DETAILED ACTION
Notice of AIA Status
The present application is being examined under the AIA the first inventor to file provisions.
Priority
Receipt is acknowledged of certified copies of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 10/28/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawing objections
The drawings are objected to under 37 CFR 1.83(a). The drawings must show every feature of the invention specified in the claims. Therefore, the remote sensing mediums must be shown or the feature(s) canceled from the claim(s). No new matter should be entered.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
Claims 1, 5, 9 are objected to because of the following informalities:
In claim 1, Page 1 Line 20 the term “hyperspectral image patches by” should be changed to “hyperspectral image patches for typographical/grammar issues to avoid clarity issues to prevent a rejection under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph.
In claim 1, Page 2 Line 2 the term “(i) a R-Top(N) technique” should be changed to “,” for typographical/grammar issues.
In claim 5, Page 4 Line 16 the term “(i) a R-Top(N) technique” should be changed to “,” for typographical/grammar issues.
In claim 9, Page 7 Line 4 the term “hyperspectral image patches by” should be changed to “hyperspectral image patches for typographical/grammar issues to avoid clarity issues to prevent a rejection.
In claim 9, Page 7 Line 6 the term “(i) a R-Top(N) technique” should be changed to “,” for typographical/grammar issues.
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.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
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.
Claims 1, 5, and 9, recites limitations that use words like “means” (or “step”) or similar terms with functional language and do invoke 35 U.S.C. 112(f):
Claim 1; recites the limitation, “using one or more remote sensing mediums” Page 1, [Line 4].
Claim 5; recites the limitation, “using one or more remote sensing mediums” Page 4, [Line 2].
Claim 9; recites the limitation, “using one or more remote sensing mediums” Page 6, [Line 11-12].
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.
After a careful analysis, as disclosed above, and a careful review of the specification the following limitations in claims 1, 5, and 9:
“remote sensing mediums” (Paragraph [0037]- the one or more remote sensing mediums may comprise but ae not limited to sensors mounted aboard aircraft flying at different altitudes, satellites, drones or ground-based instruments. (wherein the segmentation unit has sufficient structure of a sensor mounted aboard an aircraft, satellite, drone, or ground-based instrument.).)).
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 § 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 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 of this title, 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, 5, and 9 are rejected under 35 U.S.C 103 as being unpatentable over Malvar Maua et al. (US 20230316745 A1) hereafter referenced as Malvar Maua in view of Ma et al. (US 20230027514 A1) hereafter referenced as Ma, Simental et al. (US 20070031042 A1) hereafter referenced as Simental, and Robinson et al. (US 20090074297 A1) hereafter referenced as Robinson.
Regarding claim 1, Malvar Maua discloses a processor implemented method (Fig. 3, Paragraph [0028]- Malvar Maua discloses storage machine 312 is schematically depicted having instructions 316 stored thereon that are executable by logic machine 310 to perform the various operations and methods described herein.),
comprising: receiving, via one or more hardware processors (Fig. 3, Paragraph [0028]- Malvar Maua discloses logic machine 310 may include one or more processor devices, as an example.),
a plurality of hyperspectral image patches of one or more regions of earth’s surface using one or more remote sensing mediums (Fig. 1, Paragraph [0020]- Malvar Maua discloses aeronautical vehicle 100 may take the form of an orbiting satellite of the Sentinel-2 program having an onboard imaging system referred to as the MultiSpectral Instrument (MSI). The MSI, in this example, captures multispectral imagery that measures the Earth's reflected radiance in 13 spectral bands.);
computing, via the one or more hardware processors, an average of a plurality of reflectance values of each of a plurality of spectral bands in each of the plurality of hyperspectral image patches (Fig. 2a, Paragraph [0064]- Malvar Maua discloses for each inter-band intensity value, the filtered combination is an average of the band-specific intensity values of the grouped subset of the plurality of reference spectral bands.);
wherein each cluster comprises a subset of spectral bands from the plurality of spectral bands (Fig. 2a, Paragraph [0064]- Malvar Maua discloses the method further comprises, for each inter-band intensity value of the background reflectance map, classifying the plurality of reference spectral bands into the grouped subset using a clustering algorithm.);
Malvar Maua fails to explicitly teach obtaining, via the one or more hardware processors, a plurality of clusters of spectral bands based on a computed average of the plurality of reflectance values across all pixels in each of the plurality of hyperspectral image patches using a clustering technique, and performing, via the one or more hardware processors, at least one of a plurality of reflexivity-based approximate computing techniques on the plurality of hyperspectral image patches for reducing a number of the plurality of spectral bands in each of the plurality of hyperspectral image patches by, a R-Top(N) technique.
