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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on -05/31/2024- is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 2 objected to because of the following informalities:
In claim 2 an urban mask feature map is represented with (64_i). Specification and figures represent the urban mask feature map with 64i.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: urban ware module in claims 2, 4 and 9,
normalization sub-module in claims 4-9,
first block, second block, third block, fourth block, fifth block in claims 6, 8, 10
channel attention sub-module in claim 9 and 10.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
Although the specification does not recite the structure of modules, submodules, and blocks, a person of ordinary skill in the art would understand above modules, submodules, and blocks as specific components of neural network architecture.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites term “large scale” in preamble line 1. The term is considered to be a relative term and does not provide a fixed or absolute value of the intended geographical region. Therefore, the term is unclear. Applicant is suggested to amend the unclear term.
Claims 2-15 are also rejected under 35 U.S.C 112(b) due to dependency on claim 1.
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.
Claims 1-14 are rejected under 35 U.S.C 101 because the claimed invention is directed to judicial exception (i.e., a law of nature, natural phenomenon, or an abstract idea) without significantly more.
Specifically, claim 1 recites:
A method for near real-time flood detection at large scale in a geographical region covering urban areas and rural areas, said method being computer-implemented, the geographical region being subdivided into a plurality of adjacent cells, characterized in that said method comprises a training stage and an operation stage, the operation stage comprising:
pre-processing (312) Synthetic Aperture Radar—SAR data of the geographical region to determine a plurality of SAR images (2);
defining (314) an urban mask (4) of the geographical region, said urban mask providing, for each cell, a likelihood that said cell is an urban area;
applying (316) on the plurality of SAR images as input data, a deep learning classification algorithm, in order to compute, for each cell of the geographical region, a class (6) indicative that the corresponding cell is either a flooded urban area, a flooded rural area, or a non-flooded area, the deep learning classification algorithm being structured as a fully convolutional neural network (16), the deep learning classification algorithm comprising dynamic parameters and static parameters, the values of the dynamic parameters being computed from the urban mask and the values of the static parameters being set with optimal values determined at the end of the training stage.
The claim limitations in the abstract idea have been highlighted in bold above.
Under the step 1 of the eligibility analysis, it is determined whether the claims are drawn to a statutory category by considering whether the claimed subject matter fall within the four statutory categories of patentable subject matter identified by 35 U.S.C 101: process, machine, manufacture, or composition of matter. The above claim is considered to be in the statutory category of (process).
Under the step 2A, prong one, it is considered whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into groupings of subject matter when recited as such in a claim limitation, that cover mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) and mental process – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion.
For example, a step of “pre-processing (312) Synthetic Aperture Radar—SAR data of the geographical region to determine a plurality of SAR images (2) (is considered to be a mathematical step- signal processing);
defining (314) an urban mask (4) of the geographical region, said urban mask providing, for each cell, a likelihood that said cell is an urban area (is considered to be a mental step- observation, judgment, evaluation);
applying (316) on the plurality of SAR images as input data, a deep learning classification algorithm, in order to compute, for each cell of the geographical region, a class (6) indicative that the corresponding cell is either a flooded urban area, a flooded rural area, or a non-flooded area, the deep learning classification algorithm being structured as a fully convolutional neural network (16) (is considered to be a mathematical step- use of a mathematical algorithm to output a result),
the deep learning classification algorithm comprising dynamic parameters and static parameters, the values of the dynamic parameters being computed from the urban mask and the values of the static parameters being set with optimal values determined at the end of the training stage (is considered to be a mathematical step- using algorithm to calculate or determine values).
These mathematical and mental steps represent that, under its broadest reasonable interpretation, covers performance of the limitation in the mind.
Next, under the step 2A, prong two, it is considered whether the claim that recites a judicial exception is integrated into a practical application.
In this step, it is evaluated whether the claim recites meaningful additional elements that integrate the exception into a practical application of that exception.
In claim 1, the additional elements/steps are: Computer, Synthetic Aperture Radar. The above additional elements/steps (hardware or software – units) are recited in generality and represent extra solution activity to the judicial exception. The additional element in the preamble of “A method for near real-time flood detection…” is not qualified for a meaningful limitation because it only generally links the use of the judicial exception to a particular technological environment or field of use.
In conclusion, the above additional elements, considered individually and in combination with the other claim elements do not reflect an improvement to other technology or technical field, and, therefore, do not integrate the judicial exception into a practical application. Therefore, the claims are directed to a judicial exception and require further analysis under the step 2B.
However, as evidenced by the prior art of record, the above claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception (step 2B analysis).
Please see prior art rejection below that addresses claim limitations with prior arts.
Considering the claim as a whole, one of ordinary skill in the art would not know the practical application of the present invention since the claims do not apply or use the judicial exception in some meaningful way.
The independent claim, therefore, is not patent eligible.
With regards to the dependent claims, the claims 2-14 comprise the analogous subject matter and also comprise additional features/steps which are the part of an expanded abstract idea of the independent claim 1 (additionally comprising mathematical relationship/mental process steps) and, therefore, the dependent claims are not eligible without additional elements that reflect a practical application and qualified for significantly more for substantially similar reason as discussed with regards to claim 1.
