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 .
Claims 1, 4-5, and 7-12 are pending for examination. Claim 1 is independent.
Response to Amendment
The office action is responsive to the amendments filed on 04/22/2026. As
directed by the amendments claim 1 is amended.
Response to Arguments
Applicant's arguments filed 04/22/2026 have been fully considered but they are not fully persuasive.
Applicant arguments regarding 35 U.S.C. § 101:
Step 2A, Prong 1: The Claims Are Not Directed to a Mental Process
The 2019 Guidance instructs that claims must not be characterized "at a high level of abstraction untethered from the language of the claims." 84 Fed. Reg. 50, 54 (Jan. 7, 2019). The Examiner violates this instruction by reducing the full claim to generic phrases such as "extracting features" and "evaluating data," while ignoring the specific technical operations that the claim actually requires.
More fundamentally, the Examiner's characterization of each claim step as a "mental process" is incorrect. A step is a mental process only if it can practically be performed in the human mind. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372 (Fed. Cir. 2011). The following operations recited in Claim 1, at least as amended, are decidedly not mentally performable, even in principle:
(1) Automated simultaneous multi-algorithm feature extraction. The claim requires applying simultaneously a plurality of feature analysis algorithms spanning multiple categories - temporal, pattern, statistical, context, harmonic, and external features - across each data subset in a sliding window. […]
(2) High-dimensional vector fusion. As discussed in specification paragraph [0088] and elsewhere, the claim requires fusing the individual feature vectors into a single higher dimensional fused feature vector of dimension […]
(3) Automated machine learning model training and selection. The claim requires using the multi-dimensional fused feature vector dataset to automatically train at least one machine learning model. […]
(4) Real-time inference on new data. The deployment step requires processing new time-series data through the full feature extraction pipeline to generate fused feature vectors, then applying the trained model to generate forecasts - all automatically, without human intervention, and using the same computationally intensive feature extraction pipeline.
The Examiner's per se characterization of these operations as "mental processes" is erroneous. The Federal Circuit has specifically held that network traffic analysis using trained models and specific technical operations is not a mental process because it requires technical means not implementable in the human mind. SRI Int'l v. Cisco Sys., 930 F.3d 1295, 1304 (Fed. Cir. 2019). The same logic applies with equal force here.
Examiner response: Examiner respectfully disagrees, under broadest reasonable interpretation, simultaneously performing multiple algorithms is describing using a computer to execute multiple algorithms, which is mere instructions to implement an abstract idea on a computer (see MPEP 2106.05(f)). Specifying the dimensionality of the fused feature vector is understood to be a field of use limitation (See MPEP 2106.05(h)). Performing steps “automatically” describe using a computer to perform steps and generally applying a machine learning model are understood as “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). Examiner respectfully disagrees, performing data analysis on new data is practically performable in the human mind. Using a machine learning model to analyze new data is understood as “apply it” (or an equivalent) with the judicial exception (see MPEP 2106.05(f)). It is unclear how applicants claims relate to SRI Int'l v. Cisco Sys which describe a different invention and improvement entirely.
Applicant further argues:
B. Step 2A, Prong 2: The Claims Are Integrated into a Practical Application
Even assuming (for the sake of argument), that any individual claim step could be characterized as abstract (which Applicant expressly disputes), the claims as a whole are integrated into a practical application that improves the technology of time-series forecasting. Under MPEP 2106.05(a), a claim that improves computer functionality or other technology is a practical application of any identified abstract idea. […]
5. The Structural Dataset Limitation Is Not Merely a 'Field of Use' (MPEP 2106.0S(h))
The Examiner treats the time-series training dataset structure (linear array of time points, origin time point, single time-aware variable with data point value) as a mere field-of-use limitation under MPEP 2106.05(h). This is incorrect. The data structure is functional and integral to the claimed method: the sliding window mechanism, the feature extraction algorithms, and the entire static domain transformation are specifically designed to operate on this defined data structure. The data structure is not merely identifying where the invention is used - it defines the technical input to the specific technical process. See Enfish, 822 F.3d at 1337 (specific data structure used to achieve a technical improvement is not a mere field of use).