However, Ma explicitly teaches obtaining, via the one or more hardware processors, a plurality of clusters of spectral bands based on a computed average of the plurality of reflectance values across all pixels in each of the plurality of hyperspectral image patches using a clustering technique (Fig. 1, Paragraph [0135]- Ma discloses the reflectance results for each sample area, whether as curves, vectors of averages for bands, etc., are then clustered together into clusters. In this application, there will be two main clusters, one having overall higher reflectance representing the inclusion of a seed, and another with lower reflectance where the sample avoided a seed.),
and performing, via the one or more hardware processors, at least one of a plurality of reflexivity-based approximate computing techniques on the plurality of hyperspectral image patches for reducing a number of the plurality of spectral bands in each of the plurality of hyperspectral image patches by (Fig. 3, Paragraph [0101]- Ma discloses the system then compares the scores 312-314 (e.g., segmentation accuracy scores) for the respective bands to select those that indicate the highest accuracy. For example, a predetermined number of bands can be selected (e.g., the n bands having the highest scores) or a threshold can be applied (e.g., select bands having an accuracy above 80%). The selected bands are then used in a second iteration of the selection process, to evaluate potential combinations of the bands.),
a R-Top(N) technique (Fig. 3, Paragraph [0101]- Ma discloses the system then compares the scores 312-314 (e.g., segmentation accuracy scores) for the respective bands to select those that indicate the highest accuracy. For example, a predetermined number of bands can be selected (e.g., the n bands having the highest scores) or a threshold can be applied (e.g., select bands having an accuracy above 80%). The selected bands are then used in a second iteration of the selection process, to evaluate potential combinations of the bands (wherein the selection of N bands having the highest score is seen as R-top(N)).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Malvar Maua of having a processor implemented method, comprising: receiving, via one or more hardware processors, a plurality of hyperspectral image patches of one or more regions of earth’s surface using one or more remote sensing mediums with the teachings of Ma obtaining, via the one or more hardware processors, a plurality of clusters of spectral bands based on a computed average of the plurality of reflectance values across all pixels in each of the plurality of hyperspectral image patches using a clustering technique, and performing, via the one or more hardware processors, at least one of a plurality of reflexivity-based approximate computing techniques on the plurality of hyperspectral image patches for reducing a number of the plurality of spectral bands in each of the plurality of hyperspectral image patches by, a R-Top(N) technique.
Wherein having Malvar Maua’s system for hyperspectral imaging and processing wherein obtaining, via the one or more hardware processors, a plurality of clusters of spectral bands based on a computed average of the plurality of reflectance values across all pixels in each of the plurality of hyperspectral image patches using a clustering technique, and performing, via the one or more hardware processors, at least one of a plurality of reflexivity-based approximate computing techniques on the plurality of hyperspectral image patches for reducing a number of the plurality of spectral bands in each of the plurality of hyperspectral image patches by, a R-Top(N) technique.
The motivation behind the modification would have been to allow for greater accuracy of segmentation, since both Malvar Maua and Ma are both systems that use hyperspectral images. Wherein Malvar Maua’s system wherein improved the accuracy and availability of observations made with hyperspectral images, while Ma’s system provides a way to improve the accuracy of segmentation. Please see Malvar Maua et al. (US 20230316745 A1), Paragraph [0017] and Ma et al. (US 20230027514 A1) Paragraph [0013].
Malvar Maua in view of Ma fails to explicitly teach wherein the plurality of reflexivity-based approximate computing techniques comprise (i) a R-Hop(K) technique.
However, Simental explicitly teaches wherein the plurality of reflexivity-based approximate computing techniques comprise (i) a R-Hop(K) technique (Fig. 15, Paragraph [0126]- Simental discloses lag is the interval between the bands, i.e., number of skipped bands, used in this embodiment of the present invention.),
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Malvar Maua in view of Ma of having a processor implemented method, comprising: receiving, via one or more hardware processors, a plurality of hyperspectral image patches of one or more regions of earth’s surface using one or more remote sensing mediums with the teachings of Simental wherein the plurality of reflexivity-based approximate computing techniques comprise (i) a R-Hop(K) technique
Wherein having Malvar Maua’s system for hyperspectral imaging and processing wherein the plurality of reflexivity-based approximate computing techniques comprise (i) a R-Hop(K) technique
The motivation behind the modification would have been to allow for greater data anomaly filtering and detection, since both Malvar Maua and Simental are both systems that use hyperspectral images. Wherein Malvar Maua’s system wherein improved the accuracy and availability of observations made with hyperspectral images, while Simental’s system provides a way to improve data anomaly filtering and detection. Please see Malvar Maua et al. (US 20230316745 A1), Paragraph [0017] and Simental et al. (US 20070031042 A1) Paragraph [0079].
Malvar Maua in view of Ma and Simental fails to explicitly teach and a R-Proximity(N) technique.
However, Robinson explicitly teaches and a R-Proximity(N) technique (Fig. 5, Paragraph [0063]- Robinson discloses various optimization methods may be employed and in general the type of optimization strategy adopted is determined by an operator. These methods may include, but are not limited to, optimization to selection of the maximum spectral vectors (e.g., the spectral vectors having the greatest magnitudes), optimization to select the greatest range between the spectral vectors, optimization to select the spectral vectors closest to a spectral vector mean, optimization to select the spectral vectors closest to a spectral median.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Malvar Maua in view of Ma and Simental of having a processor implemented method, comprising: receiving, via one or more hardware processors, a plurality of hyperspectral image patches of one or more regions of earth’s surface using one or more remote sensing mediums with the teachings of Robinson and a R-Proximity(N) technique.
Wherein having Malvar Maua’s system for hyperspectral imaging and processing wherein and a R-Proximity(N) technique.
The motivation behind the modification would have been to allow for speed of dimensionality reduction, since both Malvar Maua and Robinson are both systems that use hyperspectral images. Wherein Malvar Maua’s system wherein improved the accuracy and availability of observations made with hyperspectral images, while Robinson’s system provides a way to improve the speed of the system. Please see Malvar Maua et al. (US 20230316745 A1), Paragraph [0017] and Robinson et al. (US 20090074297 A1) Paragraph [0044].
