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 .
Amendment filed on 12/30/2025 has been entered. Claims 1-4, 7-14, 16-19 and 22-24 are pending. Claims 5-6, 15, 20 and 21 have been canceled without prejudice. Claims 1 and 14 are currently amended. Claims 23-24 are newly added.
Claim Objections
Claim 8, third limitation includes the step of “perform step B and step C of claim 1 again”, which is improper to claim because there is no step B and step C in claim 8, and claim 8 is an independent claim, which cannot be referred back to claim.
Claim 14 is objected to under 37 CFR 1.75 as being a substantial duplicate of claim 9. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
Claim 19 is objected to because of the following informalities: claim 19 further depends on claim 5, but claim 5 has been cancelled. Please verify. For the purpose of this examination, claim 19 is considered as dependent on claim 1.
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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 nonobviousness.
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.
Claims 1-4, 7, 9-14,16-19 and 22-23 are rejected under 35 U.S.C. 103 as being unpatentable over Wolf et al. (Wolf), US Patent Application Publication No. US. 2020/0003678, in view of Wiecha et al. (Wiecha), NPL "Deep Learning in nano-photonics: inverse design and beyond", published on Jan. 12, 2021, arXiv:2011.12603v2, pages: 18, and further in view of Gao et al. (Gao), NPL “Welding Defect Detection Method, Device, Storage Medium and System”, Document ID: CN 108711151 B, published on 2022-09-13, but filed on 2018-05-22.
As to independent claim 1, Wolf discloses a method for reverse design of micro-nano structure based on a deep neural network, comprising:
step A, acquiring initial data of a micro-nano structure according to the micro-nano structure to be reversely designed (Wolf, [0060]-[0063], [0094]-[0095]: obtaining data, such as optical response and material properties of a nanostructure);
step B, inputting the initial data of the micro-nano structure into a trained optical parameter prediction model to obtain optical prediction parameters, wherein the trained optical parameter prediction model is obtained by training a deep neural network based on sample micro-nano data marked with an optical attribute parameter, and the sample micro-nano data comprises sample micro-nano structure data and sample micro-nano optical characteristic data (Wolf, [0021], [0066]-[0067], [0089], [0097], [0136], [0157], [0165]: inputting the data into a trained model to obtain optical prediction parameters by a deep neural network based on sample micro-nano structure data and optical attribute data);
step C, evaluating the optical prediction parameters based on an evaluation function and an optical target parameter; when an evaluation result does not satisfy a preset condition, optimizing the initial data of the micro-nano structure through an optimization algorithm and the evaluation result to obtain optimized data of the micro-nano structure, inputting the optimized data of the micro-nano structure into the trained optical parameter prediction model; and step D, repeating step B and step C again until the evaluation result of the optical prediction parameters obtained in a current iteration satisfies the preset condition, then performing the reverse design of micro-nano structure according to the optimized data of the micro-nano structure corresponding to the optical prediction parameters in the current iteration (Wolf, [0066 |-[0067], [0089], [0168 ]-[0169], [0179]: evaluation when the prediction is insufficiently accurate, modifying input the data, such as different material or modified spectra for optical response; repeating the predictions to design the micro-nano structure based on the evaluation result until satisfy with evaluation result),
step E, obtaining a plurality of initial data of different micro-nano structures, and inputting the initial data of each micro-nano structure into the trained optical parameter prediction model to obtain a plurality of optical prediction parameters, and obtaining an optical prediction measurement matrix according to the plurality of optical prediction parameters (Wolf, [0066]- [0067], [0089], [0094]-[0095]: inputting optical responses and material properties of different nanostructures to trained model to obtain a predicted spectrum);
step F, evaluating the optical prediction measurement matrix based on an evaluation function and optical target parameters; when an evaluation result of the measurement matrix does not satisfy a preset condition, optimizing the initial data of each micro-nano structure through an optimization algorithm and the evaluation result of the measurement matrix to obtain the optimized data of the micro-nano structure, inputting each optimized data of the micro-nano structure into the trained optical parameter prediction model, and step G, repeating step E and step F again until the evaluation result of the optical prediction measurement matrix obtained in a current iteration satisfies the preset condition, then performing the reverse design of micro-nano structure according to a plurality of optimized data of the micro-nano structure corresponding to the optical prediction measurement matrix in the current iteration, and constructing a sensor according to the plurality of micro-nano structures obtained from the reverse design (Wolf, [0027], [0066]-[0067], [0089], [0091], [0168]-[0169], [0179]: evaluation when the prediction is insufficiently accurate, modifying input the data, such as different material or modified spectra for optical response; repeating the predictions to design the micro-nano structure based on the evaluation result until satisfy with evaluation result, the micro-nano structure serves as a sensor).
