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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/16/2026 has been entered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/07/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
In light of the changes made to claim 1, the objection pertaining to incorrect grammar is withdrawn.
In light of the changes made to claim 21, the objection pertaining to a lack of antecedent basis is withdrawn.
Applicant's arguments with respect to claims 1-9, 21-25 and 29 have been considered but are moot in view of the new ground(s) of rejection.
Claim Objections
Claim 21 objected to because of the following informalities: marked as (New) when it was already previously presented in the claim set. Appropriate correction is required.
Claims 1 and 21 objected to because of the following informalities: relative term. The term “substantially” in claims 1 and 21 is a relative term which renders the claims indefinite. The term “substantially” 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. Appropriate correction is required.
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) 1-4, 8, 10, 21-25 and 29 is/are rejected under 35 U.S.C. 103 as being unpatentable over Karabutov et al. (“Kara”) (U.S. PG Publication No. 2023/0065862) in view of Miyauchi (“Miya”) (U.S. PG Publication No. 2021/0334603).
In regards to claim 1, Kara teaches a decoder, the decoder comprising circuitry (See ¶0291) configured to:
receive an MPEG encoded bitstream (See ¶0004), the bitstream including a header including a feature parameter set (Given the broadest reasonable interpretation consistent with applicant’s specification, the feature parameter set may be taught as at least machine-learning model weightings, coefficients, or supplemental data that describes parameters of the machine model as described in ¶0056-0057 of applicant’s specification, see ¶0108 of Kara wherein any machine-learning-based network, neural network [including convolutional neural network] with any pre-training creates parameters representing such training test data, with further examples of ¶0113-0119, 0226 and 0229 showing example parameters that are used, including those parameters which are included in the header and used in the base layer data), and at least a base feature layer including at least one feature map extracted at an encoder by a partial convolutional neural network (See ¶0113-0115, 0117-0118, 0126-0127 wherein feature maps are a result of a layer through CNN or neural network, and may also be termed as channels, activation map, with ¶0136 describing that base layer features are encoded within the respective base layer, with the base layer bitstream and base feature bitstream being used synonymously at times, additionally 0168-0169 further describing the trained network extracting and decoding the base layer features from the base layer bitstream; it is additionally noted that this is executed as a partial convolutional neural network through the use of a series of convolutional layers as described in at least ¶0113-0117, 0119, );
using information in the feature parameter set, decode the at least a base feature layer to decode the at least one feature map (See at least ¶0108, 0113-0119, 0168-0169, 0226 and 0229 as described above wherein features [including base layer features] are used to extract the base layer data; it is noted by the examiner that the term “feature” is such a broad term that it may encompass the features by which a neural network has been trained [thus features before any decoding has been executed], as well as features which are extracted out of the bitstream by the neural network [using its respective pre-defined features by which decoding has been executed]);
output the feature map to a machine process (See at least ¶0113-0119; also see FIG. 1-3, 6 and 11-14).
Kara, however, fails to teach the bitstream including a sequence of frames with each frame comprising multiple rectangular patches including patches of different sizes, each patch being an extracted feature map, and wherein the multiple rectangular feature map patches comprise substantially of a width and a height of a frame.
In a similar endeavor Miya teaches the bitstream including a sequence of frames with each frame comprising multiple rectangular patches including patches of different sizes, each patch being an extracted feature map, and wherein the multiple rectangular feature map patches comprise substantially of a width and a height of a frame (See ¶0007, 0065 and 0037 in view of 0002-0003 wherein a feature map, and a hierarchy map, are used as a way of determining how to divide the image out into areas, this may be visualized as seen in FIG. 3A-3F, as noted the whole image is input into feature extraction and processed by each convolution section to generate corresponding feature and hierarchy maps such that eventually the whole image is input in order to create an appropriate dividing pattern for the whole image for encoding [thus are substantially of a width and a height of a frame, with different sizes of division of rectangular “patches”]).
It would have been obvious to a person of ordinary skill in the art, and before the effective filing date of the claimed invention, to incorporate the teaching of Miya into Kara because it allows for appropriate image encoding by proper adjustment of image division, which itself may require a large amount of computation as described in ¶0002-0003, therefore improving encoding efficiency as described in at least 0030.
In regards to claim 2, Kara teaches the decoder of claim 1, wherein decoding the at least a base feature layer further comprises inversely pre-processing the at least a decoded base feature layer (See ¶0084-0086 wherein the inverse pre-processing may be taught as inverse processing of the encoded data).
