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 .
Examiner’s Note
To help the reader, examiner notes in this detailed action claim language is in bold, strikethrough limitations are not explicitly taught and language added to explain a reference mapping are isolated from quotations via square brackets.
Response to Arguments
Applicant's arguments filed 10/13/2025 have been fully considered but they are not persuasive. An explanation is provided below.
Applicant alleges on p.8:
Wang discloses a target detection method using a threshold. However, as acknowledged in the Office Action, Wang does not disclose at least the following limitations of Claim 1: "compare the detected unit data to a figure of merit to generate a priority score; assign the priority score to the detected unit data; sort the detected unit data based on the priority score to generate a sorted list of detected unit data; update the baseline detection threshold based on the sorted list; and use the feature set associated with the sorted list of detected unit data for further processing." (Office Action, p. 4 (emphasis added)). Thus, Wang lacks several limitations including the figure of merit, scoring, sorting, and most notably the feedback loop from the sorted list to updated detection threshold.
The Examiner respectfully disagrees. Applicant notes several limitations allegedly not taught. However wang in view of Parikh teaches a figure of merit (Wang 0065 “maximum and minimum percentage thresholds can be set based on extremely noisy and quiet scenarios for the environment of the CUT, respectively. The radar system 10 can then adapt the percentage threshold between these maximum and minimum percentage thresholds based on the number of false plots or false tracks”; Parikh 0037 “the track-management component may use values of parameters associated with the detected objects to determine scores for the detected objects, where the scores are associated with the priorities”),
scoring (Wang 0066 “The threshold coefficient can also be determined by examining the false track ratio as well and selecting the threshold coefficient such that the false track ratio is at an acceptable level.”; Parikh 0039 “The track-management component may use the scores to generate data (e.g., data representing a list) that sorts the detected objects based on the scores. For example, the detected object with the highest score may be first, followed by the detected object with the second highest score, followed by the detected object with the third highest score”),
sorting (Parikh 0056 “For example, the sorting data 116 may represent a list of the detected objects that is arranged based on how the sorting component 114 sorted the detected objects (e.g., in descending order).”; Parikh 0039 quoted above, and
feedback loop from the sorted list to updated detection threshold (Wang 0080 “In alternative embodiments, additional logic can be added to the comparator 162 to incorporate some feedback in the threshold decision so as to make the threshold more accurate.”; 0068 “even though the object 204(3) was selected not be tracked at the fifth time T(5), the object 204(3) was again included in the sorted list 208(6) at the sixth time T(6). This is because the priority may have changed between the fifth time T(5) and the sixth time T(6).”; 0057 “The process 100 may include a selection component 118 processing the object sorting data 116 and, based on the processing, selecting one or more detected objects for tracking and/or selecting one or more detected objects for not tracking. For example, the selection component 118 may select a threshold number of the detected objects that includes the highest scores for tracking and select the remaining objects for not tracking. In such an example, the threshold number of detected objects may include the same threshold number of objects used by the threshold component 110 or may include a different threshold number of detected objects.”).
Furthermore, Applicant alleges on p.9 that ‘Parikh operates at a higher semantic level (object tracking) after detection. It does not teach how to integrate ranking/sorting/prioritizing logic into the object detection threshold selection stage at the radar cell level. There is no teaching or suggestion in Parikh of feeding tracking-level priority scores back to influence detection thresholds at the radar cell level’.
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Here, Wang teaches Cell Under Test features (Abstract). Furthermore, the ranking/sorting/prioritizing logic happens after detection, similar to Parikh.
Furthermore, Applicant alleges on p.11 “even assuming a person of ordinary skill in the art had access to both references, there would be no teaching or motivation to combine them in the manner required to arrive at the claimed invention. The Office Action lacks the necessary "reasoned explanation.””
In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, both Wang and Parikh teach similar object tracking features using radar.
