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
The Amendment filed April 9 2026 has been entered and considered. Claims 1, 15-17, 19, 27, 29-33, and 51 have been amended. New claims 59-61 have been added. The amendment does not overcome the rejection under 35 U.S.C. 103 previously set forth. The rejection is maintained and is restated below with additional discussion of the amended limitations; accordingly, this action is made final.
Claim Objections –
In view of the amendments to claims 15-17, 19, and 51, the objection has been withdrawn as moot.
112 Rejections –
In view of the amendments to claims 19 and 51, the rejection has been withdrawn as moot.
Response to Arguments
Applicant's arguments filed 4/9/2026, Remarks Pgs. 16-17, have been fully considered but they are not persuasive.
Applicant argues that the prior art does not disclose the newly added amendments to the independent claims. Remarks of 4/9/26 at Pgs. 16-17. Examiner respectfully disagrees.
Applicant argues (Pg. 16):
For example, Applicant submits that none of the cited references teach or suggest to obtain from the spatial frequency representation velocity values and spatial data to generate one or more data cubes of the field of view and perform motion correction on one or more targets in an arrangement of spatial-velocity values based on the data cubes.
Examiner responds:
Cohen explicitly discloses performing range-azimuth-Doppler recovery with the output map functionally representing a data cube (Fig. 5). They perform this recovery by obtaining spatial frequency representations of the signal (Section IV, A) to extract velocity values and spatial data. One of ordinary skill in the art would have recognized that utilizing the data cube generation of Cohen for the input velocity values to the phase correction function of Jakowatz is a straightforward combination motivated by the shared goal of providing accurate spatial representations of moving targets.
Applicant argues (Pg. 16):
For example, Applicant submits that SAR refocusing constitutes non-analogous art and that the teachings between the claimed invention and the cited references are technically distinct.
Examiner responds:
It has been held that a prior art reference must either be in the field of the inventor’s endeavor or, if not, then be reasonably pertinent to the particular problem with which the inventor was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992). In this case, both references relate to radar signal processing and specifically address the problem of recovering spatial and velocity information about moving targets from received radar signals. In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., spherical manifold imaging, acquisition scheduling) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
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) 1-19, 23-51, 55-58, and 60-61 as best understood are rejected under 35 U.S.C. 103 as being unpatentable over Jakowatz et al. (NPL, “Refocus of constant velocity moving targets in synthetic aperture radar imagery”, published 1998, pdf attached) in view of Cohen et al. (NPL, “SUMMeR: Sub-Nyquist MIMO Radar”, published 2016, pdf attached).
Regarding claim 1, Jakowatz teaches an image processing system comprising (Pg. 1, “The refocus is accomplished by application of a two-dimensional phase function to the phase history data obtained via Fourier transformation of an image chip that contains the blurred moving target data.”, image processing is performed): a memory storing computer-program instructions; one or more processors coupled to the memory that execute the computer-program instructions to (Performing image processing indicates the usage of a processor coupled with memory): gather data samples associated with an antenna element (Pg. 1, “We consider the general problem of imaging a target (typically a vehicle) moving at constant speed in a straight line, with a SAR system that forms imagery collected in the spotlight mode.”), wherein the data samples correspond to data at a specific range (Pg. 3, “The formed SAR image is described by g(x,y), where x and y denote the ground plane cross-range and range dimensions, respectively.”); transform the data samples into a range representation for a field of view (Pg. 1, “This is sometimes referred to as a Doppler shift, and its classic demonstration is the image of a railroad train traveling in the range direction appearing displaced in cross-range off of its tracks; 2) the target signature is defocused (blurred) in the cross-range dimension by an amount that depends upon the cross-range component of the target velocity; and 3) the target is imparted a two-dimensional defocus (i.e., in both range and cross-range dimensions), the magnitude of which is determined by the range component of the target velocity.”; Pg. 5, “In this section we propose an algorithm for automatically refocusing a section (chip) of image domain data that is thought to contain the blurred signature of a moving target undergoing constant velocity.”, the image chip containing the blurred moving target data is the range representation for a field of view); sample the range representation at each range