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 .
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.
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.
Claim 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Re claim 1 the limitation of cause one or more frames of one or more videos including one or more objects identified by one or more users to be identified during recording of the one or more objects, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, cause one or more frames of one or more videos including one or more objects identified in the context of this claim encompasses the user mentally.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – A processor comprising: one or more circuits to perform the steps and performing the abstract idea using a neural network. The processor in both steps is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. The neural network is also described in a high level of generality and does little more the limit the abstract idea to the field of neural networks Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the steps amounts to no more than mere instructions to apply the exception using a generic computer component. Further implementing the abstract idea with a generic neural network does no more than limit the claims to the fields of neural networks. Mere instructions to apply an exception using a generic computer component combined with a generic neural network cannot provide an inventive concept. The claim is not patent eligible.
Re claim 2 the limitation of a video of the one or more videos comprising at least one of the one or more frames, the identification of the at least one frame of the video is based at least on a first timestamp that corresponds to detection, of an object in a first portion of the video, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, identifying a frame based on a time stamp in the context of this claim encompasses the user mentally identifying a frame based on a timestamp.
The analysis with respect to integration into an abstract idea and significantly more is not significantly different from the claim which this claim depends.
Re claim 3 the limitation of wherein the at least one frame of the video comprises a plurality of frames, and wherein the identification of the plurality of frames of the video is further based on a second timestamp that corresponds to detection, of the object in a second portion of the video recorded subsequent to the first portion of the video, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, identifying a frame based on a time stamp in the context of this claim encompasses the user mentally identifying a frame based on a timestamp.
The analysis with respect to integration into an abstract idea and significantly more is not significantly different from the claim which this claim depends.
Re claim 4 the limitation of wherein the identification of the plurality of frames of the video is further based on a determination that the first timestamp and the second timestamp correspond to time metadata of one or more files that comprise the plurality of frames of the video.
, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, identification based on a time stamp and metadata in the context of this claim encompasses the user mentally identifying frames based on time stamps and metadata.
The analysis with respect to integration into an abstract idea and significantly more is not significantly different from the claim which this claim depends.
Re claim 5 contains the same abstract idea as claim 4. This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – A processor comprising: one or more circuits to perform the steps; performing the abstract idea using a neural network and cause one or more of: the plurality of frames of the video to be played, or the plurality of frames of the video to be stored at a storage location. The processor in both steps is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. The neural network is also described in a high level of generality and does little more the limit the abstract idea to the field of neural networks. Merely playing or storing frames amounts to little more insignificant extra solution activity of storing data and displaying results. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the steps amounts to no more than mere instructions to apply the exception using a generic computer component. Further implementing the abstract idea with a generic neural network does no more than limit the claims to the fields of neural networks. Merely playing or storing frames amounts to little more insignificant extra solution activity of storing data and displaying results. Mere instructions to apply an exception using a generic computer component combined with a generic neural network and insignificant extra solution activity cannot provide an inventive concept. The claim is not patent eligible.
Re claim 6 the limitation of cause another plurality of frames of the video including the object to be identified during a subsequent recording of the object, and wherein the identification of the other plurality of frames of the video is further based at least on a third timestamp that corresponds to detection, of the object in a third portion of the video recorded subsequent to the second portion of the video and on a fourth timestamp that corresponds to detection, of the object in a fourth portion of the video recorded subsequent to the third portion of the video, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, identifying frames using time stamps in the context of this claim encompasses the user mentally identifying frames using time stamps.
The analysis with respect to integration into an abstract idea and significantly more is not significantly different from the claim which this claim depends.
Re claim 7 contains the same abstract idea as claim 6. This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – A processor comprising: one or more circuits to perform the steps; performing the abstract idea using a neural network and cause one or more of: the plurality of frames of the video to be played, or the plurality of frames of the video to be stored at a storage location and the other plurality of frames of the video to be stored at the storage location, without storing at the storage location the one or more additional frames of the video that correspond to the portion of the video recorded subsequent to the second portion and prior to the third portion. The processor in both steps is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. The neural network is also described in a high level of generality and does little more the limit the abstract idea to the field of neural networks. Merely playing or storing selected frames amounts to little more insignificant extra solution activity of storing data and displaying results. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the steps amounts to no more than mere instructions to apply the exception using a generic computer component. Further implementing the abstract idea with a generic neural network does no more than limit the claims to the fields of neural networks. Merely playing or storing frames amounts to little more insignificant extra solution activity of storing data and displaying results. Mere instructions to apply an exception using a generic computer component combined with a generic neural network and insignificant extra solution activity cannot provide an inventive concept. The claim is not patent eligible.
Re claim 8 the limitation of cause one or more frames of one or more videos including one or more objects identified by one or more users to be identified during recording of the one or more objects, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, cause one or more frames of one or more videos including one or more objects identified in the context of this claim encompasses the user mentally.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements –performing the abstract idea using a neural network. The neural network is also described in a high level of generality and does little more the limit the abstract idea to the field of neural networks Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, implementing the abstract idea with a generic neural network does no more than limit the claims to the fields of neural networks. Mere instructions to apply an exception with a generic neural network cannot provide an inventive concept. The claim is not patent eligible.
Re claim 9 the limitation of a video of the one or more videos comprising at least one of the one or more frames, the identification of the at least one frame of the video is based at least on a first timestamp that corresponds to detection, of an object in a first portion of the video, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, identifying a frame based on a time stamp in the context of this claim encompasses the user mentally identifying a frame based on a timestamp.
