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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/18/2025 has been entered.
Response to Amendment
This action is in response to the remarks filed on 12/18/2025.
The amendments filed on 12/18/2025 have been entered. Accordingly claims 1, 3-20 remain pending. Claim 2 is cancelled.
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 and 3-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims 1, 14 and 20 recite “delineating…image”, “performing reconstruction”
The limitation of “delineating”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is nothing in the claim element precludes the step from practically being performed in the mind. For example, “delineating” in the context of this claim encompasses the user manually calculating/determining the reflectance image of the skin. Similarly, the limitation of “performing”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, performing in the context of this claim encompasses the user contemplating/thinking the reconstruction of the algorithm (or planning with the simple pen and paper).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites one additional element – using a processor to perform the limitation of “delineating and reconstruction”. The processor in both steps is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of “delineating and reconstruction” such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform “delineating and reconstruction” steps amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim is not patent eligible.
The depending claims also recite process that, under their broadest reasonable interpretation, covers performance of the limitation in the mind. The additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The depending claims are directed to an abstract idea.
The judicial exceptions are not integrated into a practical application. The depending claim do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform steps amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The depending claims are not patent eligible.
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 and 3-20 are rejected under 35 U.S.C. 103 as being unpatentable over Fan (WO2021173763A1 which is the equivalent of US20230181042A1) and further in view of Noked (US 20220022754).
Regarding claim 1, Fan teaches a method for analyzing perfusion parameters of skin (§[0008] : ‘the one or more optically determined tissue features comprise at least one of a perfusion, oxygenation, or tissue homogeneity corresponding to the peri- wound pixels’), the method comprising:
acquiring a multi-band reflectance image of skin of a subject (§[0060] : 'image data that represents precise information about wavelength bands that were reflected from an imaged object);
delineating at least one clinically relevant spatial component of the multi-band reflectance image of the skin, wherein the at least one clinically relevant spatial component has substantially homogenous optical properties (§[0242] : 'Once the wound bed and callus are identified, the geometry of the wound bed may be computed.'):
performing reconstruction on the at least one clinically relevant spatial component using a corresponding at least one tailored reconstruction algorithm (“processor 1120 to generate a multispectral datacube based on intensity signals received from the photodiodes of different sensor regions. For example, the datacube generation module 1140 can estimate a disparity between the same regions of an imaged object based on a spectral channel corresponding to the common waveband passed by all multi-bandpass filters, and can use this disparity to register all spectral images across all captured channels to one another (e.g., such that the same point on the object is represented by substantially the same (x,y) pixel location across all spectral channels). The registered images collectively form the multispectral datacube, and the disparity information may be used to determine depths of different imaged objects, for example a depth difference between healthy tissue and a deepest location within a wound site” [0117]), respectively, wherein the at least one tailored reconstruction algorithm is specific to the at least one clinically relevant spatial component (“some implementations of the datacube analysis module 1145 can provide the multispectral datacube (and optionally depth information) to a machine learning model trained to classify each pixel according to a certain state. These states may be clinical states in the case of tissue imaging, for example burn states (e.g., first degree burn, second degree burn, third degree burn, or healthy tissue categories), wound states (e.g., hemostasis, inflammation, proliferation, remodeling or healthy skin categories), healing potential (e.g., a score reflecting the likelihood that the tissue will heal from a wounded state, with or without a particular therapy), perfusion states” [0118]; “. Artificial neural networks are used to model complex relationships between inputs and outputs or to find patterns in data, where the dependency between the inputs and the outputs cannot be easily ascertained. A neural network typically includes an input layer, one or more intermediate (“hidden”) layers, and an output layer, with each layer including a number of nodes. The number of nodes can vary between layers. A neural network is considered “deep” when it includes two or more hidden layers. The nodes in each layer connect to some or all nodes in the subsequent layer and the weights of these connections are typically learnt from data during the training process, for example through backpropagation in which the network parameters are tuned to produce expected outputs given corresponding inputs in labeled training data. Thus, an artificial neural network is an adaptive system that is configured to change its structure (e.g., the connection configuration and/or weights) based on information that flows through the network during training, and the weights of the hidden layers can be considered as an encoding of meaningful patterns in the data” [0141]; “A fully connected neural network is one in which each node in the input layer is connected to each node in the subsequent layer (the first hidden layer), each node in that first hidden layer is connected in turn to each node in the subsequent hidden layer, and so on until each node in the final hidden layer is connected to each node in the output layer” [0142]); and
