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 .
Specification
The disclosure is objected to because of the following informalities: In para 46, “estimate the actual (the (e.g., the most probable)” should read “estimate the actual (e.g., the most probable)”. Appropriate correction is required.
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
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 an abstract idea without significantly more. Under Step 1, claims 1-10 are process/method claims and claims 11-20 are machine claims. Under Step 2A Prong One, all claims recite abstract ideas, specifically mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III). These mental processes are more particularly recited in claim 1 as:
obtaining digital representations of an object depicted in a plurality of video frames (i.e., human obtaining numerical values characterizing object location or visual description or obtaining images with objects visually located); and performing a bidirectional tracking of the object across the plurality of video frames (i.e., following the location of objects across frames either with pen/paper or mentally), wherein the bidirectional tracking comprises:
for each of a forward direction (FD) of the bidirectional tracking and a reverse direction (RD) of the bidirectional tracking, obtaining, using (i) a current state of the object associated with an upstream video frame and (ii) the digital representation of the object for a downstream video frame, an updated state of the object associated with the downstream video frame (i.e., following the numerical values/locations of objects in both previous and subsequent frames); obtaining, using at least one of the updated state of the object for the FD or the updated state of the object for the RD, a bidirectional state of the object (i.e., determining a location of objects in one frame with respect to its location in a different frame); and determining, using the bidirectional state of the object, a trajectory of the object across the plurality of video frames (i.e., characterizing with pen/paper or mentally how an object moved across frames).
Dependent claims 2-10 provide additional limitations that are further part of the abstract idea of bidirectional tracking of objects across image frames. Claims 5-10 also provide additional limitations that are considered mathematical concepts, and are thus further part of the abstract idea.
It is noted that the above analysis is according to the 2019 Revised Patent Subject Matter Eligibility Guidance published in the Federal Register (84 FR 50) on January 7, 2019 and MPEP 2106.04(a)(2)(III). Consider also that “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 claim recites an abstract idea” as per MPEP 2106.04(a)(2)(III)(B). See also footnotes 14 and 15 of the Federal Register Notice. As detailed above, the steps for bidirectional tracking of objects across image frames may be practically performed in the human mind with or without the use of a physical aid such as a pen and paper.
Under Step 2A Prong Two, this judicial exception is not integrated into a practical application because each of claims 1-10 do not recite additional elements that integrate the exception into a practical application. The additional element of “obtaining digital representations of an object” in claim 1 adds insignificant extra-solution activity, which is not indicative of integration into a practical application as per MPEP 2106.05(g). The additional element of the machine learning model of claim 2 is recited at a high level of generality and merely equate to “apply it” or otherwise merely uses a generic computer as a tool to perform an abstract idea which is not indicative of integration into a practical application, as per MPEP 2106.05(f). Additionally, the digital representations are obtained before bidirectional tracking is performed, thus a human performing the claim solely receives the data as generated. See also MPEP 2106.04(a)(2)(III) with respect to Mental Processes: “Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer”. See also MPEP 2106.04(a)(2)(III)(C)(3) “Using a computer as tool to perform a mental process” and MPEP 2106.04(a)(2)(III)(D), as well as the case law cited therein.
The additional elements in claims 2-10 reciting mathematical concepts and/or abstract ideas do not integrate the judicial exception into a practical application. See MPEP 2106.04(II)(A)(2).
Under Step 2B, each of claims 1-10 do not recite additional elements that are indicative of an inventive concept. The additional elements are simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception as per MPEP 2106.05(d) and 2106.07(a)III. In other words, the additional elements do not amount to significantly more than the judicial exception.
Regarding claim 1, the obtaining digital representations of an object is well-known extra-solution activity (a common way for a human to identify an object in an image), and thus does not amount to significantly more (see MPEP 2106.05(g)).
Regarding claim 2, use of a machine learning model to obtain data before performing the abstract idea is considered insignificant extra-solution activity (see MPEP 2106.05(g)) and amounts to merely an instruction to apply an aspect of the abstract idea using generic computer elements (see MPEP 2106.05(f) MPEP 2106.05(I)(A)). Thus, it does not integrate the judicial exception into a practical application.
