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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 2/23/26 has been entered.
Response to Arguments
Applicant’s arguments with respect to claim(s) have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
Claims 1-5, 7-14 and 16-19 are rejected under 35 U.S.C. 103 as being unpatentable over McCormack (US Pub. 2011/0149072) in view of Rittscher et al (US Pub. 2007/0003141), Ren et al (US Pub. 2016/0092727) and Xu et al (US Pub. 2008/0166045).
With respect to claim 1 (exemplary claim), McCormack discloses A system for determining a correspondence between a tracked object in one video frame and a candidate object in a different video frame, said system comprising of:
an object tracking unit;
a processor;
a non-transitory storage element;
encoded instructions stored in said non-transitory storage element, wherein the encoded instructions when implemented by the processor, (see paragraph 0020 for processor and software and firmware; and figure 3, 105 is the tracking unit), configure the system to:
generate a neighborhood region that bounds an object tracked in a first frame and a second frame, the neighborhood region being located within and smaller than an entirety of the first and second frames, (see ‘072 paragraph 0005, wherein, …a predetermined control area “a neighborhood region”… a first video image framed….second video image frame…);
perform a search of the neighborhood region in the first frame for determining a number of tracked objects in the neighborhood region of the first frame;
perform an additional search of the neighborhood region in the second frame for determining and a number of candidate objects in the neighborhood of the second frame;
check the number of tracked objects and the number of candidate objects only in the neighborhood region; and
compare the number of tracked objects with the number of candidate objects in the neighborhood region, and if the number of tracked objects is different than the number of candidate objects, determine a correspondence between the tracked object in the neighborhood region and the candidate object in the neighborhood region by the object tracking unit, (see claim 22 wherein, …least one substantial difference between said at least one second video image frame and said at least one first video image frame, wherein the at least one substantial difference includes at least one of: at least one object has entered the predetermined control area; at least one object has exited the predetermined control area; and at least one object has moved within the predetermined control area; and paragraph 0039 wherein, …difference between …objects and objects that may have changed “correspondence”…) comparing feature-based similarities between the tracked object and the candidate object when objects in the first frame outnumber objects in the second frame, wherein the features are at least one of a group comprising size, aspect ratio, location in a scene, Histogram of Oriented Gradient (HOG), Scale-invariant feature transform (SIFT), HAAR like features, or Local Binary Pattern (LBP) of the object, as claimed.
However, McCormack fails to disclose generate a neighborhood region that bounds an object tracked in a first frame and a second frame, the neighborhood region being located within and smaller than an entirety of the first and second frames;
perform a search of the neighborhood region in the first frame for determining a number of tracked objects in the neighborhood region of the first frame;
perform an additional search of the neighborhood region in the second frame for determining and a number of candidate objects in the neighborhood of the second frame;
check the number of tracked objects and the number of candidate objects only in the neighborhood region; and
compare the number of tracked objects with the number of candidate objects in the neighborhood region, and if the number of tracked objects is different than the number of candidate objects, determine a correspondence between the tracked object in the neighborhood region and the candidate object in the neighborhood region by the object tracking unit comparing feature-based similarities between the tracked object and the candidate object when objects in the first frame outnumber objects in the second frame, wherein the features are at least one of a group comprising size, aspect ratio, location in a scene, Histogram of Oriented Gradient (HOG), Scale-invariant feature transform (SIFT), HAAR like features, or Local Binary Pattern (LBP) of the object, as claimed.
Rittscher teaches generate a neighborhood region that bounds an object tracked in a first frame and a second frame, the neighborhood region being located within and smaller than an entirety of the first and second frames; and check the number of tracked objects and the number of candidate objects only in the neighborhood region, (see paragraph 0052, wherein … system 10 detects … individuals are present in the counting area 24 “generate a neighborhood region that bounds” and are moving out of virtual gate 22, as is shown by histogram bar 50 at point a in FIG. 5 and shown in FIG. 6A. System 10 successfully counts all three individuals 44.sub.1, 44.sub.2 and 44.sub.3, as is shown by histogram bar 52 at point a in FIG. 5, “check the number of tracked objects and the number of candidate objects only in the neighborhood region” in spite of some occlusion while passing the virtual gate 22…), as claimed.
It would have been obvious to one ordinary skilled in the art at the effective date of invention to combine the two references as they are analogous because they are solving similar problem of tracing objects/humans in images using image analysis. The teaching of Rittscher to consider an area within an image in order to track within the area can be incorporated into McCormack’s system as suggested (see paragraph 0006 region of interest), for suggestion, and modifying the system yields a system to count individuals in a specific region within an image (see Abstract), for motivation.
Ren in tracking humans in a video teaches perform a search of the neighborhood region in the first frame for determining a number of tracked objects in
perform an additional search of the neighborhood region in the second frame for determining and a number of candidate objects in (see figure 1, 101, 102 and 103 images of the same area, also see paragraph 0041, wherein … the data structure 1030 may therefore be used to count the people in the frames, track the people in the frames, or predict the future location of the people in the frames…);
and
wherein the features are at least one of a group comprising size, aspect ratio, location in a scene, Histogram of Oriented Gradient (HOG), Scale-invariant feature transform (SIFT), HAAR like features, or Local Binary Pattern (LBP) of the object, (see paragraph 0018, wherein … characteristics of the keypoints may be represented by descriptors such as a binary descriptor “binary pattern (LBP) of the object” or a vector of integers ...a motion vector determined based on the locations of the bounding boxes in the video images “location in a scene” …, and paragraph 0023, wherein …Patch descriptor techniques such as the HOG “Histogram of Oriented Gradient (HOG)” technique may effectively identify the bounding boxes 305-308 for the humans 105-108 …image 301…), as claimed.
