DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Oath/Declaration
Oath/Declaration as filed on December 23, 2024 is noted by the Examiner.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 9, and 15 are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Wang et al., U.S. Patent Application Publication 2013/0166711 A1 (hereinafter Wang).
Regarding claim 1, Wang teaches a method for reducing video data storage and improving processing efficiency (130 FIGS. 2-3, 6, and 8, paragraph[0054] of Wang teaches referring next to FIG. 6, with further reference to FIGS. 1-5, a process 130 of managing transfer of information associated with a video surveillance system to a network management service, such as a cloud service 90, includes the stages shown; the process 130 is, however, an example only and not limiting; the process 130 can be altered, e.g., by having stages added, removed, rearranged, combined, and/or performed concurrently; still other alterations to the process 130 as shown and described are possible; and the process 130 can be performed by one or more entities associated with a multi-tiered surveillance management system, such as a gateway 52 interposed between a cloud service 90 and local surveillance systems 70. One or more of the operations described in process 130 can be performed in hardware and/or in software, and See also at least ABSTRACT, and paragraphs[0021], [0024], [0028], [0031], [0033], [0038], [0040], [0043]-[0044], [0046], [0055]-[0056], [0060]-[0067], [0072], and [0075] of Wang (i.e., Wang teaches a process, which is performed by a computer system, of managing transfer of information associated with a video surveillance system to a network management service such as a cloud service and even reduce video uploaded to the cloud service, wherein the computer system includes one or more processors executing one or more instructions, which are contained within memory, to perform one or more procedures of the process)), comprising:
receiving a video stream comprising a plurality of frames (132 FIGS. 2-3, 6, and 8, paragraph[0021] of Wang teaches the process 130 begins at stage 132, wherein video content and associated metadata are obtained from a plurality of network devices (e.g., edge devices such as routers 20, cameras 42, encoders, etc.); at stage 134, the obtained metadata are analyzed and processed, and noise metadata are identified; "Noise" metadata is defined as any metadata generated by a network device that does not correspond to an actual event or object; these can include, e.g., false positives identified by a network device due to background image noise, metadata identified by a gateway 52 as extraneous upon performing inter-camera analytics or temporal relationship processing, etc; the noise metadata can be identified by, for example, evaluating quality of the metadata according to one or more criteria as described above and classifying portions of the metadata having a quality below a given threshold as noise metadata; and at stage 136, a filtered set of metadata is generated by removing the noise metadata identified at stage 134 from the metadata obtained at stage 132, and See also at least ABSTRACT, and paragraphs[0021], [0024], [0028], [0031], [0033], [0038], [0040], [0043]-[0044], [0046], [0054]-[0056], [0060], [0072], and [0075] of Wang (i.e., Wang teaches the computer system obtaining video content and associated metadata from cameras, wherein the camera transmit frames of a video feed));
analyzing the video stream to identify one or more objects (FIGS. 2-3, 6, and 8, paragraph[0053] of Wang teaches for the real-time detection service 112, the rule engine 120 checks associated metadata in real-time to determine whether events are presently occurring that meet predefined rules; if so, the real-time detection service 112 triggers alarms for the certain types of events that are detected; for the operational service 114, the rule engine 120 assists in providing results of statistical data, (e.g., a list of average counts of objects visiting a specific place, such as a lane in a department store, in a predefined time period, etc.); for the camera network monitoring service 116, the rule engine 120 assists in displaying user selected or system identified objects automatically; the camera network monitoring service 116 manages multiple cameras, which can have overlapping or non-overlapping monitoring areas; the video on the display can be switched automatically to track suspects and/or other objects of interest; and further, moving objects can be labeled on a map registered with the camera scenes, and See also at least ABSTRACT, and paragraphs[0021], [0024], [0028], [0031], [0033], [0037]-[0040], [0042]-[0044], [0046], [0048], [0054]-[0056], [0060], [0072], and [0075] of Wang (i.e., Wang teaches the computer system switching automatically the video on a display to track suspects and/or objects of interest));
