DETAILED ACTION
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 .
Response to Amendment
The Amendment filed on 06/17/2026 has been entered. Claims 1-20 remain pending in the application.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1 and 15 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. Claim 1 recites “computing first data distribution characteristics for different features in the first set of features, wherein each of the first data distribution characteristics represents a respective data distribution derived from how data values within the first plurality of datasets corresponding to a respective feature from the first set of features differ from each other”. In the Remarks filed on 06/17/2026, Applicant stated that support for the amendment can be found at paragraphs [0019]-[0026]. However, the Examiner has been unable to find a description of wherein each of the first data distribution characteristics represents a respective data distribution derived from how data values within the first plurality of datasets corresponding to a respective feature from the first set of features differ from each other in [0019]-[0026] or anywhere else in the specification. For the purpose of examination, examiner will interpret as computing first data distribution characteristics for different features in the first set of features, wherein each of the first data distribution characteristics represents a respective data distribution derived based on data values corresponding to a respective feature from the first set of features as described in paragraph [0019].
Claim 15 recites “computing a first set of measures for a combination of the first plurality of datasets and the second plurality of datasets, wherein each measure in the first set of measures represents one or more statistical characteristics of how values in the first plurality of datasets and the second plurality of datasets corresponding to a respective feature from the first set of features differ from each other”. In the Remarks filed on 06/17/2026, Applicant stated that support for the amendment can be found at paragraphs [0019]-[0026]. However, the Examiner has been unable to find a description of computing a first set of measures for a combination of the first plurality of datasets and the second plurality of datasets and wherein each measure in the first set of measures represents one or more statistical characteristics of how values in the first plurality of datasets and the second plurality of datasets corresponding to a respective feature from the first set of features differ from each other in [0019]-[0026] or anywhere else in the specification. For the purpose of examination, examiner will interpret as computing, for the first set of features and the second set of features, a first set of measures representing one or more statistical characteristics of values in the first plurality of datasets and the second plurality of datasets.
Therefore, claims 1 and 15 are rejected for containing subject matter which was not described in the specification. Claims 2-7 and 16-20 are rejected for failing to cure the deficiency from their respective parent claims.
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 1-5, 7-13 and 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over SAHA et al. (hereinafter SAHA), US 20230049418 A1, in view of ARZANI et al. (hereinafter ARZANI), US 20210012239 A1.
Regarding independent claim 1, SAHA teaches a system (Fig. 1, 102) comprising:
a non-transitory memory ([0083] The electronic storages may include non-transitory storage media that electronically stores information); and
one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations comprising ([0082] In some embodiments, the various computers and subsystems illustrated in FIG. 1 may include one or more computing devices that are programmed to perform the functions described herein. The computing devices may include one or more electronic storages (e.g., database(s) 130, which may include dataset database 132, model database 134, training data database 136, etc., or other electronic storages), one or more physical processors programmed with one or more computer program instructions, and/or other components):
obtaining a first plurality of datasets usable for training a first machine learning model, wherein each dataset in the first plurality of datasets comprises a set of values corresponding to a first set of features ([0076] FIG. 12 shows a flowchart of a method 1200 for generating training data including features having minimized correlation, in accordance with one or more embodiments. In an operation 1202, datasets including a plurality of features may be obtained. The datasets may be obtained from one or more data sources (e.g., dataset database 132). Each dataset may include one or more data items, and the data items may represent various features … In some embodiments, operation 1202 may be performed by a subsystem that is the same or similar to scoring subsystem 112);
computing first data distribution characteristics for different features in the first set of features, wherein each of the first data distribution characteristics represents a respective data distribution derived from how data values within the first plurality of datasets corresponding to a respective feature from the first set of features differ from each other ([0063] In some embodiments, computer system 102 may be configured to compute a CoV to identify volatility in time series data. As an example, with reference to FIG. 9A, plot 900 may represent time series data 902 over a rolling window of 12 months … As an example, window 904 of plot 900 may represent one 12 month window within time series data 902. Within the time period of window 904, the standard deviation of the data may be 95.7 and the mean value of the data may be 1905.8; [0064] For each 12 month temporal window, the corresponding CoV may be computed and plotted in a graph 940 of FIG. 9B, indicated how the CoV varies over time series data 902. An average CoV for the entire time series data 902 may be determined based on the CoV at each point; [0067] In some embodiments, the volatility of the time series data may be computed based on the standard deviation of the time series data, or other statistical measures of the time series data; [0068] In some embodiments, scoring subsystem 112 may provide the volatility information and the trending information to user interface subsystem 120 for generating a chart to be displayed in a user interface representing the behaviors of each variable for various metrics. As an example, with reference to FIG. 9C, chart 980 may be displayed within a user interface, and may include information related to various metrics computed for a variable described by time series data 902. For example, the various metrics may include a missing rate, a mean, a median, a standard deviation, a zero rate, and a population stability index (PSI) metric; [0072] with reference to FIG. 11A, a set of plots 1100 are displayed. Each plot includes three distributions related to one feature under examination for consistency. For example, plots 1102, 1104, and 1106 may each be associated with a first feature (e.g., feature_1); [0077] In an operation 1204, a plurality of correlation scores indicating a correlation between features of the plurality of features may be computed … In some embodiments, operation 1204 may be performed by a subsystem that is the same or similar to scoring subsystem 112);
