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 .
Status of Claims
Claims 1-20 pending.
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 nonobviousness.
Claim(s) 1-2, 5-6, 15-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20230090211 A1 to Alsindi in view of US 20210117659 A1 to FOROOZAN and further in view of US 20230068523 A1 to Santra (‘523).
Regarding claim 1,
US 20230090211 A1 to Alsindi teaches:
A method, comprising:
receiving a signal; (Fig. 5 [0037] – “The target detection module 502 may process the received radar signals”)
estimating, using a processing circuit, a range spectrum, a Doppler spectrum, and an angle spectrum of the signal to generate a radar cube; (Figs. 5-6; [0041] – “radar data (e.g., a radar data cube from the wireless transceiver 500 of FIG. 5, the radar data cube including processed received signals that contain range, angle and/or doppler information for all potential targets) may be provided to a target location estimator 600 (e.g., implemented as or as part of the target detection module 502 of FIG. 5)”)
detecting, using the processing circuit, a point cloud corresponding to the radar cube; ([0041] – “In one or more implementations, the target location estimator 600 may estimate target locations for all potential targets by performing a spectral estimation operation (e.g., a 2D fast Fourier transform (FFT)). The output of the target location estimator 600 may be provided to a Constant False Alarm Rate (CFAR) detector to extract the detection point cloud described above in connection with FIG. 5. As shown in FIG. 6, the target detections (e.g., the point cloud) may be passed from the target location estimator 600”)
tracking, using the processing circuit, one or more tracked objects corresponding to the point cloud, (Figs. 5-6; [0041] – “the target detections (e.g., the point cloud) may be passed from the target location estimator 600 to a data association and tracking module 602. The data association and tracking module 602 may track one or more of the detected targets over time, as objects.”) (lined through limitations correspond to limitations not taught by reference)
generating, using the processing circuit, a micro-Doppler spectrogram for each tracked object of the one or more tracked objects; ([0041] – “Using the beam formed radar signals, for each tracked target, the RCS, a micro-doppler signal, and a high-resolution range-azimuth may be estimated for each frame or sample time.”)
generating, using the processing circuit, a micro-range spectrogram for each tracked object of the one or more tracked objects; and
classifying, using the processing circuit, each tracked object of the one or more tracked objects based on the micro-Doppler spectrogram and the micro-range spectrogram for the one or more tracked objects. ([0041] – “As shown, the object feature data 510 of a given target may then be passed to the classifier 512 (e.g., to the machine learning model 514) to classify the detected object.”)
US 20210117659 A1 to FOROOZAN teaches:
tracking, using the processing circuit, one or more tracked objects corresponding to the point cloud, to generate a centroid, a boundary, and a track velocity for each tracked object of the one or more tracked objects; ([0092] – “In step 171, a radar signal (e.g., Doppler radar) is used to generate point cloud data associated with an object (a potentially moving object)… subsequently, multi-object tracking is performed at step 174. In one example, an algorithm calculates the centroid of each cluster and estimates the boundaries of the target, sending this information to the multi-object tracking block.” [0066-72] – “final output of the multi-object tracking may have… the coordinates of the centroid of the bounding box… bounding box information… the velocity of the target”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have applied FOROOZAN’s known technique to Alsindi’s known method ready for improvement to yield predictable results. Such a finding is proper because (1) Alsindi teaches a base method of determining and tracking objects, performing further determinations (e.g., spectrograms of tracked targets), then classifying tracked objects based on these determinations; (2) FOROOZAN teaches a specific technique of determining centroid, boundaries, and velocity of tracked targets, then performing classification / activity recognition of tracked objects; (3) one of ordinary skill in the art would have recognized that applying the known technique would have yielded predictable results and resulted in an improved system with further detail of tracked objects; and (4) no additional findings based on the Graham factual inquiries are necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness (See MPEP 2143).
