DETAILED ACTION
This Office Action is in response to the Amendment filed on 07/01/2026.
In the filed response, Claims 1, 4, 7, 9, 12 and 15 have been amended, where Claims 1 and 9 are independent claims.
Accordingly, Claims 1-16 have been examined and are pending. This Action is made FINAL
Information Disclosure Statement
1. The information disclosure statement (IDS) was submitted on 07/23/2026. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Response to Arguments
2. Applicant's arguments filed 07/01/2026 (pgs. 10-15 of remarks) have been fully considered but they are not persuasive. Please see examiner’s responses below.
3. As best understood by examiner, it appears Applicant’s main arguments relate to Kim 013 having a fundamentally different architecture that does not allow for performing real-time stream-based matching based on a continuous stream of metadata from the real-time CCTV video feed (pgs. 14-15 of remarks with reference to steps S130-S170 of fig. 2). It is alleged Kim 013 “first pre-generates and stores tubes of objects of interest, unifies those tubes through clustering, and only then performs re-identification within the search candidate areas”. Applicant also points to Kim 013 not having anything that corresponds to “the concept of
new video object metadata integrated with the monitored subject, which in the present
invention is identified by the first updater 631 from the stream of real-time video object metadata
received by the second receiver 620” (pg. 15 of remarks).
4. Applicant’s remarks are all acknowledged, however, after careful review of the art of record (notably Kim 013), the examiner respectfully submits the following. First the examiner points to for e.g. ¶0049, where Kim 013 explicitly refers to system 1 acquiring a source video including an object via real-time imaging during the operation of the CCTV 30. This is also illustrated in fig. 3 (S110) showing a real-time CCTV image. From this, it is believed all subsequent operations are based on those real-time images. Although Kim 013 provides storage when analyzing CCTV video feeds and further performs pre-processing operations (e.g. pre-computed tubes), the examiner respectfully submits the claims do not necessarily preclude using any type of storage, nor do they preclude performing any type of pre-processing. Also, in response to applicant's argument that the references (notably Kim 013) fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “Kim 013 not having anything that corresponds to the concept of new video object metadata integrated with the monitored subject, which in the present invention is identified by the first updater 631 from the stream of real-time video object metadata received by the second receiver 620”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Considering the foregoing and given the BRI of the claims, the examiner respectfully submits Kim 013 is relevant art.
5. In light of the clarifying amendments, the work of Saghafi et al. “Review of person re-identification techniques”, IET Computer Vision, p. 1-20, 2013 (PTO 892), hereinafter referred to as Saghafi, is introduced to help further address “a process of matching the monitored subject with new video object metadata integrated with the monitored subject from among new video object metadata generated in real time by the plurality of cameras based on the monitored subject re-identification query”. As such, Saghafi describes person re-identification techniques across different surveillance cameras having disjoint fields of view (e.g. abstract). Tracking/matching a person(s) between cameras (inter-camera) requires overcoming problems related to illumination conditions and for e.g. a person’s clothing and gait (Sect. 1 and 2 on pgs. 2-3). Most methods rely on extracting color and texture features of an individual’s clothing between the disjoint fields of view, which in this case can be construed as the new video object metadata. In other words, with each field of view, new extracted color and texture features (i.e. new video object metadata) can be determined, associated with the person(s), that are generated by the plurality of CCTV cameras. With Saghafi’s teachings, the person can be re-identified (abstract). For these reasons, which are elaborated on below, the examiner respectfully submits the works of Kim, Kim 013, and Saghafi, reasonably teach and/or suggest under 35 U.S.C. 103, either alone or in combination, the disclosed features of the instant claims given their broadest reasonable interpretation (BRI). Other prior art deemed relevant include the NPL documents made available in the IDS dated 07/23/2026 (KR 10-1298741 and KR 10-2021-0094784 corresponding to US 2021/0225013 A1 of record), along with Nam et al. US 2022/0261577 A1.
6. Examiner notes the index of claims (dated 03/26/2026) have been updated to correctly show the claims that have been objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
7. Applicant’s responses/amendments related to the claim objections and the claim rejections under 35 U.S.C. 112(b) are acknowledged. As such, the claim objections and the claim rejections under 35 U.S.C. 112(b) are withdrawn.
8. The Examiner is available to discuss the matters of this office action to help move the Instant Application forward. Please refer to the conclusion to this office action regarding scheduling interviews.