Regarding claims 2-4, further defining the type of data used to characterize objects in images is further part of the abstract idea of claim 1. Additionally, the tracking of these values across a plurality of image frames may be practically performed in the human mind with the use of a physical aid such as a pen and paper. Regarding claims 5-9, additional limitations are directed to the abstract ideas of mathematical calculations (comparing vectors in claim 5) and comparing numerical values to each other with the use of a threshold value (claims 6-9), which are considered mathematical concepts/calculations (see MPEP 2106.04(a)(2)) and mental processes (see MPEP 2106.04(a)(2)). Regarding claim 10, the generating of a human-perceivable report is further part of the abstract idea of claim 1 and may be practically performed in the human mind with the use of a physical aid such as a pen and paper. The addition of further judicial exceptions does not amount to significantly more (see MPEP 2106.05(I)).
Regarding independent claims 11 and 20, the rationale provided in the rejection of claim 1, and corresponding dependent claims, is incorporated herein. The system of claim 1 corresponds to the system of claim 11 and the processor of claim 20, and performs the same steps disclosed in claim 1. In addition, the processing devices of claims 11 and 20 and the systems of claim 19 amount to merely an instruction to apply the abstract idea using generic computer elements, and does not integrate the judicial exception into a practical application (see MPEP 2106.05(d)). This does not amount to significantly more than the judicial exception. For all of the above reasons, taken alone or in combination, claims 1-20 recite a non-statutory mental process.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 3, 5, 7-9, 11-13, 15-17 and 20 are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by Wang et al. (U.S. Patent No. 2024/0037757 A1), hereinafter Wang.
Regarding claim 1, Wang teaches a method comprising: obtaining digital representations of an object depicted in a plurality of video frames (Wang, FIG. 2a-d, attached below – bounding boxes identify object in image frames, for example Tx=g[x] (the person without a hat), para 32: “FIG. 2(c) and FIG. 2(d) illustrate two schematic images in an output image sequence in multi-target tracking, wherein, bounding boxes that highlight detected targets have been overlaid in input images, two targets of interest have been detected in the image”); and performing a bidirectional tracking of the object across the plurality of video frames (Wang, FIG. 2e and 3, attached below – frame t’ is the downstream frame for both directions; para 32 describes detecting incorrect target identifications between time frames t, t’, and t’’, referred to throughout the disclosure as an image patch sequence in para 85, for example; para 37: “determine a candidate identification switch image patch based on similarities of adjoining image patch pairs”), wherein the bidirectional tracking comprises:
for each of a forward direction (FD) of the bidirectional tracking (Wang, Comparison of frames t'' and t') and a reverse direction (RD) of the bidirectional tracking (Wang, Comparison of frames t and t'), obtaining, using (i) a current state of the object associated with an upstream video frame (Wang, Patch[Kt''][j''] for the FD; Patch[Kt][j] for the RD) and (ii) the digital representation of the object for a downstream video frame (Wang, Patch[Kt'][j'] for both the FD and RD), an updated state of the object associated with the downstream video frame (Wang, confirmed or flagged ID switch from downstream to upstream frame, Step S303; para 39 describes the step of determining if ID/target is labeled correctly; in the example in para 32 and FIGs below, no ID switch for the FD because (id=x) and yes ID switch for the RD because (id≠x));
obtaining, using at least one of the updated state of the object for the FD or the updated state of the object for the RD, a bidirectional state of the object (Wang, determination, using ID switches, of whether tracklet for the object needs to be corrected or not, see para 32 and para 63: “The device 1000 can make attempts to split a tracklet indicative of a trajectory of a single target with the units 1001 to 1007 …determine whether a candidate identification switch image patch is present in the tracklet based on feature similarities of a plurality of re-identification feature pairs in the re-identification feature set. The verifying unit 1005 is configured to: in a case where a determination result is “yes”, verify whether it is credible that identification-switch has occurred at the candidate identification switch image patch.”); and determining, using the bidirectional state of the object, a trajectory of the object across the plurality of video frames (Wang, Tracklets are indicative of the trajectory of the object, see para 32, 63).
