DETAILED ACTION
I. 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 .
II. Response to Amendment
A. The response, filed March 11, 2026, has been entered and made of record. Claims 1-24 and 26-30 are pending in the application.
B. Applicant’s amendment to claim 11 has overcome the examiner’s objection.
III. Response to Arguments
A. Applicant’s arguments regarding claims 1-8 and the combination of Zhang et al. and Ozcan et al. have been considered but they are not persuasive. Applicant primarily challenges the examiner’s combination on the grounds that Zhang’s character erasing network cannot necessarily be implemented as an optical network. Along this line, Applicant claims that the examiner used impermissible hindsight in combining Zhang et al. and Ozcan et al. by assuming that any application of a digitally-implemented neural network can be translated to all-optical form. Lastly, Applicant stresses that Ozcan et al. contains no teaching or suggestion of using an optical network to remove or erase objects in an input image. The examiner respectfully disagrees with each of these arguments and, therefore, maintains the rejection in the Office action.
Applicant begins by asserting that knowledge of digital and optical networks prior to the instant effective filing date does not mean that any digital neural network can successfully be implemented in all-optical format, noting that large language models have not found success in an all-optical format. The examiner stipulates that large language models cannot successfully be implemented in all-optical format. However, Zhang’s network is not a large language model; it is much less complex and is designed to perform a single, specific task–character erasing. Exemplary features of Zhang’s deep learning network include multiple convolutional layers, sigmoid activation, and vector concatenation, each of which can be performed by optical neural networks. Ozcan et al. specifically discloses an optical network with multiple convolutional layers ([0089], lines 1-9) and with sigmoid activation ([00183] and [00184]) and is trained in similar fashion to Zhang et al. Moreover, Shen et al. (US # 11,734,555 B2) discloses an optical computation system that can perform concatenation of optical vectors (col. 25, lines 39-65).
Applicant’s further argues patentability because no other groups have been able to discover a way to erase undesired objects from an image using an optical neural network. This statement assumes that others have tried and failed and is only applicable to an anticipatory rejection, while claim 1 was rejected under 35 U.S.C. 103. Applicant’s argument that Ozcan et al. contains no disclosure of an optical neural network for object erasure is similarly unpersuasive because it fails to consider the combination of Zhang et al. and Ozcan et al. One cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. In re Merck & Co., 800 F.2d 1091 (Fed. Cir. 1986).
The examiner further finds unpersuasive Applicant’s argument that impermissible hindsight was used in constructing the combination of Zhang et al. and Ozcan et al. Optical neural networks present the possibility of implementing any digital neural network in that form, and the examiner has showed above that Zhang’s can be.
B. Applicant’s statements regarding amended claim 10 have been considered. The examiner agrees that the amendment distinguishes the combination Zhang et al. and Ozcan et al. Claim 10 is allowed as explained in section VI below.
IV. Claim Objections
Reconsideration of the language of claim 10 in light of claim 17 suggested. This suggestion is similar to the suggestion in the previous Office action regarding claims 19 and 25. Claim 10 now requires that “the one or more optically transmissive and/or reflective substrate layers and the plurality of physical features thereon collectively generate a pixel-wise permuted output optical field or image,” which is a feature exclusive to the embodiment of Figs. 13A and 13B. As explained in the specification, this pixel-wise permuted output is a linear transformation of the input image/ optical field. However, claim 17 requires that “the one or more optically transmissive and/or reflective substrate layers comprise at least one nonlinear optical material.” As explained in the previous Office action, these limitations are likely mutually exclusive. Any nonlinear activation within the diffractive network would mean that the transformation between the input image/optical field and output image/optical field would be nonlinear. The examiner suggests canceling claim 17 as Applicant has done to claim 25.
V. Claim Rejections - 35 USC § 103
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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 1-8 are rejected under 35 U.S.C. 103 as being unpatentable over the Chinese publication of Zhang et al. (Chinese publication number: CN 114170099 A) in view of the WIPO publication of Ozcan et al. (WIPO publication number: WO 2019/200289 A1).
Please refer to the attached translation of Zhang et al. for the cited page and line
numbers as they differ from the citations of the original Chinese document.
As to claim 1, Zhang et al. teaches a camera (Fig. 4, processing device of Fig. 4; p. 9, lines 8-10 and 13, “…image capture device…”) that captures images (p. 9, line 13) containing one or more target classes of objects (e.g., Fig. 3, image in row 1, column 1; {All objects other than text are the target class(es) of objects.}), the camera comprising:
a neural network (Fig. 2; p. 3, lines 32-37) that receives one or more input images (Fig. 2; p. 4, lines 1-5) and generates an output image that includes the one or more target classes of objects from the input images or input optical fields (Fig. 3) and substantially erases and/or distorts one or more non-target classes of objects from the input images or input optical fields (e.g., Fig. 3, image in row 1, column 6; p. 3, lines 29 and 30; p. 8, lines 10-18).
Zhang et al. discloses an electronic neural network that erases text from an input image while preserving other scene objects. Claim 1, however, requires (1) that the camera be a diffractive camera that performs all-optical computations, (2) that the camera comprises a diffractive network comprising one or more optically transmissive and/or reflective substrate layers arranged in an optical path, (3) that each of the one or more optically transmissive and/or reflective substrate layers comprises a plurality of physical features formed on or within the one or more optically transmissive or reflective substrate layers and having different transmission and/or reflection properties as a function of the lateral coordinates across each substrate layer, (4) that the one or more optically transmissive and/or reflective substrate layers and the plurality of physical features thereon collectively generate the output image, and (5) that the diffractive camera further includes one or more optical image sensors or a plurality of photodetectors configured to capture the output image resulting from the one or more optically transmissive and/or reflective substrate layers.
