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
Applicant’s Amendments filed on 06/03/2026 has been entered and made of record.
Currently pending Claim(s):
Independent Claim(s):
Amended Claim(s):
Cancelled Claim(s):
1, 3–6, 8–11 and 13–15
1, 6 and 11
1, 6 and 11
2, 7 and 12
Response to Applicant’s Arguments
This office action is responsive to Applicant’s Arguments/Remarks Made in an Amendment received on 06/03/2026.
Applicant’s Reply (June 3, 2026) includes substantive amendments to the claims. This Office action has been updated with a new grounds of rejection addressing those amendments. Further, Applicant’s Arguments/Remarks with respect to independent claims 1, 6 and 11 have been considered but are moot because the arguments do not apply to any of the references being used in the current rejection; and the claims are now rejected by newly cited art Doi et al. (US 20220075059 A1), Fleizach (US 20200265563 A1) and Bargeron et al. (US 20040240562 A1) as explained in the body of the rejection.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 1, 6 and 11 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claim 1 recites the limitation “power-law transformation” on Pg. 3, ln. 2 of the amendments to the claims. Claim 6 recites the limitation “power-law transformation” on Pg. 5, ln. 12 of the amendments to the claims. Claim 11 recites the limitation “power-law transformation” on Pg. 7, ln. 23 of the amendments to the claims. The Applicant in the Remarks page 10 references to the paragraphs [0048-0052] of the specification for the support for said amendments. This seems to reference to equation (4) of the specification on Pg. 16, paragraph [0051]. It is unclear whether the equation (4) is a power-law transformation, or something else. It is also unclear which type of power-law equation, if any, this is and the specification does not provide support or any terms that relate to “power-law transformation”.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claim(s) 1, 3–6, 8–11 and 13–15 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (US 2022/0196798 A1, hereafter, "Chen") in view of Xie et al. (US 2022/0300739 A1, hereafter, "Xie"), Doi et al. (US 20220075059 A1, hereafter, “Doi”), Bargeron et al. (US 20040240562 A1, hereafter, “Bargeron”), and further in view of Fleizach (US 20200265563 A1, hereafter, “Fleizach”).
Regarding claim 1, Chen discloses a radar object recognition system (See Chen, ¶ [0192], In some aspects, the radar detector 1110 may include a neural network machine learning algorithm to perform object detection), comprising: a storage device configured to store at least one instruction; and a processor electrically connected to the storage device, and the processor configured to execute the at least one instruction for (See Chen, ¶ [0191], The radar device 1102 may include a radar processor 1104 and a radar detector 1110 that may generate an error value 1120. See also [FIG. 11], 1104 Radar Processor):
performing a radar image generation on a radar data to generate a radar image (See Chen, ¶ [0370], Range and Doppler processing creates a range doppler map, and the AoA estimation creates a azimuth elevation map for each range-doppler bin, thus resulting in a 4D voxel. A detector may then create a point cloud, which can then be an input for a perception pipeline. See also ¶ [0363], Conventional techniques for generating high-resolution radar images (i.e. point clouds));
inputting the radar image into an object recognition model, so that the object recognition model outputs a recognition result (See Chen, ¶ [0420], The radar processor 104 may use one or more neural networks to perform radar-based perception tasks (such as object detection, classification, segmentation) based on a 3D point cloud); and
[performing a post-process on the recognition result to eliminate a recognition error from the recognition result],
wherein the radar data is a two-dimensional radar data map, and the radar image generation executed by the processor comprises (See Chen, ¶ [0370], Range and Doppler processing creates a range-doppler map. Note: the range-doppler map is a 2D radar map):
normalizing all two-dimensional bins of the radar data map to obtain a normalized radar data map (See Chen, ¶ [0244], “The graphical representation 1500b was obtained using operations described above in relation to FIGS. 11-42. The graphical representation 1500b includes a 4D map that includes estimations of the object parameters using the neural network machine learning algorithm. The graphical representation 1500b includes a binned map, the size of the FFT map, and each bin is normalized to a value of one), [wherein all signal strengths of the normalized radar data map range from 0 to 1];
performing a target enhancement on the normalized radar data map, by setting a lower bound of signal strength of the normalized radar data map, to obtain an enhanced radar data map (See Chen, ¶ [0430] In a typical input data voxel, the 4D bins in the field of view in general consists of three categories: I) 4D bins genuinely occupied by objects; II) 4D bins with clutter and ghost responses; III) 4D bins with only thermal noise. Most of the 4D bins in the field of view fall into Category III. Typically, the goal is to preserve 4D bins in Category I and remove noise, clutter and ghost responses. However, it often erroneously eliminates genuine weak targets. ¶ [0436], The pre-processor then removes the rows of X′ with Pdb,k<th from X′ wherein th=CσN 2 is a predefined threshold value which is for example based on thermal noise variance σN 2. For example the radar system includes a temperature sensor configured to measure the ambient temperature and determines the threshold from the measured temperature. The result of this removal operation is an array X″∈R.sup.N×2 with N<<