The analysis with respect to integration into an abstract idea and significantly more is not significantly different from the claim which this claim depends.
Re claim 10 the limitation of wherein the at least one frame of the video comprises a plurality of frames, and wherein the identification of the plurality of frames of the video is further based on a second timestamp that corresponds to detection, of the object in a second portion of the video recorded subsequent to the first portion of the video, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, identifying a frame based on a time stamp in the context of this claim encompasses the user mentally identifying a frame based on a timestamp.
The analysis with respect to integration into an abstract idea and significantly more is not significantly different from the claim which this claim depends.
Re claim 11 the limitation of wherein the identification of the plurality of frames of the video is further based on a determination that the first timestamp and the second timestamp correspond to time metadata of one or more files that comprise the plurality of frames of the video.
, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, identification based on a time stamp and metadata in the context of this claim encompasses the user mentally identifying frames based on time stamps and metadata.
The analysis with respect to integration into an abstract idea and significantly more is not significantly different from the claim which this claim depends.
Re claim 12 contains the same abstract idea as claim 11. This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements performing the abstract idea using a neural network and cause one or more of: the plurality of frames of the video to be played, or the plurality of frames of the video to be stored at a storage location. The neural network is also described in a high level of generality and does little more the limit the abstract idea to the field of neural networks. Merely playing or storing frames amounts to little more insignificant extra solution activity of storing data and displaying results. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element implementing the abstract idea with a generic neural network does no more than limit the claims to the fields of neural networks. Merely playing or storing frames amounts to little more insignificant extra solution activity of storing data and displaying results. Mere instructions to apply an exception using a generic neural network combined with insignificant extra solution activity cannot provide an inventive concept. The claim is not patent eligible.
Re claim 13 the limitation of cause another plurality of frames of the video including the object to be identified during a subsequent recording of the object, and wherein the identification of the other plurality of frames of the video is further based at least on a third timestamp that corresponds to detection, of the object in a third portion of the video recorded subsequent to the second portion of the video and on a fourth timestamp that corresponds to detection, of the object in a fourth portion of the video recorded subsequent to the third portion of the video, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, identifying frames using time stamps in the context of this claim encompasses the user mentally identifying frames using time stamps.
The analysis with respect to integration into an abstract idea and significantly more is not significantly different from the claim which this claim depends.
Re claim 14 contains the same abstract idea as claim 13. This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements –performing the abstract idea using a neural network and cause one or more of: the plurality of frames of the video to be played, or the plurality of frames of the video to be stored at a storage location and the other plurality of frames of the video to be stored at the storage location, without storing at the storage location the one or more additional frames of the video that correspond to the portion of the video recorded subsequent to the second portion and prior to the third portion. The neural network is also described in a high level of generality and does little more the limit the abstract idea to the field of neural networks. Merely playing or storing selected frames amounts to little more insignificant extra solution activity of storing data and displaying results. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of implementing the abstract idea with a generic neural network does no more than limit the claims to the fields of neural networks. Merely playing or storing frames amounts to little more insignificant extra solution activity of storing data and displaying results. Mere instructions to apply an exception with a generic neural network combined with insignificant extra solution activity cannot provide an inventive concept. The claim is not patent eligible.
Re claim 15 the limitation of cause one or more frames of one or more videos including one or more objects identified by one or more users to be identified during recording of the one or more objects, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, cause one or more frames of one or more videos including one or more objects identified in the context of this claim encompasses the user mentally.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – A processor comprising: one or more circuits to perform the steps performing the abstract idea using a neural network and one or more memories to store parameters associated with the one or more neural networks. The processor and memory in the steps is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function and a generic memory storing data) such that it amounts no more than mere instructions to apply the exception using a generic computer components. The neural network is also described in a high level of generality and does little more the limit the abstract idea to the field of neural networks. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the steps and a memory to store parameters amounts to no more than mere instructions to apply the exception using generic computer components. Further implementing the abstract idea with a generic neural network does no more than limit the claims to the fields of neural networks. Mere instructions to apply an exception using a generic computer component combined with a generic neural network cannot provide an inventive concept. The claim is not patent eligible.
Re claim 16 the limitation of a video of the one or more videos comprising at least one of the one or more frames, the identification of the at least one frame of the video is based at least on a first timestamp that corresponds to detection, of an object in a first portion of the video, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, identifying a frame based on a time stamp in the context of this claim encompasses the user mentally identifying a frame based on a timestamp.
The analysis with respect to integration into an abstract idea and significantly more is not significantly different from the claim which this claim depends.
Re claim 17 the limitation of wherein the at least one frame of the video comprises a plurality of frames, and wherein the identification of the plurality of frames of the video is further based on a second timestamp that corresponds to detection, of the object in a second portion of the video recorded subsequent to the first portion of the video, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, identifying a frame based on a time stamp in the context of this claim encompasses the user mentally identifying a frame based on a timestamp.
The analysis with respect to integration into an abstract idea and significantly more is not significantly different from the claim which this claim depends.
Re claim 18 the limitation of wherein the identification of the plurality of frames of the video is further based on a determination that the first timestamp and the second timestamp correspond to time metadata of one or more files that comprise the plurality of frames of the video.
, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, identification based on a time stamp and metadata in the context of this claim encompasses the user mentally identifying frames based on time stamps and metadata.
