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 .
DETAILED ACTION
The Action is responsive to the Application filed on 7/19/2024. Claims 1-20 are pending claims. Claim 1 is written in independent form.
Claim Objections
Claims 3 and 13 are objected to because of the following informalities:
Claims 3 and 13 appear to recite a typographical error in step (e) by reciting “repeating steps (b) and (d)….” which skips over step (c). Based on Figure 5 Element 418, the limitation is understood as reciting “repeating steps (b) [[and]] to (d)….”.
Appropriate correction is required.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claimed invention is directed to one or more abstract ideas without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than judicial exception. The eligibility analysis in support of these findings is provided below.
As per Claim 1,
STEP 1:In accordance with Step 1 of the eligibility inquiry (as explained in MPEP 2106), the claimed method (claims 1-20) are directed to one of the eligible categories of subject matter and therefore satisfies Step 1.
STEP 2A Prong One:The independent claim 1 recites the following limitations directed to an abstract idea:
Generating a plurality of super pixels from the multidimensional data;
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by observing and evaluating multidimensional data, and based on the observation and evaluation, making a judgement and/or opinion of a plurality of super pixels.
Clustering the plurality of super pixels into a plurality of super pixel clusters;
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by observing and evaluating the plurality of super pixels, and based on the observation and evaluation, making a judgement and/or opinion of a plurality of super pixels.
Using the plurality of super pixel clusters to train a plurality of dictionaries respectively;
The limitation recites a mathematical concept of executing a mathematical formula/function in the form of inputting a plurality of super pixel clusters and outputting a respective dictionary.
Splitting the multidimensional data into a plurality of data sub-blocks;
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by observing and evaluating the multidimensional data, and based on the observation and evaluation, making a judgement and/or opinion to split/organize the multidimensional data into data sub-blocks.
Vectorizing the plurality of data sub-blocks to obtain a plurality of vectorized data sub-blocks;
The limitation recites a mathematical concept of executing a mathematical formula/function in the form of inputting data sub-blocks and outputting the data sub-blocks in a vectorized format.
Selecting a dictionary for each of the vectorized data sub-blocks;
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by observing and evaluating each vectorized data sub-blocks and dictionaries, and based on the observation and evaluation, making a judgement and/or opinion of a selection of a dictionary for each of the vectorized data sub-blocks.
Sparse coding each of vectorized data sub-blocks with a corresponding selected dictionary to obtain a compressed representation; and
The limitation recites a mathematical concept of executing a mathematical formula/function in the form of executing sparse coding on the vectorized data sub-block using its corresponding selected dictionary, and outputting a compressed representation.
Wherein each dictionary is selected for a corresponding vectorized data sub-block by:
Calculating a similarity between the corresponding vectorized data sub-block and each of the super pixel clusters;
The limitation recites a mathematical concept of executing a mathematical formula/function in the form of a similarity calculation between a vectorized data sub-block and each of the super pixel clusters.
Finding a best-matched super pixel cluster having a highest similarity with the vectorized data sub-block;
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by observing and evaluating the similarity between each of the super pixel clusters and the vectorized data sub-block, and based on the observation and evaluation, making a judgement and/or opinion that the highest similarity corresponds to a best-batched super pixel cluster for the vectorized data sub-block.
Selecting a dictionary trained with the best-matched super pixel cluster as the coding dictionary; and
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by observing and evaluating the dictionaries trained with respective super pixel clusters, and based on the observation and evaluation, making a judgement and/or opinion to select the dictionary trained with the best-matched super pixel cluster as the coding dictionary.
Wherein each dictionary is trained with a corresponding super pixel cluster by:
Dividing the corresponding super pixel cluster into a plurality of training data sub-blocks;
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by observing and evaluating the super pixel cluster, and based on the observation and evaluation, dividing/grouping portions of the super pixel cluster into a plurality of training data sub-blocks.
Vectorizing the plurality of training data sub-blocks to obtain a plurality of initial representations respectively;
The limitation recites a mathematical concept of executing a mathematical formula/function in the form of inputting training data sub-blocks and outputting a plurality of initial representations in a vectorized format.
Calculating a similarity matrix between each pair of training data sub-blocks;
The limitation recites a mathematical concept of executing a mathematical formula/function in the form of a similarity matrix calculation between each pair of training data sub-blocks.
Computing an average similarity value for each training data sub-block;
The limitation recites a mathematical concept of executing a mathematical formula/function in the form of calculating/computing an average similarity value for each training data sub-block.
Selecting a plurality of initial training data sub-blocks from the plurality of training data sub-blocks based on the computed average similarity values;
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by observing and evaluating the computed average similarity values for each training data sub-block, and based on the observation and evaluation, making a judgement and/or opinion to select a plurality of initial training data sub-blocks from the plurality of training data sub-blocks.
Initializing the dictionary by setting the initial training data sub-blocks as initial atoms of the dictionary;
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by observing and evaluating the initial training data sub-blocks, and based on the observation and evaluation, making a judgment and/or opinion to set the initial training data sub-blocks as initial atoms of the dictionary and thus initialize the dictionary.
Training the dictionary to construct a sparse coding representation for each of the training data sub-blocks by performing a batch orthogonal matching pursuit (batch OMP) algorithm constrained with a target sparsity and a target residual threshold; and
The limitation recites a mathematical concept of executing a mathematical formula/function in the form of a batch orthogonal matching pursuit algorithm constrained with a target sparsity and a target residual threshold.
Updating the dictionary atom by atom with the constructed sparse coding representations to obtain a trained dictionary.
The limitation recites a mathematical concept of executing a mathematical formula/function in the form of executing an algorithm that updates the dictionary atom by atom with constructed sparse coding representations, resulting in “a trained dictionary”.
STEP 2A Prong Two:The claim recites the following additional elements:
Storing the obtained compressed representations and indexes of corresponding selected dictionaries in the compressed file;
The limitation also recites using “storing” data, which is a high-level recitation of generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
STEP 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
Looking at the claim as a whole does not change this conclusion and the claim is ineligible.
As per Dependent Claims 2-20,
STEP 1:In accordance with Step 1 of the eligibility inquiry (as explained in MPEP 2106), the claimed method (claims 1-20) are directed to one of the eligible categories of subject matter and therefore satisfies Step 1.
STEP 2A Prong One:The dependent claims 2-20 recite the following limitations directed to an abstract idea:
The limitation(s) of Dependent Claims 2 and 12 include the step(s) of:
Wherein the batch OMP algorithm is performed to construct a sparse coding representation for each training data sub-block by:
(a) computing a gram matrix of the dictionary;
The limitation recites a mathematical concept of executing a mathematical formula/function in the form of a computation/formula that takes as input the dictionary and outputs a gram matrix of the dictionary.
