DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
Received 04/14/2026
Claim(s) 22-38 and 40-42 is/are pending.
Claim(s) 22, 33, and 41 has/have been amended.
Claim(s) 1-21 and 39 has/have been cancelled.
Claim(s) 42 has/have been added.
The 35 U.S.C § 103 rejection to claim(s) 22-38 and 40-42 have been fully considered in view of the amendments received on 04/14/2026 and are fully addressed in the prior art rejection below.
Response to Arguments
Received 04/14/2026
Regarding independent claim(s) 1, 33, and 41:
Applicant’s arguments (Remarks, Page 8: ¶ 3-6), filed 04/14/2026, with respect to the rejection(s) of claim(s) 1, 33, and 41 under 35 U.S.C § 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn, necessitated by Applicant's amendments. However, upon further consideration, a new ground(s) of rejection is made in view of Chakraborty et al. (US PGPUB No. 20200213354 A1), and further in view of O’Shea et al. (US PGPUB No. 2018037192 A1).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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.
Claim(s) 22-38 and 40-42 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chakraborty et al., US PGPUB No. 20200213354 A1, hereinafter Chakraborty, and further in view of O’Shea et al., US PGPUB No. 2018037192 A1, hereinafter O’Shea.
Regarding claim 22, Chakraborty discloses a system (Chakraborty; a system [¶ 0030 and ¶ 0033], as illustrated within Fig. 2 and Fig. 3; moreover, apparatus utilizing processors and programming [¶ 0005]) comprising:
one or more processors (Chakraborty; the system [as addressed above] comprises one or more processors [¶ 0033 and ¶ 0035], as illustrated within Fig. 3); and
one or more storage devices on which are stored instructions that, when executed, are configured to cause the one or more processors to perform operations (Chakraborty; the system [as addressed above] comprises one or more storage devices on which are stored instructions that are configured to cause the one or more processors to perform operations when executed [¶ 0033-0035]; moreover, CRSM having computer readable program instructions [¶ 0088-0091]) comprising:
generating, using a generator machine learning network, data representing a signal for transmission over a wireless communications channel (Chakraborty; processor(s) [as addressed above] comprises generating data representing a signal for transmission over a wireless communications channel [¶ 0030-0032] using a generator machine learning network (i.e. GAN (or generator & discriminator)) [¶ 0025-0026], as illustrated within Fig. 1; additionally, a GAN of multiple devices communicating in a training phase [¶ 0056-0057 and ¶ 0061]), wherein the generator machine learning network is trained using a discriminator machine learning network and an optimizer based on signals transmitted over the wireless communications channel (Chakraborty; wherein the generator machine learning network (i.e. GAN (or generator) [as addressed above] is trained using a discriminator machine learning network (i.e. GAN (or discriminator) and an optimizer (i.e. minimizing cost function) based on signals transmitted over the wireless communications channel) [¶ 0056-0057 and ¶ 0059-0060]; moreover, signal authentication uses a decimator and generator [¶ 0025-0026] and applies a cost/loss function to update/optimize the functionality of the GAN to perform its objective(s) [¶ 0027-0028]; additionally, loss functions [¶ 0066-0067 and ¶ 0076-0077]);
in response to generating the data representing the signal for transmission, performing data generated by the generator machine learning network (Chakraborty; performing (parameter influenced) data generated by the generator machine learning network (i.e. GAN (or generator) in response to generating the data representing the signal for transmission [¶ 0026-0028]; wherein, samples generated by a generator are updated/changed based on parameters [¶ 0059]), wherein the generator machine learning network is trained (Chakraborty; wherein the generator machine learning network (i.e. GAN (or generator)) is trained [¶ 0025 and ¶ 0027]; wherein, a GAN framework may include trained discriminator models and/or generator models [¶ 0035]; additionally, one or more devices may use and/or distribute trained models to other devices [¶ 0031 and ¶ 0055]), using the discriminator machine learning network and the optimizer based on signals transmitted over the wireless communications channel (Chakraborty; training the GAN/generator [as addressed above] using the discriminator machine learning network (i.e. GAN (or discriminator)) and the optimizer (i.e. cost/loss function) based on signals transmitted over the wireless communications channel [¶ 0025 and ¶ 0027-0028]), to generate a signal (Chakraborty; to generate a signal [¶ 0030 and ¶ 0040-0042] [¶ 0055]; even further, generation of signals [¶ 0056-0057 and ¶ 0059]; wherein, a device trains a generator to spoof a signal from a device’s transmitter [¶ 0058]), and wherein the data is generated by the generator machine learning network subsequent to the training (Chakraborty; the data is generated by the generator machine learning network (i.e. GAN (or generator) subsequent to the training [¶ 0065-0066 and ¶ 0076-0077]; moreover, updating/changing parameters of a generator model [¶ 0026-0028]);
