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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statements submitted on August 14, 2023, March 10, 2025 and April 22, 2025 have been considered by the Examiner.
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.
Claim 7 is 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.
In particular, claim 7 recites “a result of the track integration process is reflected in the labeled training data without through a human check.” It is unclear as to whether this is intended to mean that the result of the track integration process is reflected in the training data without a human check, or that the result of the track integration process is reflected in the training data through a human check, or something else. For purposes of examination claim 7 is interpreted to require a result of the track integration process be reflected in the labeled training data without a human check.
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 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (e.g. a mental process) without significantly more. As described in MPEP § 2106, the analyses as to whether a claim qualifies as eligible subject matter under 35 U.S.C. § 101 includes the following determinations:
(1) Whether the claim is to a statutory category, i.e. to a process, machine, manufacture or composition of matter (“Step 1”) – see MPEP §§ 2106, subsection III, and 2106.03
(2) If the claim is to a statutory category, whether the claim recites any judicial exceptions, including certain groupings of abstract ideas (i.e., mathematical concepts, certain methods of organizing human activity, or mental processes) (“Step 2A, Prong One”) – see MPEP §§ 2106, subsection III, and 2106.04
(3) If the claim recites a judicial exception, whether the claim recites additional elements that integrate the judicial exception into a practical application (“Step 2A, Prong Two”) – see MPEP §§ 2106, subsection III, and 2106.04
(4) If the claim does not recite additional elements that integrate the judicial exception into a practical application, whether the claim recites additional elements that amount to significantly more than the judicial exception (“Step 2B”) – see MPEP §§ 2106, subsection III, and 2106.05
Claim 1
Regarding “Step 1,” independent claim 1 is to a statutory category as claim 1 is directed to a method, i.e. a process.
Accordingly, the analysis proceeds to “Step 2A, Prong One” to determine if the claim recites a judicial exception. In this case, claim 1 recites a mental process and thus recites a judicial exception. “’[T]he mental processes’ abstract idea grouping in particular is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgements, and opinions.” MPEP § 2106.04(a)(2), subsection III. “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claims recites an abstract idea. MPEP § 2106.04(a)(2), subsection III,B (citations omitted). Here, claim 1 recites the following mental process steps: “detecting a moving object in a sequence of images; tracking a same moving object in the sequence of images…[to] obtain a track that is information representing a time series of the same moving object in the sequence of images; and generating the labeled training data by giving the track as a label to the sequence of images.” Such tasks, when given their broadest reasonable interpretations, can practically be performed in the human mind.
Because claim 1 recites a judicial exception (i.e. a mental process), the analysis proceeds to “Step2A, Prong Two.” But here the claim does not recite additional elements that integrate the judicial exception into a practical application. Other than the judicial exceptions, claim 1 recites that the tracking is done “using a tracker, to automatically obtain” the track. Such use of a tracker represents no more than mere instructions to apply the judicial exception on a computer, and thus does not integrate the judicial exception into a practical application. See MPEP § 2106.05(f).
Accordingly, as claim 1 does not recite additional elements that integrate the judicial exception into a practical application, the analysis proceeds to “Step 2B” to determine whether the claim recites additional elements that amount to significantly more than the judicial exception. However, in this case, the claim does not. As noted above, in addition to the above-noted mental process, claim 1 recites that the tracking is done “using a tracker, to automatically obtain” the track. Like further noted above, this represents mere instructions to apply the abstract idea on a generic computer, and thus does not amount to significantly more than the judicial exception.
Consequently, claim 1 recites an abstract idea but does not include additional elements that integrate the abstract idea into a practical application or that amount to significantly more than the abstract idea. As a result, and for the reasons described above, claim 1 is rejected as being patent ineligible under 35 U.S.C. § 101.
Claim 2
In claim 2, the recitations of “…track[ing] the same moving object based on a movement of the bounding box, without performing feature extraction,” and wherein the “bounding box represents a location of the detected moving object in the sequence of images” is considered a mental process.
Other than the mental process, claim 2 recites that the tracking is done using the tracker. However, like noted above, such use of a tracker represents no more than mere instructions to apply the judicial exception on a computer, and thus does not integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Accordingly, claim 2 fails to recite any additional elements that integrate the abstract idea into a practical application or that amount to significantly more than the abstract idea, and as a result, claim 2 is also patent ineligible under 35 U.S.C. § 101.