extracting object metadata associated with the one or more objects from the video stream, the object metadata including at least location information of the one or more objects (136 FIGS. 2-3, 6, and 8, paragraphs[0055]-[0056] of Wang teaches the process 130 begins at stage 132, wherein video content and associated metadata are obtained from a plurality of network devices (e.g., edge devices such as routers 20, cameras 42, encoders, etc.); at stage 134, the obtained metadata are analyzed and processed, and noise metadata are identified; "Noise" metadata is defined as any metadata generated by a network device that does not correspond to an actual event or object; these can include, e.g., false positives identified by a network device due to background image noise, metadata identified by a gateway 52 as extraneous upon performing inter-camera analytics or temporal relationship processing, etc; the noise metadata can be identified by, for example, evaluating quality of the metadata according to one or more criteria as described above and classifying portions of the metadata having a quality below a given threshold as noise metadata; at stage 136, a filtered set of metadata is generated by removing the noise metadata identified at stage 134 from the metadata obtained at stage 132; at stage 138, video portions associated with first portions of the set of filtered metadata generated at stage 136 are isolated from the video content obtained at stage 132; and at stage 140, the video portions isolated at stage 138 and their associated metadata are uploaded to a network storage entity (e.g., associated with a cloud service 90), and See also at least ABSTRACT, and paragraphs[0003], [0021], [0024], [0027]-[0028], [0031], [0033], [0037]-[0040], [0042]-[0044], [0046], [0048], [0053]-[0054], [0060], [0072], and [0075] of Wang (i.e., Wang teaches the computer system filtering metadata associated with the video and corresponding to the objects));
determining movement characteristics of the one or more objects based on the object metadata (FIGS. 2-3, 6, and 8, paragraph[0037] of Wang teaches various aspects of the functionality of the system shown in FIG. 2 are illustrated by diagram 70 in FIG. 3; within the intelligent cameras/encoders, a video analytics algorithm is utilized as a scene analyzer to detect and track objects in the scene and generate metadata to describe the objects and their events; the scene analyzer operates as a background subtraction based algorithm; the scene analyzer can describe an object with its color, location in the scene, time stamp, velocity, size, moving direction, etc; and the scene analyzer can also trigger predefined metadata events such as zone or tripwire violation, counting, camera sabotage, object merging, object splitting, still objects, object loitering, etc; and object and event metadata, along with any other metadata generated by the edge device(s), are sent to the gateway 52, and See also at least ABSTRACT, and paragraphs[0003], [0021], [0024], [0027]-[0028], [0031], [0033], [0037]-[0040], [0042]-[0044], [0046], [0048], [0051], [0053]-[0056], [0060], [0072], and [0075] of Wang (i.e., Wang teaches the computer system having a scene analyzer detecting and extracting the objects and even generating metadata to describe the objects based on their velocity and direction)); and
selecting at least a subset of the plurality of frames in the video stream based on the object metadata and the movement characteristics of the one or more objects for further processing, wherein the subset of the plurality of frames includes less than all of the plurality of frames (138 FIGS. 2-3, 6, and 8, paragraphs[0056] of Wang teaches at stage 138, video portions associated with first portions of the set of filtered metadata generated at stage 136 are isolated from the video content obtained at stage 132; and at stage 140, the video portions isolated at stage 138 and their associated metadata are uploaded to a network storage entity (e.g., associated with a cloud service 90), and See also at least ABSTRACT, and paragraphs[0003], [0021], [0024], [0027]-[0028], [0031], [0033], [0037]-[0040], [0042]-[0044], [0046], [0048], [0051], [0053]-[0056], [0060], [0072], and [0075] of Wang (i.e., Wang teaches the computer system isolating video portions, which are from the obtained video content, based on the filtered metadata wherein the metadata includes describes the objects and includes at least velocity and moving direction)).