The analysis with respect to integration into an abstract idea and significantly more is not significantly different from the claim which this claim depends.
Re claim 19 the limitation of cause another plurality of frames of the video including the object to be identified during a subsequent recording of the object, and wherein the identification of the other plurality of frames of the video is further based at least on a third timestamp that corresponds to detection, of the object in a third portion of the video recorded subsequent to the second portion of the video and on a fourth timestamp that corresponds to detection, of the object in a fourth portion of the video recorded subsequent to the third portion of the video, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, identifying frames using time stamps in the context of this claim encompasses the user mentally identifying frames using time stamps.
The analysis with respect to integration into an abstract idea and significantly more is not significantly different from the claim which this claim depends.
Re claim 20 contains the same abstract idea as claim 19. This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – A processor comprising: one or more circuits to perform the steps; performing the abstract idea using a neural network; one or more memories to store parameters associated with the one or more neural networks and cause one or more of: the plurality of frames of the video to be played, or the plurality of frames of the video to be stored at a storage location and the other plurality of frames of the video to be stored at the storage location, without storing at the storage location the one or more additional frames of the video that correspond to the portion of the video recorded subsequent to the second portion and prior to the third portion. The processor and memory in both steps is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function and a generic memory storing data) such that it amounts no more than mere instructions to apply the exception using a generic computer component. The neural network is also described in a high level of generality and does little more the limit the abstract idea to the field of neural networks. Merely playing or storing selected frames amounts to little more insignificant extra solution activity of storing data and displaying results. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the steps and a memory to store parameters amounts to no more than mere instructions to apply the exception using generic computer components. Further implementing the abstract idea with a generic neural network does no more than limit the claims to the fields of neural networks. Merely playing or storing frames amounts to little more insignificant extra solution activity of storing data and displaying results. Mere instructions to apply an exception using a generic computer component combined with a generic neural network and insignificant extra solution activity cannot provide an inventive concept. The claim is not patent eligible.
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.
Claim(s)1, 8 and 15 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by IMES US 20210383124 A1
.
Re claim 1 Imes discloses A processor comprising: one or more circuits (see paragraph 202 note that a processor may be used to implement the inventions) to cause one or more frames of one or more videos including one or more objects identified by one or more users to be identified (see paragraph 205 The method can also use AI Logic to identify the specific user and characteristics, details, and/or objects of that user can be provided with the video. Upon identifying the user, the method proceeds to step 437 and extracts segments of video as they relate to the user” note that portions of the video containing a particular user are extracted using AI also see for example paragraph 32 note that the portions/segments of the video comprise play) using one or more neural networks during (see paragraph 80 note that the AI may be neural network may be used to identify objects in video images) recording of the one or more objects (see paragraph 73 “for purposes of this disclosure, an athletic monitoring and recording system can include any instrumentality or aggregate of instrumentalities operable to compute, classify, process, transmit, receive, retrieve, originate, switch, store, display, manifest, detect, record, reproduce, handle, or utilize any form of information, intelligence, or data for business, scientific, control, entertainment, or other purposes. For example, an athletic monitoring and recording system can be a personal computer, a PDA, a consumer electronic device, a smart phone, a cellular or mobile phone, a set-top box, a digital media subscriber module, a cable modem, a fiber optic enabled communications device, a media gateway, a home media management system, a network server or storage device, a switch router, wireless router, or other network communication device, or any other suitable device and can vary in size, shape, performance, functionality, and price” note that the system is a recording system).
Re claim 8 A method, comprising: causing one or more frames of one or more videos including one or more objects identified by one or more users to be identified (see paragraph 205 The method can also use AI Logic to identify the specific user and characteristics, details, and/or objects of that user can be provided with the video. Upon identifying the user, the method proceeds to step 437 and extracts segments of video as they relate to the user” note that portions of the video containing a particular user are extracted using AI also see for example paragraph 32 note that the portions/segments of the video comprise play) using one or more neural networks during (see paragraph 80 note that the AI may be neural network may be used to identify objects in video images) recording of the one or more objects (see paragraph 73 “for purposes of this disclosure, an athletic monitoring and recording system can include any instrumentality or aggregate of instrumentalities operable to compute, classify, process, transmit, receive, retrieve, originate, switch, store, display, manifest, detect, record, reproduce, handle, or utilize any form of information, intelligence, or data for business, scientific, control, entertainment, or other purposes. For example, an athletic monitoring and recording system can be a personal computer, a PDA, a consumer electronic device, a smart phone, a cellular or mobile phone, a set-top box, a digital media subscriber module, a cable modem, a fiber optic enabled communications device, a media gateway, a home media management system, a network server or storage device, a switch router, wireless router, or other network communication device, or any other suitable device and can vary in size, shape, performance, functionality, and price” note that the system is a recording system).
