claim 7 is rejected for similar reasoning as claim 1, above.
As per claims 8-12, they recite performance enhancement devices having similar limitations as the performance enhancement methods of claim 2-6, respectively, and are therefore rejected for similar reasoning as claims 2-6, respectively, above.
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.
Claims 1 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over McCormick (US PG Pub. 2019/0317879 A1), Parent (US Patent 10,740,216 B1), and Zhang et al. (herein called Zhang) (US PG Pub. 2020/0241861 A1).
As per claim 1, McCormick teaches: a performance enhancement method, performed by one or more computer processors, for a software defect prediction model, the performance enhancement method comprising:
a software defect prediction model providing step of providing the software defect prediction model that identifies a module in which a software defect occurs (pars. [0006], [0019], [0029]-[0030], [0040], [0044], neural network (model) is provided that identifies defects in software/source code/etc. (provide software defect prediction model/neural network that identifies/predicts defects in software/source code) and determines/maps/etc. location in source code/range of lines in source code/block of code (module) where defect is located (identifies module in which software defect occurs).); and
a parameter optimization step of simultaneously optimizing at least one parameter in a software defect prediction process (pars. [0016], [0029]-[0030], [0035]-[0043], neural network is provided input file/code representation/input array/etc. which it processes/analyzes/etc. to determine defects and locations of defects in source code software (software defect prediction process) and neural network/defect prediction model applies weighting factors (parameters) to input data/input file/etc. to produce desired output data/determine defects and defect location/etc. (parameter/weighting factor in software defect prediction process), and weighting factors/parameters are adjusted/modified/optimized during training/modification/updating/optimization/etc. of neural network/defect prediction (optimizing/modifying/adjusting/etc. at least one parameter/weight/factor/etc. in a software defect prediction process).).
McCormick does not explicitly disclose that the adjusting of the weight is performed by an algorithm, and as such does not explicitly state, however Parent teaches:
a parameter optimization step of simultaneously optimizing at least one parameter in a software defect prediction process by using an optimization algorithm to enhance performance of the software defect prediction model (col. 4 lines 5-30, col. 7 lines 3-25, machine learning model is used to identify/classify/etc. bugs (defects) using bug attributes (parameters) and machine learning engine (algorithm) adjusts weights of attributes, adds/corrects attributes, changes value of attribute, etc. (modifying/optimizing/etc. a parameter) in machine learning model to refine/improve (modify/enhance/etc.) accuracy and efficiency of machine learning model (to modify/enhance/etc. performance of software defect prediction model).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add a parameter optimization step of simultaneously optimizing at least one parameter in a software defect prediction process by using an optimization algorithm to enhance performance of the software defect prediction model, as conceptually taught by Parent, into that of McCormick because these modifications allow for the model to be automatically adjusted/modified/etc. to improve its performance by an algorithm/engine/program/software/etc., thereby helping to ensure that the model operates as desired and saving time and resources that would be spent by a user manually adjusting/modifying/etc. the model.
McCormick and Parent do not explicitly state, however Zhang teaches:
wherein a preprocessing step and a classification model generation step are simultaneously performed for a search space of the optimization algorithm (pars. [0006], [0024], [0046]-[0051], [0059], [0061], machine learning is used to determine location of software defects using classification models to classify defects, training of classification models (generation step of classification model) includes remedying imbalance in training data/normalizing training data/preprocessing/etc. (preprocessing step) and classified training data is used to update parameters of model to minimize loss function (optimization algorithm). As training/generating/etc. the classification model includes the normalizing/remedying imbalance/preprocessing/etc. training data, the classification model generation step/training and preprocessing step are performed simultaneously/one is part of the other/etc., and as the training data is processed/normalized/preprocessed/etc. to train the classification model and classified training data is used to update/modify/parameters to minimize loss function, the training data is a search space/data/space being searched/analyzed/processed/etc. to determine what needs to be normalized/preprocessed/remedied/etc. of the optimization algorithm.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add wherein a preprocessing step and a classification model generation step are simultaneously performed for a search space of the optimization algorithm, as conceptually taught by Zhang, into that of McCormick and Parent because these modifications allow for classification models to be generated/updated/trained/etc. using desired data to determine defects, thereby helping to ensure that the models operate as desired/correctly and defects are successfully determined/located/etc. so they may be corrected, thereby increasing the usability of the models and helping to ensure that they successfully determine/locate errors for correction.
