DETAILED ACTION
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 6 May 2026 has been entered. Claims 1-13 have been examined.
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 20220383662
Par. 3
“…reidentification is to recognize individuals tracked over a set of distributed non-overlapping cameras with different viewpoints and camera poses and the variability of image capture conditions…”
US 10814815
Col. 13, lines 45-55
Deep learning model in the cloud and another deep learning model in camera video analytics
US 20180103348
Pars. 29-39
Multiple surveillance zones with sensors that send detected metadata to a cloud-based person tracking system
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 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-13 are directed to one of the eligible categories of subject matter.
With respect to independent claims 1 and 13, the extracting, combining, updating, synchronizing limitations cover performance of the limitations manually and/or in the mind (mental processes abstract idea). The capturing, storing, providing, transmitting, receive limitations are recited at a high level of generality and do not add meaningful limitations to the abstract idea, they are directed to insignificant extra solution activities. The claims as a whole merely describe how to generally “apply” the exception in a computer environment using generic computer functions or components (such as the expressly claimed language of using deep learning algorithms). Even when viewed in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
With respect to dependent claims 2-5 the zones, geotags, information are recited at a high level of generality and do not add meaningful limitations to the abstract idea. The claims as a whole merely describe how to generally “apply” the exception in a computer environment using generic computer functions or components. Even when viewed in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
With respect to dependent claims 6-12, the querying, correlating, matching, reidentifying, generating, updating cover performance of the limitations manually and/or in the mind (mental processes abstract idea). No additional elements are recited and so the claims do not provide a practical application and are not considered to be significantly more. The claims are not eligible.
Claim Rejections - 35 USC § 103
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 1-7 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Aghdasi et al., Pub. No.: US 20160357762 A1, hereinafter Aghdasi in view of Shah et al., Pub. No.: US 20230112584 A1, hereinafter Shan, and Kwon et al., Patent No.: US 11941870 B1, hereinafter Kwon.
As per claim 1, Aghdasi discloses A method, comprising:
capturing, by a plurality of geographically distributed embedded-AI (artificial intelligence) cameras, a set of streaming videos (par. 34 cameras 102a-n);
extracting, at the cameras, metadata information from the set of streaming videos using […] algorithms on the cameras to create local metadata (par. 34) […];
storing the local metadata in local edge device metadata caches corresponding to the cameras […](par. 28 discloses cameras collect (i.e., store) metadata; additionally, the cameras operate a video analytics process to generate metadata (i.e., creating and storing local metadata));
transmitting, via a communication network, the local metadata from the local edge device metadata caches to one or more edge-cloud servers (pars. 34-36, metadata is provided to gateway 52 (i.e., edge cloud server));
extracting, at the edge-cloud servers, additional metadata using […] algorithms on the edge-cloud servers (pars. 35-36 wherein gateway 52 (i.e., edge-cloud server) extracts/adds more metadata and/or modifies existing camera generated metadata.);
combining the additional metadata with the local metadata to create global metadata (see rejection of previous limitation);
storing the global metadata in an edge-cloud metadata cache (see citations above – note that at least par. 34, 36 state that gateway 52 (i.e., edge-cloud server) stores the metadata);
transmitting, via the communication network, the global metadata from the edge-cloud metadata cache to a cloud server (see citations above and note that at least pars. 33-37, 40 disclose that metadata is sent from gateway 52 (i.e., edge-cloud server) to cloud computing server 62 (i.e., cloud server));
extracting cloud metadata from the global metadata using […] algorithms on the cloud server (see at least pars. 45-50, 54-55 disclose defining new types of metadata, values thereof, indexes etc. based on gateway 52 and camera provided metadata (i.e. extracting));
updating the global metadata based on the cloud metadata […](see rejection of previous limitation wherein the metadata (i.e., global metadata) provided by gateway 52 to cloud computing server 62 is updated by the new types of metadata, values thereof, indexes etc.);
storing the updated global metadata in a query database (see at least pars. 45-48);[…].