of one or more ranges within the field of view to obtain a spatial frequency representation of the field of view (Pg. 3, “As shown in Figure 1(b) the phase history data are denoted as G(X, Y), where X and Y are the ground-plane cross-range and range spatial frequencies, respectively. The formed SAR image is described by g(x,y), where x and y denote the ground plane cross-range and range dimensions, respectively.”, the phase history data is the spatial frequency representation); transform the spatial frequency representation to obtain spatial-velocity values for the field of view (Pg. 1, “By considering separately the phase effects of the range and cross-range components of the target velocity vector, we show how the appropriate phase correction term can be derived as a two-parameter function.”; Pg. 3, “The net effect will be a 2-D function that completely accounts for all of the effects of constant velocity motion of the target, and that is parametrized by the two components of the target velocity.”); perform motion correction on one or more targets in an arrangement of spatial- velocity values to generate a motion corrected representation of the field of view (Pg. 1, “The refocus is accomplished by application of a two-dimensional phase function to the phase history data obtained via Fourier transformation of an image chip that contains the blurred moving target data. By considering separately the phase effects of the range and cross-range components of the target velocity vector, we show how the appropriate phase correction term can be derived as a two-parameter function.”); and generate from the motion corrected representation of the field of view, a representation of the field of view at the specific range (Fig. 4, shows the motion corrected representation of the field of view at the specific range; Pg. 5, “When the optimization procedure is complete, we end up with a well-focused image of the moving target, as well as estimates for the two velocity components.”).
Jakowatz does not explicitly disclose the usage of a plurality of antenna elements or to generate data cubes and perform motion correction based on the data cubes.
Cohen teaches to gather data samples associated with a plurality of antenna elements (Pg. 1, Col. 1, “Multiple input multiple output (MIMO) [1] radar, which presents significant potential for advancing state-of-the-art modern radar in terms of flexibility and performance, poses new theoretical and practical challenges. This radar architecture combines multiple antenna elements both at the transmitter and receiver where each transmitter radiates a different waveform.”) and obtain from the spatial frequency representation velocity values and spatial data to generate one or more data cubes of the field of view (Fig. 5).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Jakowatz to incorporate the teachings of Cohen to include a plurality of antenna elements and generate data cubes to perform the motion correction. Jakowatz teaches a motion correction algorithm using SAR imager, but they do not explicitly disclose that multiple antenna are used for the imagery or generating a data cube. One of ordinary skill in the art would recognize that combining the motion correction algorithm of Jakowatz with newer MIMO-based radar systems, such as the one disclosed in Cohen, is a well-known, routine design choice to improve resolution and performance. They would further understand that utilizing a data cube, such as the one disclosed in Cohen, provides velocity estimates which feed naturally into the motion correction method of Jakowatz.
Regarding claim 2, Jakowatz as modified above teaches all of the elements of claim 1, as stated above, as well as wherein the plurality of antenna elements are arranged in a multi-input multi-output (MIMO) configuration (Cohen; Pg. 1, Col. 1, “Multiple input multiple output (MIMO) [1] radar, which presents significant potential for advancing state-of-the-art modern radar in terms of flexibility and performance, poses new theoretical and practical challenges. This radar architecture combines multiple antenna elements both at the transmitter and receiver where each transmitter radiates a different waveform.”).
Regarding claim 3, Jakowatz as modified above teaches all of the elements of claim 1, as stated above, as well as wherein the plurality of antenna elements includes a plurality of reception antennas (Cohen; Fig. 1, Pg. 2, Col. 1, “The traditional approach to collocated MIMO adopts a virtual ULA structure [26], where R receivers”, multiple receiver antennas are disclosed).
Regarding claim 4, Jakowatz as modified above teaches all of the elements of claim 1, as stated above, as well as wherein the one or more ranges correspond to reflections from respective objects located at the one or more ranges (Figs. 2-4).
Regarding claim 5, Jakowatz as modified above teaches all of the elements of claim 1, as stated above, as well as wherein the one or more processors are configured to render the field of view at the one or more ranges based on data of the motion corrected representation (Fig. 4, shows the field of view being rendered based on data of the motion corrected).