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. ¶ [0527], According to various embodiments, in other words, a radar system proceeds on the basis of radar reception values (e.g. IQ samples) which lie above a thermal noise threshold. Thus, it may for example be ensured that weak targets are considered in the further processing (e.g. are detected) while keeping the amount of data to be processed, stored and communicated, e.g. in the radar baseband processing pipeline, low. The reception data values are for example generated by a radar sensor of the radar system. It should be noted that the further processing may include removal of clutter and ghost responses (e.g. followed by detection, perception etc.). Note: Examiner is interpreting removing the thermal noise as enhancing the target), [wherein a median value of all signal strengths of all bins of the normalized radar data map is set as a lower bound of signal strength of the normalized radar data map, when a signal strength of a bin of the normalized radar data map is greater than or equal to the lower bound, a signal strength of a corresponding bin of the enhanced radar data map is derived through a power-law transformation of the signal strength of the bin of the normalized radar data map, and when a signal strength of a bin of the normalized radar data map is less than the lower bound, a signal strength of a corresponding bin of the enhanced radar data map is set to zero]; and
converting the enhanced radar data map into the radar image through a Cartesian coordinate conversion, wherein the radar image is a two-dimensional image (See Chen, ¶ [0343], The coordinate transformation block 2502 may perform transformation of polar coordinates to cartesian coordinates (i.e. polar coordinate bins to cartesian coordinate bins). See Chen, ¶ [0420], based on a 3D point cloud or projecting 4D/3D reception data to a 2D plane such as range-Doppler or range-azimuth (optionally projected to X-Y plane in Cartesian space) for reduced storage and computational complexity).
However, Chen fail(s) to teach performing a post-process on the recognition result to eliminate a recognition error from the recognition result; wherein all signal strengths of the normalized radar data map range from 0 to 1; wherein a median value of all signal strengths of all bins of the normalized radar data map is set as a lower bound of signal strength of the normalized radar data map, when a signal strength of a bin of the normalized radar data map is greater than or equal to the lower bound, a signal strength of a corresponding bin of the enhanced radar data map is derived through a power-law transformation of the signal strength of the bin of the normalized radar data map, and when a signal strength of a bin of the normalized radar data map is less than the lower bound, a signal strength of a corresponding bin of the enhanced radar data map is set to zero.
Xie, working in the same field of endeavor, teaches: performing a post-process on the recognition result to eliminate a recognition error from the recognition result f(See Xie, ¶ [0040], To overcome the excess computation and energy problem, a Fast NMS algorithm can be used as follows. The basic idea is to introduce a filter between step 1 and step 2 of the above recited algorithm as step la, which vastly reduces the number of computations required by pre-emptively removing unnecessary bounding boxes from further processing).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Chen’s reference performing a post-process on the recognition result to eliminate a recognition error from the recognition result based on the method of Xie’s reference. The suggestion/motivation would have been to vastly reduce the computation and energy required for processing (See Xie, ¶ [0001–0008, 0040]).
However, Chen and Xie fail to teach wherein all signal strengths of the normalized radar data map range from 0 to 1; wherein a median value of all signal strengths of all bins of the normalized radar data map is set as a lower bound of signal strength of the normalized radar data map, when a signal strength of a bin of the normalized radar data map is greater than or equal to the lower bound, a signal strength of a corresponding bin of the enhanced radar data map is derived through a power-law transformation of the signal strength of the bin of the normalized radar data map, and when a signal strength of a bin of the normalized radar data map is less than the lower bound, a signal strength of a corresponding bin of the enhanced radar data map is set to zero.
Doi, working in the same field of endeavor, teaches: wherein all signal strengths of the normalized radar data map range from 0 to 1 (See Doi, ¶ [0053], The intensity of the reflected radar signal may be converted into a logarithmic scale in each pixel value of the radar image indicated by the radar image information, and further the intensity of the reflected radar signal after conversion into the logarithmic scale may be normalized so that the maximum value is 1 and the minimum value is 0).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Chen’s and Xie’s reference to wherein all signal strengths of the normalized radar data map range from 0 to 1 based on the method of Doi’s reference. The suggestion/motivation would have been to for accurate object detection in radar images (See Doi, ¶ [0003–0012]).