outputting estimated perfusion parameters of the skin for the at least one reconstructed clinically relevant spatial component from the at least one tailored reconstruction algorithm (§[0242] : ‘From the wound bed and peri-wound regions, the perfusion, oxygenation, and tissue homogeneity may further be computed);and
displaying the estimated perfusion parameters for the at least one reconstructed clinically relevant spatial component in a spatially resolved manner on the multi-band reflectance image (“processors are further configured to output a visual representation of the plurality of scalar values for display to a user. In some embodiments, the visual representation comprises the image having each pixel of the subset displayed with a particular visual representation selected based on the probability of healing corresponding to the pixel, wherein pixels associated with different probabilities of healing are displayed in different visual representations” [0008]; “the multispectral datacube 1525 can be analyzed as input data 1525 into a machine learning model 1532 to generate a classified mapping 1535 of the imaged tissue. The classified mapping can assign each pixel in the image data (which, after registration, represent specific points on the imaged object 1511) to a certain tissue classification, or to a certain healing potential score. The different classifications and scores can be represented using visually distinct colors or patterns in the output classified image. Thus, even though a number of images are captured of the object 1511, the output can be a single image of the object (e.g., a typical RGB image) overlaid with visual representations of pixel-wise classification” [0140]).
Fan does not seem to point out the specifics of displaying the estimated perfusion parameters for the at least one reconstructed clinically relevant spatial component in a spatially resolved manner as an overlay on the multi-band reflectance image.
However, in the same field of endeavor, Noked teaches reconstruction algorithms to retrieve optical-spatial details of a deep tissue hot spot; and/or merging multiple UPE images to provide depth and spatial analysis of the pathological condition [0035]. Red light (RL) illumination with and without filters, images and analyzed images; incorporating white light (WL) illumination with and without filters, images and analyzed images; incorporating three-dimensional (3D) surface reconstruction, incorporating structured light (SL) images and analyzed images.; and/or incorporating signal reconstruction algorithms, including but not limited to diffusion-model based algorithms, autocorrelation-based algorithms, combination thereof, or others. In some embodiments, the spatial matching of the UPE imaging onto the surface reconstruction map may provide better visualization for analysis. In some embodiments, the spatially matched UPE imaging can be used as an actionable starting point for signal reconstruction algorithms to retrieve the optical-spatial details of a deep tissue signal [0052]. . The purpose of the NIR MSI is to assess quantitatively the blood flow and perfusion, and oxidation of the tissue. These tissue properties provide an important assessment of the viability and wellbeing of the measured tissue, as they relate to the supply of oxygen and other nutrients into the tissue, as well as the removal of waste product out of the tissue. Poor perfusion and lower oxidation harm the tissue and inhibit recovery. The MSI image can be further analyzed to remove noise, correct background, apply a smoothing algorithm, identify contours, identify hot spots by defined thresholds, or undergo other image processing procedure. By superimposing on, and comparing and contrasting the NIR MSI images, analyzed images and the oxidation and perfusion images with UPE imaging, a more detailed clinical profile may emerge [0144]. Support algorithms based on the data collected from all the measurements, for a specific condition, stage and the progression (healing/deterioration) of the pathological condition as a function of time. The medical images can be analyzed by various statistical tools and advanced analysis, including but not limited to, machine learning algorithms, deep learning algorithms, neural networks algorithms, artificial intelligence algorithms, or other [0153]. Also see [0186].
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with displaying the estimated perfusion parameters for the at least one reconstructed clinically relevant spatial component in a spatially resolved manner “as an overlay” on the multi-band reflectance image as taught by Noked because this allows for a better spatial localization of the tumor for follow-up diagnostic and imaging procedures, as well as interventions, the patient is likely to need ([0186] of Noked).
