CTNF 18/155,349 CTNF 74910 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Continued Examination Under 37 CFR 1.114 07-42-04 AIA A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 3/10/26 has been entered. Response to Arguments Applicant’s arguments with respect to claim(s) have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Claim Rejections - 35 USC § 112 07-30-02 AIA 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. 07-34-01 Claims 23-24 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. 07-34-05 AIA Claim 23 recites the limitation " the other " in line 3 . There is insufficient antecedent basis for this limitation in the claim. 07-34-05 AIA Claim 24 recites the limitation " the other " in line 3 . There is insufficient antecedent basis for this limitation in the claim. Claim 24 recites “a similarity” twice in line 2 and 3, its unclear if these terms are same or different, this makes the claim indefinite. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-20-aia AIA 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. 07-21-aia AIA Claim s 1, 4-6, 8-12 and 18-24 are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al (US Pub. 2020/0219268) in view of Kim et al (US Pub. 2012/0051594) and Yahagi et al (US Pub. 2011/0228100) . With respect to claim 1, Liu discloses An information processing apparatus comprising: at least one processor; and at least one memory having stored thereon instructions which, when executed by the at least one processor, cause the information processing apparatus at least to (see paragraph 0039, processor and memory): [determine a candidate object including a tracking target specified by a user and an object selected based on the tracking target;] retain a feature of the tracking target and [a feature of the object selected based on the tracking target] based on a learned model in one or more past images, (see paragraph 0046, …the feature of a reference image is optionally stored to a buffer “retaining unit”, and only the stored feature of the reference image needs to be invoked when predicting the position of a tracking target in an image…; and paragraph 0068, …a convolution “learned model” operation is performed on the target image to obtain…feature..); acquire a feature of a candidate object in a current image based on the learned model, (see paragraph 0010, …to obtain features of a plurality of reference images of a target image…); [perform a detection process for detecting a candidate object similar to the tracking target or a candidate object similar to the object selected based on the tracking target based on the feature of the tracking target, the feature of the object selected based on the tracking target and the feature of the candidate object acquired from the current image; and track the tracking target based on the feature of the tracking target in the current image determined based on a result of the detection process], as claimed. However, Liu fails to explicitly disclose determine a candidate object including a tracking target specified by a user and an object selected based on the tracking target; a feature of the object selected based on the tracking target; and perform a detection process for detecting a candidate object similar to the tracking target or a candidate object similar to the object selected based on the tracking target based on the feature of the tracking target, the feature of the object selected based on the tracking target and the feature of the candidate object acquired from the current image; and track the tracking target based on the feature of the tracking target in the current image determined based on a result of the detection process, as claimed. Kim teaches a feature of the object selected based on the tracking target, (see figure 2, two persons are monitored, one is tracked and other is not tracked); and perform a detection process for detecting a candidate object similar to the tracking target or a candidate object similar to the object selected based on the tracking target based on the feature of the tracking target, the feature of the object selected based on the tracking target and the feature of the candidate object acquired from the current image; and track the tracking target based on the feature of the tracking target in the current image determined based on a result of the detection process, (see paragraph 0051, wherein … The object tracking unit 150 collects per frame [this is read as current, future and any preceding frames] the feature information included in the silhouette regions of the first and second objects [this is read as both tracked and candidate target as shown in figure 9] that are separated from each other and by comparing feature information collected in a present frame with feature information collected in a previous frame, the object tracking unit 150 tracks an object to be tracked [to be tracked is read as “tracking target”] at present between the first and second objects “track the tracking target based on the feature of the tracking target in the current image”), as claimed. It would have been obvious to one ordinary skilled in the art at the effective date of invention to combine the two references as they are analogous because they are solving similar problem of object tracking using image analysis. The teaching of Kim to detect/track the objects even when they are overlapped can be incorporated in to the Liu’s system as suggested (see figure 1, numerical 120), for suggestion, and modifying the system yields a tracking system that overcomes the overlapping problems in moving objects (see Abstract of Kim), for motivation. Also, Yahagi in the same field of object tracking teaches disclose determine a candidate object including a tracking target specified by a user and an object selected based on the tracking target, (see paragraph 0080, wherein …a touch panel is formed on the display screen on which the object image 60 is displayed, and the user touches the image of the pedestrian 61 to designate the pedestrian 61, which is a tracking target), as claimed. It would have been obvious to one ordinary skilled in the art at the effective date of invention to combine the references