Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
Submission dated 06/02/2026 amends claims 1, 9 and 17. Claims 1-20 are pending.
Response to Arguments
Applicant’s arguments with respect to the 103 and double patenting rejections have been considered but are moot because the new ground of rejection does not rely on the teachings of Amiri that were challenged in the arguments.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 2, 9, 10, and 17-18 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by us patent application publication no. 2009/007202 to Williams et al. (hereinafter Williams).
For claims 1, 9 and 17, Williams a as applied discloses a method of identifying targets, comprising:
receiving a first plurality of snapshot (see, e.g., pars. 25-28 and Fig. 1A, which teach receiving a video item composed of a plurality of frames, each of which provides a still image);
generating a first plurality of descriptors representing visual appearances of one or more objects in each of the first plurality of snapshots (see, e.g., pars. 29 and 34-37 and FIG. 1A, which teach determining a visual feature for each frame of the video item);
grouping the first plurality of snapshots into at least one cluster based on the plurality of descriptors (see, e.g., pars. 29 and 38-40 and FIG. 1A, which teach dividing the video item into segments by detecting shot boundaries based on the visual features of each frame);
selecting, for each of the at least one cluster, one snapshot from the respective cluster as a representative snapshot corresponding to the respective cluster (see, e.g., pars. 42-48 and FIG. 1A, which teach selecting a keyframe for each video segment that is highly representative of the video segment);
generating at least one second descriptor for each representative snapshot, wherein the at least one second descriptor is more complex than the first plurality of descriptors (see, e.g., pars. 54-56 and FIG. 1A, which teach selecting final keyframes for groups of video segments; the final keyframes are more complex than the segment keyframes because each group includes multiple segments); and
identifying a target based on comparing the at least second descriptor and a third descriptor (see, e.g., pars. 83 and 90-92 and FIGS. 3 and 6, which teach identifying search results based on a comparison between the final keyframes and a search query).
For claims 2, 10 and 18, Williams as applied discloses that the third descriptor is associated with a second plurality of snapshots or an input query (see, e.g., pars. 83 and 90-99 and FIGS. 3 and 6-7, which teach using a search query).
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) 3, 11 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Williams in view of a non-patent literature titled “Hierarchical Keyframe-based Video Summarization Using QR-Decomposition and Modified k-Means Clustering” by Amiri et al. (hereinafter Amiri), published in 2010, in view of a non-patent literature titled “Video Shot Boundary Detection Using QR-Decomposition and
Gaussian Transition Detection” by Fathy et a. (hereinafter Fathy), published in 2009.
For claims 3, 11 and 19, while Williams does not explicitly teach, Amiri in the analogous art teaches that the input query includes one or more arrays of numbers representing an intended target (see, e.g., sections 5.1 and 5.2 of Amiri, which teach that values of Tmax are integers, representing the number of keyframes).
It would have been obvious to one of ordinary skill in the art to modify Williams in view of Amiri to use an array of number as the query as taught by Amiri because doing so would provide a way to control the number of keyframes (see, e.g., sec. 5.2 of Amiri).
Claim(s) 4 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Williams in view of the legal precedent provided in MPEP 2144.04(VI)(B).
For claims 4 and 12, while Williams as applied does not explicitly teach, the limitations of these claimed are obvious over Williams in view of Amiri and further in view of In re Harza, because the limitations of claims 4 and 12 merely duplicate the subject matter of claims 1 and 9, which have been anticipated by Williams, and as held in In re Harza, merely duplicating what has been already taught is obvious, i.e., has no patentable significance unless a new and unexpected results is produced (see 2144.04(VI)(B) and In re Harza, 274 F.2d 669, 124 USPQ 378 (CCPA 1960)).
Claim(s) 6-8, 14-16 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Williams in view of Amiri and further in view of us patent application publication no. 20200272509 to Wright et al. (hereinafter Wright).
For claims 6 and 14, while Williams does not explicitly teach, Amiri in the analogous art teaches prior to selecting a representative snapshot for each of the at least one cluster, estimating a precision value for each snapshot in the at least one cluster (see, e.g., section 5.2 of Amiri, which teach determining a distance of each frame from the center of the cluster before selecting a keyframe).
It would have been obvious to one of ordinary skill in the art to modify Williams in view of Amiri use the distance as precision value as taught by Amiri because doing so would provide a way to control the number of clusters (see, e.g., sec. 5.2 of Amiri). Amiri, however, does not explicitly teach utilizing a mean average precision value as the precision value.