comparing the first data distribution characteristics against a different data distribution characteristics associated with different groups of datasets used for training different machine learning models ([0078] In an operation 1206, a plurality of feature clusters may be generated. Each cluster may include one or more features that are determined to be correlated with one another. For example, if two features are determined to be correlated, both of those features may be clustered into a same feature cluster. Features that are determined to lack correlation (e.g., correlation score is less than a threshold correlation score, correlation score is zero) may be included in different feature clusters … In some embodiments, operation 1206 may be performed by a subsystem that is the same or similar to clustering subsystem 114);
SAHA does not explicitly teach selecting, from a plurality of machine learning model types corresponding to the different machine learning models, a particular machine learning model type for the first machine learning model based on the comparing, wherein the particular machine learning model type corresponds to a second machine learning model, and wherein a difference between the first data distribution characteristics and second data distribution characteristics associated with a second plurality of datasets used to train the second machine learning model is within a threshold extracting a configuration and a set of hyperparameters associated with the second machine learning model; configuring the first machine learning model based on the particular machine learning model type and the configuration; and training the first machine learning model using the first plurality of datasets and based on the set of hyperparameters.
However, in the same field of endeavor, ARZANI teaches selecting, from a plurality of machine learning model types corresponding to the different machine learning models, a particular machine learning model type for the first machine learning model based on the comparing, wherein the particular machine learning model type corresponds to a second machine learning model, and wherein a difference between the first data distribution characteristics and second data distribution characteristics associated with a second plurality of datasets used to train the second machine learning model is within a threshold (Fig. 2; [0033] Processing flow 200 begins with model selection process 204, which evaluates candidate machine learning model types from a model library 206 of candidate machine learning model types; [0034] The model selection process 204 can involve selecting a particular model pool based on the network context data 202; [0035] Once a given model pool is selected, the model selection process 204 can select a specific model type from the selected pool; [0071] In some cases, a meta-learning process is employed that can compare new input data sets to previously-observed input data sets and start the model selection process with a machine learning model that was determined to be effective on a similar input dataset);
extracting a configuration and a set of hyperparameters associated with the second machine learning model ([0036] In addition, the model selection process 204 can also select model hyperparameters for the selected model type … Collectively, the selected model type and selected hyperparameters can be output from the model selection process as selected model 214; [0072] In a similar manner, hyperparameter selection can also be informed by prior knowledge. For instance, as models with specific hyperparameter values are successfully identified for specific problem types, those models can be selected again when the same or similar problem types or input data are presented by different users);
configuring the first machine learning model based on the particular machine learning model type and the configuration; and training the first machine learning model using the first plurality of datasets and based on the set of hyperparameters ([0030] FIG. 2 illustrates an example processing flow 200, consistent with the disclosed implementations. Processing flow 200 utilizes network context data 202 to select, configure, and/or train one or more machine learning model; [0036] In addition, the model selection process 204 can also select model hyperparameters for the selected model type. In the case of a neural network model type, the hyperparameters can include the learning rate, number of nodes per layer, types of layers, depth, etc., each of which has a range of potential values. In the case of a random forest model type, the hyperparameters can include the number of decision trees, the number of features to consider for each tree for node-splitting, etc. In some cases, the training budget and/or memory budget in the network context data can influence the selection of hyperparameters. In other scenarios, characteristics of the input data can influence the selection of hyperparameters, as discussed more below. Collectively, the selected model type and selected hyperparameters can be output from the model selection process as selected model 214.).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of selecting, configuring, and training a machine learning model by comparing new input data sets to previously-observed input data sets as suggested in ARZANI into SAHA’s system because both of these systems are addressing training and evaluating machine learning models. This modification would have been motivated by the desire to automate generation of machine learning models to facilitate the development and deployment of machine learning models (ARZANI, [0002]).