However, Chen, Xie and Doi fail to teach wherein a median value of all signal strengths of all bins of the normalized radar data map is set as a lower bound of signal strength of the normalized radar data map, when a signal strength of a bin of the normalized radar data map is greater than or equal to the lower bound, a signal strength of a corresponding bin of the enhanced radar data map is derived through a power-law transformation of the signal strength of the bin of the normalized radar data map, and when a signal strength of a bin of the normalized radar data map is less than the lower bound, a signal strength of a corresponding bin of the enhanced radar data map is set to zero.
Bargeron, working in the same field of endeavor, teaches: wherein a median value of all signal strengths of all bins of the normalized radar data map is set as a lower bound of signal strength of the normalized radar data map when a signal strength of a bin of the normalized radar data map is less than the lower bound, a signal strength of a corresponding bin of the enhanced radar data map is set to zero (See Bargeron, ¶ [0074], Thus, everything above the threshold (i.e., the median gray value) was a "1" and everything below the threshold (i.e., the median gray value) was a "0").
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Chen’s, Xie’s and Doi’s reference to wherein a median value of all signal strengths of all bins of the normalized radar data map is set as a lower bound of signal strength of the normalized radar data map when a signal strength of a bin of the normalized radar data map is less than the lower bound, a signal strength of a corresponding bin of the enhanced radar data map is set to zero based on the method of Bargeron’s reference. The suggestion/motivation would have been to decrease the discrimination between images (See Bargeron, ¶ [0075]).
However, Chen, Xie, Doi and Bargeron fail to teach when a signal strength of a bin of the normalized radar data map is greater than or equal to the lower bound, a signal strength of a corresponding bin of the enhanced radar data map is derived through a power-law transformation of the signal strength of the bin of the normalized radar data map.
Fleizach, working in the same field of endeavor, teaches: when a signal strength of a bin of the normalized radar data map is greater than or equal to the lower bound, a signal strength of a corresponding bin of the enhanced radar data map is derived through a power-law transformation of the signal strength of the bin of the normalized radar data map (See Fleizach, ¶ [0029], If the image pixel intensity value is greater than the threshold T, then at step 140, the gamma value used in the power-law transformation is calculated differently than at step 130. In this case, at step 140, the gamma value is a function that decreases with intensity, e.g., in accordance with the following equation. See also [FIG. 1]).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Chen’s, Xie’s, Doi’s and Bargeron’s reference to when a signal strength of a bin of the normalized radar data map is greater than or equal to the lower bound, a signal strength of a corresponding bin of the enhanced radar data map is derived through a power-law transformation of the signal strength of the bin of the normalized radar data map based on the method of Fleizach’s reference. The suggestion/motivation would have been to selectively apply power law transformation to specific areas (See Fleizach, ¶ [0003–0009]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Fleizach with Chen, Xie, Doi and Bargeron to obtain the invention as specified in claim 1.
Regarding claim 3, Chen in view of Xie, Doi, Bargeron, and further in view of Fleizach teaches the radar object recognition system of claim 1, wherein the object recognition model is a deep learning object recognition model, the deep learning object recognition model recognizes the radar image to obtain the recognition result, the recognition result comprises a plurality of bounding boxes in the radar image (See Chen, ¶ [0324], The first neural network 2001 may also output bounding boxes for the various objects, i.e. bounding boxes of the 4D-bins within the input data voxel belonging to the same object. The first neural network 2001 may also generate a radial velocity estimate and/or an orientation and/or 2D/3D bounding boxes for the object associated with a segment), [the bounding boxes have a plurality of confidence values respectively, and the confidence values represent confidence levels of the deep learning object recognition model of determining whether the bounding boxes comprises an object].
However, Chen fail(s) to teach the bounding boxes having a plurality of confidence values respectively, and the confidence values represent confidence levels of the deep learning object recognition model of determining whether the bounding boxes comprises an object.
Xie, working in the same field of endeavor, teaches: the bounding boxes have a plurality of confidence values respectively, and the confidence values represent confidence levels of the deep learning object recognition model of determining whether the bounding boxes comprises an object (See Xie, ¶ [0042], Get confidence_score, class_score, box_pos_info original data output from the deep learning model inference output.).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Chen’s reference the bounding boxes have a plurality of confidence values respectively, and the confidence values represent confidence levels of the deep learning object recognition model of determining whether the bounding boxes comprises an object based on the method of Xie’s reference. The suggestion/motivation would have been to vastly reduce the computation and energy required for processing (See Xie, ¶ [0001–0008, 0040]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Xie with Chen to obtain the invention as specified in claim 3.
