DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In view of the appeal brief filed on 03/25/2026, PROSECUTION IS HEREBY REOPENED. New grounds of rejection are set forth below.
To avoid abandonment of the application, appellant must exercise one of the following two options:
(1) file a reply under 37 CFR 1.111 (if this Office action is non-final) or a reply under 37 CFR 1.113 (if this Office action is final); or,
(2) initiate a new appeal by filing a notice of appeal under 37 CFR 41.31 followed by an appeal brief under 37 CFR 41.37. The previously paid notice of appeal fee and appeal brief fee can be applied to the new appeal. If, however, the appeal fees set forth in 37 CFR 41.20 have been increased since they were previously paid, then appellant must pay the difference between the increased fees and the amount previously paid.
A Supervisory Patent Examiner (SPE) has approved of reopening prosecution by signing below:
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Response to Arguments
Applicant’s arguments with respect to claims 1 and 11 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Therefore, the claims stand rejected.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 5, 7-9, 11-13, 15, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Ntziachristos, et al., US 20150247999 A1 in view of Boyden, et al., US 20090281412 A1 and Sarvazyan, N., US 20200008681 A1
Regarding claim 1, Ntziachristos teaches a photoacoustic imaging method (see abstract) comprising:
receiving multi-spectral photoacoustic image data (paragraphs 64-66 describe collection of opto-acoustic data for multi-spectral opto-acoustic tomography (MSOT)) from a photoacoustic imaging system (paragraphs 64-66 describe collection of opto-acoustic data for multi-spectral opto-acoustic tomography (MSOT));
pre-processing the multi-spectral photoacoustic image data, wherein the pre-processing comprises determining a number of components above a background distribution of the multi-spectral photoacoustic image data (paragraph 70 indicates using multivariate data analysis and matrix factorization algorithms, such as principal component analysis (PCA), non-negative matrix factorization (NNMF), multivariate curve resolution (MCR) or independent component analysis (ICA) in spectral unmixing methods (paragraphs 66-69) to separate from background distribution The separation of the principal components from the background distribution inherently removes unwanted spectral data);
detecting tissue chromophores based on the number of components from the multi-spectral photoacoustic image data using an unsupervised spectral unmixing process based on limited or no human intervention (paragraph 60-69 indicate reconstructing a spatial distribution (image) of the chromophore of interest using the above mentioned PCA and ICA. NB: Paragraph 26 of the P.G. Pub version of the specification states that “The unsupervised spectral unmixing process or algorithm, for example, can be singular value decomposition (SVD), principal component analysis (PCA), sparse filtering (SF), independent component analysis (ICA)…” and paragraph 28 states that “the unsupervised spectral unmixing process or algorithm can allow a system, apparatus, or processor to learn from multi-spectral photoacoustic image data with limited or no human intervention by detecting or recognizing representative spectral patterns”. Since Ntziachristos teaches the processor (data processor of paragraph 87) that executes the PCA and ICA disclosed in paragraphs 60-69, which is disclosed in the specification as being the unsupervised spectral unmixing process or algorithm, Ntziachristos achieves the recited “using an unsupervised spectral unmixing process based on limited or no human intervention”), and
display the detected tissue chromophores in an abundance map (paragraph 68 indicates that a spatial distribution (image) of the chromophore is generated for display on the display in paragraph 87).
Ntziachristos does not teach that the background distribution, that is, the determining of a number of components, comprises a noise floor above which the principal components are separated, and wherein the unsupervised spectral unmixing process comprises clustering of the number components from the multi-spectral photoacoustic image data and windowing of the number of components from the multi-spectral photoacoustic image data.