Regarding claim 9, Wang teaches a computing system for reducing video data storage and improving processing efficiency, the computing system comprising: one or more processors; and memory storing one or more programs configured for execution by the one or more processors, the one or more programs comprising instructions for: (200, 225 FIGS. 2-3, 6, and 8, paragraphs[0064]-[0065] of Wang teaches the computer system 200 also can comprise software elements, shown as being currently located within the working memory 235, including an operating system 240, device drivers, executable libraries, and/or other code, such as one or more application programs 245, which may comprise computer programs provided by various configurations, and/or may be designed to implement methods, and/or configure systems, provided by other configurations, as described herein; merely by way of example, one or more procedures described with respect to the method(s) discussed above might be implemented as code and/or instructions executable by a computer (and/or a processor within a computer); in an aspect, then, such code and/or instructions can be used to configure and/or adapt a general purpose computer (or other device) to perform one or more operations in accordance with the described methods; a set of these instructions and/or code might be stored on a computer-readable storage medium, such as the storage device(s) 225 described above; in some cases, the storage medium might be incorporated within a computer system, such as the system 200; in other configurations, the storage medium might be separate from a computer system (e.g., a removable medium, such as a compact disc), and or provided in an installation package, such that the storage medium can be used to program, configure and/or adapt a general purpose computer with the instructions/code stored thereon; and these instructions might take the form of executable code, which is executable by the computer system 200 and/or might take the form of source and/or installable code, which, upon compilation and/or installation on the computer system 200 (e.g., using any of a variety of generally available compilers, installation programs, compression/decompression utilities, etc.), then takes the form of executable code, and See also at least ABSTRACT, and paragraphs[0021], [0024], [0028], [0031], [0033], [0038], [0040], [0043]-[0044], [0046], [0054]-[0056], [0060]-[0063], [0066]-[0067], [0072], and [0075] of Wang (i.e., Wang teaches a process, which is performed by a computer system, of managing transfer of information associated with a video surveillance system to a network management service such as a cloud service and even reduce video uploaded to the cloud service, wherein the computer system includes one or more processors executing one or more instructions, which are contained within memory, to perform one or more procedures of the process))
receiving a video stream comprising a plurality of frames (132 FIGS. 2-3, 6, and 8, paragraph[0021] of Wang teaches the process 130 begins at stage 132, wherein video content and associated metadata are obtained from a plurality of network devices (e.g., edge devices such as routers 20, cameras 42, encoders, etc.); at stage 134, the obtained metadata are analyzed and processed, and noise metadata are identified; "Noise" metadata is defined as any metadata generated by a network device that does not correspond to an actual event or object; these can include, e.g., false positives identified by a network device due to background image noise, metadata identified by a gateway 52 as extraneous upon performing inter-camera analytics or temporal relationship processing, etc; the noise metadata can be identified by, for example, evaluating quality of the metadata according to one or more criteria as described above and classifying portions of the metadata having a quality below a given threshold as noise metadata; and at stage 136, a filtered set of metadata is generated by removing the noise metadata identified at stage 134 from the metadata obtained at stage 132, and See also at least ABSTRACT, and paragraphs[0021], [0024], [0028], [0031], [0033], [0038], [0040], [0043]-[0044], [0046], [0054]-[0056], [0060], [0072], and [0075] of Wang (i.e., Wang teaches the computer system obtaining video content and associated metadata from cameras, wherein the camera transmit frames of a video feed));
analyzing the video stream to identify one or more objects (FIGS. 2-3, 6, and 8, paragraph[0053] of Wang teaches for the real-time detection service 112, the rule engine 120 checks associated metadata in real-time to determine whether events are presently occurring that meet predefined rules; if so, the real-time detection service 112 triggers alarms for the certain types of events that are detected; for the operational service 114, the rule engine 120 assists in providing results of statistical data, (e.g., a list of average counts of objects visiting a specific place, such as a lane in a department store, in a predefined time period, etc.); for the camera network monitoring service 116, the rule engine 120 assists in displaying user selected or system identified objects automatically; the camera network monitoring service 116 manages multiple cameras, which can have overlapping or non-overlapping monitoring areas; the video on the display can be switched automatically to track suspects and/or other objects of interest; and further, moving objects can be labeled on a map registered with the camera scenes, and See also at least ABSTRACT, and paragraphs[0021], [0024], [0028], [0031], [0033], [0037]-[0040], [0042]-[0044], [0046], [0048], [0054]-[0056], [0060], [0072], and [0075] of Wang (i.e., Wang teaches the computer system switching automatically the video on a display to track suspects and/or objects of interest));
