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 Arguments
Applicant's arguments filed 8/7/2026 have been fully considered but they are not persuasive.
To the claim objection, Applicant didn’t amend claim 15 to fix the dependency issue – objection maintained.
Applicant argues, argues the that “feature selection, feature engineering, model selection, intervention recommendation, and ways of testing for correlation cannot be equated to a mathematical relationship as it is not a relationship between variables or numbers.” Remarks 9. Each one of those steps, including intervention recommendation, has claimed mathematical relationship: LASSO for feature selection, and engineering; SHAP for model selection; and cross validation with holdout values for testing. These and other claim elements are the abstract idea, and they are a mathematical relationship.
Applicant argues that “an improved machine learning pipeline… provide multiple improvements to the function of a computer…” Remarks 10. The pipeline uses the computer to run the pipeline, it does not seek to improve the function of a computer. The alleged improvement to the algorithm, does not actually improve the algorithm compared to the prior art, see 102(a)(1) rejection below.
The prior art rejections are moot in light of new art necessitated by amendments.
Claim Objections
A series of singular dependent claims is permissible in which a dependent claim refers to a preceding claim which, in turn, refers to another preceding claim.
A claim which depends from a dependent claim should not be separated by any claim which does not also depend from said dependent claim. It should be kept in mind that a dependent claim may refer to any preceding independent claim. In general, applicant's sequence will not be changed. See MPEP § 608.01(n).
Claim 15 is out of order and should probably be amended to depend on claim 1 because it isn’t really related to claim 13.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of a mental concept and mathematical relationship without significantly more. The claims recite the mental concept and mathematical relationship of:
1. (Currently Amended) …
a feature selection block that receives the dataset from the input block and reduces a size of the dataset by:
dividing features of the dataset into a first subset of features that are predictive of a target variable and a second subset of features that are not predictive of the target variable; and
removing the second subset of features from the dataset to create a modified dataset having only columns corresponding to the first subset of features;
a model selection block that selects a candidate model from a plurality of models by:
receiving the modified dataset from the feature selection block;
feeding one or more validation values from the modified dataset to the plurality of models;
measuring performance of each of the plurality of models at estimating the target variable; and
selecting the candidate model from the plurality of models based on the candidate model having a measured performance that meets or exceeds measured performances of other models in the plurality of models; and
an output block that provides an output that identifies the candidate model as being a preferred model for processing the dataset.
2. (Currently Amended) The system of claim 1, wherein:
reducing the size of the dataset further comprises:
dividing features of the modified dataset into a third subset of features that are correlated features and a fourth subset of features that are not correlated features; and
removing the fourth subset of features from the dataset to create a second modified dataset comprising columns corresponding to the third subset of features; and
the model selection block receives the second modified dataset from the feature selection block and tests the performance of the plurality of models against the second modified dataset.
3. (Currently Amended) The system of claim 1, wherein the model selection block further tests the performance of the plurality of models by feeding a previously unseen dataset to each of the plurality of models and measuring the performance of each of the plurality of models.
4. (Currently Amended) The system of claim 1, wherein the output is delivered in one or more electronic communications to the computational device.
5. (Currently Amended) The system of claim 1, further comprising a journey optimization block that receives the modified dataset and identifies one or more interventions for an individual based on processing the modified dataset, wherein the one or more interventions comprise a recommended set of interventions for the individual.
6. (Currently Amended) The system of claim 5, wherein the journey optimization block further suggests a communication modality in the output, wherein the communication modality corresponds to a suggested mode for a care provider to communicate the one or more interventions to the individual.
7. (Currently Amended) The system of claim 1, wherein the feature selection block divides the features of the dataset into the first subset of features and the second subset of features by running an automated correlation analysis.
8. (Currently Amended) The system of claim 7, wherein the feature selection block runs the automated correlation analysis with a linear regression that uses shrinkage.
9. (Currently Amended) The system of claim 1, wherein the feature selection block comprises a correlation matrix and a Lasso model.
10. (Currently Amended) The system of claim 9, wherein the feature selection block iteratively processes the modified dataset using the correlation matrix and the Lasso model that forces the non-correlated features to have a value of zero.
11. (Currently Amended) The system of claim 10, wherein a number of times that the feature selection block iteratively processes the modified dataset is configurable by a user.
12. (Currently Amended) The system of claim 1, further comprising: a feature engineering block positioned between the input block and the feature selection block, wherein the feature engineering block checks the dataset for errors and fixes any identified errors included in the dataset.
13. (Currently Amended) The system of claim 12, wherein the feature engineering block enriches the dataset with one or more additional features.
