DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
Receipt is acknowledged of claim amendments with associated arguments/remarks, received July 21, 2026. Claims 1-17 are pending of which claims 1, 2, 4-8, 12-13 were amended and claims 9-11, 14-17 are withdrawn.
Response to Arguments
Applicant’s arguments, see Remarks, pg 8, filed 07/21/202, with respect to the objection of claim 12 has been fully considered and, in light of the associated amendment, is persuasive. Therefore, the objection has been withdrawn.
Applicant’s arguments, see Remarks, pg 8, filed 07/21/2026, with respect to the rejection of claim 13 under 35 USC § 101 has been fully considered and, in light of the associated amendment, is persuasive. Therefore, the rejection has been withdrawn.
Applicant’s arguments, see Remarks, pg 8, filed 07/21/2026, with respect to the rejections of claim 6-8 under 35 USC § 112(b) has been fully considered and, in light of the associated amendment, is persuasive. Therefore, the rejection has been withdrawn.
Applicant’s arguments, see pg 10-11, filed July 21, 2026, with respect to the rejection of claims 1, 2, 4-8, 12-13 under 35 U.S.C. §§ 102, 103 has been fully considered but is not persuasive. The examiner notes the applicant has changed the scope of the invention based on the submitted claim amendments but the examiner is not persuaded, after review of the amendments, prior art and remarks that the claims are not taught by the cited prior art.
Specifically, the applicant argues the independent claim 1 is amended such that is it not taught by the prior art Hallett et al (US 2020/0143561) or by Sriram et al (US 2019/0294889) alone or in combination. Respectfully, the examiner is not persuaded.
The applicant first argues Hallett teaches the cameras are positioned to capture the same kind of data. Claim 1 is claimed to capture first position data from a first camera that includes individuating data and capture second position data from a second camera that does not include individuating data with the viewing region of interest containing at least a portion of the same field. Hallett et al states, for example, “FIG. 2 is a flowchart of operations to determine a sequence of locations based on a set of images and sensor measurements, in accordance with some embodiments. In some embodiments, the process 200 includes acquiring a set of images from a plurality of cameras directed to different fields of view of a monitored environment, as indicated by block 204. As used herein, an image may include a mapping of a set of values to a corresponding set of spatial positions, and may represent a color image, black and white image, structured illumination, thermal image, infrared image, x-ray image, ultraviolet image, some combination thereof, or the like.” in ¶ [0026]. See also Fig 1 of Hallett et al which show adjacent facing cameras capturing partial same field of view and partial distinct field of view. Applicant does not provide substantive details in the Remarks or in the claim language for how such captured data is distinguishable from the cited art.
Applicant further argues the prior art does not teach matching positional data from the two camera images and using such positional data to track an individual from different image perspectives based on the applicant’s claim language (“matching said first and said second positional data”). Hallett et al provides numerous statements of how positional data is identified (a search for the word “position” in the reference results in 53 citations), including “an image may include a mapping of a set of values to a corresponding set of spatial positions” ¶ [0026]; “the entity-tracking system may use a SSD neural network, where the entity-tracking system may determine a set of visual features in an image and a set of visual feature positions associated with the set of visual features, where the set of visual features may be determined using a convolutional neural network, other neural network, or feature detection algorithm” ¶ [0031]; and “the first entity may detect locations using one or more computer vision methods based on a pixel position of the detected entity, where computer vision methods may include the implementation of various perspective transformation algorithms, homography methods, pose estimation algorithms, or the like” ¶ [0034].
Lastly, the applicant argues object tracking as described by Hallett et al is not applicable to the current independent claims because the prior art does not teach tracking as claimed because the applicant’s invention is “to coordinate continuity of tracking of the entity” using image data from two different cameras and perspectives. As discussed above, Hallett et al teaches different positional data is acquired from multiple cameras facing different perspectives and such data is used for entity tracking. In regarding to applicant’s argument of the example use of SIFT, context of Hallett et al ¶ [0027] is “The entity-tracking system may apply various image analysis operations and image processing operations to detect and localize the set of entities based on the set of images. In some embodiments, the system may use object recognition methods on a set of images to detect one or more entities. Object tracking methods may include detection-based tracking methods, detection-free tracking methods, single object tracking methods, multi-object tracking methods. Some embodiments may use tracking approaches may include use of a Viola-Jones object detection framework, scale-invariant feature transform (SIFT), histogram of oriented gradients (HOG), deformable parts model (DPM), or the like.”). Applicant argues the example of matching using SIFT is not applicable to the applicant’s invention. However, the applicant’s claims do not explicitly disclose a specific type of tracking method.