As per claim 7, it recites a device having similar limitations as the method of claim 1, and is therefore rejected for similar reasoning as claim 1, above.
Claims 2-3 and 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over McCormick (US PG Pub. 2019/0317879 A1), Parent (US Patent 10,740,216 B1), Zhang et al. (herein called Zhang) (US PG Pub. 2020/0241861 A1), and Kennedy et al. (herein called Kennedy) (US Patent 10,489,587 B1) in further view of Gao et al. “Harmony Search Method: Theory and Applications” (2015) (herein called Gao).
As per claim 2, McCormick, Parent, and Zhang do not explicitly state, however Kennedy teaches:
wherein the optimization algorithm uses a cost-sensitive decision tree based on search, and the cost-sensitive decision tree uses a search algorithm that is a metaheuristic algorithm (col. 3 line 60-col. 4 line 8, col. 4 lines 33-50, col. 5 lines 25-30, machine learning heuristic algorithm (optimization algorithm from Parent and Zhang) uses decision tree to classify files as different types without incurring power/space costs of having different decision trees stored and processed (cost-sensitive decision tree), and analyzes and classifies file by applying machine learning heuristic analysis (metaheuristic/heuristic algorithm) to file using decision tree to identify node/leaf of tree corresponding to file/arrived at by the heuristic on the file/etc. and that is associated with a classification type (optimization algorithm/machine learning heuristic/etc. uses a cost-sensitive decision tree based on search/analysis/etc., and the cost-sensitive decision tree uses a search algorithm that is a metaheuristic algorithm/heuristic analysis/etc.).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add wherein the optimization algorithm uses a cost-sensitive decision tree based on search, and the cost-sensitive decision tree uses a search algorithm that is a metaheuristic algorithm, as conceptually taught by Kennedy, into that of McCormick, Parent, and Zhang because these modifications allow for classification models to be generated/updated/trained/etc. using desired data to determine defects, thereby helping to ensure that the models operate as desired/correctly and defects are successfully determined/located/etc. so they may be corrected, thereby increasing the usability of the models and helping to ensure that they successfully determine/locate errors for correction.
While Kennedy teaches that the decision tree may be cost sensitive and use a heuristic/metaheuristic algorithm, it does not explicitly state that the metaheuristic algorithm is a harmony search, and as such does not explicitly state, however Gao teaches:
wherein the optimization algorithm uses a harmony search (HS-CSDT), and the harmony search algorithm (HS) that is a metaheuristic algorithm (pg. 1 par. 6/abstract paragraph, pg. 1 left column par. 1-right column par. 4, pg. 2 right column par. 1-left column par. 2, harmony search algorithms are metaheuristic optimization algorithms used in classification. As Kennedy teaches using decision trees and heuristic/metaheuristic/etc. algorithms to perform classification and Gao teaches that Harmony search is a metaheuristic optimization algorithm used in classification, it is obvious that the optimization algorithm used may be a harmony search algorithm.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify McCormick, Parent, Zhang, and Kennedy such that a harmony search algorithm that is a metaheuristic algorithm is used to perform the search/analysis, as conceptually taught by Gao, to create wherein the optimization algorithm uses a cost-sensitive decision tree based on harmony search (HS-CSDT), and the cost-sensitive decision tree uses a harmony search algorithm (HS) that is a metaheuristic algorithm, because these modifications allow for a known type of algorithm used in classification to be used in performing classification, which is desirable as it expands usability by allowing for additional algorithms to be used to perform the classification while helping to ensure that the classification is performed correctly/as desired.