14. (Currently Amended) The system of claim 1, wherein the one or more validation data values are obtained from the modified dataset.
15. (Currently Amended) The system of claim 13, further comprising: a parameter setting block that determines one or more operational parameters for the candidate model.
16. (Currently Amended) The system of claim 1, wherein the feature selection block corresponds to a callable function.
17. (Currently Amended) The system of claim 1, wherein the model selection block corresponds to a callable function.
18. (Currently Amended) The machine learning pipeline of claim 1, further comprising: a Shapley additive explanations (SHAP) model that identifies a percentage of the target variable that is driven by a feature in the first subset of features; and
the output block provides a second output to the computational device, wherein the second output identifies the percentage of the target variable that is driven by the feature of the first subset of features.
Claims 19 and 20 are substantially similar.
This judicial exception is not integrated into a practical application because the steps of an input block that receives a dataset from a data source, wherein the dataset comprises a plurality of columns that each correspond to a different feature in the dataset merely links the abstract idea to the field of data. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because elements such as “a processor; and memory storing instructions…” are directed to generic computer parts.
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 1-20 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 applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. The first subset and second subset are related to correlation. The specification never teaches the first and second subset as part of the predictive variables. This is important because: one, the claim lacks literal written description; and two, claims 7 and 18 later use the first subset and second subset as if they still related to correlated features.
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.
Claims 7-8 and 18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 7 uses an automatic correlation analysis to determine the first subset and second subset, but claim 1 says those subsets are divided based on predictiveness – that doesn’t make sense. The same is true for the SHAP model in claim 18, this test is introduced in the specification as a test for correlation.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-10, 12-15 and 19-20 are rejected under 35 U.S.C. 102(a)(1) as being described by US20210319899A1 to Liu et al.
Liu teaches claim 1. (Currently Amended) A system, comprising: a processor; and
memory storing instructions that, when executed by the processor, cause the computing device to implement a machine learning pipeline, the machine learning pipeline comprising:
an input block that receives a dataset from a data source, wherein the dataset comprises a plurality of columns that each correspond to a different feature in the dataset; (Liu para 209-210 “For each of the 21 models, seven combinations of feature types were used as the inputs to be able to evaluate performance of single- and multi-modal feature sets. These included clinical scales only, sMRI only, fMRI only, scales+sMRI, scales+fMRI, sMRI+fMRI, and scales+sMRI+fMRI.
[0210]
As input features varied in their mean values and regularized models require normally-distributed data, scaled each input feature was scaled separately to have zero mean and unit variance.”)
a feature selection block that receives the dataset from the input block and reduces a size of the dataset by: (Liu para 171-172 “full feature sets are likely to be subjected to a substantial amount of noise as well as features that are not predictive. … first the features in the transdiagnostic classifiers are rank ordered according to their feature importance measures. Next, a series of truncated models was built such that each model would only take the top k most predictive features as inputs to perform the same transdiagnostic classification problems.” (emphasis added))
dividing features of the dataset into a first subset of features that are predictive of a target variable and a second subset of features that are not predictive of the target variable; and (Liu para 171-172 “To investigate whether improved classification performances can be achieved from a reduced set of most predictive features, the following feature importance-guided sequential model selection procedure is utilized.
[0172]
Specifically, first the features in the transdiagnostic classifiers are rank ordered according to their feature importance measures. Next, a series of truncated models was built such that each model would only take the top k most predictive features as inputs…”)
removing the second subset of features from the dataset to create a modified dataset having only columns corresponding to the first subset of features; (Liu para 172 “Next, a series of truncated models was built such that each model would only take the top k most predictive features as inputs…”)
a model selection block that selects a candidate model from a plurality of models by:
receiving the modified dataset from the feature selection block; (Liu para 78 “initial rank-ordering step for ordering features by importance, a forward-selection search step for building a series of models utilizing subsets of ordered features selected from the first step…” Liu para 79 “Two different linear regression algorithms that incorporate feature selection through regularization (Lasso, Elastic Net) and one non-linear algorithm (Random Forest) are also evaluated, in order to identify the best parameters and biomarkers for our selected set of symptom types.”)
feeding one or more validation values from the modified dataset to the plurality of models; (Liu para 212 “For each of these sets of models, hyperparameters were tuned using 5-fold cross-validated grid-search on a training set of data (80% of data), and selected hyperparameters were used on a separate evaluation set of data (20% held-out sample).”)
measuring performance of each of the plurality of models at estimating the target variable; and (Liu para 213 “comparison of measured outcome scores and predicted outcome scores. For example, measured versus predicted outcome scores (right) illustrate how closely the model predictions are to actual outcome scores for individuals in the held-out sample.”)