extracting object metadata associated with the one or more objects from the video stream, the object metadata including at least location information of the one or more objects (136 FIGS. 2-3, 6, and 8, paragraphs[0055]-[0056] of Wang teaches the process 130 begins at stage 132, wherein video content and associated metadata are obtained from a plurality of network devices (e.g., edge devices such as routers 20, cameras 42, encoders, etc.); at stage 134, the obtained metadata are analyzed and processed, and noise metadata are identified; "Noise" metadata is defined as any metadata generated by a network device that does not correspond to an actual event or object; these can include, e.g., false positives identified by a network device due to background image noise, metadata identified by a gateway 52 as extraneous upon performing inter-camera analytics or temporal relationship processing, etc; the noise metadata can be identified by, for example, evaluating quality of the metadata according to one or more criteria as described above and classifying portions of the metadata having a quality below a given threshold as noise metadata; at stage 136, a filtered set of metadata is generated by removing the noise metadata identified at stage 134 from the metadata obtained at stage 132; at stage 138, video portions associated with first portions of the set of filtered metadata generated at stage 136 are isolated from the video content obtained at stage 132; and at stage 140, the video portions isolated at stage 138 and their associated metadata are uploaded to a network storage entity (e.g., associated with a cloud service 90), and See also at least ABSTRACT, and paragraphs[0003], [0021], [0024], [0027]-[0028], [0031], [0033], [0037]-[0040], [0042]-[0044], [0046], [0048], [0053]-[0054], [0060], [0072], and [0075] of Wang (i.e., Wang teaches the computer system filtering metadata associated with the video and corresponding to the objects));
determining movement characteristics of the one or more objects based on the object metadata (FIGS. 2-3, 6, and 8, paragraph[0037] of Wang teaches various aspects of the functionality of the system shown in FIG. 2 are illustrated by diagram 70 in FIG. 3; within the intelligent cameras/encoders, a video analytics algorithm is utilized as a scene analyzer to detect and track objects in the scene and generate metadata to describe the objects and their events; the scene analyzer operates as a background subtraction based algorithm; the scene analyzer can describe an object with its color, location in the scene, time stamp, velocity, size, moving direction, etc; and the scene analyzer can also trigger predefined metadata events such as zone or tripwire violation, counting, camera sabotage, object merging, object splitting, still objects, object loitering, etc; and object and event metadata, along with any other metadata generated by the edge device(s), are sent to the gateway 52, and See also at least ABSTRACT, and paragraphs[0003], [0021], [0024], [0027]-[0028], [0031], [0033], [0037]-[0040], [0042]-[0044], [0046], [0048], [0051], [0053]-[0056], [0060], [0072], and [0075] of Wang (i.e., Wang teaches the computer system having a scene analyzer detecting and extracting the objects and even generating metadata to describe the objects based on their velocity and direction)); and
selecting at least a subset of the plurality of frames in the video stream based on the object metadata and the movement characteristics of the one or more objects for further processing, wherein the subset of the plurality of frames includes less than all of the plurality of frames (138 FIGS. 2-3, 6, and 8, paragraphs[0056] of Wang teaches at stage 138, video portions associated with first portions of the set of filtered metadata generated at stage 136 are isolated from the video content obtained at stage 132; and at stage 140, the video portions isolated at stage 138 and their associated metadata are uploaded to a network storage entity (e.g., associated with a cloud service 90), and See also at least ABSTRACT, and paragraphs[0003], [0021], [0024], [0027]-[0028], [0031], [0033], [0037]-[0040], [0042]-[0044], [0046], [0048], [0051], [0053]-[0056], [0060], [0072], and [0075] of Wang (i.e., Wang teaches the computer system isolating video portions, which are from the obtained video content, based on the filtered metadata wherein the metadata includes describes the objects and includes at least velocity and moving direction)).