(b) computing a correlation vector of the dictionary with respect to the data sub-block, where each element of the correlation vector corresponds to correlation between an atom of the dictionary and the data sub-block;
The limitation recites a mathematical concept of executing a mathematical formula/function in the form of a computation/formula that takes as input the dictionary and the data sub-block, and outputs a correlation vector of the dictionary that correlates an atom of the dictionary to the data sub-block.
(c) finding the best-matched atom of the dictionary having the maximum correlation with the data sub-block;
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by observing and evaluating the correlations between the atoms of the dictionary and the data sub-block, and based on the observation and evaluation, making a judgement and/or opinion of the maximum correlation for an atom that is a best-matched atom.
(d) applying Cholesky decomposition to a sub-set of the gram matrix containing index of the best-matched atom to obtain a Cholesky decomposition result;
The limitation recites a mathematical concept of executing a mathematical formula/function in the form of applying/executing Cholesky decomposition that takes as input a sub-set of the gram matrix containing index of the best-matched atom, and outputting a Cholesky decomposition result.
(e) solving for the sparse coding representation using the Cholesky decomposition result;
The limitation recites a mathematical concept of executing a mathematical formula/function in the form of an algorithm or formula that solves for/computes the sparse coding representation using the Cholesky decomposition result.
(f) updating the correlation vector by subtracting a product of the sparse coding representation and the gram matrix;
The limitation recites a mathematical concept of executing a mathematical formula/function that subtracts a product of the sparse coding representation and the gram matrix to update the correlation vector.
(g) multiplying the dictionary with the sparse coding representation to obtain a reconstructed signal;
The limitation recites a mathematical concept of executing a mathematical formula/function that multiples the dictionary with the sparse coding representation to obtain a reconstructed signal.
(h) updating a residual between the reconstructed signal and the training data sub-block;
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by making a judgement and/or opinion to update a residual between the reconstructed signal and the training data sub-block.
(i) if the updated residual is greater than the target residual threshold, repeating steps (c) to (h); and
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by observing and evaluating the updated residual and the target residual threshold, and based on the observation and evaluation, making a judgement and/or opinion of whether the updated residual is greater than the target residual threshold, and if so, repeating steps (c) to (h).
If the target sparsity is reached or the updated residual is smaller than the target residual threshold, outputting the sparse coding representation for the training data sub-block for further operation.
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by observing and evaluating the target sparsity, the updated residual, and the target residual threshold, and based on the observation and evaluation, making a judgement and/or opinion that the target sparsity has been reached or the updated residual is smaller than the target residual threshold, and based on that judgement and/or opinion, making another judgement and/or opinion to output the sparse coding representation for the training data sub-block for further operation.
The limitation(s) of Dependent Claims 3 and 13 include the step(s) of:
wherein the plurality of super pixels is generated by:
Initializing a plurality of super pixel centers with a maximum interval;
The limitation recites a mathematical concept of executing a mathematical formula/function in the form of initializing super pixel centers with a maximum interval.
Updating locations of the plurality of super pixel centers; and
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by observing and evaluating the locations of the plurality of super pixel centers, and based on the observation and evaluation, making a judgement and/or opinion to update the locations of the plurality of super pixel centers.
(a) defining a plurality of overlapped super pixel regions centered by and corresponding to the plurality of super pixel centers respectively, each cluster region having a size equal to two times of the maximum interval of the super pixel centers;
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by observing and evaluating the plurality of super pixel centers, super pixel clusters, and the maximum interval of the super pixel centers, and based on the observation and evaluation, making a judgement and/or opinion to define a plurality of overlapped super pixel regions centered by and corresponding to the plurality of super pixel centers and having a size equal to two times of the maximum interval of the super pixel centers.
(b) for each multi-dimensional data point:
Calculating, within each corresponding overlapped super pixel regions, a plurality of distances of a plurality of neighboring super pixel centers from the multi-dimensional data point; and
The limitation recites a mathematical concept of executing a mathematical formula/function that calculates the plurality of distances of a plurality of neighboring super pixel cell centers from the multi-dimensional data point.
Assigning the multi-dimensional data point to super pixel region containing a nearest neighbor super pixel that has a shortest distance from the multi-dimensional data point;
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by observing and evaluating the distances of the plurality of neighboring super pixel centers from the multi-dimensional data point, based on the observation and evaluation, making a judgement and/or opinion of the nearest neighbor super pixel that has a shortest distance from the multi-dimensional data point, and based on the judgement and/or opinion, making another judgement and/or opinion to assign the multi-dimensional data point to the super pixel region containing the nearest neighbor super pixel.
(c) for each super pixel center: updating a current location of the super pixel center to a centroid position of all multi-dimensional data points assigned to the corresponding super pixel region; and
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by observing and evaluating each super pixel center and a centroid position of all multi-dimensional data points assigned to the corresponding super pixel region, and based on the observation and evaluation, making a judgement and/or opinion to update the location of the super pixel center to the centroid position of all multi-dimensional data points assigned to the corresponding super pixel region.
(d) calculating a residual value between the updated location and the current location for each super pixel center;
The limitation recites a mathematical concept of executing a mathematical formula/function that calculates a residual value between the updated location and the current location for each super pixel center.
(e) repeating steps (b) and (d) if any one of the residual values is greater than a threshold value.
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by observing and evaluating the residual values and a threshold value, and based on the observation and evaluation, making a judgement as to whether any one of the residual values is greater than the threshold value, and if so, repeating steps (b) and (d)
The limitation(s) of Dependent Claims 5 and 15 include the step(s) of:
Wherein the plurality of super pixels is clustered by performing a Laplacian sparse subspace clustering algorithm; and
The limitation recites a mathematical concept of executing a mathematical formula/function in the form of a Laplacian sparse subspace clustering algorithm.
The Laplacian sparse subspace clustering algorithm is performed by:
Constructing a weighted undirected graph having a plurality of nodes representing the plurality of feature vectors, edges connecting the nodes;
The limitation recites a mathematical concept of executing a mathematical formula/function that takes as input a plurality of feature vectors, and outputs a weighted undirected graph having a plurality of nodes representing the plurality of feature vectors and edges connecting the nodes.
Partitioning the weighted undirected graph to obtain a plurality of clusters of super pixels.
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by observing and evaluating the weighted undirected graph, and based on the observation and evaluation, making a judgement and/or opinion of partitions/groupings for the weighted undirected graph into a plurality of clusters of super pixels.
The limitation(s) of Dependent Claims 7 and 17 include the step(s) of:
Wherein the covariance matrix Mi of a super pixel Ci is given by:
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The limitation recites a mathematical concept of executing a mathematical formula/function in the form of a particular algorithm that generates the covariance matrix.