Furthermore, Applicant alleges on p.11 Claim 8 has been further amended to emphasis other aspects of the invention by removing the limitation "range data, doppler data" from line 6 of the claim and emphasizing the use of "SNR data, noise data, or power data" in parameter data. This refinement further distinguishes the claim over the cited references. None of the cited references discloses "determining a feature set for the cell under test, associated with multiple cells, the feature set specifying parameter data including at least one of SNR data, noise data, or power data." (emphasis added).”
The Examiner disagrees as Wang teaches 0039 “In general, the detector 32 looks for a peak at the CUT with respect to a portion of the range-Doppler values that surround the CUT and form a window. This is done by comparing the amplitude of the CUT with the amplitudes of the neighboring cells. The detection is made difficult due to the different types of noise that can be encountered within the window based on the range and azimuth of the CUT. Various CFAR detection methods are discussed herein that can deal with these various forms of noise to provide improved detection.” Here, the amplitude corresponds to power data.
Furthermore, Applicant alleges on p.11 “Claim 15 has been further amended to recite "select a baseline detection threshold based on a noise power estimate from neighboring cells." (Claim 15, (emphasis added)). This limitation is not taught or suggested in either Wang or Parikh, further distinguishing Claim 15 over the prior art.”
The Examiner disagrees as Wang teaches 0070 “The Cell Averaging (CA)-CFAR detection method assumes that the local noise environment is homogeneous and determines the background noise level in the reference window by averaging the cells in the reference window. The CA-CFAR detection method then adds a constant to the estimated background noise level or multiplies the estimated background noise level by a certain factor to obtain the threshold that is used for detection.”; claim 1 “obtaining an average value of a percentage of the ordered range-Doppler radar values based on a percentage threshold; multiplying the average value by a threshold coefficient to obtain a first threshold value”.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 20130201054 hereinafter Wang) in view of Parikh et al. (US 20240428596 hereinafter Parikh).
Regarding claim 1, Wang teaches A radar system for performing object detection on a cell under test (CUT) associated with range-Doppler radar values, comprising (0004 “in one aspect, in at least one embodiment described herein, there is provided a method of performing target detection on a Cell Under Test (CUT) associated with a reference window of range-Doppler radar values.”):
a computing device processor; and a memory device including instructions that (0007 “a processor coupled to the input and configured to process the range-Doppler radar values”), when executed by the computing device processor, enables the radar system to: determine a feature set for the cell under test (0009 “perform target detection on the CUT by classifying a local noise environment of the CUT using a first reference window”),
the cell under test associated with a plurality of cells, the feature set specifying parameter data including at least one of range data, doppler data, SNR data, noise data, or power data (0004 “there is provided a method of performing target detection on a Cell Under Test (CUT) associated with a reference window of range-Doppler radar values.”; 0039 “In general, the detector 32 looks for a peak at the CUT with respect to a portion of the range-Doppler values that surround the CUT and form a window. This is done by comparing the amplitude of the CUT with the amplitudes of the neighboring cells. The detection is made difficult due to the different types of noise that can be encountered within the window based on the range and azimuth of the CUT. Various CFAR detection methods are discussed herein that can deal with these various forms of noise to provide improved detection.”);
select a baseline detection threshold based on the feature set (0004 “The method comprises ordering the range-Doppler radar values in the reference window to produce ordered range-Doppler radar values obtaining an average value of a percentage of the ordered range-Doppler radar values based on a percentage threshold”; 0054 “The uncensored cells that remain form the background for this reference window and the new CFAR threshold will be the mean level of the remaining uncensored cells multiplied by a preset scaling factor.”);
analyze parameter data associated with the cell under test with respective parameter data associated with neighboring cells to generate detected unit data indicative of an object detection (0008 “The detector comprises an input for receiving the range-Doppler radar values of the reference window and the CUT; a processor coupled to the input and configured to process the range-Doppler radar values of the reference window and the CUT to provide an indication of target detection by ordering the range-Doppler radar values in the reference window to produce ordered range-Doppler radar values”);
compare the detected unit data to the baseline detection threshold to detect an object (0067 “At step 82, the range-Doppler radar value in the CUT is compared with the threshold value to detect a possible radar target, hi particular, a target is detected if the radar value associated with the CUT is larger than the threshold value.”);
Wang does not explicitly teach the strikethrough limitations. However, in a related field of endeavor, Parikh teaches
compare the detected unit data to a figure of merit to generate a priority score; assign the priority score to the detected unit data (0060 “As shown, the scoring component 114 may determine scores 202(1)-(33) (also referred to singularly as “score 202” or in plural as “scores 202”) for detected objects 204(1)-(10) (also referred to singularly as “object 204” or in plural as “objects 204”) over a period of time 206.”);
sort the detected unit data based on the priority score to generate a sorted list of detected unit data (0056 “For example, the sorting data 116 may represent a list of the detected objects that is arranged based on how the sorting component 114 sorted the detected objects (e.g., in descending order).”);
update the baseline detection threshold based on the sorted list; and use the feature set associated with the sorted list of detected unit data for further processing (0058 “In some examples, the selection component 118 may additionally, and/or alternatively, use a scoring threshold when selecting the detected objects. For instance, the selection component 118 may determine that detected objects that include a score that satisfies (e.g., is equal to or greater than) a threshold score may always be tracked.”; 0034 “While tracking an object, the system(s) may update the state of the tracked object using state information associated with a detected object that corresponds to the tracked object”).
