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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 05/07/2026 has been entered.
Prior art references
D1 SCHACHTER BRUCE J: "Unification of automatic target tracking and automatic target recognition"(cited in IDS and has been attached in its entirety)
D2 VEIT LEONHARDT ET AL: "A region-growing based clustering approach for extended object tracking", (cited in IDS)
D3 Yaakov Bar-Shalom ET AL: "Tracking in a Cluttered Environment with Probabilistic Data Association", (cited in IDS)
D4 POWER C MET AL: "Context-based methods for track association", (cited in IDS)
D5 US 20200377124
D6 JABBARIAN M ET AL: "Target tracking in pulse-doppler MIMO radar by extended kalman filter using velocity vector", (cited in IDS)
D0 US 20120093359 A1
Response to Arguments
Applicant's arguments filed 05/07/2026 have been fully considered but they are not persuasive. Due to broadness of the limitations Examiner presented alternative interpretation with mapping of the claim (see rejection bellow). The combination of D1 and D0 teaches the amended limitations.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-21 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claims 1, 5, 10, 15-17 and 19 include term “in particular” which is indefinite. It is indefinite whether limitation after term “in particular” required or not.
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) 1-6, 10-12 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over D1 in view of D0
Regarding claims 1, 19 D1 teaches
Tracking computation unit (introduction radar processes data and therefore processor is implicit)
detecting detection points in the radar (introduction radar in page 1) frames ;(abstract “detection”)
within a tracking loop(fig. 1 tracking loop) that processes successive radar frames(page 5 and 6 frames are analyzed one by one), associating the detection points (page 6 Measurement to track association ) of a present radar frame with a plurality of tracklets maintained in a first data(Association determines which tracked targets generated which of these measurements, and which measurements cannot be attributed to any track) structure separately from object-tracks(object track is Track management on page 5 when it is identified is it true track, need to be merged or grouped with some tracks),
wherein each tracklet is updated incrementally on a per-frame basis using a dynamical system model(tracklets are updated in Track management section with page 4 updating using Kalman Filter), wherein each tracklet is a track of at least one detection point observed over multiple radar frames, and (page 5, 6 track management)
wherein the associating of the detection points with the tracklets is performed as a first association step(fig. 1 Detection to Track association) prior to and independently of a second association step;(Track management)
The difference is that D1 does not explicitly say To which tracks “Detection and to track Association” part associates and if it is different from association presented in “Track management”.
but does not explicitly teach that
tracklets maintained in a first data are different from object-tracks
wherein each object- track is maintained in a second data structure separately from the tracklets and represents at least one physical object comprising a plurality of tracklets
D0 teaches
detecting detection points in the radar frames;(310)+(405)
associating the detection points(415) of a present radar frame with a plurality of tracklets maintained[0033](fig. 4 each detection compared with track associated with plurality other detections) in a first data structure separately from object-tracks(object track is clustered track in [0050] which is done later),
wherein each tracklet is a track of at least one detection point observed over multiple radar frames(fig. 4), and wherein the associating of the detection points with the tracklets is performed as a first association step prior[0033] to and independently of a second association step[0050](clustering part); and
associating the tracklets in the second association step within a same iteration of the tracking loop based on at least one feature-parameter with at least one object-track, [0050](relative proximity, relative velocity)
wherein each object- track is maintained in a second data structure separately from the tracklets and represents at least one physical object comprising a plurality of tracklets.(implicit/obvious as combined track is different from detection point track, meaning combined track in [0050], [0067] is not used for step in [0033])
It would be obvious to one of ordinary skills in the art at the time of the filing to modify invention by D1 with invention by D0 in order obtain individual point tracking as well as combined cluster tracking and therefore to identify and filter the rotation(D0 [0067]).
2. D1 (page 4 Kalman filter and Gausian Markov using kinematic tracking model) discloses subject matter of claim 2, wherein obtaining and/or maintaining the tracklets and the object-tracks is based on at least one dynamical system model.