Wright in the analogous art teaches using a MAP of a snapshot (see, e.g., pars. 58-59 of Wright). It would have been obvious to one of ordinary skill in the art to modify Williams in view of Amiri to use a neural network to estimate the MAP for keyframe extraction as taught by Wright because doing so would provide more accurate analysis of a snapshot (see, e.g., par. 57 of Wright) and doing so would constitute a simple substitution of one element for another to achieve more accurate analysis of a snapshot (see, e.g., MPEP 2143(I)(B)).
For claims 7 and 15, while Williams does not explicitly teach, Amiri in the analogous art teaches selecting a representative snapshot for each of the at least one cluster comprises selecting a snapshot in the at least one cluster having a highest estimated MAP (see, e.g., section 5.2 of Amiri, which teaches selecting the center most frame as the keyframe; the examiner interprets the center most frame as the claimed snapshot with the highest precision value).
It would have been obvious to one of ordinary skill in the art to modify Williams in view of Amiri use the distance as precision value as taught by Amiri because doing so would provide a way to control the number of clusters (see, e.g., sec. 5.2 of Amiri). Amiri, however, does not explicitly teach utilizing a mean average precision value as the precision value.
Wright in the analogous art teaches using a MAP of a snapshot (see, e.g., pars. 58-59 of Wright). It would have been obvious to one of ordinary skill in the art to modify Amiri to use a neural network to estimate the MAP for keyframe extraction as taught by Wright because doing so would provide more accurate analysis of a snapshot (see, e.g., par. 57 of Wright) and doing so would constitute a simple substitution of one element for another to achieve more accurate analysis of a snapshot (see, e.g., MPEP 2143(I)(B)).
For claims 8 and 16, while Williams in view of Amiri does not explicitly teach, Wright in the analogous art teaches estimating a MAP of a snapshot using a neural network (see, e.g., pars. 58-59 of Wright).
It would have been obvious to one of ordinary skill in the art to modify Williams in view of Amiri to use a neural network to estimate the MAP for keyframe extraction as taught by Wright because doing so would provide predictable results of automating and making the extraction process more robust by using a neural network (see MPEP 2143(I)(D)).
For claim 20, while Williams does not explicitly teach, Amiri in the analogous art teaches, prior to selecting a representative snapshot, estimating a mean average precision (MAP) for each snapshot in the at least one cluster (see, e.g., section 5.2, which teach determining a distance of each frame from the center of the cluster to select a keyframe, wherein the distance being the measure of precision/accuracy).
It would have been obvious to one of ordinary skill in the art to modify Williams in view of Amiri use the distance as precision value as taught by Amiri because doing so would provide a way to control the number of clusters (see, e.g., sec. 5.2 of Amiri). Amiri, however, does not explicitly teach estimating a MAP of a snapshot using a neural network.
Wright in the analogous art teaches estimating a MAP of a snapshot using a neural network (see, e.g., par. 58 of Wright).
It would have been obvious to one of ordinary skill in the art to modify Williams in view of Amiri to use a neural network to estimate the MAP for keyframe extraction as taught by Wright because doing so would provide predictable results of automating and making the extraction process more robust by using a neural network (see MPEP 2143(I)(D)).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Us patent application publication no. 17/882208
Us patent no. 11423248
1. A method of identifying targets, comprising:
receiving a first plurality of snapshots;
generating a first plurality of descriptors representing visual appearances of one or more objects in each of the first plurality of snapshots;
grouping the first plurality of snapshots into at least one cluster based on the plurality of descriptors;
selecting, for each of the at least one cluster, one snapshot from the respective cluster as a representative snapshot corresponding to the respective cluster;
generating at least one second descriptor for each representative snapshot, wherein the at least one second descriptor is more complex than the first plurality of descriptors; and
identifying a target based on comparing the at least second descriptor and a third descriptor.
1. A method of hierarchical sampling, comprising:
receiving a first plurality of snapshots;
generating a first plurality of descriptors each associated with the first plurality of snapshots;
grouping the first plurality of snapshots into at least one cluster based on the plurality of descriptors;
selecting a representative snapshot for each of the at least one cluster;
generating at least one second descriptor for the representative snapshot for each of the at least one cluster, wherein the at least one second descriptor is more complex than the first plurality of descriptors; and
identifying a target by applying the at least second descriptor to a second plurality of snapshots.
