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 1-18 are presented in the case.
Priority
Acknowledgment is made of applicant's claim for foreign priority based on application GR20210100506 filed in Greece on 07/07/2021. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statements are submitted on 01/26/2024 and 02/20/2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Objections
Claims 1-2, 4, 14 and 16 are objected to because of the following informalities:
Claim 1, line 6 recites the phrase “parameterised” which should be “parameterized”
Claim 2, line 7 recites the phrase “parameterised” which should be “parameterized”
Claim 4, line 2 recites the phrase “regularisation” which should be “regularization”
Claim 14, line 2 recites the phrase “generalised” which should be “generalized”
Claim 16, line 6 recites the phrase “generalised” which should be “generalized”
For the informalities above and wherever else they may occur appropriate correction is required.
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.
Claim 17 is rejected under 35 U.S.C. 101 because the claim limitation recites “A computer program product comprising computer-readable code that”. However, the usage of the phrase “computer program product” is broad enough to include both “non-transitory” and “transitory” media. The specification further explicitly does not limit the utilization of a computer program product where transitory and non-transitory mediums are discussed, however, the program product is not defined. (see USPGPUB ¶108) When the specification is silent, the BRI of a CRM or computer product in view of the state of the art covers a signal per se. See Ex parte Mewherter, 2012-007962 (PTAB, 2013). Therefore, the claim must be rejected under 35 U.S.C. § 101 as covering non-statutory subject matter. See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007) (transitory embodiments are not directed to statutory subject matter).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 5, 7, 8, 16 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 5 recites the limitation "filtered EEG channels", “filtered brain activity channels” and “the plurality of channels of EEG signals.” Where claim 2 does not mention EEG, filtered brain activity channels or the plurality of channels of EEG signals and thus the claim lacks antecedent basis.
Claim 7 recites “the comprises” on line 9 which should be “comprises”.
Claim 8 recites “The method claim 6,” on line 1 which should be “The method of claim 6,”.
Claim 16 recites “the received channels of EEG signals,” which should be “the received channels of EEG signals,”
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 20210267474 A1) hereinafter Wang in view of Balasubramaniam et al. (US 20210272571 A1) hereinafter Balasubramaniam.
As to independent claim 1, Wang teaches a computer implemented method of classifying brain activity signals, the method comprising: [classify subjects brain for fatigue state ¶112]
receiving a plurality of channels of brain activity signals; [receives EEG signals across 24 channels of a subjects brain ¶112 "CNN-Attention-based network is developed for both driving fatigue state classification and PI with EEG signals. Specifically, 24-channel EEG signals from a subject who participate in a simulated driving environment are collected."]
generating a plurality of channels of filtered brain activity signals by applying a plurality of filters to the received channels of brain activity signals, [applies bandpass and FastICA filtering of signals generating channels ¶112]
determining, using a differentiable feature module, a plurality of feature maps from the plurality of channels of filtered brain activity signals; and [creates a feature map using CNN/attention extraction (differentiable) ¶82-83 " forms a feature map of this layer"]
determining, using a classification model, one or more classifications for the received plurality of channels of brain activity signals based on the determined feature maps. [classification model for fatigue detection such as awake ¶50, ¶68 "attention-mechanism-based convolutional neural network (hereinafter referred to as Att-CNN, or as CNN-Attention-based network, as shown in FIG. 2) is used for PI and driving fatigue state classification"]
Wang does not specifically teach wherein the plurality of filters comprises a plurality of learned parameterised bandpass filters.
However, Balasubramaniam teaches wherein the plurality of filters comprises a plurality of learned parameterised bandpass filters; [applies an array of bandpass filters (plurality) (of which are learned ¶281), ¶277 " filter bank is an array of band-pass filters that separates the input signal into multiple components."]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the EEG classification disclosed by Wang by incorporating the wherein the plurality of filters comprises a plurality of learned parameterised bandpass filters disclosed by Balasubramaniam because both techniques address the same field of machine learning and by incorporating Balasubramaniam into Wang improves detection systems for better performance and help retrieval of signals [Balasubramaniam ¶281]
As to dependent claim 17, the rejection of claim 1 is incorporated, Wang and Balasubramaniam further teach a computer program product comprising computer-readable code that, when executed by a computing system, causes the computing system to perform a method according to claim 1. [Wang program executed by processor and system ¶134-135]
As to dependent claim 18, the rejection of claim 1 is incorporated, Wang and Balasubramaniam further teach a system comprising one or more processors and a memory, the memory storing computer readable instructions that, when executed by the one or more processors, causes the system to perform a method according to claim 1. [Wang program executed by processor and system ¶134-135]
Claims 2, 9-11, 13 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Balasubramaniam and Sainath et al. (US 20150161995 A1) hereinafter Sainath.
