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 .
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.
DETAILED ACTION
Claims status
Claims 1-20 are pending as the applicant filed on 06/26/2024.
Claim Rejections - 35 USC § 112
2. 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 1-8 and 13-19 are 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.
Regarding claims 1-8 and 13-19, the term “ground truth” is vague and a relative term that renders the claim indefinite. The term “ground truth” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably appraised of the scope of the invention. An artisan doing measuring and testing would not know at what point " ground truth" within the scope of the claim had been accomplished because nothing within the disclosure establishes when a sufficient “ground truth” occurs.
Note: In view of the PTO compact prosecution, the Examiner notes that due to the indefiniteness issues described above all consideration of the merits of the claims in view of prior art is as best understood.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 1, Step 1 the claim is a process (or machine) (Yes),
Step 2A Prong One, does the claim recite an abstract idea? current claim related to a method of training an object detector, the method comprising:
obtaining a first tracklet set based on an object detection result output corresponding to a plurality of frames; obtaining a second tracklet set from ground truth data predetermined corresponding to the plurality of frames;
obtaining a first bipartite matching result of a bounding box level, the bounding box level corresponding to each of first tracklets comprised in the first tracklet set and each of second tracklets comprised in the second tracklet set appears is an abstract idea of mental process (MPEP 2106.04(a)) or data gathering equivalent to mathematical concept or mathematical manipulation function (MPEP 2106.04 (a) (2) (concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula), (OR Mathematical Concepts and Mental Processes) Step 2A Prong One: Yes.
Step 2A Prong Two, is the claim directed to an abstract idea? In other words, does claim recite additional elements that integrate the Judicial Exception into a practical application? the additional elements of obtaining a second bipartite matching result of a tracklet level, the tracklet level corresponding to the first tracklet set and the second tracklet set, based on the first bipartite matching result are recited at a high level of generality and merely amount to a particular field of use (see MPEP 2106.05(h)) and/or insignificant post-solution activity (MPEP 2106.05(g)), this does not integrate the Judicial Exception into a practical application,
Step 2A Prong Two: NO.
Step 2B, Does the claim recite additional element that amount to significantly more than the Judicial exception? the additional elements of assigning a second tracklet determined to be one of a pair including a first tracklet, as a paired first tracklet and second tracklet, to ground truth data of the first tracklet, based on the second bipartite matching result appears to be field of use (See MPEP 2106.05(h) and MPEP 2106.05(f)) and/or merely amounts to insignificant extra-solution output of the results (see MPEP 2106.05(g)) and therefore fails to integrate the abstract idea into a practical application or amount to significantly more. Step 2B: No. claim 1 not eligible.
Claim 9, Step 1 the claim is a process (or machine) (Yes),
Step 2A Prong One, does the claim recite an abstract idea? current claim related to an object detection method, the method comprising: obtaining a first tracklet set and a second tracklet set based on an object detection result; obtaining a first bipartite matching result of a bounding box level, the bounding box level corresponding to each of first tracklets comprised in the first tracklet set and each of second tracklets comprised in the second tracklet set appears is an abstract idea of mental process (MPEP 2106.04(a)) or data gathering equivalent to mathematical concept or mathematical manipulation function (MPEP 2106.04 (a) (2) (concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula), (OR Mathematical Concepts and Mental Processes) Step 2A Prong One: Yes.
Step 2A Prong Two, is the claim directed to an abstract idea? In other words, does claim recite additional elements that integrate the Judicial Exception into a practical application? the additional elements of obtaining a second bipartite matching result of a tracklet level, the tracklet level corresponding to the first tracklet set and the second tracklet set, based on the first bipartite matching result are recited at a high level of generality and merely amount to a particular field of use (see MPEP 2106.05(h)) and/or insignificant post-solution activity (MPEP 2106.05(g)), this does not integrate the Judicial Exception into a practical application,
Step 2A Prong Two: NO.