Regarding dependent claim 2, the combination of SAHA and ARZANI teaches all the limitations as set forth in the rejection of claim 1 that is incorporated. SAHA further teaches wherein the computing the first data distribution characteristics (Fig. 3) comprises:
obtaining, from the first plurality of datasets, first data values corresponding to a first feature in the first set of features, wherein each of the first data values is obtained from a distinct dataset from the first plurality of datasets ([0041] At 304, scoring subsystem 112, upon receipt of datasets 302, may extract features from each of datasets 302. In some embodiments, scoring subsystem 112 may parse datasets 302 to identify the data items stored therein, and further identify which features are represented by each of those data items. Identifying the features may include performing a semantic analysis of the data items to identify entities described by or included within each data item, resolving a label (e.g., tag) for each entity, and attributing the label to each entity (if those entities are not labeled));
deriving a statistical value from the first data values ([0041] After the data items are parsed, the features included within the data items may be grouped together by the data item with which they were extracted from. In some embodiments, the features may be grouped based on similarity to one another. For example, each of groups 308 represents a group of features extracted from datasets 302. For instance, a first group may include features Xai, XA2, ..., XAN, a second group may include features XB1, XB2, ..., XBN, and an M-th group including features XM1, XM2, ..., XMN. In some embodiments, each of the M groups may include a same number of features (e.g., N features), however, some groups may include fewer (or more) features); and
computing a first distribution characteristics in the first distribution characteristics that corresponds to the first feature based on the statistical value ([0042] At 306, a correlation score 310 for pairs of features may be computed. In some embodiments, the correlation score may be between features of a same group (e.g., a correlation score between feature XA1 and feature XA2), between features of different groups (e.g., a correlation score between feature XA1 and feature XB1), or other combinations).
Regarding dependent claim 3, the combination of SAHA and ARZANI teaches all the limitations as set forth in the rejection of claim 1 that is incorporated. SAHA further teaches wherein operations further comprise:
determining a relationship between the first plurality of datasets and the second plurality of datasets based on the comparing the first data distribution characteristics against the second data distribution characteristics ([0044] In some embodiments, at 404, correlated features may be clustered together to generate clustering data 408. Clustering data 408 may include each feature extracted from the datasets (e.g., datasets 302) and, for each of the features, any other features determined to be correlated thereto; [0045] clustering subsystem 114 may further be configured to generate a ranking of the features included within a given feature cluster. The ranking may be determined based on the correlation scores for each feature).
Regarding dependent claim 4, the combination of SAHA and ARZANI teaches all the limitations as set forth in the rejection of claim 1 that is incorporated. SAHA further teaches wherein the operations further comprise:
obtaining a third plurality of datasets usable for training the first machine learning model ([0026] The training data may be used to train the machine learning model, whereas the test data may be used to determine how well the machine learning model has been trained. In some embodiments, if the machine learning model is determined to be trained poorly (e.g., an accuracy of the model is less than a threshold accuracy), then new datasets may be retrieved from dataset database 132, and the new datasets may be used to develop new training data and new test data to retrain the model; [0058] if the accuracy does not satisfy the threshold accuracy condition, then new training data may be generated from datasets 806, additional datasets retrieved from dataset database 132 that also include input features 802);
updating the first set of distribution characteristics based on the third plurality of datasets ([0058] This process may be repeated until the accuracy of the machine learning model satisfies the threshold accuracy condition, or until another stopping criterion is satisfied; [0077] In an operation 1204, a plurality of correlation scores indicating a correlation between features of the plurality of features may be computed);
selecting, from the plurality of machine learning model types, a second machine learning model type for the first machine learning model based on the updated first set of distribution characteristics ([0058] This process may be repeated until the accuracy of the machine learning model satisfies the threshold accuracy condition, or until another stopping criterion is satisfied; [0079] In an operation 1208, a machine learning model may be selected based on a set of input features of the machine learning model and the plurality of clusters);
re-configuring the first machine learning model based on the second machine learning model type ([0058] This process may be repeated until the accuracy of the machine learning model satisfies the threshold accuracy condition, or until another stopping criterion is satisfied; [0080] In an operation 1210, a subset of datasets may be selected based on the set of input features of the selected machine learning model);
determining a second plurality of hyperparameters for training the first machine learning model based on the updated first distribution characteristics ([0058] This process may be repeated until the accuracy of the machine learning model satisfies the threshold accuracy condition, or until another stopping criterion is satisfied; [0081] In an operation 1212, training data may be generated based on the selected subset of datasets. The training data may be generated such that the training data includes some or all of the subset of datasets. In some embodiments, generating the training data may be part of generating build data. The build data may include the training data and test data, where the test data is used to test an accuracy of the trained machine learning model. The training data, upon generation, may be stored in training data database 136 and used to train the selected machine learning model. In some embodiments, operation 1212 may be performed by a subset that is the same or similar to training subsystem 118; Fig. 8; [0058] In some embodiments, training subsystem 118 may be configured to generate build data 808 based on datasets 806. Generating build data 808 may include formatting, transforming, curating, or performing other processes to engineer the features included in datasets 806 for being used to training a machine learning model. For instance, datasets 806 may be organized such that data items included in datasets 806 can be input to a machine learning model, and the hyperparameters of the machine learning model can be adjusted to minimize a cost function of the mode); and
training the re-configured first machine learning model using at least one of the first plurality of datasets or the third plurality of datasets based on the second plurality of hyperparameters ([0058] This process may be repeated until the accuracy of the machine learning model satisfies the threshold accuracy condition, or until another stopping criterion is satisfied; [0060] In some embodiments, training subsystem 118 may automatically begin training the machine learning model after build data 808 has been generated; [0106] training the first machine learning model using the training data to obtain a trained machine learning model).