As per claim 3, McCormick and Parent do not explicitly state, however Zhang teaches:
wherein the preprocessing step includes normalization, feature selection, and class imbalance learning (pars. [0006], [0016], [0024], [0032], [0046]-[0051], [0058]-[0059], [0061], training of classification models includes normalizing training data (preprocessing includes normalization), generating feature vector (feature selection), determining non-uniform distribution in training data and remedying imbalance in training data (class imbalance learning) and classified training data is used to update parameters of model to minimize loss function (optimization algorithm).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add wherein the preprocessing step includes normalization, feature selection, and class imbalance learning, as conceptually taught by Zhang, into that of McCormick, Parent, Kennedy, and Gao because these modifications allow for additional types of information/data/etc. to be used to train/modify/improve/etc. the model thereby helping to ensure that the model operates correctly/as desired/etc..
McCormick, Parent, and Zhang do not explicitly state, however Kennedy teaches:
and the classification model generation step includes a decision tree (DT) model (col. 3 line 60-col. 4 line 8, col. 4 lines 33-50, col. 5 lines 25-30, machine learning uses decision tree to classify files as different types by analyzing and classifying a file by applying machine learning heuristic analysis to file using decision tree to identify node/leaf of tree corresponding to file/arrived at by the heuristic on the file/etc. and that is associated with a classification type. As machine learning/models/neural networks/etc. use decision trees to classify files, it is obvious they are classification models that use decision trees/decision tree model/etc., and as such generating the classification model includes a decision tree model.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add and the classification model generation step includes a decision tree (DT) model, as conceptually taught by Kennedy Zhang, into that of McCormick, Parent, Zhang, and Gao because these modifications allow for an effective method of generating the classification model with additional information thereby helping to ensure that the classification model operates correctly/as desired/etc..
As per claims 8-9, they recite device’s having similar limitations as the methods of claims 2-3, respectively, and are therefore rejected for similar reasoning as claims 2-3, respectively, above.
Allowable Subject Matter Over Prior Art
The following is a statement of reasons for the indication of allowable subject matter:
The prior art of record teaches that machine learning/trained models/neural networks/etc. may be used to determine/predict/identify software defects, that the parameters/attributes/etc. may be adjusted/modified/etc. to tune/train/modify/etc. the models and improve/modify/etc. performance of the models; that models may be classification models/trained to classify input data/etc. and training/generating/tuning/optimizing/etc. the models may include preprocessing data/information/etc. used in training/generating/optimizing/tuning/etc. the models; that pre-processing may include normalizing data, selecting features, class imbalance learning, etc.; and that modifying/optimizing/tuning/etc. the models may include using harmony search and cost-sensitive decision trees. However, the prior art of record fails to render an obviousness of modifying/updating/etc. parameters/attributes of the model/training the model/etc. including a parameter extraction step of extracting parameters in the normalization, the feature selection, and the class imbalance learning and hyperparameters of the decision tree model by executing the cost-sensitive decision tree based on the harmony search with training data; and a performance evaluation step of evaluating performance of the software defect prediction model by using the extracted parameters in the normalization, the feature selection, and the class imbalance learning, and the extracted hyperparameters of the decision tree model, as required by dependent claims 4 and 10.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kochura et al. US Patent 10,838,849 B2 teaches using natural language processing/machine learning/etc. to determine origin/cause/etc. of failures of software.
Du et al. US PG Pub. 2021/0089285 A1 teaches optimizing machine learning models including preprocessing parameters/data/information used by the machine learning model.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DOUGLAS M SLACHTA whose telephone number is (571)270-0653. The examiner can normally be reached Monday-Friday 6:30am-4pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chat Do can be reached at 571-272-3721. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DOUGLAS M SLACHTA/Examiner, Art Unit 2193
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