The limitation(s) of Dependent Claims 9 and 19 include the step(s) of:
Wherein the weighted undirected graph is partitioned by:
Defining a minimization problem constrained by the Laplacian matrix;
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by observing and evaluating the Laplacian matrix, and based on the observation and evaluation, making a judgement and/or opinion of a minimization problem constrained by the Laplacian matrix.
Solving the minimization problem for each super pixel to obtain a sparse coefficient for the super pixel;
The limitation recites a mathematical concept of executing a mathematical formula/function in the form of an algorithm or formula that solves for/computes, using the minimization problem for each super pixel, the sparse coefficient for the super pixel.
Constructing a sparse coefficient matrix for the R subpixel super pixels
The limitation recites a mathematical concept of executing a mathematical formula/function that takes as input the R subpixel super pixels, and outputs a sparse coefficient matrix.
Updating a symmetry matrix with the sparse coefficient matrix;
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by observing and evaluating the symmetry matrix and the sparse coefficient matrix, and based on the observation and evaluation, making a judgement and/or opinion to update the symmetry matrix with the sparse coefficient matrix.
Partitioning the weighted undirected graph based on the updated symmetry matrix.
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by observing and evaluating the weighted undirected graph and the updated symmetry matrix, and based on the observation and evaluation, making a judgement and/or opinion of partitions/groupings for the weighted undirected graph into a plurality of clusters of super pixels.
The limitation(s) of Dependent Claim 11 include the step(s) of:
Decoding the plurality of compressed representations to obtain a plurality of decompressed data sub-blocks; and
The limitation recites a mathematical concept of executing a mathematical formula/function in the form of a decoding formula that decodes/decompresses data sub-blocks.
Joining the plurality of decompressed data sub-blocks to reconstruct the multidimensional data; and
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by observing and evaluating the information for plurality of decompressed data sub-blocks, and based on the observation and evaluation, making a judgement and/or opinion to reconstruct the multidimensional data by joining the plurality of decompressed data sub-blocks.
Wherein the decompressed data sub-bock is obtained by:
Multiplying the compressed representations with the retrieved dictionary to reconstruct a vectorized data sub-block; and
The limitation recites a mathematical concept of executing a mathematical formula/function that multiplies the compressed representations with the retrieved dictionary and outputs a reconstructed vectorized data sub-block.
De-vectorizing the reconstructed vectorized data sub-block to obtain the decompressed data sub-block.
The limitation recites a mathematical concept of executing a mathematical formula/function in the form of inputting data sub-blocks in a vectorized format and outputting the de-vectorized data sub-blocks.
STEP 2A Prong Two:The claim(s) recite the following additional elements:
The limitation(s) of Dependent Claims 4 and 14 include the step(s) of:
Wherein, the distance of a neighboring super pixel center to a multi-dimensional data point is calculated on basis of a color distance and a spatial distance between neighboring super pixel center to an image pixel.
The limitation recites an insignificant extra-solution activity as selecting a particular type of distance features being used to calculate the distance of a neighboring super pixel center to a multi-dimensional data point as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
The limitation(s) of Dependent Claims 5 and 15 include the step(s) of:
Extracting a plurality of feature vectors for the plurality of super pixels respectively;
The limitation recites an insignificant extra solution activity as sending/receiving/extracting data (ie. Mere data gathering) such as ‘obtaining information’ as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Obtaining a covariance matrix for each super pixel;
The limitation recites an insignificant extra solution activity as sending/receiving/extracting data (ie. Mere data gathering) such as ‘obtaining information’ as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Obtaining a similarity value for each pair of super pixels;
The limitation recites an insignificant extra solution activity as sending/receiving/extracting data (ie. Mere data gathering) such as ‘obtaining information’ as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Wherein each edge has a weight representing a similarity value between a pair of nodes connected by the edge;
The limitation recites an insignificant extra-solution activity as selecting a particular type of data being used to represent the edge connecting a pair of nodes as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
The limitation(s) of Dependent Claims 6 and 16 include the step(s) of:
Wherein the feature vector of each super pixel is an average of feature vectors of all pixels within the super pixel.
The limitation recites an insignificant extra-solution activity as selecting a particular type of data being used to represent the feature vector of each super pixel as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
The limitation(s) of Dependent Claims 7 and 17 include the step(s) of:
Where Mi(a,b) denotes covariance matrix elements of features a and b in super pixel i, fi and ui are the pixel feature vector and mean feature vector within the super pixel j, fj(a’) denotes the a’th element of the feature vector and ui(a’’) denotes the a’’th element of the mean feature vector, fj(b’) denotes the b’th element of the feature vector and ui(b’’) denotes the b’’th element of the mean feature vector.
The limitation recites an insignificant extra-solution activity as selecting a particular type of data being used to represent the particular variable in the equation as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
The limitation(s) of Dependent Claims 8 and 18 include the step(s) of:
Wherein the similarity values for a super pixel i1 and a super pixel i2 is calculated on basis of a distance between feature vectors of the super pixels i1 and i2.
The limitation recites an insignificant extra-solution activity as selecting a particular type of data/metric(s) being used to calculate the similarity values between two super pixels as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
The limitation(s) of Dependent Claims 9 and 19 include the step(s) of:
Wherein the weighted undirected graph is partitioned by:
Obtaining a Laplacian matrix for the weighted undirected graph;
The limitation recites an insignificant extra solution activity as sending/receiving/extracting data (ie. Mere data gathering) such as ‘obtaining information’ as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
The limitation(s) of Dependent Claims 10 and 20 include the step(s) of:
The minimization problem is defined with the sparse coefficient, a linear transformation matrix used to project the sparse coefficient into a low-dimensional space, a target vector in the low-dimensional space, and L1 norm of the sparse coefficient and sparse coefficients of other data points.
The limitation recites an insignificant extra-solution activity as selecting a particular type of data being used to define the minimization problem as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
The limitation(s) of Dependent Claim 11 include the step(s) of:
Retrieving a corresponding dictionary based on a corresponding index;
The limitation recites an insignificant extra solution activity as sending/receiving/extracting data (ie. Mere data gathering) such as ‘obtaining information’ as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
STEP 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
With respect to Claims 4 and 14 reciting “Wherein, the distance of a neighboring super pixel center to a multi-dimensional data point is calculated on basis of a color distance and a spatial distance between neighboring super pixel center to an image pixel.” identified as insignificant extra-solution activity above this is also WURC as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to Claims 5 and 15 reciting “Extracting a plurality of feature vectors for the plurality of super pixels respectively;” identified as insignificant extra-solution activity above this is also WURC as court-identified see MPEP 2106.05(d)(II)(i).
With respect to Claims 5 and 15 reciting “Obtaining a covariance matrix for each super pixel;” identified as insignificant extra-solution activity above this is also WURC as court-identified see MPEP 2106.05(d)(II)(i).
