Detailed Action
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The present application was filed on 04/06/2023. Claims 1-20 are pending and have been examined.
Priority
The examiner acknowledges the priority benefit to parent application 16/176,949 filed on 10/31/2018.
Information Disclosure Statement
As required by M.P.E.P 609(c), the applicant’s submissions of the Information Disclosure Statements dated 03/12/2025, 02/06/2025 and 09/04/2024 are acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending. As required by M.P.E.P 609 C(2), copies of the PTOL-1449 initialed and dated by the examiner are attached to the instant office action.
Claim Objection
Claims 1-20 are objected to because of the following informalities:
In claim 6, the claim sentence is not ended with a period. Appropriate correction is required.
Claims 1, 10 and 15 recite “one or more of: the whole video scene, the important spatial regions” These recitations are grammatically incorrect and appear to be missing one or more words between “scene” and “the important”. If supported by the original specification, the examiner suggests that one possible way to address this objection would be to amend “one or more of: the whole video scene, the important spatial regions”. to recite “one or more of: the whole video scene, or the important spatial regions”. Appropriate correction is required.
Claims 2-9 depend on claim 1 and do not cure the deficiencies of the claim 1, therefore claims 2-9 are objected for the same rationales.
Claims 11-14 depend on claim 10 and do not cure the deficiencies of the claim 10, therefore claims 11-14 are objected for the same rationales.
Claims 16-20 depend on claim 15 and do not cure the deficiencies of the claim 16-20, therefore claims 15 are objected for the same rationales.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-9 are rejected under 35 U.S.C 112(b) or 35 U.S.C 112 (pre-AIA ), second paragraph, as failing to set forth the subject matter which the inventor or a joint inventor, or for application subject to pre-AIA 35 U.S.C 112, the application regards, as the invention.
The term “important spatial regions” in claim 1 is a relative term which renders the claim indefinite. The term “important” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. For examination purposes “important spatial regions” are being interpreted as any kind of region or body part considered to be important spatial regions (Page 18 “Beginning at block 200, modification of a common convolutional neural network (CNN) to a spatial region extraction network (SREN) may be performed so that feature vectors of a whole scene of video and important spatial regions (e.g., objects, body parts, etc.) can be extracted”).
Claims 2-9 depend on claim 1 and do not cure the deficiencies of the claim 1 and therefore are rejected under 35 U.S.C. 112(b) as being indefinite under the same rationale as claim 1.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1:
Step 1: Claim 1 recites an apparatus; thus, it is a machine, one of the four statutory categories of patentable subject matter.
Step 2A Prong 1: The claim recites the limitations:
modify the CNN to a spatial region extraction network (SREN) to extract feature vectors of a whole video scene and important spatial regions - In the context of the claim limitation, this encompasses a mental process of evaluating using pen paper (to modify the CNN based on the extracted feature vectors which can be done using pen and paper).
identify, based on the extraction, first and second outputs from the CNN - In the context of the claim limitation, this encompasses a mental process of evaluating and observing outputs.
concatenate the first and second outputs into frame-level feature vectors - In the context of the claim limitation, this encompasses a mental process of evaluating the feature vectors.
modify, based on the temporal dynamic information, a classifier to classify one or more of: the whole video scene, the important spatial regions - In the context of the claim limitation, this encompasses a mental process of evaluating by classifying an observed video scene.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “at least one processor configured with instructions executable by the at least one processor to”; “access convolutional neural network (CNN)”; “a recurrent neural network (RNN) to model temporal dynamic information” – these are mere instructions to apply the judicial exception using a generic computer programmed with instructions/program code/logic. See MPEP 2106.05(f). Regarding the “convolutional neural network (CNN)”, “spatial region extraction network (SREN)” and “recurrent neural network (RNN)”, no details of the networks or their training are recited and the networks are recited at a high level of generality and can be constructed by hand with pen and paper. The claimed CNN, SREN and RNN, under the broadest reasonable interpretation (BRI), in light of the specification, could be constructed by hand with pen and paper based on a reasonable amount of observed data (i.e., the feature vectors and video scene). The networks are recited at a high level of generality and therefore is being interpreted as performing an abstract idea (mental process) on a generic computer. See MPEP 2106.04(a)(2) § III.C which states that “a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept” still recite a mental process. The claim also recites “provide the frame-level feature vectors ”, which recite insignificant extra-solution activity of mere data gathering and output. MPEP 2106.05(g). The additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: 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, some of the additional elements are directed to mere instructions to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). The recitation of “provide…” is directed to insignificant extra-solution activities that is well known, routine and conventional because the limitations are directed to receiving or transmitting data over a network, e.g., using the Internet to gather data. See MPEP 2106.05(d)(II), OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Therefore, the claim does not include additional elements which provide an inventive concept nor represent significantly more than the abstract idea, and the claim is not patent eligible.
Claim 2:
Step 1: Claim 2 recites an apparatus; thus, it is a machine, one of the four statutory categories of patentable subject matter.