Furthermore, it would have been obvious to one of ordinary skill in the art, at the time of filing of the instant application, to include the teachings of Parikh with the teachings of Wang. One would have been motivated to do so in order to advantageously improve processing and reliability (Parikh 0246). Further still, the Supreme Court in KSR International Co. v. Teleflex Inc. (KSR), 550 U.S. 398, 82 USPQ2d 1385 (2007) provides that combining prior art elements according to known methods to yield predictable results may render a claimed invention obvious over such combination. Here, Parikh merely teaches that it is well-known to incorporate the particular priority scoring features. Since Wang and Parikh disclose similar object tracking using radar, one of ordinary skill in the art would recognize that the combination of elements here has previously been executed according to known methods, thereby evidencing that such combination would yield predictable results.
Regarding claim 2, the cited prior art teaches The radar system of claim 1, wherein the figure of merit is updated based on radar system constraints including environment considerations for updating the figure of merit and threshold (Parikh Abstract “the number of objects that are tracked may be limited to a threshold number of objects when a number of detected objects exceeds a threshold.”; 0052 “the scoring component 112 may determine a respective score for each detected objects, where the respective score indicates a priority for tracking the detected object.”).
Furthermore, it would have been obvious to one of ordinary skill in the art, at the time of filing of the instant application, to include the teachings of Parikh with the teachings of Wang. One would have been motivated to do so in order to advantageously improve processing and reliability (Parikh 0246). Further still, the Supreme Court in KSR International Co. v. Teleflex Inc. (KSR), 550 U.S. 398, 82 USPQ2d 1385 (2007) provides that combining prior art elements according to known methods to yield predictable results may render a claimed invention obvious over such combination. Here, Parikh merely teaches that it is well-known to incorporate the particular priority scoring features. Since Wang and Parikh disclose similar object tracking using radar, one of ordinary skill in the art would recognize that the combination of elements here has previously been executed according to known methods, thereby evidencing that such combination would yield predictable results.
Regarding claim 3, The radar system of claim 2, wherein the radar system constraints include one of a time constraint or quantity (Parikh 0055 “For instance, the scoring component 112 may determine the score based on an average of the current score and one or more (e.g., a threshold number) of the previous scores.”).
Furthermore, it would have been obvious to one of ordinary skill in the art, at the time of filing of the instant application, to include the teachings of Parikh with the teachings of Wang. One would have been motivated to do so in order to advantageously improve processing and reliability (Parikh 0246). Further still, the Supreme Court in KSR International Co. v. Teleflex Inc. (KSR), 550 U.S. 398, 82 USPQ2d 1385 (2007) provides that combining prior art elements according to known methods to yield predictable results may render a claimed invention obvious over such combination. Here, Parikh merely teaches that it is well-known to incorporate the particular priority scoring features. Since Wang and Parikh disclose similar object tracking using radar, one of ordinary skill in the art would recognize that the combination of elements here has previously been executed according to known methods, thereby evidencing that such combination would yield predictable results.