transmitting the signal generated by performing data over the wireless communications channel (Chakraborty; processor(s) [as addressed above] comprises transmitting the signal generated by performing (the parameter influenced of the) data over the wireless communications [¶ 0026-0028] implicit channel (given typical communication techniques) [¶ 0002]; wherein, communication is over a network [¶ 0030]);
in response to transmitting the signal over the wireless communications channel, receiving one or more communication metrics (Chakraborty; processor(s) [as addressed above] comprises receiving one or more communication metrics in response to transmitting the signal over the wireless communications [¶ 0061-0062 and ¶ 0065-0067] implicit channel (given typical communication techniques) [¶ 0002], as illustrated within Figs. 7A-C; wherein, the GAN [¶ 0056] involves the sending and receiving of feedback [¶ 0057-0059]; moreover, updates/changes to a model are in relation with a cost/loss function [¶ 0027-0028]); and
updating the generator machine learning network, which has been trained using the discriminator machine learning network and the optimizer based on signals transmitted over the wireless communications channel, based on the received one or more communication metrics (Chakraborty; processor(s) [as addressed above] comprises updating the generator machine learning network (i.e. GAN (or generator) based on the received one or more communication metrics which has been trained using the discriminator machine learning network (i.e. GAN (or discriminator)) and the optimizer (i.e. cost/loss function) based on signals transmitted over the wireless communications [¶ 0061-0062 and ¶ 0065-0067] implicit channel (given typical communication techniques) [¶ 0002], as illustrated within Figs. 7A-C).
Chakraborty fails to explicitly disclose performing one or more encodings of the data generated;
to generate a radio frequency signal;
transmitting the radio frequency signal generated by performing the one or more encodings of the data over the wireless communications channel; and
transmitting the radio frequency signal over the wireless communications channel.
However, O’Shea teaches in response to generating the data representing the signal for transmission, performing one or more encodings of the data generated by the generator machine learning network (O’Shea; performing one or more encodings of the data generated by the generator ML network (i.e. encoder of a ML) in response to generating the data representing the signal for transmission [¶ 0100-0101 and ¶ 0105]; moreover, a plurality of encoding models [¶ 0116-0117]).
to generate a radio frequency signal (O’Shea; generate a RF signal [¶ 0087-0088]; moreover, training an RF system [¶ 0099-0101]; wherein, training a model of an RF channel via simulation [¶ 0102-0105]), and wherein the data is generated by the generator machine learning network subsequent to the training (O’Shea; the data is generated by the generator machine learning network subsequent to the training [¶ 0099-0101]; wherein, Fig. 4 illustrates a ML comprising an encoder (i.e. generator) and decoder (i.e. decoder));
transmitting the radio frequency signal generated by performing the one or more encodings of the data over the wireless communications channel (O’Shea; transmitting the RF signal generated by performing the one or more encodings of the data over the wireless communications channel [¶ 0102-0105]); and
in response to transmitting the radio frequency signal over the wireless communications channel, receiving one or more communication metrics (O’Shea; receiving one or more communication metrics in response to transmitting the RF signal over the wireless communications channel [¶ 0101-0105]; wherein, the loss function is in relation with information corresponding to metrics [¶ 0107-0110]; moreover, the network update process may update the encoder network, the decoder network, and/or the CSI estimator to achieve a desired objective function, which may include the loss function and other performance metric [¶ 0112]; moreover, trained objectives (i.e. performance results) [¶ 0047-0049 and ¶0108-0109]).