9. Accordingly, Claims 1-16 have been examined and are pending.
Claim Rejections - 35 USC § 103
10. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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 1, 6, 9, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Kim KR102589401B1 (See IDS), in view of Kim et al. US 2021/0225013 A1, and in further view of Saghafi et al. “Review of person re-identification techniques”, IET Computer Vision, p. 1-20, 2013, hereinafter referred to as Kim, Kim 013, and Saghafi, respectively.
Regarding claim 1, Given the broadest reasonable interpretation (BRI) of the following limitations, Kim teaches and/or suggests “A method for CCTV-integrated monitoring [See abstract which describes a real-time object search using collected image information from a plurality of CCTVs], the method comprising: a process of generating video object metadata by receiving videos photographed by a plurality of cameras [For e.g. ¶0036 teaches metadata information of the object being searched (e.g. object capture time, object location, etc.)]; a process of generating a movement path re-identification query [Given the BRI, “movement path re-identification query” can be construed to mean any query that returns a movement path of a re-identified/re-recognized object. See for e.g. the displayed path in fig. 7 on the screen of a user’s terminal], by using a camera list [Although not explicit, determining a movement time and path of an object via a plurality of CCTVs deployed in a plurality of regions implies keeping track of each camera at each location (e.g. ¶0007). This can be considered a “camera list” given its BRI. Please see Kim 013 below for more explicit support] overlapped with a GPS movement path of a monitored subject [¶0036 describes information about the object’s location which can be obtained through GPS (¶0075)] and search time zone information [See ¶0035-¶0036 regarding movement time of the object along its path. Also refer to ¶0034 (inputting inquiry date and time zone)] integrated with the monitored subject [See ¶0007 and ¶0035-¶0036 regarding the object being tracked. The above information is integrated to yield information about the object (e.g. fig. 7)]; a process of searching video object metadata integrated with the monitored subject based on the generated movement path re-identification query [For e.g., ¶0035, with reference to fig. 7, shows various metadata information may be used for providing the movement time and path of the re-recognized object. Also see ¶0053 regarding recognizing the object based on clothing details. For further support, please see Kim 013 below]; a process of deriving a plurality of persons which move along the GPS movement path, and a camera unit movement path of each person and an image of each person, based on the searched video object metadata [Please see above citations regarding a movement time and path for an object based on metadata information (e.g. abstract). More than one object can be tracked. See for e.g. ¶0003, ¶0036, ¶0051, fig. 5)]; a process of visualizing and providing the derived movement path information and person image to an interface based on a GPS [See for e.g. the displayed path in fig. 7 on the screen of a user’s terminal. See ¶0036 and ¶0075 regarding GPS]; a process of generating a monitored subject re-identification query based on a monitored subject image selected from the interface and video object metadata corresponding to the monitored subject image [Kim does not appear to address the foregoing limitation. Please see Kim 013 below for support]; a process of matching the monitored subject with new video object metadata integrated with the monitored subject from among new video object metadata generated in real time by the plurality of cameras based on the monitored subject re-identification query [Kim does not appear to address the foregoing limitation. See Kim 013 for support]; and a process of tracking a real-time location of the monitored subject based on a matching result for the monitored subject re-identification query.” [Kim does not appear to address the foregoing limitation. See Kim 013 for support] Although Kim’s teachings are deemed relevant given the BRI of the aforementioned limitation, Kim does not clearly address “a camera list”. Kim 013 on the other hand from the same or similar field of support, does teach and/or suggest this feature [See ¶0052 and ¶0160 regarding location information (e.g. coordinates) of the CCTV 30 in system 1 which may appear in a separate database or memory, i.e. “a camera list”] Also regarding “a process of searching video object metadata integrated with the monitored subject based on the generated movement path re-identification query”, although Kim provides support as noted above, Kim 013 also teaches and/or suggests these features [See for e.g. ¶0024, ¶0144 and ¶0168]. As to the remaining limitation, Kim does not address these features, however Kim 013 is found to teach and/or suggest “a process of generating a monitored subject re-identification query based on a monitored subject