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 .
Claim Rejections – 35 U.S.C. 103
Applicant’s prior art arguments have been fully considered and they are persuasive.
Applicant asserts (pg. 9-12) that the cited references do not teach the amendments "selecting, from a plurality of candidate covariance matrix computation options, based on the output value, a method for computing a covariance matrix for interference whitening" in claim 1.
Examiner agrees. Accordingly, a new reference, Thirukannan et al. (US9762272B1) has been added to the rejection, as further detailed below.
The foregoing applies to all independent claims and their dependent claims.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as "configured to" or "so that"; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step for”) in a claim with functional language creates a rebuttable presumption that the claim element is to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is invoked is rebutted when the function is recited with sufficient structure, material, or acts within the claim itself to entirely perform the recited function.
Absence of the word “means” (or “step for”) in a claim creates a rebuttable presumption that the claim element is not to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is not invoked is rebutted when the claim element recites function but fails to recite sufficiently definite structure, material or acts to perform that function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
The following limitations are interpreted as invoking 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
Claim 17, “and means for processing, the means for processing being configured to …” The corresponding structure in the disclosure for performing the claimed processing is any combination of hardware, firmware, and software, employed to process data or digital signals (see detailed description [0062]). Therefore, the interpretation of the “means for processing…” is a generic processor with software that makes it able to receive signals, extract features from said signal, make a selection via neural network, and select a covariance matrix.
Claim 20, “means for processing is further configured to …” The corresponding structure in the disclosure for performing the claimed processing is any combination of hardware, firmware, and software, employed to process data or digital signals (see detailed description [0062]). Therefore, the interpretation of the “means for processing…” is a generic processor with software that makes it able to extract features from a signal and make a selection via neural network.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-20 are rejected under 35 U.S.C. 103 as being anticipated by Wu et al. (EP 3739356 A1) hereinafter known as Wu in view of Fang et al. (US 20210218483 A1) hereinafter known as Fang in view of Thirukannan (US9762272B1) hereinafter known as Thirukannan in view of Venkatesan et al. (“An iterative algorithm for computing a spatial whitening filter”) hereinafter known as Venkatesan.
Regarding independent claim 1, Wu teaches:
A method, comprising: receiving a signal; (Wu [Page 6, Lines 1-2]: “The receiver is configured for: receiving the wireless signal through the wireless multipath channel, extracting a plurality of time series of channel information (TSCI) of the wireless multipath channel from the wireless signal.” Wu teaches a receiver that can receive a wireless signal.)
extracting a first set of features from the received signal; (Wu [Page 6, Lines 1-2]: “The receiver is configured for: receiving the wireless signal through the wireless multipath channel, extracting a plurality of time series of channel information (TSCI) of the wireless multipath channel from the wireless signal.” Wu teaches a receiver that can extract a plurality of time series channel information sets from the signal.)
…
…
…
Wu does not explicitly teach:
computing, using a first neural network, an output value corresponding to a first resource block (RB), based on the first set of features, the output value being an indication of estimated signal to interference ratio in the first resource block;
… for interference whitening;
However, Fang teaches:
computing, using a first neural network, an output value corresponding to a first resource block (RB), based on the first set of features, the output value being an indication of estimated signal to interference ratio in the first resource block; (Fang [¶ 0103]: “calculate a post-signal-to-noise ratio (SINR) value for a sub-band of the wireless communications; determine, using a neural network (NN) and the post-SINR value as input to the NN, a label for each of a plurality of channel quality indicator” Fang teaches that a computation, the post-signal-to-noise-ratio of a resource block, or sub-band of wireless communication, is calculated. This is used to create the label of the channel quality indicator, which thus reflects the signal to noise/interference ratio.)
… for interference whitening; (Fang [Figure 15]: Fang teaches that the generation of the covariance matrix R is dependent on the whitening matrix W. Indeed in the figure, an equation is listed as R = sum(WH)^2(WH)). This shows that the selection of the whitening matrix and the selection of the covariance matrix is thus for interference whitening.)