Step 2B, Does the claim recite additional element that amount to significantly more than the Judicial exception? the additional elements of correcting the object detection result based on the second bipartite matching result appears to be field of use (See MPEP 2106.05(h) and MPEP 2106.05(f)) and/or merely amounts to insignificant extra-solution output of the results (see MPEP 2106.05(g)) and therefore fails to integrate the abstract idea into a practical application or amount to significantly more. Step 2B: No. claim 9 not eligible.
Claim 14, Step 1 the claim is a process (or machine) (Yes),
Step 2A Prong One, does the claim recite an abstract idea? current claim related to an apparatus for training an object detector, the apparatus comprising:
processors configured to execute instructions; and a memory storing the instructions, wherein execution of the instructions configures the processors to:
obtain a first tracklet set based on an object detection result output corresponding to a plurality of frames appears is an abstract idea of mental process (MPEP 2106.04(a)) or data gathering equivalent to mathematical concept or mathematical manipulation function (MPEP 2106.04 (a) (2) (concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula), (OR Mathematical Concepts and Mental Processes) Step 2A Prong One: Yes.
Step 2A Prong Two, is the claim directed to an abstract idea? In other words, does claim recite additional elements that integrate the Judicial Exception into a practical application? the additional elements of obtain a second tracklet set from ground truth data predetermined corresponding to the plurality of frames; obtain a first bipartite matching result of a bounding box level, the bounding box level corresponding to each of first tracklets comprised in the first tracklet set and each of second tracklets comprised in the second tracklet set; obtain a second bipartite matching result of a tracklet level, the tracklet level corresponding to the first tracklet set and the second tracklet set, based on the first bipartite matching result are recited at a high level of generality and merely amount to a particular field of use (see MPEP 2106.05(h)) and/or insignificant post-solution activity (MPEP 2106.05(g)), this does not integrate the Judicial Exception into a practical application,
Step 2A Prong Two: NO.
Step 2B, Does the claim recite additional element that amount to significantly more than the Judicial exception? the additional elements of assign a second tracklet determined to be one of a pair including a first tracklet, as a paired first tracklet and second tracklet, to ground truth data of the first tracklet, based on the second bipartite matching result appears to be field of use (See MPEP 2106.05(h) and MPEP 2106.05(f)) and/or merely amounts to insignificant extra-solution output of the results (see MPEP 2106.05(g)) and therefore fails to integrate the abstract idea into a practical application or amount to significantly more. Step 2B: No. claim 14 not eligible.
Claim 20, Step 1 the claim is a process (or machine) (Yes),
Step 2A Prong One, does the claim recite an abstract idea? current claim related to an apparatus for object detection, the apparatus comprising:
processors configured to execute instructions; and a memory storing the instructions, wherein execution of the instructions configures the processors to:
obtain a first tracklet set and a second tracklet set based on an object detection result, obtain a first bipartite matching result of a bounding box level, the bounding box level corresponding to each of first tracklets comprised in the first tracklet set and each of second tracklets comprised in the second tracklet set appears is an abstract idea of mental process (MPEP 2106.04(a)) or data gathering equivalent to mathematical concept or mathematical manipulation function (MPEP 2106.04 (a) (2) (concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula), (OR Mathematical Concepts and Mental Processes) Step 2A Prong One: Yes.
Step 2A Prong Two, is the claim directed to an abstract idea? In other words, does claim recite additional elements that integrate the Judicial Exception into a practical application? the additional elements of obtain a second bipartite matching result of a tracklet level, the tracklet level corresponding to the first tracklet set and the second tracklet set, based on the first bipartite matching result are recited at a high level of generality and merely amount to a particular field of use (see MPEP 2106.05(h)) and/or insignificant post-solution activity (MPEP 2106.05(g)), this does not integrate the Judicial Exception into a practical application,
Step 2A Prong Two: NO.