US 20230068523 A1 to Santra teaches:
generating, using the processing circuit, a micro-range spectrogram for each tracked object of the one or more tracked objects; (Figs. 10-11, 13-14; [0146] – “FIG. 11 and FIG. 12 illustrate the positional time spectrograms 101-104, 101*-104* for range, velocity, azimuthal and elevation angle, respectively.” [0152] – “From each MTI-filtered RDI, a range and a Doppler vector can be extracted (cf. FIG. 13: 7020 and 7025; FIG. 14: boxes 7120 and 7125). The selected vectors—within the gesture duration 250 at which a gesture 501-510 is detected—are aggregated/concatenated and form the range and Doppler spectrograms respectively. The range vectors and correspondingly the Doppler vectors are selected based on marginalization along each axis, they are appended across time to generate the range spectrogram and Doppler spectrogram respectively (cf. FIG. 14: boxes 7130 and 7135).”) and
classifying, using the processing circuit, each tracked object of the one or more tracked objects based on the micro-Doppler spectrogram and the micro-range spectrogram for the one or more tracked objects. ([0102] – “positional time spectrograms can be used as input to the VAENN 111. For instance, one or more positional time spectrograms can be used which are selected from the group including: range time spectrogram, velocity time spectrogram, an azimuthal angle time spectrogram, or an elevation angle time spectrogram.” [0111] – “VAENN 111 performs gesture classification based on the measurement data 64. The VAENN 111 provides, as output, a label 115 that is indicative of the particular gesture class 520 of the gesture recognized in the positional time spectrograms.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have applied Santra (‘523)’s known technique to Alsindi’s known method ready for improvement to yield predictable results. Such a finding is proper because (1) Alsindi teaches a base method of determining micro-Doppler spectrograms of tracked targets then classifying tracked objects based on these determinations; (2) Santra (‘523) teaches a specific technique of inputting both range and doppler spectrograms for gesture classification; (3) one of ordinary skill in the art would have recognized that applying the known technique would have yielded predictable results and resulted in improved classification due to increased input to the classification algorithm; and (4) no additional findings based on the Graham factual inquiries are necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness (See MPEP 2143).
Regarding claim 2,
Alsindi in view of FOROOZAN and further in view of Santra (‘523) teaches the invention as claimed and discussed above.
Alsindi further teaches:
The method of claim 1, wherein generating the micro-Doppler spectrogram of a first tracked object of the one or more tracked objects includes summing signal strengths in range-angle indices ([0047] – “range-angle profiles extracted from radar returns”) of the boundary of the first tracked object ([0041] – “The data association and tracking module 602 may track one or more of the detected targets over time, as objects. As shown, the data association and tracking module 602 may provide a target location estimate for each tracked object at a time-sample, to a beam former 606. The beam former 606 may apply beam forming to the radar data cube using the provided target location, to isolate a given target corresponding to the target location from the other targets detected by the target location estimator 600.”) to generate range-angle sums, (Fig. 8; [0041] – “Using the beam formed radar signals, for each tracked target, the RCS, a micro-doppler signal, and a high-resolution range-azimuth may be estimated for each frame or sample time.” Range-angle sum corresponds to sum of signal strengths of the range-angle profile for a given velocity/doppler value, which corresponds to the value of the given velocity index in Fig. 8) so that range-angle sums corresponding to different Doppler indices of the boundary correspond to different frequency bins of a one dimensional time slice of the micro-Doppler spectrogram of the first tracked object; (Fig. 8 – Velocity bins in Fig. 8 correspond to frequency bins of the micro-Doppler spectrogram. 1D time slice corresponds to a slice at a one time index of Fig. 8; [0041] – “micro-doppler signal, and a high-resolution range-azimuth may be estimated for each frame or sample time.”) and
Alsindi does not explicitly teach the additional elements of the claim.
Santra (‘523) further teaches:
wherein generating the micro-range spectrogram of the first tracked object includes summing signal strengths in Doppler-angle indices of the boundary of the first tracked object to generate Doppler-angle sums, so that Doppler-angle sums corresponding to different range indices of the boundary correspond to different frequency bins of the one dimensional time slice of the micro-range spectrogram of the first tracked object (Figs. 10-14)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have applied Santra (‘523)’s known technique to Alsindi’s known method ready for improvement to yield predictable results. Such a finding is proper because (1) Alsindi teaches a base method of determining micro-Doppler spectrograms of tracked targets then classifying tracked objects based on these determinations; (2) Santra (‘523) teaches a specific technique of inputting both range and doppler spectrograms for gesture classification; (3) one of ordinary skill in the art would have recognized that applying the known technique would have yielded predictable results and resulted in improved classification due to increased input to the classification algorithm; and (4) no additional findings based on the Graham factual inquiries are necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness (See MPEP 2143).