Wu and Fang are in the same field of endeavor as the present invention, since the references are directed to processing signals and generating a selection of the features using a neural network, and determining the channel quality indicator of a signal using a neural network and covariances of the signal, respectively. It would have been obvious, before the effective filing date of the claimed invention, to a person of ordinary skill in the art, to combine making a selection based on features as taught in Wu with making decisions in the neural network using the signal to interference ratio as taught in Fang. Fang provides this additional functionality. As such, it would have been obvious to one of ordinary skill in the art to modify the teachings of Wu to include teachings of Fang because the combination would allow for decreasing the error of the selection when there are inaccuracies with transferring data through the signal in a network. This has the potential benefit of mitigating the effect of interference in signal processing.
Wu and Fang do not explicitly teach:
selecting, from a plurality of candidate covariance matrix computation options, based on the output value, a method for computing a covariance matrix …;
However, Thirukannan teaches:
selecting, from a plurality of candidate covariance matrix computation options, based on the output value, a method for computing a covariance matrix …; (Thirukannan [Col. 10, Lines 1-18]: “table of different sub-band lengths (bandwidths) to be used as a function of quantized estimated SNR and quantized estimated delay spread … the quantized delay spread and quantized estimated SNR may be used to look up the configured table for sub-band length (bandwidth) to be used for averaging in covariance estimation … the covariance may be estimated and averaged according to the sub - band length (bandwidth)” Thirukannan teaches a table of sub-band lengths that can be looked up. A subset of these bandwidths as resource blocks can be selected to calculate the covariance, showing that there are effectively many ways to calculate the covariance (each with different subset of bandwidths) and lookup/selection of bandwidths is equivalent to choosing a method.)
Thirukannan is in the same field of endeavor as the present invention, since it is directed to adaptive covariance matrix estimation for suppressing inter-cell interference. It would have been obvious, before the effective filing date of the claimed invention, to a person of ordinary skill in the art, to combine making a selection based on the features as taught in Wu as modified by Fang with selecting a method of calculation from candidates of covariance matrix computation options as taught in Thirukannan. Thirukannan provides this additional functionality. As such, it would have been obvious to one of ordinary skill in the art to modify the teachings of Wu as modified by Fang to include teachings of Thirukannan because the combination would allow for making a selection based on features using covariance estimates from contiguous resource blocks. This has the potential benefit of being able to make predictions on the error in contiguous data, such as audio.
Wu, Fang, and Thirukannan do not explicitly teach:
and improving the received signal by performing interference whitening on the received signal based on the selected first covariance matrix.
However, Venkatesan teaches:
and improving the received signal by performing interference whitening on the received signal based on the selected first covariance matrix. (Venkatesan [Page 338, Column 1, Paragraph 1]: “iterative algorithm to compute a spatial whitening filter for a given covariance matrix.” Venkatesan teaches an algorithm for a signal whitening filter based on a given covariance matrix. Venkatesan [Page 342, Column 2, Paragraph 1]: “Through simulation, we demonstrated its usefulness in the context of a multiple-antenna wireless link with spatially colored interference at the receiver.” Venkatesan teaches that this filtering improved the wireless link signal.)
Venkatesan is in the same field as the present invention, since it is directed to signal whitening using a given covariance matrix. It would have been obvious, before the effective filing date of the claimed invention, to a person of ordinary skill in the art, to combine the selection of the covariance matrix as taught in Wu as modified by Fang as modified by Thirukannan with using the covariance matrix to whiten the signal as taught in Venkatesan. Venkatesan provides this additional functionality. As such, it would have been obvious to one of ordinary skill in the art to modify the teachings of Wu as modified by Fang as modified by Thirukannan to include teachings of Venkatesan because the combination would allow for signals in wireless links to have their noise reduced, or whitened. This has the potential benefit of improving the communications between wireless devices, as the signal can be more accurate.
Regarding dependent claim 2, Wu, Fang, Thirukannan, and Venkatesan teach:
The method of claim 1,
Fang teaches:
wherein the computing of the output value comprises computing the output value based on a plurality of initial covariance estimates, each corresponding to a respective resource block (RB) of a contiguous set of resource blocks. (Fang [0051]: “while each sub-band includes six RBs” Fang [0104]: “post-SINR value is calculated for each resource block (RB) of a specified sub-band” Fang [0080]: “determines an RI corresponding to the channel, using the covariance matrix R as input to a NN or DNN” Fang teaches computing RI values across contiguous RBs by inputting this and a covariance matrix into a neural network.)