In the same field of endeavor as the instant application, Ozcan et al. discloses a diffractive deep-learning neural network (Fig. 1, diffractive deep neural network “10”) that can be implemented in a processor of a camera (1), (2) ([00100], lines 8-11). The network includes a plurality of transmissive or reflective layers in an optical path of the camera (Fig. 1), each having a number of surface features across the layers that perform optical transformations on an input signal (3) ([0098], lines 1-9). The optical network is designed to accept an optical input, perform a total transformation on the input as dictated by the transmissive or reflective coefficients of the surface features, and output a transformed optical field (4) (Fig. 11C). The output optical field is then captured by an image sensor at an output plane of the network (5) ([00100], lines 1-5). Like electronic neural networks, the diffractive network is trained to perform a specific task, when the transmissive or reflective coefficients of the surface features are optimized to output an optical image conforming to that task ([0098], lines 9-16). Also, while Ozcan et al. discloses specific application of the optical network to object classification ([00122]), the reference notes that the disclosed optical deep-learning framework has broad applicability ([0002], lines 3-6).
In light of the teaching of Ozcan et al., the examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to design Zhang’s electronic neural network as a diffractive neural network, where Zhang’s network accepts an optical input and produces an optical output with erased text at an image sensor through optimized transmissive or reflective transformations on the optical input. One of ordinary skill in the art would have been motivated to modify Zhang et al. in this way because optical neural networks can perform the various complex functions that electronic neural networks perform but at the speed of light (see Ozcan et al., [0002]).
As to claim 2, Zhang et al., as modified by Ozcan et al., teaches the diffractive camera of claim 1, wherein the one or more optically transmissive and/or reflective substrate layers are computationally designed during a training phase to define the plurality of physical features formed on or within the one or more optically transmissive or reflective substrate layers such that the diffractive network outputs the output image that includes the one or more target classes of objects and substantially erases and/or distorts the one or more non-target classes of objects (see Ozcan et al., [0098], lines 9-16; see Zhang et al., p. 6, lines 12-14).
As to claim 3, Zhang et al., as modified by Ozcan et al., teaches the diffractive camera of claim 1, wherein the plurality of physical features of the one or more optically transmissive and/or reflective substrate layers comprise regions of varied thicknesses (see Ozcan et al., [00102], lines 2 and 3) and/or varied optical properties (see Ozcan et al., [00103], lines 2-4).
As to claim 4, Zhang et al., as modified by Ozcan et al., teaches the diffractive camera of claim 1, wherein the plurality of physical features of the one or more optically transmissive and/or reflective substrate layers comprise regions having different refractive index (see Ozcan et al., [00103], lines 4-6) and/or absorption and/or spectral features.
As to claim 5, Zhang et al., as modified by Ozcan et al., teaches the diffractive camera of claim 1, wherein the plurality of physical features of the one or more optically transmissive and/or reflective substrate layers comprise metamaterials and/or metasurfaces (see Ozcan et al., [00104], lines 1 and 2).
As to claim 6, Zhang et al., as modified by Ozcan et al., teaches the diffractive camera of claim 1, wherein the one or more optically transmissive and/or reflective substrate layers comprise at least one nonlinear optical material (see Ozcan et al., [00103], lines 7-10).
As to claim 7, Ozcan et al. teaches the diffractive camera of claim 1, wherein the one or more optically transmissive and/or reflective substrate layers comprises one or more physical substrate layers that comprise reconfigurable physical features that can change as a function of time (see Ozcan et al., [00138], lines 1-4).
As to claim 8, Ozcan et al. teaches the diffractive camera of claim 1, wherein the images are captured within a region or part of the electromagnetic spectrum by the one or more optical image sensors or the plurality of photodetectors (see Ozcan et al., [0097]).
VI. Allowable Subject Matter
A. Claims 10-24 and 26-30 are allowed, and the following is the examiner’s statement of reasons for allowance: As to claim 10, Byrne et al. discloses an image processing system that performs image analysis, via an electronic neural network, on optically encrypted image data. That is, the input light field is optically pixel-wise permuted, converted to electronic form, processed by the neural network, then inverse permuted to its original form. As explained in the previous Office action, the transformations between the input image and the output image are only either linear operations or rectified linear unit nonlinear operations. As also explained, one of ordinary skill in the art would not find it obvious to, first, encrypt an input image then subject it to an optical character-erasing network, produced by the combination of Zhang et al. and Ozcan et al., because this initial encryption would alleviate the need to erase characters from the image. Furthermore, it is unclear whether Zhang’s network can successfully erase a character when the input image data is encrypted. Claims 11-18 are allowed because they depend on claim 10. The reasons for allowance of claims 19-24 and 26-30 can be found in the previous Office action.
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.”
B. Claim 9 is 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. The reasons for indicating allowable subject matter of claim 9 can be found in the previous Office action.
VII. Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANTHONY J DANIELS whose telephone number is (571)272-7362. The examiner can normally be reached M-F 9:00 AM - 5:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sinh Tran can be reached at 571-272-7564. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANTHONY J DANIELS/Primary Examiner, Art Unit 2637
4/21/2026