2. The method of claim 1, wherein the third descriptor is associated with a second plurality of snapshots or an input query.
2. The method of claim 1, further comprising, prior to receiving the first plurality of snapshots:
receiving a third plurality of snapshots;
generating a third plurality of descriptors each associated with the third plurality of snapshots, wherein the third plurality of descriptors are less complex than the first plurality of descriptors;
grouping the third plurality of snapshots into a plurality of clusters based on the third plurality of descriptors; and
selecting a second plurality of representative snapshots as the first plurality of snapshots.
3. The method of claim 2, wherein the input query includes one or more arrays of numbers representing an intended target.
2. The method of claim 1, further comprising, prior to receiving the first plurality of snapshots:
receiving a third plurality of snapshots;
generating a third plurality of descriptors each associated with the third plurality of snapshots, wherein the third plurality of descriptors are less complex than the first plurality of descriptors;
grouping the third plurality of snapshots into a plurality of clusters based on the third plurality of descriptors; and
selecting a second plurality of representative snapshots as the first plurality of snapshots.
4. The method of claim 1, further comprising, prior to receiving the first plurality of snapshots:
receiving a second plurality of snapshots;
generating a third plurality of descriptors each associated with the second plurality of snapshots, wherein the third plurality of descriptors are less complex than the first plurality of descriptors;
grouping the second plurality of snapshots into a plurality of clusters based on the third plurality of descriptors; and
selecting a second plurality of representative snapshots as the first plurality of snapshots.
2. The method of claim 1, further comprising, prior to receiving the first plurality of snapshots:
receiving a third plurality of snapshots;
generating a third plurality of descriptors each associated with the third plurality of snapshots, wherein the third plurality of descriptors are less complex than the first plurality of descriptors;
grouping the third plurality of snapshots into a plurality of clusters based on the third plurality of descriptors; and
selecting a second plurality of representative snapshots as the first plurality of snapshots.
5. The method of claim 1, further comprising:
classifying each representative snapshot for each of the at least one cluster;
aggregating classification scores of each representative snapshot for each of the at least one cluster;
determining a class based on the aggregated classification scores; and
wherein the at least one second descriptor is a class-specific descriptor.
4. The method of claim 1, further comprising:
classifying the representative snapshot for each of the at least one cluster;
aggregating classification scores of the representative snapshot for each of the at least one cluster;
determining a class based on the aggregated classification scores; and
wherein the at least one descriptor is a class-specific descriptor.
6. The method of claim 1, further comprising, prior to selecting a representative snapshot for each of the at least one cluster, estimating a mean average precision (MAP) for each snapshot in the at least one cluster.
5. The method of claim 1, further comprising, prior to selecting the representative snapshot, estimating a mean average precision (MAP) for each snapshot in the at least one cluster.
7. The method of claim 6, wherein selecting a representative snapshot for each of the at least one cluster comprises selecting a snapshot in the at least one cluster having a highest estimated MAP.
6. The method of claim 5, wherein selecting the representative snapshot for each of the at least one cluster comprises selecting a snapshot in the at least one cluster having a highest estimated MAP.
8. The method of claim 6, wherein estimating a MAP comprises using a neural network to estimate the MAP.
8. The method of claim 5, wherein estimating a MAP comprises using a neural network to estimate the MAP.
9. A non-transitory computer readable medium comprising instructions stored therein that, when executed by a processor of a system, cause the processor to:
receive a first plurality of snapshots;
generate a first plurality of descriptors representing visual appearances of one or more objects in each of the first plurality of snapshots;
group the first plurality of snapshots into at least one cluster based on the plurality of descriptors;
select, for each of the at least one cluster, one snapshot from the respective cluster as a representative snapshot corresponding to the respective cluster;
generate at least one second descriptor for each representative snapshot, wherein the at least one second descriptor is more complex than the first plurality of descriptors; and
identify a target based on comparing the at least second descriptor and a third descriptor.
9. A non-transitory computer readable medium comprising instructions stored therein that, when executed by a processor of a system, cause the processor to:
receive a first plurality of snapshots;
generate a first plurality of descriptors each associated with the first plurality of snapshots;
group the first plurality of snapshots into at least one cluster based on the plurality of descriptors;
select a representative snapshot for each of the at least one cluster;
generate at least one second descriptor for the representative snapshot for each of the at least one cluster, wherein the at least one second descriptor is more complex than the first plurality of descriptors; and
identify a target by applying the at least second descriptor to a second plurality of snapshots.