The reasons to combine are substantially similar to those of claim 1.
Regarding dependent claim 3, Wu, Thirukannan, and Venkatesan teach:
The method of claim 2,
Fang teaches:
wherein the contiguous set of resource blocks comprises all of the resource blocks in a bandwidth part. (Fang ¶ [0079]: “the covariance matrix R includes respective values corresponding to each sub-band of the communication channel” Fang teaches that the covariance matrix spans every sub-band of the communication channel, showing that the RB set comprises all resource blocks in the bandwidth part.)
The reasons to combine are substantially similar to those of claim 1.
Regarding dependent claim 4, Wu, Thirukannan, and Venkatesan teach:
The method of claim 2,
Wu teaches:
further comprising: extracting a second set of features from the signal; (Wu [Page 6, Lines 1-2]: “The receiver is configured for: receiving the wireless signal through the wireless multipath channel, extracting a plurality of time series of channel information (TSCI) of the wireless multipath channel from the wireless signal.” Wu teaches a receiver that can extract a plurality of time series channel information sets from the signal.)
and computing, using a second neural network, a second output value based on the second set of features, wherein the first set of features corresponds to a first resource block, and the second set of features corresponds to a second resource block. (Wu ¶ [0092]: “The classifier may be applied to at least one of: each first section of the first time duration of the first TSCI, and/or each second section of the second time duration of the second TSCI, to obtain at least one tentative classification results. Each tentative classification result may be associated with a respective first section and a respective second section.” Wu teaches that a classifier is applied to the second set of time series features and makes a second selection by obtaining the second tentative classification result. This is based on the second resource block, which is the second set of time series data. Wu ¶ [0094]: “A projection for each Cl may be trained using a dimension reduction method based on the training TSCI. The dimension reduction method may comprise at least one of: … neural network, deep neural network … The projection may be applied to at least one of: the training TSCI associated with the at least one event, and/or the current TSCI, for the classifier. Wu teaches that the PCA for the classification can be done using a neural network – which would necessarily be different from the first neural network.)
The reasons to combine are substantially similar to those of claim 1.
Regarding dependent claim 5, Wu, Fang, Thirukannan, and Venkatesan teach:
…
wherein: the output value is an indication of estimated signal to interference ratio in the first resource block; (Fang ¶ [0048]: “User device 100 circuitry (e.g., baseband processor 110) can calculate a SINR of a specified sub-band of the plurality of sub-bands 606 at blocks 610. When the training process is complete, the user device 100 may have calculated all, or a subset of all, of the post-SINR for each sub-band or resource block (RB) thereof In some available systems, Mutual Information Effective SNR Mapping (MIESM) can be used to generate the effective SINR for CQI mapping. Then, the best-M method is used to filter the sub-band with best channel status to provide a best MCS at block 612.” Fang teaches generating a model via selection based on the estimated signal to interference ratio.)
the output value corresponds to a signal to interference ratio less than a first threshold; (Fang ¶ [0049]: “FIG. 7 illustrates fields of the circle buffer database 618 according to some aspects. For each MCS, the database 618 stores information 702 for a plurality of sub-bands. Data 704 for each sub-band includes at least of r-dimensional ordered post-SINR 706, where r is determined by the number of RBs used (and is related to system bandwidth). The CRC calibration result 708 is used to verify that the packet error rate (PER) is below a threshold, e.g., below about 10% although aspects are not limited thereto.” Fang teaches that a threshold on the PER is used. As PER and SIR are inversely causally related, there is effectively a threshold on the SIR.)
the method further comprises selecting a covariance matrix based on a first initial covariance estimate, the first initial covariance estimate corresponding to the first resource block. (Thirukannan [Col. 10, Lines 1-18]: “the covariance may be estimated and averaged according to the sub - band length (bandwidth)” Thirukannan teaches a table of sub-band lengths that can be looked up. A subset of these bandwidths as resource blocks can be selected to calculate the covariance, showing that the selection of the sub-band is effectively selecting a covariance matrix.)
The reasons to combine are substantially similar to those of claim 1.