It would have been obvious to one ordinary skilled in the art at the effective date of invention to combine the references as they are analogous because they are solving similar problem of tracing objects/humans in images using image analysis. The teaching of Ren to count the objects in first and second frames to calculate the future locations of the people and considering features like location, binary pattern and HOG for the purpose to obtain the correspondence between the first and second frames can be incorporated in to McCormack’s system as suggested in paragraph 0014 of McCormack (…if difference are determined ….investigate the difference…), and modifying the system yields identifying and tracking humans in a video (see Ren paragraph 0002), for motivation.
Xu in the same field teaches compare the number of tracked objects with the number of candidate objects , and if the number of tracked objects is different than the number of candidate objects, determine a correspondence between the tracked object in the neighborhood region and the candidate object in the neighborhood region by the object tracking unit comparing feature-based similarities between the tracked object and the candidate object when objects in the first frame outnumber objects in the second frame (see paragraphs 0050-0054, wherein in the conditions of the difference between the two frames are detailed i.e. comparison; see paragraph 0005, wherein, matching process “similarities” is performed using the features from each received blob and the objects identified in previous frames; paragraph 0050, wherein, …detect an occlusion, …status of objects in the current frame and the respective status of objects already being tracked… i.e. a condition “when” and paragraph 0051, …number of blobs in the incoming frame is less than the number of objects currently tracked “outnumbered objects”), as claimed.
It would have been obvious to one ordinary skilled in the art at the effective date of invention to combine the references as they are analogous because they are solving similar problem of tracing objects in images using image analysis. The teaching of Xu to compare them when there is any discrepancy between the frames can be incorporated in to McCormack’s system as suggested in paragraph 0014 of McCormack (…whether a threshold difference …exist…if difference are determined ….investigate the difference…), and modifying the system yields an intelligent surveillance system (see Xu paragraph 0034) for motivation.
With respect to claim 2, combination of McCormack, Rittscher, Ren and Xu further discloses wherein the first frame is derived from at least one of a live video, an archived video stored in a data storage, or a recorded video, (see McCormack figure 1, numerical 105 camera), as claimed.
With respect to claim 3, combination of McCormack, Rittscher, Ren and Xu further discloses wherein the object tracking system is a part of at least one of a group comprising a surveillance system, a security system, a retail system, a monitoring system and a business intelligence-based system, (see McCormack Abstract first two lines), as claimed.
With respect to claim 4, combination of McCormack, Rittscher, Ren and Xu further discloses further comprising a classifier unit configured to classify any one of the object of the one or more objects in one or more categories, (see Xu paragraph 0006, classification block), as claimed.
With respect to claim 5, combination of McCormack, Rittscher, Ren and Xu further discloses wherein the classifier classifies the object based on at least one feature of any one of the object, (see Xu paragraph 0006, wherein, …classified in terms of their resemblance…), as claimed.
With respect to claim 7, combination of McCormack, Rittscher, Ren and Xu further discloses wherein the at least one object is detected by an object detection unit executing a blob detection or merging algorithm, (see Xu paragraph 0009, wherein, …match the newly detecte3d blobs in the incoming frame…), as claimed.
With respect to claim 8, combination of McCormack, Rittscher, Ren and Xu further discloses further comprising a cost-function based model for tracking objects across video frames dependent on a lack of a correspondence issue of objects across frames, (see Xu paragraph 0050 wherein, …in order to detect an occlusion “a lack of a correspondence issue of objects across frames”…), as claimed.
With respect to claim 9, combination of McCormack, Rittscher, Ren and Xu further discloses further comprising a dynamic state change between the feature-based or cost-function models for object tracking depending on object correspondence across frames, (see Xu paragraph 0017, wherein, …models can represent the respective color distribution…), as claimed.
Claims 10 and 19 are rejected for the same reasons as set forth in the rejections for claim 1, because claims 10 and 19 are claiming subject matter of similar scope as claimed in the exemplary claim 1.
Claims 11-14 and 16-18 are rejected for the same reasons as set forth in the rejections of claims 2-5 and 7-9, because claims 11-14 and 16-18 are claiming subject matter of similar scope as claimed in claims 2-5 and 7-9 respectively.
Claims 6 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over McCormack (US Pub. 2011/0149072) in view of Rittscher et al (US Pub. 2007/0003141), Ren et al (US Pub. 2016/0092727) and Xu et al (US Pub. 2008/0166045) as applied to claim 1 above, and further in view of Sharma (US Pub. 2012/0148093).
With respect to claim 6, combination of McCormack, Rittscher, Ren and Xu discloses all the limitations as claimed and as rejected in claim 1 above. However, they fail to explicitly disclose wherein the at least one object is tracked by a bounding box prediction using at least one of an optical flow, mean shift, or dense-sampling search, as claimed.
Sharma in the same field teaches wherein the at least one object is tracked by a bounding box prediction using at least one of an optical flow, mean shift, or dense-sampling search, (see figure 3B, numerical 324 “…bounding box size of each blob”), as claimed.
It would have been obvious to one ordinary skilled in the art at the effective date of invention to combine the references as they are analogous because they are solving similar problem of tracing objects in images using image analysis. The teaching of Sharma to use a bounding box for blob in order to track the object in a sequence of frames can be incorporated in to McCormack, Ren and Xu system as suggested in paragraph 0067 of McCormack (……track the object…), and modifying the system yields an intelligent surveillance system that incorporates a well-known technology (see Sharma paragraph 0001), for motivation.
Claim 15 is rejected for the same reasons as set forth in the rejections of claim 6, because claim 15 is claiming subject matter of similar scope as claimed in claim 6.
Conclusion
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/VIKKRAM BALI/Primary Examiner, Art Unit 2663
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