PNG
media_image1.png
514
753
media_image1.png
Greyscale
PNG
media_image2.png
694
712
media_image2.png
Greyscale
Regarding claim 3 (dependent on claim 1), Wang teaches wherein the current state of the object comprises: a feature vector representative of a depiction of the object in at least the upstream video frame (Wang, para 32 describes features for each patch; para 38 indicates a cosine similarity between features), and a location of the object associated with the upstream video frame (Wang, bounding boxes associated with features demonstrate location of objects, para 32).
Regarding claim 5 (dependent on claim 1), Wang teaches wherein obtaining the bidirectional state of the object comprises:
computing an FD similarity between an FD feature vector associated with the current state of the object for the FD of the bidirectional tracking and a feature vector associated with the digital representation of the object for the downstream frame (Wang, process of determining whether adjoining frames contain a “special feature similarity”, para 39, determined using cosine similarity, para 38: “adjoining image patch pairs can be obtained, and a j-th feature similarity Sim[j] is a similarity Sim(F[j+1], F[j]) between a re-identification feature F[j+1] of an image patch Patch[j+1] and a re-identification feature F[j] of an image patch Patch[j]. The similarity can be a cosine similarity between re-identification feature”);
computing an RD similarity between an RD feature vector associated with the current state of the object for the RD of the bidirectional tracking and the feature vector associated with the digital representation of the object for the downstream frame (Wang, same processing above is computed for the number of patch sets in a sequence – para 38: “feature similarities of re-identification feature pairs of a plurality of adjoining image patch pairs in the image patch sequence SqPatch[i] are determined.”; para 40: “it is possible to find at a time all special feature similarities in the plurality of feature similarities, and to designate image patches associated therewith as candidate identification switch image patches”); and
obtaining the bidirectional state of the object using the FD similarity and the RD similarity (Wang, para 39: “it is determined whether a candidate identification switch image patch is present in the tracklet Trk[j] according to whether a special feature similarity Simp less than a predetermined similarity threshold sTh is present in the plurality of feature similarities”).
Regarding claim 7 (dependent on claim 5), Wang teaches wherein obtaining the bidirectional state of the object using the FD similarity and the RD similarity comprises: determining that the FD similarity is above a threshold similarity and that the RD similarity is below the threshold similarity (Wang, a case when only the RD comparison contains a special feature similarity; para 39: “whether a special feature similarity Simp less than a predetermined similarity threshold sTh is present in the plurality of feature similarities”); resetting the state of the object for the RD (Wang, candidate for ID switch, para 39: “when a special feature similarity Simp is present, it is determined that a candidate identification switch image patch is present in the tracklet Trk[j], and, an image patch associated with the special feature similarity Simp is designated as the candidate identification switch image patch. For example, when the special feature similarity Simp is Sim[j] (i.e., Sim[j]<sTh), the image patch Patch[j] is designated as the candidate identification switch image patch.”); assigning a new object identification (ID) as an object ID for the RD of the bidirectional tracking (Wang, identification switch for the object in the patch by correcting the tracklet; tracklets are associated with target object ID, see FIG. 2, so splitting the tracklets corrects the IDs for all patches in each tracklet, see FIG. 1); and mapping the object ID for the RD of the bidirectional tracking to an object ID for the FD of the bidirectional tracking (Wang, correcting tracklets results in correct IDs in both directions, see para 53-54).