Hallett et al additionally teaches entity tracking based on a bounding-box tracking methodology, which was cited as a means to track (not just match points between two images) the object based on a tracking reference tagged with said individuating data (see Hallett et al ¶ [0027, [0030]-[0036]). Applicant did not argue the applicant’s invention is distinct from the citations pertaining to the use of bounding box tracking as cited by the examiner. The citations of Hallett et al using a bounding box to track an entity across multiple images is similar to that disclosed by in applicant’s specification (specification – 03/21/2024 pg 7 ln 5-26). Respectfully, the examiner is not persuaded.
Applicant relies on arguments above to argue against Sriram et al, which are, respectfully, not persuasive for the aforementioned reasons.
Additional substantially similar prior art was identified in the updated search and incorporated such references in the pertinent prior art. The examiner recommends further explanation as to the applicant’s reasoning as to why the applicant believes the claimed invention is novel to one of ordinary skill in the art and carefully considering amendments to support the novelty of the invention over the prior art.
Respectfully, the examiner is not persuaded.
All arguments were addressed.
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.
Claims 1-5, 12, 13 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hallett et al (US 2020/0143561, cited in Non-Final Rejection – 03/23/2026).
Regarding Claim 1, Hallett et al teach a method of operating a space monitoring apparatus (process 200 of monitoring an environment 101 using an entity-tracking system 100; Fig 1, 2 and ¶ [0019], [0025]) in networked communication with a set of cameras (system 100 includes first camera 102 and second camera 103 connected with server 180; Fig 1 and ¶ [0019]-[0020]) including at least a first camera positioned to capture position data and individuation data of a subject (first camera 102 with field of view for monitored environment 101 to identify and track individual entities (subjects), such as first entity 110; Fig 1 and ¶ [0019]) and a second camera positioned to capture position data of a subject and not to capture individuating data (second camera 103 have a partially associated field of view for monitored environment 101 to identify and track individual entities (subjects), such as first entity 110 but may not collect identical attributes at the same time (thereby not capture individuating data of the identify at the same time as the first camera); Fig 1 and ¶ [0019]), said first and said second cameras sharing at least a portion of a field of view (the first camera 102 and second camera 103 acquire images for an associated field of view for the monitored environment 101; Fig 1 and ¶ [0019]), comprising:
extracting first individuating data and first position data of a first subject from a first image captured by said first camera (the first camera 102 acquires a set of images of the monitored environment 101, including of entity 110, with analysis including use of a spatial coordinate system to determine positional data of the entity; Fig 1, 2 and ¶ [0019]-[0020], [0026], [0033]);
extracting second position data of a second subject from a second image captured by said second camera (the second camera 103 acquires a set of images of the monitored environment 101, including of entity 110, with analysis including use of a spatial coordinate system to determine positional data of the entity; Fig 1, 2 and ¶ [0019]-[0020], [0026], [0033]);
matching said first and said second position data (images of the entity 110, acquired by the cameras 102, 103 with same positional data from spatial coordinate system, are matched for single object tracking, with matching of the multiple images (for example, SIFT matches coordinates of objects between two images ¶ [0027] but noted other matching methodology is disclosed ); Fig 1, 2 and ¶ [0019]-[0020], [0026]-[0028], [0034], [0081]);
responsive to a positive match between said first position data and said second position data, indicating that said first and said second subject are an identical entity, creating a tracking reference tagged with said first individuating data (the image data from the multiple fields of view matched for a given object is used for entity tracking by tracking the motion of the entity (such as via tracking bounding box motion with bounding box tagged to detected entity); Fig 1, 2 and ¶ [0030], [0034]-[0035]);
storing said tracking reference tagged with said first individuating data in a data store for reuse (the images and associated measurement data (positioning, tracking) are stored on the server 180 in a database and may be retrieved at a later time; Fig 1, 2 and ¶ [0023], [0026]); and
signalling said tracking reference to a tracking logic component to coordinate continuity of tracking of said identical entity with said set of cameras (the entity-tracking system may include a single shot multibox detection (SSD) neural network, which SSD neural network uses the visual feature positions identified in the multiple camera 102, 103 images to perform tracking of the entity 110 concurrently during imaging, thereby providing real-time tracking of the entity; Fig 1, 2 and ¶ [0024]-[0027], [0030]-[0031]).