Claim 3
In claim 3, the recitations of “…associates multiple bounding boxes representing the same moving object in the sequence of images with each other” and “the track is information indicating the multiple bounding boxes representing the same moving object in the sequence of images” are considered a recitation of a mental process.
Other than the mental process, claim 3 recites that the association of the multiple bounding boxes is performed using the tracker. However, such use of a tracker represents no more than mere instructions to apply the judicial exception on a computer, and thus does not integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Accordingly, claim 3 fails to recite any additional elements that integrate the abstract idea into a practical application or that amount to significantly more than the abstract idea, and as a result, claim 3 is also patent ineligible under 35 U.S.C. § 101.
Claim 4
In claim 4, the recitations of “detecting two or more different tracks that are given to the same moving object,” and “integrating the two or more different tracks into a single track” are considered a recitation of a mental process.
Claim 4 fails to recite any additional elements that integrate the abstract idea into a practical application or that amount to significantly more than the abstract idea, and as a result, claim 4 is also patent ineligible under 35 U.S.C. § 101.
Claim 5
In claim 5, the following are considered recitations of a mental process: “…extract a feature amount of each moving object detected in the sequence of images and calculating a degree of similarity between moving objects based on the extracted feature amount” and “where the degree of similarity between a first moving object of a first track and a second moving object of a second track is higher than a threshold, determining that the first moving object and the second moving object are identical and integrating the first track and the second track into a single track.”
Other than the mental process, claim 5 recites that the sequence of images is input into a “feature extraction model” to extract the feature amount and calculate the degree of similarity. Such recitation of the “feature extraction model” represents no more than mere instructions to apply the judicial exception on a computer, and thus does not integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Accordingly, claim 5 fails to recite any additional elements that integrate the abstract idea into a practical application or that amount to significantly more than the abstract idea, and as a result, claim 5 is also patent ineligible under 35 U.S.C. § 101.
Claim 6
In claim 6, the recitations of “presenting a result of the track integration process to a human checker” is considered a recitation of a mental process.
Claim 6 fails to recite any additional elements that integrate the abstract idea into a practical application or that amount to significantly more than the abstract idea, and as a result, claim 6 is also patent ineligible under 35 U.S.C. § 101.
Claim 7
Claim 7 recites “a result of the track integration process is reflected in the labeled training data without through a human check.” This is considered a recitation of applying the judicial exception on a computer, e.g. to automatically perform the track integration process, and thus does not integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Accordingly, claim 7 fails to recite any additional elements that integrate the abstract idea into a practical application or that amount to significantly more than the abstract idea, and as a result, claim 7 is also patent ineligible under 35 U.S.C. § 101.
Claim 8
Claim 8 recites that “the object identification model is a human re-identification model.” This is considered a recitation of applying the judicial exception on a computer, and thus does not integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Accordingly, claim 8 fails to recite any additional elements that integrate the abstract idea into a practical application or that amount to significantly more than the abstract idea, and as a result, claim 8 is also patent ineligible under 35 U.S.C. § 101.
Claim 9
Regarding “Step 1,” independent claim 9 is to a statutory category as claim 9 is directed to a system, which can be considered a machine.
Accordingly, the analysis proceeds to “Step 2A, Prong One” to determine if the claim recites a judicial exception. In this case, claim 9 recites a mental process and thus recites a judicial exception. Similar to claim 1 described above, claim 9 recites the following mental process steps: “detect a moving object in a sequence of images; track a same moving object in the sequence of images…[to] obtain a track that is information representing a time series of the same moving object in the sequence of images; and generate the labeled training data by giving the track as a label to the sequence of images.” Such tasks, when given their broadest reasonable interpretations, can practically be performed in the human mind.
Because claim 9 recites a judicial exception (i.e. a mental process), the analysis proceeds to “Step2A, Prong Two.” But here the claim does not recite additional elements that integrate the judicial exception into a practical application. Other than the judicial exceptions, claim 9 recites that the tracking is done “using a tracker, to automatically obtain” the track, and that the above noted-tasks are performed via one or more processors. These limitations represent no more than mere instructions to apply the judicial exception on a computer, and thus do not integrate the judicial exception into a practical application. See MPEP § 2106.05(f).