Regarding claim 3 D1 teaches
3. (Currently Amended) The method according to claim 1 further comprising:
predicting one or a plurality of parameters of each tracklet for the present radar frame by propagating the dynamical system model, wherein the parameters of each tracklet include at least one of a position or a velocity or an acceleration, and (page 3, 4 section tracking, page 5 fig 1 Track filtering prediction)
a covariance of the tracklet in a radar frame(implicit each target is tracked and hence covariance of target in different frames is also taken into account to make sure that the it is the same target in different frames); and
correcting the parameters of each tracklet based on the detection points that are associated with a corresponding tracklet,(page 4 eq 1 state vector xk+1 obtained from state vector xk, fig. 5 Detection track association + track management)
wherein the predicting is performed before the associating of the detection points with the tracklets and the correcting is performed after the associating of the detection points with the tracklets.(fig. 5 track filtering/prediction is fed into Detection track association and then track management is used after track association)
4. D1 (figure 1: gating, spatial primacy; page 6: 2. Gating; page 5: a. Track Initiation) discloses the subject-matter of claim 4, wherein in the associating of the detection points to tracklets, a detection point is associated with a tracklet, if a position of the detection point is within a gate of a tracklet, wherein new tracklets are initialized from the detection points whenever a criterion for assignment of a detection is not met for any existing tracklets.
5. D1 (page 6: 2. Gating : "rectangular"; "kinematic gates") discloses the subject- matter of claim 5, wherein a gate for each tracklet is either fixed in size or is adaptive in size, wherein the size of the gate correlates with a covariance of the tracklet, in particular such that the size of the gate is increased if the covariance increases, or vice versa.
6. D1 (page 6: "nearest neighbor assignment) discloses the subject-matter of claim 6, wherein in the associating of the detection points with the tracklets, a detection point is associated with the tracklet having a position closest to the detecting point.).
10. D1 (figure 1: Track Filtering; page 6) discloses implicitly the subject-matter of claim 10, wherein the method further comprises correcting parameters of an object-track by updating the parameters of the object-track based on a predicted velocity, as this process is inherent to the Kalman filtering process.).
11. D1 (page 6: “track record") discloses the subject-matter of claim 11, wherein each tracklet comprises metadata including at least one of a status of the tracklet.).
12. D1 (page 5: "track initiation", figure 1 : initiate, track files) discloses the subject- matter of claim 12, wherein the method further comprises: updating the metadata of the tracklets; and initializing detection points as new tracklets that are not associated toexisting tracklets, wherein the updating of the metadata and the initializing of detection points as new tracklets are performed after the associating of the detection points with the tracklets.).
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) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over D1 in view of D3.
Regarding claim 7 D1 teaches
7. (Currently Amended) The method according to claim 1,
wherein in the associating of the detection points with tracklets, a detection point is probabilistically associated with multiple tracklets,(page 5 probabilistic data association filter)
but does not teach while D3 teaches
wherein probabilistic values determining a probability that a detection point is associated with a tracklet are increased if a distance between a position of the detection point and a predicted position of the tracklet decreases, or vice versa.(fig. 5 and fig. 6)
It would be obvious to one of ordinary skills in the art at the time of the filing to modify invention by D1 with invention by D3 in order to identify the cluster of points associated with single target.
Claim(s) 8-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over D1 in view of D4.
Regarding claims 8-9 D1 does not teach but D4 teaches
8. (Currently Amended) The method according to claim 1,
wherein a feature-parameter for grouping of the tracklets, based on which the tracklets are clustered into the object-tracks, comprises an overlap of gates of individual tracklets in at least the present radar frame and/or a summed overlap of the gates of the individual tracklets in multiple previous radar frames.(page 1136 “nearest neighbor algorithm”)
9. (Currently Amended) The method according to claim 1, wherein grouping of the tracklets is performed by a clustering method. (page 1136 “Mahalonobis distance”)
It would be obvious to one of ordinary skills in the art at the time of the filing to modify invention by D1 with invention by D4 in order to identify the cluster of points associated with single target.
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over D1 in view of D5.
Regarding claim 14 D1 teaches using Kalman filter but does not teach while D5 teaches
Using multiple models .[0002,0005, 0044, fig. 1A,b]
And therefore limitation
wherein an object model is inferred from a library of object models for each object-track and a switching Kalman filter is used for modelling the object-tracks, wherein a switch state of the switching Kalman filter represents an object class is just obvious known modification (as evidenced by https://en.wikipedia.org/wiki/Switching_Kalman_filter) in order to increase robustness of the result.