As to independent claim 2, Wang teaches a method of training a model for classifying brain activity signals, the method comprising:
for each of one or more of training examples from a training set, each training example comprising a plurality of channels of brain activity signals and a ground-truth classification: [labeled training data set with EEG and labels (classification) ¶68, ¶7 "pre-processing the EEG data, and labeling the EEG data to obtain a labeled training data set, wherein the training data set comprises the pre-processed and labeled EEG data"]
generating a plurality of channels of filtered brain activity signals by applying a plurality of filters to the plurality of brain activity signals of said training example, [applies bandpass and FastICA filtering ¶112]
determining, using a differentiable feature module, a plurality of feature maps from the plurality of channels of filtered brain activity signals, [creates a feature map using CNN/attention extraction (differentiable) ¶82-83 " forms a feature map of this layer"]
wherein the differentiable feature module applies one or more fixed functions to the filtered brain activity signals to determine the feature maps; and [applies a Swish where B is 1 (fixed) as activation function ¶80, forms feature map ¶82]
determining, using a classification model, one or more classifications for the plurality of channels of brain activity signals of said training example based on the determined feature maps, and [classification model for fatigue detection such as awake ¶50, ¶68 "attention-mechanism-based convolutional neural network (hereinafter referred to as Att-CNN, or as CNN-Attention-based network, as shown in FIG. 2) is used for PI and driving fatigue state classification"]
Wang does not specifically teach wherein the plurality of filters comprises a plurality of learned parameterised bandpass filters.
However, Balasubramaniam teaches wherein the plurality of filters comprises a plurality of learned parameterised bandpass filters; [applies an array of bandpass filters (plurality) (of which are learned ¶281), ¶277 " filter bank is an array of band-pass filters that separates the input signal into multiple components."]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the EEG classification disclosed by Wang by incorporating the wherein the plurality of filters comprises a plurality of learned parameterised bandpass filters disclosed by Balasubramaniam because both techniques address the same field of machine learning and by incorporating Balasubramaniam into Wang improves detection systems for better performance and help retrieval of signals [Balasubramaniam ¶281]
Wang and Balasubramaniam do not specifically teach updating parameters of the plurality of filters and the classification model based on a comparison of the one or more classifications to corresponding ground-truth classifications, wherein the comparison is made using an objective function and wherein the updates are determined using backpropagation of gradients.
However, Sainath teaches updating parameters of the plurality of filters and the classification model based on a comparison of the one or more classifications to corresponding ground-truth classifications, wherein the comparison is made using an objective function and wherein the updates are determined using backpropagation of gradients. [updates filter banks and weights via comparing in neural network (model) using back propagation ¶28, ¶4 "comparing the output classification to a target classification to compute an error measure; and applying back propagation to adjust the plurality of weights as a layer of the neural network based on the error measure"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the classification disclosed by Wang and Balasubramaniam by incorporating the updating parameters of the plurality of filters and the classification model based on a comparison of the one or more classifications to corresponding ground-truth classifications, wherein the comparison is made using an objective function and wherein the updates are determined using backpropagation of gradients disclosed by Sainath because all techniques address the same field of machine learning and by incorporating Sainath into Wang and Balasubramaniam reduce the parameters needed while enabling better feature extraction in models [Sainath ¶16].