as they are analogous because they are solving similar problem of object tracking using image analysis. The teaching of Yahagi to select a tracking object can be incorporated into Liu and Kim and Li as suggested (see Liu figure 1, numerical 120), for suggestion, and modifying the system yields a system for tracking an object selected by the user (see Yahagi paragraph 0080), for motivation. with respect to claim 4, combination of Liu, Kim and Yahagi further discloses wherein at least one of the feature of the tracking target retained and the feature of the object selected based on the tracking target is updated (see Liu paragraph 0120, …after determining the final position of the tracking target in the target image, …update the reference image list using the target image…[the updating information regarding the object/features is needed for future use as required]), as claimed. with respect to claim 5, combination of Liu, Kim and Yahagi further discloses wherein, in a case where the candidate object having a similarity between the feature of the tracking target retained from a past image of the one or more past images and the feature of the candidate object in the current image higher than a predetermined threshold value is detected in the current image, the feature of the tracking target is updated with the feature of the candidate object acquired from the current image, (see Liu paragraph 0117, ….the at least one initial predicted position having the similarity greater than the first preset threshold may be directly averaged to obtain the position of the tracking target in the target image …This is not limited in the embodiments of the present disclosure.…; and paragraph 0119, … in the embodiments of the present disclosure, the location of the tracking target in the target image may be stored in the first buffer in an overwritten or incremental manner “update”, …the tracking target in the target image may be stored under any condition; or the tracking target is stored in the first buffer if the position of the tracking target in the target image satisfies a certain preset condition, such as, for example, the interval between the target image and an image frame corresponding to the appearance reference position stored in the first buffer is a preset value; or the position of the tracking target in the target image satisfies a preset condition “threshold”, such as, for example, the difference between the target image and the appearance reference position stored in the first buffer exceeds a certain threshold “predetermined threshold value”, or the like. The condition for storing the position of the tracking target in the target image is not limited in the embodiments of the present disclosure…), as claimed. with respect to claim 6, combination of Liu, Kim and Yahagi further discloses wherein the information processing apparatus detects a position of the candidate object, and wherein, in a case where the candidate object having a similarity between the feature of the tracking target retained from a past image of the one or more past images and the feature of the candidate object in the current image higher than the predetermined threshold value is detected in the current image, a position of the tracking target is updated with the feature of the candidate object acquired from the current image, (see Liu paragraph 0118, ….the determined final position of the tracking target in the target image is stored into a first buffer, where the first buffer is used for storing the appearance reference position of the tracking target…; and paragraph 0119, … in the embodiments of the present disclosure, the location of the tracking target in the target image may be stored in the first buffer in an overwritten or incremental manner “update”, …the tracking target in the target image may be stored under any condition; or the tracking target is stored in the first buffer if the position of the tracking target in the target image satisfies a certain preset condition, such as, for example, the interval between the target image and an image frame corresponding to the appearance reference position stored in the first buffer is a preset value; or the position of the tracking target in the target image satisfies a preset condition “threshold”, such as, for example, the difference between the target image and the appearance reference position stored in the first buffer exceeds a certain threshold “predetermined threshold value”, or the like. The condition for storing the position of the tracking target in the target image is not limited in the embodiments of the present disclosure…), as claimed. with respect to claim 8, combination of Liu, Kim and Yahagi further discloses wherein the feature of the tracking target is a feature of an object specified by a user, (see Liu paragraph 0141, …When a user uses a cell phone or a camera to detect a face and a common object…), as claimed. with respect to claim 9, combination of Liu, Kim and Yahagi further discloses wherein each of the one or more past images is an image captured before the current image, wherein the instruction further cause the information processing apparatus to extract, from the current image, a partial image to detect the candidate object similar to the tracking target and the candidate object similar to the object selected based on the tracking target based on a position of the tracking target and a position of the object selected based on the tracking target detected in the one or more past images, and wherein the instruction further cause the information processing apparatus to acquire the feature of the candidate object from the current image based on the partial image of the current image extracted, (see Liu paragraph 0072, … the ROI alignment operation is performed by using the position of a bounding box, corresponding to the tracking target in the at least one of the plurality of reference images, in the reference image as an ROI, so as to obtain the feature of the target image. The bounding box corresponding to the tracking target may be the bounding box per se of the tracking target, or may be obtained by processing the bounding box of the tracking target. For