Regarding claim 6, Jakowatz as modified above teaches all of the elements of claim 1, as stated above, as well as wherein the one or more processors execute the computer-program instructions to: perform a summation on data of the motion corrected representation of the field of view to generate one or more images of the field of view at the specific range (Fig. 4, Pg. 5, “For each guess at the two components of moving target velocity, the conjugates of the hyperbolic and quadratic phase terms are applied to the Fourier transform of the mover's image chip. Upon inverse Fourier transformation, an estimate of the refocused image chip is produced and a value for the contrast is produced.”, an inverse Fourier transformation is performed, which sums the frequency components to produce the final focused image).
Regarding claim 7, Jakowatz as modified above teaches all of the elements of claim 6, as stated above, as well as wherein the summation comprises a coherent summation on the motion corrected representation of the field of view (Figs. 3-4, Pg. 5, “For each guess at the two components of moving target velocity, the conjugates of the hyperbolic and quadratic phase terms are applied to the Fourier transform of the mover's image chip. Upon inverse Fourier transformation, an estimate of the refocused image chip is produced and a value for the contrast is produced.”, coherent summation is necessarily performed in order to generate a high-resolution image that has been refocused as shown in the above figures).
Regarding claim 8, Jakowatz as modified above teaches all of the elements of claim 6, as stated above, as well as wherein the one or more images comprise one or more two-dimensional (2D) images of the field of view (Fig. 4).
Regarding claim 9, Jakowatz as modified above teaches all of the elements of claim 1, as stated above, as well as wherein one or more ranges comprise a plurality of ranges (Pg. 4, “The effect of the hyperbolic term will be to blur the image domain data in both range and cross-range dimensions, while the effect of the linear term will be to translate the formed moving target signature in the image cross-range dimension.”, multiple ranges are disclosed as being used in the processing).
Regarding claim 10, Jakowatz as modified above teaches all of the elements of claim 1, as stated above, as well as wherein the data samples comprise samples of a modulated radar signal (Pg. 3, “Each pulse is transmitted, received, and demodulated at a particular position & along the synthetic aperture, yielding one column of Fourier transform (phase history) data for the associated value of spatial frequency X.”, the radar signal is demodulated, necessarily meaning that the signal was a modulated radar signal).
Regarding claim 11, Jakowatz as modified above teaches all of the elements of claim 10, as stated above, as well as wherein the modulated radar signal comprises a time modulated radar signal or a frequency modulated radar signal (Cohen; Pg. 3, Col. 1, “Each transmitting antenna sends P pulses, such that the mth transmitted signal is given by (Eq 1) where hm (t), 0 ≤ m ≤ T −1 are narrowband and orthogonal pulses with bandwidth Bh, modulated with carrier frequency fc.”. One of ordinary skill in the art would also understand that the high range resolution necessary for the SAR images in Jakowatz is well-known to be achieved by using Frequency Modulation signals).
Regarding claim 12, Jakowatz as modified above teaches all of the elements of claim 10, as stated above, as well as wherein the modulated radar signal comprises a time domain multiplexed (TDM) modulated radar signal or a frequency division multiplexing (FDM) modulated radar signal (Cohen; Pg. 3, Col. 1, “This property implies that the orthogonal signals cannot overlap in frequency [19], leading to FDMA. Alternatively, time invariant orthogonality can be approximately achieved using CDMA.”, when there are a plurality of antennas, the signals need to be separated/orthogonalized).
Regarding claim 13, Jakowatz as modified above teaches all of the elements of claim 1, as stated above, as well as further comprising an analog to digital converter that samples the data samples associated with the plurality of antenna elements when a first set of data samples are generated (Cohen; Pgs. 2-3, Cols. 2-1, “Low-rate data acquisition is based on the ideas of Xampling [15], [16], which consist of an ADC performing analog prefiltering of the signal before taking point-wise samples.”, digital processing performed on samples of the received signal necessitates the usage of an analog to digital converter. One of ordinary skill in the art would recognize that this applies to the system of Jakowatz as well).