As to dependent claim 9, the rejection of claim 2 is incorporated, Wang, Balasubramaniam and Sainath further teach wherein the one or more feature maps comprises one or more of: a measure of magnitude of each of the filtered brain activity signals; a signal power; a signal variance; and/or a signal entropy. [Sainath magnitude of filter banked signals ¶39]
As to dependent claim 10, the rejection of claim 9 is incorporated, Wang, Balasubramaniam and Sainath further teach wherein the measure of magnitude of each of the filtered brain activity signals comprises a sum of the magnitude of the filtered brain activity signal over frequency bins of the filtered brain activity signal. [Sainath frequency regions and sum of filtered signals ¶24]
As to dependent claim 11, the rejection of claim 2 is incorporated, Wang, Balasubramaniam and Sainath further teach wherein the plurality of channels of brain activity signals are processed in the frequency domain. [Sainath uses power spectrum into frequency components ¶4]
As to dependent claim 13, the rejection of claim 2 is incorporated, Wang, Balasubramaniam and Sainath further teach wherein the one or more classifications for the plurality of channels of brain activity signals comprises: a classification of a resting or active state; a classification of a dynamic state triggered by/underlying the physical or imaginary movement of extremities; a classification of a dynamic state triggered by/underlying a conscious or non-conscious cognitive process related to attention tasks, perception tasks, planning tasks, memory tasks, language tasks, arithmetic tasks, reading tasks, control interface tasks, and specialized tasks like flight or driving, either in a simulator or in a real vehicle action; a classification of an affective state; a classification of an anomaly; a classification of a control intention for an external device; and/or a classification of clinical states. [Wang classify into awake, fatigue or mixed state (dynamic) in driving (vehicle) ¶68]
As to dependent claim 15, the rejection of claim 2 is incorporated, Wang, Balasubramaniam and Sainath further teach wherein the brain activity signals are EEG signals. [Wang brain EEG ¶137]
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Balasubramaniam and Sainath as applied to the rejection of claim 2 above, and further in view of MALAESCU et al. (US 20210089831 A1) hereinafter Malaescu.
As to dependent claim 3, the combination of Wang, Balasubramaniam and Sainath teach all the limitations of claim 2 that is incorporated.
Wang, Balasubramaniam and Sainath do not specifically teach wherein the objective function comprises a comparison term comprising a norm of a difference between the classifications and corresponding ground-truth classifications.
However, Malaescu teaches wherein the objective function comprises a comparison term comprising a norm of a difference between the classifications and corresponding ground-truth classifications. [norm between X as ground truths and Y as predictions ¶49-53 "L1 norm: L(X, Y)=sum(|x[j]−y[j]|)"…"ground-truths"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify data modelling disclosed by Wang, Balasubramaniam and Sainath by incorporating the wherein the objective function comprises a comparison term comprising a norm of a difference between the classifications and corresponding ground-truth classifications disclosed by Malaescu because all techniques address the same field of machine learning and by incorporating Malaescu into Wang, Balasubramaniam and Sainath maintains model performance and ability to process data unrelated to its training data [Malaescu ¶2-3]
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Balasubramaniam, Sainath and Malaescu as applied to the rejection of claim 6 above, and further in view of Menon et al. (US 20210049442 A1) hereinafter Menon.
As to dependent claim 4, the combination of Wang, Balasubramaniam, Sainath and Malaescu teach all the limitations of claim 2 that is incorporated.
Wang, Balasubramaniam, Sainath and Malaescu do not specifically teach wherein the objective function further comprises a regularisation term comprising a sum of norms of weights of the classification model.
However, Menon teaches wherein the objective function further comprises a regularisation term comprising a sum of norms of weights of the classification model. [regularized and summation of norms ¶51 "regularizer, and the second term is a sum of l2-norms of all the weight matrices in the architecture 502"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify data modelling disclosed by Wang, Balasubramaniam, Sainath and Malaescu by incorporating the wherein the objective function further comprises a regularisation term comprising a sum of norms of weights of the classification model disclosed by Menon because all techniques address the same field of machine learning and by incorporating Menon into Wang, Balasubramaniam, Sainath and Malaescu preserve accuracy and maintenance cycles while capturing features for model [Menon ¶3]
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Balasubramaniam and Sainath as applied to the rejection of claim 2 above, and further in view of Ang et al. (US 20130138011 A1) hereinafter Ang.