Regarding claim 5, Malvar Maua teaches a system (Fig. 3, Paragraph [0027]- Malvar Maua discloses computing system 150 may include one or more computing devices having a co-located and/or distributed configuration. Computing system 150 in this example includes a logic machine 310, a storage machine 312, and an input/output (I/O) subsystem 314 depicted schematically in FIG. 3.),
comprising: a memory storing instructions (Fig. 3, Paragraph [0028]- Malvar Maua discloses storage machine 312 is schematically depicted having instructions 316 stored thereon that are executable by logic machine 310 to perform the various operations and methods described herein.);
wherein the one or more hardware processors are configured by the instructions to (Fig. 3, Paragraph [0028]- Malvar Maua discloses storage machine 312 is schematically depicted having instructions 316 stored thereon that are executable by logic machine 310 to perform the various operations and methods described herein.):
receive a plurality of hyperspectral image patches of one or more regions of earth’s surface using one or more remote sensing mediums (Fig. 1, Paragraph [0020]- Malvar Maua discloses aeronautical vehicle 100 may take the form of an orbiting satellite of the Sentinel-2 program having an onboard imaging system referred to as the MultiSpectral Instrument (MSI). The MSI, in this example, captures multispectral imagery that measures the Earth's reflected radiance in 13 spectral bands.);
compute an average of a plurality of reflectance values of each of a plurality of spectral bands in each of the plurality of hyperspectral image patches (Fig. 2a, Paragraph [0064]- Malvar Maua discloses for each inter-band intensity value, the filtered combination is an average of the band-specific intensity values of the grouped subset of the plurality of reference spectral bands.);
wherein each cluster comprises a subset of spectral bands from the plurality of spectral bands (Fig. 2a, Paragraph [0064]- Malvar Maua discloses the method further comprises, for each inter-band intensity value of the background reflectance map, classifying the plurality of reference spectral bands into the grouped subset using a clustering algorithm.);
Malvar Maua fails to explicitly teach one or more communication interfaces; and one or more hardware processors coupled to the memory via the one or more communication interfaces, obtain a plurality of clusters of spectral bands based on a computed average of the plurality of reflectance values across all pixels in each of the plurality of hyperspectral image patches using a clustering technique, and perform at least one of a plurality of reflexivity-based approximate computing techniques on the plurality of hyperspectral image patches for reducing a number of the plurality of spectral bands in each of the plurality of hyperspectral image patches, a R-Top(N) technique.
However, Ma explicitly teaches one or more communication interfaces (Fig. 7, Paragraph [0182]- Ma discloses these various implementations can include implementation in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.);
and one or more hardware processors coupled to the memory via the one or more communication interfaces (Fig. 7, Paragraph [0182]- Ma discloses these various implementations can include implementation in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.),
obtain a plurality of clusters of spectral bands based on a computed average of the plurality of reflectance values across all pixels in each of the plurality of hyperspectral image patches using a clustering technique (Fig. 1, Paragraph [0135]- Ma discloses the reflectance results for each sample area, whether as curves, vectors of averages for bands, etc., are then clustered together into clusters. In this application, there will be two main clusters, one having overall higher reflectance representing the inclusion of a seed, and another with lower reflectance where the sample avoided a seed.),
and perform at least one of a plurality of reflexivity-based approximate computing techniques on the plurality of hyperspectral image patches for reducing a number of the plurality of spectral bands in each of the plurality of hyperspectral image patches (Fig. 3, Paragraph [0101]- Ma discloses the system then compares the scores 312-314 (e.g., segmentation accuracy scores) for the respective bands to select those that indicate the highest accuracy. For example, a predetermined number of bands can be selected (e.g., the n bands having the highest scores) or a threshold can be applied (e.g., select bands having an accuracy above 80%). The selected bands are then used in a second iteration of the selection process, to evaluate potential combinations of the bands.),
a R-Top(N) technique (Fig. 3, Paragraph [0101]- Ma discloses the system then compares the scores 312-314 (e.g., segmentation accuracy scores) for the respective bands to select those that indicate the highest accuracy. For example, a predetermined number of bands can be selected (e.g., the n bands having the highest scores) or a threshold can be applied (e.g., select bands having an accuracy above 80%). The selected bands are then used in a second iteration of the selection process, to evaluate potential combinations of the bands (wherein the selection of N bands having the highest score is seen as R-top(N)).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Malvar Maua of having a system, comprising: a memory storing instructions; wherein the one or more hardware processors are configured by the instructions to: receive a plurality of hyperspectral image patches of one or more regions of earth’s surface using one or more remote sensing mediums with the teachings of Ma one or more communication interfaces; and one or more hardware processors coupled to the memory via the one or more communication interfaces, obtain a plurality of clusters of spectral bands based on a computed average of the plurality of reflectance values across all pixels in each of the plurality of hyperspectral image patches using a clustering technique, and perform at least one of a plurality of reflexivity-based approximate computing techniques on the plurality of hyperspectral image patches for reducing a number of the plurality of spectral bands in each of the plurality of hyperspectral image patches, a R-Top(N) technique.
Wherein having Malvar Maua’s system for hyperspectral imaging and processing wherein one or more communication interfaces; and one or more hardware processors coupled to the memory via the one or more communication interfaces, obtain a plurality of clusters of spectral bands based on a computed average of the plurality of reflectance values across all pixels in each of the plurality of hyperspectral image patches using a clustering technique, and perform at least one of a plurality of reflexivity-based approximate computing techniques on the plurality of hyperspectral image patches for reducing a number of the plurality of spectral bands in each of the plurality of hyperspectral image patches, a R-Top(N) technique.
The motivation behind the modification would have been to allow for greater accuracy of segmentation, since both Malvar Maua and Ma are both systems that use hyperspectral images. Wherein Malvar Maua’s system wherein improved the accuracy and availability of observations made with hyperspectral images, while Ma’s system provides a way to improve the accuracy of segmentation. Please see Malvar Maua et al. (US 20230316745 A1), Paragraph [0017] and Ma et al. (US 20230027514 A1) Paragraph [0013].