Chakraborty and O’Shea are considered to be analogous art because both pertain to generating and/or managing data in relation with utilizing a machine learning model, wherein one or more computerized units process data through a neural network.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Chakraborty, to incorporate in response to generating the data representing the signal for transmission, performing one or more encodings of the data generated by the generator machine learning network; to generate a radio frequency signal, and wherein the data is generated by the generator machine learning network subsequent to the training; transmitting the radio frequency signal generated by performing the one or more encodings of the data over the wireless communications channel; and in response to transmitting the radio frequency signal over the wireless communications channel, receiving one or more communication metrics (as taught by O’Shea), in order to provide an improved performance for multiple input multiple output communications (O’Shea; [¶ 0032-0034 and ¶ 0039]).
Regarding claim 23, Chakraborty in view of O’Shea further discloses the system of claim 22, wherein receiving the one or more communication metrics (O’Shea; receiving the one or more communication metrics [as addressed within parent claim(s)]) comprises:
receiving data indicating at least one of construction error, power consumption, or delay corresponding to transmitting the radio frequency signal over the wireless communications channel (O’Shea; receiving data indicating (at least one of) power consumption [¶ 0047-0049 and ¶0108-0109] corresponding to transmitting the RF signal over the wireless communications channel [as addressed within the parent claim(s)]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Chakraborty as modified by O’Shea, to incorporate receiving the one or more communication metrics comprises: receiving data indicating at least one of construction error, power consumption, or delay corresponding to transmitting the radio frequency signal over the wireless communications channel (as taught by O’Shea), in order to provide an improved performance for multiple input multiple output communications (O’Shea; [¶ 0032-0034 and ¶ 0039]).
Regarding claim 24, Chakraborty in view of O’Shea further discloses the system of claim 22, wherein receiving the one or more communication metrics based on transmitting the radio frequency signal over the wireless communications channel (Chakraborty; receiving the one or more communication metrics based on transmitting the RF signal over the wireless communications channel [¶ 0065-0067]; wherein, data samples are able to be distributed [¶ 0025] and parameter can be changed/updated [¶ 0026-0028]; and wherein, a channel is implicit within wireless communications [as addressed within the parent claim(s)]) comprises:
receiving error feedback corresponding to transmitting the signal over the wireless communications channel (Chakraborty; receiving error feedback corresponding to transmitting the signal over the wireless communications channel [¶ 0065-0067] wherein, a channel is implicit within wireless communications [as addressed within the parent claim(s)]), and wherein updating the generator machine learning network based on the received one or more communication metrics (Chakraborty; wherein updating the generator machine learning network (i.e. GAN (or generator)) based on the received one or more communication metrics [¶ 0027-0028 and ¶ 0067]) comprises:
updating the generator machine learning network based on the received error feedback (Chakraborty; updating the generator machine learning network (i.e. GAN (or generator)) based on the received error feedback [¶ 0065-0067]; moreover, parameter updates/changes [¶ 0026-0028]).
O’Shea further teaches receiving error feedback corresponding to transmitting the radio frequency signal over the wireless communications channel (O’Shea; receiving error feedback corresponding to transmitting the radio frequency signal over the wireless communications channel [¶ 0102-0105]; wherein, the loss function is in relation with information corresponding to metrics [¶ 0107-0110]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Chakraborty as modified by O’Shea, to incorporate receiving error feedback corresponding to transmitting the radio frequency signal over the wireless communications channel (as taught by O’Shea), in order to provide an improved performance for multiple input multiple output communications (O’Shea; [¶ 0032-0034 and ¶ 0039]).