Regarding dependent claim 5, the combination of SAHA and ARZANI teaches all the limitations as set forth in the rejection of claim 1 that is incorporated. SAHA further teaches wherein the second distribution characteristics correspond to a second set of features different from the first set of features ([0042] a correlation score 310 for pairs of features may be computed. In some embodiments, the correlation score may be between features of a same group (e.g., a correlation score between feature XA1 and feature XA2), between features of different groups (e.g., a correlation score between feature XA1 and feature XB1), or other combinations).
Regarding dependent claim 7, the combination of SAHA and ARZANI teaches all the limitations as set forth in the rejection of claim 1 that is incorporated. SAHA further teaches wherein the first distribution characteristics comprise at least one of statistical features of the first plurality of datasets, a central tendency of the first plurality of datasets, a skewness of the first plurality of datasets, a spread among datasets in the first plurality of datasets, one or more patterns of the first plurality of datasets, a frequency of a value in the first plurality of datasets, a presence of outliers in the first plurality of datasets, a correlation between every two distribution characteristics in the first distribution characteristics, or a type of probability distribution of the first plurality of datasets ([0027] At data cleaning 216, the build data may be treated if missing observations are present, or if it is determined that any outliers are present in the data. In order to ensure that the model is accurately trained, the build data should accurately reflect the types of data the model is to expect in real-world applications. Therefore, identifying outliers, or other abnormalities, in the data prior to being used to train the model can eliminate potential sources of error; [0034] For each feature included in the datasets, a correlation score may be computed. The correlation score may indicate how well correlated each feature is to each other feature. In some embodiments, the correlation score may be represented using a Pearson score, computed using a Pearson Correlation Coefficient. In some embodiments, the correlation score may be represented using a Spearman Coefficient score computed using a Spearman Correlation Coefficient. In some embodiments, the correlation score may be represented using a Variance Inflation Factor (VIF) computed by determining how much a variance of an estimated regression coefficient is increased due to collinearity).
Regarding independent claim 8, claim 8 contains substantially similar limitations to those found in claim 1. Therefore, it is rejected for the same reason as claim 1 above.
Regarding dependent claim 9, claim 9 contains substantially similar limitations to those found in claim 1. Therefore, it is rejected for the same reason as claim 1 above.
Regarding dependent claim 10, claim 10 contains substantially similar limitations to those found in claim 2. Therefore, it is rejected for the same reason as claim 2 above.
Regarding dependent claim 11, claim 11 contains substantially similar limitations to those found in claim 3. Therefore, it is rejected for the same reason as claim 3 above.
Regarding dependent claim 12, claim 12 contains substantially similar limitations to those found in claim 4. Therefore, it is rejected for the same reason as claim 4 above.
Regarding dependent claim 13, claim 13 contains substantially similar limitations to those found in claim 5. Therefore, it is rejected for the same reason as claim 5 above.
Regarding independent claim 15, claim 15 contains substantially similar limitations to those found in claims 1 and 4. Therefore, it is rejected for the same reason as claims 1 and 4 above.
Regarding dependent claim 16, claim 16 contains substantially similar limitations to those found in claims 1 and 2. Therefore, it is rejected for the same reason as claims 1 and 2 above.
Regarding dependent claim 17, claim 17 contains substantially similar limitations to those found in claim 3. Therefore, it is rejected for the same reason as claim 3 above.