Malvar Maua in view of Ma fails to explicitly teach wherein the plurality of reflexivity-based approximate computing techniques comprise (i) a R-Hop(K) technique.
However, Simental explicitly teaches wherein the plurality of reflexivity-based approximate computing techniques comprise (i) a R-Hop(K) technique (Fig. 15, Paragraph [0126]- Simental discloses lag is the interval between the bands, i.e., number of skipped bands, used in this embodiment of the present invention.),
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Malvar Maua in view of Ma of having a system, comprising: a memory storing instructions; wherein the one or more hardware processors are configured by the instructions to: receive a plurality of hyperspectral image patches of one or more regions of earth’s surface using one or more remote sensing mediums with the teachings of Simental wherein the plurality of reflexivity-based approximate computing techniques comprise (i) a R-Hop(K) technique.
Wherein having Malvar Maua’s system for hyperspectral imaging and processing wherein the plurality of reflexivity-based approximate computing techniques comprise (i) a R-Hop(K) technique.
The motivation behind the modification would have been to allow for greater data anomaly filtering and detection, since both Malvar Maua and Simental are both systems that use hyperspectral images. Wherein Malvar Maua’s system wherein improved the accuracy and availability of observations made with hyperspectral images, while Simental’s system provides a way to improve data anomaly filtering and detection. Please see Malvar Maua et al. (US 20230316745 A1), Paragraph [0017] and Simental et al. (US 20070031042 A1) Paragraph [0079].
Malvar Maua in view of Ma and Simental fails to explicitly teach and a R-Proximity(N) technique.
However, Robinson explicitly teaches and a R-Proximity(N) technique (Fig. 5, Paragraph [0063]- Robinson discloses various optimization methods may be employed and in general the type of optimization strategy adopted is determined by an operator. These methods may include, but are not limited to, optimization to selection of the maximum spectral vectors (e.g., the spectral vectors having the greatest magnitudes), optimization to select the greatest range between the spectral vectors, optimization to select the spectral vectors closest to a spectral vector mean, optimization to select the spectral vectors closest to a spectral median.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Malvar Maua in view of Ma and Simental of having a system, comprising: a memory storing instructions; wherein the one or more hardware processors are configured by the instructions to: receive a plurality of hyperspectral image patches of one or more regions of earth’s surface using one or more remote sensing mediums with the teachings of Robinson and a R-Proximity(N) technique.
Wherein having Malvar Maua’s system for hyperspectral imaging and processing wherein and a R-Proximity(N) technique.
The motivation behind the modification would have been to allow for speed of dimensionality reduction, since both Malvar Maua and Robinson are both systems that use hyperspectral images. Wherein Malvar Maua’s system wherein improved the accuracy and availability of observations made with hyperspectral images, while Robinson’s system provides a way to improve the speed of the system. Please see Malvar Maua et al. (US 20230316745 A1), Paragraph [0017] and Robinson et al. (US 20090074297 A1) Paragraph [0044].
Regarding claim 9, Malvar Maua teaches one or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause (Fig. 1, paragraph [0028]- Malvar Maua discloses program suite 152 may include one or more computer-executable programs having one or more data processing workflows 154. Storage machine 312 further includes data 318 stored thereon, which may include input data 140 received by computing system 150 and output data 160 generated by computing system 150, as well as other types of data generated by program suite 152 implementing data processing workflows 154.):
receiving a plurality of hyperspectral image patches of one or more regions of earth’s surface using one or more remote sensing mediums (Fig. 1, Paragraph [0020]- Malvar Maua discloses aeronautical vehicle 100 may take the form of an orbiting satellite of the Sentinel-2 program having an onboard imaging system referred to as the MultiSpectral Instrument (MSI). The MSI, in this example, captures multispectral imagery that measures the Earth's reflected radiance in 13 spectral bands.);
computing an average of a plurality of reflectance values of each of a plurality of spectral bands in each of the plurality of hyperspectral image patches (Fig. 2a, Paragraph [0064]- Malvar Maua discloses for each inter-band intensity value, the filtered combination is an average of the band-specific intensity values of the grouped subset of the plurality of reference spectral bands.);
wherein each cluster comprises a subset of spectral bands from the plurality of spectral bands (Fig. 2a, Paragraph [0064]- Malvar Maua discloses the method further comprises, for each inter-band intensity value of the background reflectance map, classifying the plurality of reference spectral bands into the grouped subset using a clustering algorithm.);
Malvar Maua fails to explicitly teach obtaining a plurality of clusters of spectral bands based on a computed average of the plurality of reflectance values across all pixels in each of the plurality of hyperspectral image patches using a clustering technique, and performing at least one of a plurality of reflexivity-based approximate computing techniques on the plurality of hyperspectral image patches for reducing a number of the plurality of spectral bands in each of the plurality of hyperspectral image patches by, a R-Top(N) technique.