As to dependent claim 5, the combination of Wang, Balasubramaniam and Sainath teach all the limitations of claim 2 that is incorporated.
Wang, Balasubramaniam and Sainath do not specifically teach wherein each of a plurality of feature maps in the plurality of feature maps is associated with a respective plurality of channels of filtered EEG channels corresponding to the plurality of channels of brain activity signals, and wherein each of the respective plurality of channels of filtered brain activity channels is determined by applying a respective plurality of filters to the plurality of channels of EEG signals.
However, Ang teaches wherein each of a plurality of feature maps in the plurality of feature maps is associated with a respective plurality of channels of filtered EEG channels corresponding to the plurality of channels of brain activity signals, and wherein each of the respective plurality of channels of filtered brain activity channels is determined by applying a respective plurality of filters to the plurality of channels of EEG signals. [Signal is segmented into bands and filters ¶12 channels being filtered ¶56 " EEG from each time segment and each filter bank can be linearly transformed"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify data modelling disclosed by Wang, Balasubramaniam and Sainath by incorporating the wherein each of a plurality of feature maps in the plurality of feature maps is associated with a respective plurality of channels of filtered EEG channels corresponding to the plurality of channels of brain activity signals, and wherein each of the respective plurality of channels of filtered brain activity channels is determined by applying a respective plurality of filters to the plurality of channels of EEG signals disclosed by Ang because all techniques address the same field of machine learning and by incorporating Ang into Wang, Balasubramaniam and Sainath improve input acquisition minimizing false positive detections [Ang ¶60]
Claim 6-8 is rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Balasubramaniam and Sainath as applied to the rejection of claim 2 above, and further in view of Galan (US 20140107521 A1).
As to dependent claim 6, the combination of Wang, Balasubramaniam and Sainath teach all the limitations of claim 2 that is incorporated.
Wang, Balasubramaniam and Sainath do not specifically teach wherein the one or more feature maps comprises one or more measures of functional connectivity between brain activity signals in the plurality of filtered brain activity signals.
However, Galan teaches wherein the one or more feature maps comprises one or more measures of functional connectivity between brain activity signals in the plurality of filtered brain activity signals. [determines functional connectivity using EEG ¶48 " detects the direction of functional connections, i.e. whether area A excites (or inhibits) area B more strongly or vice versa."]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify data modelling disclosed by Wang, Balasubramaniam and Sainath by incorporating the wherein the one or more feature maps comprises one or more measures of functional connectivity between brain activity signals in the plurality of filtered brain activity signal disclosed by Galan because all techniques address the same field of machine learning and by incorporating Galan into Wang, Balasubramaniam and Sainath better explores the brains activity [Galan ¶46].
As to dependent claim 7, the rejection of claim 6 is incorporated, Wang, Balasubramaniam, Sainath and Galan further teach determining a connectivity matrix between two or more of the filtered brain activity signals; and [Galan pairwise correlation matrices ¶75-76, ¶62]
extracting a feature vector from the connectivity matrix, wherein the feature vector corresponds to an upper triangular or lower triangular of the connectivity matrix, and [Galan reshapes into a vector ¶62 " connectivity matrix W (size: 141.times.141) reshaped into a one-dimensional list of values, a vector of features of dimension 19,88161."]
wherein determining, using the classification model, the one or more classifications for the plurality of channels of brain activity signals the comprises inputting the extracted feature vector into the classification model. [Galan used in classifier ¶62]
As to dependent claim 8, the rejection of claim 6 is incorporated, Wang, Balasubramaniam, Sainath and Galan wherein the one or more measures of functional connectivity between filtered brain activity signals comprises one or more of: a correlation function between two of the filtered brain activity signals; a phase locking value; amplitude envelope correlations; and/or signal envelope correlations. [Galan correlation function including Pearsons between signals ¶11 "cross-correlations (Pearson's correlation coefficient) between SEEG signals as a measure of functional brain connectivity"]
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Balasubramaniam and Sainath as applied to the rejection of claim 2 above, and further in view of Shafran et al. (US 20190156819 A1) hereinafter Shafran
As to dependent claim 12, the combination of Wang, Balasubramaniam and Sainath teach all the limitations of claim 2 that is incorporated.
