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 § 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 (i.e., changing from AIA to pre-AIA ) 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.
Claims 1-2, 4-5, 12 are rejected under 35 U.S.C. 103 as being unpatentable over Maharana et al. (US20240114162A1, hereinafter referred to as Maharana) in view of Lee (US20250113049A1).
The applied reference has a common assignee with the instant application. Based upon the earlier effectively filed date of the reference, it constitutes prior art under 35 U.S.C. 102(a)(2).
This rejection under 35 U.S.C. 103 might be overcome by: (1) a showing under 37 CFR 1.130(a) that the subject matter disclosed in the reference was obtained directly or indirectly from the inventor or a joint inventor of this application and is thus not prior art in accordance with 35 U.S.C.102(b)(2)(A); (2) a showing under 37 CFR 1.130(b) of a prior public disclosure under 35 U.S.C. 102(b)(2)(B); or (3) a statement pursuant to 35 U.S.C. 102(b)(2)(C) establishing that, not later than the effective filing date of the claimed invention, the subject matter disclosed and the claimed invention were either owned by the same person or subject to an obligation of assignment to the same person or subject to a joint research agreement. See generally MPEP § 717.02.
Regarding claim 1, Maharana teaches one or more processors comprising: one or more circuits to: extract, from an encoded representation of an image frame, the image frame and an indication of a reference characteristic of one or more objects represented by the image frame (met by, during encoding, assemble, based at least in part on differences of an attribute of an object, a set of reference frames; further met by reconstruction of an object, during decoding based at least in part on a selection from different ones of a set of reference frames); apply the image frame as input to one or more vision models to cause the one or more vision models to generate inference data regarding the one or more objects represented by the image frame (met by reconstruction of an object). This is read in (Paragraph [0061]).
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Maharana fails to teach determining a metric of operation of the one or more vision models based at least on the inference data and the reference characteristic. However, Lee amends this deficiency.
Lee teaches various forms of machine learning including unsupervised learning and self-learning which involve training a model to enable the model to process further data to make inferences, wherein said model may include input and output layers in addition to multiple hidden layers in between that that are configured and weighted to make inferences about an appropriate output. This meets the intended process of the claimed invention by the ability to analyze the outputs of the model while also having access to the input layers. This is read in (Paragraph [0043]-[0044]).
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Therefore, it would have been prima facia obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to have modified Maharana to incorporate the teachings of Lee in order to provide training a machine learning (ML) model to determine, e.g., based on bit rate capacity, whether to encode a frame of video or not. (Abstract).
Regarding claim 2, Maharana as read in the rejection of claim 1, incorporated herein, meets wherein the one or more circuits are to receive the encoded representation as at least one of (i) a stream of image data or (ii) compressed video data (met by image associated with a video stream).
Regarding claim 4, Lee teaches wherein the one or more circuits are to determine the metric of operation based at least on comparing the inference data with the reference characteristic (met by inputting training data set to model at state (600), training set including sequences of video frames at a low frame rate along with ground truth). This is read in (Paragraph [0054]). Further, (Paragraph [0057]) teaches the training data may include complete frames of data and ground truth data.
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Regarding claim 5, Lee teaches wherein the one or more circuits are to at least one of (i) assign a flag to one or more parameters of the one or more vision models, the flag corresponding to the metric, (met by training data may include ground truth indication based on a subjective index which may be a subjective video quality metric) or (ii) update the one or more parameters based at least on the metric. This is read in (Paragraph [0047]).
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Regarding claim 12, the claim is substantially identical to claim 1, the analysis of which is incorporated herein.
Regarding claim 13, the claim is substantially identical to claim 2, the analysis of which is incorporated herein.
Regarding claim 15, the claim is substantially identical to claims 4-5, the analyses of which are incorporated herein.
Regarding claim 19, the claim is substantially identical to claim 1, the analysis of which is incorporated herein.
Claims 3, 6-10 are rejected under 35 U.S.C. 103 as being unpatentable over Maharana in view of Lee and in further view of Shin et al. (US011983928B1, hereinafter referred to as Shin).