Step 2B, Does the claim recite additional element that amount to significantly more than the Judicial exception? the additional elements of correct the object detection result based on the second bipartite matching result appears to be field of use (See MPEP 2106.05(h) and MPEP 2106.05(f)) and/or merely amounts to insignificant extra-solution output of the results (see MPEP 2106.05(g)) and therefore fails to integrate the abstract idea into a practical application or amount to significantly more. Step 2B: No. claim 20 not eligible.
Claim 2 related to wherein the obtaining the first bipartite matching result comprises: obtaining the first bipartite matching result based on a first cost, the first cost resulting from a first similarity of a first bounding box comprised in the first tracklet and a second bounding box comprised in the second tracklet, its recites further data characterization and mathematical concepts that are part of the abstract idea, claim 2 not eligible.
Claim 3 related to wherein the first cost is determined based on at least one of: a probability in which a first class of the first bounding box is a similar class as a second class of the second bounding box; a difference between first coordinates of the first bounding box and second coordinates of the second bounding box; a difference between a first size of the first bounding box and a second size the second bounding box; and a difference in a rotation degree between the first bounding box and the second bounding box, its recites further data characterization and mathematical concepts that are part of the abstract idea, claim 3 not eligible.
Claim 4 related to wherein the obtaining the second bipartite matching result comprises: obtaining the second bipartite matching result based on a second cost, the second cost resulting from a second similarity of the first tracklet and the second tracklet determined from the first bipartite matching result, its recites further data characterization and mathematical concepts that are part of the abstract idea, claim 4 not eligible.
Claim 5 related to wherein the obtaining the second bipartite matching result comprising: obtaining a first cost of a first bounding box comprised in the first tracklet determined to be the paired first tracklet and a second bounding box comprised in the second tracklet, based on the first bipartite matching result; determining a second cost of the first tracklet and the second tracklet, based on the obtained first cost; and obtaining the second bipartite matching result based on the second cost, its recites further data characterization and mathematical concepts that are part of the abstract idea, claim 5 not eligible.
Claim 6 related to wherein the object detector comprises an object detector of a 2-stage detector type, and wherein the obtaining of the first tracklet set comprises obtaining the first tracklet set corresponding to respective trajectories of respective detected objects, based on an object detection result output from a region proposal module of the object detector corresponding to the plurality of frames, its recites further data characterization and mathematical concepts that are part of the abstract idea, claim 6 not eligible.
Claim 7 related to training the object detector based on the ground truth data of the first tracklet, its recites further data characterization and mathematical concepts that are part of the abstract idea, claim 7 not eligible.
Claim 8 related to wherein the first tracklet comprises a plurality of first bounding boxes corresponding to a time interval, and the second tracklet comprises a plurality of second bounding boxes corresponding to the time interval, its recites further data characterization and mathematical concepts that are part of the abstract idea, claim 8 not eligible.
Claim 10 related to wherein the correcting the object detection result comprises: synthesizing a first tracklet of the first tracklets that is paired to a second tracklet of the second tracklets as a pair, the pair resulting from the second bipartite matching result, its recites further data characterization and mathematical concepts that are part of the abstract idea, claim 10 not eligible.
Claim 11 related to wherein the obtaining the first tracklet set and the second tracklet set comprises: obtaining the first tracklet set corresponding to a first respective trajectory of a first respective detected object of first detected objects, based on a first object detection result output from a first object detector corresponding to a plurality of frames; and obtaining the second tracklet set corresponding to a second respective trajectory of a second respective object of second detected objects, based on a second object detection result output from a second object detector corresponding to the plurality of frames, its recites further data characterization and mathematical concepts that are part of the abstract idea, claim 11 not eligible.
Claim 12 related to obtaining the first tracklet set corresponding to a first respective trajectory of a first respective detected object of first detected objects, based on a first object detection result output from a first object detector corresponding to a first plurality of frames obtained from a first sensor; and obtaining the second tracklet set corresponding to a second respective trajectory of a second respective object of second detected objects, based on a second object detection result output from a second object detector corresponding to a plurality of frames obtained from a second sensor, its recites further data characterization and mathematical concepts that are part of the abstract idea, claim 12 not eligible.