Regarding Claim 2, Hallett et al teach the method of claim 1 (as described above), said extracting first individuating data further comprising operating a machine-learning model to determine characteristics of said first subject in said first image (features of the first entity 110 are extracted from the first camera 102 images and classified using a neural network system; Fig 1, 2 and ¶ [0024], [0031], [0036]-[0037]).
Regarding Claim 3, Hallett et al teach the method of claim 1 (as described above), further comprising: extracting second individuating data from a further image (features of the first entity 110 are extracted from the second camera 103 images; Fig 1, 2 and ¶ [0019], [0031], [0036]-[0037]); querying said data store for stored said first individuating data matching said second individuating data (accessing the database data for the first camera 102 images and associated measurement data (positioning, tracking) stored on the server 180 and using the data for entity 110 feature matching to the second camera 103 images using SIFT techniques; Fig 1, 2, 3 and ¶ [0023]-[0028], [0031], [0036]-[0037], [0075]); and in response to a positive match from said querying (matching performed between image data from cameras 102, 103; ¶ [0075]), indicating that said first and said second subject are an identical entity, reusing said tracking reference (the matching data may be stored and used again to match an entity detected from the current visit to a previously-detected entity visit; ¶ [0062], [0075]).
Regarding Claim 4, Hallett et al teach the method of claim 1 (as described above), said signalling comprising signalling said tracking reference to a tracking logic component associated with a third camera in said set of cameras to coordinate tracking of said identical entity in images captured by said third camera (a third camera can be used to capture additional image data (“PEO3” ¶ [0089]) to additionally track the entity 110, including third location and positional data for matching and entity tracking via SIFT (¶ [0027]), with the entity attributes and positional location for tracking based on third measurements and confidence values; Fig 1, 2, 3 and ¶ [0043], [0087]-[0090]).
Regarding Claim 5, Hallett et al teach the method of claim 4 (as described above), said tracking comprising replacing a new tracking reference generated for an image captured by said third camera with said tracking reference signalled to said tracking logic component (a reference position is used for spatial coordinates for the monitored environment (¶ [0020]) and a reference model associated with the given entity will update the given numerical attributes from the first camera to the second camera (or given third camera by matching PEO1 to PEO3 for same entity (Entity 1 ¶ [0095]) and the record (position) is updated for the entity-tracking system; Fig 1, 2, 3 and ¶ [0082], [0087]).
Regarding Claim 12, Hallett et al teach a space monitoring apparatus (entity-tracking system 100; Fig 1, 2 and ¶ [0019], [0025]) operable in networked communication with a set of cameras (system 100 includes first camera 102 and second camera 103 connected with server 180; Fig 1 and ¶ [0019]-[0020]) including at least a first camera positioned to capture position data and image data suitable for and individuation data of a subject (first camera 102 with field of view for monitored environment 101 to identify and track individual entities (subjects), such as first entity 110; Fig 1 and ¶ [0019]) and a second camera positioned to capture position data of a subject and not to capture individuating data (second camera 103 have a partially associated field of view for monitored environment 101 to identify and track individual entities (subjects), such as first entity 110 but may not collect identical attributes at the same time (thereby not capture individuating data of the identify at the same time as the first camera); Fig 1 and ¶ [0019]), said first and said second cameras sharing at least a portion of a field of view (the first camera 102 and second camera 103 acquire images for an associated field of view for the monitored environment 101; Fig 1 and ¶ [0019]), the space monitoring apparatus comprising:
electronic logic circuitry (server 180 performs the operations for the entity-tracking system 100; Fig 1, 2 and ¶ [0020], [0023]-[0026]) operable to perform the method according to claim 1 (as described above).
Regarding Claim 13, Hallett et al teach the method according to claim 1 (as described above), wherein all the steps of the method are performed by a computer program comprising computer program code (the entity-tracking system 100 include instructions of computer code; Fig 1, 2 and ¶ [0025]) loaded into a computer system and executed thereon (the computer code instructions may be tangible to be loaded on machine-readable medium and executed by processor; Fig 1, 2 and ¶ [0025]).