Regarding claim 3, Fan teaches mapping the estimated perfusion parameters back into a perfusion parameter map across the skin ([0140] “the multispectral datacube 1525 can be analyzed as input data 1525 into a machine learning model 1532 to generate a classified mapping 1535 of the imaged tissue. The classified mapping can assign each pixel in the image data (which, after registration, represent specific points on the imaged object 1511) to a certain tissue classification, or to a certain healing potential score.”).
Regarding claims 4 and 15, Fan teaches receiving an RGB-value camera and/or a grey-value camera configured to provide an RGB-value image and/or a grey-value image of the skin ([0114] “multi-aperture spectral camera 1160 for capturing images. The multi-aperture spectral camera 1160 can be, for example, any of the devices of FIGS. 3A-10B.”; [0059] “images may be captured with a monochrome, RGB, and/or infrared imaging device”), respectively,
wherein the RGB-value image and/or the grey-value image are spatially aligned with the multi- band reflectance image ([0066] “For line scanning and wavelength scanning imaging systems, this can be due to the fact that each spectral or spatial image is captured using the entire area of the image sensor.”; [0070] “the multi-aperture system may enable the collection of 3D spatial images of or relating to object curvature, depth, volume, and/or area based on the calculated disparity of the perspective differences between each aperture.”); and
displaying the estimated perfusion parameters for the at least one reconstructed clinically relevant spatial component in a spatially resolved manner as an overlay on the RGB-value image and/or the grey-value image ([0140] “number of images are captured of the object 1511, the output can be a single image of the object (e.g., a typical RGB image) overlaid with visual representations of pixel-wise classification”).
Regarding claims 5, 6 and 17, Fan teaches accumulating the estimated perfusion parameters within the at least one reconstructed clinically relevant spatial component ([0118] “The datacube analysis module 1145 … analyze the multispectral datacube …provide the multispectral datacube (and optionally depth information) to a machine learning model trained to classify each pixel according to a certain state…healing potential (e.g., a score reflecting the likelihood that the tissue will heal from a wounded state, with or without a particular therapy), … or other wound-related tissue states. The datacube analysis module 1145 can also analyze the multispectral datacube for biometric recognition and/or materials analysis.”) comprises taking an average, a mean, a median, a quantile and/or a variance of the estimated perfusion parameters within the at least one reconstructed clinically relevant spatial component ([0176] “the mean of all pixel values, the median of all pixel values, and the standard deviation of all pixel values”); and
determining a vector of the estimated perfusion properties of the at least one clinically relevant spatial component based on the accumulated estimated perfusion parameters ([0081] “A “spectral vector” refers to a vector describing the spectral data at a particular (x, y) position in a datacube (e.g., the spectrum of light received from a particular point in the object space)”; [0144] “metrics can be converted into a vector representation through appropriate processing, for example through word-to-vec embeddings, a vector having binary values representing whether the patient does or does not have the patient metric (e.g., does or does not have type I diabetes), or numerical values representing a degree to which the patient has each patient metric.”).
Regarding claim 7, Fan teaches wherein the at least one clinically relevant spatial component of the multi-band reflectance image of the skin is delineated automatically ([0222] “systems and methods of the present technology are suitable for automated detection of wound margins and identification of tissue types in the wound area. In some embodiments, the systems and methods of the present technology can be configured for automated segmentation of wound images into at least wound pixels and non-wound pixels, such that any aggregate quantitiative features calculated based on the subset of wound pixels achieve a desirable level of accuracy.”).
Regarding claim 8, Fan teaches wherein delineating the at least one clinically relevant spatial component comprises performing automatic segmentation on a local backscatter spectrum of the multi-band reflectance image ([0222] “systems and methods of the present technology are suitable for automated detection of wound margins and identification of tissue types in the wound area. In some embodiments, the systems and methods of the present technology can be configured for automated segmentation of wound images into at least wound pixels and non-wound pixels, such that any aggregate quantitiative features calculated based on the subset of wound pixels achieve a desirable level of accuracy.”).
Regarding claim 9, Fan teaches wherein delineating the at least one clinically relevant spatial component comprises performing automatic segmentation on the RGB-value image and/or the grey-value image ([0222] “systems and methods of the present technology are suitable for automated detection of wound margins and identification of tissue types in the wound area. In some embodiments, the systems and methods of the present technology can be configured for automated segmentation of wound images into at least wound pixels and non-wound pixels, such that any aggregate quantitiative features calculated based on the subset of wound pixels achieve a desirable level of accuracy.”).