6. Distinguishing Example 47 of the July 2024 Subject Matter Eligibility Examples […]
Examiner response: Examiner respectfully disagrees, under broadest reasonable interpretation, transforming time series-training data set to a static domain of features is practically performable in the human mind. Performing abstract ideas “automatically” or by applying a machine learning model are understood as “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). Therefore, applicants claimed improvement is describing an improvement to an abstract idea and not to an improvement to a computer or technical field. MPEP 2106.05(a) says an improvement in the abstract idea itself is not an improvement in technology. Many of the cases describe different inventions and improvements to applicant’s limitations and it is unclear how they correspond.
Examiner respectfully disagrees, the claim limitations describing using the time series-training dataset to perform transformations, extractions, and create data subset and are all treated as mental processes in step 2A Prong 1. The specific claim limitations stating “wherein said time-series training dataset comprises a linear array of time points starting from an origin time point, each time point having a single associated data point with a data point value;” further specifies the dataset and is understood to be a field of use limitation (See MPEP 2106.05(h)).
Example 47 from the “July 2024 Subject Matter Eligibility Examples” describes generally training and applying a neural network (i.e. machine learning model). The example similarly describes how using and applying a high level machine learning model recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer (See MPEP 2106.05(f)).
Applicant further argues
C. Step 2B: The Additional Elements Amount to Significantly More
Even if the claims were directed to an abstract idea - which Applicant expressly denies – Step 2B is satisfied. The claimed combination of a specific computer processor, a static machine learning system comprising an ANN, and a plurality of specific feature analysis algorithms, operating together in the specific pipeline described, amounts to significantly more than any individual abstract step alone.[…]
Claims 7 and 8 (Sliding Window Specification): The specification of incrementally sliding time windows (Claim 7) and constant-length sliding windows (Claim 8) define the specific technical mechanism by which the feature extraction operates - they are not merely specifying where the invention is used. The window structure directly controls the feature extraction process 14 and the machine-learning training dataset generated. These are technical constraints on the computational process, not field-of-use limitations.
Claims 4 and 9 (Feature Categories and Variable Window Length): Claim 4's enumeration of temporal, pattern, statistical, context, harmonic, and external features, and Claim 9's variable maximum window length per feature type, define specific technical parameters of the feature extraction algorithm - parameters that directly affect the quality and nature of the extracted feature vectors. These are technical specifications, not field-of-use designations.
Claim 5 (Feature Analysis Algorithm Enumeration): The specific list of feature analysis algorithms in Claim 5 (including Fourier and wavelet transform coefficients, dominant frequencies, spectral energy distribution, harmonic ratios, autocorrelation, entropy, cross entropy, kurtosis, skewness, etc.) are the very computations that make the claimed method non performable in the human mind. They are specific algorithmic choices that define the technical character of the invention.
Claims 11 and 12 (Automated Algorithm Optimization): Claims 11 and 12 recite automated iterative optimization -varying model hyperparameters through MSE error metrics (Claim 11) and automatically iterating over different sets of feature analysis algorithms to find the best performing set (Claim 12). These are computational optimization loops that directly improve the machine learning model's performance. The Examiner found Claim 12 passes 2A Prong I but has no additional elements for 2A Prong 2/2B. Applicant respectfully submits that the automated iteration and determination of which algorithm sets produce superior models is a non-generic computational process - it is an additional element that amounts to significantly more, particularly in combination with all of Claim l's elements. […]
Examiner response: Examiner respectfully disagrees, the additional elements disclosed in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are field of use limitations in combination of generic computer functions that are implemented to perform the disclosed abstract ideas. The claim limitations are a combination of mental steps under step 2A Prong 1, and additional elements under steps 2A Prong 2 & 2B as detailed in the 101 rejection below.
Examiner respectfully disagrees, claim 1 describes using the sliding time window to create a data subset and is addressed in step 2A Prong 1 as a mental process. Claims 7-8 further specify the time window itself and is understood to be field of use or technological environment (See MPEP 2106.05(h)).
Examiner respectfully disagrees, claim 1 describes extracting features, which is a mental process. Claim 4 and 9 further specify the features itself and is understood to be field of use or technological environment (See MPEP 2106.05(h)).
Examiner respectfully disagrees, claim 1 describes using feature analysis algorithms, which is a mental process. Claim 5 further specify all the possible different algorithms and is understood to be field of use or technological environment (See MPEP 2106.05(h)).