Claim(s) 15-18, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over D1 in view of D6.
Regarding claim 15 D1 teaches
15. (Currently Amended) The method according claim 1, for tracking at least one object in measurement data of a radar system including a plurality of, in particular consecutive, radar frames acquired by a radar system, comprising:
detecting detection points in the radar frames; (introduction)
wherein the plurality of radar frames comprised in the measurement data is a first plurality of radar frames acquired by a first radar unit; (introduction)
wherein the radar frames contain range, doppler and angle measurements, (Section 1.0, 2.0)
wherein a multidimensional velocity vector is determined from the doppler measurements for at least one detection point that is detectable in synchronized radar frames of the first and the second plurality of radar frames, wherein the determining of the multidimensional velocity vector is based on the corresponding doppler measurements of the first and the second radar units(Section 1.0, 2.0)
D1 also teaches
16. (Currently Amended) The method according to claim 15,
wherein the multidimensional velocity vectors(section 3.0. 3D system x is a vector and hence x dot is also vector in 3D) are used in a correcting of parameters of a track, in particular in the correcting of the parameters of the tracklet.(eq. 1)
17. (Currently Amended) The method according to claim 15,
wherein the multidimensional velocity vectors are used in an updating of metadata of a track and in an initializing of detection points as new tracks, in particular in the updating of the metadata of the tracklets and in the initializing of detection points as new tracklets.(Section 3.0 eq.1)
but does not teach which D6 teaches
wherein the measurement data further includes a second plurality of radar frames acquired by a second radar unit that is non-colocated to the first radar unit,(fig. 1)
wherein the first and the second plurality of radar frames are synchronized and at least partially overlap, (fig. 1)
20. (Currently Amended) The radar system according to claim 19, further comprising:
a second radar unit configured to acquire a plurality of radar frames by transmitting and receiving radar signals reflected on potential objects to be tracked in a field-of-view of the second radar unit,(fig 1)
wherein the field of view of the first radar unit and the field-of-view of the second radar unit at least partially overlap.(fig. 1)
It would be obvious to one of ordinary skills in the art at the time of the filing to modify invention by D1 with invention by D6 in order triangulate the position of the target and based on obtained position to track the target.
Regarding 18 D1 also teaches
18. (Currently Amended) The method according to claim 15,
wherein a status of a track is changed immediately from a tentative state to a tracked state if the track is inside an area around a position of a detection point for which a multidimensional vector is determined, and if a comparison measure, of the multidimensional velocity vector and multidimensional velocity vectors of a detection point's neighboring multidimensional velocity vectors is equal or greater than a predetermined threshold (page 6 gating).
if not explicit then ant least It would be obvious to one of ordinary skills in the art at the time of the filing to modify invention by D1 in order to perform greedy nearest neighbor algorithm and match not only positions but velocities and other parameters.
Claim(s) 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over D1.
Although does not explicitly teach
21. (Currently Amended) A vehicle in which a radar system according to claim 19 is mounted, wherein the vehicle is an aircraft or watercraft or land vehicle, wherein the vehicle is either manned or unmanned.
It would be obvious to one of ordinary skills in the art at the time of the filing to modify invention by D1 in order to create mobile radar system which can change the position and therefore get radar data in different regions.
Allowable Subject Matter
Claim 3 is 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.
The following is a statement of reasons for the indication of allowable subject matter: the Examiner has not found any prior art that would render obvious the claim limitations directed to “wherein a first dynamical model comprising an alpha-beta filter is used for modelling dynamics of the tracklets and a second dynamical model, different from the first dynamical model, comprising a Kalman filter is used for modelling dynamics of the object-tracks”.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HELENA SERAYDARYAN whose telephone number is (571)270-0706. The examiner can normally be reached on M-T, 7:30-5pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Resha Desai can be reached on (571) 270-7792. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HELENA H SERAYDARYAN/ Examiner, Art Unit 3648C
/TIMOTHY A BRAINARD/Primary Examiner, Art Unit 3648
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