With respect to Claims 5 and 15 reciting “Obtaining a similarity value for each pair of super pixels;” identified as insignificant extra-solution activity above this is also WURC as court-identified see MPEP 2106.05(d)(II)(i).
With respect to Claims 5 and 15 reciting “Wherein each edge has a weight representing a similarity value between a pair of nodes connected by the edge;” identified as insignificant extra-solution activity above this is also WURC as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to Claims 6 and 16 reciting “Wherein the feature vector of each super pixel is an average of feature vectors of all pixels within the super pixel.” identified as insignificant extra-solution activity above this is also WURC as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to Claims 7 and 17 reciting “Where Mi(a,b) denotes covariance matrix elements of features a and b in super pixel i, fi and ui are the pixel feature vector and mean feature vector within the super pixel j, fj(a’) denotes the a’th element of the feature vector and ui(a’’) denotes the a’’th element of the mean feature vector, fj(b’) denotes the b’th element of the feature vector and ui(b’’) denotes the b’’th element of the mean feature vector.” identified as insignificant extra-solution activity above this is also WURC as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to Claims 8 and 18 reciting “Wherein the similarity values for a super pixel i1 and a super pixel i2 is calculated on basis of a distance between feature vectors of the super pixels i1 and i2.” identified as insignificant extra-solution activity above this is also WURC as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to Claims 9 and 19 reciting “Obtaining a Laplacian matrix for the weighted undirected graph;” identified as insignificant extra-solution activity above this is also WURC as court-identified see MPEP 2106.05(d)(II)(i).
With respect to Claims 10 and 20 reciting “The minimization problem is defined with the sparse coefficient, a linear transformation matrix used to project the sparse coefficient into a low-dimensional space, a target vector in the low-dimensional space, and L1 norm of the sparse coefficient and sparse coefficients of other data points.” identified as insignificant extra-solution activity above this is also WURC as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to Claim 11 reciting “Retrieving a corresponding dictionary based on a corresponding index;” identified as insignificant extra-solution activity above this is also WURC as court-identified see MPEP 2106.05(d)(II)(i).
Looking at the claim as a whole does not change this conclusion and the claim is ineligible.
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) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Lin et al. (U.S. Pre-Grant Publication No. 2020/0019817, hereinafter referred to as Lin) and further in view of Moody et al. (U.S. Pre-Grant Publication No. 2017/0213109, hereinafter referred to as Moody)
Regarding Claim 1:
Moody teaches a method for compressing multidimensional data having a plurality of data point into a compressed file, comprising:
Generating a plurality of super pixels from the multidimensional data;
Lin teaches “The scale of superpixel segmentation is different from that of traditional object-level segmentation; compared with the target of capturing a complete ground object, the superpixel focuses more on one part of the ground object accurately, and then covers the whole ground object completely through the combination of several superpixels. Such excessive segmentation scale enables a single segmentation area (superpixel) to more accurately match the boundary of the ground object locally. Therefore, for an ideal superpixel, we can assume that all of the pixels therein belong to the same ground object.” (Para. [0070])
Clustering the plurality of super pixels into a plurality of super pixel clusters;
Lin teaches “The scale of superpixel segmentation is different from that of traditional object-level segmentation; compared with the target of capturing a complete ground object, the superpixel focuses more on one part of the ground object accurately, and then covers the whole ground object completely through the combination of several superpixels. Such excessive segmentation scale enables a single segmentation area (superpixel) to more accurately match the boundary of the ground object locally. Therefore, for an ideal superpixel, we can assume that all of the pixels therein belong to the same ground object.” (Para. [0070])
Using the plurality of super pixel clusters to train a dictionary.
Lin teaches “ carrying out semi-supervised K-SVD dictionary learning on the training samples of a hyperspectral image, and thus to obtain an overcomplete dictionary;” (Para. [0011]).
Splitting the multidimensional data into a plurality of data sub-blocks;
Lin teaches “we usually can obtain superpixel split images of three scales that each superpixel contains an average of about 16, 64 and 256 pixels by using the existing superpixel split algorithm” (Para. [0085]).
Vectorizing the plurality of data sub-blocks to obtain a plurality of vectorized data sub-blocks;
Lin teaches “many pixel-level classifiers have been developed, including Support Vector Machine (SVM), support vector condition stochastic classifier, neural network, etc.” (Para. [0004]) thereby teaching vectorizing data for performing operations.
Wherein each dictionary is trained with a corresponding super pixel cluster by:
Dividing the corresponding super pixel cluster into a plurality of training data sub-blocks;
Lin teaches “The scale of superpixel segmentation is different from that of traditional object-level segmentation; compared with the target of capturing a complete ground object, the superpixel focuses more on one part of the ground object accurately, and then covers the whole ground object completely through the combination of several superpixels. Such excessive segmentation scale enables a single segmentation area (superpixel) to more accurately match the boundary of the ground object locally. Therefore, for an ideal superpixel, we can assume that all of the pixels therein belong to the same ground object.” (Para. [0070])
Vectorizing the plurality of training data sub-blocks to obtain a plurality of initial representations respectively;
Lin teaches “many pixel-level classifiers have been developed, including Support Vector Machine (SVM), support vector condition stochastic classifier, neural network, etc.” (Para. [0004]) thereby teaching vectorizing data for performing operations.
Calculating a similarity matrix between each pair of training data sub-blocks;
Lin teahes “in the field of classification of sparse representations, ideal sparse representations of samples of the same class shall be highly similar, and therefore, the Joint Sparse Model (JSM) is introduced for the sparse solution during the dictionary learning process.” (para. [0074])
Computing an average similarity value for each training data sub-block;
Lin teaches “in the field of classification of sparse representations, ideal sparse representations of samples of the same class shall be highly similar, and therefore, the Joint Sparse Model (JSM) is introduced for the sparse solution during the dictionary learning process.” (para. [0074])
Selecting a plurality of initial training data sub-blocks from the plurality of training data sub-blocks based on the computed average similarity values;
Lin teaches “To prevent the atomic number of the dictionary from being out of control, the original training samples are used as the initial dictionary.” (para. [0072]).
Initializing the dictionary by setting the initial training data sub-blocks as initial atoms of the dictionary;
Lin teaches “using the training samples of the hyperspectral image as the initial dictionary, and denoting the superpixel where the training samples xp are located” (Para. [0014]) and “To prevent the atomic number of the dictionary from being out of control, the original training samples are used as the initial dictionary.” (para. [0072]).