Re claim 15 Imes discloses A system, comprising: one or more processors to ( see paragraph 202 note that a processor may be used to implement the inventions) to cause one or more frames of one or more videos including one or more objects identified by one or more users to be identified (see paragraph 205 The method can also use AI Logic to identify the specific user and characteristics, details, and/or objects of that user can be provided with the video. Upon identifying the user, the method proceeds to step 437 and extracts segments of video as they relate to the user” note that portions of the video containing a particular user are extracted using AI also see for example paragraph 32 note that the portions/segments of the video comprise play) using one or more neural networks during (see paragraph 80 note that the AI may be neural network may be used to identify objects in video images) recording of the one or more objects (see paragraph 73 “for purposes of this disclosure, an athletic monitoring and recording system can include any instrumentality or aggregate of instrumentalities operable to compute, classify, process, transmit, receive, retrieve, originate, switch, store, display, manifest, detect, record, reproduce, handle, or utilize any form of information, intelligence, or data for business, scientific, control, entertainment, or other purposes. For example, an athletic monitoring and recording system can be a personal computer, a PDA, a consumer electronic device, a smart phone, a cellular or mobile phone, a set-top box, a digital media subscriber module, a cable modem, a fiber optic enabled communications device, a media gateway, a home media management system, a network server or storage device, a switch router, wireless router, or other network communication device, or any other suitable device and can vary in size, shape, performance, functionality, and price” note that the system is a recording system) and one or more memories to store parameters associated with the one or more neural networks see paragraph 200 ” GPU 410 can also access AI logic 412 that can include a variety of stored AI enabled logic that are designed to automate various aspects of autonomous video processing” and 203 “Various activities as described herein can be stored within various AI logic that has been created using Machine Learning as a Neural Network” note that see also paragraph 82 “ Various aspects of the disclosure may refer generically to hardware, software, modules, or the like distributed across various systems. Various hardware and software may be used to facilitate the features and functionality described herein, including, but not limited to: an NVIDIA Jetson TX2 computer, having a 256-core NVIDIA Pascal GPU architecture with 256 NVIDIA CUDA cores, and a Dual-Core NVIDIA Denver 2 64-bit CPU and Quad-Core ARM Cortex-A57 MPCore, including 8 GB 128-bit LPDDR4 memory and 32 GB eMMC storage”).
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, 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(s) 2-7, 9-14, 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over IMES US 20210383124 A1 in view of Sharma US 8570376 B1.
Re claim 2 Imes discloses, wherein, a video of the one or more videos comprising at least one of the one or more frames, the identification of the at least one frame of the video by the one or more neural networks, of an object in a first portion of the video ((see paragraph 205 The method can also use AI Logic to identify the specific user and characteristics, details, and/or objects of that user can be provided with the video. Upon identifying the user, the method proceeds to step 437 and extracts segments of video as they relate to the user” note that segments of the video containing a particular user are extracted using AI, note that one of ordinary skill in the art would understand segments of a video to include frames see for example paragraph 32).
Imes does not expressly disclose Identification is based at least on a first timestamp that corresponds to detection. Sharma discloses Identification is based at least on a first timestamp that corresponds to detection (see column 10 lines 60- column 11 line 20 and figure 2 and 3 Note that various timestamps are used to determine the start end of video segments).
The motivation to combine is “The timestamps of a track segment mark the start and finish of each track segment per person” (see column 11 lines 4 and 5). One of ordinary skill in the art could have easily used multiple time stamps to mark the start and finish of each video portion. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Imes and Sharma to reach the aforementioned advantage.
Re claim 3 Imes further discloses, wherein the at least one frame of the video comprises a plurality of frames, , by the one or more neural networks, of the object in a second portion of the video recorded subsequent to the first portion of the video (see paragraph 205 The method can also use AI Logic to identify the specific user and characteristics, details, and/or objects of that user can be provided with the video. Upon identifying the user, the method proceeds to step 437 and extracts segments of video as they relate to the user” note that multiple segments segments of the video containing a particular user are extracted using AI, note that one of ordinary skill in the art would understand segments of a video to include frames see paragraph 206 those multiple segments may be combined, note that one of ordinary skill in the art would understand segments of a video to include frames see for example paragraph 32).
Imes does not expressly disclose Identification is further based on a second timestamp that corresponds to detection. Sharama discloses Identification is further based on a second timestamp that corresponds to detection (see column 10 lines 60- column 11 line 20 and figure 2 and 3 Note that various timestamps are used to determine the start end of video segments).
The motivation to combine is “The timestamps of a track segment mark the start and finish of each track segment per person” (see column 11 lines 4 and 5). One of ordinary skill in the art could have easily used multiple time stamps to mark the start and finish of each video portion. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Imes and Sharma to reach the aforementioned advantage.
Re claim 4 Imes further does not expressly disclose wherein the identification of the plurality of frames of the video is further based on a determination that the first timestamp and the second timestamp correspond to time metadata of one or more files that comprise the plurality of frames of the video. Sharma discloses wherein the identification of the plurality of frames of the video is further based on a determination that the first timestamp and the second timestamp correspond to time metadata of one or more files that comprise the plurality of frames of the video (see figure 3 and column 10 lines 20-40 note that the time constraint corresponds to the meta data the meta data see also figure 4 and column 10 lines 60- column 11 line 20 note that time stamps are used to identify video segments ) The motivation to combine is “For example, if a goal of the behavior analysis is to know how many women shopped a predefined target product during a predefined window of time, the user of an embodiment of the present invention does not need all of the detected tracks, e.g., 100 k tracks, from the entire video streams. The user can select only (.alpha..times.100K) tracks that appear during the predefined window of time, where .alpha. is a temporal constraint in this exemplary case. Therefore, the size of sampled video data depends on the level of the spatiotemporal constraints” (see column 8 lines 15-20). One of ordinary skill in the art could have easily used the teachings of Sharma a time window to constrain the extracted segments to a particular time window in the invention of Imes. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Imes and Sharma to reach the aforementioned advantage.