Claim 13 related to a non-transitory computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to perform, its recites further data characterization and mathematical concepts that are part of the abstract idea, claim 13 not eligible.
Claim 15 related to wherein the processors are further configured to: when obtaining the first bipartite matching result, obtain the first bipartite matching result based on a first cost, the first cost resulting from a first similarity of a first bounding box comprised in the first tracklet and a second bounding box comprised in the second tracklet, its recites further data characterization and mathematical concepts that are part of the abstract idea, claim 15 not eligible.
Claim 16 related to wherein the processors are further configured to: when obtaining the second bipartite matching result, obtain the second bipartite matching result based on a second cost, the second cost resulting from a second similarity of the first tracklet and the second tracklet determined from the first bipartite matching result, its recites further data characterization and mathematical concepts that are part of the abstract idea, claim 16 not eligible.
Claim 17 related to wherein the processors are further configured to, when obtaining the second bipartite matching result, obtain a first cost of a first bounding box comprised in the first tracklet determined to be the paired first tracklet and a second bounding box comprised in the second tracklet, based on the first bipartite matching result, determine a second cost of the first tracklet and the second tracklet, based on the obtained first cost, and obtain the second bipartite matching result based on the second cost, its recites further data characterization and mathematical concepts that are part of the abstract idea, claim 17 not eligible.
Claim 18 related to wherein the object detector comprises an object detector of a 2-stage detector type, and wherein the processors are further configured to: when obtaining the first tracklet set, obtain the first tracklet set corresponding to respective trajectories of respective detected objects, based on an object detection result output from a region proposal module of the object detector corresponding to the plurality of frames, its recites further data characterization and mathematical concepts that are part of the abstract idea, claim 18 not eligible.
Claim 19 related to to train the object detector based on the ground truth data of the first tracklet, its recites further data characterization and mathematical concepts that are part of the abstract idea, claim 19 not eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-20 are rejected under 35 U.S.C. 102 (a) (1) as being anticipated by UNNIKRISHNAN et al. (US Patent Application Publication 2020/0218913 A1, Date Published: 2020-07-09).
Regarding claim 1:
UNNIKRISHNAN described a method of training an object detector (abstract, detects the target object in one or more images), the method comprising: obtaining a first tracklet set based on an object detection result output corresponding to a plurality of frames (0080-0085, spawn new tracklets);
obtaining a second tracklet set from ground truth data predetermined corresponding to the plurality of frames (0080-0085, spawn new tracklets);
obtaining a first bipartite matching result of a bounding box level, the bounding box level corresponding to each of first tracklets comprised in the first tracklet set and each of second tracklets comprised in the second tracklet set (0079-0080, the data association problem in sensor fusion is formulated as a weighted bipartite graph matching);
obtaining a second bipartite matching result of a tracklet level, the tracklet level corresponding to the first tracklet set and the second tracklet set, based on the first bipartite matching result (0079-0080, the data association problem in sensor fusion is formulated as a weighted bipartite graph matching, weighted bipartite graph matching is also commonly referred to as “linear sum assignment.” For each new observation y.sub.j received in frame n and an existing trackle); and
assigning a second tracklet determined to be one of a pair including a first tracklet, as a paired first tracklet and second tracklet, to ground truth data of the first tracklet, based on the second bipartite matching result (0079-0080, 0090, the data association problem in sensor fusion is formulated as a weighted bipartite graph matching, weighted bipartite graph matching is also commonly referred to as “linear sum assignment.” For each new observation y.sub.j received in frame n and an existing trackle).