However, Ma explicitly teaches obtaining a plurality of clusters of spectral bands based on a computed average of the plurality of reflectance values across all pixels in each of the plurality of hyperspectral image patches using a clustering technique (Fig. 1, Paragraph [0135]- Ma discloses the reflectance results for each sample area, whether as curves, vectors of averages for bands, etc., are then clustered together into clusters. In this application, there will be two main clusters, one having overall higher reflectance representing the inclusion of a seed, and another with lower reflectance where the sample avoided a seed.),
and performing at least one of a plurality of reflexivity-based approximate computing techniques on the plurality of hyperspectral image patches for reducing a number of the plurality of spectral bands in each of the plurality of hyperspectral image patches by (Fig. 3, Paragraph [0101]- Ma discloses the system then compares the scores 312-314 (e.g., segmentation accuracy scores) for the respective bands to select those that indicate the highest accuracy. For example, a predetermined number of bands can be selected (e.g., the n bands having the highest scores) or a threshold can be applied (e.g., select bands having an accuracy above 80%). The selected bands are then used in a second iteration of the selection process, to evaluate potential combinations of the bands.),
a R-Top(N) technique (Fig. 3, Paragraph [0101]- Ma discloses the system then compares the scores 312-314 (e.g., segmentation accuracy scores) for the respective bands to select those that indicate the highest accuracy. For example, a predetermined number of bands can be selected (e.g., the n bands having the highest scores) or a threshold can be applied (e.g., select bands having an accuracy above 80%). The selected bands are then used in a second iteration of the selection process, to evaluate potential combinations of the bands (wherein the selection of N bands having the highest score is seen as R-top(N)).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Malvar Maua of having one or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause: receiving a plurality of hyperspectral image patches of one or more regions of earth’s surface using one or more remote sensing mediums with the teachings of Ma obtaining a plurality of clusters of spectral bands based on a computed average of the plurality of reflectance values across all pixels in each of the plurality of hyperspectral image patches using a clustering technique, and performing at least one of a plurality of reflexivity-based approximate computing techniques on the plurality of hyperspectral image patches for reducing a number of the plurality of spectral bands in each of the plurality of hyperspectral image patches by, a R-Top(N) technique.
Wherein having Malvar Maua’s system for hyperspectral imaging and processing wherein obtaining a plurality of clusters of spectral bands based on a computed average of the plurality of reflectance values across all pixels in each of the plurality of hyperspectral image patches using a clustering technique, and performing at least one of a plurality of reflexivity-based approximate computing techniques on the plurality of hyperspectral image patches for reducing a number of the plurality of spectral bands in each of the plurality of hyperspectral image patches by, a R-Top(N) technique.
The motivation behind the modification would have been to allow for greater accuracy of segmentation, since both Malvar Maua and Ma are both systems that use hyperspectral images. Wherein Malvar Maua’s system wherein improved the accuracy and availability of observations made with hyperspectral images, while Ma’s system provides a way to improve the accuracy of segmentation. Please see Malvar Maua et al. (US 20230316745 A1), Paragraph [0017] and Ma et al. (US 20230027514 A1) Paragraph [0013].
Malvar Maua in view of Ma fails to explicitly teach wherein the plurality of reflexivity-based approximate computing techniques comprise (i) a R-Hop(K) technique.
However, Simental explicitly teaches wherein the plurality of reflexivity-based approximate computing techniques comprise (i) a R-Hop(K) technique (Fig. 15, Paragraph [0126]- Simental discloses lag is the interval between the bands, i.e., number of skipped bands, used in this embodiment of the present invention.),
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Malvar Maua in view of Ma of having one or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause: receiving a plurality of hyperspectral image patches of one or more regions of earth’s surface using one or more remote sensing mediums with the teachings of Simental wherein the plurality of reflexivity-based approximate computing techniques comprise (i) a R-Hop(K) technique.
Wherein having Malvar Maua’s system for hyperspectral imaging and processing wherein the plurality of reflexivity-based approximate computing techniques comprise (i) a R-Hop(K) technique.
The motivation behind the modification would have been to allow for greater data anomaly filtering and detection, since both Malvar Maua and Simental are both systems that use hyperspectral images. Wherein Malvar Maua’s system wherein improved the accuracy and availability of observations made with hyperspectral images, while Simental’s system provides a way to improve data anomaly filtering and detection. Please see Malvar Maua et al. (US 20230316745 A1), Paragraph [0017] and Simental et al. (US 20070031042 A1) Paragraph [0079].
Malvar Maua in view of Ma and Simental fails to explicitly teach and a R-Proximity(N) technique.
However, Robinson explicitly teaches and a R-Proximity(N) technique (Fig. 5, Paragraph [0063]- Robinson discloses various optimization methods may be employed and in general the type of optimization strategy adopted is determined by an operator. These methods may include, but are not limited to, optimization to selection of the maximum spectral vectors (e.g., the spectral vectors having the greatest magnitudes), optimization to select the greatest range between the spectral vectors, optimization to select the spectral vectors closest to a spectral vector mean, optimization to select the spectral vectors closest to a spectral median.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Malvar Maua in view of Ma and Simental of having one or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause: receiving a plurality of hyperspectral image patches of one or more regions of earth’s surface using one or more remote sensing mediums with the teachings of Robinson and a R-Proximity(N) technique.
Wherein having Malvar Maua’s system for hyperspectral imaging and processing wherein and a R-Proximity(N) technique.
The motivation behind the modification would have been to allow for speed of dimensionality reduction, since both Malvar Maua and Robinson are both systems that use hyperspectral images. Wherein Malvar Maua’s system wherein improved the accuracy and availability of observations made with hyperspectral images, while Robinson’s system provides a way to improve the speed of the system. Please see Malvar Maua et al. (US 20230316745 A1), Paragraph [0017] and Robinson et al. (US 20090074297 A1) Paragraph [0044].
Claims 2, 6, and 10 are rejected under 35 U.S.C 103 as being unpatentable over Malvar Maua et al. (US 20230316745 A1) hereafter referenced as Malvar Maua in view of Ma et al. (US 20230027514 A1) hereafter referenced as Ma, Simental et al. (US 20070031042 A1) hereafter referenced as Simental, Robinson et al. (US 20090074297 A1) hereafter referenced as Robinson, and Jacquot et al. (US 20210374448 A1) hereafter referenced as Jacquot.