Regarding claim 15, Wang teaches a non-transitory computer-readable storage medium storing one or more programs configured for execution by one or more processors of a computing system, wherein the computing system includes a memory hierarchy, and the one or more programs comprise instructions for: (225, 200 FIGS. 2-3, 6, and 8, paragraphs[0064]-[0065] of Wang teaches the computer system 200 also can comprise software elements, shown as being currently located within the working memory 235, including an operating system 240, device drivers, executable libraries, and/or other code, such as one or more application programs 245, which may comprise computer programs provided by various configurations, and/or may be designed to implement methods, and/or configure systems, provided by other configurations, as described herein; merely by way of example, one or more procedures described with respect to the method(s) discussed above might be implemented as code and/or instructions executable by a computer (and/or a processor within a computer); in an aspect, then, such code and/or instructions can be used to configure and/or adapt a general purpose computer (or other device) to perform one or more operations in accordance with the described methods; a set of these instructions and/or code might be stored on a computer-readable storage medium, such as the storage device(s) 225 described above; in some cases, the storage medium might be incorporated within a computer system, such as the system 200; in other configurations, the storage medium might be separate from a computer system (e.g., a removable medium, such as a compact disc), and or provided in an installation package, such that the storage medium can be used to program, configure and/or adapt a general purpose computer with the instructions/code stored thereon; and these instructions might take the form of executable code, which is executable by the computer system 200 and/or might take the form of source and/or installable code, which, upon compilation and/or installation on the computer system 200 (e.g., using any of a variety of generally available compilers, installation programs, compression/decompression utilities, etc.), then takes the form of executable code, and See also at least ABSTRACT, and paragraphs[0021], [0024], [0028], [0031], [0033], [0038], [0040], [0043]-[0044], [0046], [0054]-[0056], [0060]-[0063], [0066]-[0067], [0072], and [0075] of Wang (i.e., Wang teaches a process, which is performed by a computer system, of managing transfer of information associated with a video surveillance system to a network management service such as a cloud service and even reduce video uploaded to the cloud service, wherein the computer system includes one or more processors executing one or more instructions, which are contained within memory, to perform one or more procedures of the process))
receiving a video stream comprising a plurality of frames (132 FIGS. 2-3, 6, and 8, paragraph[0021] of Wang teaches the process 130 begins at stage 132, wherein video content and associated metadata are obtained from a plurality of network devices (e.g., edge devices such as routers 20, cameras 42, encoders, etc.); at stage 134, the obtained metadata are analyzed and processed, and noise metadata are identified; "Noise" metadata is defined as any metadata generated by a network device that does not correspond to an actual event or object; these can include, e.g., false positives identified by a network device due to background image noise, metadata identified by a gateway 52 as extraneous upon performing inter-camera analytics or temporal relationship processing, etc; the noise metadata can be identified by, for example, evaluating quality of the metadata according to one or more criteria as described above and classifying portions of the metadata having a quality below a given threshold as noise metadata; and at stage 136, a filtered set of metadata is generated by removing the noise metadata identified at stage 134 from the metadata obtained at stage 132, and See also at least ABSTRACT, and paragraphs[0021], [0024], [0028], [0031], [0033], [0038], [0040], [0043]-[0044], [0046], [0054]-[0056], [0060], [0072], and [0075] of Wang (i.e., Wang teaches the computer system obtaining video content and associated metadata from cameras, wherein the camera transmit frames of a video feed));
analyzing the video stream to identify one or more objects (FIGS. 2-3, 6, and 8, paragraph[0053] of Wang teaches for the real-time detection service 112, the rule engine 120 checks associated metadata in real-time to determine whether events are presently occurring that meet predefined rules; if so, the real-time detection service 112 triggers alarms for the certain types of events that are detected; for the operational service 114, the rule engine 120 assists in providing results of statistical data, (e.g., a list of average counts of objects visiting a specific place, such as a lane in a department store, in a predefined time period, etc.); for the camera network monitoring service 116, the rule engine 120 assists in displaying user selected or system identified objects automatically; the camera network monitoring service 116 manages multiple cameras, which can have overlapping or non-overlapping monitoring areas; the video on the display can be switched automatically to track suspects and/or other objects of interest; and further, moving objects can be labeled on a map registered with the camera scenes, and See also at least ABSTRACT, and paragraphs[0021], [0024], [0028], [0031], [0033], [0037]-[0040], [0042]-[0044], [0046], [0048], [0054]-[0056], [0060], [0072], and [0075] of Wang (i.e., Wang teaches the computer system switching automatically the video on a display to track suspects and/or objects of interest));