Accordingly, as claim 9 does not recite additional elements that integrate the judicial exception into a practical application, the analysis proceeds to “Step 2B” to determine whether the claim recites additional elements that amount to significantly more than the judicial exception. However, in this case, the claim does not. As noted above, in addition to the above-noted mental process, claim 9 recites that the tracking is done “using a tracker, to automatically obtain” the track, and that the above noted-tasks are performed via one or more processors. Like further noted above, this represents mere instructions to apply the abstract idea on a generic computer, and thus does not amount to significantly more than the judicial exception.
Consequently, claim 9 recites an abstract idea but does not include additional elements that integrate the abstract idea into a practical application or that amount to significantly more than the abstract idea. As a result, and for the reasons described above, claim 9 is rejected as being patent ineligible under 35 U.S.C. § 101.
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.
Claims 1 and 9 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by the article entitled, “A semi-automatic system for ground truth generation of soccer video sequences” by D’Orazio et al. (“D’Orazio”).
Regarding claims 1 and 9, D’Orazio describes a semi-automatic system that generates an initial ground truth estimation for image sequences, and then provides a user interface for the manual validation or correction of the ground truth estimations (see e.g. the Abstract). Like claimed, D’Orazio particularly teaches:
detecting a moving object in a sequences of images, and tracking a same moving object in the sequence of images by using a tracker, to automatically obtain a track that is information representing a time series of the same moving object in the sequence of images (D’Orazio discloses that the system generates the initial ground truth estimates by, in part, processing the sequence of images to detect moving objects therein, and using a tracking algorithm to provide a track for each detected moving object:
In this paper we present a semiautomatic system for ground truth generation that provides video annotation file in XML format, compatible with the ViPER toolkit that can be used for image visualization. The proposed system consists on two steps for the initial generation of ground truth estimations and their manual validation by a human operator. In the first step, the whole sequence is processed applying a background segmentation that detects the moving objects, and a tracking algorithm that provides for each blob the track data. In the second step, by using a user friendly interface, all the data provided in the first step, is validated frame by frame by the human operator. In this way he has only to correct the blob dimensions, if the segmentation algorithm was not precise, and also to solve the group blobs if the tracking algorithm failed. In addition, the operator has the possibility to associate some semantic information to each blob (as the type of interactions among objects), and eventually propagates this label to all the frames in which the same blob remains in the scene. The system was tested on multi-view soccer image sequences. Experiments demonstrated that the ground truth data were generated faster than by using the ViPER toolkit. The sequence download is available at the authors’ Institute website [13].
(Section I. “Introduction.” Emphasis added.).
The automatic processing of the whole sequences consists on two steps: first of all a foreground segmentation algorithm is applied to detect the moving object in the scene. Then a tracking algorithm, based an the spatial contiguity of the moving blobs, associates the track information to each blob. In this paper we propose a background subtraction and a tracking algorithm that were demonstrated to be effective with static cameras, people moving during all the sequence and varying lighting conditions. Anyway the initial processing could be substituted with any other algorithms that work in different context providing different results.
(Section II. “Automatic Processing.” Emphasis added.).
D’Orazio describes particular algorithms for detecting the moving objects and for tracking the detected moving objects – see section II.A “Moving Object Segmentation” and section II.B “Moving Object Tracking” – but suggests that other algorithms can alternatively be used as well, as is indicated in the above excerpt. The algorithm for tracking the detected moving objects is considered a tracker like claimed, and understandably obtains information representing a time series of the same moving object in the sequence of images.); and
generating labeled training data by giving the track as a label to the sequence of images (D’Orazio describes a user interface that enables an editor to validate or correct the detected objects and tracks:
In this paper we present a semiautomatic system for ground truth generation that provides video annotation file in XML format, compatible with the ViPER toolkit that can be used for image visualization. The proposed system consists on two steps for the initial generation of ground truth estimations and their manual validation by a human operator. In the first step, the whole sequence is processed applying a background segmentation that detects the moving objects, and a tracking algorithm that provides for each blob the track data. In the second step, by using a user friendly interface, all the data provided in the first step, is validated frame by frame by the human operator. In this way he has only to correct the blob dimensions, if the segmentation algorithm was not precise, and also to solve the group blobs if the tracking algorithm failed. In addition, the operator has the possibility to associate some semantic information to each blob (as the type of interactions among objects), and eventually propagates this label to all the frames in which the same blob remains in the scene. The system was tested on multi-view soccer image sequences. Experiments demonstrated that the ground truth data were generated faster than by using the ViPER toolkit. The sequence download is available at the authors’ Institute website [13].