Regarding claim 25, Chakraborty in view of O’Shea further discloses the system of claim 24, wherein receiving the error feedback corresponding to transmitting the signal over the wireless communications channel (Chakraborty; receiving the error feedback [as addressed within the parent claim(s)] corresponding to transmitting the signal over the wireless communications [¶ 0056-0057] implicit channel (given typical communication techniques) [¶ 0002]; wherein, communications are wireless over a network [¶ 0030-0031]) comprises:
receiving data indicating the error feedback using at least one of a communications bus or protocol message (Chakraborty; receiving data indicating the error feedback [as addressed above] using (at least one of) an implicit communications protocol message (given a unique inherent signature) [¶ 0030]; wherein, the feedback/output of a discriminator is provided to a generator [¶ 0027] over a network between two devices [¶ 0065-0066]; even further, network devices embodies a GAN-style framework [¶ 0030-0032]).
O’Shea further teaches receiving error feedback corresponding to transmitting the radio frequency signal over the wireless communications channel (O’Shea; receiving error feedback corresponding to transmitting the radio frequency signal over the wireless communications channel [as addressed within the parent claim(s)]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Chakraborty as modified by O’Shea, to incorporate receiving error feedback corresponding to transmitting the radio frequency signal over the wireless communications channel (as taught by O’Shea), in order to provide an improved performance for multiple input multiple output communications (O’Shea; [¶ 0032-0034 and ¶ 0039]).
Regarding claim 26, Chakraborty in view of O’Shea further discloses the system of claim 22, wherein training the generator machine learning network (Chakraborty; training the generator machine learning network (i.e. GAN (or generator)) [as addressed within the parent claim(s)]) comprises:
updating the generator machine learning network using the discriminator machine learning network communicably coupled to the optimizer (Chakraborty; updating the generator machine learning network (i.e. GAN (or generator)) using the discriminator machine learning network (i.e. GAN (or discriminator)) communicably coupled to the optimizer (i.e. cost/loss function) [¶ 0056-0057 and ¶ 0059-0060]; additionally, loss functions [¶ 0066-0067 and ¶ 0076-0077]), wherein the optimizer is configured to process decision information indicating a determination performed by the discriminator machine learning network (Chakraborty; the optimizer (i.e. cost/loss function) is configured to process decision information (i.e. determinations of a spoof) indicating a determination performed by the discriminator machine learning network (i.e. GAN (or discriminator)) [¶ 0065-0067]; wherein, training the GAN involves discriminator and generator loss functions [¶ 0076-0078]; wherein, the adversarial relationship between a discriminator and a generator requires back and forth feedback and in relation with parameter changes/updates [¶ 0026-0208]).
Regarding claim 27, Chakraborty in view of O’Shea further discloses the system of claim 26, wherein the optimizer is configured to process decision information indicating a determination performed by the discriminator machine learning network using one or more iterative optimization techniques (Chakraborty; the optimizer (i.e. cost/loss function) [as addressed within the parent claim(s)] is configured to process decision information indicating a determination (i.e. a low enough or reduced value, or a value according to a threshold) performed by the discriminator machine learning network (i.e. GAN (or discriminator)) using one or more iterative/repeated optimization techniques [¶ 0076-0078]).
O’Shea further teaches machine learning network using one or more iterative optimization techniques (O’Shea; machine learning network using one or more iterative optimization techniques [¶ 0068-0069 and ¶ 0071]; wherein, the ML networks may be trained jointly or in an iterative manner [¶ 0036]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Chakraborty as modified by O’Shea, to incorporate machine learning network using one or more iterative optimization techniques (as taught by O’Shea), in order to provide an improved performance for multiple input multiple output communications (O’Shea; [¶ 0032-0034 and ¶ 0039]).
Regarding claim 28, Chakraborty in view of O’Shea further discloses the system of claim 27, wherein the one or more iterative optimization techniques include optimization algorithm (Chakraborty; the one or more iterative optimization techniques include optimization algorithm [¶ 0066-0067 and ¶ 0076-0078).