Regarding claim 8 (dependent on claim 5), Wang teaches wherein obtaining the bidirectional state of the object using the FD similarity and the RD similarity comprises: determining that the RD similarity is above a threshold similarity and that the FD similarity is below the threshold similarity (Wang, a case when only the FD comparison contains a special feature similarity; para 39: “whether a special feature similarity Simp less than a predetermined similarity threshold sTh is present in the plurality of feature similarities”); resetting the state of the object for the FD (Wang, candidate for ID switch, para 39: “when a special feature similarity Simp is present, it is determined that a candidate identification switch image patch is present in the tracklet Trk[j], and, an image patch associated with the special feature similarity Simp is designated as the candidate identification switch image patch. For example, when the special feature similarity Simp is Sim[j] (i.e., Sim[j]<sTh), the image patch Patch[j] is designated as the candidate identification switch image patch.”); assigning a new object ID as the object for the FD of the bidirectional tracking (Wang, identification switch for the object in the patch by correcting the tracklet; tracklets are associated with target object ID, see FIG. 2, so splitting the tracklets corrects the IDs for all patches in each tracklet, see FIG. 1); and mapping the object ID for the FD of the bidirectional tracking to an object ID for the RD of the bidirectional tracking (Wang, correcting tracklets results in correct IDs in both directions, see para 53-54).
Regarding claim 9 (dependent on claim 5), Wang teaches wherein obtaining the bidirectional state of the object using the FD similarity and the RD similarity comprises: determining that each of the FD similarity and the RD similarity is below a threshold similarity (Wang, a case when both the FD and RD are special feature similarities due to both being below the similarity threshold, sTh, see para 39); resetting the updated state of the object for the FD; resetting the updated state of the object for the RD (Wang, candidate for ID switch for both sets of patches outlined in claim 1, see para 39); and assigning new object IDs to each of the FD of the bidirectional tracking and the RD of the bidirectional tracking (Wang, identification switch for the object in each patch set by correcting the tracklet; tracklets are associated with target object ID, see FIG. 2, so splitting the tracklets corrects the IDs for all patches in each tracklet, see FIG. 1).
Regarding claim 11, Wang teaches a system comprising: a processing device (Wang, para 64: “The device 1100 comprises: a memory 1101 having instructions stored thereon; and at least one processor 1103 connected to the memory 1101 and used to execute the instructions on the memory 1101 to split a tracklet indicative of a trajectory of a single target”). All claim limitations carried out by the processing device are met by Wang because the method steps of claim 1 are the same as the steps in claim 11.
Regarding claim 12 (dependent on claim 11), Wang teaches wherein the current state of the object comprises: a feature vector representative of a depiction of the object in at least the upstream video frame (Wang, para 32 describes features for each patch; para 38 indicates a cosine similarity between features), a size of the object, and a location of the object associated with the upstream video frame (Wang, bounding boxes associated with object features, para 32).
Regarding claim 13 (dependent on claim 11), all claim limitations are met by Wang because the method steps of claim 5 are the same as the steps in claim 13.
Regarding claim 15 (dependent on claim 13), all claim limitations are met by Wang because the method steps of claim 7 are the same as the steps in claim 15.
Regarding claim 16 (dependent on claim 13), all claim limitations are met by Wang because the method steps of claim 8 are the same as the steps in claim 16.
Regarding claim 17 (dependent on claim 13), all claim limitations are met by Wang because the method steps of claim 9 are the same as the steps in claim 17.
Regarding claim 20, Wang teaches a processor comprising one or more processing devices (Wang, para 64: “The device 1100 comprises: a memory 1101 having instructions stored thereon; and at least one processor 1103 connected to the memory 1101 and used to execute the instructions on the memory 1101 to split a tracklet indicative of a trajectory of a single target”). All claim limitations carried out by the one or more processing devices are met by Wang because the method steps of claim 1 are the same as the steps in claim 20.
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.
Claims 2 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Yasutomi et al. (U.S. Patent No. 2024/0119739 A1), hereinafter Yasutomi.