Re claim 5 Imes discloses wherein the one or more circuits further cause one or more of: the plurality of frames of the video to be played (see paragraph 10 note that the processed recording may be displayed), or the plurality of frames of the video to be stored at a storage location (see paragraph 198 “For example, some content may be stored for immediate access while other forms of content can be stored for delayed access using a deep storage technique. This will enable flexibility in access to content such as video while reducing the overall cost on a user by user basis. For example, if a user has elected to pay for longer term storage, image processing system can modify the type of storage on a rate by rate basis. As such, cloud storage and services 404 can include various different types of on-line services and according to one aspect, can include Amazon Web Services (AWS) Glacier for storing video in the cloud. Additionally, Content manager 418 and distribution manager/communication 408 can utilize AWS Cloudfront as a content delivery service that distributes videos to end users.” Note that the content may be stored).
Re claim 6 Imes discloses wherein the one or more circuits further cause another plurality of frames of the video including the object to be identified using the one or more neural networks during a subsequent recording of the object, (see paragraph 205 “Upon identifying the user, the method proceeds to step 437 and extracts segments of video as they relate to the user. For example, a user that is identified may be a football player having a specific jersey number and name. The method would locate all segments of the video where the football player is present, and extract those segments from other players. In another form, a golfer may be playing a hole on a golf course with other players and the video may include numerous other shots or activities taken by the other golfers. As such, the method can identify the specific user and activity within various segments of video and remove the segments that don't include the user. In this manner, a video of just the golfer can be created” note that multiple segments of a user during and event extracted from the video note that one of ordinary skill in the art would understand segments of a video to include frames see for example paragraph 32)
Imes does not expressly disclose and wherein the identification of the other plurality of frames of the video is further based at least on a third timestamp that corresponds to detection, , of the object in a third portion of the video recorded subsequent to the second portion of the video and on a fourth timestamp that corresponds to detection, , of the object in a fourth portion of the video recorded subsequent to the third portion of the video.
Sharma discloses cause another plurality of frames of the video including the object to be identified during a subsequent recording of the object wherein the identification of the other plurality of frames of the video is further based at least on a third timestamp that corresponds to detection (see for example figures 3 and 4 also see column 10 lines 20-column 11 lines 20 note that portions of video which contain a person are segmented from multiple video streams at various times with various time steps see in particular elements SG1-1 SG7-1 note that these corresponds to various segments of video each with time stamps corresponding to the start and finish of the segment which represent a user tracked (i.e. identified) through that video segment), of the object in a third portion of the video recorded subsequent to the second portion of the video and on a fourth timestamp that corresponds to detection, of the object in a fourth portion of the video recorded subsequent to the third portion of the video (see for example figures 3 and 4 also see column 10 lines 20-column 11 lines 20 note that portions of video which contain a person are segmented from multiple video streams at various times with various time steps see in particular elements SG1-1 SG7-1 note that these corresponds to various segments of video each with time stamps corresponding to the start and finish of the segment which represent a user tracked (i.e. identified) through that video segment). The motivation to combine is “The timestamps of a track segment mark the start and finish of each track segment per person” (see column 11 lines 4 and 5). One of ordinary skill in the art could have easily used multiple time stamps to mark the start and finish of each video portion. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Imes and Sharma to reach the aforementioned advantage.
Re claim 7 Imes further discloses wherein the one or more circuits further cause one or more of: the other plurality of frames of the video to be played after the plurality of frames of the video are played, wherein one or more additional frames of the video that correspond to a portion of the video recorded subsequent to the second portion and prior to the third portion is skipped over without being played or is played at a faster rate than a rate that the plurality of frames and the other plurality of frames are played, or the other plurality of frames of the video to be stored at the storage location, without storing at the storage location the one or more additional frames of the video that correspond to the portion of the video recorded subsequent to the second portion and prior to the third portion (see paragraph 205 “Upon identifying the user, the method proceeds to step 437 and extracts segments of video as they relate to the user. For example, a user that is identified may be a football player having a specific jersey number and name. The method would locate all segments of the video where the football player is present, and extract those segments from other players. In another form, a golfer may be playing a hole on a golf course with other players and the video may include numerous other shots or activities taken by the other golfers. As such, the method can identify the specific user and activity within various segments of video and remove the segments that don't include the user” Note that multiple video segments are determined from video portions which do not include the user are removed see also paragraph 206 note that multiple video segments are extracted and combined into a single video see also paragraph 206 and step 447 figure 4b note that a video file is created for the user.
Re claim 9 Imes discloses, wherein, a video of the one or more videos comprising at least one of the one or more frames, the identification of the at least one frame of the video by the one or more neural networks, of an object in a first portion of the video ((see paragraph 205 The method can also use AI Logic to identify the specific user and characteristics, details, and/or objects of that user can be provided with the video. Upon identifying the user, the method proceeds to step 437 and extracts segments of video as they relate to the user” note that segments of the video containing a particular user are extracted using AI, note that one of ordinary skill in the art would understand segments of a video to include frames see for example paragraph 32 ).
Imes does not expressly disclose Identification is based at least on a first timestamp that corresponds to detection. Sharma discloses Identification is based at least on a first timestamp that corresponds to detection (see column 10 lines 60- column 11 line 20 and figure 2 and 3 Note that various timestamps are used to determine the start end of video segments).
The motivation to combine is “The timestamps of a track segment mark the start and finish of each track segment per person” (see column 11 lines 4 and 5). One of ordinary skill in the art could have easily used multiple time stamps to mark the start and finish of each video portion. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Imes and Sharma to reach the aforementioned advantage.