selecting the candidate model from the plurality of models based on the candidate model having a measured performance that meets or exceeds measured performances of other models in the plurality of models; and (Liu para 217 “The best model overall was selected by finding the maximum median r2 value over all feature subsets and selecting the model that corresponded to that max median r2 value (FIGS. 9A-9B).”)
an output block that provides an output that identifies the candidate model as being a preferred model for processing the dataset. (Liu para 217 “The best model overall was selected…”)
Liu teaches claim 2. (Currently Amended) The system of claim 1, wherein:
reducing the size of the dataset further comprises:
dividing features of the modified dataset into a third subset of features that are correlated features and a fourth subset of features that are not correlated features; and (Liu para 165 “Third, functional connectivity between ROIs was estimated via the Pearson's correlation coefficient between the average time series from all pairs of brain regions. This resulted in a 264-by-264 correlation matrix, from which 34,716 are unique correlations between two distinct ROIs and were used as input features to the models.”)
removing the fourth subset of features from the dataset to create a second modified dataset comprising columns corresponding to the third subset of features; and (Liu para 165 “This resulted in a 264-by-264 correlation matrix, from which 34,716 are unique correlations between two distinct ROIs and were used as input features to the models.” 34,716 is less than 2642.)
the model selection block receives the second modified dataset from the feature selection block and tests the performance of the plurality of models against the second modified dataset. (Liu para 217 “The best model overall was selected by finding the maximum median r2 value over all feature subsets and selecting the model that corresponded to that max median r2 value (FIGS. 9A-9B).” The remaining correlated features are used as input, so they are used in selection/validation also.)
Liu teaches claim 3. (Currently Amended) The system of claim 1, wherein the model selection block further tests the performance of the plurality of models by feeding a previously unseen dataset to each of the plurality of models and measuring the performance of each of the plurality of models. (Liu para 168 “Then the model was trained on the entire development set using the best hyperparameters and was further tested on the remaining 20% of evaluation set which the model had never seen before to obtain testing performance.”)
Liu teaches claim 4. (Currently Amended) The system of claim 1, wherein the output is delivered in one or more electronic communications to the computational device. (Liu para 83 “display 2802 is on a smart phone…. The multiple displays can output identical or different information, according to instructions by the control system 2806.”)
Liu teaches claim 5. (Currently Amended) The system of claim 1, further comprising a journey optimization block that receives the modified dataset and identifies one or more interventions for an individual based on processing the modified dataset, wherein the one or more interventions comprise a recommended set of interventions for the individual. (Liu para 15 “the machine learning system further includes using the features of the diagnostic classifier as a screening tool to assess at least one of intermediate or end-point outcomes in at least one clinical trial testing for treatment responses.”)
Liu teaches claim 6. (Currently Amended) The system of claim 5, wherein the journey optimization block further suggests a communication modality in the output, wherein the communication modality corresponds to a suggested mode for a care provider to communicate the one or more interventions to the individual.
Liu teaches claim 7. (Currently Amended) The system of claim 1, wherein the feature selection block divides the features of the dataset into the first subset of features and the second subset of features by running an automated correlation analysis. (Liu para 165 “Third, functional connectivity between ROIs was estimated via the Pearson's correlation coefficient between the average time series from all pairs of brain regions. This resulted in a 264-by-264 correlation matrix, from which 34,716 are unique correlations between two distinct ROIs and were used as input features to the models.” Liu para 79 “Two different linear regression algorithms that incorporate feature selection through regularization (Lasso, Elastic Net) and…”)
Liu teaches claim 8. (Currently Amended) The system of claim 7, wherein the feature selection block runs the automated correlation analysis with a linear regression that uses shrinkage. (Liu para 211 “The Lasso approach uses regularization by imposing an L1-penalty parameter to force some coefficients to zero…” This is regularization/shrinkage linear regression.)