Step 2A Prong 1: The claim recites the limitations:
wherein the important spatial regions comprise objects - In the context of the claim limitation, this encompasses a mental process of evaluating an observed video scene.
Step 2A Prong 2: Please see analysis of an independent claim 1.
Step 2B Analysis: Please see analysis of the independent claim 1.
Claim 3:
Step 1: Claim 3 recites an apparatus; thus, it is a machine, one of the four statutory categories of patentable subject matter.
Step 2A Prong 1: The claim recites the limitations:
wherein the important spatial regions comprise body parts - In the context of the claim limitation, this encompasses a mental process of evaluating an observed video scene.
Step 2A Prong 2: Please see analysis of the independent claim 1.
Step 2B Analysis: Please see analysis of the independent claim 1.
Claim 4:
Step 1: Claim 4 recites an apparatus; thus, it is a machine, one of the four statutory categories of patentable subject matter.
Step 2A Prong 1: The claim recites the limitations:
wherein the first output comprises one or more region features - In the context of the claim limitation, this encompasses a mental process of evaluating output comprising by evaluating an observed video scene.
Step 2A Prong 2: Please see analysis of the independent claim 1.
Step 2B Analysis: Please see analysis of the independent claim 1.
Claim 5:
Step 1: Claim 5 recites an apparatus; thus, it is a machine, one of the four statutory categories of patentable subject matter.
Step 2A Prong 1: The claim recites the limitations:
wherein the second output comprises one or more scene features - In the context of the claim limitation, this encompasses a mental process of evaluating output comprising by evaluating an observed video scene .
Step 2A Prong 2: Please see analysis of the claim 4.
Step 2B Analysis: Please see analysis of the claim 4.
Claim 6:
Step 1: Claim 6 recites an apparatus; thus, it is a machine, one of the four statutory categories of patentable subject matter.
Step 2A Prong 1: The claim recites the limitations:
wherein the first output is a first output type, and wherein the second output is a second output type - In the context of the claim limitation, this encompasses a mental process of evaluating output based on the output type.
Step 2A Prong 2: Please see analysis of the claim 5.
Step 2B Analysis: Please see analysis of the claim 5.
Claim 7:
Step 1: Claim 7 recites an apparatus; thus, it is a machine, one of the four statutory categories of patentable subject matter.
Step 2A Prong 1: The claim recites the limitations:
modify the classifier to classify the whole video scene - In the context of the claim limitation, this encompasses a mental process of evaluating a video scene to perform the classification.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “wherein the at least one processor is configured to” – this is a mere instruction to apply the judicial exception using a generic computer programmed with instructions/program code/logic. See MPEP 2106.05(f). The additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: 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, some of the additional elements are directed to mere instructions to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). Therefore, the claim does not include additional elements which provide an inventive concept nor represent significantly more than the abstract idea, and the claim is not patent eligible.
Claim 8:
Step 1: Claim 8 recites an apparatus; thus, it is a machine, one of the four statutory categories of patentable subject matter.
Step 2A Prong 1: The claim recites the limitations:
modify the classifier to classify the important spatial regions - In the context of the claim limitation, this encompasses a mental process of evaluating a video scene to perform the classification.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “wherein the at least one processor is configured to” – this is a mere instruction to apply the judicial exception using a generic computer programmed with instructions/program code/logic. See MPEP 2106.05(f). The additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: 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, some of the additional elements are directed to mere instructions to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). Therefore, the claim does not include additional elements which provide an inventive concept nor represent significantly more than the abstract idea, and the claim is not patent eligible.
Claim 9:
Step 1: Claim 9 recites an apparatus; thus, it is a machine, one of the four statutory categories of patentable subject matter.
Step 2A Prong 1: The claim recites the limitations:
modify the classifier to classify both of the whole video scene and the important spatial regions - In the context of the claim limitation, this encompasses a mental process of evaluating a video scene to perform the classification.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “wherein the at least one processor is configured to” – this is a mere instruction to apply the judicial exception using a generic computer programmed with instructions/program code/logic. See MPEP 2106.05(f). The additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: 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, some of the additional elements are directed to mere instructions to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). Therefore, the claim does not include additional elements which provide an inventive concept nor represent significantly more than the abstract idea, and the claim is not patent eligible.
Claim 10:
Step 1: Claim 10 recites a method; thus, it is a process, one of the four statutory categories of patentable subject matter.