(Section I. “Introduction.” Emphasis added.).
D’Orazio discloses that the user interface presents the results of the object detection and tracking by superimposing bounding boxes and other information on image frames in the sequence of images:
In figure 3 a portion of one of the images after the first automatic processing step of our system is shown. In this image, the segmentation algorithm and the tracking algorithm have suggested the bounding boxes on the players and the lifetime of each blob.
At this point the human operator has to modify those situations in which algorithms provided imprecisions. As it can be seen in figure 4 some blobs have not the correct dimensions since either the shoes or the hands were not correctly segmented. The numbers superimposed on each blob represent the track ID and the corresponding lifetime provided by the tracking algorithm. Generally the lifetime is correctly updated also when there is a temporary failure of the segmentation algorithm. Indeed in these cases the tracking algorithm maintains the information on previous blobs and when a new one appears in the image in close positions compatible with those of previous tracks, it checks their similarity and associates the track parameters between the two blobs. In figure 4 the ground truth editor allows the human operator to draw manually the two bounding boxes that solve the merge blob and to associate at each of them the attribute parameters of previous frames. These bounding boxes chaining can be done both in the backward and in the forward direction and the editor automatically updates all the dynamic attributes of that track (such as the lifetime). In the manual data validation phase the operator has the possibility to associate to each blob also other information such as the belonging team (Team A, Team B, Goalkeeper A, Goalkeeper B, and Referee). This step is done just on one frame and also in this case it is propagated to all the bounding boxes of the same track. In figure 4 each bounding box has also the class attribute that is the same for players of the same team.
(Section IV. “Experimental Results.” Emphasis added.).
The sequence of images having the superimposed track annotations can be considered labeled training data in which a track is given as a label to the sequence of images. Alternatively, the sequence of images after the user validation step also can be considered labeled training data like claimed.).
D’Orazio thus teaches a training data generation method like that of claim 1, which is for generating training data that can be used for training an object identification model that is based on machine learning. The computing system necessary for performing the object detection and tracking, and for the presenting the user interface described by D’Orazio, is considered a training data generation system like that of claim 9.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 2 and 3 are rejected under 35 U.S.C. 103 as being unpatentable over the article by D’Orazio described above, and also over WIPO Publication No. WO 2021/201774 A1 to Wang et al. (“Wang”).
Regarding claim 2, D’Orazio teaches a training data generation method like that of claim 1, as is described above, and which comprises steps for detecting a moving object in a sequence of images and for tracking a same moving object in the sequence of images by using a tracker. D’Orazio further teaches using a bounding box to represent a location of the detected moving object in the sequence of images (see e.g. Section IV. “Experimental Results,” which recites: “In figure 3 a portion of one of the images after the first automatic processing step of our system is shown. In this image, the segmentation algorithm and the tracking algorithm have suggested the bounding boxes on the players and the lifetime of each blob.”). D’Orazio however does not explicitly disclose that the tracker tracks the same moving object based on a movement of the bounding box, without performing feature extraction, as is further required by claim 2.
Wang nevertheless describes a tracker for tracking a moving object within a sequence of images, wherein a target object label (e.g. a bounding box) is applied to each image in the sequence to represent a location of the moving object in the sequence of images, and the tracker tracks the same moving object in the sequence of images based on a movement of the bounding box, without performing feature extraction (i.e. by detecting the bounding box movement instead of movement of the object itself) (see e.g. page 2, line 20 – page 3, line 6; page 3, lines 18-23; page 20, lines 15-27; and page 21, line 27 – page 22, line 12).