However, within the same field of endeavor, Boyden teaches a photoacoustic blood occlusion monitoring system (paragraph 32) wherein the background distribution, that is, the determining of a number of components, comprises a noise floor above which the principal components are separated (“spectral information associated with a detected emitted or remitted energy is clustered into related groups based on similarity, dissimilarity, pairwise similarities, distances from a threshold value (e.g., a cluster centroid deviation)” paragraph 229) and wherein the unsupervised spectral unmixing process comprises clustering of the number components from the multi-spectral photoacoustic image data (“clustering includes assigning spectral information into clusters such that spectral parameters from the same cluster are more similar to each other than spectral parameters from different clusters” paragraph 230); and windowing of the number of components from the multi-spectral photoacoustic image data (“At 1020, the method 1000 includes partitioning the spectral information into one or more information subsets. At 1022, partitioning the spectral information into the one or more information subsets includes grouping the spectral information into one or more information subsets using a clustering protocol” paragraph 259).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Ntziachristos wherein the background distribution, that is, the determining of a number of components, comprises a noise floor above which the principal components are separated, and wherein the unsupervised spectral unmixing process comprises clustering of the number components from the multi-spectral photoacoustic image data; and windowing of the number of components from the multi-spectral photoacoustic image data, as taught by Boyden, to allow for better signal to noise, and may minimize the effect of other spectral parameters of the body that vary over time, paragraph 89.
Ntziachristos in view of Boyden fails to teach wherein the clustering includes grouping the number of components from the multi-spectral photoacoustic image data based on a correlation value; and wherein the windowing includes determining a group of the number of components from the multi-spectral photoacoustic image data based on a comparison of the correlation value to a threshold value; and characterizing at least one biomarker based on the detected tissue chromophores; and providing the at least one biomarker for assessment or monitoring of a disease or medical condition.
However, within the same field of endeavor, Sarvazyan teaches systems and methods for hyperspectral analysis of cardiac tissue including visualizing ablation lesions includes illuminating at one or more illumination wavelengths a surface of tissue having an ablation lesion; collecting a spectral data set comprising spectral images of the illuminated tissue acquired at multiple spectral bands each at one or more acquisition wavelengths; distinguishing between the ablation lesion and an unablated tissue based on one or more spectral differences between the ablation lesion and unablated tissue; and creating a composite image of the tissue showing the ablation lesion and the unablated tissue (see abstract). Paragraphs 49-50 of Sarvazyan indicate that as shown in FIG. 2A, FIG. 2B and FIG. 2C, the spatially resolved spectral imaging obtained by HSI can provide diagnostic information about the tissue physiology, morphology, and composition. The spectra from each pixel can be classified into different subsets using principal component analysis or other mathematical algorithms referred hereafter as spectral unmixing. Sarvazyan then teaches wherein the clustering includes grouping the number of components from the multi-spectral photoacoustic image data based on a correlation value; and wherein the windowing includes determining a group of the number of components from the multi-spectral photoacoustic image data based on a comparison of the correlation value to a threshold value, indicating in paragraph 50 that pixels with spectra that match the target spectrum to a specified level of confidence are then marked as potential targets. The target spectrum comprising the claimed correlation value and the specified level of confidence comprising the threshold. Sarvazyan also teaches characterizing at least one biomarker based on the detected tissue chromophores in paragraph 51; and providing the at least one biomarker for assessment or monitoring of a disease or medical condition (paragraphs 54 and 66).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Ntziachristos, as modified by Boyden, wherein the clustering includes grouping the number of components from the multi-spectral photoacoustic image data based on a correlation value; and wherein the windowing includes determining a group of the number of components from the multi-spectral photoacoustic image data based on a comparison of the correlation value to a threshold value; and characterizing at least one biomarker based on the detected tissue chromophores; and providing the at least one biomarker for assessment or monitoring of a disease or medical condition, as taught by Sarvazyan, to precisely identify locations, shape, size, and depth of the tissue of interest (paragraph 82).
Regarding claim 2, Ntziachristos in view of Boyden and Sarvazyan teaches all the limitations of claim 1.
Ntziachristos fails to teach displaying a component spectra with the determined number of components from the multi-spectral photoacoustic image data.