Regarding claim 4, the cited prior art teaches The radar system of claim 1, wherein one of the baseline detection threshold or the figure of merit is updated to satisfy a system performance metric (Parikh 0058 “the selection component 118 may additionally, and/or alternatively, use a scoring threshold when selecting the detected objects. For instance, the selection component 118 may determine that detected objects that include a score that satisfies (e.g., is equal to or greater than) a threshold score may always be tracked. For a first example, if the threshold number of objects to track includes one hundred objects and the number of detected objects includes one hundred fifty objects, the selection component 118 may initially select the top one hundred detected objects with the highest one hundred scores to track.”).
Furthermore, it would have been obvious to one of ordinary skill in the art, at the time of filing of the instant application, to include the teachings of Parikh with the teachings of Wang. One would have been motivated to do so in order to advantageously improve processing and reliability (Parikh 0246). Further still, the Supreme Court in KSR International Co. v. Teleflex Inc. (KSR), 550 U.S. 398, 82 USPQ2d 1385 (2007) provides that combining prior art elements according to known methods to yield predictable results may render a claimed invention obvious over such combination. Here, Parikh merely teaches that it is well-known to incorporate the particular priority scoring features. Since Wang and Parikh disclose similar object tracking using radar, one of ordinary skill in the art would recognize that the combination of elements here has previously been executed according to known methods, thereby evidencing that such combination would yield predictable results.
Regarding claim 5, the cited prior art teaches The radar system of claim 4, wherein the system performance metric includes an amount detected objects (Parikh 0055 “For instance, the scoring component 112 may determine the score based on an average of the current score and one or more (e.g., a threshold number) of the previous scores.”).
Furthermore, it would have been obvious to one of ordinary skill in the art, at the time of filing of the instant application, to include the teachings of Parikh with the teachings of Wang. One would have been motivated to do so in order to advantageously improve processing and reliability (Parikh 0246). Further still, the Supreme Court in KSR International Co. v. Teleflex Inc. (KSR), 550 U.S. 398, 82 USPQ2d 1385 (2007) provides that combining prior art elements according to known methods to yield predictable results may render a claimed invention obvious over such combination. Here, Parikh merely teaches that it is well-known to incorporate the particular priority scoring features. Since Wang and Parikh disclose similar object tracking using radar, one of ordinary skill in the art would recognize that the combination of elements here has previously been executed according to known methods, thereby evidencing that such combination would yield predictable results.
Regarding claim 6, the cited prior art teaches The radar system of claim 1 wherein the instructions, when executed by the computing device processor, further enables the radar system to: process detected unit data satisfying a priority score threshold (Parikh 0035 “the track-management component may begin to prioritize the detected objects based on the number of detected objects satisfying (e.g., being equal to or greater than) a threshold number of objects.”).
Furthermore, it would have been obvious to one of ordinary skill in the art, at the time of filing of the instant application, to include the teachings of Parikh with the teachings of Wang. One would have been motivated to do so in order to advantageously improve processing and reliability (Parikh 0246). Further still, the Supreme Court in KSR International Co. v. Teleflex Inc. (KSR), 550 U.S. 398, 82 USPQ2d 1385 (2007) provides that combining prior art elements according to known methods to yield predictable results may render a claimed invention obvious over such combination. Here, Parikh merely teaches that it is well-known to incorporate the particular priority scoring features. Since Wang and Parikh disclose similar object tracking using radar, one of ordinary skill in the art would recognize that the combination of elements here has previously been executed according to known methods, thereby evidencing that such combination would yield predictable results.
Regarding claim 7, the cited prior art teaches The radar system of claim 1 wherein the instructions, when executed by the computing device processor, further enables the radar system to: use a machine learning based approach to determine the baseline detection threshold for the feature set (Parikh 0083 “the detection component 402 may use a machine learning approach (e.g., scale-invariant feature transform (SIFT), histogram of oriented gradients (HOG), etc.) followed by a support vector machine (SVM) to classify objects depicted in images represented by the sensor data 404.”).