It would have been obvious to one of ordinary skill in the art, having the teachings of D’Orazio and Wang before the effective filing date of the claimed invention, to modify the method taught by D’Orazio so as to use the tracker taught by Wang to track the moving objects in the sequence of images, whereby a bounding box represents a location of a detected moving object in the sequence of images, and the tracker tracks the same moving object based on a movement of the bounding box, without performing feature extraction. It would have been advantageous to one of ordinary skill to utilize such a tracker because it can improve the accuracy of the object detection and tracking, as is taught by Wang (see e.g. page 3, lines 1-6). Accordingly, D’Orazio and Wang are considered to teach, to one of ordinary skill in the art, a training data generation method like that of claim 2.
As per claim 3, it would have been obvious, as is described above, to modify the method taught by D’Orazio so as to use the tracker taught by Wang to track the moving objects in the sequence of images. Wang particularly teaches that the tracker associates multiple bounding boxes representing the same moving object in the sequence of images with each other (e.g. based on a threshold distance), and provides a track comprising information indicating the multiple bounding boxes representing the same moving object in the sequence of images (see e.g. page 21, line 27 – page 22, line 12; and page 23, lines 1-18). Accordingly, the above-described combination of D’Orazio and Wang is further considered to teach a method like that of claim 3.
Claims 4-7 are rejected under 35 U.S.C. 103 as being unpatentable over the article by D’Orazio described above, and also over U.S. Patent No. 12,236,685 to Pan et al. (“Pan”).
Regarding claim 4, D’Orazio teaches a training data generation method like that of claim 1, as is described above, and which comprises steps for detecting a moving object in a sequence of images and for tracking a same moving object in the sequence of images by using a tracker. Although D’Orazio suggests enabling a user to integrate two or more tracks into a single track (e.g. by “chaining” bounding boxes – see e.g. section III. “Manual Data Validation”), D’Orazio does not teach employing a track integration process that includes detecting two or more different tracks that are given to the same moving object, and integrating the two or more tracks into a single track like required by claim 4.
Pan nevertheless describes a track integration process that involves detecting two or more tracks that are given to the same moving object (e.g. the same pedestrian), and integrating the two or more tracks into a single track (see e.g. column 1, lines 50-59; and column 9, lines 26-52).
It would have been obvious to one of ordinary skill in the art, having the teachings of D’Orazio and Pan before the effective filing date of the claimed invention, to modify the method taught by D’Orazio so as to also utilize a track integration process like taught by Pan, which detects two or more tracks that are given to the same moving object, and integrates the two or more tracks into a single track. It would have been advantageous to one of ordinary skill to utilize such a track integration process because it can reduce the user’s manual workload, as is evident from Pan (see e.g. column 1, lines 37-59). Accordingly, D’Orazio and Pan are considered to teach, to one of ordinary skill in the art, a training data generation method like that of claim 4.
As per claim 5, it would have been obvious, as is described above, to modify the method taught by D’Orazio so as to also utilize a track integration process like taught by Pan. Pan suggests that the track integration process includes: (i) inputting a sequence of images into a feature extraction model to extract a feature amount of each moving object (e.g. pedestrian) detected in the sequence of images and calculate a degree of similarity between the moving objects based on the extracted feature amount; and (ii) when the degree of similarity between a first moving object of a first track and a second moving object of a second track is higher than a threshold, determining that the first moving object and the second moving object are identical and integrating the first track and the second track into a single track (see e.g. column 2, line 63-67; column 3, line 28 – column 4, line 18; and column 5, lines 7-22). Accordingly, the above-described combination of D’Orazio and Pan is further considered to teach a training data generation method like that of claim 5.
As per claim 6, D’Orazio teaches presenting results of the object detection and tracking to a human checker via a user interface (see e.g. section I. “Introduction,” which recites: “In the first step, the whole sequence is processed applying a background segmentation that detects the moving objects, and a tracking algorithm that provides for each blob the track data. In the second step, by using a user friendly interface, all the data provided in the first step, is validated frame by frame by the human operator.”). As described above, it would have been obvious to modify the method taught by D’Orazio so as to also utilize a track integration process like taught by Pan. It thus follows that the presentation of the results of the object detection and tracking would further entail presenting a result of the track integration process to the human checker. Accordingly, the above-described combination of D’Orazio and Pan is further considered to teach a training data generation method like that of claim 6.