However, Boyden further teaches displaying a component spectra with the determined number of components from the multi-spectral photoacoustic image data (paragraph 170 discloses “a visual display of at least one spectral parameter, and the like” ).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Ntziachristos including displaying a component spectra with the determined number of components from the multi-spectral photoacoustic image data, as taught by Boyden, to allow for better signal to noise, and may minimize the effect of other spectral parameters of the body that vary over time, paragraph 89.
Regarding claim 3, Ntziachristos in view of Boyden and Sarvazyan teaches all the limitations of claim 2.
Ntziachristos further teaches determining a disease or medical condition based on at least one of the abundance map or the component spectra (paragraph 102 indicates using the disclosed multi-spectral method for oral cancers, peripheral arterial disease, oxygenation measurements).
Regarding claim 5, Ntziachristos in view of Boyden and Sarvazyan teaches all the limitations of claim 1.
Ntziachristos further teaches wherein the unsupervised spectral unmixing process comprises nonnegative matrix factorization(paragraph 70 discloses using non-negative matrix factorization (NNMF)).
Regarding claim 7, Ntziachristos in view of Boyden and Sarvazyan teaches all the limitations of claim 1.
Ntziachristos further teaches wherein the unsupervised spectral unmixing process comprises singular value decomposition, principal component analysis, independent component analysis, reconstruction independent component analysis, or sparse filtering (paragraph 70 discloses using PCA and ICA).
Regarding claim 8, Ntziachristos in view of Boyden and Sarvazyan teaches all the limitations of claim 1.
Ntziachristos further teaches wherein at least one of the number of components or noise floor is determined using an eigenvalue algorithm(paragraph 71).
Regarding claim 9, Ntziachristos in view of Boyden and Sarvazyan teaches all the limitations of claim 1.
Ntziachristos in view of Boyden fail to teach wherein the clustering and windowing comprises: dividing the multi-spectral photoacoustic image data into one or more subsets; and searching for the number of components in the one or more subsets.
However, Boyden further teaches wherein the clustering and windowing comprises: dividing the multi-spectral photoacoustic image data into one or more subsets; and searching for the number of components in the one or more subsets (paragraph 259 states that “At 1022, partitioning the spectral information into the one or more information subsets includes grouping the spectral information into one or more information subsets using a clustering protocol” and paragraph 260 states that “At 1030, the method 1000 includes comparing at least one parameter associated with a second spectral information from a biological subject associated to at least one parameter associated with at least one of the one or more information subsets. At 1040, the method 1000 may include generating a response based on the comparison of the at least one parameter associated with the second spectral information to the at least one parameter associated with at least one of the one or more information subset”. In performing the comparison step, the subset is searched for the one or more spectral information within the subset).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Ntziachristos wherein the clustering and windowing comprises: dividing the multi-spectral photoacoustic image data into one or more subsets; and searching for the number of components in the one or more subsets, as taught by Boyden, to allow for better signal to noise, and may minimize the effect of other spectral parameters of the body that vary over time, paragraph 89.