extracting object metadata associated with the one or more objects from the video stream, the object metadata including at least location information of the one or more objects (136 FIGS. 2-3, 6, and 8, paragraphs[0055]-[0056] of Wang teaches the process 130 begins at stage 132, wherein video content and associated metadata are obtained from a plurality of network devices (e.g., edge devices such as routers 20, cameras 42, encoders, etc.); at stage 134, the obtained metadata are analyzed and processed, and noise metadata are identified; "Noise" metadata is defined as any metadata generated by a network device that does not correspond to an actual event or object; these can include, e.g., false positives identified by a network device due to background image noise, metadata identified by a gateway 52 as extraneous upon performing inter-camera analytics or temporal relationship processing, etc; the noise metadata can be identified by, for example, evaluating quality of the metadata according to one or more criteria as described above and classifying portions of the metadata having a quality below a given threshold as noise metadata; at stage 136, a filtered set of metadata is generated by removing the noise metadata identified at stage 134 from the metadata obtained at stage 132; at stage 138, video portions associated with first portions of the set of filtered metadata generated at stage 136 are isolated from the video content obtained at stage 132; and at stage 140, the video portions isolated at stage 138 and their associated metadata are uploaded to a network storage entity (e.g., associated with a cloud service 90), and See also at least ABSTRACT, and paragraphs[0003], [0021], [0024], [0027]-[0028], [0031], [0033], [0037]-[0040], [0042]-[0044], [0046], [0048], [0053]-[0054], [0060], [0072], and [0075] of Wang (i.e., Wang teaches the computer system filtering metadata associated with the video and corresponding to the objects));
determining movement characteristics of the one or more objects based on the object metadata (FIGS. 2-3, 6, and 8, paragraph[0037] of Wang teaches various aspects of the functionality of the system shown in FIG. 2 are illustrated by diagram 70 in FIG. 3; within the intelligent cameras/encoders, a video analytics algorithm is utilized as a scene analyzer to detect and track objects in the scene and generate metadata to describe the objects and their events; the scene analyzer operates as a background subtraction based algorithm; the scene analyzer can describe an object with its color, location in the scene, time stamp, velocity, size, moving direction, etc; and the scene analyzer can also trigger predefined metadata events such as zone or tripwire violation, counting, camera sabotage, object merging, object splitting, still objects, object loitering, etc; and object and event metadata, along with any other metadata generated by the edge device(s), are sent to the gateway 52, and See also at least ABSTRACT, and paragraphs[0003], [0021], [0024], [0027]-[0028], [0031], [0033], [0037]-[0040], [0042]-[0044], [0046], [0048], [0051], [0053]-[0056], [0060], [0072], and [0075] of Wang (i.e., Wang teaches the computer system having a scene analyzer detecting and extracting the objects and even generating metadata to describe the objects based on their velocity and direction)); and
selecting at least a subset of the plurality of frames in the video stream based on the object metadata and the movement characteristics of the one or more objects for further processing, wherein the subset of the plurality of frames includes less than all of the plurality of frames (138 FIGS. 2-3, 6, and 8, paragraphs[0056] of Wang teaches at stage 138, video portions associated with first portions of the set of filtered metadata generated at stage 136 are isolated from the video content obtained at stage 132; and at stage 140, the video portions isolated at stage 138 and their associated metadata are uploaded to a network storage entity (e.g., associated with a cloud service 90), and See also at least ABSTRACT, and paragraphs[0003], [0021], [0024], [0027]-[0028], [0031], [0033], [0037]-[0040], [0042]-[0044], [0046], [0048], [0051], [0053]-[0056], [0060], [0072], and [0075] of Wang (i.e., Wang teaches the computer system isolating video portions, which are from the obtained video content, based on the filtered metadata wherein the metadata includes describes the objects and includes at least velocity and moving direction)).
