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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 04/22/2026 has been entered.
Response to Amendment
This communication is filed in response to the action filed on 04/22/2026.
Claims 1, 4, 11, and 14 are currently amended. Claims 2-3, 5-6, 9, 12-13, 15, and 17-23 are canceled. Claims 1, 4, 10-11, and 14 are pending.
Response to Arguments
Applicant’s arguments filed on 04/22/2026 on pages 5-8, under REMARKS with respect to 35 U.S.C. 103 have been fully considered but they are not persuasive. Regarding claim 1 applicants on page 7 states that:
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The examiner respectfully disagrees. The examiner would like to point to specific sections of BESENBURCH, including fig 110; paragraphs [0484], [0600-0602] specifically paragraph [0484] states “an AI-based compression pipeline with functional fine-tuning, using a hyperprior HP to represent the additional parameters cp. An integer-valued hyper-parameter z is found on a per-image basis, which is encoded into the bitstream. The parameter z is used to parameterize the additional parameter cp”. Further stating at paragraph [0602] “Parameters are a set of arbitrarily grouped vector and/or matrix quantities that encompasses for example all the weight matrices and biases vectors of a network,
or the parametrization of a probability model which could consist of a mean vector and a covariance matrix” clearly showing the basis vector for parameter z would be the average vector value of parameters z throughout the image data set and the common basis vector could be selected to be any variable basis vector based on the system rule to use the average vector as the basis value. Please see full rejection to claims below.
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.
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 non-obviousness.
Claims 1, 4, 10, 11, and 14 are rejected under 35 § U.S.C. 103 as being obvious over US 2022/0092827 A1 to DO et al. (hereinafter “DO”) in view of US 2023/0154055 A1 to BESENBRUCH et al. (hereinafter “BESENBRUCH”).
As per claim 1, DO discloses a method of encoding a feature map (a computing system and corresponding method for selective image feature encoding/decoding via/through a residual feature map bitstream; abstract; fig 1; paragraphs [0057], [0063-0067]), comprising: extracting a plurality of feature maps from input image data (the computing system performs the steps of extracting a feature map from an input image by using a first feature extraction unit 130 and second feature extraction unit 140, wherein as stated in paragraph [0053] all singular phrases referencing an element are also to be interpreted to mean plural of those referenced elements, for example “feature map” stated throughout the reference would be interpreted as “feature maps” the plural meaning of the phrase and “input image” would be interpreted as a plurality of “input images” to produce a similar and related plurality of feature maps of the extracted features of the images; fig 1; paragraphs [0057], [0063-0067]); generating a concatenated feature map based on the plurality of features maps (based on the below claimed definition of concatenated feature maps the preprocessing steps of resolution sub sampling the input encoded images used to generate encoded feature maps into the bit stream are all standardized to the same sub sample resolution and the system includes a resolution up sampler steps in fig 15 and down sampler steps in fig 4 and each would be applied as needed to match resolutions of input images used to generate a similar feature map; figs 3-4, 15; paragraphs [0073], [0083-0095], [0122-0129], [0400-0415]); wherein generating the concatenated feature map comprises: adjusting resolutions of the plurality of feature maps to the same resolution (the user has the ability to adjust the resolution of the encoded feature maps using the resolution sub sampling module provided by the system here in fig 15 describes the steps to up sample resolution and fig 4 describes steps to down sample or reduce resolution as desired by the user; figs 3-4, 15; paragraphs [0073], [0107-0113], [0122-0129], [0400-0415]); and concatenating the plurality of feature maps having the same resolution, and wherein information on the common basis vector is further encoded into a bitstream (taking the concatenated similar resolution feature maps and at step 270, the feature-information-encoding unit 170 performs encoding on the preprocessed residual feature information generating a residual feature map which is considered the residual feature map bitstream and includes a plurality of feature vector value and would include the common basis vector of BESENBRUCH to be encoded into said bitstream; figs 3-4; paragraphs [0093-0097], [0107-0113]). DO fails to disclose obtaining a common basis vector from the concatenated feature maps; performing a transform on the concatenated feature map based on the common basis vector; and encoding transform coefficients resulting from the transform on the concatenated feature map.