O’Shea further teaches optimization techniques include a stochastic gradient descent (SGD) or Adam optimization algorithm (O’Shea; the one or more iterative optimization techniques include a stochastic gradient descent (SGD) or Adam optimization algorithm [¶ 0114]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Chakraborty as modified by O’Shea, to incorporate optimization techniques include a stochastic gradient descent (SGD) or Adam optimization algorithm (as taught by O’Shea), in order to provide an improved performance for multiple input multiple output communications (O’Shea; [¶ 0032-0034 and ¶ 0039]).
Regarding claim 29, Chakraborty in view of O’Shea further discloses the system of claim 26, wherein the decision information indicates a result of the discriminator machine learning network processing (i) a first set of information corresponding to information transmitted across the wireless communications channel (Chakraborty; the decision information (i.e. determinations of a spoof) indicates a result of the discriminator machine learning network (i.e. GAN (or discriminator)) processing (i) a 1st set of information corresponding to information transmitted across the wireless communications channel [¶ 0056-0060]; moreover, communication between two entities [¶ 0030] further corresponds to communications between entities (e.g. generator(s) and discriminator(s)) of a GAN [¶ 0026-0028]; wherein, the channel implicit within wireless communications [as addressed within the parent claim(s)]) and (ii) a second set of information corresponding to data generated by at least one of (a) the generator machine learning network or (b) sampling from a target information source (Chakraborty; the decision information (i.e. determinations of a spoof) indicates a result of the discriminator machine learning network (i.e. GAN (or discriminator)) processing (ii) a 2nd set of information corresponding to data generated by at least one of (a) the generator machine learning network (i.e. GAN (or generator)) [¶ 0056-0060] or (b) sampling from a target information source (i.e. actual/true data) [¶ 0025-0028]).
Regarding claim 30, Chakraborty in view of O’Shea further discloses the system of claim 29, wherein the first set of information corresponding to information transmitted across the wireless communications channel comprises data generated by the generator machine learning network (Chakraborty; the 1st set of information corresponding to information transmitted across the wireless communications channel [as addressed with parent claim(s)] comprises data generated by the generator machine learning network (i.e. GAN (or generator)) [¶ 0058-0059], as illustrated within Fig. 7A).
Regarding claim 31, Chakraborty in view of O’Shea further discloses the system of claim 29, wherein the first set of information corresponding to information transmitted across the wireless communications channel is an altered version of information generated by the generator machine learning network that is obtained by processing the information using either a real or simulated communications channel (Chakraborty; the 1st set of information corresponding to information transmitted across the wireless communications channel [as addressed within the parent claim(s)] is an altered version of information generated by the generator machine learning network that is obtained by processing the information using either a real or simulated communications channel [¶ 0058-0060], as illustrated within Fig. 7A; moreover, samples are sent to the decimator from the generator [¶ 0025], as illustrated within Fig. 1).
Regarding claim 32, Chakraborty in view of O’Shea further discloses the system of claim 22, wherein performing the one or more encodings of the data to generate the radio frequency signal (O’Shea; performing the one or more encodings of the data to generate the RF signal [as addressed within the parent claim(s)]) comprises:
generating a signal using a time-frequency modulation basis (O’Shea; performing the one or more encodings of the data to generate the radio frequency signal [as addressed above] comprises generating a signal using a time-frequency modulation basis [¶ 0050 and ¶ 0056]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Chakraborty as modified by O’Shea, to incorporate performing the one or more encodings of the data to generate the radio frequency signal comprises: generating a signal using a time-frequency modulation basis (as taught by O’Shea), in order to provide an improved performance for multiple input multiple output communications (O’Shea; [¶ 0032-0034 and ¶ 0039]).