Claim Rejections - 35 USC § 103
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 of this title, 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.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Wang, in view of One, U.S. Patent Application Publication 2022/0185625 A1 (hereinafter One).
Regarding claim 2, Wang teaches the method of claim 1, analyzing the video stream further comprising: (FIGS. 2-3, 6, and 8, paragraph[0053] of Wang teaches for the real-time detection service 112, the rule engine 120 checks associated metadata in real-time to determine whether events are presently occurring that meet predefined rules; if so, the real-time detection service 112 triggers alarms for the certain types of events that are detected; for the operational service 114, the rule engine 120 assists in providing results of statistical data, (e.g., a list of average counts of objects visiting a specific place, such as a lane in a department store, in a predefined time period, etc.); for the camera network monitoring service 116, the rule engine 120 assists in displaying user selected or system identified objects automatically; the camera network monitoring service 116 manages multiple cameras, which can have overlapping or non-overlapping monitoring areas; the video on the display can be switched automatically to track suspects and/or other objects of interest; and further, moving objects can be labeled on a map registered with the camera scenes, and See also at least ABSTRACT, and paragraphs[0021], [0024], [0028], [0031], [0033], [0037]-[0040], [0042]-[0044], [0046], [0048], [0054]-[0056], [0060], [0072], and [0075] of Wang (i.e., Wang teaches the computer system switching automatically the video on a display to track suspects and/or objects of interest and thus not only analyzing the video but also analyzing metadata, which is associated with images of moving objects, received from multiple cameras)); but does not expressly teach segmenting the one or more objects of interest using an image segmentation model, the image segmentation model including a transformer or a convolutional neural network.
However, One teaches segmenting the one or more objects of interest using an image segmentation model, the image segmentation model including a transformer or a convolutional neural network (FIG. 4A, paragraph[0107] of One teaches In some embodiments, a separate neural network (e.g., based at least in part on an image classification, image segmentation, or convolutional neural network) may be trained to perform feature extraction for the purposes of enhancing the accuracy of object tracking as described above; for example, a feature extraction network may be trained on a person re-identification dataset, which may be comprised of sets of images—each set containing multiple image segments of the same person captured from different angles and/or different points in time; in some implementations, the sensor module may be configured to capture images and/or videos for the purposes of gathering training data, which may subsequently be labeled manually or via an automated process to generate, among other datasets, a person re-identification dataset; and in this manner, feature extraction network may therefore be improved over time (i.e., One teaches a convolutional neural network to perform feature extraction to enhance accuracy of object tracking)).
Furthermore, Wang and One are considered to be analogous art because they are from the same field of endeavor with respect to a detection system, and involve the same problem of forming the detection system to suitably detect objects by using a camera. Therefore, before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to modify the method of Wang based on One for segmenting the one or more objects of interest using an image segmentation model, the image segmentation model including a transformer or a convolutional neural network. One reason for the modification as taught by One is to perform object detection (paragraph[0002] of One).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Wang, in view of Reitan, U.S. Patent Application Publication 2013/0050260 A1 (hereinafter Reitan).
Regarding claim 6, Wang teaches the method of claim 1, but does not expressly teach wherein the video stream corresponds to a light source, the method further comprising: capturing first metadata of the light source in the plurality of frames, wherein the first metadata further includes a location and an intensity level of the light source.