Training the dictionary to construct a sparse coding representation for each of the training data sub-blocks by performing a batch orthogonal matching pursuit (batch OMP) algorithm constrained with a target sparsity and a target residual threshold; and
Lin teaches “For the researches on solving the sparse representation of the original signal, the most classic ones are Matching Pursuit (MP) and Orthogonal Matching Pursuit (OMP). In MP and OMP, the solution of sparse representation is based on a signal (pixel) and the influence of spatial context is not considered. Based on this, Joint Sparse Model (JSM) and Synchronous Orthogonal Matching Pursuit (SOMP) appeared subsequently..” (Para. [0007]) and “Preferably, the sparse representation problem of T scales is solved by orthogonal matching pursuit.” (para. [0035]).
Updating the dictionary atom by atom with the constructed sparse coding representations to obtain a trained dictionary.
Lin teaches “optimize OMP solution method during dictionary update to a solution method based on Joint Sparse Model (JSM)” (Para. [0068]).
Lin explicitly teaches all of the elements of the claimed invention as recited above except:
Training a plurality of dictionaries respectively;
Selecting a dictionary for each of the vectorized data sub-blocks;
Sparse coding each of vectorized data sub-blocks with a corresponding selected dictionary to obtain a compressed representation; and
Storing the obtained compressed representations and indexes of corresponding selected dictionaries in the compressed file;
Wherein each dictionary is selected for a corresponding vectorized data sub-block by:
Calculating a similarity between the corresponding vectorized data sub-block and each of the super pixel clusters;
Finding a best-matched super pixel cluster having a highest similarity with the vectorized data sub-block;
Selecting a dictionary trained with the best-matched super pixel cluster as the coding dictionary;
However, in the related field of endeavor of classification of multispectral or hyperspectral satellite imagery, Moody teaches:
Training a plurality of dictionaries respectively;
Moody teaches “a plurality of different spatial resolutions is used to learn multiple distinct dictionaries.” (Para. [0114])
Selecting a dictionary for each of the vectorized data sub-blocks;
Moody teaches “a plurality of different spatial resolutions is used to learn multiple distinct dictionaries.” (Para. [0114]) and “A sparse representation is computed with respect to the learned dictionary at 220.” (Para. [0116]) thereby teaching necessarily selecting one of the multiple distinct dictionaries.
Sparse coding each of vectorized data sub-blocks with a corresponding selected dictionary to obtain a compressed representation; and
Moody teaches “The sparse representations, the learned dictionaries, or both, may be obtained using efficient convolutional sparse coding” (Para. [0003]) and “Using learned dictionaries provides dimensionality reduction, which is desirable in high data rate applications.” (Para. [0021]) where “A sparse representation is computed with respect to the learned dictionary at 220.” (Para. [0116]).
Storing the obtained compressed representations and indexes of corresponding selected dictionaries in the compressed file;
Moody teaches “outputting the dictionary {gm} and coefficient maps {ym} at 140.” (Para. [0112]) at the end of the dictionary learning process.
Wherein each dictionary is selected for a corresponding vectorized data sub-block by:
Calculating a similarity between the corresponding vectorized data sub-block and each of the super pixel clusters;
Moody teaches “The CoSA algorithm seeks to automatically identify land cover categories in an unsupervised fashion. In order to accomplish this, a k-means clustering algorithm may be used on feature vectors (e.g., patches including a spatial neighborhood) extracted from sparse approximations of multispectral or hyperspectral images found using dictionaries learned from efficient convolutional sparse coding” (para. [0099]).
Finding a best-matched super pixel cluster having a highest similarity with the vectorized data sub-block;
Moody teaches “The CoSA algorithm seeks to automatically identify land cover categories in an unsupervised fashion. In order to accomplish this, a k-means clustering algorithm may be used on feature vectors (e.g., patches including a spatial neighborhood) extracted from sparse approximations of multispectral or hyperspectral images found using dictionaries learned from efficient convolutional sparse coding” (para. [0099]) and “A large number of clustering scenarios should be considered in some embodiments. An important step is determining the number of clusters necessary for good classification from a domain expert point of view. Trained cluster centers may be used to generate land cover labels at the same spatial pixel resolution of an original image, such as a satellite image. Specifically, for every pixel in the image, a clustering classification label may be given based on its surrounding context (i.e., a patch centered on the respective pixel in the convolutional sparse representation). Each central pixel in a patch may therefore be assigned a classification level based on both its surrounding context and its spectral properties. One way to assess the quality of the full image land cover labels generated by CoSA is to quantify the resulting Euclidian intracluster distances when the entire image has been clustered.” (Para. [0100]).
Selecting a dictionary trained with the best-matched super pixel cluster as the coding dictionary;
Moody teaches “The CoSA algorithm seeks to automatically identify land cover categories in an unsupervised fashion. In order to accomplish this, a k-means clustering algorithm may be used on feature vectors (e.g., patches including a spatial neighborhood) extracted from sparse approximations of multispectral or hyperspectral images found using dictionaries learned from efficient convolutional sparse coding” (para. [0099]) and “A large number of clustering scenarios should be considered in some embodiments. An important step is determining the number of clusters necessary for good classification from a domain expert point of view.” (Para. [0100]) thereby using the best matched dictionary
Thus, it would have been obvious to one of ordinary skill in the art, having the teachings of Moody and Lin at the time that the claimed invention was effectively filed, to have modified the systems and methods for hyperspectral image classification technology as taught by Lin with the use of distinct dictionaries, as taught by Moody.
One would have been motivated to make such combination because Moody teaches “If the dictionary D is analytically defined and corresponds to a linear operator with a fast transform (e.g., the Discrete Wavelet Transform), a representation for an entire signal or image can readily be computed. More recently, however, it has been realized that improved performance can be obtained by learning the dictionary from a set of training data relevant to a specific problem” (Para. [0031]).
Regarding Claim 2:
Moody and Lin further teach
Wherein the batch OMP algorithm is performed to construct a sparse coding representation for each training data sub-block by:
(a) computing a gram matrix of the dictionary;
Lin teaches “K-SVD is composed of a sparse coding stage and a dictionary update stage: firstly, the sparse representation matrix Atrain in formula (1) is solved by fixed dictionary D and OMP algorithm.” (Para. [0063]).
(b) computing a correlation vector of the dictionary with respect to the data sub-block, where each element of the correlation vector corresponds to correlation between an atom of the dictionary and the data sub-block;
Lin teaches “the dictionary with ND atoms to be learned” (Para. [0063]) and “the second stage is started once A.sup.train is obtained, all the other atoms in the dictionary are fixed, the current atom is updated by SVD decomposition of error terms, and the second stage is ended when all the atoms in the dictionary are updated. Assume that the k.sup.th column of atoms are currently updated, denote the k.sup.th column as d.sub.k, and denote the corresponding k.sup.th row in the sparse matrix A.sup.train as a.sub.T.sup.k, so the sample matrix and dictionary approximation error after d.sub.ka.sub.T.sup.k is removed” (Para. [0064])
(c) finding the best-matched atom of the dictionary having the maximum correlation with the data sub-block;
Lin teaches “ the steps in selecting a new adaptive set are as follows: 1) finding a representing atom with the minimum residual error for each scale of each class; 2) combining the optimum atoms of all scales of each class into a cluster; 3) selecting the optimum cluster from clusters of all the classes and recording the index of atoms in the cluster as the new adaptive set.” (para. [0094]) and “wherein the function maxnum(L.sub.1) is used to obtain the label value l.sub.maxnum that occurs most often in the L.sub.1 vector. The final superpixel classification algorithm of multiscale sparse representation is obtained.” (Para. [0102]).