Regarding claim 4, Chen in view of Xie, Doi, Bargeron and further in view of Fleizach teaches the radar object recognition system of claim 3, [wherein the post-process comprises overlap elimination, and the processor executes the overlap elimination through a non-maximum suppression of different classes based on the confidence values to eliminate overlapping bounding boxes from the bounding boxes].
However, Chen fail(s) to teach wherein the post-process comprises overlap elimination, and the processor executes the overlap elimination through a non-maximum suppression of different classes based on the confidence values to eliminate overlapping bounding boxes from the bounding boxes.
Xie, working in the same field of endeavor, teaches: wherein the post-process comprises overlap elimination, and the processor executes the overlap elimination through a non-maximum suppression of different classes based on the confidence values to eliminate overlapping bounding boxes from the bounding boxes (See Xie, ¶ [0040], To overcome the excess computation and energy problem, a Fast NMS algorithm can be used as follows. The basic idea is to introduce a filter between step 1 and step 2 of the above recited algorithm as step la, which vastly reduces the number of computations required by pre-emptively removing unnecessary bounding boxes from further processing.).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Chen’s reference wherein the post-process comprises overlap elimination, and the processor executes the overlap elimination through a non-maximum suppression of different classes based on the confidence values to eliminate overlapping bounding boxes from the bounding boxes based on the method of Xie’s reference. The suggestion/motivation would have been to vastly reduce the computation and energy required for processing (See Xie, ¶ [0001–0008, 0040]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Xie with Chen to obtain the invention as specified in claim 4.
Regarding claim 5, Chen in view of Xie, Doi, Bargeron and further in view of Fleizach teaches the radar object recognition system of claim 4, [wherein the non-maximum suppression of the different classes performed by the processor comprises operations of:
(A) sorting these confidence values;
(B) selecting one having a maximum confidence value from the bounding boxes to be a candidate in each round;
(C) when a value of an intersection between the candidate and at least one of remaining bounding boxes in the bounding boxes is greater than a predetermined threshold value, setting the confidence value of the at least one of the remaining bounding boxes to zero; and
(D) performing and repeating operations (B) to (C) on the remaining bounding boxes in a next round until a last bounding box serves as the candidate].
However, Chen fail(s) to teach wherein the non-maximum suppression of the different classes performed by the processor comprises operations of: (A) sorting these confidence values; (B) selecting one having a maximum confidence value from the bounding boxes to be a candidate in each round; (C) when a value of an intersection between the candidate and at least one of remaining bounding boxes in the bounding boxes is greater than a predetermined threshold value, setting the confidence value of the at least one of the remaining bounding boxes to zero; and (D) performing and repeating operations (B) to (C) on the remaining bounding boxes in a next round until a last bounding box serves as the candidate.
Xie, working in the same field of endeavor, teaches: wherein the non-maximum suppression of the different classes performed by the processor comprises operations of (See Xie, ¶ [0040], To overcome the excess computation and energy problem, a Fast NMS algorithm can be used as follows. The basic idea is to introduce a filter between step 1 and step 2 of the above recited algorithm as step la, which vastly reduces the number of computations required by pre-emptively removing unnecessary bounding boxes from further processing):
(A) sorting these confidence values (See Xie, ¶ [0037], In each run, the remaining scores are sorted);
(B) selecting one having a maximum confidence value from the bounding boxes to be a candidate in each round (See Xie, ¶ [0037], In each run, the remaining scores are sorted and the box having the highest score is selected for further processing);
(C) when a value of an intersection between the candidate and at least one of remaining bounding boxes in the bounding boxes is greater than a predetermined threshold value, setting the confidence value of the at least one of the remaining bounding boxes to zero (See Xie, ¶ [0037], With the remaining boxes, the intersection over union (IoU) for the boxes are calculated, with scores of boxes having a level of calculated result greater than a threshold (in this example, the threshold is set to 0.7) set to 0. If a plurality of boxes remain, the remaining boxes are passed to a second run where they are again sorted, high score selected, and IoU calculations done); and
(D) performing and repeating operations (B) to (C) on the remaining bounding boxes in a next round until a last bounding box serves as the candidate (See Xie, ¶ [0037], The process continues until there does not remain a plurality of boxes having a non-zero score as shown).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Chen’s reference wherein the non-maximum suppression of the different classes performed by the processor comprises operations of: (A) sorting these confidence values; (B) selecting one having a maximum confidence value from the bounding boxes to be a candidate in each round; (C) when a value of an intersection between the candidate and at least one of remaining bounding boxes in the bounding boxes is greater than a predetermined threshold value, setting the confidence value of the at least one of the remaining bounding boxes to zero; and (D) performing and repeating operations (B) to (C) on the remaining bounding boxes in a next round until a last bounding box serves as the candidate based on the method of Xie’s reference. The suggestion/motivation would have been to vastly reduce the computation and energy required for processing (See Xie, ¶ [0001–0008, 0040]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Xie with Chen to obtain the invention as specified in claim 5.