Regarding dependent claim 6, Wu, Thirukannan, and Venkatesan teach:
The method of claim 4,
Fang teaches:
wherein: the output value is an indication of estimated signal to interference ratio in the first resource block; (Fang ¶ [0048]: “User device 100 circuitry (e.g., baseband processor 110) can calculate a SINR of a specified sub-band of the plurality of sub-bands 606 at blocks 610. When the training process is complete, the user device 100 may have calculated all, or a subset of all, of the post-SINR for each sub-band or resource block (RB) thereof In some available systems, Mutual Information Effective SNR Mapping (MIESM) can be used to generate the effective SINR for CQI mapping. Then, the best-M method is used to filter the sub-band with best channel status to provide a best MCS at block 612.” Fang teaches generating a plurality of models via selection based on the estimated signal to interference ratios of the plurality of resource blocks.)
the output value corresponds to a signal to interference ratio greater than a first threshold; (Fang ¶ [0049]: “FIG. 7 illustrates fields of the circle buffer database 618 according to some aspects. For each MCS, the database 618 stores information 702 for a plurality of sub-bands. Data 704 for each sub-band includes at least of r-dimensional ordered post-SINR 706, where r is determined by the number of RBs used (and is related to system bandwidth). The CRC calibration result 708 is used to verify that the packet error rate (PER) is below a threshold, e.g., below about 10% although aspects are not limited thereto.” Fang teaches that a threshold on the PER is used. As PER and SIR are inversely causally related, there is effectively a threshold on the SIR. Since there are a plurality of sub-bands, there may be a plurality of thresholds.)
the second output value is an indication of estimated signal to interference ratio in the second resource block; (Fang ¶ [0048]: “User device 100 circuitry (e.g., baseband processor 110) can calculate a SINR of a specified sub-band of the plurality of sub-bands 606 at blocks 610. When the training process is complete, the user device 100 may have calculated all, or a subset of all, of the post-SINR for each sub-band or resource block (RB) thereof In some available systems, Mutual Information Effective SNR Mapping (MIESM) can be used to generate the effective SINR for CQI mapping. Then, the best-M method is used to filter the sub-band with best channel status to provide a best MCS at block 612.” Fang teaches generating a plurality of models via selection based on the estimated signal to interference ratios of the plurality of resource blocks.)
the second output value corresponds to a signal to interference ratio greater than the first threshold; (Fang ¶ [0049]: “FIG. 7 illustrates fields of the circle buffer database 618 according to some aspects. For each MCS, the database 618 stores information 702 for a plurality of sub-bands. Data 704 for each sub-band includes at least of r-dimensional ordered post-SINR 706, where r is determined by the number of RBs used (and is related to system bandwidth). The CRC calibration result 708 is used to verify that the packet error rate (PER) is below a threshold, e.g., below about 10% although aspects are not limited thereto.” Fang teaches that a threshold on the PER is used. As PER and SIR are inversely causally related, there is effectively a threshold on the SIR. Since there are a plurality of sub-bands, there may be a plurality of thresholds.)
Thirukannan teaches:
the method further comprises selecting a covariance matrix based on a first initial covariance estimate and on a second initial covariance estimate; (Thirukannan [Col. 10, Lines 1-18]: “the covariance may be estimated and averaged according to the sub - band length (bandwidth)” Thirukannan teaches a table of sub-band lengths that can be looked up. A subset of these bandwidths as resource blocks can be selected to calculate the covariance, showing that the selection of the sub-band is effectively selecting a covariance matrix.)
the first initial covariance estimate corresponds to the first resource block; (Thirukannan [Col. 4, Lines 50-53]: “wherein the estimated covariance matrix is determined using reference signals (RS) … averaged over a bandwidth length” Thirukannan teaches that the covariance matrix is determined by the RS, which occupy resource elements located within particular RBs. Therefore, the covariance estimate corresponds to the RB.)
and the second initial covariance estimate corresponds to the second resource block. (Thirukannan [Col. 4, Lines 50-53]: “wherein the estimated covariance matrix is determined using reference signals (RS) … averaged over a bandwidth length” Thirukannan teaches that the covariance matrix is determined by the RS, which occupy resource elements located within particular RBs. Therefore, the covariance estimate corresponds to the RB.)
The reasons to combine are substantially similar to those of claim 1.