Regarding claim 2, Malvar Maua in view of Ma, Simental, and Robinson teaches the processor implemented method of claim 1,
Malvar Maua in view of Ma and Robinson fails to explicitly teach and performing a uniform sampling on the ranked plurality of spectral bands by selecting a plurality of alternate bands from the ranked plurality of spectral bands based on a prespecified hop size k, wherein the plurality of alternate bands represent a reduced number of the plurality of spectral bands.
However, Simental explicitly teaches and performing a uniform sampling on the ranked plurality of spectral bands by selecting a plurality of alternate bands from the ranked plurality of spectral bands based on a prespecified hop size K (Fig. 15, Paragraph [0126]- Simental discloses lag is the interval between the bands, i.e., number of skipped bands, used in this embodiment of the present invention),
wherein the plurality of alternate bands represent a reduced number of the plurality of spectral bands (Fig. 15, Paragraph [0126]- Simental discloses lag is the interval between the bands, i.e., number of skipped bands, used in this embodiment of the present invention).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Malvar Maua in view of Ma and Robinson of having a processor implemented method, comprising: receiving, via one or more hardware processors, a plurality of hyperspectral image patches of one or more regions of earth’s surface using one or more remote sensing mediums with the teachings of Simental and performing a uniform sampling on the ranked plurality of spectral bands by selecting a plurality of alternate bands from the ranked plurality of spectral bands based on a prespecified hop size k, wherein the plurality of alternate bands represent a reduced number of the plurality of spectral bands.
Wherein having Malvar Maua’s system for hyperspectral imaging and processing and performing a uniform sampling on the ranked plurality of spectral bands by selecting a plurality of alternate bands from the ranked plurality of spectral bands based on a prespecified hop size k, wherein the plurality of alternate bands represent a reduced number of the plurality of spectral bands.
The motivation behind the modification would have been to allow for greater data anomaly filtering and detection, since both Malvar Maua and Simental are both systems that use hyperspectral images. Wherein Malvar Maua’s system wherein improved the accuracy and availability of observations made with hyperspectral images, while Simental’s system provides a way to improve data anomaly filtering and detection. Please see Malvar Maua et al. (US 20230316745 A1), Paragraph [0017] and Simental et al. (US 20070031042 A1) Paragraph [0079].
Malvar Maua in view of Ma, Simental, and Robinson fails to explicitly teach wherein the R-Hop(K) technique comprises: ranking each spectral band in the plurality of spectral bands in an order of the plurality of reflectance values.
However, Jacquot explicitly teaches wherein the R-Hop(K) technique comprises: ranking each spectral band in the plurality of spectral bands in an order of the plurality of reflectance values (Fig. 3, Paragraph [0093]- Jacquot discloses the selection module 114 can rank the bands based on their respective scores 230 to select bands from among all N bands.);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Malvar Maua in view of Ma, Simental, and Robinson of having a processor implemented method, comprising: receiving, via one or more hardware processors, a plurality of hyperspectral image patches of one or more regions of earth’s surface using one or more remote sensing mediums with the teachings of Jacquot wherein the R-Hop(K) technique comprises: ranking each spectral band in the plurality of spectral bands in an order of the plurality of reflectance values.
Wherein having Malvar Maua’s system for hyperspectral imaging and processing wherein the R-Hop(K) technique comprises: ranking each spectral band in the plurality of spectral bands in an order of the plurality of reflectance values.
The motivation behind the modification would have been to allow for greater accuracy and efficiency of processing multispectral images, since both Malvar Maua and Jacquot are both systems that use hyperspectral images. Wherein Malvar Maua’s system wherein improved the accuracy and availability of observations made with hyperspectral images, while Jacquot’s system provides a way to improve the accuracy and efficiency of the system. Please see Malvar Maua et al. (US 20230316745 A1), Paragraph [0017] and Jacquot et al. (US 20210374448 A1) Paragraph [0010].
Regarding claim 6, Malvar Maua in view of Ma, Simental, and Robinson teaches the system of claim 5,
Malvar Maua in view of Ma and Robinson fails to explicitly teach and performing a uniform sampling on the ranked plurality of spectral bands by selecting a plurality of alternate bands from the ranked plurality of spectral bands based on a prespecified hop size, wherein the plurality of alternate bands represent a reduced number of the plurality of spectral bands.
However, Simental explicitly teaches and performing a uniform sampling on the ranked plurality of spectral bands by selecting a plurality of alternate bands from the ranked plurality of spectral bands based on a prespecified hop size K (Fig. 15, Paragraph [0126]- Simental discloses lag is the interval between the bands, i.e., number of skipped bands, used in this embodiment of the present invention),
wherein the plurality of alternate bands represent a reduced number of the plurality of spectral bands (Fig. 15, Paragraph [0126]- Simental discloses lag is the interval between the bands, i.e., number of skipped bands, used in this embodiment of the present invention).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Malvar Maua in view of Ma and Robinson of having a system, comprising: a memory storing instructions; wherein the one or more hardware processors are configured by the instructions to: receive a plurality of hyperspectral image patches of one or more regions of earth’s surface using one or more remote sensing mediums with the teachings of Simental and performing a uniform sampling on the ranked plurality of spectral bands by selecting a plurality of alternate bands from the ranked plurality of spectral bands based on a prespecified hop size k, wherein the plurality of alternate bands represent a reduced number of the plurality of spectral bands.