Furthermore, it would have been obvious to one of ordinary skill in the art, at the time of filing of the instant application, to include the teachings of Parikh with the teachings of Wang. One would have been motivated to do so in order to advantageously improve processing and reliability (Parikh 0246). Further still, the Supreme Court in KSR International Co. v. Teleflex Inc. (KSR), 550 U.S. 398, 82 USPQ2d 1385 (2007) provides that combining prior art elements according to known methods to yield predictable results may render a claimed invention obvious over such combination. Here, Parikh merely teaches that it is well-known to incorporate the particular priority scoring features. Since Wang and Parikh disclose similar object tracking using radar, one of ordinary skill in the art would recognize that the combination of elements here has previously been executed according to known methods, thereby evidencing that such combination would yield predictable results.
Regarding claim 8, claim 8 recites substantially the same limitations as claim 1. Therefore, claim 8 is rejected for substantially the same reasons as claim 1.
Regarding claim 9, claim 9 recites substantially the same limitations as claim 2. Therefore, claim 9 is rejected for substantially the same reasons as claim 2.
Regarding claim 10, claim 10 recites substantially the same limitations as claim 3. Therefore, claim 10 is rejected for substantially the same reasons as claim 3.
Regarding claim 11, claim 11 recites substantially the same limitations as claim 4. Therefore, claim 11 is rejected for substantially the same reasons as claim 4.
Regarding claim 12, claim 12 recites substantially the same limitations as claim 5. Therefore, claim 12 is rejected for substantially the same reasons as claim 5.
Regarding claim 13, claim 13 recites substantially the same limitations as claim 6. Therefore, claim 13 is rejected for substantially the same reasons as claim 6.
Regarding claim 14, claim 14 recites substantially the same limitations as claim 7. Therefore, claim 14 is rejected for substantially the same reasons as claim 7.
Regarding claim 15, claim 15 recites substantially the same limitations as claim 1. Therefore, claim 15 is rejected for substantially the same reasons as claim 1. Wang further teaches select a baseline detection threshold based on a noise power estimate from neighboring cells (0070 “The Cell Averaging (CA)-CFAR detection method assumes that the local noise environment is homogeneous and determines the background noise level in the reference window by averaging the cells in the reference window. The CA-CFAR detection method then adds a constant to the estimated background noise level or multiplies the estimated background noise level by a certain factor to obtain the threshold that is used for detection.”; claim 1 “obtaining an average value of a percentage of the ordered range-Doppler radar values based on a percentage threshold; multiplying the average value by a threshold coefficient to obtain a first threshold value”).
Regarding claim 16, claim 16 recites substantially the same limitations as claim 2. Therefore, claim 16 is rejected for substantially the same reasons as claim 2.
Regarding claim 17, claim 17 recites substantially the same limitations as claim 3. Therefore, claim 17 is rejected for substantially the same reasons as claim 3.
Regarding claim 18, claim 18 recites substantially the same limitations as claim 4. Therefore, claim 18 is rejected for substantially the same reasons as claim 4.
Regarding claim 19, claim 19 recites substantially the same limitations as claim 5. Therefore, claim 19 is rejected for substantially the same reasons as claim 5.
Regarding claim 20, claim 20 recites substantially the same limitations as claim 6. Therefore, claim 20 is rejected for substantially the same reasons as claim 6.
Conclusion
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.
The prior art made of record and not relied upon is considered pertinent to application’s disclosure:
Mostajeran et al. (US 20210200209) discloses “In one embodiment, a method includes accessing a set of data points captured using a radar system of the vehicle. Each data point is associated with at least three measurements include a Doppler measurement, a range measurement, and an azimuth measurement in reference to the radar system. The method also includes clustering the set of data points into one or more first clusters based on a first pair of the three measurements associated with each of the data points (See abstract)”
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/ISMAAEEL A. SIDDIQUEE/
Examiner, Art Unit 3648
/William Kelleher/Supervisory Patent Examiner, Art Unit 3648