10. The non-transitory computer readable medium of claim 9, wherein the third descriptor is associated with a second plurality of snapshots or an input query.
10. The non-transitory computer readable medium of claim 9, further comprising instructions stored therein that, when executed by the processor of the system, cause the processor to, prior to receiving the first plurality of snapshots:
receive a third plurality of snapshots;
generate a third plurality of descriptors each associated with the third plurality of snapshots, wherein the third plurality of descriptors are less complex than the first plurality of descriptors;
group the third plurality of snapshots into a plurality of clusters based on the third plurality of descriptors; and
select a second plurality of representative snapshots as the first plurality of snapshots.
11. The non-transitory computer readable medium of claim 10, wherein the input query includes one or more arrays of numbers representing an intended target.
10. The non-transitory computer readable medium of claim 9, further comprising instructions stored therein that, when executed by the processor of the system, cause the processor to, prior to receiving the first plurality of snapshots:
receive a third plurality of snapshots;
generate a third plurality of descriptors each associated with the third plurality of snapshots, wherein the third plurality of descriptors are less complex than the first plurality of descriptors;
group the third plurality of snapshots into a plurality of clusters based on the third plurality of descriptors; and
select a second plurality of representative snapshots as the first plurality of snapshots.
12. The non-transitory computer readable medium of claim 9, further comprising instructions that, prior to receiving the first plurality of snapshots, cause the processor to:
receive a second plurality of snapshots;
generate a third plurality of descriptors each associated with the second plurality of snapshots, wherein the third plurality of descriptors are less complex than the first plurality of descriptors;
group the second plurality of snapshots into a plurality of clusters based on the third plurality of descriptors; and
select a second plurality of representative snapshots as the first plurality of snapshots.
10. The non-transitory computer readable medium of claim 9, further comprising instructions stored therein that, when executed by the processor of the system, cause the processor to, prior to receiving the first plurality of snapshots:
receive a third plurality of snapshots;
generate a third plurality of descriptors each associated with the third plurality of snapshots, wherein the third plurality of descriptors are less complex than the first plurality of descriptors;
group the third plurality of snapshots into a plurality of clusters based on the third plurality of descriptors; and
select a second plurality of representative snapshots as the first plurality of snapshots.
13. The non-transitory computer readable medium of claim 9, further comprising instructions that cause the processor to:
classify each representative snapshot for each of the at least one cluster;
aggregate classification scores of each representative snapshot for each of the at least one cluster;
determine a class based on the aggregated classification scores; and
wherein the at least one descriptor is a class-specific descriptor.
12. The non-transitory computer readable medium of claim 9, further comprising instructions stored therein that, when executed by the processor of the system, cause the processor to:
classify the representative snapshot for each of the at least one cluster;
aggregate classification scores of the representative snapshot for each of the at least one cluster;
determine a class based on the aggregated classification scores; and
wherein the at least one descriptor is a class-specific descriptor.
14. The non-transitory computer readable medium of claim 9, further comprising instructions that, prior to selecting a representative snapshot for each of the at least one cluster, cause to processor to estimate a mean average precision (MAP) for each snapshot in the at least one cluster.
13. The non-transitory computer readable medium of claim 9, further comprising instructions stored therein that, when executed by the processor of the system, cause the processor to, prior to select the representative snapshot, estimate a Mean Average Precision for each snapshot in the at least one cluster.
15. The non-transitory computer readable medium of claim 14, wherein the instructions for selecting a representative snapshot for each of the at least one cluster comprises instructions for selecting a snapshot in the at least one cluster having a highest estimated MAP.
14. The non-transitory computer readable medium of claim 13, wherein the instructions for selecting the representative snapshot for each of the at least one cluster comprises instructions that, when executed by the processor of the system, cause the processor to select a snapshot in the at least one cluster having a highest estimated mean average precision (MAP).
16. The non-transitory computer readable medium of claim 14, wherein the instructions for estimating a MAP comprises instructions for using a neural network to estimate the MAP.
16. The non-transitory computer readable medium of claim 13, wherein the instructions for estimating a MAP comprises instructions that, when executed by the processor of the system, cause the processor to use a neural network to estimate the MAP.