Wang, Balasubramaniam and Sainath do not specifically teach wherein one or more of the parameters of the plurality of filters controls a phase response of a filter.
However, Shafran teaches wherein one or more of the parameters of the plurality of filters controls a phase response of a filter. [Model used phase information converting inputs into frequency via FFT ¶88 "CLP model integrates speech phase information because of the complex weight representation"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify data modelling disclosed by Wang, Balasubramaniam and Sainath by incorporating the wherein one or more of the parameters of the plurality of filters controls a phase response of a filter disclosed by Shafran because all techniques address the same field of machine learning and by incorporating Shafran into Wang, Balasubramaniam and Sainath improves performance of models with advantageous efficiency [Shafran ¶4]
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Balasubramaniam and Sainath as applied to the rejection of claim 2 above, and further in view of Santra et al. (US 20210396843 A1) hereinafter Santra.
As to dependent claim 14, the combination of Wang, Balasubramaniam and Sainath teach all the limitations of claim 2 that is incorporated.
Wang, Balasubramaniam and Sainath do not specifically teach wherein the plurality of filters comprises a plurality of generalised Gaussian filters.
However, Santra teaches wherein the plurality of filters comprises a plurality of generalised Gaussian filters. [Morlet wavelet filters with a Gaussian window ¶101-102 "A Morlet wavelet may be understood as the multiplication of an underlying frequency (carrier) by a Gaussian window (envelope). In some embodiments, each 2D Morlet wavelet filter of 2D convolutional layer"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify data modelling disclosed by Wang, Balasubramaniam and Sainath by incorporating the wherein the plurality of filters comprises a plurality of generalised Gaussian filters. disclosed by Santra because all techniques address the same field of machine learning and by incorporating Santra into Wang, Balasubramaniam and Sainath enables more coherent signal processing [Santra ¶3]
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Santra
As to independent claim 16, Wang teaches a computer implemented method of classifying signal data, the method comprising: [classify subjects’ brain for fatigue state ¶112]
receiving a plurality of channels of signal data; [receives EEG signals across 24 channels of a subjects brain ¶112 "CNN-Attention-based network is developed for both driving fatigue state classification and PI with EEG signals. Specifically, 24-channel EEG signals from a subject who participate in a simulated driving environment are collected."]
generating a plurality of channels of filtered signal data by applying a plurality of filters to the received channels of EEG signals, [applies bandpass and FastICA filtering of signals generating channels ¶112]
determining, using a differentiable feature module, a plurality of feature maps from the plurality of channels of filtered signal data; and [creates a feature map using CNN/attention extraction (differentiable) ¶82-83 " forms a feature map of this layer"]
determining, using a classification model, one or more classifications for the received plurality of channels of signal data based on the determined feature maps. [classification model for fatigue detection such as awake ¶50, ¶68 "attention-mechanism-based convolutional neural network (hereinafter referred to as Att-CNN, or as CNN-Attention-based network, as shown in FIG. 2) is used for PI and driving fatigue state classification"]
Wang does not specifically teach wherein the plurality of filters comprises a plurality of generalised Gaussian filters.
However, Santra teaches wherein the plurality of filters comprises a plurality of generalised Gaussian filters. [Morlet wavelet filters with a Gaussian window ¶101-102 "A Morlet wavelet may be understood as the multiplication of an underlying frequency (carrier) by a Gaussian window (envelope). In some embodiments, each 2D Morlet wavelet filter of 2D convolutional layer"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify data modelling disclosed by Wang by incorporating the wherein the plurality of filters comprises a plurality of generalised Gaussian filters disclosed by Santra because both techniques address the same field of machine learning and by incorporating Santra into Wang enables more coherent signal processing [Santra ¶3]
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.
Azemi et al. (US 20210286429 A1) teaches a feature map derived from EEG signals (see ¶38)
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)).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Beau Spratt whose telephone number is 571 272 9919. The examiner can normally be reached 8:30am to 5:00pm (PST).
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 571 483 7388.
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/BEAU D SPRATT/ Primary Examiner, Art Unit 2143
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