Regarding claim 9:
UNNIKRISHNAN described an object detection method (abstract, detects the target object in one or more images), the method comprising:
obtaining a first tracklet set and a second tracklet set based on an object detection result (0080-0085, spawn new tracklets); obtaining a first bipartite matching result of a bounding box level, the bounding box level corresponding to each of first tracklets comprised in the first tracklet set and each of second tracklets comprised in the second tracklet set (0079-0080, the data association problem in sensor fusion is formulated as a weighted bipartite graph matching, weighted bipartite graph matching is also commonly referred to as “linear sum assignment.” For each new observation y.sub.j received in frame n and an existing trackle);
obtaining a second bipartite matching result of a tracklet level, the tracklet level corresponding to the first tracklet set and the second tracklet set, based on the first bipartite matching result (0079-0080, the data association problem in sensor fusion is formulated as a weighted bipartite graph matching, weighted bipartite graph matching is also commonly referred to as “linear sum assignment.” For each new observation y.sub.j received in frame n and an existing trackle); and
correcting the object detection result based on the second bipartite matching result (0090, detections in a frame to the tracks is obtained by identifying the minimum weight matching in this bipartite graph. Optimizing the cost function ensures that the weights are well-chosen, which would then help in maximizing the accuracy in correctly associating detections to tracks).
Regarding claim 14:
UNNIKRISHNAN described an apparatus for training an object detector, the apparatus comprising (abstract, detects the target object in one or more images, fig. 2):
processors configured to execute instructions; and a memory storing the instructions, wherein execution of the instructions configures the processors to (0009, use computer):
obtain a first tracklet set based on an object detection result output corresponding to a plurality of frames (0030, camera images and radar frames, 0080-0085, spawn new tracklets);
obtain a second tracklet set from ground truth data predetermined corresponding to the plurality of frames; obtain a first bipartite matching result of a bounding box level, the bounding box level corresponding to each of first tracklets comprised in the first tracklet set and each of second tracklets comprised in the second tracklet set (0079-0080, the data association problem in sensor fusion is formulated as a weighted bipartite graph matching, weighted bipartite graph matching is also commonly referred to as “linear sum assignment.” For each new observation y.sub.j received in frame n and an existing trackle);
obtain a second bipartite matching result of a tracklet level, the tracklet level corresponding to the first tracklet set and the second tracklet set, based on the first bipartite matching result (0079-0080, the data association problem in sensor fusion is formulated as a weighted bipartite graph matching, weighted bipartite graph matching is also commonly referred to as “linear sum assignment.” For each new observation y.sub.j received in frame n and an existing trackle); and
assign a second tracklet determined to be one of a pair including a first tracklet, as a paired first tracklet and second tracklet, to ground truth data of the first tracklet, based on the second bipartite matching result (0079-0080, the data association problem in sensor fusion is formulated as a weighted bipartite graph matching, weighted bipartite graph matching is also commonly referred to as “linear sum assignment.” For each new observation y.sub.j received in frame n and an existing trackle).
Regarding claim 20:
UNNIKRISHNAN described an apparatus for object detection, the apparatus (abstract, detects the target object in one or more images, fig. 2) comprising:
processors configured to execute instructions; and a memory storing the instructions, wherein execution of the instructions configures the processors to (0009, use computer):
obtain a first tracklet set and a second tracklet set based on an object detection result (0030, camera images and radar frames, 0080-0085, spawn new tracklets),
obtain a first bipartite matching result of a bounding box level, the bounding box level corresponding to each of first tracklets comprised in the first tracklet set and each of second tracklets comprised in the second tracklet set (0079-0080, the data association problem in sensor fusion is formulated as a weighted bipartite graph matching, weighted bipartite graph matching is also commonly referred to as “linear sum assignment.” For each new observation y.sub.j received in frame n and an existing trackle),
obtain a second bipartite matching result of a tracklet level, the tracklet level corresponding to the first tracklet set and the second tracklet set, based on the first bipartite matching result (0079-0080, the data association problem in sensor fusion is formulated as a weighted bipartite graph matching, weighted bipartite graph matching is also commonly referred to as “linear sum assignment.” For each new observation y.sub.j received in frame n and an existing trackle), and
correct the object detection result based on the second bipartite matching result (0090, detections in a frame to the tracks is obtained by identifying the minimum weight matching in this bipartite graph. Optimizing the cost function ensures that the weights are well-chosen, which would then help in maximizing the accuracy in correctly associating detections to tracks).