Regarding claim 6, claim 6 is rejected the same as claim 1 and the arguments similar to that presented above for claim 1 are equally applicable to the claim 6, and all of the other limitations similar to claim 1 are not repeated herein, but incorporated by reference. Furthermore, Chen teaches a radar object recognition method (See Chen, ¶ [0220], FIG. 13 illustrates a flowchart of an example method 1300 to perform object detection by the radar detector 1110, in accordance with at least one aspect described in the present disclosure).
Regarding claim 8, claim 8 is rejected the same as claim 3 and the arguments similar to that presented above for claim 3 are equally applicable to the claim 8, and all of the other limitations similar to claim 3 are not repeated herein, but incorporated by reference.
Regarding claim 9, claim 9 is rejected the same as claim 4 and the arguments similar to that presented above for claim 4 are equally applicable to the claim 9, and all of the other limitations similar to claim 4 are not repeated herein, but incorporated by reference.
Regarding claim 10, claim 10 is rejected the same as claim 5 and the arguments similar to that presented above for claim 5 are equally applicable to the claim 10, and all of the other limitations similar to claim 5 are not repeated herein, but incorporated by reference.
Regarding claim 11, claim 11 is rejected the same as claim 1 and the arguments similar to that presented above for claim 1 are equally applicable to the claim 11, and all of the other limitations similar to claim 1 are not repeated herein, but incorporated by reference. Furthermore, Chen teaches a non-transitory computer readable medium to store a plurality of instructions for commanding a computer to execute a radar object recognition method (See Chen, ¶ [0191], The radar device 1102 may include a radar processor 1104 and a radar detector 1110 that may generate an error value 1120. See also [FIG. 3], 309 Radar Processor).
Regarding claim 13, claim 13 is rejected the same as claim 3 and the arguments similar to that presented above for claim 3 are equally applicable to the claim 13, and all of the other limitations similar to claim 3 are not repeated herein, but incorporated by reference.
Regarding claim 14, claim 14 is rejected the same as claim 4 and the arguments similar to that presented above for claim 4 are equally applicable to the claim 14, and all of the other limitations similar to claim 4 are not repeated herein, but incorporated by reference.
Regarding claim 15, claim 15 is rejected the same as claim 5 and the arguments similar to that presented above for claim 5 are equally applicable to the claim 15, and all of the other limitations similar to claim 5 are not repeated herein, but incorporated by reference.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Castorina et al. (US 7881554 B2) teaches a luminance intensity of pixels of an input digital image is corrected for generating a corrected digital image. A luminance of each pixel is calculated as a function of the luminance of a corresponding pixel in an original image according to a parametric function. A mask of the input digital image to be corrected is generated according to a bilateral filtering technique. For each pixel of the input digital image, a respective value of at least one parameter of the parametric function is established based upon the luminance of a corresponding pixel of the mask.
Gao et al. (See NPL attached, “Image denoising in the presence of non-Gaussian, power-law noise”) teaches assign zero to a pixel if its value is larger than a chosen threshold. It turns out there is a generic choice for the threshold, independent of the testing images. For normalized clean images with pixel values ranging from 0 to 1, the generic threshold value for noisy images is 2, noting that with unbounded power-law noise, the pixel values in the resulting noisy image can be much larger than 2.
Mohapatra et al. (See NPL attached, “Post Processing Techniques for Inverse Synthetic Aperture Radar Imaging”) teaches where K is a constant, that can be tuned based on experimentation. Typical values lie between 1 to 5. The pixel value above threshold are retained with values reduced by T while those below threshold are made zero.
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 DION J SATCHER whose telephone number is (703)756-5849. The examiner can normally be reached Monday - Thursday 5:30 am - 2:30 pm, Friday 5:30 am - 9:30 am PST.
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/DION J SATCHER/Patent Examiner, Art Unit 2676
/Henok Shiferaw/Supervisory Patent Examiner, Art Unit 2676
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