Step 2A Prong 1: The claim recites the limitations:
modifying the CNN to a spatial region extraction network (SREN) to extract feature vectors of a whole video scene and particular spatial regions - In the context of the claim limitation, this encompasses a mental process of evaluating using pen paper (to modify the CNN based on the extracted feature vectors which can be done using pen and paper).
identifying, based on the extraction, first and second outputs from the CNN - In the context of the claim limitation, this encompasses a mental process of evaluating and observing outputs.
concatenating the first and second outputs into frame-level feature vectors - In the context of the claim limitation, this encompasses a mental process of evaluating the feature vectors.
modifying, based on the temporal dynamic information, a classifier to classify one or more of: the whole video scene, the particular spatial regions - In the context of the claim limitation, this encompasses a mental process of evaluating by classifying an observed video scene.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “accessing convolutional neural network (CNN)”; “to a recurrent neural network (RNN) to model temporal dynamic information” – these are mere instructions to apply the judicial exception using a generic computer programmed with instructions/program code/logic. See MPEP 2106.05(f). Regarding the “convolutional neural network (CNN)”, “spatial region extraction network (SREN)” and “recurrent neural network (RNN)”, no details of the networks or their training are recited and the networks are recited at a high level of generality and can be constructed by hand with pen and paper. The claimed CNN, SREN and RNN, under the broadest reasonable interpretation (BRI), in light of the specification, could be constructed by hand with pen and paper based on a reasonable amount of observed data (i.e., the feature vectors and video scene). The networks are recited at a high level of generality and therefore is being interpreted as performing an abstract idea (mental process) on a generic computer. See MPEP 2106.04(a)(2) § III.C which states that “a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept” still recite a mental process. The claim also recites “providing the frame-level feature vectors”, which recite insignificant extra-solution activity of mere data gathering and output. MPEP 2106.05(g). The additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: 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, some of the additional elements are directed to mere instructions to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). The recitations of “providing…” is directed to insignificant extra-solution activities that is well known, routine and conventional because the limitations are directed to receiving or transmitting data over a network, e.g., using the Internet to gather data. See MPEP 2106.05(d)(II), OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Therefore, the claim does not include additional elements which provide an inventive concept nor represent significantly more than the abstract idea, and the claim is not patent eligible.
Claim 11:
Step 1: Claim 11 recites a method; thus, it is a process, one of the four statutory categories of patentable subject matter.
Step 2A Prong 1: The claim recites the limitations:
wherein the particular spatial regions comprise objects - In the context of the claim limitation, this encompasses a mental process of evaluating an observed video scene.
Step 2A Prong 2: Please see analysis of an independent claim 10.
Step 2B Analysis: Please see analysis of the independent claim 10.
Claim 12:
Step 1: Claim 12 recites a method; thus, it is a process, one of the four statutory categories of patentable subject matter.
Step 2A Prong 1: The claim recites the limitations:
wherein the particular spatial regions comprise body parts - In the context of the claim limitation, this encompasses a mental process of evaluating an observed video scene.
Step 2A Prong 2: Please see analysis of the independent claim 10.
Step 2B Analysis: Please see analysis of the independent claim 10.
Claim 13:
Step 1: Claim 13 recites a method; thus, it is a process, one of the four statutory categories of patentable subject matter.
Step 2A Prong 1: The claim recites the limitations:
wherein the first output comprises one or more region features - In the context of the claim limitation, this encompasses a mental process of evaluating output comprising by evaluating an observed video scene.
Step 2A Prong 2: Please see analysis of the independent claim 10.
Step 2B Analysis: Please see analysis of the independent claim 10.
Claim 14:
Step 1: Claim 14 recites a method; thus, it is a process, one of the four statutory categories of patentable subject matter.
Step 2A Prong 1: The claim recites the limitations:
wherein the first output comprises one or more scene features - In the context of the claim limitation, this encompasses a mental process of evaluating output comprising by evaluating an observed video scene.
Step 2A Prong 2: Please see analysis of the claim 10.
Step 2B Analysis: Please see analysis of the claim 10.
Claim 15:
Step 1: Claim 15 recites an apparatus; thus, it is a machine, one of the four statutory categories of patentable subject matter.
Step 2A Prong 1: The claim recites the limitations:
modify the first NN to extract feature vectors of a whole video scene and particular spatial regions - In the context of the claim limitation, this encompasses a mental process of evaluating using pen paper (to modify the CNN based on the extracted feature vectors which can be done using pen and paper).
identify, based on the extraction, first and second outputs from the first NN - In the context of the claim limitation, this encompasses a mental process of evaluating and observing outputs.
concatenate the first and second outputs into frame-level feature vectors - In the context of the claim limitation, this encompasses a mental process of evaluating the feature vectors.