Claims 6-8 are rejected under 35 U.S.C. 103 as being unpatentable over Hallett et al (US 2020/0143561, cited in Non-Final Rejection – 03/23/2026) in view of Sriram et al (US 2019/0294889, cited in Non-Final Rejection – 03/23/2026).
Regarding Claim 6, Hallett et al teach the method of claim 1 (as described above).
Hallett et al does not explicitly teach positioning said first camera to capture a horizontal view. Hallett teaches the camera is positioned to obtain image data at a monitored space relative to a reference position or given coordinate (¶ [0020], [0026]).
Sriram et al is analogous art pertinent to the technological problem addressed in the current application and teach positioning said first camera to capture a horizontal view (an image sensor camera 240 is used to monitor the area of interest 252A of area 200 (entrance/exit) from a first perspective 164B; Fig 1A, 2, 3B and ¶ [0061]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to combine the teachings of Hallett et al with Sriram et al including positioning said first camera to capture a horizontal view. By capturing a field of view with multiples cameras from different perspectives and analyzing the environment using machine learning techniques, detailed information and behavior is detected and tracked, thereby allowing for enhanced security and convenience is achieved, as recognized by Sriram et al (¶ [0006]-[0008]).
Regarding Claim 7, Hallett et al teach the method of claim 1 (as described above).
Hallett et al does not teach positioning said second camera to capture a downward-looking view.
Sriram et al is analogous art pertinent to the technological problem addressed in the current application and teach positioning said second camera to capture a downward-looking view (interpreted as a camera imaging from a bird’s eye view) (a fisheye lens camera 236 may be installed to monitor from above to capture 360 degree field of view (vertical), corresponding to at least some adjacent corresponding field of view of the first camera 240 and second camera 246; Fig 1A, 2, 3B and ¶ [0061]-[0063]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to combine the teachings of Hallett et al with Sriram et al including positioning said second camera to capture a downward-looking view. By capturing a field of view with multiples cameras from different perspectives and analyzing the environment using machine learning techniques, detailed information and behavior is detected and tracked, thereby allowing for enhanced security and convenience is achieved, as recognized by Sriram et al (¶ [0006]-[0008]).
Regarding Claim 8, Hallett et al teach the method of claim 1 (as described above).
Hallett et al does not teach positioning said third camera to capture a further downward-looking view.
Sriram et al is analogous art pertinent to the technological problem addressed in the current application and teach positioning said third camera to capture a further downward-looking view (interpreted as a second bird’s eye view camera) (additional fisheye lens camera 228 may be installed to monitor from above to capture 360 degree field of view (vertical) from a different area of view from the first bird’s eye view fisheye camera (second camera), corresponding to a different at least partial field of view of the first camera 240 and second camera 246; Fig 1A, 2, 3B and ¶ [0061]-[0063]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to combine the teachings of Hallett et al with Sriram et al including positioning said third camera to capture a further downward-looking view. By capturing a field of view with multiples cameras from different perspectives and analyzing the environment using machine learning techniques, detailed information and behavior is detected and tracked, thereby allowing for enhanced security and convenience is achieved, as recognized by Sriram et al (¶ [0006]-[0008]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
El-Khamy et al (US 2018/0089505, cited in Non-Final Rejection – 03/23/2026) teach a method and apparatus for detecting an object in image data including use of multiple object detectors for generating the image data and use of multiple neural network models for detecting, classifying and tracking of an object.
Fisher et al (US 2019/0156274, cited in Non-Final Rejection – 03/23/2026) teach a system and method for machine learning subject tracking including use of a plurality of cameras with overlapping fields of view to image an environment and used to identify and track a subject.
Ma et al (US 2016/0217417, cited in Non-Final Rejection – 03/23/2026) teach a system and method for detecting an object, including motion of the object and the direction of motion, which may be used to track the object over time.
Mande et al (US 2017/0344855) teach a method and system for determining a likelihood for a collision between different vehicles including using bounding box data to identify and track an entity.
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 KATHLEEN M BROUGHTON whose telephone number is (571)270-7380. The examiner can normally be reached Monday-Friday 8:00-5:00.
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/KATHLEEN M BROUGHTON/Primary Examiner, Art Unit 2661
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