Liu teaches claim 9. (Currently Amended) The system of claim 1, wherein the feature selection block comprises a correlation matrix and a Lasso model. (Liu para 165 “Third, functional connectivity between ROIs was estimated via the Pearson's correlation coefficient between the average time series from all pairs of brain regions. This resulted in a 264-by-264 correlation matrix, from which 34,716 are unique correlations between two distinct ROIs and were used as input features to the models.” Liu para 79 “Two different linear regression algorithms that incorporate feature selection through regularization (Lasso, Elastic Net) and…”)
Liu teaches claim 10. (Currently Amended) The system of claim 9, wherein the feature selection block iteratively processes the modified dataset using the correlation matrix and the Lasso model that forces the non-correlated features to have a value of zero. (Liu para 211 “The Lasso approach uses regularization by imposing an L1-penalty parameter to force some coefficients to zero…”)
Liu teaches claim 12. (Currently Amended) The system of claim 1, further comprising: a feature engineering block positioned between the input block and the feature selection block, wherein the feature engineering block checks the dataset for errors and fixes any identified errors included in the dataset. (Liu para 162 “Specifically, the first 3 volumes in the data were discarded to remove any transient magnetization effects in the data. Spikes in the resting-state fMRI data were then removed and all volumes were spatially registered with the 4th volume to correct for any head motion.”)
Liu teaches claim 13. (Currently Amended) The system of claim 12, wherein the feature engineering block enriches the dataset with one or more additional features. (Liu para 165 “ For resting-state fMRI features, the brain is first parceled into 264 regions. Specifically, a 5-mm radius spherical ROI was seeded according to the MNI coordinates of each brain region specified in the atlas. Second, the clean resting-state BOLD time series from all voxels within a given 5-mm radius spherical ROI were averaged to create the representative time series for the brain region. Third, functional connectivity between ROIs was estimated via the Pearson's correlation coefficient between the average time series from all pairs of brain regions.” The ROI are extra features, the correlation is an extra connectivity feature.)
Liu teaches claim 14. (Currently Amended) The system of claim 1, wherein the one or more validation data values are obtained from the modified dataset. (Liu para 212 “ For each of these sets of models, hyperparameters were tuned using 5-fold cross-validated grid-search on a training set of data (80% of data), and selected hyperparameters were used on a separate evaluation set of data (20% held-out sample).” Liu para 214 “a forward-selection search step for building a series of models utilizing subsets of ordered features selected from the first step, and (3) an evaluation step for evaluating each of these models using these candidate subsets according to a pre-specified criterion to find the optimal model.”)
Liu teaches claim 15. (Currently Amended) The system of claim 13, further comprising: a parameter setting block that determines one or more operational parameters for the candidate model. (Liu para 212 “ For each of these sets of models, hyperparameters were tuned using 5-fold cross-validated grid-search on a training set of data (80% of data), and selected hyperparameters were used on a separate evaluation set of data (20% held-out sample).”)
Liu teaches claim 19. (Currently Amended) A computer memory device comprising a codebase, wherein the codebase provides access to blocks of a machine learning pipeline comprising:
an input block that receives a dataset from a data source, wherein the dataset comprises a plurality of columns that each correspond to a different feature in the dataset; (Liu para 209-210 “For each of the 21 models, seven combinations of feature types were used as the inputs to be able to evaluate performance of single- and multi-modal feature sets. These included clinical scales only, sMRI only, fMRI only, scales+sMRI, scales+fMRI, sMRI+fMRI, and scales+sMRI+fMRI.
[0210]
As input features varied in their mean values and regularized models require normally-distributed data, scaled each input feature was scaled separately to have zero mean and unit variance.”)
a feature selection block that receives the dataset from the input block and produces a modified dataset by dividing features of the dataset into a first subset of features that are predictive of a target variable and a second subset of features that are not predictive of the target variable; and (Liu para 171-172 “To investigate whether improved classification performances can be achieved from a reduced set of most predictive features, the following feature importance-guided sequential model selection procedure is utilized.
[0172]
Specifically, first the features in the transdiagnostic classifiers are rank ordered according to their feature importance measures. Next, a series of truncated models was built such that each model would only take the top k most predictive features as inputs…”)
an output block that outputs a candidate model that is optimized to process the first subset of features of the modified dataset and not process the second subset of features of the modified dataset. (Liu para 217 “The best model overall was selected…”)
Liu teaches claim 20. (Currently Amended) The computer memory device of claim 19, wherein the blocks of the machine learning pipeline further comprise: a model selection block that selects a candidate model from a plurality of models by:
receiving the modified dataset from the feature selection block; (Liu para 78 “initial rank-ordering step for ordering features by importance, a forward-selection search step for building a series of models utilizing subsets of ordered features selected from the first step…” Liu para 79 “Two different linear regression algorithms that incorporate feature selection through regularization (Lasso, Elastic Net) and one non-linear algorithm (Random Forest) are also evaluated, in order to identify the best parameters and biomarkers for our selected set of symptom types.”)
feeding one or more validation values from the modified dataset to the plurality of models; (Liu para 212 “For each of these sets of models, hyperparameters were tuned using 5-fold cross-validated grid-search on a training set of data (80% of data), and selected hyperparameters were used on a separate evaluation set of data (20% held-out sample).”)
measuring performance of each of the plurality of models at estimating the target variable; and(Liu para 213 “comparison of measured outcome scores and predicted outcome scores. For example, measured versus predicted outcome scores (right) illustrate how closely the model predictions are to actual outcome scores for individuals in the held-out sample.”)
selecting the candidate model from the plurality of models based on the candidate model having a measured performance that meets or exceeds measured performances of other models in the plurality of models. (Liu para 217 “The best model overall was selected by finding the maximum median r2 value over all feature subsets and selecting the model that corresponded to that max median r2 value (FIGS. 9A-9B).”)