Regarding claim 2 (dependent on claim 1), Wang fails to explicitly teach wherein the digital representations of the object comprise one or more feature vectors generated by a machine learning model for respective video frames of the plurality of video frames. However, Yasutomi similarly teaches an object tracking method (Yasutomi, abstract). Yasutomi teaches wherein the digital representations of an object comprise one or more feature vectors generated by a machine learning model for respective video frames of the plurality of video frames (Yasutomi, para 50: “Image data 226 and 227 of a plurality of objects 223a and 223b recognized as the same object by an object tracking model (to be described later) in frame images 221 (221a and 221b) at different times of unlabeled moving image data 220 (moving image) are used for contrastive learning”; see FIG. 4, attached below, wherein images are input to an object detection model before the objects are input to a tracking model; see examples of trained detection models in para 58 and “feature vectors” in para 59).
Wang discloses a base method for extracting features from a plurality of video frames, but does not specify specific methods for feature extraction. Yasutomi teaches a method for object feature detection using a known technique of generating feature vectors by a machine learning model. A person having ordinary skill in the art, before the effective filing date of the claimed invention, could have applied the known technique, as taught by Yasutomi, in the same way to the method of Wang and achieved predictable results of accurately identifying objects in image frames using a trained and validated model.
PNG
media_image3.png
472
568
media_image3.png
Greyscale
Regarding claim 19 (dependent on claim 11), Wang teaches wherein multi-target tracking inventions may be applied to autonomous driving (Wang, para 4: “MTT is a key technology in the field of computer vision, and has been widely applied in fields such as autonomous driving, intelligent monitoring, behavior recognition and the like.”), but fails to explicitly teach wherein the system is comprised in at least one of: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing simulation operations; a system for performing digital twin operations; a system for performing light transport simulation; a system for performing collaborative content creation for 3D assets; a system for performing deep learning operations; a system implemented using an edge device; a system for generating or presenting at least one of augmented reality content, virtual reality content, or mixed reality content; a system implemented using a robot; a system for performing conversational AI operations; a system for generating synthetic data using AI operations; a system incorporating one or more virtual machines (VMs); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources.
However, Yasutomi similarly teaches an object tracking method (Yasutomi, abstract). Yasutomi teaches wherein the system performing the object tracking method is comprised in a system for performing deep learning operations (Yasutomi, the system includes a deep neural network, see para 81). Wang discloses a base method for extracting features from a plurality of video frames in a target tracking system, but does not specify a specific type of system for carrying out the operations. Yasutomi teaches a method for multi-target tracking using a known technique of implementing the method in a system performing deep learning operations to carry out the method. A person having ordinary skill in the art, before the effective filing date of the claimed invention, could have applied the known technique of utilizing deep learning operations to perform computer vision tasks, as taught by Yasutomi, in the same way to the system of Wang and achieved predictable results of accurately and efficiently identifying and tracking objects across image frames using a trained and validated model.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Kirsch et al. (U.S. Patent No. 2019/0353775 A1), hereinafter Kirsch.
Regarding claim 4 (dependent on claim 3), Wang teaches wherein the current state of the object further comprises: a size of the object (Wang, bounding boxes associated with object features, para 32), but fails to explicitly teach further comprising a velocity of the object associated with the upstream video frame, and wherein the location of the object, the size of the object and the velocity of the object are determined using a statistical filter.
However, Kirsch teaches a method for tracking objects in images, including determining the location of the object, the size of the object and the velocity of the object using a statistical filter (Kirsch, para 100: “a process 500 is shown for detecting and tracking an object. The process 500 can be performed by the camera manager 318 with…a Kalman filter”; para 121: “the camera manager 318 can predict an object bounding box using a Kalman filter”; para 125: “The camera manager 318 can retrieve a predicted speed of the object from the Kalman filter for each frame of a sequence of frames analyzed by the camera manager 318”). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have combined the determining of the velocity of the object and use of a Kalman filter, as taught by Kirsch, with the method of Wang in order to improve the accuracy of tracking objects by identifying the speed of objects over times and predicting movement/locations over time using a Kalman filter (Kirsch, para 121: “The prediction by the Kalman filter can be made based on one or multiple past known locations of the object (e.g., past bounding boxes). The Kalman filter can track one or multiple different objects, generating a predicted location for each.”; para 112: “the camera manager 318 can perform filtering of the tracks of the objects generated in the step 804 (or over multiple iterations of the steps 802 and 804) based on speed and size of the objects”).
