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 .
In the response to this Office Action, the Examiner respectfully requests that support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line numbers in the specification and/or drawing figure(s). This will assist the Examiner in prosecuting this application.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 25 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because claim 25 recites a “computer readable storage medium”. The broadest reasonable interpretation of a claim drawn to a computer-readable storage medium typically covers forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of “computer readable storage medium”, as variations of the term “computer readable storage medium“ are not necessarily considered to limit a medium claim to non-transitory embodiments because many disclosures conflate storage media and signals, particularly when the specification is silent. See MPEP 2111.01.
The USPTO recognizes that applicants may have claims directed to “computer readable storage media”, which the USPTO must reject under 35 U.S.C. § 101 as covering both non-statutory subject matter and statutory subject matter. In an effort to assist the patent community in overcoming a rejection or potential rejection under 35 U.S.C. § 101 in this situation, the USPTO suggests the following approach. A claim drawn to such computer readable storage medium that covers both transitory and non-transitory embodiments may be amended to narrow the claim to cover only statutory embodiments to avoid a rejection under 35 U.S.C. § I01 by rewriting the claim to recite "non-transitory computer-readable storage medium" to the claim. Cf. Animals -Patentability, 1 077 0ff. Gaz. Pat. Office 24 (April 21, 1987) (suggesting that applicants add the limitation "non-human" to a claim covering a multi-cellular organism to avoid a rejection under 35 U.S.C. § 101). Such an amendment would typically not raise the issue of new matter, even when the specification is silent because the broadest reasonable interpretation relies on the ordinary and customary meaning that includes signals per se. The limited situations in which such an amendment could raise issues of new matter occur, for example, when the specification does not support a non-transitory embodiment because a signal per se is the only viable embodiment such that the amended claim is impermissibly broadened beyond the supporting disclosure. See, e.g., Gentry Gallery, Inc. v. Berkline Corp., 134 F.3d 1473 (Fed. Cir. 1998).
Allowable Subject Matter
Claims 1-3, 6-11, 24, and 26-34 are allowed.
The following is an Examiner’s statement of reasons for allowance:
Applicant’s claims presented on 06/09/2026 constitute the basis for the reasons of allowance as the current prior art of record, considered individually or in combination, fails to teach or reasonably suggest the claimed features of claims 1 and 26 structurally and functionally interconnected with other limitations in the manner as cited in the claims and dependent claims. Examiner notes that the current invention as disclosed in the independent claims is allowed in its entirety. Each and every limitation working together in concert realizes the current claimed invention’s novelty. No single limitation alone accomplishes the allowability of the inventive independent claim(s), rather each and every limitation of the claim(s) and their disclosed relationships are integral.
None of the references, either singularly or in combination, teach or fairly suggest an invertible image transformation method, in which a source image is subjected to a plurality of decomposition stages involving subband transformation, so as to produce a plurality of subbands, where one or more of the subband transformation stages incorporates a non-linear processing method, which involves separate proposal and opacity processing branches, each producing outputs within a plurality of channels with the same number of channels in each case, where the channels are combined using a non-linear point-wise operation, to form the processed outputs, wherein: (a) the proposal processing branch employs linear filters to generate its channel outputs, where each channel has a separate set of filter coefficients; (b) the opacity processing branch employs a neural network to generate each of its channel output samples, containing at least one layer with non-linear activation functions; and (c) the non-linear point-wise operation produces an output sample value at each location from proposal and opacity channel output sample values at the same spatial location.
None of the references, either singularly or in combination, teach or fairly suggest an inverse transformation method, in which a plurality of subbands are subjected to a plurality of inverse decomposition stages involving inverse subband transformation, so as to recover a source image, where one or more of the inverse subband transformation stages incorporate a non-linear processing method, which involves separate proposal and opacity processing branches, each producing outputs within a plurality of channels with the same number of channels in each case, where the channels are combined using a non-linear point-wise operation, to form the processed outputs, wherein: (a) the proposal processing branch employs linear filters to generate its channel outputs, where each channel has a separate set of filter coefficients; (b) the opacity processing branch employs a neural network to generate each of its channel output samples, containing at least one layer with non-linear activation functions; and (c) the non-linear point-wise operation produces an output sample value at each location from proposal and opacity channel output sample values at the same spatial location.
Foreign Publication CN114529833A to Feng discloses a remote sensing image segmentation method and device, which relate to the technical field of deep learning and can be used in the financial field or other technical fields. The method includes acquiring a remote sensing image, and carrying out image processing on the remote sensing image; carrying out segmentation processing on the remote sensing image subjected to image processing based on a preset remote sensing image overlapping shadow segmentation model to obtain a shadow segmentation image subjected to overlap removal; the preset remote sensing image overlapping shadow segmentation model is a convolutional neural network which is trained in advance; the convolutional neural network contains scale information and direction information of a convolutional kernel.
Non-Patent Publication “Machine-Learning Based Secondary Transform for Improved Image Compression in JPEG2000“to Li at al. discloses a CNN based method for predicting aliased components of the LL band from HL, LH and HH detail bands at the same level within the DWT decomposition, demonstrating not only improved energy compaction over LeGall 5/3 wavelet transform, but also more visually significant LL bands at different scales within a JPEG 2000 compression framework. The CNN in this work exploits information in detail sub-bands to predict some of the aliasing information in the corresponding approximation or low-pass sub-band; this reduction in aliasing, although not perfect, improves the compressibility of the “cleaned” approximation subband. This process is repeated in subsequent wavelet decomposition levels to further improve coding efficiency. Experimental results show that, at high bit rates, the proposed network outperforms conventional JPEG2000 compression framework by up to 1.2 dB, especially for images with strong geometric flow.
However, none of the above disclose the invertible image transformation method and inverse transformation method as claimed.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ABHISHEK SARMA whose telephone number is (571)272-9887. The examiner can normally be reached on Mon - Fri 8:00-5:00.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Amr Awad can be reached on 571-272-7764. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ABHISHEK SARMA/
Primary Examiner, Art Unit 2621
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