Regarding claim 5,
Alsindi in view of FOROOZAN and further in view of Santra (‘523) teaches the invention as claimed and discussed above.
Alsindi further teaches:
The method of claim 1, further comprising extracting, using the processing circuit, feature values from the micro-Doppler spectrogram for a first tracked object of the one or more tracked objects to form one dimensional feature vectors, and performing the classifying of the first tracked object based on the ([0038-39] – “for a given target of interest that is identified by the TOI identifier 504, the feature extractor 508 may extract object feature data 510 (e.g., surface features, including static features and/or or time-varying features) from the radar signals. Examples of object feature data 510 (e.g., surface features) that can be extracted from radar signals include a time-varying radar cross-section (RCS), a micro-doppler feature, and high-resolution range/angle information.”)
A modification of the combination of Alsindi to use a 1D feature vector would have been obvious to try as one of a finite number of identified, predictable solutions with a reasonable expectation of success. Such a finding is proper because (1) at the time of the invention, there had been a recognized problem or need in the art, in this case a need to choose the dimension of the feature vector taught by Alsindi; (2) there are a finite number of identified, predictable potential solutions to the recognized need or problem; (3) one of ordinary skill in the art could have pursued the known potential solutions with a reasonable expectation of success; and (4) no additional findings based on the Graham factual inquiries are necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness (See MPEP 2143).
Regarding claim 6,
Alsindi in view of FOROOZAN and further in view of Santra (‘523) teaches the invention as claimed and discussed above.
Alsindi further teaches:
The method of claim 5, wherein the features extracted to form the one dimensional feature vectors include one or more of:
a normalized bandwidth power, a mean Doppler, a median Doppler, an upper Doppler intercept, a lower Doppler intercept, or a spectral entropy. ([0038-39, 44, 48] – “mean value 802 that is doppler shifted from mean zero 803 by an amount that corresponds to the mean speed of the walking user. As shown, the measured velocity of the glass door also includes micro-doppler features such as oscillations 804 that oscillate with a frequency that corresponds to the cadence of the device user's gait... FIG. 10 illustrates a transform 1000 of the micro-doppler feature of FIG. 8, in which the cadence 1002 can be seen, and can be seen to be substantially constant over time. The cadence 1002 may correspond to the frequency of the oscillations 804 of FIG. 8, and to the cadence of the user's steps as the user walks toward the glass door.” Mean doppler values, cadence correspond to micro doppler features as described in [0038-39])
Regarding claim(s) 15, 16, and 19,
Claim(s) 15, 16, 19 is/are claims corresponding to claim(s) 5, 6, and 1, respectively. Accordingly, the Examiner’s remarks and application of the prior art with respect to claim(s) 15, 16, 19 are substantially the same as those made above with respect to claim(s) 5, 6, and 1,
Claim(s) 7, 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20230090211 A1 to Alsindi in view of US 20210117659 A1 to FOROOZAN and further in view of US 20230068523 A1 to Santra (‘523) and further in view of US 20090012398 A1 to Zhang.
Regarding claim 7,
Alsindi in view of FOROOZAN and further in view of Santra (‘523) teaches the invention as claimed and discussed above.
Alsindi in view of FOROOZAN and further in view of Santra (‘523) does not teach the additional elements of the claim.