Examiner respectfully disagrees, calculating a mean square error is a recitation of a mental process or mathematical calculation. Performing steps “automatically” describe using a computer to perform steps and generally applying a machine learning model are understood as “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).
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.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
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 of carrying out his invention.
Claims 1, 4-5, and 7-12 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.
Regarding Claim 1 recites “wherein said extracting comprises simultaneously applying said plurality of feature analysis algorithms from at least two different feature types selected from temporal features, pattern features, statistical features, context features, harmonic features, and external features, to data within a sliding time window of said time series-training dataset;” Support for this limitation does not appears in the Speciation, drawings, or claims as originally submitted. Based on Examiner review, the nearest support disclosure for this limitation is found in para 0051:
[Para 0051] Terminology: in this disclosure, the term “algorithm” will generally refer to various mathematical data analysis methods that can be performed with standard computer processors. This includes, but is not limited to, mathematical operations such as lagged values, moving averages, […]
As Highlighted in the passage above, the specification recites performing methods with standard computer processors. Examiner notes that the specification does not appear to disclose “simultaneously applying said plurality of feature analysis algorithms” as described in the amended limitations.
Dependent claims 4-5 and 7-12 do not resolve the 112(a) rejection
from independent claim 1 and are also rejected under 112(a).
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, 4-5, and 7-12 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
According to the first part of the analysis, in the instant case, claims 1, 4-5, and 7-12 are directed to a method. Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Regarding Claim 1
2A Prong 1:
An
using at least one time series-training dataset, (This step for extracting features from a time series is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., judgment/evaluation).)
for at least some later time points after said origin time point, window; (This step for creating a data subset is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
for each said data subset, using (This step for producing a plurality of data subset individual feature vectors is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
for each said data subset, fusing said plurality of data subset individual feature vectors by concatenation to produce a single data subset fused feature vector, thus preserving the individual information of each said individual feature vector while aligning them into a higher-dimensional feature space; (This step for fusing and producing a fused vector is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
using a plurality of single data subset fused feature vectors, obtained over a plurality of different sliding time windows, as a machine-learning dataset; (This step for using feature vectors is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., judgment/evaluation).) and
analyzing said new data by (This step for analyzing new data to create fused feature vectors is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
An automated method… said method comprising: at least one computer processor, and at least one static machine learning system comprising an artificial neural network (ANN) (The processor, static machine learning system, and automatically are understood to be generic computer elements and the limitation is merely using generic computer elements as a tool to perform an abstract idea . See MPEP 2106.05(f).)
wherein said extracting comprises simultaneously applying said plurality of feature analysis algorithms from at least two different feature types selected from temporal features, pattern features, statistical features, context features, harmonic features, and external features, to data within a sliding time window of said time series-training dataset; (This step describing simultaneously applying (e.g. parallel processing) is adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (i.e. applying feature analysis algorithms) - see MPEP 2106.05(f))
wherein said at least one static machine learning system predicts a target variable based on said automatically extracted features irrespective of their temporal sequence in said dataset; (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying machine learning as a tool to perform the abstract idea (i.e., predicting) - see MPEP 2106.05(f).)
using said automatically extracted features to automatically train at least one machine learning model, thus producing at least one trained machine learning model; (Training a machine learning model is understood as mere instructions to implement an abstract idea (e.g., generate inferences) on a computer - see MPEP 2106.05(f).))
wherein said time-series training dataset comprises a linear array of time points starting from an origin time point, each time point having a single associated data point with a data point value; (The specification of data to be stored is understood to be a field of use limitation. The limitation further specifies the time-series training dataset - See MPEP 2106.05(h).)
using said at least one static machine learning system comprising an artificial neural network (ANN), (The static machine learning system and processor are understood to be generic computer elements and the limitation is merely using generic computer elements as a tool to perform an abstract idea . See MPEP 2106.05(f).)
wherein said fused feature vector has a dimensionality equal to the sum of the individual dimensions of each of said individual feature vectors; (The specification of data to be stored is understood to be a field of use limitation. The limitation further specifies the fused feature vector- See MPEP 2106.05(h).)
using said machine-learning dataset and said at least one static machine learning system, to automatically train at least one said machine learning model, producing at least one trained machine learning model for forecasting future time series values; (Training a machine learning model is understood as mere instructions to implement an abstract idea (e.g., generate inferences) on a computer - see MPEP 2106.05(f).)) and
using at least one said trained machine learning model for forecasting future time series values to implement a time-series forecasting system for new data by the steps of; (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a machine learning model as a tool to perform the abstract idea (i.e., a time-series forecasting) - see MPEP 2106.05(f).)