As per claim 7, D’Orazio teaches presenting results of the object detection and tracking to a human checker via a user interface (see e.g. section I. “Introduction,” which recites: “In the first step, the whole sequence is processed applying a background segmentation that detects the moving objects, and a tracking algorithm that provides for each blob the track data. In the second step, by using a user friendly interface, all the data provided in the first step, is validated frame by frame by the human operator.”). As described above, it would have been obvious to modify the method taught by D’Orazio so as to also utilize a track integration process like taught by Pan. The track integration process taught by Pan is automatic (see e.g. column 1, lines 37-59). It thus follows that the results of the track integration process can be reflected in the labeled training data without a human check. Accordingly, the above-described combination of D’Orazio and Pan is further considered to teach a training data generation method like that of claim 7.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over the article by D’Orazio described above, and also over U.S. Patent Application Publication No. 2022/0092348 to Jakobsen et al. (“Jakobsen”).
Regarding claim 8, D’Orazio teaches a training data generation method like that of claim 1, as is described above, and which comprises steps for detecting a moving object in a sequence of images and for tracking a same moving object in the sequence of images by using a tracker. D’Orazio, however, does not explicitly teach that the training data generated by the method is used to train an object identification model that is a human re-identification model, as is required by claim 8.
Jakobsen nevertheless teaches generating training data from a sequence of images, wherein the training data is used for training an object identification model that is based on machine learning, and particularly where the object identification model is a human re-identification model (see e.g. paragraphs 0002-0003 and 0006-0008).
It would have been obvious to one of ordinary skill in the art, having the teachings of D’Orazio and Jakobsen before the effective filing date of the claimed invention, to modify the method taught by D’Orazio so as to use the generated training data to train an object identification that is a human re-identification model like taught by Jakobsen. It would have been advantageous to one of ordinary skill to utilize such a combination, because it would provide for a more efficient training, as is suggested by Jakobsen (see e.g. paragraphs 0003 and 0022). Accordingly, D’Orazio and Jakobsen are considered to teach, to one of ordinary skill in the art, a training data generation method like that of claim 8.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-9 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 2-8 of copending Application No. 18/233,447 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the reference application anticipate the claims of the instant application. This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Particularly, like in claim 1 of the instant application, claim 2 of the reference application describes a method comprising: (i) detecting a moving object in a sequence of images (i.e. claim 2 of the reference application recites, “detecting a moving object in the sequence of images”); (ii) tracking a same moving object in the sequence of images by using a tracker, to automatically obtain a track that is information representing a time series of the same moving object in the sequence of images (i.e. claim 2 of the reference application recites, “tracking the same moving object in the sequence of images by using the tracker to automatically obtain the track;” and claim 2 depends from claim 1 of the reference application, which further recites “wherein the track is information representing a time series of a same moving object in the sequence of images”); and (ii) generating the labeled training data by giving the track as a label to the sequence of images (i.e. claim 2 of the reference application recites, “generating the labeled training data by giving the track as the label to the sequence of images”). Accordingly, claim 1 of the instant application is anticipated by claim 2 of the reference application.
Like in claim 2 of the instant application, claim 3 of the reference application teaches that: (i) a bounding box represents a location of the detected moving object in the sequence of images (i.e. claim 3 of the reference application recites, “a bounding box represents a location of the detected moving object in the sequence of images”); and (ii) the tracker tracks the same moving object based on a movement of the bounding box, without performing feature extraction (i.e. claim 3 of the reference application recites, “the tracker tracks the same moving object based on a movement of the bounding box, without performing feature extraction”). Accordingly, claim 2 of the instant application is anticipated by claim 3 of the reference application.
Like in claim 3 of the instant application, claim 4 of the reference application teaches that: (i) the tracker associates multiple bounding boxes representing the same moving object in the sequence of images with each other (i.e. claim 4 of the reference application recites, “the tracker associates multiple bounding boxes representing the same moving object in the sequence of images with each other”); and (ii) the track is information indicating the multiple bounding boxes representing the same moving object in the sequence of images (i.e. claim 4 of the reference application recites, “the track is information indicating the multiple bounding boxes representing the same moving object in the sequence of images”). Accordingly, claim 3 of the instant application is anticipated by claim 4 of the reference application.