Regarding dependent claim 18, claim 18 contains substantially similar limitations to those found in claim 2. Therefore, it is rejected for the same reason as claim 2 above.
Regarding dependent claim 19, claim 19 contains substantially similar limitations to those found in claim 5. Therefore, it is rejected for the same reason as claim 5 above.
Claims 6, 14 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over SAHA, in view of ARZANI as applied in claims 1, 8 and 15, in view of Ma et al. (hereinafter Ma), US 20200401948 A1.
Regarding dependent claim 6, the combination of SAHA and ARZANI teaches all the limitations as set forth in the rejection of claim 1 that is incorporated. The combination of SAHA and ARZANI does not explicitly teach wherein the operations further comprise:
configuring and training a plurality of different versions of the first machine learning model generated using different ones of the plurality of machine learning model types, a plurality of configuration parameters, and a plurality of hyperparameters;
evaluating the plurality of different versions of the second machine learning model;
determining a particular configuration and particular training setting for the first machine learning model based on the evaluating; and
associating the configuration and training setting with the second distribution characteristics.
However, in the same field of endeavor, Ma teaches
configuring and training a plurality of different versions of the second machine learning model generated using different ones of the plurality of machine learning model types, a plurality of configuration parameters, and a plurality of hyperparameters (Fig. 2; [0045] an exploration apparatus 212 trains each machine learning model using a different training configuration (e.g., training configuration 1 244, training configuration n 246). Each training configuration contains a set of features to be inputted into the corresponding machine learning model; [0048] Each training configuration also, or instead, includes one or more hyperparameters for the corresponding machine learning model. For example, the hyperparameters include a convergence parameter that adjusts the rate of convergence of the machine-learning model. In another example, the hyperparameters include a clustering parameter that controls the amount of clustering (e.g., number of clusters) in a clustering technique and/or classification technique that utilizes clusters);
evaluating the plurality of different versions of the second machine learning model ([0051] After exploration apparatus 212 trains global and personalized versions of a given machine learning model using training dataset 216 and the corresponding training configuration, exploration apparatus 212 evaluates the performance (e.g., performance 1 240, performance n 242) of the machine learning model using evaluation dataset 218);
determining a particular configuration and a particular training setting for the second machine learning model based on the evaluating ([0052] exploration apparatus 212 trains and evaluates multiple machine learning models using different training configurations to explore different feature sets and/or hyperparameters for the machine learning models. In turn, exploration apparatus 212 identifies feature sets and/or hyperparameters that result in the best-performing machine learning model.); and
associating the particular configuration and the particular training setting with the second distribution characteristics ([0055] a sampling apparatus 204 generates a sampled training dataset 224 that includes records 228 that are sampled from training dataset 216 and a sampled evaluation dataset 226 that includes records 230 that are sampled from evaluation dataset 218).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of generating sampled evaluation and training datasets repeated a number of times and using each sampled training dataset and the corresponding sampled evaluation dataset with a different training configuration to train and evaluate the machine learning model as suggested in Ma into SAHA and ARZANI’s system because both of these systems are addressing training and evaluating machine learning models. This modification would have been motivated by the desire to facilitate machine learning and/or analytics by mechanisms for improving the creation, profiling, management, sharing, and reuse of features and/or machine learning models (Ma, [0005]).
Regarding dependent claim 14, claim 14 contains substantially similar limitations to those found in claim 6. Therefore, it is rejected for the same reason as claim 6 above.
Regarding dependent claim 20, claim 20 contains substantially similar limitations to those found in claim 6. Therefore, it is rejected for the same reason as claim 6 above.
Response to Arguments
Applicant's arguments filed 06/17/2026 have been fully considered. Each of applicant’s remarks is set forth, followed by examiner’s response.
(1) Regarding 35 U.S.C. 101 rejections, Applicant’s amendments to the claims 1-20 have overcome the rejections. Rejections under 35 U.S.C 101 to claims 1-20 are withdrawn.
(2) Applicant’s prior art arguments with respect to the pending claims have been considered but they are moot in view of the new ground(s) of rejections presented above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
FENG et al. (US 20220253725 A1) discloses generating a trained machine learning model for performing entity resolution.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
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 AMY P HOANG whose telephone number is (469)295-9134. The examiner can normally be reached M-TH 8:30-5:00PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, JENNIFER WELCH can be reached at 571-272-7212. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/AMY P HOANG/ Examiner, Art Unit 2143
/JENNIFER N WELCH/ Supervisory Patent Examiner, Art Unit 2143
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