However, Reitan teaches wherein the video stream corresponds to a light source, the method further comprising: (paragraph[0080] of Reitan teaches in accordance with various embodiments, because video images are mapped to an infinity model, or to a background virtual flat view surface, virtual reality objects can be rendered as overlays to the video stream of content 110 and/or, using alpha-media stream management, as video underlay; because the video media is mapped to an infinity model, objects can be placed into the images that appear to a user as being closer in space than anything that was recorded and sent as an input media stream to the playback device 104; in other words, if the recording is of a "background" image, objects can be mapped in front of that background image using rendering component 105; for example, if the background image is of a bridge, a ship can be mapped to virtual display space 134 to appear to pass in between the bridge and the viewer's position in space; in accordance with various embodiments, each of media streams 108 and 109 further comprises meta-data which facilitates identifying the 3-D reality of the media streams which the meta-data describes; this can include, but is not limited to, luminance levels, chrominance, direction(s) of light source(s), atmospheric effects, etc. which can be used so that the object can be overlaid in a realistic manner in which the lighting of the background image and the overlaid object appears to come from the same source(s) and is subject to the same effects; in various embodiments, digital matting, using alpha channel management, is implemented to lay objects over other portions of the images mapped to virtual surfaces; by mapping images to an infinity model, the overlays appear to be embedded in, or part of, the original media stream; additionally, alpha channel management can be implemented in various embodiments to facilitate underlays of embedded objects as well; underlays make an embedded object appear to pass behind an object which is interpreted to be in the foreground of an image mapped to virtual display surface in various embodiments; in one embodiment, the birds are modeled, using rendering component 105, in 3-D space within the virtual display space; as an example, an invisible 3-D object is mapped to a bird which appears to be passing between the ship and the viewer's position; and again, using alpha channel management allows under laying the ship relative to the bird so that the bird appears to pass between the position of the ship and that of the viewer, and See also at least ABSTRACT, paragraphs[0060], [0915], and [0918] of Reitan (i.e., Reitan teaches a lens/microphone array, which captures media streams that include metadata, wherein the metadata includes light sources used so that an object can be overlaid in a realistic manner)) capturing first metadata of the light source in the plurality of frames, wherein the first metadata further includes a location and an intensity level of the light source (paragraph[0918] of Reitan teaches metadata information can additionally include: frame time, camera position, camera orientation vector, camera frame orientation vector (up indicator), camera frustum (camera lens: zoom/perspective), camera aperture, camera focus, light source positions, light source intensity, light source chrominance, flying mobility boundaries, floating mobility boundaries, hard surface mobility boundaries, video object positions, ghost bot positions ("invisible" functional interactive potential video reality objects), video object depth (used for matting approach to hidden object removal and stereoscopy), video object shape models (used for 3D model approach to hidden object removal and stereoscopy), ghost bot identity (action) mapping, video clarity (visibility), video resolution, video luminance, video chrominance, audio source positions, audio range, dialogue, dialogue to audio source mapping, infinity mapping, effective distance, interpolation, extrapolation, behavioral cues, proximity, periodicity, dialogue, value of user interaction, significance (relative weighting of value), and the like, and See also at least ABSTRACT, paragraphs[0060], [0080], [0915], and [0918] of Reitan (i.e., Reitan teaches a lens/microphone array, which captures media streams that include metadata, wherein the metadata includes light sources used so that an object can be overlaid in a realistic manner, wherein metadata information, which is obtained by a web cam, includes light source positions and even light source intensity)).
Furthermore, Wang and Reitan are considered to be analogous art because they are from the same field of endeavor with respect to a detection system, and involve the same problem of forming the detection system to suitably detect objects. Therefore, before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to modify the method of Wang based on Reitan for segmenting the one or more objects of interest using an image segmentation model, the image segmentation model including a transformer or a convolutional neural network. One reason for the modification as taught by Reitan is to perform suitable object detection which includes edge detection (paragraph[1067] of Reitan).
Potentially Allowable Subject Matter
Claims 3-5, 7-8, 10-14, and 16-20 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, because for each of claims 3-5, 7-8, 10-14, and 16-20 the prior art references of record do not teach the combination of all element limitations as presently claimed.
Conclusion
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/ABDUL-SAMAD A ADEDIRAN/Primary Examiner, Art Unit 2621
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