example, by enlarging the bounding box of the tracking target in the reference image by a first preset multiple, a bounding box corresponding to the tracking target in the reference image is obtained), as claimed. with respect to claim 10, combination of Liu, Kim and Yahagi further discloses wherein, in the current image, the instruction cause the information processing apparatus to extract the partial image in a predetermined size from a region corresponding to a vicinity of the tracking target or the object selected based on the tracking target detected in the one or more past images, (see Liu paragraph 0072, … the ROI alignment operation is performed by using the position of a bounding box, corresponding to the tracking target in the at least one of the plurality of reference images, in the reference image as an ROI, so as to obtain the feature of the target image. The bounding box corresponding to the tracking target may be the bounding box per se of the tracking target, or may be obtained by processing the bounding box of the tracking target. For example, by enlarging the bounding box of the tracking target in the reference image by a first preset multiple, a bounding box corresponding to the tracking target in the reference image is obtained; also see Kim figure 2, numerical 120 Object detecting unit), as claimed. with respect to claims 11 and 12, combination of Liu, Kim and Yahagi further discloses wherein parameters of the learned model are updated based on teacher data indicating a position of the tracking target in an image; and wherein a loss for a position in the image where an object similar to the tracking target is estimated is acquired, based on the teacher data indicating the position of the tracking target in the image, and wherein the parameters of the learned model are updated based on the loss acquired, (see Liu paragraph 015, … A conventional deep recurrent network “a learning unit” mainly consists of a reference image branch and a target frame branch, and implements position prediction of a target object by modeling the displacement of the target object between two frames. The present disclosure propose that the intermediate feature obtained through calculation is stored by means of a location exemplar buffer (a location exemplar obtained by a reference image branch through calculation), and the previously buffered location exemplar is reused in subsequent prediction, thereby quickly predicting an object by using a plurality of reference image exemplars. The method provided in the present disclosure may be applied to various target tracking scenarios, increases the running speed of a deep recurrent network-based target tracking algorithm to nearly twice the original speed, improves the real-time performance of tracking, and reduces the power consumption of a device…), as claimed. Claims 18 and 19 are rejected for the same reasons as set forth in the rejections for claim 1, because claims 18 and 19 are claiming subject matter of similar scope as claimed in claim 1. Furthermore, Liu discloses computer program product see paragraph 0019. with respect to claim 20, combination of Liu, Kim and Yahagi further discloses wherein the feature of the tracking target retained in the one or more past images is not updated with the feature of the object selected based on the tracking target from the current image, (see Liu paragraph 0120, …after determining the final position of the tracking target in the target image, whether [is read as should update or not update, i.e. a condition] to add the target image to a reference image…), as claimed. with respect to claim 21, combination of Liu, Kim and Yahagi further discloses wherein, in a case where the candidate object having the similarity between the feature of the tracking target retained from the past image and the feature of the candidate object in the current image higher than the predetermined threshold value is not detected in the current image, the feature of the tracking target is not updated with the feature of the candidate object acquired from the current image, (see Liu paragraph 0120, …after determining the final position of the tracking target in the target image, whether [is read as should update or not update, i.e. a condition] to add the target image to a reference image…), as claimed. with respect to claim 22, combination of Liu, Kim and Yahagi further discloses wherein, in a case where the candidate object having the similarity between the feature of the tracking target retained from the past image and the feature of the candidate object in the current image higher than the predetermined threshold value is not detected in the current image, the feature of the tracking target is not updated with the feature of the candidate object acquired from the current image, (see Liu paragraph 0120, …after determining the final position of the tracking target in the target image, whether [is read as should update or not update, i.e. a condition] to add the target image to a reference image…), as claimed. with respect to claims 23 and 24, combination of Liu, Kim and Yahagi further discloses wherein one of the feature of the tracking target retained and the feature of the object selected based on the tracking target retained is updated, and the other of the feature of the tracking target retained and the feature of the object selected based on the tracking target retained is not updated; and wherein a similarity between the one of the feature that is updated and the feature of candidate object in a current image is higher than a similarity between the other of the feature that is not updated and the feature of the candidate object in the current image, (see Liu paragraph 0120, …after determining the final position of the tracking target in the target image, whether [is read as should update or not update, i.e. a condition] to add the target image to a reference image…), as claimed . 