Aghdasi does not explicitly disclose, however, Shah in the related field of endeavor of multi-camera person reidentification and tracking discloses:
using deep learning algorithms (Shah, pars. 25, 33, 35, 52),
wherein each local edge device metadata cache associates a local identifier and a global identifier to the local metadata, the local identifier corresponding to the local edge device metadata cache and the global identifier corresponding to an edge-cloud server associated with the local edge device metadata cache (Shah, pars. 37, 40 disclose “assigned ID” as a local identifier of data at a local camera information store and pars. 26, 44, 54, 55 disclose “global ID” as a global identifier assigned to data at the local camera information store based on a match, the global ID/global track being maintained at a multicamera track association subsystem 114 as well as at a large data store 518 (i.e. global identifier corresponding to an edge-cloud server associated with…));
wherein updating the global metadata includes updating the global identifier based on the local metadata (Shah pars. 25-26, 37-40, 52-54 disclose that the global ID is assigned/updated based on the visual features of the camera-specific (local) track; when the camera-specific track matches a global track, the camera-specific track is linked to the global track, assigned the matched global ID, and the global track’s expiration time is reset (i.e., the global track is updated); when the camera specific track doesn’t match any existing global track, a new global ID is created for the camera-specific track and added to the global track collection).
and synchronizing the global identifier across the local edge device metadata caches (Shah as cited above including pars. 25-27, 41-45 disclose that camera specific local tracks from different cameras that correspond to the same person are each assigned the same global ID as the matched global track, such that a common global ID is applied across and made consistent among per camera local tracks (i.e., synchronizing the global ID across the local data stores of the multiple cameras)).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the cited references because Shah would have allowed Aghdasi to implement deep learning algorithms to more accurately detect and re-identify objects across video streams, and to implement local/global identifiers as claimed in order to maintain identification of an object across geographically distributed cameras throughout the objects presence in the monitored environment. This provides more enhanced cross-camera/cross-zone re-identification and anomaly detection. See Shah, par. 20.
Aghdasi as modified does not explicitly disclose using wherein the local metadata includes an adjacency matrix representing relationships between objects in the set of streaming videos and actions of the objects. However, Kwon in the related field of endeavor of image analysis discloses this limitation in at least (Kwon, col. 8, lines 18-60 matrices that describe graph structures (i.e. adjacency matrices) including relationships between objects and actions).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the cited references because Kwon would have allowed Aghdasi as modified to use matrices to capture and process image information including objects and actions that correspond to spatial-temporal scene graphs for a variety of applications that use object/action recognition as stated in Kwon, col. 10, line 50 to col. 11, line 6.
As per claim 13, it includes the same or similar subject matter as claim 1 and is therefore likewise rejected. See Aghdasi pars. 29, 59-61 for the memories and processors.
In addition, regarding the limitations a plurality of geographically distributed embedded-AI (artificial intelligence) cameras; one or more geographically distributed edge-cloud servers; and a cloud server (Aghdasi, see rejection of claim 1 including pars. 28-34),
wherein the one or more geographically distributed edge-cloud servers are coupled, via the communication network, to subgroups of the cameras and the local edge device metadata caches (Aghdasi, see rejection of claim 1 including pars. 27-28, 30-31),
receive, via the communication network, the local metadata from the local edge device metadata caches… receive, via the communication network, the local metadata from the local edge device metadata caches (Aghdasi, see rejection of claim 1 including pars. 27-28, 33-37, 40),
As per claim 2, Aghdasi as modified discloses The method of claim 1, wherein the edge-cloud servers are geographically distributed across one or more zones (pars. 30, 44).
As per claim 3, Aghdasi as modified discloses The method of claim 2, wherein the edge-cloud servers are geotagged according to geographical locations of the edge-cloud servers (pars. 3-6, 22, 23, 30, 31).
As per claim 4, Aghdasi as modified discloses The method of claim 1, wherein the local metadata includes information describing the objects and attributes in the set of streaming videos (par. 34).
As per claim 5, Aghdasi as modified discloses The method of claim 1, wherein the global metadata includes information describing the objects and attributes in the set of streaming videos based on the local metadata (pars. 35-39).
As per claim 6, Aghdasi as modified discloses The method of claim 1, further comprising implementing human-level querying of the query database for geographically distributed queries based on identification of specific objects and attributes of interest in the set of streaming videos (pars. 30, 35, 42, 43, 51, 55, 57).