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 11, 16 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over US20210319899A1 to Liu et al and https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html as archived 7/18/2019 (SciKit).
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over US20210319899A1 to Liu et al and US10510022B1 to Tharrington et al.
Liu teaches claim 11. (Currently Amended) The system of claim 10, wherein a number of times that the feature selection block iteratively processes the modified dataset is (Liu para 216 “ In order to generate descriptive statistics for this step, twenty-five (25) iterations of modeling for each feature subset…”)
Liu doesn’t teach user configuration.
However, Scikit teaches iterations configurable by a user. (Scikit p. 1 shows “max_iter” which is the maximum number of iterations set by the user of the LASSO function.)
The claims, Liu and Scikit are all related to the LASSO method for determining correlated features. It would have been obvious to a person having ordinary skill in the art, at the time of filing, to use iteration and cap the number of iterations because “scikit-learn toolbox is utilized” (Liu para 166) and in order to solve for the optimum penalty for each coefficient without hanging up on an intractable optimization problem.
Liu teaches claim 16. (Currently Amended) The system of claim 1, wherein the feature selection block corresponds to a (Liu para 79 “ linear regression algorithms that incorporate feature selection through regularization (Lasso…”)
Liu doesn’t teach callable functions.
However, Scikit teaches feature selection block corresponds to a callable function. (Scikit p. 1 shows the LASSO function is a callable function.)
The claims, Liu and Scikit are all related to the LASSO method for determining correlated features. It would have been obvious to a person having ordinary skill in the art, at the time of filing, to use iteration and cap the number of iterations because “scikit-learn toolbox is utilized” (Liu para 166).
Liu teaches claim 17. (Currently Amended) The system of claim 1, wherein the model selection block corresponds to a (Liu para 214 “a forward-selection search step for building a series of models utilizing subsets of ordered features selected from the first step, and (3) an evaluation step for evaluating each of these models using these candidate subsets according to a pre-specified criterion to find the optimal model.”)
Liu doesn’t teach callable functions.
However, Scikit teaches a callable function. (Scikit p. 1 shows the LASSO function is a callable function.)
The claims, Liu and Scikit are all related to the LASSO method for determining correlated features. It would have been obvious to a person having ordinary skill in the art, at the time of filing, to use iteration and cap the number of iterations because “scikit-learn toolbox is utilized” (Liu para 166) and callable functions are well-known in the art.
Liu teaches claim 18. (Currently Amended) The machine learning pipeline of claim 1, further comprising: a (Liu para 213 “FIG. 8B illustrates a comparison of measured outcome scores and predicted outcome scores. For example, measured versus predicted outcome scores (right) illustrate how closely the model predictions are to actual outcome scores for individuals in the held-out sample.”)
Liu doesn’t teach SHAP models and a second output.
However, Tharrington teaches Shapley additive explanations (SHAP) model that identifies a percentage… (Tharrington 3-4:65-2 “a Shapley absolute percent error generated by a plurality of algorithms in computing a feature contribution for a first dataset…”
the output block provides a second output to the computational device, wherein the second output identifies the percentage of the target variable that is driven by the feature of the first subset of features. (Tharrington 4:55 “Output interface 104 provides an interface for outputting information for review by a user of feature contribution device 100 and/or for use by another application or device.” Tharrington 2:65 “The computed Shapley estimate value is output for each variable of the plurality of variables as a contribution of each variable to a predicted value for the predefined query.”)
Liu, the claims and Tharrington all judge model performance. It would have been obvious to a person having ordinary skill in the art, at the time of filing, “To apply the Shapley values for the purpose of predictive model explanation…” Tharrington 4:50.
Conclusion
THIS ACTION IS MADE FINAL. 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 Austin Hicks whose telephone number is (571)270-3377. The examiner can normally be reached Monday - Thursday 8-4 PST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mariela Reyes can be reached at (571) 270-1006. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/AUSTIN HICKS/Primary Examiner, Art Unit 2142
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