Regarding claim 3, Maharana in view of Lee fails to teach wherein the one or more vision models comprise at least one of (i) an object detector to assign a bounding box to a portion of the image frame corresponding to at least one object of the one or more objects detected by the object detector or (ii) an object tracker to generate the inference data to include an identifier to track the one or more objects across the image frame and a second image frame. However, Shin amends this deficiency.
Shin teaches Object localization module (234) which may provide an indication of a bounding box region and/or a correlation response region associated with a detected object. This meets the claimed inventions desire to assign a bounding box to a portion of the image corresponding to at least one object detected by the object detector. This is read in (Column 16, Line 3-6).
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Regarding claim 6, Shin as read in the rejection of claim 3, incorporated herein, meets wherein the one or more circuits are to generate the encoded representation of the image frame using an encoder, wherein the encoder is configured to insert the indication of the reference characteristic into the encoded representation (met by object localization module (234) may generate a set of similarity metric values indicating a similarity between a detected object and an existing target; further met by object localization module (234) may assign the detected object a particular similarity metric; further met by the target object itself being the characteristic that is subject to comparison; further met by comparing the visual features of the detected object with the visual features associated with each target tracked).
Regarding claim 7, Shin as read in the rejection of claims 3 and 6, both of which are incorporated herein, meets wherein the encoder is configured to insert the indication of the reference characteristic as a supplemental enhancement information (SEI) message within the encoded representation of the image frame (met by object localization module (234) may assign the detected object a particular similarity metric; further met by the target object itself being the characteristic that is subject to comparison), and wherein the indication of the reference characteristic corresponds to ground truth (GT) data (met by the pre-existing target object which is what the detected object is being compared to).
Regarding claim 8, Shin as read in the rejection of claims 3, 6-7, incorporated herein, meets wherein inserting the GT data comprises embedding the GT data into the image frame of a plurality of image frames of a stream of image data or compressed video data (met by target object data), and wherein the GT data comprises at least one of one or more bounding boxes, one or more class labels, or one or more object identifiers (IDs) (met by object localization may provide an indication of a bounding box region and/or a correlation response region associated with a detected object). Because Shin is capable of determining a bounding box of a detected object, and uses a previously detected target object as ground truth data, it is within the capability of the prior art to use GT data which comprises one or more bounding boxes.
Regarding claim 9, Maharana as read in the rejection of claim 1, incorporated herein, meets wherein the encoded representation is received from a real-time stream (met by image associated with a video stream).
Further, Shin as read in the rejections of claims 3, 6-8, incorporated herein, meets wherein extracting the indication of the reference characteristic comprises extracting the SEI message comprising the GT data and storing the GT data as metadata in a buffer corresponding with an extracted representation of the image frame. This is met implicitly by determining and utilizing such ground truth data which, in order to be used in any meaningful capacity, must be stored a memory buffer as is the case in all software-implemented logic.
Regarding claim 10, Shin as read in the rejection of claims 3, 6-9, incorporated herein, meets wherein applying the image frame as the input to the one or more vision models comprises identifying the metadata in the buffer (met by comparing the detected object to the target object, the target object implicitly containing metadata which must by identified in order to be used for such a process).
Regarding claim 14, the claim is substantially identical to claim 3, the analysis of which is incorporated herein.
Regarding claim 16, the claim is substantially identical to claim 6, the analysis of which is incorporated herein.
Regarding claim 17, the claim is substantially identical to claims 7-8, the analyses of which are incorporated herein.
Regarding claim 18, the claim is substantially identical to claims 9-10, the analyses of which are incorporated herein.
Regarding claim 20, the claim is substantially identical to claims 2-3, the analyses of which are incorporated herein.
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW JAMES BODNARK whose telephone number is (703)756-5378. The examiner can normally be reached 8a-5p.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vu Le can be reached at (571) 272-7332. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MATTHEW JAMES BODNARK/Examiner, Art Unit 2668
/VU LE/Supervisory Patent Examiner, Art Unit 2668
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