In regards to claim 3, Kara teaches the decoder of claim 2, wherein the at least a header includes at least a pre-processing parameter (See ¶0229-0230 wherein the header includes information associated with the base data as well as information related to the neural network, feature information, number of features, locations of features, as well as other parameters of feature data); and
decoding the at least a base feature layer further comprises inversely pre-processing the at least a decoded base feature layer as a function of the at least a pre-processing parameter (See ¶0084-0086 in view of 0229-0230, wherein it is further noted that parameters are a part of pre-training for neural networks as described in 0108).
In regards to claim 4, Kara teaches the decoder of claim 1, wherein the bitstream includes at least a residual visual layer, and decoding further comprises: decoding the residual layer (See at least FIG. 2 and 6 wherein the residual visual layer is taught as the enhancement layer); and
combining the decoded base feature layer and the decoded residual layer to form a human viewable video signal (See FIG. 2, 6 and 20A).
In regards to claim 8, Kara teaches the decoder of claim 1, wherein the circuitry is further configured to output at least a feature parameter, signaled in the at least a header, to the at least a machine (See ¶0229-0230 and FIG. 4 in view FIG. 5).
In regards to claim 10, Kara teaches the decoder of claim 1, wherein the MPEG encoded bitstream is one of an AVC compliant bitstream or VVC compliant bitstream (See ¶0004-0005, 0088 and 0199).
In regards to claim 21, Kara teaches video encoder for encoding a bit stream to be used by a machine video application, the encoder comprising:
a feature extractor, the feature extractor receiving an input video signal (See FIG. 1, 3 and 11) and extracting feature data including at least one feature map (See ¶0113-0115 and 0126-0127);
a feature encoder comprising a temporal predictor, a transformer, and a quantizer (See ¶0085-0086, 0204, 0208, 0212 and 0222), the feature encoder receiving the feature signal from the feature extractor and providing an encoded feature signal (See at least 0088-0094 0100-0108 wherein various features are taught; it is noted by the examiner that the “feature signal” is unclear and is not well-defined, as “features” can comprise of just about anything within the encoding process as per currently claimed and will thus be interpreted as such within prior art references); and
an MPEG encoder coupled to the output of the feature encoder and generating an MPEG coded bitstream (See ¶0004 in view of FIG. 1, 3 and 11), the MPEG bitstream including signaling information including a feature parameter set and compressed feature data (Given the broadest reasonable interpretation consistent with applicant’s specification, the feature parameter set may be taught as at least machine-learning model weightings, coefficients, or supplemental data that describes parameters of the machine model as described in ¶0056-0057 of applicant’s specification, see ¶0108 of Kara wherein any machine-learning-based network, neural network [including convolutional neural network] with any pre-training creates parameters representing such training test data, with further examples of ¶0113-0119, 0226 and 0229 showing example parameters that are used, including those parameters which are included in the header and used in the base layer data).
Kara, however, fails to teach comprising a sequence of frames with each frame comprising multiple rectangular patches including patches of different sizes, each patch being an extracted feature map, and wherein the multiple rectangular feature map patches comprise substantially of a width and a height of a frame.
In a similar endeavor Miya teaches comprising a sequence of frames the bitstream including a sequence of frames with each frame comprising multiple rectangular patches including patches of different sizes, each patch being an extracted feature map, and wherein the multiple rectangular feature map patches comprise substantially of a width and a height of a frame (See ¶0007, 0065 and 0037 in view of 0002-0003 wherein a feature map, and a hierarchy map, are used as a way of determining how to divide the image out into areas, this may be visualized as seen in FIG. 3A-3F, as noted the whole image is input into feature extraction and processed by each convolution section to generate corresponding feature and hierarchy maps such that eventually the whole image is input in order to create an appropriate dividing pattern for the whole image for encoding [thus are substantially of a width and a height of a frame, with different sizes of division of rectangular “patches”]).
It would have been obvious to a person of ordinary skill in the art, and before the effective filing date of the claimed invention, to incorporate the teaching of Miya into Kara because it allows for appropriate image encoding by proper adjustment of image division, which itself may require a large amount of computation as described in ¶0002-0003, therefore improving encoding efficiency as described in at least 0030.
In regards to claim 22, Kara teaches the video encoder of claim 21 wherein the feature extractor is a convolutional neural network (See ¶0108, 0112 and 0117).