image selected from the interface and video object metadata corresponding to the monitored subject image [Kim 013 teaches a re-identified tube of the object of interest as a query result (e.g. fig. 2 and ¶0009, along with ¶0057). Also see for e.g. ¶0142 regarding selecting the representative image]; a process of matching the monitored subject with new video object metadata integrated with the monitored subject from among new video object metadata generated in real time by the plurality of cameras based on the monitored subject re-identification query [Please refer to ‘matching’ attribute information in ¶0024-¶0025 and ¶0155. Also see ¶0172-¶0174 with reference to the re-identification model of fig. 10 in Kim 013. Kim 013’s attribute matching can be understood as occurring between the different views of the CCTV cameras. “New video object metadata” is believed to have support during the attribute matching process, where ‘new’ video object attributes coincide with a new camera view. For further details regarding real-time CCTV video feeds, please see examiner’s responses above. Also for further support, please see Saghafi below]; and a process of tracking a real-time location of the monitored subject based on a matching result for the monitored subject re-identification query.” [Lastly Kim 013 teaches and/or suggests tracking the identified object of interest (e.g. abstract)] Given the teachings of Kim 013 above, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the image analysis system of Kim related to object identification and tracking (abstract) with the teachings of Kim 013 for re-identifying a target object based on CCTV location information and movement information of said object (e.g. abstract) thus enabling an efficient search to be performed, which can help find a missing child quickly to ensure prompt initial action to be taken (¶0030). Although the work of Kim 013 is deemed relevant for the reasons presented, Saghafi from the same or similar field of endeavor is brought in to further teach and/or suggest “a process of matching the monitored subject with new video object metadata integrated with the monitored subject from among new video object metadata generated in real time by the plurality of cameras based on the monitored subject re-identification query” [Given Saghafi’s re-identification techniques (abstract), tracking/matching a person’s gait or clothing (i.e. video object metadata) between cameras of a CCTV system is taken to mean with each new field of view, gait or clothing (i.e. color and texture) information can be obtained (construed as new video object metadata) corresponding with the person (i.e. is integrated with the person) which is generated by the cameras of the CCTV system. See for e.g. Sect. 1 and 2 on pgs. 2-3] Given the teachings of Saghafi above, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the image analysis system of Kim related to object identification and tracking (abstract) along with the target re-identifying techniques of Kim 013 (abstract), with the teachings of Saghafi for overcoming issues associated with re-identification so as to help make surveillance systems more operator-independent than before (pg. 1, Sect. 1 Introduction)
Regarding claim 6, Kim, Kim 013, and Saghafi teach all the limitations of claim 1, and are analyzed as previously discussed with respect to that claim. Although Kim suggests a query that can return a movement path of a re-identified/re-recognized object as shown in the displayed path of fig. 7 on the screen of a user’s terminal, Kim does not appear to address the remaining features of claim 6. Kim 013 on the other hand from the same or similar field of endeavor is brought in to teach and/or suggest “wherein the monitored subject re-identification query includes the monitored subject image [See fig. 2 regarding receiving an image query requesting a tube of a target object in a source video, followed by finding a search candidate area and then re-identifying said target object], video object metadata of the monitored subject, [See fig. 3 with respect to clustering of frame based on attribute information] and a camera list to find the monitored subject.” [Although not explicit, fig. 3 depicts an arrangement of cameras. Cameras must be identified to show which ones captured the images (e.g. CAM#1, CAM#2, etc.). Also see ¶0052 and ¶0160 regarding location information (e.g. coordinates) of the CCTV 30 in system 1 which may appear in a separate database or memory, i.e. “a camera list”] The motivation for combining Kim and Kim 013 has been discussed in connection with claim 1, above.
Regarding claim 9, claim 9 is rejected under the same art and evidentiary limitations as determined for the method of Claim 1.
Regarding claim 14, claim 14 is rejected under the same art and evidentiary limitations as determined for the method of Claim 6.
Claims 2-3, 10, and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Kim, in view of Kim 013, in further view of Saghafi, and in further view of Khan et al. US 2024/0338946 A1, hereinafter referred to as Khan.