Regarding claims 10 and 13, Fan teaches wherein delineating the at least one clinically relevant spatial component comprises performing automatic segmentation by applying a convolutional neural network that is optimized based on a set of annotated data samples from the at least one clinically relevant spatial component, or by applying purpose-driven heuristics to the at least one clinically relevant spatial component ([0224] “a convolutional neural network (CNN) can be used for the automated segmentation of these tissue categories. In some embodiments, the algorithm structure can be a shallow U-net with a plurality of convolutional layers. In one example implementation, desirable segmentation outcomes were achieved with 31 convolutional layers. However, many other algorithms for image segmentation could be applied”).
Regarding claims 11 and 18, Fan teaches initially estimating perfusion parameters by applying an initial perfusion parameter reconstruction algorithm to the multi-band reflectance image to provide an estimated perfusion parameter map ([0140] “a classified mapping 1535 of the imaged tissue. The classified mapping can assign each pixel in the image data (which, after registration, represent specific points on the imaged object 1511) to a certain tissue classification, or to a certain healing potential score”); and performing segmentation of the estimated perfusion parameter map to identify the at least one clinically relevant spatial component for delineation ([0049] “image segmentation approaches for generating a conditional healing probability map”).
Regarding claims 12 and 19, Fan teaches delineating at least one clinically irrelevant spatial component of the multi-band reflectance image of the skin, wherein the at least one clinically irrelevant spatial component comprises a disturbance; and inpainting the at least one clinically irrelevant spatial component ([0223] “The resulting labeled images, known as ground truth makes, may include a number of colors corresponding to the number of labeled categories in the image. FIG. 37 illustrates an example image of a DFU (left), and corresponding ground truth mask (right). The example ground truth mask of FIG. 37 includes a purple region corresponding to background pixels, a yellow region corresponding to callus pixels, and a cyan region corresponding to wound pixels.”).
Regarding claim 14, Fan teaches system for analyzing perfusion parameters of skin (§[0008] : ‘the one or more optically determined tissue features comprise at least one of a perfusion, oxygenation, or tissue homogeneity corresponding to the peri- wound pixels’), the system comprising:
an imaging system configured to acquire a multi-band reflectance image of skin (§[0060]: 'image data that represents precise information about wavelength bands that were reflected from an imaged object);
at least one processor coupled to the imaging system to receive the multi-band reflectance image of the skin of a subject (“processors are configured to receive a signal from the at least one light detection element, the signal representing light of the first wavelength reflected from the tissue region; generate, based on the signal, an image having a plurality of pixels depicting the tissue region; automatically segment the plurality of pixels of the image into at least wound pixels and non-wound pixels” [0007]); and
at least one non-transitory memory storing instructions that, when executed by the at least one processor, cause the at least one processor to:
delineating at least one clinically relevant spatial component of the multi-band reflectance image of the skin, wherein the at least one clinically relevant spatial component has substantially homogenous optical properties (§[0242] : 'Once the wound bed and callus are identified, the geometry of the wound bed may be computed.'):
performing reconstruction on the at least one clinically relevant spatial component using a corresponding at least one tailored reconstruction algorithm, respectively, wherein the at least one tailored reconstruction algorithm is specific to the at least one clinically relevant spatial component (“processor 1120 to generate a multispectral datacube based on intensity signals received from the photodiodes of different sensor regions. For example, the datacube generation module 1140 can estimate a disparity between the same regions of an imaged object based on a spectral channel corresponding to the common waveband passed by all multi-bandpass filters, and can use this disparity to register all spectral images across all captured channels to one another (e.g., such that the same point on the object is represented by substantially the same (x,y) pixel location across all spectral channels). The registered images collectively form the multispectral datacube, and the disparity information may be used to determine depths of different imaged objects, for example a depth difference between healthy tissue and a deepest location within a wound site” [0117]), respectively, wherein the at least one tailored reconstruction algorithm is specific to the at least one clinically relevant spatial component (“some implementations of the datacube analysis module 1145 can provide the multispectral datacube (and optionally depth information) to a machine learning model trained to classify each pixel according to a certain state. These states may be clinical states in the case of tissue imaging, for example burn states (e.g., first degree burn, second degree burn, third degree burn, or healthy tissue categories), wound states (e.g., hemostasis, inflammation, proliferation, remodeling or healthy skin categories), healing potential (e.g., a score reflecting the likelihood that the tissue will heal from a wounded state, with or without a particular therapy), perfusion states” [0118]; “. Artificial neural networks are used to model complex relationships between inputs and outputs or to find patterns in data, where the dependency between the inputs and the outputs cannot be easily ascertained. A neural network typically includes an input layer, one or more intermediate (“hidden”) layers, and an output layer, with each layer including a number of nodes. The number of nodes can vary between layers. A neural network is considered “deep” when it includes two or more hidden layers. The nodes in each layer connect to some or all nodes in the subsequent layer and the weights of these connections are typically learnt from data during the training process, for example through backpropagation in which the network parameters are tuned to produce expected outputs given corresponding inputs in labeled training data. Thus, an artificial neural network is an adaptive system that is configured to change its structure (e.g., the connection configuration and/or weights) based on information that flows through the network during training, and the weights of the hidden layers can be considered as an encoding of meaningful patterns in the data” [0141]; “A fully connected neural network is one in which each node in the input layer is connected to each node in the subsequent layer (the first hidden layer), each node in that first hidden layer is connected in turn to each node in the subsequent hidden layer, and so on until each node in the final hidden layer is connected to each node in the output layer” [0142]); and
outputting estimated perfusion parameters of the skin for the at least one reconstructed clinically relevant spatial component from the at least one tailored reconstruction algorithm (§[0242] : ‘From the wound bed and peri-wound regions, the perfusion, oxygenation, and tissue homogeneity may further be computed).
a display configured to display the estimated perfusion parameters for the at least one reconstructed clinically relevant spatial component in a spatially resolved manner ([0008] “output a visual representation of the plurality of scalar values for display to a user. In some embodiments, the visual representation comprises the image having each pixel of the subset displayed with a particular visual representation selected based on the probability of healing corresponding to the pixel, wherein pixels associated with different probabilities of healing are displayed in different visual representations”).
A can be clearly seen above, Fan taches all the claimed limitations; yet, in an interpretation, if one argues otherwise (which the office does not concede), Noked reference is brought in to show the teachings in an effort to provide compact prosecution.
Noked, in the same field of endeavor, teaches reconstruction algorithms to retrieve optical-spatial details of a deep tissue hot spot; and/or merging multiple UPE images to provide depth and spatial analysis of the pathological condition [0035]. Red light (RL) illumination with and without filters, images and analyzed images; incorporating white light (WL) illumination with and without filters, images and analyzed images; incorporating three-dimensional (3D) surface reconstruction, incorporating structured light (SL) images and analyzed images.; and/or incorporating signal reconstruction algorithms, including but not limited to diffusion-model based algorithms, autocorrelation-based algorithms, combination thereof, or others. In some embodiments, the spatial matching of the UPE imaging onto the surface reconstruction map may provide better visualization for analysis. In some embodiments, the spatially matched UPE imaging can be used as an actionable starting point for signal reconstruction algorithms to retrieve the optical-spatial details of a deep tissue signal [0052]. . The purpose of the NIR MSI is to assess quantitatively the blood flow and perfusion, and oxidation of the tissue. These tissue properties provide an important assessment of the viability and wellbeing of the measured tissue, as they relate to the supply of oxygen and other nutrients into the tissue, as well as the removal of waste product out of the tissue. Poor perfusion and lower oxidation harm the tissue and inhibit recovery. The MSI image can be further analyzed to remove noise, correct background, apply a smoothing algorithm, identify contours, identify hot spots by defined thresholds, or undergo other image processing procedure. By superimposing on, and comparing and contrasting the NIR MSI images, analyzed images and the oxidation and perfusion images with UPE imaging, a more detailed clinical profile may emerge [0144]. Support algorithms based on the data collected from all the measurements, for a specific condition, stage and the progression (healing/deterioration) of the pathological condition as a function of time. The medical images can be analyzed by various statistical tools and advanced analysis, including but not limited to, machine learning algorithms, deep learning algorithms, neural networks algorithms, artificial intelligence algorithms, or other [0153]. Also see [0186].