Like in claim 4 of the instant application, claim 5 of the reference application teaches a track integration process that includes: (i) detecting two or more different tracks that are given to the same moving object (i.e. claim 5 of the reference application recites, “detecting two or more different tracks that are given to the same moving object”); and (ii) integrating the two or more tracks into a single track (i.e. claim 5 of the reference application recites, “integrating the two or more different tracks into a single track”). Accordingly, claim 4 of the instant application is anticipated by claim 5 of the reference application.
Like in claim 5 of the instant application, claim 6 of the reference application teaches that the track integration process includes: (i) inputting the sequence of images into a feature extraction model to extract a feature amount of each moving object detected in the sequence of images and calculate a degree of similarity between moving objects based on the extracted feature amount (i.e. claim 6 of the reference application recites, “inputting the sequence of images into a feature extraction model to extract a feature amount of each moving object detected in the sequence of images and calculate a degree of similarity between moving objects based on the extracted feature amount”); and (ii) when the degree of similarity between a first moving object of a first track and a second moving object of a second track is higher than a threshold, determining that the first moving object and the second moving object are identical and integrating the first track and the second track into a single track (i.e. claim 6 of the reference application recites, “when the degree of similarity between a first moving object of a first track and a second moving object of a second track is higher than a threshold, determining that the first moving object and the second moving object are identical and integrating the first track and the second track into a single track”). Accordingly, claim 5 of the instant application is anticipated by claim 6 of the reference application.
Like in claim 6 of the instant application, claim 7 of the reference application teaches presenting a result of the track integration process to a human checker (i.e. claim 7 of the reference application recites, “a result of the track integration process is reflected in the labeled training data without through a human check”). Accordingly, claim 6 of the instant application is anticipated by claim 7 of the reference application.
Like in claim 7 of the instant application, claim 7 of the reference application teaches that a result of the track integration process is reflected in the labeled training data without through a human check (i.e. claim 7 of the reference application recites, “a result of the track integration process is reflected in the labeled training data without through a human check”). Accordingly, claim 7 of the instant application is anticipated by claim 7 of the reference application.
Like in claim 8 of the instant application, claim 8 of the reference application teaches that the object identification model is a human re-identification model (i.e. claim 8 of the reference application recites, “the object identification model is a human re-identification model”). Accordingly, claim 8 of the instant application is anticipated by claim 8 of the reference application.
Like in claim 9 of the instant application, claim 2 of the reference application teaches: (i) detecting a moving object in a sequence of images (i.e. claim 2 of the reference application recites, “detecting a moving object in the sequence of images”); (ii) tracking a same moving object in the sequence of images by using a tracker, to automatically obtain a track that is information representing a time series of the same moving object in the sequence of images (i.e. claim 2 of the reference application recites, “tracking the same moving object in the sequence of images by using the tracker to automatically obtain the track;” and claim 2 depends from claim 1 of the reference application, which further recites “wherein the track is information representing a time series of a same moving object in the sequence of images”); and (ii) generating the labeled training data by giving the track as a label to the sequence of images (i.e. claim 2 of the reference application recites, “generating the labeled training data by giving the track as the label to the sequence of images”). A processor necessary to perform such tasks is considered a training data generation system like that of claim 9 of the instant application. Accordingly, claim 9 of the instant application is anticipated by claim 2 of the reference application.
Conclusion
The prior art made of record on form PTO-892 and not relied upon is considered pertinent to applicant’s disclosure. The applicant is required under 37 C.F.R. §1.111(C) to consider these references fully when responding to this action. In particular, the article by Bianco et al. cited therein describes an interactive video annotation tool that supports manual, semi-automatic and automatic annotations. The U.S. Patent Application Publication to Shen et al. cited therein describes a method for generating and editing object track labels for objects detected in video data. The U.S. Patent Application Publication to Brower cited therein describes using object detections of a machine learning model to automatically generate new ground truth data for images captured at different perspectives. The U.S. Patent Application Publication to Vajapey et al. cited therein describes an automated annotated object tracking tool that allows machine-learning teams to annotate an object within a frame and have that annotation persist across frames as the annotated object is tracked within a series of frames.
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/BTB/
3/21/2026
/MATTHEW ELL/Supervisory Patent Examiner, Art Unit 2141