17. A system, comprising:
memory that stores instructions; and
a processor configured to execute the instructions to:
receive a first plurality of snapshots;
generate a first plurality of descriptors representing visual appearances of one or more objects in each of the first plurality of snapshots;
group the first plurality of snapshots into at least one cluster based on the plurality of descriptors;
select, for each of the at least one cluster, one snapshot from the respective cluster as a representative snapshot corresponding to the respective cluster;
generate at least one second descriptor for each representative snapshot, wherein the at least one second descriptor is more complex than the first plurality of descriptors; and
identify a target based on comparing the at least second descriptor and a third descriptor.
17. A system, comprising:
memory that stores instructions; and
a processor configured to execute the instructions to:
receive a first plurality of snapshots;
generate a first plurality of descriptors each associated with the first plurality of snapshots;
group the first plurality of snapshots into at least one cluster based on the plurality of descriptors;
select a representative snapshot for each of the at least one cluster;
generate at least one second descriptor for the representative snapshot for each of the at least one cluster, wherein the at least one second descriptor is more complex than the first plurality of descriptors; and
identify a target by applying the at least second descriptor to a second plurality of snapshots.
18. The system of claim 17, wherein the third descriptor is associated with a second plurality of snapshots or an input query.
18. The system of claim 17, wherein the processor is further configured to execute the instructions to, prior to receiving the first plurality of snapshots:
receive a third plurality of snapshots;
generate a third plurality of descriptors each associated with the third plurality of snapshots, wherein the third plurality of descriptors are less complex than the first plurality of descriptors;
group the third plurality of snapshots into a plurality of clusters based on the third plurality of descriptors; and
select a second plurality of representative snapshots as the first plurality of snapshots.
19. The system of claim 18, wherein the input query includes one or more arrays of numbers representing an intended target.
18. The system of claim 17, wherein the processor is further configured to execute the instructions to, prior to receiving the first plurality of snapshots:
receive a third plurality of snapshots;
generate a third plurality of descriptors each associated with the third plurality of snapshots, wherein the third plurality of descriptors are less complex than the first plurality of descriptors;
group the third plurality of snapshots into a plurality of clusters based on the third plurality of descriptors; and
select a second plurality of representative snapshots as the first plurality of snapshots.
20. The system of claim 17, wherein the processor is further configured to, prior to selecting a representative snapshot for each of the at least one cluster, estimate a mean average precision (MAP) for each snapshot in the at least one cluster using a neural network.
8. The method of claim 5, wherein estimating a MAP comprises using a neural network to estimate the MAP.
16. The non-transitory computer readable medium of claim 13, wherein the instructions for estimating a MAP comprises instructions that, when executed by the processor of the system, cause the processor to use a neural network to estimate the MAP.
Claims 1, 9 and 17 are rejected on the ground of nonstatutory double patenting as being unpatentable over patent claims 1, 9 and 17 of U.S. Patent No. 11423248 (hereinafter the patent) in view of Williams. For the difference between claims 1, 9 and 17 and the patent claims 1, 9 and 17, Williams in the analogous art teaches identifying a target based on comparing the at least second descriptor and a third descriptor (see, e.g., pars. 83 and 90-92 and FIGS. 3 and 6, which teach identifying the search results based on the comparison between the final keyframes and the search query).
It would have been obvious to modify the teaching of claims 1, 9 and 17 to identify a target as taught by Williams because doing so would yield predictable results of providing an additional layer of classification and a more accurate identification of target (see MPEP 2143(I)(D)).
Claims 2, 10 and 18 are rejected on the ground of nonstatutory double patenting as being unpatentable over the patent claims 1, 2, 9, 10, 17, and 18 in view of Williams.
For claims 2, 10 and 18, patent claims 2, 10 and 18 teach that the third descriptor is associated with a second plurality of snapshots.
Claims 3, 11 and 19 are rejected on the ground of nonstatutory double patenting as being unpatentable over patent claims 1, 2, 9, 10, 17, and 18 in view of Williams and Amiri.
For the difference between claims 3, 11 and 19 and patent claims 1, 2, 9, 10, 17, and 18, Amiri as applied teaches that the input query includes one or more arrays of numbers representing an intended target (see, e.g., sections 5.1 and 5.2, which teach that β and Tmax are integers, representing the number of keyframes).