However, Wolf does not teach constructing a compressed sensor, which is composed of the different micro-nano structures.
In the same field of endeavor, Wiecha discloses deep learning in the context of nano-photonics discussed in terms of its potential for inverse design of photonic devices or nanostructures (page 1). Wiecha further disclose deep learning has been found to be very powerful in solving inverse problems occurring in imaging experiments, and in this context often sparsity assumptions are required to enable deconstruction of undersampled data, which demands computationally complex inverse solving techniques like compressive sensing (page 13). Wiecha further discloses Artificial Neural Networks (ANNs) can be applied for instance to real-time image enhancement, microscopy stabilizing feed-back system or to conduct sparse data acquisition schemes for acceleration of scanning microscopy systems via compressive sensing (page 13).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the system of Wolf to include constructing a compressed sensor, which is composed of the different micro-nano structures, as taught by Wiecha for providing computationally complex inverse solving technique.
Wolf and Wiecha, however, do not disclose a quality of the compressed sensor is equivalent to an average value of correlation of the column vectors of the optical prediction measurement matrix.
Gao discloses the observation matrix can be deterministic random matrix constructed based on deterministic random sequence, for example, random Gaussian measurement matrix (page 8). Gao further disclose in order to use the observation matrix to reduce the sparse matrix from the high-dimensional to low-dimensional to obtain the original information as many different information, which needs to observe the matrix of column vector correlation is small (page 8). Gao further discloses in random Gaussian measurement matrix is the most common measurement matrix in the compression sensing research, wherein the elements in the matrix obedire the normal distribution when the average value is zero (page 8).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to modify the systems of Wolf and Wiecha to include “a quality of the compressed sensor is equivalent to an average value of correlation of the column vectors of the optical prediction measurement matrix”, as taught by Gao. Gao suggest that using compressed sensing reconstruction algorithm of Lp norm for solving sparse optimization problem.
As to dependent claim 2, Wolf discloses wherein the trained optical parameter prediction model is obtained by training in the following steps:
marking each sample micro-nano data with a corresponding label according to the optical attribute parameter, and constructing a training sample set according to the labeled sample micro-nano data and a corresponding sample optical parameter; and
inputting the training sample set into the deep neural network for training, and obtaining a trained optical parameter prediction model (Wolf, [0158]-[0168]: horizontal spectrum sample, vertical spectrum samples; spectrum predicting subnetwork (SPN) and geometry predicting subnetwork (GPN)).
As to dependent claim 3, Wolf discloses wherein the optimization algorithm comprises a simulated annealing algorithm, a neural network algorithm and a genetic algorithm (Wolf, [0193]: stochastic optimization methods; abstract, artificial neural network; [0043], [0190]: genetic algorithm).
As to dependent claim 4, Wolf discloses wherein an input layer of the deep neural network is connected with a plurality of convolutional layers (Wolf, Abstract; Figures 2, 6A; layers, hidden layers).
Regarding claim 7, which is dependent on claim 2, Wolf teaches wherein the sample micro-nano optical characteristic data comprises at least a dielectric constant and a dispersion of a micro-nano material (Wolf, [(0064]-[0065], [0122], dielectric; [0137], [0186] dispersion of indium)
Claims 9, 11-14 are for an electronic apparatus claims, comprising a memory, a processor, and computer programs stored on the memory and executable by the processor performing the method of claims 1, 2, 3, 4, 1, respectively. Therefore, claims 9 and 11-14 are rejected under the same rationale.