BESENBRUCH discloses obtaining a common basis vector from the concatenated feature maps (the computing system comprise inputs such as feature maps to derive latent space basis vectors which are made common by sorting each vector by clustering vectors into predetermined bins according to their value, and mapped to a fixed centroid of that bin and therefore each vector bin would comprise common basis vectors in the latent space; fig 110; paragraphs [0484], [0600-0602], [0617-0618]); performing a transform on the concatenated feature map based on the common basis vector (the computing system is adapted to perform multiple transforms on the image/image feature data including a Fourier Transform a discrete cosine transform, an inverse discrete cosine transform as image segmentation transforms; fig 4, 14, and 110; paragraphs [0027-0028], [0224], [0484], [0581], [0600-0602], [0611-0621]); and encoding transform coefficients resulting from the transform on the concatenated feature map (and the encoder step of the auto encoder described in paragraph [0612] encodes the transformed data from the feature maps having a standardized resolution from DO and encodes them into the bitstream; figs 4, 14, and 110; paragraphs [0027-0028], [0224], [0484], [0581], [0600-0602] [0611-0621]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify DO to have obtaining a common basis vector from the concatenated feature maps and performing a transform based on the vector of BESENBRUCH reference. The Suggestion/motivation for doing so would have been to provide the ability to during quantization step, latent vectors are clustered into predetermined bins according to their value, and mapped to a fixed centroid of that bin. One way of doing this is by rounding the latent vectors to the nearest integer value. The overall effect is that the set of possible values for the latent vectors is reduced significantly which allows for shorter descriptors and helps reduce and prevent bottlenecking of the computing system as suggested by paragraphs [0617-0618] of BESENBRUCH. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine BESENBRUCH with DO to obtain the invention as specified in claim 1.
As per claim 4, DO in view of BESENBRUCH discloses the encoding method of claim 1. DO fails to disclose wherein the information on the common basis vector represents differential basis vector which represents, a difference between the common basis vector and a fixed common basis vector pre-defined in a device for encoding the feature map.
BESENBRUCH discloses wherein the information on the common basis vector represents differential basis vector which represents, a difference between the common basis vector and a fixed common basis vector pre-defined in a device for encoding the feature map (during network training rate estimation function R is used to measure the difference between the input vector x (fixed common basis vector of a plurality of input feature maps) of a latent space and quininized vector x^ (common basis vector) having its vector values sorted into bins to ensure the values for each vector within an associated bin is common, and when the difference is found between the two values the resultant vector is a common differential basis vector; fig 106; paragraphs [0600], [0617-0618], [1520-1524]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify DO to have information on the common basis vector represents differential basis vector which represents, a difference between the common basis vector and a fixed common basis vector pre-defined in a device of BESENBRUCH reference. The Suggestion/motivation for doing so would have been to provide a rate estimation function allowing the user to optimize as desired the value of the function and may maximize or minimize various feature values as desired by the user for the specific use case as suggested by BESENBRUCH paragraph [1524]. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine BESENBRUCH with DO to obtain the invention as specified in claim 4.
As per claim 10, DO in view of BESENBRUCH discloses the encoding method of claim 1. Modified DO further discloses a non-transitory computer-readable storage medium for storing the bitstream generated by the method (the system comprises a computer readable storage medium for storing programs and instructions and would store the encoded bitstream; paragraphs [0302-0310], [0315], [0425]).
As per claim 11, DO discloses a method of decoding a feature map (a computing system and corresponding method for selective image feature encoding/decoding a residual feature map bitstream; abstract; fig 12-13; paragraphs [0074], [0152]), comprising: and adjusting each of the plurality of feature maps to a respective original resolution (image-postprocessing unit 1260 may include a resolution-up-sampling unit 1510 and an inverse color format transformation unit 1520, a reconstructed image and image-postprocessing information is input to the resolution-up-sampling unit 1510, and the resolution-up-sampling unit 1510 generates an up-sampled reconstructed image; fig 15; paragraphs [0400-0404]), and wherein the common basis vector is obtained based on information decoded from the bitstream (taking the concatenated similar resolution feature maps and at step 270, the feature-information-decoding unit 1220 performs decoding on the encoded sampling criterion information, thereby generating sampling criterion information including feature vectors which is a part of the residual feature map bitstream and includes a plurality of feature vector values and would include the common basis vector of BESENBRUCH to be decoded from the decoded information in said bitstream; figs 3-4; paragraphs [0093-0097], [0107-0113]). DO fails to disclose decoding a concatenated feature map from a bitstream; performing an inverse-transform on the concatenated feature map based on a common basis vector; and reconstructing a plurality of feature maps from the concatenated feature map, wherein reconstructing the plurality of feature maps from the concatenated feature map comprises: separating the concatenated feature map into the plurality of feature maps.