(d) applying Cholesky decomposition to a sub-set of the gram matrix containing index of the best-matched atom to obtain a Cholesky decomposition result;
Moody teaches “This can be effective when it is possible to precompute and store a lower upper (LU), which is a standard decomposition in linear algebra, or a Cholesky decomposition (or similar decomposition) of the system matrix.” (Para. [0050]).
(e) solving for the sparse coding representation using the Cholesky decomposition result;
Moody teaches “This can be effective when it is possible to precompute and store a lower upper (LU), which is a standard decomposition in linear algebra, or a Cholesky decomposition (or similar decomposition) of the system matrix.” (Para. [0050]).
(f) updating the correlation vector by subtracting a product of the sparse coding representation and the gram matrix;
Moody teaches “The only vector operations are scalar multiplication, subtraction, and inner products, rendering this method O(M) instead of O(M.sup.3) as in Eq. (20). The cost of solving such a system at all N frequency indices is O(MN), and the cost of the FFTs is O(MN log N). The cost of the FFTs dominates the computational complexity, whereas in Eq. (20), the cost of the solutions of the linear systems in the DFT domain dominates the cost of the FFTs.” (Para. [0059])
(g) multiplying the dictionary with the sparse coding representation to obtain a reconstructed signal;
Moody teaches “The only vector operations are scalar multiplication, subtraction, and inner products, rendering this method O(M) instead of O(M.sup.3) as in Eq. (20). The cost of solving such a system at all N frequency indices is O(MN), and the cost of the FFTs is O(MN log N). The cost of the FFTs dominates the computational complexity, whereas in Eq. (20), the cost of the solutions of the linear systems in the DFT domain dominates the cost of the FFTs.” (Para. [0059])
(h) updating a residual between the reconstructed signal and the training data sub-block;
Moody teaches “Some typical values for the penalty auto-update parameters in some embodiments” (para. [0061]).
(i) if the updated residual is greater than the target residual threshold, repeating steps (c) to (h); and
Moody teaches a threshold by teaching “where D is a dictionary matrix, x is the sparse representation, λ is a regularization parameter, E is a reconstruction error threshold, τ is a sparsity threshold, and R(•) denotes a sparsity inducing function such as the l.sup.1 norm or the l.sup.0 “norm.” While the l.sup.0 norm does not conform to all of the requirements of a real norm, it is convenient to write it using norm notation” (Para. [0029])
If the target sparsity is reached or the updated residual is smaller than the target residual threshold, outputting the sparse coding representation for the training data sub-block for further operation.
Moody teaches a threshold by teaching “where D is a dictionary matrix, x is the sparse representation, λ is a regularization parameter, E is a reconstruction error threshold, τ is a sparsity threshold, and R(•) denotes a sparsity inducing function such as the l.sup.1 norm or the l.sup.0 “norm.” While the l.sup.0 norm does not conform to all of the requirements of a real norm, it is convenient to write it using norm notation” (Para. [0029])
Regarding Claim 3:
Moody and Lin further teach wherein the plurality of super pixels is generated by:
Initializing a plurality of super pixel centers with a maximum interval;
Moody teaches “Specifically, for every pixel in the image, a clustering classification label may be given based on its surrounding context (i.e., a patch centered on the respective pixel in the convolutional sparse representation)” (Para. [0100]
Updating locations of the plurality of super pixel centers; and
(a) defining a plurality of overlapped super pixel regions centered by and corresponding to the plurality of super pixel centers respectively, each cluster region having a size equal to two times of the maximum interval of the super pixel centers;
(b) for each multi-dimensional data point:
Calculating, within each corresponding overlapped super pixel regions, a plurality of distances of a plurality of neighboring super pixel centers from the multi-dimensional data point; and
Moody teaches “When applied to images, this decomposition is usually applied independently to a set of overlapping image patches covering the image. This approach is convenient, but often necessitates somewhat ad hoc subsequent handling of the overlap between patches. This results in a representation over the whole image that is suboptimal.” (Para. [0029]).
Assigning the multi-dimensional data point to super pixel region containing a nearest neighbor super pixel that has a shortest distance from the multi-dimensional data point;
Moody teaches “ Vectors extracted from these sparse representations (e.g., pixel patches including a spatial neighborhood) can be used to perform unsupervised k-means clustering into land cover categories.” (Para. [0024])
(c) for each super pixel center: updating a current location of the super pixel center to a centroid position of all multi-dimensional data points assigned to the corresponding super pixel region; and
Moody teaches “Trained cluster centers may be used to generate land cover labels at the same spatial pixel resolution of an original image, such as a satellite image. Specifically, for every pixel in the image, a clustering classification label may be given based on its surrounding context (i.e., a patch centered on the respective pixel in the convolutional sparse representation). Each central pixel in a patch may therefore be assigned a classification level based on both its surrounding context and its spectral properties. One way to assess the quality of the full image land cover labels generated by CoSA is to quantify the resulting Euclidian intracluster distances when the entire image has been clustered.” (Para. [0100]).
(d) calculating a residual value between the updated location and the current location for each super pixel center;
Moody teaches a threshold by teaching “where D is a dictionary matrix, x is the sparse representation, λ is a regularization parameter, E is a reconstruction error threshold, τ is a sparsity threshold, and R(•) denotes a sparsity inducing function such as the l.sup.1 norm or the l.sup.0 “norm.” While the l.sup.0 norm does not conform to all of the requirements of a real norm, it is convenient to write it using norm notation” (Para. [0029])
(e) repeating steps (b) and (d) if any one of the residual values is greater than a threshold value.
Moody teaches a threshold by teaching “where D is a dictionary matrix, x is the sparse representation, λ is a regularization parameter, E is a reconstruction error threshold, τ is a sparsity threshold, and R(•) denotes a sparsity inducing function such as the l.sup.1 norm or the l.sup.0 “norm.” While the l.sup.0 norm does not conform to all of the requirements of a real norm, it is convenient to write it using norm notation” (Para. [0029])
Regarding Claim 4:
Moody and Lin further teach:
Wherein, the distance of a neighboring super pixel center to a multi-dimensional data point is calculated on basis of a color distance and a spatial distance between neighboring super pixel center to an image pixel.