In regards to claim 23, Kara fails to teach the video encoder of claim 1 wherein the feature extractor is a partial convolutional neural network (See ¶0113-0117, 0119 and 0126).
In regards to claim 24, Kara teaches the encoder of claim 21 wherein the feature extractor uses a machine learning model to extract the features and the MPEG bitstream stream includes information about the model (See ¶0100 and 0113-0115 in view of 0007, 0090, 0108-0110).
In regards to claim 25, Kara teaches the encoder of claim 21 wherein the MPEG encoder is an AVC encoder, and the bitstream is an AVC compliant bit stream (See ¶0004-0005 and 0088).
In regards to claim 29, Kara teaches the encoder of claim 21 wherein the MPEG encoder is a VVC encoder and the bitstream is a VVC compliant bit stream (See ¶0004 and 0199).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Karabutov et al. (“Kara”) (U.S. PG Publication No. 2023/0065862) in view of Miyauchi (“Miya”) (U.S. PG Publication No. 2021/0334603) and Hendry et al. (“Hendry”) (U.S. PG Publication No. 2020/0252634).
In regards to claim 5, Kara fails to teach the decoder of claim 4, wherein the bitstream includes a plurality of residual layers and the number of residual visual layers is signaled within the at least a header.
In a similar endeavor Hendry teaches wherein the bitstream includes a plurality of residual layers and the number of residual visual layers is signaled within the at least a header (See ¶0113 and 0146-0147 wherein, for example, sps_max_sub_layers_minus1 is a parameter within the sps header that may signal a number of layers).
It would have been obvious to a person of ordinary skill in the art, and before the effective filing date of the claimed invention, to incorporate the teaching of Hendry into Kara because it allows for the for the parsing of header information, as well as parameters further specifying how the encoding stream should be structured such as is described in ¶0146.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Karabutov et al. (“Kara”) (U.S. PG Publication No. 2023/0065862) in view of Miyauchi (“Miya”) (U.S. PG Publication No. 2021/0334603) and Hendry et al. (“Hendry”) (U.S. PG Publication No. 2020/0252634), in further view of Meardi et al. (“Meardi”) (WO 2020/188273).
In regards to claim 6, Kara fails to teach the decoder of claim 5, wherein the circuitry is further configured to combine the at least a decoded base feature layer with the first residual visual layer; and combine the at least a combined decoded base feature and first residual visual layer with the second residual visual layer.
In a similar endeavor Meardi teaches wherein the circuitry is further configured to combine the at least a decoded base feature layer with the first residual visual layer (See FIG. 2 with regards to 220, FIG. 5A-5C and 26 as examples); and
combine the at least a combined decoded base feature and first residual visual layer with the second residual visual layer (See FIG. 2 with regards to 220, FIG. 5A-5C and 26 as examples).
It would have been obvious to a person of ordinary skill in the art, and before the effective filing date of the claimed invention, to incorporate the teaching of Meardi into Kara because it allows for the necessary inverse-processing step of the overall decoding process, which includes parameter data from the header as described by Meardi, thus allowing for proper decoding of data.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Karabutov et al. (“Kara”) (U.S. PG Publication No. 2023/0065862) in view of Miyauchi (“Miya”) (U.S. PG Publication No. 2021/0334603) and Meardi et al. (“Meardi”) (WO 2020/188273).
In regards to claim 9, Kara fails to explicitly teach the decoder of claim 1, wherein the circuitry is further configured to inversely pre-process the at least a decoded base feature layer (See col. 270, li. 21 – col. 271, li. 4).
In a similar endeavor Meardi teaches wherein the circuitry is further configured to inversely pre-process the at least a decoded base feature layer (See col. 270, li. 21 – col. 271, li. 4).
It would have been obvious to a person of ordinary skill in the art, and before the effective filing date of the claimed invention, to incorporate the teaching of Meardi into Kara because it allows for the necessary inverse-processing step of the overall decoding process, which includes parameter data from the header as described by Meardi, thus allowing for proper decoding of data.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EDEMIO NAVAS JR whose telephone number is (571)270-1067. The examiner can normally be reached M-F, ~ 9 AM -6 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Joseph Ustaris can be reached at 5712727383. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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EDEMIO NAVAS JR
Primary Examiner
Art Unit 2483
/EDEMIO NAVAS JR/Primary Examiner, Art Unit 2483
Prosecution Insights