Regarding claim 33, the rejection of claim 33 is addressed within the rejection of claim 22, due to the similarities claim 33 and claim 22 share, therefore refer to the rejection of claim 22 regarding the rejection of claim 33. Although, claim 33 and claim 22 may not be identical, they are considerably comparable or substantially equivalent given their overlapping subject matter. Thus, it is reasonable to reject claim 33 based on the teachings and rational in relation with the prior art within the rejection of claim 22.
Regarding claim 34, the rejection of claim 34 is addressed within the rejection of claim 23, due to the similarities claim 34 and claim 23 share, therefore refer to the rejection of claim 23 regarding the rejection of claim 34.
Regarding claim 35, the rejection of claim 35 is addressed within the rejection of claim 24, due to the similarities claim 35 and claim 24 share, therefore refer to the rejection of claim 24 regarding the rejection of claim 35.
Regarding claim 36, the rejection of claim 36 is addressed within the rejection of claim 25, due to the similarities claim 36 and claim 25 share, therefore refer to the rejection of claim 25 regarding the rejection of claim 36.
Regarding claim 37, the rejection of claim 37 is addressed within the rejection of claim 26, due to the similarities claim 37 and claim 26 share, therefore refer to the rejection of claim 26 regarding the rejection of claim 37.
Regarding claim 38, the rejection of claim 38 is addressed within the rejection of claim 27, due to the similarities claim 38 and claim 27 share, therefore refer to the rejection of claim 27 regarding the rejection of claim 38.
Regarding claim 40, the rejection of claim 40 is addressed within the rejection of claim 29, due to the similarities claim 40 and claim 29 share, therefore refer to the rejection of claim 29 regarding the rejection of claim 40.
Regarding claim 41, the rejection of claim 41 is addressed within the rejection of claim 22, due to the similarities claim 41 and claim 22 share, therefore refer to the rejection of claim 22 regarding the rejection of claim 41. Although, claim 41 and claim 22 may not be identical, they are considerably comparable or substantially equivalent given their overlapping subject matter. Thus, it is reasonable to reject claim 41 based on the teachings and rational in relation with the prior art within the rejection of claim 22.
Regarding claim 42, Chakraborty in view of O’Shea further discloses the system of claim 22, wherein performing the one or more encodings of the data to generate the radio frequency signal (O’Shea; performing the one or more encodings of the data [as addressed within the parent claim(s)] to generate the RF signal [¶ 0089-0090]; moreover, simulated propagation models of real-world RF channel data [¶ 0102 and ¶ 0105]) comprises:
performing digital to analog conversion of the data to generate an analog radio frequency waveform (O’Shea; performing digital to analog conversion of the data to generate an analog RF waveform [¶ 0119-0120 and ¶ 0122]), and wherein transmitting the radio frequency signal over the wireless communications channel (O’Shea; transmitting the RF signal over the wireless communications channel [¶ 0120 and ¶ 0122]) comprises:
transmitting the generated analog radio frequency waveform using a transmitting antenna (O’Shea; transmitting the generated analog radio frequency waveform using a transmitting antenna [¶ 0120 and ¶ 0122]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Chakraborty as modified by O’Shea, to incorporate wherein performing the one or more encodings of the data to generate the radio frequency signal comprises: performing digital to analog conversion of the data to generate an analog radio frequency waveform, and wherein transmitting the radio frequency signal over the wireless communications channel comprises: transmitting the generated analog radio frequency waveform using a transmitting antenna (as taught by O’Shea), in order to provide an improved performance for multiple input multiple output communications (O’Shea; [¶ 0032-0034 and ¶ 0039]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Refer to PTO-892, Notice of Reference Cited for a listing of analogous art.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Charles Lloyd Beard whose telephone number is (571)272-5735. The examiner can normally be reached Monday - Friday, 8:00 AM - 5: 00 PM, alternate Fridays EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tammy Goddard can be reached at (571) 272-7773. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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CHARLES LLOYD. BEARD
Primary Examiner
Art Unit 2611
/CHARLES L BEARD/Primary Examiner, Art Unit 2611
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