DETAILED ACTION
Response to Amendment
Applicant’s response to the last Office Action, filed on 3/23/2026 has been entered and made of record.
Applicant’s amendments necessitated the new ground of rejection set forth herein; therefore, this action is made Final.
Rejection under 35 USC 101 is withdrawn in view of amendments.
Rejection under 35 USC 112(b) is withdrawn in view of amendments.
Response to Arguments
Applicant's arguments filed on 3/23/2026 have been fully considered but they are not persuasive.
Examiner finds that Matteucci discloses a correction module arranged to correct a trajectory of the at least one object with reference to a template image representing a stabilizing region in the sequence of images, wherein the correction module calculates coordinates moved to apply to each track of the object to reduce an influence of movement caused by instability of the unmanned aircraft system.
Matteucci teaches motion stabilization for a UAV displacement between certain image regions in a template and the acquired images. Pg. 20, Figure 3.1 and 3.2 shows the global motion estimation process of tracking features between successive frames, in order to estimate motion of the tracks and thereafter compensate for inter-frame motion in the tracks. Pg. 22, Section 3.2.1 Feature Tracking teaches tracking features from frame to frame after features are selected, using the Lucas-Kanade optical flow image registration method. Figure 3.4 shows this process for tracking features on a moving vehicle. The paragraph spanning pgs. 23 and 24 teaches the template region of interest in the previous frame for performing the feature tracking. Transformation from image to image is defined by parametrized W warping function. Local tracking is performed simply using translational warps for the feature patches (calculating coordinates moved to apply to each track of the object to reduce an influence of movement caused by instability of the unmanned aircraft system).
Applicant remarks “As detailed in Matteucci's abstract and conclusion, its system is designed to provide motion stabilization for the acquired images. Matteucci teaches geometrically transforming (warping) the pixels of a video frame so that the resulting video appears smooth. Whilst Matteucci teaches image warping to correct a video feed, the present invention is different as it emphasizes mathematical offsetting of tracking data to correct a numerical trajectory. Matteucci is silent on calculating corrective coordinates and applying them specifically to the underlying tracking data ("each track of the object") of individual tracked targets.” Examiner notes that this analysis is inconsistent with Matteucci’s disclosure at 3.2 Motion Estimation which is computing Lucas-Kanade optical flow using parametrized warping in order to provide translational shift from image to image of the tracked features. These are specifically steps of mathematical offsetting of tracking data to correct a numerical trajectory for motion in the frames and calculating corrective coordinates and finally applying them specifically to the underlying tracking data. Contrary to Applicant’s remarks, these steps are all for local motion estimation of the feature tracks, they are not the motion compensation steps in order to make the images appear smooth. Later, Section 3.3 Motion Stabilizing teaches the motion compensation for using the motion estimation tracks in order to stabilize the video.
Allowable Subject Matter
Claims 8 and 18 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, provided all remaining rejections are withdrawn.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: image processing module, object detection module, object tracking module, and correction module in claims 1-10.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claim(s) 1, 9, 10, 11, 19 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kanhere (US PGPub 2010/0322476) in view of Matteucci (“Online Video Stabilization for UAV”).
Regarding claim 1, Kanhere discloses an object tracking system for use in traffic flow analytics, comprising: (Kanhere teaches a method for detecting and tracking vehicles for depicting traffic flow on a road, see Abstract)
an image processing module arranged to receiving a sequence of images capturing at least one object moving along a predetermined path in an area; (See Fig. 1 and ¶ 0066 which teaches processing the obtained sequence of images. Also see Figs. 2, 3 and 4 which show the camera setup in reference to the predetermined path in the detection zone/area.)
an object detection module arranged to detect the at least one object including identifying a predetermined category of the detected object; and (¶ 0097 teaches classifying the object into predetermined categories such as car and truck.)
an object tracking module arranged to track the at least one object travelling from an entrance to an exit of the predetermined path based on coordinates of the object in the sequence of images being detected. (¶ 0069 teaches the setup of the exit and entrance boundaries of the detection zone/area, ¶ 0116 teaches detecting the vehicle at the beginning of the zone, ¶ 0096 teaches the vehicle exiting the zone, ¶ 0064 and 0114 teach tracking based on vehicle coordinates.)