Re claim 10 Imes further discloses, wherein the at least one frame of the video comprises a plurality of frames, by the one or more neural networks, of the object in a second portion of the video recorded subsequent to the first portion of the video (see paragraph 205 The method can also use AI Logic to identify the specific user and characteristics, details, and/or objects of that user can be provided with the video. Upon identifying the user, the method proceeds to step 437 and extracts segments of video as they relate to the user” note that multiple segments of the video containing a particular user are extracted using AI, note that one of ordinary skill in the art would understand segments of a video to include frames see paragraph 206 those multiple segments may be combined, note that one of ordinary skill in the art would understand segments of a video to include frames see for example paragraph 32).
Imes does not expressly disclose Identification is further based on a second timestamp that corresponds to detection. Sharama discloses Identification is further based on a second timestamp that corresponds to detection (see column 10 lines 60- column 11 line 20 and figure 2 and 3 Note that various timestamps are used to determine the start end of video segments).
The motivation to combine is “The timestamps of a track segment mark the start and finish of each track segment per person” (see column 11 lines 4 and 5). One of ordinary skill in the art could have easily used multiple time stamps to mark the start and finish of each video portion. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Imes and Sharma to reach the aforementioned advantage.
Re claim 11 Imes further does not expressly disclose wherein the identification of the plurality of frames of the video is further based on a determination that the first timestamp and the second timestamp correspond to time metadata of one or more files that comprise the plurality of frames of the video. Sharma discloses wherein the identification of the plurality of frames of the video is further based on a determination that the first timestamp and the second timestamp correspond to time metadata of one or more files that comprise the plurality of frames of the video (see figure 3 and column 10 lines 20-40 note that the time constraint corresponds to the meta data the meta data see also figure 4 and column 10 lines 60- column 11 line 20 note that time stamps are used to identify video segments ) The motivation to combine is “For example, if a goal of the behavior analysis is to know how many women shopped a predefined target product during a predefined window of time, the user of an embodiment of the present invention does not need all of the detected tracks, e.g., 100 k tracks, from the entire video streams. The user can select only (.alpha..times.100K) tracks that appear during the predefined window of time, where .alpha. is a temporal constraint in this exemplary case. Therefore, the size of sampled video data depends on the level of the spatiotemporal constraints” (see column 8 lines 15-20). One of ordinary skill in the art could have easily used the teachings of Sharma a time window to constrain the extracted segments to a particular time window in the invention of Imes. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Imes and Sharma to reach the aforementioned advantage.
Re claim 12 Imes discloses cause one or more of: the plurality of frames of the video to be played (see paragraph 10 note that the processed recording may be displayed), or the plurality of frames of the video to be stored at a storage location (see paragraph 198 “For example, some content may be stored for immediate access while other forms of content can be stored for delayed access using a deep storage technique. This will enable flexibility in access to content such as video while reducing the overall cost on a user by user basis. For example, if a user has elected to pay for longer term storage, image processing system can modify the type of storage on a rate by rate basis. As such, cloud storage and services 404 can include various different types of on-line services and according to one aspect, can include Amazon Web Services (AWS) Glacier for storing video in the cloud. Additionally, Content manager 418 and distribution manager/communication 408 can utilize AWS Cloudfront as a content delivery service that distributes videos to end users.” Note that the content may be stored).
Re claim 13 Imes discloses cause another plurality of frames of the video including the object to be identified using the one or more neural networks during a subsequent recording of the object, (see paragraph 205 “Upon identifying the user, the method proceeds to step 437 and extracts segments of video as they relate to the user. For example, a user that is identified may be a football player having a specific jersey number and name. The method would locate all segments of the video where the football player is present, and extract those segments from other players. In another form, a golfer may be playing a hole on a golf course with other players and the video may include numerous other shots or activities taken by the other golfers. As such, the method can identify the specific user and activity within various segments of video and remove the segments that don't include the user. In this manner, a video of just the golfer can be created” note that multiple segments of a user during and event extracted from the video note that one of ordinary skill in the art would understand segments of a video to include frames see for example paragraph 32)
Imes does not expressly disclose and wherein the identification of the other plurality of frames of the video is further based at least on a third timestamp that corresponds to detection, , of the object in a third portion of the video recorded subsequent to the second portion of the video and on a fourth timestamp that corresponds to detection, , of the object in a fourth portion of the video recorded subsequent to the third portion of the video.
Sharma discloses cause another plurality of frames of the video including the object to be identified during a subsequent recording of the object wherein the identification of the other plurality of frames of the video is further based at least on a third timestamp that corresponds to detection (see for example figures 3 and 4 also see column 10 lines 20-column 11 lines 20 note that portions of video which contain a person are segmented from multiple video streams at various times with various time steps see in particular elements SG1-1 SG7-1 note that these corresponds to various segments of video each with time stamps corresponding to the start and finish of the segment which represent a user tracked (i.e. identified) through that video segment), of the object in a third portion of the video recorded subsequent to the second portion of the video and on a fourth timestamp that corresponds to detection, of the object in a fourth portion of the video recorded subsequent to the third portion of the video (see for example figures 3 and 4 also see column 10 lines 20-column 11 lines 20 note that portions of video which contain a person are segmented from multiple video streams at various times with various time steps see in particular elements SG1-1 SG7-1 note that these corresponds to various segments of video each with time stamps corresponding to the start and finish of the segment which represent a user tracked (i.e. identified) through that video segment). The motivation to combine is “The timestamps of a track segment mark the start and finish of each track segment per person” (see column 11 lines 4 and 5). One of ordinary skill in the art could have easily used multiple time stamps to mark the start and finish of each video portion. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Imes and Sharma to reach the aforementioned advantage.