BESENBRUCH discloses decoding a concatenated feature map from a bitstream (the computing system is adapted to arithmetically encode the quantized latent representation y into a bitstream, and assuming that the identical likelihoods, parameters, resolution are evaluated/reconstructed on the decoding side, the bitstream can be arithmetically decoded into y exactly i.e. lossless, for example, see FIG. 122; figs 67 and 122; paragraphs [1207]); performing an inverse-transform on the concatenated feature map based on a common basis vector (the computing system is adapted to perform a the inverse transformation T-1 is applied on the quantized transformed common basis vector; paragraphs [1017-1025]); and reconstructing a plurality of feature maps from the concatenated feature map (the variable x/y is able to be reconstructed at decode time by solving a system of equations for x/y in terms of p of the reconstructed feature maps; paragraphs [0799-0803]), wherein reconstructing the plurality of feature maps from the concatenated feature map comprises: separating the concatenated feature map into the plurality of feature maps (the computing system is adapted to transform each image back to its original resolution via a multiresolution sorting neural network that further performed concatenation on the maps to have a standard resolution and during decoding process reverts the images back to the original input resolution; fig 31; paragraphs [0854-0857], [0861-0863], [1069]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify DO to have obtaining a common basis vector from the concatenated feature maps and performing a transform based on the vector of BESENBRUCH reference. The Suggestion/motivation for doing so would have been to provide the ability to sort the vectors according to value in order to have common basis vectors of the common value selected for each respective bin as suggested by BESEBURCH paragraph [0618]. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine BESENBRUCH with DO to obtain the invention as specified in claim 11.
As per claim 14, DO discloses the decoding method of claim 11. DO fails to disclose wherein the information represents differential basis vector which represents a difference between the common basis vector and a fixed common basis vector pre-defined in a device for decoding the feature map.
BESENBRUCH discloses wherein the information represents differential basis vector which represents a difference between the common basis vector and a fixed common basis vector pre-defined in a device for decoding the feature map (during network training rate estimation function R is used to measure the difference between the input vector x (fixed common basis vector of a plurality of input feature maps) of a latent space and quininized vector x^ (common basis vector) having its vector values sorted into bins to ensure the values for each vector within an associated bin is common, and when the difference is found between the two values the resultant vector is a common differential basis vector; fig 106; paragraphs [0600], [0617-0618], [1520-1524]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify DO to have wherein the information represents differential basis vector which represents a difference between the common basis vector and a fixed common basis vector pre-defined in a device of BESENBRUCH reference. The Suggestion/motivation for doing so would have been to provide a rate estimation function allowing the user to optimize as desired the value of the function and may maximize or minimize various feature values as desired by the user for the specific use case as suggested by BESENBRUCH paragraph [1524]. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine BESENBRUCH with DO to obtain the invention as specified in claim 14.
Conclusion
Examiner's Note: Examiner has cited figures, and paragraphs in the references as applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested for the applicant, in preparing the responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. Examiner has also cited references in PTO892 but not relied on, which are relevant and pertinent to the applicant’s disclosure, and may also be reading (anticipatory/obvious) on the claims and claimed limitations. Applicant is advised to consider the references in preparing the response/amendments in-order to expedite the prosecution.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DEVIN JACOB DHOOGE whose telephone number is (571) 270-0999. The examiner can normally be reached 7:30-5:00.
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/Devin Dhooge/
USPTO Patent Examiner
Art Unit 2677
/ANDREW W BEE/Supervisory Patent Examiner, Art Unit 2677
Prosecution Insights