Regarding claim 2, Kim, Kim 013, and Saghafi teach all the limitations of claim 1, and are analyzed as previously discussed with respect to that claim. Kim further teaches and/or suggests “wherein the process of generating the video object metadata includes a process of detecting one or more human objects from the received videos by using a pretrained first deep neural network model [See ¶0023-¶0025 with respect to implementing Kim’s image analysis system 10 via an AI model based on DNN, CNN, etc. Said system includes components for performing object detection (e.g. abstract). Kim 013 also provides similar support in fig. 10 and ¶0172], and generating bounding box information of each object [Kim however does not address the foregoing feature. See Kim 013 below for support. Khan too teaches this.], a process of tracking a location of the detected human object [Fig. 7 of Kim, for example, discloses a movement time and path of an object, which permits tracking said object. Kim 013 (fig. 5) and Khan (fig. 5) also illustrate this feature. For e.g. Khan shows Track-lets for tracking detected people], and assigning an individual unique number to each object [Kim, Kim 013, and Saghafi do not address this feature. See Khan below for support]; a process of applying a human area image cropped based on the bounding box of the human object to a pretrained second deep neural network model [Kim, Kim 013, and Saghafi do not address this feature. See Khan below for support], and converting the human area image to a multi-dimensional re-identification feature vector [Kim, Kim 013, and Saghafi do not address this feature. See Khan below for support], and a process of quantifying a re-identification difficulty of the human area image by using coordinates of the bounding box and the human area image.” [Kim, Kim 013, and Saghafi do not address this feature. See Khan below for support]. Although the teachings of Kim is deemed relevant, Kim does not address “generating bounding box information of each object”. Kim 013 on the other hand from the same or similar field of endeavor is brought in to teach and/or suggest this feature [See the bounding boxes in fig. 5. This is also shown in for e.g. figs. 5-6 of Khan] The motivation for combining Kim and Kim 013 been discussed in connection with claim 1, above. Kim, Kim 013, and Saghafi however do not appear to address the remaining limitations. Khan on the other hand from the same or similar field of endeavor is brought in to teach and/or suggest “and assigning an individual unique number to each object [See for e.g. ¶0107 of Khan, where unique IDs for each detected object are generated]; a process of applying a human area image cropped based on the bounding box of the human object to a pretrained second deep neural network model [See for e.g. figs. 5-7 of Khan and associated text regarding cropped images], and converting the human area image to a multi-dimensional re-identification feature vector [Figs. 5-7 show how various spatial features of a person can then be extracted], and a process of quantifying a re-identification difficulty of the human area image by using coordinates of the bounding box and the human area image.” [“Re-identification difficulty” is construed to mean whether re-identification was successful or not, or whether there was any confusion in the process (see for e.g. ¶0090-¶0092). This may be due to for e.g. occlusions (¶0093, ¶0120, and ¶0170)] Given the teachings of Khan above for the customized detection tracking and counting of people (abstract), it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the systems of Kim, Kim 013, and Saghafi for re-identifying/re-recognizing a target object, with the teachings of Khan for accurately detecting, identifying, counting, classifying, and tracking people from one or more video footages of crowd scenes; hence, better crowd control measures can be achieved (e.g. ¶0013 and ¶0016)
Regarding claim 3, Kim, Kim 013, Saghafi, and Khan teach all the limitations of claim 2, and are analyzed as previously discussed with respect to that claim. Kim further teaches and/or suggests “wherein the video object metadata [Kim describes metadata information of the re-recognized object (e.g. abstract). Kim 013 also refers to attribute information (e.g. ¶0143-¶0144)]includes a unique number of a camera photographing each video [Although not explicit, determining a movement time and path of an object via a plurality of CCTVs deployed in a plurality of regions implies keeping track of each camera at each location (e.g. ¶0007). This suggests each camera has an identifier. For explicit support, see Kim 013 below], a timestamp in which the video is photographed [Since Kim describes a movement “time” and path of the object (above), this also suggests a timestamp associated with the captured video. For support, please refer to Kim 013 below], a unique number of the tracked individual object [Kim, Kim 013, and Saghafi do not appear to address this feature. Please see Khan below], bounding box information [Kim does not address this limitation. See Kim 013 below for support. This is also found in Khan below], a human re-identification feature vector extracted with respect to the tracked individual object [Kim does not address this limitation. See Kim 013 below for support], a re- identification difficulty [Kim also does not appear to address this limitation. See Kim 013 below for support], and the human area image [Kim also does not appear to address this limitation. See Kim 013 below for support]. Although Kim addresses metadata/attributes