Regarding claim 2, UNNIKRISHNAN further described obtaining the first bipartite matching result based on a first cost, the first cost resulting from a first similarity of a first bounding box comprised in the first tracklet and a second bounding box comprised in the second tracklet (0079-0080, the data association problem in sensor fusion is formulated as a weighted bipartite graph matching, weighted bipartite graph matching is also commonly referred to as “linear sum assignment.” For each new observation y.sub.j received in frame n and an existing trackle).
Regarding claim 3, UNNIKRISHNAN further described a probability in which a first class of the first bounding box is a similar class as a second class of the second bounding box; a difference between first coordinates of the first bounding box and second coordinates of the second bounding box; a difference between a first size of the first bounding box and a second size the second bounding box; and a difference in a rotation degree between the first bounding box and the second bounding box (fig. 3, 4, 0033, 0055, different direction rotation, speed, path).
Regarding claim 4, UNNIKRISHNAN further described obtaining the second bipartite matching result based on a second cost, the second cost resulting from a second similarity of the first tracklet and the second tracklet determined from the first bipartite matching result (0056, camera/radar frames are matched to currently detected object(s) and associates them with each other across the camera/radar frames).
Regarding claim 5, UNNIKRISHNAN further described obtaining a first cost of a first bounding box comprised in the first tracklet determined to be the paired first tracklet and a second bounding box comprised in the second tracklet, based on the first bipartite matching result; determining a second cost of the first tracklet and the second tracklet, based on the obtained first cost; and obtaining the second bipartite matching result based on the second cost 0079-0080, the data association problem in sensor fusion is formulated as a weighted bipartite graph matching, weighted bipartite graph matching is also commonly referred to as “linear sum assignment.” For each new observation y.sub.j received in frame n and an existing trackle.
Regarding claim 6, UNNIKRISHNAN further described an object detector of a 2-stage detector type, and wherein the obtaining of the first tracklet set comprises obtaining the first tracklet set corresponding to respective trajectories of respective detected objects, based on an object detection result output from a region proposal module of the object detector corresponding to the plurality of frames (0079-0080, the data association problem in sensor fusion is formulated as a weighted bipartite graph matching, weighted bipartite graph matching is also commonly referred to as “linear sum assignment.” For each new observation y.sub.j received in frame n and an existing trackle).
Regarding claim 7, UNNIKRISHNAN further described training the object detector based on the ground truth data of the first tracklet (0054, 0080, can train tracklet ).
Regarding claim 8, UNNIKRISHNAN further described a plurality of first bounding boxes corresponding to a time interval, and the second tracklet comprises a plurality of second bounding boxes corresponding to the time interval (fig. 3, 4, 0091, over time, 0079-0080, the data association problem in sensor fusion is formulated as a weighted bipartite graph matching, weighted bipartite graph matching is also commonly referred to as “linear sum assignment.” For each new observation y.sub.j received in frame n and an existing trackle).
Regarding claim 10, UNNIKRISHNAN further described wherein the correcting the object detection result comprises: synthesizing a first tracklet of the first tracklets that is paired to a second tracklet of the second tracklets as a pair, the pair resulting from the second bipartite matching result (0090, detections in a frame to the tracks is obtained by identifying the minimum weight matching in this bipartite graph. Optimizing the cost function ensures that the weights are well-chosen, which would then help in maximizing the accuracy in correctly associating detections to tracks).
Regarding claim 11, UNNIKRISHNAN further described obtaining the first tracklet set corresponding to a first respective trajectory of a first respective detected object of first detected objects, based on a first object detection result output from a first object detector corresponding to a plurality of frames; and obtaining the second tracklet set corresponding to a second respective trajectory of a second respective object of second detected objects, based on a second object detection result output from a second object detector corresponding to the plurality of frames (0079-0080, the data association problem in sensor fusion is formulated as a weighted bipartite graph matching, weighted bipartite graph matching is also commonly referred to as “linear sum assignment.” For each new observation y.sub.j received in frame n and an existing trackle).