Claims 10, 16-19 are for a non-transitory computer-readable storage medium, with computer programs stored on the non-transitory computer-readable storage medium, the computer programs are performed by a processor to perform the method of claims 1, 2, 3, 4, 1, respectively and are rejected under the same rationale.
As to dependent claim 22, Wolf discloses wherein the optical parameters include at least one selected from the group consisting of resonance wavelength, resonance Q value, pass spectrum, amplitude response, and phase response (Wolf, [0063]: optical response is in the form of a discrete spectrum, which can comprise a set of one or more, optical transmission or optical reflection coefficient dips corresponding to wavelengths or frequencies at which an interaction of an object with an electromagnetic field exhibits a resonance).
As to dependent claim 23, Wolf discloses wherein the sample micro-nano structure data comprises at least single-period micro-nano structure shape data and micro-nano structure period data (Wolf, [0049]: designing the nanostructure or retrieving one or parameters describing the nanostructure such as shape; [0204: each nanostructure is repeating itself periodically in an array configuration, it means that the network have been able to predict the best fitting configuration and obtain an accurate solution but only with a different period length between the nanostructures).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Wolf et al. (Wolf), US Patent Application Publication No. US. 2020/0003678, and further in view of Gao et al. (Gao), NPL "Optical film and shell with the optical film", published on 2023-11-07, but filed on 2017-09-08, Document ID: CN-117008221-A, pages: 10.
As to independent claim 8, Wolf discloses a system for reverse design of micro-nano structure based on a deep neural network, comprising:
a micro-nano structure initial parameter acquired configured to acquire initial data of a micro-nano structure according to the micro-nano structure to be reversely designed (Wolf, [0060]-[0063], [0094]-[0095]: obtaining data, such as optical response and material properties of a nanostructure);
an optical parameter predictor configured to input the initial data of the micro-nano structure into a trained optical parameter prediction model to obtain optical prediction parameters, wherein the trained optical parameter prediction model is obtained by training a deep neural network based on sample micro-nano data marked with an optical attribute parameter, and the sample micro-nano data comprises sample micro-nano structure data and sample micro-nano optical characteristic data (Wolf, [0021], [0066]-[0067], [0089], [0097], [0136], [0157], [0165]: inputting the data into a trained model to obtain optical prediction parameters by a deep neural network based on sample micro-nano structure data and optical attribute data);
an evaluation and optimization module configured to evaluate the optical prediction parameters based on an evaluation function and an optical target parameter; when an evaluation result does not satisfy a preset condition, optimizing the initial data of the micro-nano structure through an optimization algorithm and the evaluation result to obtain optimized data of the micro-nano structure, inputting the optimized data of the micro-nano structure into the trained optical parameter prediction model; and step D, repeating step B and step C again until the evaluation result of the optical prediction parameters obtained in a current iteration satisfies the preset condition, then performing the reverse design of micro-nano structure according to the optimized data of the micro-nano structure corresponding to the optical prediction parameters in the current iteration (Wolf, [0066 |-[0067], [0089], [0168 ]-[0169], [0179]: evaluation when the prediction is insufficiently accurate, modifying input the data, such as different material or modified spectra for optical response; repeating the predictions to design the micro-nano structure based on the evaluation result until satisfy with evaluation result),
wherein the sample micro-nano structure data comprises at least single-period micro-nano structure shape data and micro-nano structure period data (Wolf, [0049]: designing the nanostructure or retrieving one or parameters describing the nanostructure such as shape; [0204: each nanostructure is repeating itself periodically in an array configuration, it means that the network have been able to predict the best fitting configuration and obtain an accurate solution but only with a different period length between the nanostructures).
Wolf discloses in paragraph [0073] that the nanostructure can be described as a graph with vertices and edges. Wolf, however, does not disclose wherein the single-period micro-nano structure shape data includes at least one selected from the group consisting of a number of sides of a polygon, a height of the polygon, edge point data of the micro-nano structure, and a thickness of the micro-nano plate.