In the field of UAV motion stabilization, Matteucci discloses captured images from an unmanned aircraft system and a correction module arranged to correct a trajectory of the at least one object with reference to a template image representing a stabilizing region in the sequence of images, wherein the correction module calculates coordinates moved to apply to each track of the object to reduce an influence of movement caused by instability of the unmanned aircraft system. (Matteucci teaches motion stabilization for a UAV displacement between certain image regions in a template and the acquired images. Pg. 20, Figure 3.1 and 3.2 shows the global motion estimation process of tracking features between successive frames, in order to estimate motion of the tracks and compensate for inter-frame motion in the tracks. Pg. 22, Section 3.2.1 Feature Tracking teaches tracking features from frame to frame after features are selected, using the Lucas-Kanade optical flow image registration method. Figure 3.4 shows this for tracking features on a moving vehicle. The paragraph spanning pgs. 23 and 24 teaches the template region of interest in the previous frame for performing the feature tracking. Transformation from image to image is defined by parametrized W warping function. Local tracking is performed simply using translational warps for the feature patches.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the above combination’s object tracking via UAV with Matteucci motion stabilization for UAV. Kanhere teaches monitoring traffic flow on a roadway from above, but does not expressly disclose doing so from an unmanned aircraft/UAV. Matteucci teaches motion stabilization for a UAV to stabilize the imaging system that is in flight. Matteucci’s motion stabilization allows traffic monitoring from a UAV extending Kanhere’s traffic monitoring system to allow more flexible and cost-efficient traffic monitoring across road locations. Simply incorporating Matteucci technique here to accomplish its original goal of stabilization cannot be considered a non-obvious improvement in view of the relevant prior art here. Using known engineering design, no “fundamental” operating principle of the teachings are changed; they continue to perform the same functions as originally taught prior to being combined.
Regarding claim 9, the above combination discloses the object tracking system in accordance with claim 1, wherein the correction module is arranged to slide the template image defined by a user selected from one of the images in the sequence of image to match with an identical region in each of the images in the sequence; and to correcting the coordinate of the detected object by offsetting the movement of the unmanned aircraft system based on a shift of the template image at different frames of the sequence of image. (As above, Matteucci teaches motion stabilization for a UAV displacement between certain image regions in a user defined template and subsequent the acquired image frames, see Abstract and pg. 23, last paragraph.)
Regarding claim 10, the above combination discloses the object tracking system in accordance with claim 1, wherein the predetermined category of the detected object includes at least one of pedestrians, a taxi, a coach, a bus, a tram, a collection truck and a special purpose vehicle. (See classification at ¶ 0097 as above of a car versus truck/special purpose vehicle.)
Claims 11, 19 and 20 are the method corresponding to the system of claims 1, 9 and 10. The method steps are necessitated by the system. Remaining limitations are rejected similarly. See detailed analysis above.
Claim(s) 2-5 and 12-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kanhere (US PGPub 2010/0322476) in view of Unel (“The Power of Tiling for Small Object Detection”)
Regarding claim 2, the above combination discloses the object tracking system in accordance with claim 1, including the sequence of images are captured by an aerial angle. (See rejection of claim 1)
In the field of object detection monitoring Unel teaches capturing at a shooting angle of a top view to provide a higher accuracy of traffic counts, or at a tilt view to provide a higher accuracy of vehicle type classification and pedestrian count. (Unel teaches the detection of pedestrians and vehicles via top view and tilt view from a micro aerial vehicle via a deep neural network technique, see Abstract)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Kanhere’s vehicle traffic monitoring with Unel’s vehicle traffic monitoring. Kanhere teaches monitoring traffic flow on a roadway from above, but does not expressly disclose doing so from an unmanned aircraft/UAV. Unel teaches the detection of pedestrians and vehicles from a micro aerial vehicle via a deep neural network technique. This cannot be considered a non-obvious improvement in view of the relevant prior art here. Using known engineering design, no “fundamental” operating principle of the teachings are changed; they continue to perform the same functions as originally taught prior to being combined.
