DETAILED ACTION
1. The communication is in response to the application received 02/18/2025, wherein claims 1-16 are pending and are examined as follows.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
3. The information disclosure statement (IDS) was submitted on 02/18/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority
4. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Objections
5. Claims 1 and 9 are objected to because of the following informalities: the claims recite “and integrated new video object metadata in new video object metadata”. Examiner understands this to mean the metadata corresponding to the object is updated (i.e. is new) during the re-identification query, which can be different from the initial metadata. However, it is not entirely clear what is meant by integrating new video object metadata in new video object metadata. Please clarify. Appropriate correction is required.
Claim Rejections - 35 USC § 112
6. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 4, 7, 8, 9, 12, 15, and 16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding Claims 1 and 9, they recite the limitation phrase “integrated new video object metadata”; however, it is not entirely clear what is meant by this feature when compared to “video object metadata”, and moreover, what is meant by being ‘integrated’. The specification also does not appear to define these metadata in a way that clearly illustrates their differences. For these reasons, the metes and bounds of the claim cannot be unequivocally ascertained.
Regarding Claims 7, 8, 15, and 16, these depend on claims 1 and 9 above, and therefore include all of their features. As such, Claims 7, 8, 15, and 16 are also rejected under 35 U.S.C. 112(b) for the same reasons given.
Regarding claim 4, claim 4 recites “calculating a score for a non-full body (partial) degree of the human area image by using the third deep neural network model”, however, it is not entirely clear what is meant by “non-full body (partial) degree” as this can have plural interpretations. For e.g., this can be considered a human area image that has been cropped as suggested in ¶0056 of the filed specification. This can also be considered an occluded human area image or one that represents a departure from a camera photographing range (e.g. ¶0056), i.e. a partial degree of the human area image. Further, this can also be considered a cropped non-full body (partial) which is occluded or which departs from said range. For these reasons, the metes and bounds of the claim cannot be unequivocally ascertained.
Regarding claim 12, claim 12 recites similar limitation as claim 4 above. For the same reasons given, claim 12 is also rejected under 35 U.S.C. 112(b).
Regarding claims 4 and 12, the same limitation “calculating a score for a non-full body (partial) degree of the human area image” can also be construed in two ways. According to ¶0056 of the specification, it appears the score is a “non-full body score” for classifying the human area image either as a full body image or a partial image. However in the claims, the score can be interpreted as any score for a partial image which doesn’t necessarily have to be a “non-full body score”. For these reasons, the metes and bounds of the claim cannot be unequivocally ascertained.
Claim Rejections - 35 USC § 103
7. 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 1-3, 6, 9-11, 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 Khan et al. US 2024/0338946 A1, hereinafter referred to as Kim, Kim 013, and Khan, 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 and integrated new video object metadata in new video object metadata generated in real time by the plurality of cameras based 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 and integrated new video object metadata in new video object metadata generated in real time by the plurality of cameras based 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]; 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).
Regarding claim 2, Kim and Kim 013 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 and Kim 013 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 and Kim 013 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 and Kim 013 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 and Kim 013 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 and Kim 013 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 both Kim and Kim 013 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, 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 and Kim 013 do not appear to address this feature. Please see Khan below], bounding box information [Kim and 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. 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, and Khan has been discussed in connection with claim 2, above.
Regarding claim 6, Kim and Kim 13 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 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.
Regarding claim 14, claim 14 is rejected under the same art and evidentiary limitations as determined for the method of Claim 6.
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 Khan, 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 and Kim 13 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 does not 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 does not 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 (see for e.g. ¶0045 of Fang et al. US 2022/0198684 A1)] 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 Khan, 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
8. 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. As to claims 4 and 12, the closest prior art are Alemu et al. US 2025/0191205 A1 and Agarwal et al. US 2025/0278844 A1, hereinafter referred to as Alemu and Agarwal, respectively. Alemu teaches an adjacency overlap score AdO of overlapping boundary boxes (e.g. abstract), which can be construed as an “occluded score” given its BRI and is calculated as a ratio per ¶0084. However, Alemu does not appear to address “a process of calculating an average of the occluded score and the score for the non-full body degree as the re-identification difficulty”. Similarly, Agarwal teaches various metrics for a set of boundary boxes (e.g. IoU and IoMA) as per ¶0039-¶0043, however, Agarwal too does not appear to address the aforementioned features. Regarding claims 7-8 and 15-16, the art of record fails to address “a process of dynamically adjusting a matching threshold based on a re-identification difficulty score of the monitored subject re-identification query and a re-identification difficulty score of the new video object metadata” when considering the claims as a whole. Please note, the claim rejections under 35 U.S.C. 112(b) must first be addressed. 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:
4. The method of claim 2, wherein the process of quantifying the re-identification difficulty includes a process of determining whether the human object in the video is occluded by another human object within the human area image by using the bounding box information, a process of calculating an overlapping area of a bounding box area of the other person overlapped with the human area image, a process of calculating an occluded score by the bounding box of the human object and a ratio occupied by the overlapping area, a process of calculating a score for a non-full body (partial) degree of the human area image by using the third deep neural network model, and a process of calculating an average of the occluded score and the score for the non-full body degree as the re-identification difficulty.
7. The method of claim 2, wherein the process of matching the monitored subject and the integrated new video object metadata includes a process of calculating a similarity between a re-identification feature vector of the new video object metadata and a re-identification feature vector of the monitored subject re-identification query, a process of dynamically adjusting a matching threshold based on a re-identification difficulty score of the monitored subject re-identification query and a re-identification difficulty score of the new video object metadata, and a process of determining whether the monitored subject and a human corresponding to the new video object metadata are objects having the same identity by comparing the similarity and the adjusted matching threshold.
8. The method of claim 7, wherein the similarity is calculated by using a cosine similarity or a Euclidean distance.
12. The system of claim 10, wherein in the process of quantifying the re-identification difficulty, it is determined whether the human object in the video is occluded by another human object within the human area image by using the bounding box information, an overlapping area of a bounding box area of the other person overlapped with the human area image is calculated, an occluded score is calculated by the bounding box of the human object and a ratio occupied by the overlapping area, a score for a non-full body (partial) degree of the human area image is calculated by using the third deep neural network model, and an average of the occluded score and the score for the non-full body degree is calculated as the re-identification difficulty.
15. The system of claim 10, wherein in the process of matching the monitored subject and the integrated new video object metadata, a similarity between a re-identification feature vector of the new video object metadata and a re-identification feature vector of the monitored subject re-identification query is calculated, a matching threshold is dynamically adjusted based on a re-identification difficulty score of the monitored subject re-identification query and a re-identification difficulty score of the new video object metadata, and it is determined whether the monitored subject and a human corresponding to the new video object metadata are objects having the same identity by comparing the similarity and the adjusted matching threshold.
16. The system of claim 15, wherein the similarity is calculated by using a cosine similarity or a Euclidean distance.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see PTO 892 for additional references.
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/RICHARD A HANSELL JR./Primary Examiner, Art Unit 2486
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