modify, based on the temporal dynamic information, a classifier to classify one or more of: the whole video scene, the particular spatial regions- In the context of the claim limitation, this encompasses a mental process of evaluating by classifying and observed video scene.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “at least one computer storage that is not a transitory signal and that comprises instructions executable by at least one processor to”; “access a first neural network (NN)”; “evaluating output comprising by evaluating an observed video scene” – these are mere instructions to apply the judicial exception using a generic computer programmed with instructions/program code/logic. See MPEP 2106.05(f). Regarding the “convolutional neural network (CNN)”, “spatial region extraction network (SREN)” and “recurrent neural network (RNN)”, no details of the networks or their training are recited and the networks are recited at a high level of generality and can be constructed by hand with pen and paper. The claimed CNN, SREN and RNN, under the broadest reasonable interpretation (BRI), in light of the specification, could be constructed by hand with pen and paper based on a reasonable amount of observed data (i.e., the feature vectors and video scene). The networks are recited at a high level of generality and therefore is being interpreted as performing an abstract idea (mental process) on a generic computer. See MPEP 2106.04(a)(2) § III.C which states that “a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept” still recite a mental process. The claim also recites “provide the frame-level feature vectors”, which recite insignificant extra-solution activity of mere data gathering and output. MPEP 2106.05(g). The additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: 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, some of the additional elements are directed to mere instructions to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). The recitation of “provide…” is directed to insignificant extra-solution activities that is well known, routine and conventional because the limitations are directed to receiving or transmitting data over a network, e.g., using the Internet to gather data. See MPEP 2106.05(d)(II), OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Therefore, the claim does not include additional elements which provide an inventive concept nor represent significantly more than the abstract idea, and the claim is not patent eligible.
Claim 16:
Step 1: Claim 16 recites an apparatus; thus, it is a machine, one of the four statutory categories of patentable subject matter.
Step 2A Prong 1: Please see analysis of an independent claim 15.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “wherein the first NN is a convolutional NN” – this is a mere instruction to apply the judicial exception using a generic computer programmed with instructions/program code/logic. See MPEP 2106.05(f). Regarding the “convolutional neural network (CNN)”, “spatial region extraction network (SREN)” and “recurrent neural network (RNN)”, no details of the networks or their training are recited and the networks are recited at a high level of generality and can be constructed by hand with pen and paper. The claimed CNN, SREN and RNN, under the broadest reasonable interpretation (BRI), in light of the specification, could be constructed by hand with pen and paper based on a reasonable amount of observed data (i.e., the feature vectors and video scene). The networks are recited at a high level of generality and therefore is being interpreted as performing an abstract idea (mental process) on a generic computer. See MPEP 2106.04(a)(2) § III.C which states that “a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept” still recite a mental process. The additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: 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, some of the additional elements are directed to mere instructions to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). Therefore, the claim does not include additional elements which provide an inventive concept nor represent significantly more than the abstract idea, and the claim is not patent eligible.
Claim 17:
Step 1: Claim 17 recites an apparatus; thus, it is a machine, one of the four statutory categories of patentable subject matter.
Step 2A Prong 1: Please see analysis of an independent claim 15.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “wherein the second NN is recurrent NN” – this is a mere instruction to apply the judicial exception using a generic computer programmed with instructions/program code/logic. See MPEP 2106.05(f). Regarding the “convolutional neural network (CNN)”, “spatial region extraction network (SREN)” and “recurrent neural network (RNN)”, no details of the networks or their training are recited and the networks are recited at a high level of generality and can be constructed by hand with pen and paper. The claimed CNN, SREN and RNN, under the broadest reasonable interpretation (BRI), in light of the specification, could be constructed by hand with pen and paper based on a reasonable amount of observed data (i.e., the feature vectors and video scene). The networks are recited at a high level of generality and therefore is being interpreted as performing an abstract idea (mental process) on a generic computer. See MPEP 2106.04(a)(2) § III.C which states that “a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept” still recite a mental process. The additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: 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, some of the additional elements are directed to mere instructions to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). Therefore, the claim does not include additional elements which provide an inventive concept nor represent significantly more than the abstract idea, and the claim is not patent eligible.
Claim 18:
Step 1: Claim 18 recites an apparatus; thus, it is a machine, one of the four statutory categories of patentable subject matter.
Step 2A Prong 1: The claim recites the limitations:
use a spatial region extraction network (SREN) to extract the feature vectors of the whole video scene and the particular spatial regions - In the context of the claim limitation, this encompasses a mental process of evaluating output which comprising features.
Step 2A Prong 2: Please see analysis of the independent claim 15.
Step 2B Analysis: Please see analysis of the independent claim 15.
Claim 19:
Step 1: Claim 19 recites an apparatus; thus, it is a machine, one of the four statutory categories of patentable subject matter.
Step 2A Prong 1: The claim recites the limitations:
wherein the first output comprises one or more region features, and wherein the second output comprises one or more scene features - In the context of the claim limitation, this encompasses a mental process of evaluating outputs comprising features.
Step 2A Prong 2: Please see analysis of the independent claim 15.
Step 2B Analysis: Please see analysis of the independent claim 15.
Claim 20:
Step 1: Claim 20 recites an apparatus; thus, it is a machine, one of the four statutory categories of patentable subject matter.
Step 2A Prong 1: The claim recites the limitations:
modify, based on the temporal dynamic information, the classifier to classify both of the whole video scene and the particular spatial regions - n the context of the claim limitation, this encompasses a mental process of evaluating a video scene to perform the classification
Step 2A Prong 2: Please see analysis of the independent claim 15.
