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 .
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-2 and 11-12 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by D1.1
With regard to claim 1, D1 teach video action localization device (see abstract: online temporal action localization) comprising: a memory configured to store at least one instruction, a processor configured to execute the at least one instruction, and a neural network (see § 3.1 ¶ 1, fig. 2: convolution neural network; implicitly implemented on a programmed computer with a processor and memory); wherein the processor receives a first segment included in a video at a current timestamp, extracts features of the first segment, and inputs the extracted features of the first segment and features of segments stored in a memory queue of the memory at previous timestamps to the neural network to acquire a predicted start timestamp, end timestamp, and action class of an action region in the video (see abstract, § 2.1: predict start time, end time, and action class; see fig. 2, § 4.1: current frame features and a certain number of past consecutive frames from memory are input), and each of the segments stored in the memory queue at the previous timestamps satisfies a certain condition (see § 3.1 ¶ 1, § 5.1 ¶ 2: the past frames have to be consecutive).
With regard to claim 2, D1 teach wherein each of the segments stored in the memory queue at the previous timestamps is predicted to include at least a part of the action region (see § 3.1 ¶ 1, § 5.1, figs. 2, 3: consecutive past frames include action; see 3.2 ¶ 2: past visual context which is a set of past visual features and their corresponding action probabilities).
With regard to claim 11, see discussion of claim 1.
With regard to claim 12, see discussion of claim 2.
Claims 3-10 and 13-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
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/AVINASH YENTRAPATI/Primary Examiner, Art Unit 2672
1 Tang, Tuan N., et al. "Simon: A simple framework for online temporal action localization." arXiv preprint arXiv:2211.04905 (2022).
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