Regarding dependent claim 7, Wu, Thirukannan, and Venkatesan teach:
The method of claim 1,
Thirukannan teaches:
further comprising calculating a first initial covariance estimate, wherein a first feature of the first set of features is based on the first initial covariance estimate. (Thirukannan [Col. 8, Line 14-17]: “estimation of covariance matrix comprising of interference and noise power is of prime importance as it plays a crucial role in suppressing the interfering signals” Thirukannan teaches that the features, including the interference and noise power, play a role in calculating the covariance estimate.)
The reasons to combine are substantially similar to those of claim 1.
Regarding dependent claim 8, Wu, Thirukannan, and Venkatesan teach:
The method of claim 7,
Wu teaches:
wherein the first feature includes an eigenvalue of the first initial covariance estimate. (Wu ¶ [0353]: “Therefore, the correlation matrix is used instead, which can be expressed accordingly as … The eigenvalues LAMBDA_1, …, LAMBDA_M of R are sorted in a non-descending order.” Wu teaches that the correlation matrix, which is effectively a covariance matrix when the standard deviation is known, comprises eigenvalues. The first feature is based on the covariance matrix so therefore the first feature includes an eigenvalue of the first initial covariance estimate.)
The reasons to combine are substantially similar to those of claim 1.
Regarding dependent claim 9, Wu, Thirukannan, and Venkatesan teach:
The method of claim 7,
Wu teaches:
wherein the first feature includes a QR decomposition of the first initial covariance estimate. (Wu ¶ [0116]: “An event may be monitored based on the TSCI. … The task or the wireless smart sensing task may comprise: … eigen-decomposition … other decomposition” Wu teaches that the first feature, which is based on the time series information, includes other types of matrix decomposition other than eigen-decomposition, which includes QR decomposition.)
The reasons to combine are substantially similar to those of claim 1.
Regarding dependent claim 10, Wu, Thirukannan, and Venkatesan teach:
The method of claim 7,
Thirukannan teaches:
wherein the first feature includes an element of the first initial covariance estimate. (Thirukannan [Col. 8, Line 14-17]: “estimation of covariance matrix comprising of interference and noise power is of prime importance as it plays a crucial role in suppressing the interfering signals” Thirukannan teaches that the features, including the interference and noise power, play a role in calculating the covariance estimate.)
The reasons to combine are substantially similar to those of claim 1.
Independent claim 11 is rejected on the same grounds under 35 U.S.C. 103 as claim 1, as claim 11 is substantially similar to claim 1, but has the following additional elements:
Wu teaches:
A device, comprising: a radio; (Wu ¶ [0036]: “wherein each of the N1 TSCI is associated with an antenna of the transmitter and an antenna of the receiver.” Wu teaches an a device with an antenna, which is also known as a radio.)
and a processing circuit, the processing circuit being configured to: receive, through the radio, a signal; (Wu ¶ [0048]: “The Type 1 /Type 2 device may comprise at least one of: electronics, circuitry, transmitter (TX)/receivers (RX)/transceiver, RF interface, "Origin Satellite"/"Tracker Bot", unicast/multicast/broadcasting device, wireless source device…” Wu teaches a device that comprises a processing circuit and a radio. Wu ¶ [0036]: “wherein each of the N1 TSCI is associated with an antenna of the transmitter and an antenna of the receiver.” Wu teaches receiving the signal through the radio.)
The reasons to combine are substantially similar to those of claim 1.
Claims 12-14 are rejected on the same grounds under 35 U.S.C. 103 as claims 2-4 as they are substantially similar, respectively. Mutatis mutandis.
Claims 15-16 are rejected on the same grounds under 35 U.S.C. 103 as claims 5-6 as they are substantially similar, respectively. Mutatis mutandis.
Claim 17 is rejected on the same grounds under 35 U.S.C. 103 as claim 11 as they are substantially similar, respectively. Mutatis mutandis.
Claims 18-20 are rejected on the same grounds under 35 U.S.C. 103 as claims 12-14 as they are substantially similar, respectively. Mutatis mutandis.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, 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 KYU HYUNG HAN whose telephone number is (703) 756-5529. The examiner can normally be reached on MF 9-5.
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/Kyu Hyung Han/
Examiner
Art Unit 2123
/ALEXEY SHMATOV/Supervisory Patent Examiner, Art Unit 2123
Prosecution Insights