Step 2B Analysis: Please see analysis of the independent claim 15.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ullah (Action Recognition in Video Sequences using Deep Bi-Directional LSTM With CNN Features) in view of Ma (Region-sequence based six-stream CNN features for general and fine-grained human action recognition in videos).
Claim 1.
Ullah teaches an apparatus, comprising: at least one processor configured with instructions executable by the at least one processor to (SECTION IV. Experimental Evaluation, Page 1159 “We have used Caffe toolbox for deep features extraction, tensorflow for DB-LSTM, and GeForce-Titan-X GPU for implementation” teaches an apparatus comprising a GPU/processor):
access convolutional neural network (CNN) (III. PROPOSED FRAMEWORK, Page 1157 “First, we extract CNN features from the frames of video VVI with jump JJF in sequence of frames such that the jump JJF does not affect the sequence of the action AAI in the video” teaches accessing a CNN convolutional neural network);
modify the CNN to a spatial region extraction network (SREN) to extract feature vectors of a whole video scene and important spatial regions (A. Preparation and Features Extraction, Page 1157 “CNN is a dominant source for the representation and classification of images. In the case of video data, each individual frame is represented by CNN features, followed by finding the sequential information between them using DB-LSTM… As CNN finds hidden patterns in images, it captures all the tiny changes in each frame” and II. RELATED WORKS, Page 1156 “applied 3D convolutional kernels on video frames in a time axis to capture both spatial and temporal information. They also claimed that their approach can capture motion and optical flow information because frames are connected by fully connected layers at the end. A multi-resolution CNN framework for connectivity of features in time domain is proposed by [21] to capture local spatio-temporal information. This method is experimentally evaluated on a new ‘‘YouTube 1 million videos dataset’’ of 487 classes. The authors claimed to have speed up the training complexity by foveated architecture of CNN. They improved the recognition rate for large dataset up to 63.9% but their recognition rate on UCF101 is 63.3%, which is still too low for such important task of action recognition. A two-stream CNN architecture is proposed by [22] in which first stream captures spatial and temporal information between frames and second one demonstrates the dense optical flow of multiple frames” and Fig. 1 teaches extracting features of the video to modify the CNN, in the CMM architecture two stream captures spatial and temporal information between frames);
identify, based on the extraction, first and second outputs from the CNN (A. Preparation and Features Extraction, Page 1158 “The extracted features vector from FC8 layer is one thousand dimensional. The features of each frame are considered as one chunk for one input step of RNN. CCN chunks for TTS time interval are feed to RNN” and Figure 1 teaches from CNN provide output first and second);
provide the frame-level feature vectors to a recurrent neural network (RNN) to model temporal dynamic information (A. Preparation and Features Extraction, Page 1158 “The extracted features vector from FC8 layer is one thousand dimensional. The features of each frame are considered as one chunk for one input step of RNN. CCN chunks for TTS time interval are feed to RNN” and Figure 1 teaches providing feature vectors/features from the CNN to the RNN);
and modify, based on the temporal dynamic information, a classifier to classify one or more of: the whole video scene, the important spatial regions (SECTION III. Proposed Framework, Page 1157 “Second, the features representing the sequence of action AAI for time interval TTS (such as TTS=1 sec) are fed to the proposed DB-LSTM in CCN chunks, where each CCI chunk is the features representation of the video frame and input to one RNN step. At the end, the final state of each time internal TTS is analyzed for final recognition of an action in a video” and figure 1 and Table 4 teaches based on the information classify the video by diving, skydiving etc.).
Ullah does not explicitly teach concatenate the first and second outputs into frame-level feature vectors.
However, Ma teaches concatenate the first and second outputs into frame-level feature vectors (3.2.2. Motion cues, Page 511 “Finally, all three channels are concatenated to form the new optical flow image Ut. Fig. 8 shows the computed three channels of the optical flow image” teaches a concatenated the outputs into features);
Ullah and Ma are analogous art because they are both directed to systems computing classification of video using convolution neural network.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Ma into the disclosed invention of Ullah.
One of ordinary skill in the arts would have been motivated to make this modification because of the following, “These results clearly demonstrates that utilizing pool5 layer outputs makes an appropriate balance between adequate spatial information and distinguishable features, which once again proves the effectiveness of the proposed method in recognizing human actions in videos” (Ma, 4.5. Contribution of each system component to the overall performance, Page 519).
Claim 2.
Ullah in view of Ma teaches the apparatus of Claim 1,
Ullah further teaches wherein the important spatial regions comprise objects (Figure 5 teaches objects like ball, brush hair etc.).
Claim 3.
Ullah in view of Ma teaches the apparatus of Claim 1,
Ullah further teaches wherein the important spatial regions comprise body parts (Figure 5 teaches body parts like hair, smile etc.).
Claim 4.