(This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a machine learning model as a tool to perform the abstract idea - see MPEP 2106.05(f).)
wherein said time-series forecasting system uses said plurality of new single data subset fused feature vectors representing said new data, and said trained machine learning model for forecasting future time series values, to forecast future time series values. (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a machine learning model as a tool to perform the abstract idea (i.e., a time-series forecasting) - see MPEP 2106.05(f).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are field of use in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
An automated method… said method comprising: at least one computer processor, and at least one static machine learning system comprising an artificial neural network (ANN) (The processor, static machine learning system, and automatically are understood to be generic computer elements and the limitation is merely using generic computer elements as a tool to perform an abstract idea . See MPEP 2106.05(f).)
wherein said extracting comprises simultaneously applying said plurality of feature analysis algorithms from at least two different feature types selected from temporal features, pattern features, statistical features, context features, harmonic features, and external features, to data within a sliding time window of said time series-training dataset; (This step describing simultaneously applying (e.g. parallel processing) is adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (i.e. applying feature analysis algorithms) - see MPEP 2106.05(f))
wherein said at least one static machine learning system predicts a target variable based on said automatically extracted features irrespective of their temporal sequence in said dataset; (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying machine learning as a tool to perform the abstract idea (i.e., predicting) - see MPEP 2106.05(f).)
using said automatically extracted features to automatically train at least one machine learning model, thus producing at least one trained machine learning model; (Training a machine learning model is understood as mere instructions to implement an abstract idea (e.g., generate inferences) on a computer - see MPEP 2106.05(f).))
wherein said time-series training dataset comprises a linear array of time points starting from an origin time point, each time point having a single associated data point with a data point value; (The specification of data to be stored is understood to be a field of use limitation. The limitation further specifies the time-series training dataset - See MPEP 2106.05(h).)
using said at least one static machine learning system comprising an artificial neural network (ANN), (The static machine learning system and processor are understood to be generic computer elements and the limitation is merely using generic computer elements as a tool to perform an abstract idea . See MPEP 2106.05(f).)
wherein said fused feature vector has a dimensionality equal to the sum of the individual dimensions of each of said individual feature vectors; (The specification of data to be stored is understood to be a field of use limitation. The limitation further specifies the fused feature vector- See MPEP 2106.05(h).)
using said machine-learning dataset and said at least one static machine learning system, to automatically train at least one said machine learning model, producing at least one trained machine learning model for forecasting future time series values; (Training a machine learning model is understood as mere instructions to implement an abstract idea (e.g., generate inferences) on a computer - see MPEP 2106.05(f).)) and
using at least one said trained machine learning model for forecasting future time series values to implement a time-series forecasting system for new data by the steps of; (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a machine learning model as a tool to perform the abstract idea (i.e., a time-series forecasting) - see MPEP 2106.05(f).)
(This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a machine learning model as a tool to perform the abstract idea - see MPEP 2106.05(f).)
wherein said time-series forecasting system uses said plurality of new single data subset fused feature vectors representing said new data, and said trained machine learning model for forecasting future time series values, to forecast future time series values. (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a machine learning model as a tool to perform the abstract idea (i.e., a time-series forecasting) - see MPEP 2106.05(f).)
The additional elements as disclosed above in combination of the abstract idea
are not sufficient to amount to significantly more than the judicial exception as they are
field of use as disclosed in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Regarding Claim 4
2A Prong 1: The claim does not recite any Abstract idea.
2A Prong 2 & 2B:
The method of claim 1, wherein said features extracted by said feature analysis algorithms comprise any of temporal, pattern, statistical, context, harmonic, and external features. (The specification of data to be stored is understood to be a field of use limitation. The limitation further specifies the features- See MPEP 2106.05(h).)
Regarding Claim 5
2A Prong 1: The claim does not recite any Abstract idea.