Regarding claim 11, Ntziachristos teaches a photoacoustic imaging apparatus (hybrid optical/opto-acoustic imaging apparatus 100 of paragraph 83) comprising: at least one memory comprising computer program code: at least one processor (data processor of paragraph 87):
wherein the at least one memory comprising the computer program code are configured, with the at least one processor (paragraph 87 states that “data processor which is adapted to process the signals collected with the light detector device 20 and the acoustic detector device 30 and to create an optical image, an opto-acoustic image and/or a hybrid optical/opto-acoustic image 42” and it is inherent/necessarily require that the image reconstruction device 40 further comprises at least one memory/storage comprising computer program code for processing the signals into an image), to cause the photoacoustic imaging apparatus at least to:
receive multi-spectral photoacoustic image data (paragraphs 64-66 describe collection of opto-acoustic data for multi-spectral opto-acoustic tomography (MSOT)):
pre-process the multi-spectral photoacoustic image data, wherein the pre-processing comprises determining a number of components above a background distribution of the multi-spectral photoacoustic image data (paragraph 70 indicates using multivariate data analysis and matrix factorization algorithms, such as principal component analysis (PCA), non-negative matrix factorization (NNMF), multivariate curve resolution (MCR) or independent component analysis (ICA) in spectral unmixing methods (paragraphs 66-69) to separate from a background distribution . The separation of the principal components from the background distribution inherently removes unwanted spectral data):
detect tissue chromophores based on the number of significant components from the multi-spectral photoacoustic image data using an unsupervised spectral unmixing process based on limited or no human intervention (paragraph 60-69 indicate reconstructing a spatial distribution (image) of the chromophore of interest using the above mentioned PCA and ICA. NB: Paragraph 26 of the P.G. Pub version of the specification states that “The unsupervised spectral unmixing process or algorithm, for example, can be singular value decomposition (SVD), principal component analysis (PCA), sparse filtering (SF), independent component analysis (ICA)…” and paragraph 28 states that “the unsupervised spectral unmixing process or algorithm can allow a system, apparatus, or processor to learn from multi-spectral photoacoustic image data with limited or no human intervention by detecting or recognizing representative spectral patterns”. Since Ntziachristos teaches the processor (data processor of paragraph 87) that executes the PCA and ICA disclosed in paragraphs 60-69, which is disclosed in the specification as being the unsupervised spectral unmixing process or algorithm, Ntziachristos achieves the recited “using an unsupervised spectral unmixing process based on limited or no human intervention”).
display the detected tissue chromophores in an abundance map (paragraph 68 indicates that a spatial distribution (image) of the chromophore is generated for display on the display in paragraph 87).
Ntziachristos does not teach that the background distribution, that is, the determining of a number of components, comprises a noise floor above which the principal components are separated, and wherein the unsupervised spectral unmixing process comprises clustering of the number components from the multi-spectral photoacoustic image data; and windowing of the number of components from the multi-spectral photoacoustic image data.
However, within the same field of endeavor, Boyden teaches a photoacoustic blood occlusion monitoring system (paragraph 32) wherein the background distribution, that is, the determining of a number of components, comprises a noise floor above which the principal components are separated (“spectral information associated with a detected emitted or remitted energy is clustered into related groups based on similarity, dissimilarity, pairwise similarities, distances from a threshold value (e.g., a cluster centroid deviation)” paragraph 229) and wherein the unsupervised spectral unmixing process comprises clustering of the number components from the multi-spectral photoacoustic image data (“clustering includes assigning spectral information into clusters such that spectral parameters from the same cluster are more similar to each other than spectral parameters from different clusters” paragraph 230); and windowing of the number of components from the multi-spectral photoacoustic image data (“At 1020, the method 1000 includes partitioning the spectral information into one or more information subsets. At 1022, partitioning the spectral information into the one or more information subsets includes grouping the spectral information into one or more information subsets using a clustering protocol” paragraph 259).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Ntziachristos wherein the background distribution, that is, the determining of a number of components, comprises a noise floor above which the principal components are separated, and wherein the unsupervised spectral unmixing process comprises clustering of the number components from the multi-spectral photoacoustic image data; and windowing of the number of components from the multi-spectral photoacoustic image data, as taught by Boyden, to allow for better signal to noise, and may minimize the effect of other spectral parameters of the body that vary over time, paragraph 89.
Ntziachristos in view of Boyden fails to teach wherein the clustering includes grouping the number of components from the multi-spectral photoacoustic image data based on a correlation value; and wherein the windowing includes determining a group of the number of components from the multi-spectral photoacoustic image data based on a comparison of the correlation value to a threshold value; and characterizing at least one biomarker based on the detected tissue chromophores; and providing the at least one biomarker for assessment or monitoring of a disease or medical condition.