Ullah in view of Ma teaches the apparatus of Claim 1,
Ullah further teaches wherein the first output comprises one or more region features (A. Preparation and Features Extraction, Page 1157 “The scenario of the features representation is given in Fig. 2, where the first row represents the frames in a sequence and second row shows features maps of the corresponding frames. A basketball is moving from one player to another where a small change in players’ position and orientation can be observed” teaches output comprises feature map of a basketball player).
Claim 5.
Ullah in view of Ma teaches the apparatus of Claim 4,
Ullah further teaches wherein the second output comprises one or more scene features (A. Preparation and Features Extraction, Page 1157 “The scenario of the features representation is given in Fig. 2, where the first row represents the frames in a sequence and second row shows features maps of the corresponding frames. A basketball is moving from one player to another where a small change in players’ position and orientation can be observed” teaches output comprises feature map of a basketball court).
Claim 6.
Ullah in view of Ma teaches the apparatus of Claim 5,
Ullah further teaches wherein the first output is a first output type, and wherein the second output is a second output type (Figure 1 teaches more than two output)
Claim 7.
Ullah in view of Ma teaches the apparatus of Claim 1,
Ullah further teaches wherein the at least one processor is configured to: modify the classifier to classify the whole video scene (C. YouTube Actions Dataset, Page 1162 “The dataset is collected from 11 sports action categories including volleyball, basketball, golf, horse riding, biking/cycling, tennis, diving, football, swinging, jumping, and walking with a dog. The dataset contains 25 different subjects with more than four video clips for each subject” and Figure 1 teaches classification of the video like walking, soccer etc.).
Claim 8.
Ullah in view of Ma teaches the apparatus of Claim 1,
Ullah further teaches wherein the at least one processor is configured to: modify the classifier to classify the important spatial regions (C. YouTube Actions Dataset, Page 1162 “The dataset is collected from 11 sports action categories including volleyball, basketball, golf, horse riding, biking/cycling, tennis, diving, football, swinging, jumping, and walking with a dog. The dataset contains 25 different subjects with more than four video clips for each subject” and Figure 1 teaches classification of the sky diving, basketball etc.).
Claim 9.
Ullah in view of Ma teaches the apparatus of Claim 1,
Ullah further teaches wherein the at least one processor is configured to: modify the classifier to classify both of the whole video scene and the important spatial regions (C. YouTube Actions Dataset, Page 1162 “The dataset is collected from 11 sports action categories including volleyball, basketball, golf, horse riding, biking/cycling, tennis, diving, football, swinging, jumping, and walking with a dog. The dataset contains 25 different subjects with more than four video clips for each subject” and Figure 1 teaches classification of the sky diving, basketball, running, walking etc.).
Claim 10.
Ullah teaches a method, comprising: accessing convolutional neural network (CNN) (III. PROPOSED FRAMEWORK, Page 1157 “First, we extract CNN features from the frames of video VVI with jump JJF in sequence of frames such that the jump JJF does not affect the sequence of the action AAI in the video” teaches extract convolutional neural network);
modifying the CNN to a spatial region extraction network (SREN) to extract feature vectors of a whole video scene and particular spatial regions (A. Preparation and Features Extraction, Page 1157 “CNN is a dominant source for the representation and classification of images. In the case of video data, each individual frame is represented by CNN features, followed by finding the sequential information between them using DB-LSTM… As CNN finds hidden patterns in images, it captures all the tiny changes in each frame” and II. RELATED WORKS, Page 1156 “applied 3D convolutional kernels on video frames in a time axis to capture both spatial and temporal information. They also claimed that their approach can capture motion and optical flow information because frames are connected by fully connected layers at the end. A multi-resolution CNN framework for connectivity of features in time domain is proposed by [21] to capture local spatio-temporal information. This method is experimentally evaluated on a new ‘‘YouTube 1 million videos dataset’’ of 487 classes. The authors claimed to have speed up the training complexity by foveated architecture of CNN. They improved the recognition rate for large dataset up to 63.9% but their recognition rate on UCF101 is 63.3%, which is still too low for such important task of action recognition. A two-stream CNN architecture is proposed by [22] in which first stream captures spatial and temporal information between frames and second one demonstrates the dense optical flow of multiple frames” and Fig. 1 teaches extracting features of the video to modify the CNN, in the CMM architecture two stream captures spatial and temporal information between frames);
identifying, based on the extraction, first and second outputs from the CNN (A. Preparation and Features Extraction, Page 1158 “The extracted features vector from FC8 layer is one thousand dimensional. The features of each frame are considered as one chunk for one input step of RNN. CCN chunks for TTS time interval are feed to RNN” and Figure 1 teaches from CNN provide output first and second);
providing the frame-level feature vectors to a recurrent neural network (RNN) to model temporal dynamic information (A. Preparation and Features Extraction, Page 1158 “The extracted features vector from FC8 layer is one thousand dimensional. The features of each frame are considered as one chunk for one input step of RNN. CCN chunks for TTS time interval are feed to RNN” and Figure 1 teaches providing feature vectors/features from the CNN to the RNN); and
modifying, based on the temporal dynamic information, a classifier to classify one or more of: the whole video scene, the particular spatial regions (SECTION III. Proposed Framework, Page 1157 “Second, the features representing the sequence of action AAI for time interval TTS (such as TTS=1 sec) are fed to the proposed DB-LSTM in CCN chunks, where each CCI chunk is the features representation of the video frame and input to one RNN step. At the end, the final state of each time internal TTS is analyzed for final recognition of an action in a video” and figure 1 and Table 4 teaches based on the information classify the video by diving, skydiving etc.).