It would have been obvious to modify the teaching of claims 1, 9 and 17 to identify a target based on the comparison between a description of the last classification and another description such as the value of Tmax as taught by Amiri because doing so would yield a predictable results of providing an additional layer of classification and a more accurate identification of target (see MPEP 2143(I)(D)).
Claims 4 and 12 are rejected on the ground of nonstatutory double patenting as being unpatentable over patent claims 1, 2, 9, 10, 17, and 18 in view of Williams.
For claims 4 and 12, patent claims in view of Williams teaches, prior to receiving the first plurality of snapshots:
receiving a second plurality of snapshots; generating a third plurality of descriptors each associated with the second plurality of snapshots, wherein the third plurality of descriptors are less complex than the first plurality of descriptors; grouping the second plurality of snapshots into a plurality of clusters based on the third plurality of descriptors; and
selecting a second plurality of representative snapshots as the first plurality of snapshots (see, e.g., patent claims 2, 10 and 18).
Claims 5 and 13 are rejected on the ground of nonstatutory double patenting as being unpatentable over patent claims 1, 4, 9, 12, 17, and 20 in view of Williams.
For claims 5 and 13, patent claims in view of Williams teaches:
classifying the representative snapshot for each of the at least one cluster;
aggregating classification scores of the representative snapshot for each of the at least one cluster;
determining a class based on the aggregated classification scores; and
wherein the at least one descriptor is a class-specific descriptor (see, e.g., patent claims 4, 12 and 20).
Claims 6 and 14 are rejected on the ground of nonstatutory double patenting as being unpatentable over the patent claims 1, 5, 9, 13, and 17 in view of Williams.
For claims 6 and 14, patent claims in view of Williams teaches, prior to selecting the representative snapshot, estimating a mean average precision (MAP) for each snapshot in the at least one cluster (see, e.g., patent claims 5 and 13).
Claims 7 and 15 are rejected on the ground of nonstatutory double patenting as being unpatentable over patent claims 1, 6, 9, 14, and 17 in view of Williams.
For claims 7 and 15, patent claims in view of Williams teaches selecting the representative snapshot for each of the at least one cluster comprises selecting a snapshot in the at least one cluster having a highest estimated MAP (see, e.g., patent claims 6 and 14).
Claims 8, 16, and 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over patent claims 1, 8, 9, 16, and 17 in view of Williams.
For claims 8, 16 and 20, patent claims in view of Williams teaches using a neural network for keyframe extraction (see, e.g., patent claims 8 and 16).
Allowable Subject Matter
Claims 5 and 13 would be allowable if the double patenting can be overcome.
In regard to claims 5 and 13, when considering each as a whole, prior art of record fails to disclose or render obvious, alone or in combination:
“classifying each representative snapshot for each of the at least one cluster;
aggregating classification scores of each representative snapshot for each of the at least one cluster;
determining a class based on the aggregated classification scores; and
wherein the at least one second descriptor is a class-specific descriptor.”
Additional Citations
The following table lists several references that are relevant to the subject matter claimed and disclosed in this Application. The references are not relied on by the Examiner, but are provided to assist the Applicant in responding to this Office action.
Citation
Relevance
Huh et al. (us pat. pub. 2020/0349355)
Describes a method for determining a representative image of a video with reference to a representative object, and an electronic apparatus for processing the method. A method for determining a representative image of a video may comprise acquiring a video, determining a representative object of the video from at least one object appearing in the video, and determining a representative image of the video on the basis of an image score representing visual importance of the representative object. Accordingly, an image in which a representative object is the most visually conspicuous may be determined as a representative image of a video.
Koval et al. (us pat. pub. 2020/0372294)
Describes a method of indexing and searching for video content. For each frame of a first plurality of frames, a first global feature and a first plurality of local features may be identified. The first plurality of local features may be clustered around a first plurality of cluster centers. The first plurality of local features may be converted into a first plurality of binary signatures. An index that maps the first plurality of cluster centers and the first plurality of binary signatures to the first plurality of frames may be generated. A search request associated with a second video may be received and its direct and indirect features may be identified. The identified features of the second video may be compared against the index and a candidate video may be selected as a result of the search request.
Table 1
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See Table 1 and form 892.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WOO RHIM whose telephone number is (571)272-6560. The examiner can normally be reached Mon - Fri 9:30 am - 6:00 pm et.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Henok Shiferaw can be reached at 571-272-4637. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/WOO C RHIM/Examiner, Art Unit 2676
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