Re claim 14 Imes further discloses wherein the one or more circuits further cause one or more of: the other plurality of frames of the video to be played after the plurality of frames of the video are played, wherein one or more additional frames of the video that correspond to a portion of the video recorded subsequent to the second portion and prior to the third portion is skipped over without being played or is played at a faster rate than a rate that the plurality of frames and the other plurality of frames are played, or the other plurality of frames of the video to be stored at the storage location, without storing at the storage location the one or more additional frames of the video that correspond to the portion of the video recorded subsequent to the second portion and prior to the third portion (see paragraph 205 “Upon identifying the user, the method proceeds to step 437 and extracts segments of video as they relate to the user. For example, a user that is identified may be a football player having a specific jersey number and name. The method would locate all segments of the video where the football player is present, and extract those segments from other players. In another form, a golfer may be playing a hole on a golf course with other players and the video may include numerous other shots or activities taken by the other golfers. As such, the method can identify the specific user and activity within various segments of video and remove the segments that don't include the user” Note that multiple video segments are determined from video portions which do not include the user are removed see also paragraph 206 note that multiple video segments are extracted and combined into a single video see also paragraph 206 and step 447 figure 4b note that a video file is created for the user.
Re claim 16 Imes discloses, wherein, a video of the one or more videos comprising at least one of the one or more frames, the identification of the at least one frame of the video by the one or more neural networks, of an object in a first portion of the video ((see paragraph 205 The method can also use AI Logic to identify the specific user and characteristics, details, and/or objects of that user can be provided with the video. Upon identifying the user, the method proceeds to step 437 and extracts segments of video as they relate to the user” note that segments of the video containing a particular user are extracted using AI, note that one of ordinary skill in the art would understand segments of a video to include frames see for example paragraph 32).
Imes does not expressly disclose Identification is based at least on a first timestamp that corresponds to detection. Sharma discloses Identification is based at least on a first timestamp that corresponds to detection (see column 10 lines 60- column 11 line 20 and figure 2 and 3 Note that various timestamps are used to determine the start end of video segments).
The motivation to combine is “The timestamps of a track segment mark the start and finish of each track segment per person” (see column 11 lines 4 and 5). One of ordinary skill in the art could have easily used multiple time stamps to mark the start and finish of each video portion. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Imes and Sharma to reach the aforementioned advantage.
Re claim 17 Imes further discloses, wherein the at least one frame of the video comprises a plurality of frames, by the one or more neural networks, of the object in a second portion of the video recorded subsequent to the first portion of the video (see paragraph 205 The method can also use AI Logic to identify the specific user and characteristics, details, and/or objects of that user can be provided with the video. Upon identifying the user, the method proceeds to step 437 and extracts segments of video as they relate to the user” note that multiple segments of the video containing a particular user are extracted using AI, note that one of ordinary skill in the art would understand segments of a video to include frames see paragraph 206 those multiple segments may be combined, note that one of ordinary skill in the art would understand segments of a video to include frames see for example paragraph 32).
Imes does not expressly disclose Identification is further based on a second timestamp that corresponds to detection. Sharama discloses Identification is further based on a second timestamp that corresponds to detection (see column 10 lines 60- column 11 line 20 and figure 2 and 3 Note that various timestamps are used to determine the start end of video segments).
The motivation to combine is “The timestamps of a track segment mark the start and finish of each track segment per person” (see column 11 lines 4 and 5). One of ordinary skill in the art could have easily used multiple time stamps to mark the start and finish of each video portion. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Imes and Sharma to reach the aforementioned advantage.
Re claim 18 Imes further does not expressly disclose wherein the identification of the plurality of frames of the video is further based on a determination that the first timestamp and the second timestamp correspond to time metadata of one or more files that comprise the plurality of frames of the video. Sharma discloses wherein the identification of the plurality of frames of the video is further based on a determination that the first timestamp and the second timestamp correspond to time metadata of one or more files that comprise the plurality of frames of the video (see figure 3 and column 10 lines 20-40 note that the time constraint corresponds to the meta data the meta data see also figure 4 and column 10 lines 60- column 11 line 20 note that time stamps are used to identify video segments ) The motivation to combine is “For example, if a goal of the behavior analysis is to know how many women shopped a predefined target product during a predefined window of time, the user of an embodiment of the present invention does not need all of the detected tracks, e.g., 100 k tracks, from the entire video streams. The user can select only (.alpha..times.100K) tracks that appear during the predefined window of time, where .alpha. is a temporal constraint in this exemplary case. Therefore, the size of sampled video data depends on the level of the spatiotemporal constraints” (see column 8 lines 15-20). One of ordinary skill in the art could have easily used the teachings of Sharma a time window to constrain the extracted segments to a particular time window in the invention of Imes. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Imes and Sharma to reach the aforementioned advantage.