Moody teaches “This approach may combine spectral and spatial textural characteristics to detect geologic, vegetative, and hydrologic features” (Para. [0017]) and “a k-means clustering algorithm may be used on feature vectors (e.g., patches including a spatial neighborhood)” (Para. [0024])
Regarding Claim 5:
Moody and Lin further teach:
Wherein the plurality of super pixels is clustered by performing a Laplacian sparse subspace clustering algorithm; and
Moody teaches “The amount of such training and validation data would have to be relatively large considering the dimensionality of the satellite imagery, the complexity of the terrain, and the parameter space of the CoSA algorithm, e.g., of the order of thousands up to tens of thousands of pixels at the equivalent WorldView-2™ resolution, for example. This may present an opportunity for field ecologists to generate such verified ground truth data. Meanwhile, in the absence of pixel-level ground truth, one way to assess the quality of the full image land cover labels generated by CoSA is to quantify their resulting intracluster distances.” (Para. [0101]).
The Laplacian sparse subspace clustering algorithm is performed by:
Extracting a plurality of feature vectors for the plurality of super pixels respectively;
Moody teaches “For each pixel in a given cluster, the values corresponding to its four normalized difference band indices may be extracted.” (para. [0104]).
Obtaining a covariance matrix for each super pixel;
Moody teaches “In some embodiments, the dictionary in the frequency domain is concatenated as a set of block matrices and each block matrix is a diagonal.” (Para. [0115] and “A sparse representation is computed with respect to the learned dictionary at 220. The sparse representation of the features in the first image are clustered into land cover categories at 230. In some embodiments, the clustering of the features in the first image includes using unsupervised k-means clustering.” (Para. [0116]).
Obtaining a similarity value for each pair of super pixels;
Lin teaches “selecting the optimum cluster from clusters of all the classes and recording the index of atoms in the cluster as the adaptive set.” (Para. [0039]).
Constructing a weighted undirected graph having a plurality of nodes representing the plurality of feature vectors, edges connecting the nodes;
Lin teaches “selecting the optimum cluster from clusters of all the classes and recording the index of atoms in the cluster as the adaptive set.” (para. [0039]).
Wherein each edge has a weight representing a similarity value between a pair of nodes connected by the edge;
Lin teaches “selecting the optimum cluster from clusters of all the classes and recording the index of atoms in the cluster as the adaptive set.” (Para. [0039]) where clusters are understood as having nodes and edges connecting the nodes based on similarities.
Partitioning the weighted undirected graph to obtain a plurality of clusters of super pixels.
Lin teaches “selecting the optimum cluster from clusters of all the classes and recording the index of atoms in the cluster as the adaptive set.” (Para. [0039])
Regarding Claim 6:
Moody and Lin further teach:
Wherein the feature vector of each super pixel is an average of feature vectors of all pixels with in the super pixel.
Lin teaches “we usually can obtain superpixel split images of three scales that each superpixel contains an average of about 16, 64 and 256 pixels by using the existing superpixel split algorithm” (Para. [0085]).
Regarding Claim 7:
Moody and Lin further teach:
Wherein the covariance matrix Mi of a super pixel Ci is given by:
M
i
a
,
b
=
1
r
i
-
1
*
(
∑
j
=
1
r
i
f
j
a
'
-
u
i
a
'
'
*
f
j
b
'
-
u
i
b
'
'
)
where Mi(a,b) denotes covariance matrix elements of features a and b in super pixel i, fi and ui are the pixel feature vector and mean feature vector within the super pixel j, fj(a’) denotes the a’th element of the feature vector and ui(a’’) denotes the a’’th element of the mean feature vector, fj(b’) denotes the b’th element of the feature vector and ui(b’’) denotes the b’’th element of the mean feature vector.
It is noted that the claim is merely reciting a standard structure of a sample covariance algorithm such as:
C
o
v
a
,
b
=
1
N
-
1
*
(
∑
a
-
a
'
*
b
-
b
'
)
And Moody teaches “shrinkage/soft thresholding” (Para. [0042]) which is understood as a regularization technique for high-dimensional covariance matrices and “diagonal matrices” (Para. [0049]) which are understood as being covariance matrices.
Regarding Claim 8:
Moody and Lin further teach:
Wherein the similarity values for a super pixel i1 and a super pixel i2 is calculated on basis of a distance between feature vectors of the super pixels i1 and i2.
Moody teaches “Normally, a domain expert manually derives land cover classification by taking into account both spectral information (e.g., normalized difference indices such as normalized difference vegetative index (NDVI), normalized difference wetness index (NDWI), normalized difference soil index (NDSI), and/or non-homogeneous feature distance (NHFD)), as well as spatial texture (i.e., context given by adjacent pixels).” (Para. [0023]).
Regarding Claim 9:
Moody and Lin further teach:
Wherein the weighted undirected graph is partitioned by:
Obtaining a Laplacian matrix for the weighted undirected graph;
Moody teaches “For each pixel in a given cluster, the values corresponding to its four normalized difference band indices may be extracted.” (para. [0104]).
Moody teaches “In some embodiments, the dictionary in the frequency domain is concatenated as a set of block matrices and each block matrix is a diagonal.” (Para. [0115] and “A sparse representation is computed with respect to the learned dictionary at 220. The sparse representation of the features in the first image are clustered into land cover categories at 230. In some embodiments, the clustering of the features in the first image includes using unsupervised k-means clustering.” (Para. [0116]).
Defining a minimization problem constrained by the Laplacian matrix;
Moody teaches “where D is a dictionary matrix, x is the sparse representation, λ is a regularization parameter, E is a reconstruction error threshold, τ is a sparsity threshold, and R(•) denotes a sparsity inducing function such as the l.sup.1 norm or the l.sup.0 “norm.”” (Par.a [0029]).
Solving the minimization problem for each super pixel to obtain a sparse coefficient for the super pixel;
Moody teaches “The two leading families of sparse coding methods are: (1) a wide variety of convex optimization algorithms (e.g., Alternating Direction Method of Multipliers (ADMM)) for solving Eq. (1) when R(x)=∥x∥.sub.1; and (2) a family of greedy algorithms (e.g., matching Pursuit (MP) and Orthogonal Matching Pursuit (OMP)) for providing an approximate solution for Eq. (2) or Eq. (3) when R(x)=∥x∥.sub.0.” (Para. [0030]).