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with system for analyzing perfusion parameters of skin as taught by Noked because this allows for a better spatial localization of the tumor for follow-up diagnostic and imaging procedures, as well as interventions, the patient is likely to need ([0186] of Noked).
Regarding claim 20, Fan teaches a non-transitory computer readable medium that stores instructions for analyzing perfusion parameters of skin of a subject that, when executed by at least one processor, cause the at least one processor to (§[0008] : ‘the one or more optically determined tissue features comprise at least one of a perfusion, oxygenation, or tissue homogeneity corresponding to the peri- wound pixels’):
receiving a multi-band reflectance image of skin of a subject (§[0060] : 'image data that represents precise information about wavelength bands that were reflected from an imaged object);
delineating at least one clinically relevant spatial component of the multi-band reflectance image of the skin, wherein the at least one clinically relevant spatial component has substantially homogenous optical properties (§[0242] : 'Once the wound bed and callus are identified, the geometry of the wound bed may be computed.'):
perform reconstruction on the at least one clinically relevant spatial component using a corresponding at least one tailored reconstruction algorithm (“processor 1120 to generate a multispectral datacube based on intensity signals received from the photodiodes of different sensor regions. For example, the datacube generation module 1140 can estimate a disparity between the same regions of an imaged object based on a spectral channel corresponding to the common waveband passed by all multi-bandpass filters, and can use this disparity to register all spectral images across all captured channels to one another (e.g., such that the same point on the object is represented by substantially the same (x,y) pixel location across all spectral channels). The registered images collectively form the multispectral datacube, and the disparity information may be used to determine depths of different imaged objects, for example a depth difference between healthy tissue and a deepest location within a wound site” [0117]), respectively, wherein the at least one tailored reconstruction algorithm is specific to the at least one clinically relevant spatial component (“some implementations of the datacube analysis module 1145 can provide the multispectral datacube (and optionally depth information) to a machine learning model trained to classify each pixel according to a certain state. These states may be clinical states in the case of tissue imaging, for example burn states (e.g., first degree burn, second degree burn, third degree burn, or healthy tissue categories), wound states (e.g., hemostasis, inflammation, proliferation, remodeling or healthy skin categories), healing potential (e.g., a score reflecting the likelihood that the tissue will heal from a wounded state, with or without a particular therapy), perfusion states” [0118]; “. Artificial neural networks are used to model complex relationships between inputs and outputs or to find patterns in data, where the dependency between the inputs and the outputs cannot be easily ascertained. A neural network typically includes an input layer, one or more intermediate (“hidden”) layers, and an output layer, with each layer including a number of nodes. The number of nodes can vary between layers. A neural network is considered “deep” when it includes two or more hidden layers. The nodes in each layer connect to some or all nodes in the subsequent layer and the weights of these connections are typically learnt from data during the training process, for example through backpropagation in which the network parameters are tuned to produce expected outputs given corresponding inputs in labeled training data. Thus, an artificial neural network is an adaptive system that is configured to change its structure (e.g., the connection configuration and/or weights) based on information that flows through the network during training, and the weights of the hidden layers can be considered as an encoding of meaningful patterns in the data” [0141]; “A fully connected neural network is one in which each node in the input layer is connected to each node in the subsequent layer (the first hidden layer), each node in that first hidden layer is connected in turn to each node in the subsequent hidden layer, and so on until each node in the final hidden layer is connected to each node in the output layer” [0142]); and
outputting estimated perfusion parameters of the skin for the at least one reconstructed clinically relevant spatial component from the at least one tailored reconstruction algorithm (§[0242] : ‘From the wound bed and peri-wound regions, the perfusion, oxygenation, and tissue homogeneity may further be computed).
cause a display of the estimated perfusion parameters for the at least one reconstructed clinically relevant spatial component in a spatially resolved manner ([0008] “output a visual representation of the plurality of scalar values for display to a user. In some embodiments, the visual representation comprises the image having each pixel of the subset displayed with a particular visual representation selected based on the probability of healing corresponding to the pixel, wherein pixels associated with different probabilities of healing are displayed in different visual representations”).