Regarding claim 12, UNNIKRISHNAN further described obtaining the first tracklet set corresponding to a first respective trajectory of a first respective detected object of first detected objects, based on a first object detection result output from a first object detector corresponding to a first plurality of frames obtained from a first sensor; and obtaining the second tracklet set corresponding to a second respective trajectory of a second respective object of second detected objects, based on a second object detection result output from a second object detector corresponding to a plurality of frames obtained from a second sensor (0079-0080, the data association problem in sensor fusion is formulated as a weighted bipartite graph matching, weighted bipartite graph matching is also commonly referred to as “linear sum assignment.” For each new observation y.sub.j received in frame n and an existing trackle, 0091, sensors over time to track the positions and orientations of all objects detected around the ego vehicle).
Regarding claim 13, UNNIKRISHNAN further described a non-transitory computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to perform the method (0028, 0036, 00172, computer programming).
Regarding claim 15, UNNIKRISHNAN further described when obtaining the first bipartite matching result, obtain the first bipartite matching result based on a first cost, the first cost resulting from a first similarity of a first bounding box comprised in the first tracklet and a second bounding box comprised in the second tracklet (fig. 3-4, bounding box , 0079-0080, the data association problem in sensor fusion is formulated as a weighted bipartite graph matching, weighted bipartite graph matching is also commonly referred to as “linear sum assignment.” For each new observation y.sub.j received in frame n and an existing trackle).
Regarding claim 16, UNNIKRISHNAN further described when obtaining the second bipartite matching result, obtain the second bipartite matching result based on a second cost, the second cost resulting from a second similarity of the first tracklet and the second tracklet determined from the first bipartite matching result (0079-0080, the data association problem in sensor fusion is formulated as a weighted bipartite graph matching, weighted bipartite graph matching is also commonly referred to as “linear sum assignment.” For each new observation y.sub.j received in frame n and an existing trackle).
Regarding claim 17, UNNIKRISHNAN further described when obtaining the second bipartite matching result, obtain a first cost of a first bounding box comprised in the first tracklet determined to be the paired first tracklet and a second bounding box comprised in the second tracklet, based on the first bipartite matching result, determine a second cost of the first tracklet and the second tracklet, based on the obtained first cost, and obtain the second bipartite matching result based on the second cost (0080, weighted bipartite graph matching is also commonly referred to as “linear sum assignment.” For each new observation y.sub.j received in frame n and an existing tracklet A.sub.i, let C.sub.ij=C(A.sub.i,y.sub.i) denote the cost of associating y.sub.i with A.sub.i. Note that a “tracklet” is a small subsection of the path).
Regarding claim 18, UNNIKRISHNAN further described object detector of a 2-stage detector type, and wherein the processors are further configured to: when obtaining the first tracklet set, obtain the first tracklet set corresponding to respective trajectories of respective detected objects, based on an object detection result output from a region proposal module of the object detector corresponding to the plurality of frames cost (0080, weighted bipartite graph matching is also commonly referred to as “linear sum assignment.” For each new observation y.sub.j received in frame n and an existing tracklet A.sub.i, let C.sub.ij=C(A.sub.i,y.sub.i) denote the cost of associating y.sub.i with A.sub.i. Note that a “tracklet” is a small subsection of the path, 0029, any object(s) in their path, 0030, radar frame data).
Regarding claim 19, UNNIKRISHNAN further described configured to train the object detector based on the ground truth data of the first tracklet (0054, can train, 0080-0085, spawn new tracklets).
Contact information
5. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Tung Lau whose telephone number is (571)272-2274, email is Tungs.lau@uspto.gov. The examiner can normally be reached on Tuesday-Friday 7:00 AM-5:00 PM EST.
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/TUNG S LAU/Primary Examiner, Art Unit 2857
Technology Center 2800
September 9, 2026
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