Ullah does not explicitly teach concatenating the first and second outputs into frame-level feature vectors;
However, Ma teaches concatenating the first and second outputs into frame-level feature vectors (3.2.2. Motion cues, Page 511 “Finally, all three channels are concatenated to form the new optical flow image Ut. Fig. 8 shows the computed three channels of the optical flow image” teaches concatenating the first and second outputs into feature vectors);
Ullah and Ma are analogous art because they are both directed to systems computing classification of video using convolution neural networks.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Ma into the disclosed invention of Ullah.
One of ordinary skill in the arts would have been motivated to make this modification because of the following, “These results clearly demonstrates that utilizing pool5 layer outputs makes an appropriate balance between adequate spatial information and distinguishable features, which once again proves the effectiveness of the proposed method in recognizing human actions in videos” (Ma, 4.5. Contribution of each system component to the overall performance, Page 519).
Claim 11.
Ullah in view of Ma teaches the method of claim 10,
Ullah further teaches wherein the particular spatial regions comprise objects (Figure 5 teaches objects like ball, brush hair etc.).
Claim 12.
Ullah in view of Ma teaches the method of claim 10,
Ullah further teaches wherein the particular spatial regions comprise body parts (Figure 5 teaches body parts like hair, smile etc.).
Claim 13.
Ullah in view of Ma teaches the method of claim 10,
Ullah further teaches wherein the first output comprises one or more region features (A. Preparation and Features Extraction, Page 1157 “The scenario of the features representation is given in Fig. 2, where the first row represents the frames in a sequence and second row shows features maps of the corresponding frames. A basketball is moving from one player to another where a small change in players’ position and orientation can be observed” teaches output comprises feature map of a basketball player).
Claim 14.
Ullah in view of Ma teaches the method of claim 10,
Ullah further teaches wherein the first output comprises one or more scene features (A. Preparation and Features Extraction, Page 1157 “The scenario of the features representation is given in Fig. 2, where the first row represents the frames in a sequence and second row shows features maps of the corresponding frames. A basketball is moving from one player to another where a small change in players’ position and orientation can be observed” teaches output comprises feature map of a basketball court).
Claim 15.
Ullah teaches an apparatus, comprising: at least one computer storage that is not a transitory signal and that comprises instructions executable by at least one processor to (SECTION IV. Experimental Evaluation, Page 1159 “We have used Caffe toolbox for deep features extraction, tensorflow for DB-LSTM, and GeForce-Titan-X GPU for implementation” teaches an apparatus comprising a GPU/processor):
access a first neural network (NN) (III. PROPOSED FRAMEWORK, Page 1157 “First, we extract CNN features from the frames of video VVI with jump JJF in sequence of frames such that the jump JJF does not affect the sequence of the action AAI in the video” teaches extract convolutional neural network);
modify the first NN to extract feature vectors of a whole video scene and particular spatial regions (A. Preparation and Features Extraction, Page 1157 “CNN is a dominant source for the representation and classification of images. In the case of video data, each individual frame is represented by CNN features, followed by finding the sequential information between them using DB-LSTM… As CNN finds hidden patterns in images, it captures all the tiny changes in each frame” and II. RELATED WORKS, Page 1156 “applied 3D convolutional kernels on video frames in a time axis to capture both spatial and temporal information. They also claimed that their approach can capture motion and optical flow information because frames are connected by fully connected layers at the end. A multi-resolution CNN framework for connectivity of features in time domain is proposed by [21] to capture local spatio-temporal information. This method is experimentally evaluated on a new ‘‘YouTube 1 million videos dataset’’ of 487 classes. The authors claimed to have speed up the training complexity by foveated architecture of CNN. They improved the recognition rate for large dataset up to 63.9% but their recognition rate on UCF101 is 63.3%, which is still too low for such important task of action recognition. A two-stream CNN architecture is proposed by [22] in which first stream captures spatial and temporal information between frames and second one demonstrates the dense optical flow of multiple frames” and Fig. 1 teaches extracting features of the video to modify the CNN, in the CMM architecture two stream captures spatial and temporal information between frames);
identify, based on the extraction, first and second outputs from the first NN (A. Preparation and Features Extraction, Page 1158 “The extracted features vector from FC8 layer is one thousand dimensional. The features of each frame are considered as one chunk for one input step of RNN. CCN chunks for TTS time interval are feed to RNN” and Figure 1 teaches from CNN provide output first and second);
provide the frame-level feature vectors to a second NN to model temporal dynamic information, the second NN being different from the first NN (A. Preparation and Features Extraction, Page 1158 “The extracted features vector from FC8 layer is one thousand dimensional. The features of each frame are considered as one chunk for one input step of RNN. CCN chunks for TTS time interval are feed to RNN” and Figure 1 teaches providing feature vectors/features from the CNN to the RNN); and
modify, based on the temporal dynamic information, a classifier to classify one or more of: the whole video scene, the particular spatial regions (SECTION III. Proposed Framework, Page 1157 “Second, the features representing the sequence of action AAI for time interval TTS (such as TTS=1 sec) are fed to the proposed DB-LSTM in CCN chunks, where each CCI chunk is the features representation of the video frame and input to one RNN step. At the end, the final state of each time internal TTS is analyzed for final recognition of an action in a video” and figure 1 and Table 4 teaches based on the information classify the video by diving, skydiving etc.).