Regarding claim 14, Jakowatz as modified above teaches all of the elements of claim 1, as stated above, as well as wherein signals associated with the plurality of antenna elements (Cohen; Pg. 1, Col. 1, “Multiple input multiple output (MIMO) [1] radar, which presents significant potential for advancing state-of-the-art modern radar in terms of flexibility and performance, poses new theoretical and practical challenges. This radar architecture combines multiple antenna elements both at the transmitter and receiver where each transmitter radiates a different waveform.”) are sampled to generate a plurality of data samples (Pg. 1, “That is, we will assume that the image resolution and patch size are such that the processing step of polar-to-Cartesian interpolation (also known as polar reformatting) of the phase history samples prior to Fourier transformation is not required.”, multiple samples are disclosed) at a plurality of ranges (Pg. 4, “The effect of the hyperbolic term will be to blur the image domain data in both range and cross-range dimensions, while the effect of the linear term will be to translate the formed moving target signature in the image cross-range dimension.”).
Regarding claim 15, Jakowatz as modified above teaches all of the elements of claim 1, as stated above, as well as wherein the one or more processors execute the computer-program instructions to: arrange data samples for each range of the one or more ranges along the one or more spatial dimensions to generate one or more first representations of the field of view at the one or more ranges (Pg. 4, “The formed SAR image is described by g(x,y), where x and y denote the ground plane cross-range and range dimensions, respectively.”).
Regarding claim 16, Jakowatz as modified above teaches all of the elements of claim 15, as stated above, as well as wherein the one or more first representations comprise one or more first data cubes of the field of view at the one or more ranges (Cohen; Pg. 2, Col. 2, “we separate the three dimensions (range, azimuth and Doppler) by adapting OMP to matrix form, with several matrix system equations.”, the raw data is initially organized into a three-dimensional structure, or “data cube”).
Regarding claim 17, Jakowatz as modified above teaches all of the elements of claim 1, as stated above, as well as wherein the one or more processors execute the computer-program instructions to: arrange the velocity values for each range of the one or more ranges along the one or more spatial dimensions to generate one or more second representations of the field of view at the one or more ranges (Pg. 4, “In addition, the range component gives rise to a linear phase in the X dimension that accounts for the Doppler shift of the mover. The cross-range component of target velocity will be shown to impose a one-dimensional quadratic phase term in X onto the target phase history. The net effect will be a 2-D function that completely accounts for all of the effects of constant velocity motion of the target, and that is parametrized by the two components of the target velocity.”).
Regarding claim 18, Jakowatz as modified above teaches all of the elements of claim 17, as stated above, as well as wherein the one or more second representations comprise one or more second data cubes of the field of view at the one or more ranges(Cohen; Pg. 9, Col. 1, “To recover jointly the range, azimuth and Doppler frequency of the targets, we apply the concept of Doppler focusing from [12] to our setting.”, Jakowatz discloses a representation across a 2D image plane, however for a high-resolution MIMO system, this analysis must be performed on data organized in a structure that separates all three spatial-velocity dimensions. The joint recovery operation of Cohen discloses that the data is structured as the three-dimensional data cube. One of ordinary skill in the art would recognize that when performing an analysis to represent the radar data in a MIMO system, a three-dimensional data structure is necessary, as disclosed by Cohen).
Regarding claim 19, Jakowatz as modified above teaches all of the elements of claim 17, as stated above, as well as wherein the one or more processors execute the computer-program instructions to: identify one or more targets (Pg. 1, “In this paper we address the problem of refocusing a blurred signature that has by some means been identified as a moving target.”) in the one or more second representations of the field of view at a first range of the one or more ranges (Pg. 4, “In addition, the range component gives rise to a linear phase in the X dimension that accounts for the Doppler shift of the mover. The cross-range component of target velocity will be shown to impose a one-dimensional quadratic phase term in X onto the target phase history. The net effect will be a 2-D function that completely accounts for all of the effects of constant velocity motion of the target, and that is parametrized by the two components of the target velocity.”).