Constructing a sparse coefficient matrix for the R subpixel super pixels
Moody teaches a threshold by teaching “where D is a dictionary matrix, x is the sparse representation, λ is a regularization parameter, E is a reconstruction error threshold, τ is a sparsity threshold, and R(•) denotes a sparsity inducing function such as the l.sup.1 norm or the l.sup.0 “norm.” While the l.sup.0 norm does not conform to all of the requirements of a real norm, it is convenient to write it using norm notation” (Para. [0029])
Updating a symmetry matrix with the sparse coefficient matrix;
Moody teaches “ using an iterated Sherman-Morrison algorithm for a dictionary update, and output a dictionary when stopping tolerances are met.” (Para. [0010])
Partitioning the weighted undirected graph based on the updated symmetry matrix.
Lin teaches “selecting the optimum cluster from clusters of all the classes and recording the index of atoms in the cluster as the adaptive set.” (Para. [0039])
Regarding Claim 10:
Moody and Lin further teach:
The minimization problem is defined with the sparse coefficient, a linear transformation matrix used to project the sparse coefficient into a low-dimensional space, a target vector in the low-dimensional space, and L1 norm of the sparse coefficient and sparse coefficients of other data points.
Moody teaches “However, the introduction of these copies makes it possible to try to solve the problem by an alternating minimization approach, holding {y.sub.m} constant and minimizing {x.sub.m}, followed by minimizing {y.sub.m}, then returning to {x.sub.m}, and so on. In some cases, such as here, solving these two sub-problems turns out to be easier than solving the original problem” (Para. [0039]).
Regarding Claim 11:
All of the limitations herein are similar to some or all of the limitations as recited in Claim 1.
Regarding Claim 12:
All of the limitations herein are similar to some or all of the limitations as recited in Claim 2.
Regarding Claim 13:
All of the limitations herein are similar to some or all of the limitations as recited in Claim 3.
Regarding Claim 14:
All of the limitations herein are similar to some or all of the limitations as recited in Claim 4.
Regarding Claim 15:
All of the limitations herein are similar to some or all of the limitations as recited in Claim 5.
Regarding Claim 16:
All of the limitations herein are similar to some or all of the limitations as recited in Claim 6.
Regarding Claim 17:
All of the limitations herein are similar to some or all of the limitations as recited in Claim 7.
Regarding Claim 18:
All of the limitations herein are similar to some or all of the limitations as recited in Claim 8.
Regarding Claim 19:
All of the limitations herein are similar to some or all of the limitations as recited in Claim 9.
Regarding Claim 20:
All of the limitations herein are similar to some or all of the limitations as recited in Claim 10.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Porikli (U.S. Patent No. 8,433,148) teaches compressing an image partitioned into blocks of pixels, for each block the method converts the block to a 2D matrix. The matrix is decomposing into a column matrix and a row matrix, wherein a width of the column matrix is substantially smaller than a height of the column matrix and the height of the row matrix is substantially smaller than the width of the row matrix. The column matrix and the row matrix are compressed, and the compressed matrices are then combined to form a compressed image.The reference further teaches “The SVD represents an expansion of the original data in a coordinate system where a covariance matrix is diagonal.” (Col. 4 Lines 57-58).
Zakhor et al. (U.S. Patent No. 7,003,039) teaches the creation of dictionary functions for the encoding of video signals using matching pursuit compression techniques. After an initial set of reference dictionary images is chosen, training video sequences are selected, and motion residuals are calculated. High energy portions of the residual images are extracted and stored when they match selection criteria with the reference dictionary. An energy threshold is used to limit the number of video signal "atoms" encoded for each frame, thus avoiding the encoding of noise. A new dictionary is then synthesized from the stored portions of the image residuals and the original reference dictionary. The process can then be repeated using the synthesized dictionary as the new reference dictionary. This achieves low bit rate signals with a higher signal-to-noise ratio than have been previously achieved.
Bresler et al. (U.S. Pre-Grant Publication No. 2015/0287223) teaches high quality image reconstructions from a relatively small number of noisy (or degraded) sensor imaging measurements or scans. The system includes a processing device and instructions. The processing device executes the instructions to employ transform learning as a regularizer for solving inverse problems when reconstructing an image from the imaging measurements, the instructions executable to: adapt a transform model to a first set of image patches of a first set of images containing at least a first image, to model the first set of image patches as sparse in a transform domain while allowing deviation from perfect sparsity; reconstruct a second image by minimizing an optimization objective comprising a transform-based regularizer that employs the transform model, and a data fidelity term formed using the imaging measurements; and store the second image in the computer-readable medium, the second image displayable on a display device.
Wohlberg (U.S. Pre-Grant Publication No. 2016/0335224) teaches fast dictionary learning solving the convolutional sparse coding problem in the Fourier domain. More specifically, efficient convolutional sparse coding may be derived within an alternating direction method of multipliers (ADMM) framework that utilizes fast Fourier transforms (FFT) to solve the main linear system in the frequency domain. Such algorithms may enable a significant reduction in computational cost over conventional approaches by implementing a linear solver for the most critical and computationally expensive component of the conventional iterative algorithm. The theoretical computational cost of the algorithm may be reduced from O(M.sup.3N) to O(MN log N), where N is the dimensionality of the data and M is the number of elements in the dictionary. This significant improvement in efficiency may greatly increase the range of problems that can practically be addressed via convolutional sparse representations.
Aharon et al. (U.S. Pre-Grant Publication No. 2014/0037199) teaches a signal processing system adapted for sparse representation of signals is provided, comprising: (i) one or more training signals; (ii) a dictionary containing signal-atoms; (iii) a representation of each training signal using a linear combination of said dictionary's signal-atoms; (iv) means for updating the representation of the training signal; (v) means for updating the dictionary one group of atoms at a time, wherein each atom update may include all representations referring to said updated atom; and (vi) means for iterating (iv) and (v) until a stopping rule is fulfilled. The system uses the K-SVD algorithm for designing dictionaries for sparse representation of signals.The reference further teaches a laplace distribution approach (Paras. [0009] & [0037]) and “Based on the expectation maximization procedure, the K-Means can be extended to suggest a fuzzy assignment and a covariance matrix per each cluster, so that the data is modeled as a mixture of Gaussians” (Para. [0062]).
Stanitsas et al. (U.S. Pre-Grant Publication No. 2018/0165809) teaches an imager, a processor, and an output module. The imager is configured to provide a plurality of tissue images. The processor is coupled to the imager and is configured to receive the plurality of images. The processor is coupled to a memory. The memory has instructions for determining classification of a region of tissue associated with the plurality of tissue images. Determining classification includes fusing discriminator outputs from a region covariance descriptor and from a normalized color histogram discriminator. The output module is coupled to the processor. The output module is configured to provide a three dimensional representation of the tissue.The reference further teaches constructing a covariance matrix (Paras. [0069] and [0139]-[0141])
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/ROBERT F MAY/Examiner, Art Unit 2154 2/7/2026
/BORIS GORNEY/Supervisory Patent Examiner, Art Unit 2154