A can be clearly seen above, Fan taches all the claimed limitations; yet, in an interpretation, if one argues otherwise (which the office does not concede), Noked reference is brought in to show the teachings in an effort to provide compact prosecution.
Noked, in the same field of endeavor, teaches reconstruction algorithms to retrieve optical-spatial details of a deep tissue hot spot; and/or merging multiple UPE images to provide depth and spatial analysis of the pathological condition [0035]. Red light (RL) illumination with and without filters, images and analyzed images; incorporating white light (WL) illumination with and without filters, images and analyzed images; incorporating three-dimensional (3D) surface reconstruction, incorporating structured light (SL) images and analyzed images.; and/or incorporating signal reconstruction algorithms, including but not limited to diffusion-model based algorithms, autocorrelation-based algorithms, combination thereof, or others. In some embodiments, the spatial matching of the UPE imaging onto the surface reconstruction map may provide better visualization for analysis. In some embodiments, the spatially matched UPE imaging can be used as an actionable starting point for signal reconstruction algorithms to retrieve the optical-spatial details of a deep tissue signal [0052]. . The purpose of the NIR MSI is to assess quantitatively the blood flow and perfusion, and oxidation of the tissue. These tissue properties provide an important assessment of the viability and wellbeing of the measured tissue, as they relate to the supply of oxygen and other nutrients into the tissue, as well as the removal of waste product out of the tissue. Poor perfusion and lower oxidation harm the tissue and inhibit recovery. The MSI image can be further analyzed to remove noise, correct background, apply a smoothing algorithm, identify contours, identify hot spots by defined thresholds, or undergo other image processing procedure. By superimposing on, and comparing and contrasting the NIR MSI images, analyzed images and the oxidation and perfusion images with UPE imaging, a more detailed clinical profile may emerge [0144]. Support algorithms based on the data collected from all the measurements, for a specific condition, stage and the progression (healing/deterioration) of the pathological condition as a function of time. The medical images can be analyzed by various statistical tools and advanced analysis, including but not limited to, machine learning algorithms, deep learning algorithms, neural networks algorithms, artificial intelligence algorithms, or other [0153]. Also see [0186].
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with system for analyzing perfusion parameters of skin as taught by Noked because this allows for a better spatial localization of the tumor for follow-up diagnostic and imaging procedures, as well as interventions, the patient is likely to need ([0186] of Noked).
Response to Arguments
Applicant’s arguments 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.
Regarding the rejection of claims under 35 USC 101, the applicant argues the following;
Claim 1 of the present Application, as amended, recites that the at least one tailored reconstruction algorithm is individually trained for estimating perfusion parameters specific to the at least one clinically relevant spatial component. Individual training of a reconstruction algorithm cannot be practically performed din the human mind. Further, Applicant respectfully submits that claim 1 is analogous to "Example 39 - Method for Training a Neural Network for Facial Detection" of the Subject Matter Eligibility Examples: Abstract Ideas, used in conjunction with the 2019 Revised Patent Subject Matter Eligibility Guidance. In the Example, a computer-implemented method claim, including the steps of "training the neural network in a first stage using the first training set" and "training the neural network in a second stage using the second training set" does not recite a judicial exception under Step 2A, Prong One, stating in relevant part: "Further the claim does not recite a mental process because the steps are not practically performed in the human mind.".
Contrary to the applicant’s assertion, claims still recite abstract idea as “[executed by the at least one processor] "delineate" and " perform reconstruction" which is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. Other than the recitation of generic computer components (“processor”) nothing in the claim element precludes the step from practically being performed in the mind.
Further, the applicant also argues that “[claimed] elements amount to "significantly more" than the assertedly abstract idea itself, transforming the abstract idea into a patent-eligible application.” Yet those are all generic component that is used for mere displaying/data gathering which are examples of activities that courts have found to be insignificant extra-solution activity. These components are widely practiced and commonly known with no specificity which courts have found to be insignificant extra-solution activity.
Therefore, under its broadest reasonable interpretation, claims cover performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Judicial exception is not integrated into a practical application since the claim only recites additional element generic computer components (“processor”).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claims are not patent eligible.
Conclusion
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/SERKAN AKAR/ Primary Examiner, Art Unit 3797
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