Regarding claim 23, Jakowatz as modified above teaches all of the elements of claim 1, as stated above, as well as wherein the transform comprises a fast Fourier transform (FFT) (Cohen; Pg. 9, Col. 1, “Note that step 1 can be performed using fast Fourier transform (FFT)”).
Regarding claim 24, Jakowatz as modified above teaches all of the elements of claim 23, as stated above, as well as wherein the FFT is a three-dimensional (3D) FFT (Cohen; Pg. 9, Col. 1, “Note that step 1 can be performed using fast Fourier transform (FFT)”, step 1 is part of an algorithm for sparse 3D recovery, indicating the usage of a 3D FFT).
Regarding claim 25, Jakowatz as modified above teaches all of the elements of claim 1, as stated above, as well as wherein the motion correction is performed by decoupling spatial data from Doppler data (Pg. 1, “By considering separately the phase effects of the range and cross-range components of the target velocity vector, we show how the appropriate phase correction term can be derived as a two-parameter function.”).
Regarding claim 26, Jakowatz as modified above teaches all of the elements of claim 1, as stated above, as well as wherein the motion correction is performed by decoupling spatial data from Doppler data by decoupling the velocity values from the one or more spatial dimensions orthogonal to a first range (Pg. 4, “In addition, the range component gives rise to a linear phase in the X dimension that accounts for the Doppler shift of the mover. The cross-range component of target velocity will be shown to impose a one-dimensional quadratic phase term in X onto the target phase history.”).
Claim 27 corresponds to claim 1 and is rejected under the same analysis.
Regarding claim 28, Jakowatz as modified above teaches all of the elements of claim 1, as stated above, as well as one or more pre-processing units configured to generate the data samples from responses to the representation of the modulated signal (Pg. 1, “We consider the general problem of imaging a target (typically a vehicle) moving at constant speed in a straight line, with a SAR system that forms imagery collected in the spotlight mode.”, necessarily pre-processing units are configured to generate data samples of the modulated signal in a SAR system).
Claim 29 corresponds to claim 1 and is rejected under the same analysis.
Claim 30 corresponds to claim 1 and is rejected under the same analysis.
Claim 31 corresponds to claim 1 and is rejected under the same analysis.
Regarding claim 32, the recited image processing system performs variably the same function as the image processing system of claim 1 and claim 5. It is rejected under the same analysis.
Claim 33-51 correspond to claims 1-19 respectively and are rejected under the same analysis.
Claims 55-58 correspond to claims 23-26 respectively and are rejected under the same analysis.
Regarding claim 60, Jakowatz as modified above teaches all of the elements of claim 1, as stated above, as well as wherein each of the plurality of antenna elements have a characteristic spacing between one another, wherein the characteristic spacing depends on a characteristic wavelength of a signal associated with the data samples (Cohen; Pg. 3, “The traditional approach to collocated MIMO adopts a virtual ULA structure [26], where R receivers, spaced by λ/2 and T transmitters, spaced by R λ/2 (or vice versa), form two ULAs. Here, λ is the signal wavelength”).
Regarding claim 61, Jakowatz as modified above teaches all of the elements of claim 1, as stated above, as well as wherein the one or more processors execute the computer-program instructions to arrange the velocity values and the spatial data along one or more spatial dimensions to generate one or more data cubes of the field of view, wherein the one or more data cubes represent the velocity values and the spatial data at the one or more ranges (Cohen; Fig. 5).
Claim(s) 20-22 and 52-54 are rejected under 35 U.S.C. 103 as being unpatentable over Jakowatz as modified by Cohen above, further in view of Rohling (NPL, “Radar CFAR Thresholding in Clutter and Multiple Target Situations”, published 1983, pdf attached).
Regarding claim 20, Jakowatz as modified in view of Cohen teaches all of the elements of claim 19, as stated above. They do not explicitly disclose wherein the one or more objects identified in the one or more second representations of the field of view at the first range are identified based on an adaptive threshold.