Claims 15 is rejected under 35 U.S.C 101 because the claimed invention is directed to non-statutory subject matter.
Claim 15 recites, Computer program product comprising a computer readable program for causing a computer to realize a method for near real-time flood detection in a geographical region covering both urban areas and rural areas according to claim 1.
The claim limitations with the non-statutory subject matter and abstract idea have been highlighted in bold above.
Under the step 1 of the eligibility analysis, it is determined whether the claims are drawn to a statutory or non-statutory category. In the above claim, the highlighted portion (Computer program product comprising a computer readable program) constitutes a non-statutory subject matter under a broadest reasonable interpretation.
The claim does/do not fall within at least one of the four categories of patent eligible subject matter because under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into groupings of subject matter when recited as such in a claim limitation, that cover “products that do not have a physical or tangible form, such as information (often referred to as "data per se") or a computer program per se (often referred to as "software per se") when claimed as a product without any structural recitations”. Refer to MPEP 2106.03.
Since claim 15 fails the patent eligibility under patent eligibility step 2A, prong one, further analysis under step 2A, prong two and step 2B is ineligible.
Therefore, the independent claim 15 is not patent eligible.
Applicant is suggested to amend claim 15 as: A non-transitory computer readable medium storing a program that when executed by a computer said computer to perform said method according to claim 1.
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 is/are rejected under 35 U.S.C. 103 as being unpatentable over Peng et al. Urban Flood Mapping with Bitemporal Multispectral Imagery Via a Self-Supervised Learning Framework. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 14, 2021” herein after “Peng” in view of Reinstein et al US 20190050625 A1 herein after “Reinstein”.
Regarding claim 1, Peng teaches a method for near real-time flood detection at large scale in a geographical region covering urban areas and rural areas (Abstract—Near realtime flood mapping in densely populated urban areas is critical for emergency response. The strong heterogeneity of urban areas poses a big challenge for accurate near real time flood mapping.), said method being computer-implemented, the geographical region being subdivided into a plurality of adjacent cells (page 2002. Left col. line 11 Both pixel and patch-wise mapping have been explored for flood extent mapping, since each has unique advantages in specific scenarios. Patch-wise mapping has been widely used for flood mapping over heterogeneous urban areas [12], [13], [21], for several reasons.);
Examiner views the pixel or patch mapping is implemented for geographical regions being divided into multiple adjacent pixel or patches.
characterized in that said method comprises a training stage (page 2002, right col, line 6. In this study, patch features were learned in a self-supervised manner using an autoencoder model [28].) and an operation stage (page 2002, right col. II related work, line 1 Recent studies have shown promising results of supervised patch-based land cover mapping and its potential in flood mapping. Li et al. [21] developed an active self-learning convolutional neural network (CNN) to classify the synthetic-aperture radar (SAR) image patches into three classes (i.e., NF, FL with buildings, and FL without buildings));
Examiner views the images were used at learning or training stage and the use of convolutional neural network used at operational stage, the operation stage comprising:
pre-processing (312) Synthetic Aperture Radar—SAR data of the geographical region to determine a plurality of SAR images (2) (page 2003, left col. line 24 Schlaffer et al. [32] conducted harmonic analysis of multitemporal SAR imagery to identify FL pixels, which showed strong deviations from the corresponding NF pixels. Giustarini et al. [33] developed a hybrid framework integrating SAR backscatter thresholding, FL region growing, and change detection for flood mapping using bitemporal SAR imagery.);
Examiner views the SAR images are analyzed (i.e., image preprocessed) before the image data is input to the convolutional network.
applying (316) on the plurality of SAR images as input data, a deep learning classification algorithm, in order to compute, for each cell of the geographical region, a class (6) indicative that the corresponding cell is either a flooded urban area, a flooded rural area, or a non-flooded area, the deep learning classification algorithm being structured as a fully convolutional neural network (16) (page 2004, Fig. 3. The network
architecture of the autoencoder developed in this study is shown in Fig. 3(b)with hyperparameters listed in Table II, which includes Encoder and Decoder subnetworks. The Encoder consists of several layers of convolutional nets (Conv), while the Decoder is composed of a stack of transposed convolutional nets (ConvTrans)
1) Results of Patch-CVA: Using the pretrained encoder described in Section IV-B, we encoded both pre- and post flood patches into low-dimensional feature vectors… We thus obtained two different binary classification maps [see Fig. 8(a) and 9(a)] associated with the direction and magnitude change maps, respectively.),
From above paragraph and fig. 3, 8 and 9 examiner views the SAR images from geographical regions are used as input to the deep learning convolutional network to classify and indicate a flood map (for flooded urban area, a flooded rural area, or a non-flooded area) in the cells of input images as shown in Fig. 8 and 9.
the deep learning classification algorithm comprising dynamic parameters and static parameters (page 2006, In Fig. 3 Encoder for Ipost is viewed with dynamic parameter (i.e., changes with time for flooded data) and Encoder for Ipre is viewed with static parameter (i.e., initial value used for training)), the values of the static parameters being set with optimal values determined at the end of the training stage (page 2004 left col, line 4 Fig. 3(a) illustrates the workflow of the proposed framework. Since the pre and post-flood images were captured with a time interval of less than one month, we assume that the land cover changes over the study areas mainly resulted from floods. Accordingly, patch similarity estimation based on patch feature vectors was used for patch change detection and initial flood mapping. The more similar (Iprei,j , Iposti,j ), the less likely that Iposti,j was indeed FL.).