Wherein having Malvar Maua’s system for hyperspectral imaging and processing and performing a uniform sampling on the ranked plurality of spectral bands by selecting a plurality of alternate bands from the ranked plurality of spectral bands based on a prespecified hop size k, wherein the plurality of alternate bands represent a reduced number of the plurality of spectral bands.
The motivation behind the modification would have been to allow for greater data anomaly filtering and detection, since both Malvar Maua and Simental are both systems that use hyperspectral images. Wherein Malvar Maua’s system wherein improved the accuracy and availability of observations made with hyperspectral images, while Simental’s system provides a way to improve data anomaly filtering and detection. Please see Malvar Maua et al. (US 20230316745 A1), Paragraph [0017] and Simental et al. (US 20070031042 A1) Paragraph [0079].
Malvar Maua in view of Ma, Simental, and Robinson fails to explicitly teach wherein the R-Hop(K) technique comprising: ranking each spectral band in the plurality of spectral bands in an order of the plurality of reflectance values.
However, Jacquot explicitly teaches wherein the R-Hop(K) technique comprising: ranking each spectral band in the plurality of spectral bands in an order of the plurality of reflectance values (Fig. 3, Paragraph [0093]- Jacquot discloses the selection module 114 can rank the bands based on their respective scores 230 to select bands from among all N bands.);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Malvar Maua of having a system, comprising: a memory storing instructions; wherein the one or more hardware processors are configured by the instructions to: receive a plurality of hyperspectral image patches of one or more regions of earth’s surface using one or more remote sensing mediums with the teachings of Jacquot wherein the R-Hop(K) technique comprising: ranking each spectral band in the plurality of spectral bands in an order of the plurality of reflectance values.
Wherein having Malvar Maua’s system for hyperspectral imaging and processing wherein the R-Hop(K) technique comprising: ranking each spectral band in the plurality of spectral bands in an order of the plurality of reflectance values.
The motivation behind the modification would have been to allow for greater accuracy and efficiency of processing multispectral images, since both Malvar Maua and Jacquot are both systems that use hyperspectral images. Wherein Malvar Maua’s system wherein improved the accuracy and availability of observations made with hyperspectral images, while Jacquot’s system provides a way to improve the accuracy and efficiency of the system. Please see Malvar Maua et al. (US 20230316745 A1), Paragraph [0017] and Jacquot et al. (US 20210374448 A1) Paragraph [0010].
Regarding claim 10, Malvar Maua in view of Ma, Simental, and Robinson teaches the one or more non-transitory machine-readable information storage mediums of claim 9,
Malvar Maua in view of Ma and Robinson fails to explicitly teach and performing a uniform sampling on the ranked plurality of spectral bands by selecting a plurality of alternate bands from the ranked plurality of spectral bands based on a prespecified hop size, wherein the plurality of alternate bands represent a reduced number of the plurality of spectral bands.
However, Simental explicitly teaches and performing a uniform sampling on the ranked plurality of spectral bands by selecting a plurality of alternate bands from the ranked plurality of spectral bands based on a prespecified hop size K (Fig. 15, Paragraph [0126]- Simental discloses lag is the interval between the bands, i.e., number of skipped bands, used in this embodiment of the present invention),
wherein the plurality of alternate bands represent a reduced number of the plurality of spectral bands (Fig. 15, Paragraph [0126]- Simental discloses lag is the interval between the bands, i.e., number of skipped bands, used in this embodiment of the present invention).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Malvar Maua in view of Ma and Robinson of having one or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause: receiving a plurality of hyperspectral image patches of one or more regions of earth’s surface using one or more remote sensing mediums with the teachings of Simental and performing a uniform sampling on the ranked plurality of spectral bands by selecting a plurality of alternate bands from the ranked plurality of spectral bands based on a prespecified hop size, wherein the plurality of alternate bands represent a reduced number of the plurality of spectral bands.
Wherein having Malvar Maua’s system for hyperspectral imaging and processing and performing a uniform sampling on the ranked plurality of spectral bands by selecting a plurality of alternate bands from the ranked plurality of spectral bands based on a prespecified hop size, wherein the plurality of alternate bands represent a reduced number of the plurality of spectral bands.
The motivation behind the modification would have been to allow for greater data anomaly filtering and detection, since both Malvar Maua and Simental are both systems that use hyperspectral images. Wherein Malvar Maua’s system wherein improved the accuracy and availability of observations made with hyperspectral images, while Simental’s system provides a way to improve data anomaly filtering and detection. Please see Malvar Maua et al. (US 20230316745 A1), Paragraph [0017] and Simental et al. (US 20070031042 A1) Paragraph [0079].
Malvar Maua in view of Ma, Simental, and Robinson fails to explicitly teach wherein the R-Hop(K) technique comprising: ranking each spectral band in the plurality of spectral bands in an order of the plurality of reflectance values.
However, Jacquot explicitly teaches wherein the R-Hop(K) technique comprises: ranking each spectral band in the plurality of spectral bands in an order of the plurality of reflectance values (Fig. 3, Paragraph [0093]- Jacquot discloses the selection module 114 can rank the bands based on their respective scores 230 to select bands from among all N bands.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Malvar Maua in view of Ma, Simental, and Robinson of having one or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause: receiving a plurality of hyperspectral image patches of one or more regions of earth’s surface using one or more remote sensing mediums with the teachings of Jacquot wherein the R-Hop(K) technique comprising: ranking each spectral band in the plurality of spectral bands in an order of the plurality of reflectance values.
Wherein having Malvar Maua’s system for hyperspectral imaging and processing wherein the R-Hop(K) technique comprising: ranking each spectral band in the plurality of spectral bands in an order of the plurality of reflectance values.