However, within the same field of endeavor, Sarvazyan teaches systems and methods for hyperspectral analysis of cardiac tissue including visualizing ablation lesions includes illuminating at one or more illumination wavelengths a surface of tissue having an ablation lesion; collecting a spectral data set comprising spectral images of the illuminated tissue acquired at multiple spectral bands each at one or more acquisition wavelengths; distinguishing between the ablation lesion and an unablated tissue based on one or more spectral differences between the ablation lesion and unablated tissue; and creating a composite image of the tissue showing the ablation lesion and the unablated tissue (see abstract). Paragraphs 49-50 of Sarvazyan indicate that as shown in FIG. 2A, FIG. 2B and FIG. 2C, the spatially resolved spectral imaging obtained by HSI can provide diagnostic information about the tissue physiology, morphology, and composition. The spectra from each pixel can be classified into different subsets using principal component analysis or other mathematical algorithms referred hereafter as spectral unmixing. Sarvazyan then teaches wherein the clustering includes grouping the number of components from the multi-spectral photoacoustic image data based on a correlation value; and wherein the windowing includes determining a group of the number of components from the multi-spectral photoacoustic image data based on a comparison of the correlation value to a threshold value, indicating in paragraph 50 that pixels with spectra that match the target spectrum to a specified level of confidence are then marked as potential targets. The target spectrum comprising the claimed correlation value and the specified level of confidence comprising the threshold. Sarvazyan also teaches characterizing at least one biomarker based on the detected tissue chromophores in paragraph 51; and providing the at least one biomarker for assessment or monitoring of a disease or medical condition (paragraphs 54 and 66).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Ntziachristos, as modified by Boyden, wherein the clustering includes grouping the number of components from the multi-spectral photoacoustic image data based on a correlation value; and wherein the windowing includes determining a group of the number of components from the multi-spectral photoacoustic image data based on a comparison of the correlation value to a threshold value; and characterizing at least one biomarker based on the detected tissue chromophores; and providing the at least one biomarker for assessment or monitoring of a disease or medical condition, as taught by Sarvazyan, to precisely identify locations, shape, size, and depth of the tissue of interest (paragraph 82).
Regarding claim 12, Ntziachristos in view of Boyden and Sarvazyan teaches all the limitations of claim 11.
Ntziachristos fails to teach wherein the at least one memory comprising the computer program code are configured, with the at least one processor. to cause the apparatus at least to: display a component spectra with the determined number of components from the multi-spectral photoacoustic image data.
However, Boyden further teaches wherein the at least one memory (paragraph 34) comprising the computer program code are configured, with the at least one processor (processor of paragraph 39) to cause the apparatus at least to: display a component spectra with the determined number of components from the multi-spectral photoacoustic image data (paragraph 170 discloses “a visual display of at least one spectral parameter, and the like”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Ntziachristos, wherein the at least one memory comprising the computer program code are configured, with the at least one processor to cause the apparatus at least to: display a component spectra with the determined number of components from the multi-spectral photoacoustic image data, as taught by Boyden, hence providing an easy and accurate method of identifying regions of interest within the spectral data (paragraphs 11-12)
Regarding claim 13, Ntziachristos in view of Boyden and Sarvazyan teaches all the limitations of claim 12.
Ntziachristos fails to teach wherein the at least Ntziachristos further teaches wherein the at least one memory comprising the computer program code are configured, with the at least one processor, to cause the apparatus at least to: determine a disease or medical condition based on at least one of the abundance map or the component spectra (paragraph 102 indicates using the disclosed multi-spectral method for oral cancers, peripheral arterial disease, oxygenation measurements).
Regarding claim 15, Ntziachristos in view of Boyden and Sarvazyan teaches all the limitations of claim 11.
Ntziachristos fails to teach wherein the at least Ntziachristos further teaches wherein the unsupervised spectral unmixing process comprises nonnegative matrix factorization (paragraph 70 discloses using non-negative matrix factorization (NNMF)).
Regarding claim 17, Ntziachristos in view of Boyden and Sarvazyan teaches all the limitations of claim 11.