Examiner views the dynamic parameters Iposti,j is determined from land data (i.e., urban, city, rural), this data changes as the flood is detected. The static parameter Iprei,j is a set value or fixed or threshold value for comparison or similarity (i.e., optimal value through binarization) and the value is used for final flood map (i.e., at the end of training stage).
Peng does not teach defining (314) an urban mask (4) of the geographical region, said urban mask providing, for each cell, a likelihood that said cell is an urban area and
the values of the dynamic parameters being computed from urban mask.
Reinstein teaches defining (314) an urban mask (4) of the geographical region, said urban mask providing, for each cell, a likelihood that said cell is an urban area (para [0044 FIG. 4 depicts a visualization 405 of the multi-class mask 403 over the input satellite image 202/204. In this example, the output from mask prediction module 205 comprises a multi-class mask 403 that identifies pixels in the input satellite image that correspond to water (yellow), urban (red), non-urban (green), and clouds (blue)).
Examiner views the mask 403 in fig. 4 as an urban mask of a geographical region provided by an input satellite image. The cells in the mask are divided into multiple likelihood visualizations for urban (red) areas.
the values of the dynamic parameters being computed from urban mask (para [0045] Referring back to FIG. 2, the mask prediction module 205 of change detection system 100 may provide the predicted mask to a mask fusion module 210. The mask fusion module may comprise an algorithm for determining what area(s) of the input satellite images 202, 204 will be used for change detection, based on the user input 206 and configuration file 208).
Examiner views the changing data, or the dynamic parameters are being detected or computed on the areas inputs images (i.e, urban mask) by the mask prediction module (i.e., includes water/flood color changes in images).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have incorporated Reinstein into Peng for the purpose of using a geological masking method in the satellite images so that geological areas with water or flood can be accurately determined in the images.
Regarding claim 11, the combination of Peng and Reinstein teach the method according to claim 1, Reinstein teaches wherein the urban mask (4) is defined by processing geographical data of the geographical region (para [0044] In this example, the output from mask prediction module 205 comprises a multi-class mask 403 that identifies pixels in the input satellite image that correspond to water (yellow), urban (red), non-urban (green), and clouds (blue)).
Examiner views mask 403 as urban mask that is defined by processing geographical data (water, urban, non-urban) of the geographical region in fig. 4.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have incorporated Reinstein into Peng for the purpose of using a geological masking method in the satellite images so that geological areas with water or flood can be accurately determined in the images.
Regarding claim 15, the combination of Peng and Reinstein teach Computer program product comprising a computer readable program for causing a computer to realize a method for near real-time flood detection in a geographical region covering both urban areas and rural areas according to claim 1 (Peng: page 2001, Abstract Abstract—Near realtime flood mapping in densely populated urban areas is critical for emergency response… The proposed method shows strong performance for self-supervised urban flood mapping.).
Examiner views Peng’s method is performed by a computer using a flood detection program or algorithm.
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Peng and Reinstein in view of Gong CN 110197517 A.
Regarding claim 12, the combination of Peng and Reinstein the method according to claim 1, the combination does not teach wherein the urban mask (4) is defined by processing SAR data of the geographical region.
Gong teaches wherein the urban mask (4) is defined by processing SAR data of the geographical region (page 3, line 18. S2, the SAR image by the feature vector of the connected optical image to be mask vector generated by conventional convolution of the first generator…, the mask vector representative of geographic region specified colour domain. mask vector wherein n refers to the number of n different geographic areas).
Examiner views different geographic areas images are captured in SAR. The mask vector (i.e., urban mask) is connected to the SAR data for processing and SAR data defines the mask vector.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have incorporated Gong into Peng for the purpose of masking SAR images of geographical regions so that the images can be represented with flood, water, cities, urban or rural areas in the geographic region.
Allowable Subject Matter
There are no prior art rejections for claims 2-10, 13-14. However, examiner cannot comment on their allowability until the rejections under 35 U.S.C 112 (b) and 101 are adequately addressed.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Reynolds US 20160026865 A1 discusses detecting weather events using images.
Costantini et al US 20120188119 A9 discusses analyzing SAR images for landslides, earthquakes.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHARAD TIMILSINA whose telephone number is (571)272-7104. The examiner can normally be reached Monday-Friday 9:00-5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Catherine Rastovski can be reached at 571-270-0349. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SHARAD TIMILSINA/ Examiner, Art Unit 2857
/Catherine T. Rastovski/ Supervisory Primary Examiner, Art Unit 2857
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