As per claim 7, Aghdasi as modified discloses The method of claim 6, further comprising implementing the querying by video stream content correlation through metadata matching and reidentification at the edge-cloud metadata cache (see rejection of claim 6 including pars. 26, 34, 36, 39, 40).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Aghdasi in view of Shah and Kwon and further in view of Maheshwari et al., Pub. No.: US 20220391433 A1, hereinafter Maheshwari.
As per claim 8, Aghdasi as modified discloses the method of claim 6. The combination does not expressly disclose however Maheshwari in the related field of endeavor of computer vision discloses further comprising implementing a classification algorithm to generate a hierarchical knowledge-graph representation of one or more images in the set of streaming videos (Maheshwari, pars. 6, 46-50).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the cited references because Maheshwari would have allowed the combination to implement “Scene graphs 405 [to] encapsulate the constituent objects and their relationships, and encode object attributes and spatial information. Scene graphs 405 can be applied to multiple downstream applications (for example, visual question answering, scene classification, image manipulation and visual relationship detection)” (Maheshwari, par. 50).
Claims 9-12 are rejected under 35 U.S.C. 103 as being unpatentable over Aghdasi in view of Shah and Kwon and further in view of Schei et al., Pub. No.: US 20230128577 A1, hereinafter Schei.
As per claim 9, Aghdasi as modified discloses The method of claim 1. The combination does not expressly disclose however Schei in the related field of endeavor of computer vision discloses further comprising implementing updating for the local metadata on the local edge device metadata caches based on distance correlations between feature vectors corresponding to the objects and entries in the local edge device metadata caches (Schei, pars. 75,81-84 disclose updating identifier information for a track moment (metadata) based on cosine distances with other stored face data objects/entries; see rejection of claim 1 for local metadata, devices, caches and corresponding limitations).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the cited references because Schei would have allowed the combination to perform “a continuous re-identification process that integrates the face matching steps within a face tracker such as the simple online and real time tracking with a deep association metric algorithm … to track each detection over time by performing a series of updates to the identifiers” (Schei, par. 75).
As per claim 10, Aghdasi as modified by discloses the method of claim 9, wherein updating the local metadata includes updating the local identifiers for the objects and attributes in the local metadata (see rejection of claim 9 and rationale to combine).
As per claim 11, Aghdasi as modified discloses The method of claim 1. The combination does not expressly disclose however Schei in the related field of endeavor of computer vision discloses further comprising implementing updating for the global metadata on the edge-cloud metadata cache based on distance correlations between feature vectors corresponding to entries in the local edge device metadata caches and entries in the edge-cloud metadata cache (Schei, pars. 75,81-84 disclose updating identifier information for a track moment (metadata) based on cosine distances with other stored face data objects/entries; see rejection of claim 1 for global metadata, devices, caches and corresponding limitations).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the cited references because Schei would have allowed the combination to perform “a continuous re-identification process that integrates the face matching steps within a face tracker such as the simple online and real time tracking with a deep association metric algorithm … to track each detection over time by performing a series of updates to the identifiers” (Schei, par. 75).
As per claim 12, Aghdasi as modified discloses The method of claim 11, wherein updating the global metadata includes updating the global identifier correlation based on the distance correlations (see rejection of claim 11 and rationale to combine).
Response to Arguments
Applicant's arguments filed 6 May 2026 have been fully considered.
Regarding the 35 USC 101 rejection, Applicant asserts that claim 1 amount to significantly more than the abstract idea and provides a technological improvement in view of par. 59 of the specification. Examiner respectfully disagrees. Claim 1 does not include specific algorithmic/technical steps relating to the broadly disclosed subject matter of par. 59 (i.e., algorithmic steps that specify what DNN embedding values are, how they are obtained, how they replace raw video streams, how they provide better privacy safeguards, and the exact steps/technical details that result in bandwidth reduction). The claim more broadly recites extracting metadata, transmitting metadata, updating identifiers, synchronizing identifiers, etc. Thus, it should be clear that the alleged improvement is not present in the claim.
With respect to the prior art rejection, Shah et al., Pub. No.: US 20230112584 A1, has been applied in response to claim amendments.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SYED HASAN whose telephone number is (571)270-5008. The examiner can normally be reached M-F 8am - 5 pm.
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, Boris Gorney can be reached at (571)270-5626. 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.
/SYED H HASAN/Primary Examiner, Art Unit 2154
Prosecution Insights