Ntziachristos fails to teach wherein the at least Ntziachristos further teaches wherein the unsupervised spectral unmixing process comprises singular value decomposition, principal component analysis, independent component analysis, reconstruction independent component analysis, or sparse filtering (paragraph 70 discloses using PCA and ICA).
Regarding claim 18, Ntziachristos in view of Boyden Sarvazyan teaches all the limitations of claim 11.
Ntziachristos fails to teach wherein the at least Ntziachristos further teaches wherein at least one of the number of components or noise floor is determined using an eigenvalue algorithm (paragraph 71).
Regarding claim 19, Ntziachristos in view of Boyden and Sarvazyan teaches all the limitations of claim 11.
Ntziachristos fails to teach wherein the at least Ntziachristos in view of Boyden fail to teach wherein the clustering and windowing comprises: dividing the multi-spectral photoacoustic image data into one or more subsets; and searching for the number of components in the one or more subsets.
However, Boyden further teaches wherein the clustering and windowing comprises: dividing the multi-spectral photoacoustic image data into one or more subsets; and searching for the number of components in the one or more subsets (paragraph 259 states that “At 1022, partitioning the spectral information into the one or more information subsets includes grouping the spectral information into one or more information subsets using a clustering protocol” and paragraph 260 states that “At 1030, the method 1000 includes comparing at least one parameter associated with a second spectral information from a biological subject associated to at least one parameter associated with at least one of the one or more information subsets. At 1040, the method 1000 may include generating a response based on the comparison of the at least one parameter associated with the second spectral information to the at least one parameter associated with at least one of the one or more information subset”. In performing the comparison step, the subset is searched for the one or more spectral information within the subset).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Ntziachristos wherein the clustering and windowing comprises: dividing the multi-spectral photoacoustic image data into one or more subsets; and searching for the number of components in the one or more subsets, as taught by Boyden, to allow for better signal to noise, and may minimize the effect of other spectral parameters of the body that vary over time, paragraph 89.
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Ntziachristos, et al., US 20150247999 A1 in view of Boyden and Sarvazyan, as applied to claims 1 and 11, respectively above, and further in view of Alfano, et al., US 20050240107 A1.
Regarding claim 4, Ntziachristos in view of Boyden and Sarvazyan teaches all the limitations of claim 2.
Ntziachristos in view of Boyden and Sarvazyan fails to teach wherein the component spectra represents a pure molecule absorption spectrum extracted from the multi-spectral photoacoustic image data.
However, Alfano teaches spectral optical imaging at one or more key water absorption fingerprint wavelengths measures the difference in water content between a region of cancerous or precancerous tissue and a region of normal tissue (see abstract), including the calculation of chromophore in tissues (paragraphs 11-12) wherein the component spectra represents a pure molecule absorption spectrum extracted from the multi-spectral photoacoustic image data (see fig. 5 and paragraph 62 where the absorption spectra of pure water is disclosed).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Ntziachristos as modified by Boyden and Sarvazyan such that the component spectra represents a pure molecule absorption spectrum extracted from the multi-spectral photoacoustic image data, as taught by Alfano, providing an effective way of differentiating tissues suspected of being diseased (paragraphs 25-28).
Regarding claim 14, Ntziachristos in view of Boyden and Sarvazyan teaches all the limitations of claim 11.
Ntziachristos in view of Boyden and Sarvazyan fails to teach wherein the component spectra represents a pure molecule absorption spectrum extracted from the multi-spectral photoacoustic image data.
However, Alfano teaches spectral optical imaging at one or more key water absorption fingerprint wavelengths measures the difference in water content between a region of cancerous or precancerous tissue and a region of normal tissue (see abstract), including the calculation of chromophore in tissues (paragraphs 11-12) wherein the component spectra represents a pure molecule absorption spectrum extracted from the multi-spectral photoacoustic image data (see fig. 5 and paragraph 62 where the absorption spectra of pure water is disclosed).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Ntziachristos as modified by Boyden and Sarvazyan such that the component spectra represents a pure molecule absorption spectrum extracted from the multi-spectral photoacoustic image data, as taught by Alfano, providing an effective way of differentiating tissues suspected of being diseased (paragraphs 25-28).