Re claim 19 Imes discloses wherein the one or more circuits further cause another plurality of frames of the video including the object to be identified using the one or more neural networks during a subsequent recording of the object, (see paragraph 205 “Upon identifying the user, the method proceeds to step 437 and extracts segments of video as they relate to the user. For example, a user that is identified may be a football player having a specific jersey number and name. The method would locate all segments of the video where the football player is present, and extract those segments from other players. In another form, a golfer may be playing a hole on a golf course with other players and the video may include numerous other shots or activities taken by the other golfers. As such, the method can identify the specific user and activity within various segments of video and remove the segments that don't include the user. In this manner, a video of just the golfer can be created” note that multiple segments of a user during and event extracted from the video note that one of ordinary skill in the art would understand segments of a video to include frames see for example paragraph 32)
Imes does not expressly disclose and wherein the identification of the other plurality of frames of the video is further based at least on a third timestamp that corresponds to detection, , of the object in a third portion of the video recorded subsequent to the second portion of the video and on a fourth timestamp that corresponds to detection, , of the object in a fourth portion of the video recorded subsequent to the third portion of the video.
Sharma discloses cause another plurality of frames of the video including the object to be identified during a subsequent recording of the object wherein the identification of the other plurality of frames of the video is further based at least on a third timestamp that corresponds to detection (see for example figures 3 and 4 also see column 10 lines 20-column 11 lines 20 note that portions of video which contain a person are segmented from multiple video streams at various times with various time steps see in particular elements SG1-1 SG7-1 note that these corresponds to various segments of video each with time stamps corresponding to the start and finish of the segment which represent a user tracked (i.e. identified) through that video segment), of the object in a third portion of the video recorded subsequent to the second portion of the video and on a fourth timestamp that corresponds to detection, of the object in a fourth portion of the video recorded subsequent to the third portion of the video (see for example figures 3 and 4 also see column 10 lines 20-column 11 lines 20 note that portions of video which contain a person are segmented from multiple video streams at various times with various time steps see in particular elements SG1-1 SG7-1 note that these corresponds to various segments of video each with time stamps corresponding to the start and finish of the segment which represent a user tracked (i.e. identified) through that video segment). The motivation to combine is “The timestamps of a track segment mark the start and finish of each track segment per person” (see column 11 lines 4 and 5). One of ordinary skill in the art could have easily used multiple time stamps to mark the start and finish of each video portion. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Imes and Sharma to reach the aforementioned advantage.
Re claim 20 Imes further discloses wherein the one or more circuits further cause one or more of: the other plurality of frames of the video to be played after the plurality of frames of the video are played, wherein one or more additional frames of the video that correspond to a portion of the video recorded subsequent to the second portion and prior to the third portion is skipped over without being played or is played at a faster rate than a rate that the plurality of frames and the other plurality of frames are played,
or the other plurality of frames of the video to be stored at the storage location, without storing at the storage location the one or more additional frames of the video that correspond to the portion of the video recorded subsequent to the second portion and prior to the third portion (see paragraph 205 “Upon identifying the user, the method proceeds to step 437 and extracts segments of video as they relate to the user. For example, a user that is identified may be a football player having a specific jersey number and name. The method would locate all segments of the video where the football player is present, and extract those segments from other players. In another form, a golfer may be playing a hole on a golf course with other players and the video may include numerous other shots or activities taken by the other golfers. As such, the method can identify the specific user and activity within various segments of video and remove the segments that don't include the user” Note that multiple video segments are determined from video portions which do not include the user are removed see also paragraph 206 note that multiple video segments are extracted and combined into a single video see also paragraph 206 and step 447 figure 4b note that a video file is created for the user.)
Cited Art
The following is a recitation of prior art considered relevant but not cited in a rejection above:
IMES US 20200364462 A1 discloses A system for monitoring and recording and processing an activity includes one or more cameras for automatically recording video of the activity. A processor and memory associated and in communication with the camera is disposed near the location of the activity. The system may include AI logic configured to identify a user recorded within a video frame captured by the camera. The system may also detect and identify a user when the user is located within a predetermined area. The system may include a video processing engine configured to process images within the video frame to identify the user and may modify and format the video upon identifying the user and the activity. The system may include a communication module to communicate formatted video to a remote video processing system, which may further process the video and enable access to a mobile app of the user. (see abstract)
Aguilar US 20180174616 A1 discloses Systems, methods, and non-transitory computer-readable media can determine one or more source video clips. A plurality of video segments are selected from the one or more source video clips based on video segment selection criteria. A compiled video is generated comprising the plurality of video segments. (see abstract).
Kumar US 20230395096 A1 discloses Systems, methods, and software to manage video streams for a timeline based on objects of interest identified in the video streams. In one example, a video processing system obtains video streams from video sources for a physical area and identifies one or more objects of interest in the physical area. The video processing system further identifies, for each of the video streams, one or more portions of the video stream that include at least one object of interest of the one or more objects of interest and generates a timeline to provide a visual display of the identified portions. (see abstract)
Bentley US 9721165 B1 discloses A system and method for generating a short video summary from video data. For example, the system may receive input video data including video clips and may select snippets from each video clip to include in the short video summary. To select a snippet, the system may calculate a priority metric for individual frames in a video clip, may generate a priority metric graph for the video clip and may select a portion of the video clip associated with a peak of the priority metric graph. Thus, the snippets may include a short duration of time (e.g., 1-4 seconds) corresponding to the peak of the priority metric graph. The system may reorder the snippets based on characteristics of content represented in the snippet. (see abstract).
Conclusion
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/SEAN T MOTSINGER/Primary Examiner, Art Unit 2673
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