In the same field of endeavor, Gao discloses a plurality of colored micro-nano structure 2 arranged on one side of the bearing layer 1or in the bearing layer 1 and arranged in a predetermined pattern, and the “colored micro-nano structure 2” refers to: The micro-nano structure with any shape prepared by coloured polymer can reflect the electromagnetic wave corresponding to the colour (page 5, 2nd and 3rd paragraphs). Gao further disclose the absolute value of the difference between the structure parameters of any two coloured micro-nano structures 2 and the structure parameters comprises the height along the thickness direction of the bearing layer1 (page 5, last paragraph). Gao further discloses the shape of the plurality of colored micro-nano structures 2 is a polygon and the number of sides of the plurality of colored micro-nano structures 2 is gradually increased (page 6, 5th and 6th paragraphs).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to modify the system of Wolf to include the single-period micro-nano structure shape data includes at least one selected from the group consisting of a number of sides of a polygon, a height of the polygon, edge point data of the micro-nano structure, and a thickness of the micro-nano plate, as taught by Gao. Gao suggests that the shape of the projection of the colored micro-nano structure 2 can be adaptively adjusted according to the optical pattern to be presented by the optical film (pages 7-8).
Claim 24 is rejected under 35 U.S.C. 103 as being unpatentable over Wolf, Wiecha, and Gao (CN 108711151 B) as applied to claims 1-4, 7, 9-14,16-19 and 22-23 above, and further in view of Gao et al. (Gao), NPL "Optical film and shell with the optical film", published on 2023-11-07, but filed on 2017-09-08, Document ID: CN-117008221-A, pages: 10.
As to dependent claim 24, Wolf discloses in paragraph [0073] that the nanostructure can be described as a graph with vertices and edges. Wolf, however, does not disclose wherein the single-period micro-nano structure shape data includes at least one selected from the group consisting of a number of sides of a polygon, a height of the polygon, edge point data of the micro-nano structure, and a thickness of the micro-nano plate.
In the same field of endeavor, Gao discloses a plurality of colored micro-nano structure 2 arranged on one side of the bearing layer 1or in the bearing layer 1 and arranged in a predetermined pattern, and the “colored micro-nano structure 2” refers to: The micro-nano structure with any shape prepared by coloured polymer can reflect the electromagnetic wave corresponding to the colour (page 5, 2nd and 3rd paragraphs). Gao further disclose the absolute value of the difference between the structure parameters of any two coloured micro-nano structures 2 and the structure parameters comprises the height along the thickness direction of the bearing layer1 (page 5, last paragraph). Gao further discloses the shape of the plurality of colored micro-nano structures 2 is a polygon and the number of sides of the plurality of colored micro-nano structures 2 is gradually increased (page 6, 5th and 6th paragraphs).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to modify the system of Wolf to include the single-period micro-nano structure shape data includes at least one selected from the group consisting of a number of sides of a polygon, a height of the polygon, edge point data of the micro-nano structure, and a thickness of the micro-nano plate, as taught by Gao. Gao suggests that the shape of the projection of the colored micro-nano structure 2 can be adaptively adjusted according to the optical pattern to be presented by the optical film (pages 7-8).
Response to Arguments
Applicant’s arguments and amendments filed on 08/28/2025 have been fully considered but they are not deemed fully persuasive. Applicant’s arguments with respect to claims 1-4, 7-14, 16-19, and 22-24 have been considered but are moot in view of the new ground(s) of rejection as explained here below, necessitated by Applicant’s substantial amendment (i.e., wherein the compressed sensor is composed of the different micro-nano structures and a quality of the compressed sensor is equivalent to an average value of correlation of the column vectors of the optical prediction measurement matrix) to the claims which significantly affected the scope thereof. Please see the rejection with newly cited prior art Gao (CN 108711151 B) above.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37CFR 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 extension fee 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 CHAU T NGUYEN whose telephone number is (571)272-4092. The examiner can normally be reached on Monday-Friday from 8am to 5pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Cesar Paula, can be reached at telephone number 5712724128. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHAU T NGUYEN/Primary Examiner, Art Unit 2145
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