Regarding claim 3, the above combination discloses the object tracking system in accordance with claim 1, wherein the sequence of images are captured at a shooting angle of a top view or a tilt view. (See Kanhere Fig. 3a)
Regarding claim 4, the above combination discloses the object tracking system in accordance with claim 1, wherein the object detection module comprises a modified tiny object detection network arranged to process the sequence of images. (Unel pg. 2, left column, ¶ 2 teaches a small object detection system based on high-resolution images. Also see Fig. 3 which teaches the tiling based approach for small/tiny object detection.)
Regarding claim 5, the above combination discloses the object tracking system in accordance with claim 4, wherein the modified tiny object detection network is arranged to process a plurality of at least partially overlapping image tiles having an image resolution smaller than an original resolution of the sequence of images. (See Fig. 3 which teaches the tiling based approach for small/tiny object detection, based on setting up partially overlapping image tiles having an image resolution smaller than an original resolution.)
Claims 12-15 are the method corresponding to the system of claims 2-5. The method steps are necessitated by the system. Remaining limitations are rejected similarly. See detailed analysis above.
Claim(s) 6 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kanhere (US PGPub 2010/0322476) in view of Wojke (“Simple Online and Realtime Tracking With A Deep Association Metric”)
Regarding claim 6, the above combination discloses the object tracking system in accordance with claim 1, wherein the object tracking module is arranged to assign a track identification for each of the at least one object being detected. (See Kanhere ¶ 0141)
In the field of object detection monitoring Wojke teaches tracking the movement of the at least one object using a Simple Online and Realtime Tracking with a Deep Association Metric process. (Wojke teaches the Simple Online and Realtime Tracking (SORT) approach to multiple object tracking. A deep association metric is learned on a largescale person re-identification dataset. See Abstract and pg. 2, Section 2.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Kanhere’s object tracking and flow monitoring with Wojke object tracking technique. Wojke teaches a technique to improve the performance of the widely-used Simple Online and Realtime Tracking (SORT) approach to multiple object tracking by learning a deep association metric based on an object re-identification dataset. Simply incorporating Wojke’s technique here cannot be considered a non-obvious improvement in view of the relevant prior art here. Using known engineering design, no “fundamental” operating principle of the teachings are changed; they continue to perform the same functions as originally taught prior to being combined.
Claims 16 is the method corresponding to the system of claim 6. The method steps are necessitated by the system. Remaining limitations are rejected similarly. See detailed analysis above.
Claim(s) 7 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kanhere (US PGPub 2010/0322476) in view of Unel (“The Power of Tiling for Small Object Detection”) and Wojke (“Simple Online and Realtime Tracking With A Deep Association Metric”)
Regarding claim 7, the above combination discloses the object tracking system in accordance with claim 6, wherein the object tracking module is further arranged to assign a new track identification to a lost object which is not detectable in one or more previous frames; and to perform a merge track process to determine whether an identical object assigned with different track identifications travelled from the entrance to the exit of the predetermined path. (Unel, pg. 3, right column, ¶ 1 teaches that object proposals are merged for final detections in order to merge duplicate tracks.)
Claims 17 is the method corresponding to the system of claim 7. The method steps are necessitated by the system. Remaining limitations are rejected similarly. See detailed analysis above.
Conclusion
Based on these facts, 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 extension fee 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.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Raphael Schwartz whose telephone number is (571)270-3822. The examiner can normally be reached Monday to Friday 9am-5pm CT.
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, Vincent Rudolph can be reached at (571) 272-8243. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RAPHAEL SCHWARTZ/ Examiner, Art Unit 2671
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