The motivation behind the modification would have been to allow for greater accuracy and efficiency of processing multispectral images, since both Malvar Maua and Jacquot are both systems that use hyperspectral images. Wherein Malvar Maua’s system wherein improved the accuracy and availability of observations made with hyperspectral images, while Jacquot’s system provides a way to improve the accuracy and efficiency of the system. Please see Malvar Maua et al. (US 20230316745 A1), Paragraph [0017] and Jacquot et al. (US 20210374448 A1) Paragraph [0010].
Allowable Subject Matter
Claims 3-4, 7-8, and 11-12 along with their dependent claims respectively, are therefrom objected to as being dependent upon rejected base claim, claims 1, 5, 9, respectively but would be allowable if rewritten in independent form including all of the limitations of the base claims and any intervening claims and to overcome the claim objections.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 3, the prior arts fail to explicitly teach, ranking each spectral band in the subset of spectral bands comprised in each cluster in an order of the plurality of reflectance values; and performing a uniform sampling on the ranked subset of spectral bands in each cluster by selecting a plurality of N high ranked bands from the ranked subset of spectral bands, as claimed in claim 3.
Regarding claim 4, the prior arts fail to explicitly teach, ranking each spectral band in the subset of spectral bands comprised in each cluster in an order of the plurality of reflectance values; and performing a uniform sampling on the ranked subset of spectral bands in each cluster by selecting a plurality of N closest bands from the ranked subset of spectral bands based on a distance from a centroid of each cluster, as claimed in claim 4.
Regarding claim 7, the prior arts fail to explicitly teach, ranking each spectral band in the subset of spectral bands comprised in each cluster in an order of the plurality of reflectance values; and performing a uniform sampling on the ranked subset of spectral bands in each cluster by selecting a plurality of N high ranked bands from the ranked subset of spectral bands, as claimed in claim 7.
Regarding claim 8, the prior arts fail to explicitly teach, ranking each spectral band in the subset of spectral bands comprised in each cluster in an order of the plurality of reflectance values; and performing a uniform sampling on the ranked subset of spectral bands in each cluster by selecting a plurality of N closest bands from the ranked subset of spectral bands based on a distance from a centroid of each cluster, as claimed in claim 8.
Regarding claim 11, the prior arts fail to explicitly teach, ranking each spectral band in the subset of spectral bands comprised in each cluster in an order of the plurality of reflectance values; and performing a uniform sampling on the ranked subset of spectral bands in each cluster by selecting a plurality of N high ranked bands from the ranked subset of spectral bands, as claimed in claim 11.
Regarding claim 12, the prior arts fail to explicitly teach, ranking each spectral band in the subset of spectral bands comprised in each cluster in an order of the plurality of reflectance values; and performing a uniform sampling on the ranked subset of spectral bands in each cluster by selecting a plurality of N closest bands from the ranked subset of spectral bands based on a distance from a centroid of each cluster, as claimed in claim 12.
Conclusion
Listed below are the prior arts made of record and not relied upon but are considered
pertinent to applicant`s disclosure.
Sunkavalli et al. (US 20190347526 A1)- Systems, methods, and non-transitory computer-readable media are disclosed for extracting material properties from a single digital image portraying one or more materials by utilizing a neural network encoder, a neural network material classifier, and one or more neural network material property decoders. In particular, in one or more embodiments, the disclosed systems and methods train the neural network encoder, the neural network material classifier, and one or more neural network material property decoders to accurately extract material properties from a single digital image portraying one or more materials. Furthermore, in one or more embodiments, the disclosed systems and methods train and utilize a rendering layer to generate model images from the extracted material properties....................Please see Fig. 1. Abstract.
Marciano et al. (US 20220301301 A1)- The present invention generally relates to systems and methods of classification and localization of features of interest in remote aerial images. It relates particularly to a system and method of classifying and localizing features of interest on satellite images by semantic segmentation using a trained deep learning convolutional neural network. Increasing the accuracy of classification and localization requires that the neural network to decipher the difference between the feature of interest and other features in the background. This invention addresses the problem of low accuracy in classifying and localizing pixels corresponding to the feature of interest by enabling the user to include more information together with the original pixel values in the satellite images. An exemplary embodiment of this invention is a system and method of locating mango trees in a plantation in Bataan province, Philippines using a U-net convolutional network......................Please see Fig. 1. Abstract.
LIU et al. (US 20220207856 A1)- A method for extracting spectral information of a substance under test includes: identifying a pixel region A(x, y) occupied by an object under test from a hyperspectral image acquired; extracting a specular reflection region A.sub.q and a diffuse reflection region A.sub.r from the pixel region A(x, y), and calculating a representative spectrum I.sub.q(ω) of the specular reflection region A.sub.q and a representative spectrum I.sub.r(ω) of the diffuse reflection region A.sub.r, respectively; by comparing each element in the representative spectrum I.sub.q(ω) of the specular reflection region A.sub.q with each element in the representative spectrum I.sub.r(ω) of the diffuse reflection region A.sub.r, separating information of a light source from spectral information of the object to obtain a first spectral invariant C(ω). This method does not require additional spectral information of the light source, which improves the analysis efficiency......................Please see Fig. 1. Abstract.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LUCIUS C.G. ALLEN whose telephone number is (703)756-5987. The examiner can normally be reached Mon - Fri 8-5pm (EST).
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/LUCIUS CAMERON GREEN ALLEN/Examiner, Art Unit 2673
/CHINEYERE WILLS-BURNS/Supervisory Patent Examiner, Art Unit 2673
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