Ullah does not explicitly teach concatenate the first and second outputs into frame-level feature vectors.
However, Ma teaches concatenate the first and second outputs into frame-level feature vectors (3.2.2. Motion cues, Page 511 “Finally, all three channels are concatenated to form the new optical flow image Ut. Fig. 8 shows the computed three channels of the optical flow image” teaches concatenate the outputs into features);
Ullah and Ma are analogous art because they are both directed to systems computing classification of video using convolution neural networks.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Ma into the disclosed invention of Ullah.
One of ordinary skill in the arts would have been motivated to make this modification because of the following, “These results clearly demonstrates that utilizing pool5 layer outputs makes an appropriate balance between adequate spatial information and distinguishable features, which once again proves the effectiveness of the proposed method in recognizing human actions in videos” (Ma, 4.5. Contribution of each system component to the overall performance, Page 519).
Claim 16.
Ullah in view of Ma teaches the apparatus of claim 15,
Ullah further teaches wherein the first NN is a convolutional NN (III. PROPOSED FRAMEWORK, Page 1157 “First, we extract CNN features from the frames of video VVI with jump JJF in sequence of frames such that the jump JJF does not affect the sequence of the action AAI in the video” and Figure 1 teaches convolutional neural network).
Claim 17.
Ullah in view of Ma teaches the apparatus of claim 15,
Ullah further teaches wherein the second NN is recurrent NN (A. Preparation and Features Extraction, Page 1158 “The extracted features vector from FC8 layer is one thousand dimensional. The features of each frame are considered as one chunk for one input step of RNN. CCN chunks for TTS time interval are feed to RNN” and Figure 1 teaches second neural network is recurrent NN).
Claim 18.
Ullah in view of Ma teaches the apparatus of claim 15, wherein the instructions are executable to:
Ullah further teaches use a spatial region extraction network (SREN) to extract the feature vectors of the whole video scene and the particular spatial regions (A. Preparation and Features Extraction, Page 1157 “CNN is a dominant source for the representation and classification of images. In the case of video data, each individual frame is represented by CNN features, followed by finding the sequential information between them using DB-LSTM… As CNN finds hidden patterns in images, it captures all the tiny changes in each frame” and C. YouTube Actions Dataset, Page 1162 “The dataset is collected from 11 sports action categories including volleyball, basketball, golf, horse riding, biking/cycling, tennis, diving, football, swinging, jumping, and walking with a dog. The dataset contains 25 different subjects with more than four video clips for each subject” and Fig. 1 teaches extracting feature of the video like sky diving, basketball, running, walking, etc.).
Claim 19.
Ullah in view of Ma teaches the apparatus of claim 15,
Ullah further teaches wherein the first output comprises one or more region features, and wherein the second output comprises one or more scene features (A. Preparation and Features Extraction, Page 1157 “The scenario of the features representation is given in Fig. 2, where the first row represents the frames in a sequence and second row shows features maps of the corresponding frames. A basketball is moving from one player to another where a small change in players’ position and orientation can be observed” teaches output comprises feature map of a basketball player and a basketball court).
Claim 20.
Ullah in view of Ma teaches the apparatus of claim 15,
Ullah further teaches wherein the instructions are executable to: modify, based on the temporal dynamic information, the classifier to classify both of the whole video scene and the particular spatial regions (C. YouTube Actions Dataset, Page 1162 “The dataset is collected from 11 sports action categories including volleyball, basketball, golf, horse riding, biking/cycling, tennis, diving, football, swinging, jumping, and walking with a dog. The dataset contains 25 different subjects with more than four video clips for each subject” and Figure 1 teaches classification of the sky diving, basketball, running, walking etc.).
Conclusion
8. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Lokesha Patel whose telephone number is (571)272-6267. The examiner can normally be reached 8 AM - 4 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamran Afshar can be reached at (571) 272-7796. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LOKESHA PATEL/Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125