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Ntziachristos, et al., US 20150247999 A1 in view of Boyden and Sarvazyan, as applied to claims 5 and 15, respectively, and further in view of Montcuquet, et al., US 20120032094 A1.
Regarding claim 6, Ntziachristos in view of Boyden and Sarvazyan teaches all the limitations of claim 5.
Ntziachristos in view of Boyden and Sarvazyan fails to teach wherein the nonnegative matrix factorization is represented by
PNG
media_image1.png
41
270
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Greyscale
, where W represents abundance distribution component values, S represents main spectral curves, and X represents the multi-spectral observations.
However, Montcuquet teaches locating at least one fluorescent tag in a scattering medium, wherein: a) at least one tag is introduced into the medium, b) a fluorescence image is performed by an infrared excitation of the medium along a first axis, the image including a fluorescence component due to the tag, and an auto-fluorescence component due to a medium part other than the tags, c) the image is processed by factorizing into two non-negative matrices, and d) an image of the distribution of the tag(s) is determined, without the auto-fluorescence component (see abstract), wherein the nonnegative matrix factorization (paragraph 149 indicates an algorithm for factorizing into non-negative matrices) is represented by
PNG
media_image1.png
41
270
media_image1.png
Greyscale
, where W represents abundance distribution component values, S represents main spectral curves, and X represents the multi-spectral observations (paragraphs 151-170 indicates minimization of a cost function QFMN. Specifically, the equations in paragraph 156, 159 and 164).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to Ntziachristos as modified Boyden and Sarvazyan such that the nonnegative matrix factorization is represented by
PNG
media_image1.png
41
270
media_image1.png
Greyscale
, where W represents abundance distribution component values, S represents main spectral curves, and X represents the multi-spectral observations, allowing smoothing of spectral data (paragraph 156), and hence allowing differentiation of the data from unwanted data (paragraph 3).
Regarding claim 16, Ntziachristos in view of Boyden teaches all the limitations of claim 15.
Ntziachristos in view of Boyden and Sarvazyan fail to teach wherein the nonnegative matrix factorization is represented by
PNG
media_image1.png
41
270
media_image1.png
Greyscale
, where W represents abundance distribution component values, S represents main spectral curves, and X represents the multi-spectral observations.
However, Montcuquet teaches locating at least one fluorescent tag in a scattering medium, wherein: a) at least one tag is introduced into the medium, b) a fluorescence image is performed by an infrared excitation of the medium along a first axis, the image including a fluorescence component due to the tag, and an auto-fluorescence component due to a medium part other than the tags, c) the image is processed by factorizing into two non-negative matrices, and d) an image of the distribution of the tag(s) is determined, without the auto-fluorescence component (see abstract), wherein the nonnegative matrix factorization (paragraph 149 indicates an algorithm for factorizing into non-negative matrices) is represented by
PNG
media_image1.png
41
270
media_image1.png
Greyscale
, where W represents abundance distribution component values, S represents main spectral curves, and X represents the multi-spectral observations (paragraphs 151-170 indicates minimization of a cost function QFMN. Specifically, the equations in paragraph 156, 159 and 164).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to Ntziachristos as modified Boyden and Sarvazyan such that the nonnegative matrix factorization is represented by
PNG
media_image1.png
41
270
media_image1.png
Greyscale
, where W represents abundance distribution component values, S represents main spectral curves, and X represents the multi-spectral observations, allowing smoothing of spectral data (paragraph 156), and hence allowing differentiation of the data from unwanted data (paragraph 3).
Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Ntziachristos, et al., US 20150247999 A1 in view of Boyden and Sarvazyan as applied to claims 1 and 11, respectively above, and further in view of Mihashi, et al., US 20080007691 A1.
Regarding claim 10, Ntziachristos in view of Boyden teaches all the limitations of claim 1.