Rohling teaches wherein the one or more objects identified in the one or more second representations of the field of view at the first range are identified based on an adaptive threshold (Pg. 1, Abstract, “Radar detection procedures involve the comparison of the received signal amplitude to a threshold. In order to obtain a constant false-alarm rate (CFAR), an adaptive threshold must be applied reflecting the local clutter situation. The cell averaging approach, for example, is an adaptive procedure.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Jakowatz and Cohen to incorporate the teachings of Rohling to include the one or more objects identified based on an adaptive threshold. CFAR is the standard adaptive thresholding technique applied after radar target recovery. One of ordinary skill in the art would recognize the advantage of utilizing this well-known technique to improve the accuracy of object identification.
Regarding claim 21, Jakowatz as modified in view of Cohen teaches all of the elements of claim 19, as stated above, and when modified further in view of Rohling teaches wherein the one or more objects identified in the one or more second representations of the field of view at the first range are identified based on a CFAR (Rohling; Fig. 3, Pgs. 608-609, Cols. 2-1, “The idea is to modify the common CFAR techniques by replacing the usual clutter power estimation based on arithmetic averaging by a new procedure which has proven useful for similar tasks in general image processing applications.”).
Regarding claim 22, Jakowatz as modified in view of Cohen teaches all of the elements of claim 19, as stated above, and when modified further in view of Rohling teaches wherein the CFAR (Rohling; Fig. 3) is a three-dimensional (3D) CFAR (Cohen; Pg. 9, Col. 1, “To recover jointly the range, azimuth and Doppler frequency of the targets, we apply the concept of Doppler focusing from [12] to our setting.”, In view of the 3D Range-Azimuth-Doppler data cube generated by Cohen, one of ordinary skill in the art would realize that implementing a 3D CFAR algorithm to accurately estimate the background noise in all three-dimensions would be a routine adaptation of the known CFAR methods).
Claim 52-54 correspond to claims 20-22 respectively and are rejected under the same analysis.
Claim 59 is rejected under 35 U.S.C. 103 as being unpatentable over Jakowatz as modified by Cohen above, further in view of Rambach et al. (NPL, “Optimal Time Division Multiplexing Schemes for DOA Estimation of a Moving Target Using a Colocated MIMO Radar”, published 2014).
Regarding claim 59, Jakowatz as modified in view of Cohen teaches all of the elements of claim 1, as stated above, as well as performing motion correction.
They do not explicitly disclose wherein the motion correction is in response to time domain multiplexing signals at different antenna elements of the plurality of antenna elements.
Rambach teaches that there are time domain multiplexing signals at different antenna elements of the plurality of antenna elements (Pg. 1, Col. 1, “Since the target is moving, the Doppler effect causes additional phase shifts… In [4], [5] the DOA estimation of one and two moving targets using a TDM-MIMO radar has been investigated. It was shown that the DOA accuracy depends on the position as well as on the transmission sequence of the Tx antennas.”; Fig. 1; Eqs. 1 and 2; Pg. 2, Col. 1, “The expression exp(jtω) is the phase change due to the Doppler frequency ω”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Jakowatz and Cohen to incorporate the teachings of Rambach to include wherein the motion correction is in response to time domain multiplexing signals at different antenna elements of the plurality of antenna elements. Jakowatz teaches performing motion-based phase correction on received radar signals to generate a motion-corrected representation. Rambach establishes that TDM-MIMO is a well-known radar architecture in which different antenna elements transmit at sequentially offset time instances, and that this structure introduces motion-dependent phase shifts into the received signal, degrading spatial estimation accuracy. While Rambach addresses this through optimization of the TDM transmission scheme, this approach operates at the hardware design level and cannot be used when system requirements are constrained. Jakowatz’s phase correction methodology directly addresses the same underlying motion-induced phase error in the signal processing domain, independent of TDM scheme structure. One of ordinary skill in the art would have recognized TDM-MIMO as a standard architecture presenting known motion-induced phase error challenges and would have predictably applied Jakowatz’s established correction approach to improve motion correction accuracy in such a system.
Conclusion
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 DAVID A WAMBST whose telephone number is (703)756-1750. The examiner can normally be reached M-F 9-6:30 EST.
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/DAVID ALEXANDER WAMBST/Examiner, Art Unit 2663
/GREGORY A MORSE/Supervisory Patent Examiner, Art Unit 2698
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