Zhang teaches:
The method of claim 1, further comprising interpolating spectral values of a missing data frame with respect to a first tracked object of the one or more tracked objects ([abstract] – “storing Doppler signals before and after a gap; analyzing spectral characteristics of the Doppler signals to be filled; judging whether the Doppler signals are to be frequency compensated according to the spectral parameters; compensating the Doppler signals; and filling the gap by means of weighting and superposing the frequency compensated Doppler signals to be filled and the original Doppler signals before and after the gap based on the judging result. According to the method of the present invention, the Doppler signals before and after the gap are first subjected to frequency compensation, and then weighted and superposed with the acquired Doppler signals, thus obtaining a continuous spectrogram and audio output and maintaining the original spectral characteristics of the Doppler signal.” [0021] – “estimating mean frequencies of the Doppler signals in the gap from the mean frequencies of the Doppler signals before and after the gap by means of interpolation;” [0045] – “The system determines the gap length… the Doppler processing unit judges whether there is a signal gap from the length of the received Doppler signal.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have applied Zhang’s known technique to Alsindi’s known method ready for improvement to yield predictable results. Such a finding is proper because (1) Alsindi teaches a base method of determining micro-Doppler spectrograms of tracked targets then classifying tracked objects based on these determinations; (2) Zhang teaches a specific technique of supplementing gaps in Doppler signals to improve spectrograms; (3) one of ordinary skill in the art would have recognized that applying the known technique would have yielded predictable results and resulted in improved spectrograms ; and (4) no additional findings based on the Graham factual inquiries are necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness (See MPEP 2143).
A modification of the combination of Alsindi in view of Zhang to use a threshold number of point cloud points to determine a data gap would have been obvious to try as one of a finite number of identified, predictable solutions with a reasonable expectation of success. Such a finding is proper because (1) at the time of the invention, there had been a recognized problem or need in the art, in this case a need to choose gap criteria; (2) there are a finite number of identified, predictable potential solutions to the recognized need or problem, e.g. thresholding length of received Doppler signal at para. 45 or using number of points determined from a given frame (correlated with length of received Doppler signal); (3) one of ordinary skill in the art could have pursued the known potential solutions with a reasonable expectation of success; and (4) no additional findings based on the Graham factual inquiries are necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness (See MPEP 2143).
Regarding claim(s) 18,
Claim(s) 18 is/are claims corresponding to claim(s) 7 respectively. Accordingly, the Examiner’s remarks and application of the prior art with respect to claim(s) 18 are substantially the same as those made above with respect to claim(s) 7.
Allowable Subject Matter
Claims 3-4, 17 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claims 8-14, 20 allowable.
The following is an examiner’s statement of reasons for allowance: The closest prior art of record (US 20230090211 A1 to Alsindi; US 20210117659 A1 to FOROOZAN; US 20230068523 A1 to Santra (‘523); US 20090012398 A1 to Zhang) neither teaches nor fairly renders obvious the combinations set forth in claims 3-4, 8-14, 17, and 20. See analysis regarding claims 3 and 4 below. Claim(s) 8, 17, and 20 recite similar limitation(s) and is/are indicated allowable for similar reasons. Dependent claims allowed at least as depending from allowed claims.
Regarding claim 3, the prior art of record does not teach, in combination with the remaining elements of the claim:
circularly shifting, using the processing circuit, the micro-Doppler spectrogram of a first tracked object of the one or more tracked objects around the track velocity of the first tracked object.
Regarding claim 4, the prior art of record does not teach, in combination with the remaining elements of the claim:
circularly shifting, using the processing circuit, the micro-range spectrogram or the micro-Doppler spectrogram for a first tracked object of the one or more tracked objects around mean frequencies or median frequencies, weighted by spectral power of respective frequency bins, of corresponding one dimensional time slices of the micro-range spectrogram or the micro-Doppler spectrogram for the first tracked object, to generate a circularly shifted micro-range spectrogram or a micro-Doppler spectrogram for the first tracked object;
wherein the classifying of the first tracked object is performed based on the circularly shifted micro-range spectrogram or the circularly shifted micro-Doppler spectrogram for the first tracked object.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JULIANA CROSS whose telephone number is (571)272-8721. The examiner can normally be reached Mon-Fri 9am-5pm Pacific time.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, William Kelleher can be reached on (571) 272-7753. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JULIANA CROSS/Examiner, Art Unit 3648
/William Kelleher/Supervisory Patent Examiner, Art Unit 3648