07-22-aia AIA Claim s 13 and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al (US Pub. 2020/0219268) in view of Kim et al (US Pub. 2012/0051594) and Yahagi et al (US PUB. 2011/0228100) as applied to claim 1 above, and further in view of A real time algorithm for people tracking using contextual reasoning, by Lascio et al . With respect to claim 13, combination of Liu, Kim and Yahagi discloses all the limitations as claimed and rejected above in claim 1. Furthermore, Liu discloses each of the one or more past images is an image captured before the current image, (see paragraph 0002, …target tracking issue generally refers to predicting, in condition that the position information of a target object in the first frame of a video sequence is given, subsequent positions of the object in the video…), as claimed. However, combination of Liu, Kim and Yahagi fail to explicitly disclose wherein the instruction further cause the information processing apparatus determine presence or absence of a blocked region where the candidate object is blocked based on a position of the candidate object in the current image, and wherein the candidate object and the object selected based on the tracking target are detected based on a result of determination, as claimed. Lascio teaches wherein the instruction further cause the information processing apparatus determine presence or absence of a blocked region where the candidate object is blocked based on a position of the candidate object in the current image, and wherein the candidate object and the object selected based on the tracking target are detected based on a result of determination, (see page 892, left hand column, … uses the overlap of the areas as a criterion to find a correspondence “correlation” between the objects at the current and at the previous frame. When this criterion selects multiple objects, the algorithm considers split or merge hypotheses to deal with detection errors or with occlusions “object is blocked”. After an occlusion, an appearance model of the objects is used to reassign the original object identities…), as claimed. It would have been obvious to one ordinary skilled in the art at the effective date of invention to combine the references as they are analogous because they are solving similar problem of object tracking using image analysis. The teaching of Lascio to detect/track the objects even when they are occluded can be incorporated into the Liu’s system as suggested (see figure 1, numerical 120), for suggestion, and modifying the system yields a tracking system that overcomes the occlusions (see Abstract of Lascio), for motivation. With respect to claim 15, combination of Liu, Kim, Yahagi and Lascio further discloses wherein the instruction further cause the information processing apparatus to determine a presence of the blocked region for an object having a similarity with the tracking target in the current image smaller than a predetermined threshold value, (see Lascio page 900, figure 11©, and left hand column, …second phase is shown in Fig. 11c. It follows a similar scheme, except that it considers only the objects in the new state, and does not perform the checks for merges, splits, starting and ending occlusions. Moreover, the similarity matrix is built using less features than in the first phase since we have experimentally verified that only the position information (see Section 4.4) is sufficiently reliable for such objects...), as claimed. With respect to claim 16, combination of Liu, Kim, Yahagi and Lascio further discloses wherein, for the candidate object in the current image having a similarity with the tracking target in one of the past images smaller than a predetermined threshold value, the instruction further cause the information processing apparatus to determine a degree of overlapping between a region of the candidate object and the tracking target in the current image, and, in a case where the degree of overlapping is higher than a predetermined threshold value, determines that the tracking target is blocked, (see Lascio page 901, right hand column, … we have used the following indices: the Average Tracking Accuracy (ATA), the Multiple Object Tracking Accuracy (MOTA) and the Multiple Object Tracking Precision (MOTP)….; and …The latter measures the overlap in the spatiotemporal dimensions of the detected object over the ground truth…; and page 902, left hand column, … he MOTP is a precision score that calculates the spatiotemporal overlap between the reference tracks and the system output tracks…), as claimed. With respect to claim 17, combination of Liu, Kim, Yahagi and Lascio further discloses wherein, in the case where the tracking target is blocked, the candidate object blocking the tracking target is determined as an occluder, (see Lascio page 900, left hand column, section 4.4 Similarity evaluation, … the similarity matrix is used to match one or more blobs with one or more objects. An example is depicted in Fig. 12. In order to measure the similarity between an object oi and a blob bj, the tracking system uses an index based on three kinds of information: the position, the shape and the appearance…; and equation (3)), as claimed. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to VIKKRAM BALI whose telephone number is (571)272-7415. The examiner can normally be reached Monday-Friday 7:00AM-3:00PM. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /VIKKRAM BALI/Primary Examiner, Art Unit 2663 Application/Control Number: 18/155,349 Page 2 Art Unit: 2663 Application/Control Number: 18/155,349 Page 3 Art Unit: 2663 Application/Control Number: 18/155,349 Page 4 Art Unit: 2663 Application/Control Number: 18/155,349 Page 5 Art Unit: 2663 Application/Control Number: 18/155,349 Page 6 Art Unit: 2663 Application/Control Number: 18/155,349 Page 7 Art Unit: 2663 Application/Control Number: 18/155,349 Page 8 Art Unit: 2663 Application/Control Number: 18/155,349 Page 9 Art Unit: 2663 Application/Control Number: 18/155,349 Page 10 Art Unit: 2663 Application/Control Number: 18/155,349 Page 11 Art Unit: 2663 Application/Control Number: 18/155,349 Page 12 Art Unit: 2663 Application/Control Number: 18/155,349 Page 13 Art Unit: 2663 Application/Control Number: 18/155,349 Page 14 Art Unit: 2663 Application/Control Number: 18/155,349 Page 15 Art Unit: 2663 Application/Control Number: 18/155,349 Page 16 Art Unit: 2663
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