2A Prong 2 & 2B:
The method of claim 1, wherein said feature analysis algorithms comprise any of lagged values, moving averages, exponential moving averages, temporal differences, cumulative sums, time delta features, moving window replicated features, seasonality indicators, autocorrelation, local maxima, local minima, mean, median, standard deviation, variance, autocovariance, skewness, kurtosis, minimum values, maximum values, percentiles, interquartile ranges, energy, entropy, cross-entropy, time values, season values, binary indicators for events, time-frequency coefficients from Fourier and wavelet transforms, dominant frequencies, spectral energy distribution, and harmonic ratios. (This limitation further specifies the algorithms and is merely indicating a field of use or technological environment - See MPEP 2106.05(h).)
Regarding Claim 7
2A Prong 1: The claim does not recite any Abstract idea.
2A Prong 2 & 2B:
The method of claim 1, wherein said at least one sliding time window sliding time window used to create at least one data subset, each at least one said data subset comprising a portion of said linear array of time points, is a plurality of incrementally sliding time windows, where each successive sliding time window advances by at least one time point over a proceeding sliding time window. (This limitation further specifies the sliding time window and is merely indicating a field of use or technological environment - See MPEP 2106.05(h).)
Regarding Claim 8
2A Prong 1: The claim does not recite any Abstract idea.
2A Prong 2 & 2B:
The method of claim 1, wherein said sliding time window sliding time window to create at least one data subset, each at least one said data subset comprising a portion of said linear array of time points has constant length per analyzed time-series dataset. (This limitation further specifies the sliding time window and is merely indicating a field of use or technological environment - See MPEP 2106.05(h).)
Regarding Claim 9
2A Prong 1: The claim does not recite any Abstract idea.
2A Prong 2 & 2B:
The method of claim 1, wherein said features further comprise feature types comprising any of temporal, pattern, statistical, context, harmonic, and external feature types, further varying a maximum length of said sliding time windows according to said feature types per analyzed time- series dataset. (The specification of data to be stored is understood to be a field of use limitation. The limitation further specifies the features- See MPEP 2106.05(h).)
Regarding Claim 10
2A Prong 1: The claim does not recite any Abstract idea.
2A Prong 2 & 2B:
The method of claim 1, wherein said at least one static machine learning system used to automatically extract features from said time series-training dataset is selected from any of a Sklearn, ML.NET, TensorFlow, Keras, PyTorch, XGBoost, CatBoost or other deep learning system. (This limitation further specifies the static machine learning system and is merely indicating a field of use or technological environment - See MPEP 2106.05(h).)
Regarding Claim 11
2A Prong 1:
wherein said static machine learning system further optimizes either said machine learning model or said time-series forecasting system using any of a mean squared error (MSE) or other error metrics through any of iterative hyperparameter tuning and ensemble methods (This step is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation) or mathematical calculation.).
2A Prong 2 & 2B:
The method of claim 1, wherein using said static machine learning system to automatically train either said machine learning model or said time-series forecasting system by using any of genetic algorithms, grid search, ensemble models, stacking, linear regression, support vector regression, Bayesian regression, k-nearest neighbors, decision trees, gradient boosting algorithms, and neural networks to automatically extract features from said time series-training dataset, thus creating a plurality of data subset individual feature vectors used to build said machine learning model and said time-series forecasting system (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying machine learning as a tool to perform the abstract idea - see MPEP 2106.05(f).); and
Regarding Claim 12
2A Prong 1:
The method of claim 11, further using said at least one computer processor and said static machine learning system to automatically optimize said algorithms by automatically iterating over a plurality of different sets of feature analysis algorithms and automatically determining which sets of feature analysis algorithms produce a better-optimized machine learning model or time-series forecasting system. (This step for determining is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., judgment).).
2A Prong 2 & 2B: The claim does not recite any additional elements.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Nguyen et al. (US 20240256915 A1) similarly describes time series forecasting.
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 TEWODROS E MENGISTU whose telephone number is (571)270-7714. The examiner can normally be reached Mon-Fri 9:30-5:30.
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, ABDULLAH KAWSAR can be reached at (571)270-3169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/TEWODROS E MENGISTU/ Examiner, Art Unit 2127
Prosecution Insights