Ntziachristos further teaches wherein the data reduction comprises using a squared region of interest of 4x4 pixels, stating in paragraph 67 that “Spectral unmixing methods based on differential or fitting algorithms use the known spectral information to process the image on a pixel-by-pixel basis. Those methods try to find the source component (e.g. a distribution of a certain contrast agent) that best fits its known absorption spectrum in the least-squares sense”. While Ntziachristos does not specifically state that the region is a 4x4 matrix of pixels, selecting such an area as the region of interest comprises Use of Known Technique To Improve Similar Devices (Methods, or Products) in the Same Way. See MPEP 2143(I)(C).
Ntziachristos in view of Boyden and Sarvazyan fails to teach wherein the pre-processing of the multi- spectral photoacoustic image data further comprises at least one of data correction or data reduction, wherein the data correction comprises a Gaussian filter.
However, Mihashi further teaches wherein the pre-processing of the multi-spectral photoacoustic image data further comprises at least one of data correction or data reduction, wherein the data correction comprises a Gaussian filter (paragraph 88 indicates applying a Laplacian-Gaussian filter to extract edges with high contrast and applies a gaussian function to smooth them in order to remove noise).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Ntziachristos as modified Boyden and Sarvazyan, such that the pre-processing of the multi- spectral photoacoustic image data further comprises at least one of data correction or data reduction, wherein the data correction comprises a Gaussian filter, as taught by Mihashi, hence providing a resultant high-contrast image (paragraph 89) that provide a way of identifying each part in spectral fundus images based on its spectral characteristic easily and accurately (paragraph 90).
Regarding claim 20, Ntziachristos in view of Boyden and Sarvazyan teaches all the limitations of claim 11.
Ntziachristos further teaches wherein the data reduction comprises using a squared region of interest of 4x4 pixels, stating in paragraph 67 that “Spectral unmixing methods based on differential or fitting algorithms use the known spectral information to process the image on a pixel-by-pixel basis. Those methods try to find the source component (e.g. a distribution of a certain contrast agent) that best fits its known absorption spectrum in the least-squares sense”. While Ntziachristos does not specifically state that the region is a 4x4 matrix of pixels, selecting such an area as the region of interest comprises Use of Known Technique To Improve Similar Devices (Methods, or Products) in the Same Way. See MPEP 2143(I)(C).
Ntziachristos in view of Boyden and Sarvazyan fails to teach wherein the pre-processing of the multi- spectral photoacoustic image data further comprises at least one of data correction or data reduction, wherein the data correction comprises a Gaussian filter.
However, Mihashi further teaches wherein the pre-processing of the multi-spectral photoacoustic image data further comprises at least one of data correction or data reduction, wherein the data correction comprises a Gaussian filter (paragraph 88 indicates applying a Laplacian-Gaussian filter to extract edges with high contrast and applies a gaussian function to smooth them in order to remove noise).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Ntziachristos as modified Boyden and Sarvazyan such that the pre-processing of the multi- spectral photoacoustic image data further comprises at least one of data correction or data reduction, wherein the data correction comprises a Gaussian filter, as taught by Mihashi, hence providing a resultant high-contrast image (paragraph 89) that provide a way of identifying each part in spectral fundus images based on its spectral characteristic easily and accurately (paragraph 90).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Hysi, et al., US 20230050956 A1 discloses a system and associated method for performing collagen assessment of an object using Photoacoustic Image (PA) data obtained for the object, wherein the method is performed by a processing unit and the method comprises: obtaining beamformed PA image data for the object using at least three wavelengths related to chromophores including collagen, oxyhemoglobin and deoxyhemoglobin, the three wavelengths being less than 1000 nm; performing spectral decomposition on the beamformed PA image data using the three wavelengths to obtain data that is used for generating at least one collagen map; and determining a collagen score for the at least one collagen map.
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/FAROUK A BRUCE/Examiner, Art Unit 3797
/CHRISTOPHER KOHARSKI/Supervisory Patent Examiner, Art Unit 3797
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