in the disclosure, Kim does not appear to identify the remaining features. Kim 013 on the other hand from the same or similar field of endeavor is brought in to teach and/or suggest “includes a unique number of a camera photographing each video” [For support, see ¶0050, ¶0052 and ¶0160], “a timestamp in which the video is photographed” [See timecodes in ¶0052], “bounding box information” [See the bounding boxes in fig. 5], “a human re-identification feature vector extracted with respect to the tracked individual object” [See extracted features in ¶0172 of Kim 013, with reference to the re-identification model of fig. 10], “a re- identification difficulty” [See for e.g. the image quality score in Kim 013 (¶0121). Also refer to the degree of occlusion of the object of interest in ¶0134 and ¶0144 regarding blur information. Although “re-identification difficulty” is not used, Kim 013 provides other metrics (above) that can be used to identify the difficulty associated with identifying said object], “and the human area image” [See for e.g. the human area image in fig. 10 of Kim 013] The motivation for combining Kim and Kim 013 has been discussed in connection with claim 1, above. Although Kim 013 is deemed relevant art, “a unique number of the tracked individual object” is not disclosed/suggested. Saghafi also does not appear to address this feature. Khan on the other hand from the same or similar field of endeavor is brought in to teach and/or suggest “a unique number of the tracked individual object” [Please see for e.g. ¶0107 of Khan, where unique IDs for each detected object are generated]. Khan further provides additional support for “bounding box information” [See for e.g. figs. 5-6 of Khan]. The motivation for combining Kim, Kim 013, Saghafi and Khan has been discussed in connection with claim 2, above.
Regarding claim 10, claim 10 is rejected under the same art and evidentiary limitations as determined for the method of Claim 2.
Regarding claim 11, claim 11 is rejected under the same art and evidentiary limitations as determined for the method of Claim 3.
Claims 5 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Kim, in view of Kim 013, in further view of Saghafi, and in further view of Agarwal et al. US 2025/0278844 A1 (with reference to Provisional application No. 63/559,330), hereinafter referred to as Agarwal.
Regarding claim 5, Kim, Kim 13, and Saghafi teach all the limitations of claim 1, and are analyzed as previously discussed with respect to that claim. Kim describes “the process of deriving the plurality of persons which move along the GPS movement path, and the camera unit movement path of each person” as indicated in claim 1. Kim also describes clustering image information as in ¶0051. However, Kim does not appear to address the remaining features of claim 5. Kim 013 on the other hand from the same or similar field of endeavor is brought in to teach and/or suggest “wherein the process of deriving the plurality of persons which move along the GPS movement path, and the camera unit movement path of each person and the image of each person includes a process of acquiring an identical person cluster by performing clustering for searched video object metadata for each single camera [See for e.g. fig. 3 of Kim 013 with respect to clustering of an individual frame such as color-based or motion-based clustering], and a process of determining an identical cluster pair in which a plurality of different cameras are similar within the camera list by using a linear assignment algorithm.” [However, Kim 013 and Saghafi do not appear to address this feature. See Agarwal below for support] Although Kim 013 searches for a cluster that matches attribute information of a target patch (e.g. ¶0168), Kim 03 nor Saghafi address the foregoing limitation. Agarwal on the other hand from the same or similar field of endeavor is brought in to teach and/or suggest “and a process of determining an identical cluster pair in which a plurality of different cameras are similar within the camera list by using a linear assignment algorithm.” [See for e.g. ¶0075-¶0078, where the Hungarian algorithm is believed to be analogous to the linear assignment algorithm] Given the teachings of Agarwal above for tracking persons in an environment (abstract), it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the systems of both Kim, Kim 013, and Saghafi, with the teachings of Agarwal to facilitate keeping track of which bounding boxes correspond to which detected persons within an environment being monitored by an increased number of cameras (e.g. ¶0060); hence, a person(s) can be more reliably tracked.
Regarding claim 13, claim 13 is rejected under the same art and evidentiary limitations as determined for the method of Claim 5.
Allowable Subject Matter
11. Claims 4, 7-8, 12, 15, and 16 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. In light of the specification, the Examiner finds the claimed invention to be patentably distinct from the prior art of records. Thus, the prior art of record, taken individually or in combination fail to explicitly teach or render obvious within the context of the respective independent claims the limitations of Claims 4, 7-8, 12, 15, and 16.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RICHARD A HANSELL JR. whose telephone number is (571)270-0615. The examiner can normally be reached Mon - Fri 10 am- 7 pm.
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/RICHARD A HANSELL JR./Primary Examiner, Art Unit 2486
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