Claims 6 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Zhang (CN Patent No. 110400329 A).
Regarding claim 6 (dependent on claim 5), Wang teaches wherein obtaining the bidirectional state of the object using the FD similarity and the RD similarity comprises: determining that each of the FD similarity and the RD similarity is above a threshold similarity (Wang, a case when both the FD and RD are not special feature similarities due to both being above the similarity threshold, sTh, see para 39), but fails to explicitly teach modifying the updated state for the FD using the updated state for the RD; and modifying the updated state for the RD using the updated state for the FD.
However, Zhang teaches a similar FD/RD tracking method wherein when both directions meet a threshold condition, modifying the updated state for the FD using the updated state for the RD; and modifying the updated state for the RD using the updated state for the FD (Zhang, pg. 35, para 71: “For the objects in the forward detection process and the inverse detection process of the reference area and the tracking process have very similar tracking values, they will be updated to be the same object. On the other hand, the forward tracking value and the inverse tracking value in the reference area may not be in a very close range, but both meet the preset tracking threshold condition.”; modifying the state by updating to the same object). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have combined the method for modifying the updated states based on a threshold value, as taught by Zhang, with the method of Wang in order to correctly identify the objects as being the same state in both directions when they are determined to be the same object in the downstream frame (See Zhang citation above).
Regarding claim 14 (dependent on claim 13), all claim limitations are met and rendered obvious by Wang in view of Zhang because the method steps of claim 6 are the same as the steps in claim 14.
Claims 10 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Roshtkhari et al. (U.S. Patent No. 2016/0335502 A1), hereinafter Roshtkhari.
Regarding claim 10 (dependent on claim 1), Wang teaches outputting a processing result in para 53, but fails to explicitly teach further comprising: generating at least one of: a human-perceivable report that is based, at least in part, on the trajectory of the object, a statistical report that is based, at least in part, on the trajectory of the object, or a video that depicts, at least a portion of the trajectory of the object. However, Roshtkhari teaches a similar method for tracking objects (Roshtkhari, abstract), further comprising: generating a human-perceivable report that is based, at least in part, on the trajectory of the object (Roshtkhari, final tracking result in FIG. 3d, attached below, and para 38; para 34: “The tracking system also includes a trajectory creation module 20 for using the tracklets to generate trajectories for the objects being tracked”). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have combined the generated human-perceivable result, taught by Roshtkhari, with the method of Wang in order to visualize the complete trajectories of objects (Roshtkhari, see para 34, 38, and FIG 2-3d).
PNG
media_image4.png
337
592
media_image4.png
Greyscale
Regarding claim 18 (dependent on claim 11), all claim limitations are met and rendered obvious by Wang in view of Roshtkhari because the method steps of claim 10 are the same as the steps in claim 18.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
U.S. Patent No. 2022/0301183 A1
U.S. Patent No. 12,423,832 B2
U.S. Patent No. 2024/0404084 A1
Zhang, J., Zhang, X., Huang, Z., Cheng, X., Feng, J., & Jiao, L. (2023). Bidirectional multiple object tracking based on trajectory criteria in satellite videos. IEEE Transactions on Geoscience and Remote Sensing, 61, 1-14.
Li, X., Wang, K., Wang, W., & Li, Y. (2010, June). A multiple object tracking method using Kalman filter. In The 2010 IEEE international conference on information and automation (pp. 1862-1866). IEEE.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EMMA E DRYDEN whose telephone number is (571)272-1179. The examiner can normally be reached M-F 9-5 EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, ANDREW BEE can be reached at (571) 270-5183. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/EMMA E DRYDEN/Examiner, Art Unit 2677
/ANDREW W BEE/Supervisory Patent Examiner, Art Unit 2677
Prosecution Insights