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 .
Notice to Applicants
This communication is in response to the action filed on 10/28/2024.
Claims 1-28 are currently pending.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 3-6, 8, 10-11, 13-15, 17-20, 22, 24-25, 27-28 are rejected under 35 § U.S.C. 102(a)(1) as being anticipated by US 2022/0084660 A1 to GEORGESCU et al. (hereinafter “GEORGESCU”).
As per claim 1, GEORGESCU discloses a system, comprising: at least one data processor (a computing system and corresponding method of operation wherein the computing system comprises a computing processor adapted to execute said method of operation; abstract; fig 11; paragraphs [0224-0225]); and at least one memory storing instructions, which when executed by the at least one data processor (and the computing system further comprises a memory component to store instructions related to the method of operation for the processor to execute; abstract; fig 11; paragraph [0224-0225]), result in operations comprising: determining, within an image of a biological sample, a plurality of tiles (images are captured of biological samples on a slide which contains a plurality of tiles breaking the sample image up into a grid of said tiles; figs 3-4; paragraphs [0016-0019], [0044], [0106-0109], [0116], [0221-0222]), each tile of the plurality of tiles depicting a portion of the biological sample (wherein each tile acts as a representative slice of the tumor tissue sample being images and separated into said tiles for analysis; figs 3-4; paragraphs [0016-0019], [0044], [0106-0109], [0116], [0221-0222]); applying a first machine learning model to determine a molecular subtype for the portion of the biological sample depicted in each tile of the plurality of tiles (the machine learning CNN model is designed for tumor finding in a digital pathology histological image, e.g. to classify each image pixel/tile into either a nontumor class or one of a plurality of tumor classes and this is a determination of molecular sub type; figs 6A-6B, 7A-7B, and 8-9; paragraphs [0006], [0022], [0093-0094], [0116-0118] [0211-0212]); and determining, based at least on the molecular subtype of each tile of the plurality of tiles, an overall molecular subtype for the biological sample (determining and producing based on the classification of tumor tissue containing tiles a heat map of the image in order to identify invasive tumors and noninvasive tumor areas based on the culmination of each individual tile test showing an overall output image of the medical image with the tissues types color labeled as desired by the user; figs 6A-6B, 7A-7B, and 8-9; paragraphs [0006], [0022], [0093-0094], [0116-0118] [0211-0212]).
As per claim 3, GEORGESCU discloses the system of claim 2, wherein the second machine learning model is trained to determine the overall molecular subtype by at least determining a representational encoding of the plurality of tiles (the model is adapted to include encoder and decoder steps and assist in determining molecular subtype by encoding information into a bitstream/signal to be decoded at the step of analyzation; paragraphs [0248], [0289-0291]).
As per claim 4, GEORGESCU discloses the system of claim 2, wherein the second machine learning model comprises a multiple instance learning (MIL) model (the model is a machine learning model having multiple training stages and training parameters which is substantially a multiple instance machine learning model; figs 6A-6B, 7A-7B, and 8-9; paragraphs [0006], [0022], [0093-0094], [0116-0118] [0211-0212]).
As per claim 5, GEORGESCU discloses the system of claim 1, wherein the operations further comprise: generating a first visual representation of a reduced dimension representation of the plurality of tiles (each visual representation slice within a representative tile is a height dimension and a width dimension of the tile and is broken apart using a deconvolutional layer, and further using a coordinate mapping between different whole slide/tile images s of a set comprising differently stained adjacent sections, the tile images can be merged into a single composite Image of multiple tiles/slides from which composite patches may be extracted for processing by the CNN, where such composite patches would have dimensions NxNx3 m, where 'm' is the number of composited tile/slide images forming the composite; paragraphs [0148], [0158-0164], [0169-0170]).
As per claim 6, GEORGESCU discloses the system of claim 1, wherein the overall molecular subtype of the biological sample is determined based at least on a quantity of each molecular subtype present within the plurality of cells (the overall region imaged of the patients anatomy is assigned color coded tissue /tumor type representations and the overall determination is based on the amount of tumor containing tissue is observed/analyzed; paragraphs [0200-0201], [0208-0214], [0221-0222], [0286-0287]; claim 43).
As per claim 8, GEORGESCU discloses the system of claim 1, wherein the operations further comprise: generating a visual representation depicting a first tile of the plurality of tiles having a first subtype along with a second tile of the first subtype from a same biological sample or a different biological sample (the whole image slides acting as tiles are images as shown in fig 7b and includes virtual tile/slide representations of tissues of the following types producing/generating a tumor probability heatmap by the CNN for the heatmap, different arbitrarily chosen colors indicate different classes, namely green for nontumor, reddish-brown for invasive tumor, and blue for in situ tumor, it can be seen how the approach of pixel-level prediction produces areas with smooth perimeter outline; fig 7B; paragraphs [0164-0170]).
As per claim 10, GEORGESCU discloses the system of claim 1, wherein the first machine learning model is trained to determine the molecular subtype associated with each tile of the plurality of tiles based on a morphological pattern present within the portion of the biological sample depicted in each tile (the computing system is adapted to make its tissue molecular subtype tile by tile/pixel by pixel determination using morphological patterns of the tissues; paragraphs [0170-0183]).
As per claim 11, GEORGESCU discloses the system of claim 1, wherein the first machine learning model comprises an artificial neural network (ANN) (the CNN used in the computing system is a trained CNN acting substantially as an ANN since a CNN is a type of specialized ANN; figs 6A-6B, 7A-7B, and 8-9; paragraphs [0006], [0022], [0093-0094], [0116-0118] [0211-0212]).
As per claim 13, GEORGESCU discloses the system of claim 1, wherein the operations further comprise: identifying, based at least on transcriptome data associated with a plurality of tumor tissue samples, a plurality of molecular subtypes (as detailed in fig 7B and paragraph [0164] the algorithm/model is adapted to determine data on a plurality of tissue sample types and molecular subtypes of different tissues; figs 6A-6B, 7A-7B, and 8-9; paragraphs [0006], [0022], [0093-0094], [0116-0118], [0164], [0211-0212]).
As per claim 14, GEORGESCU discloses the system of claim 1, wherein the image depicts a plurality of cells comprising the biological sample, and wherein each tile of the plurality of tiles depict a portion of the plurality of cells comprising the biological sample (wherein as seen in figs 7A-7B the plurality of cells making up the sample can be observed individually and are color coated pixel by pixel/tile by tile to differentiate various tissue types by assigning arbitrary colors to each type observed in the image; figs 3-4, 7A-7B; paragraphs [0016-0019], [0044], [0106-0109], [0116], [0221-0222]).
As per claim 15, GEORGESCU discloses a computer-implemented method (a computing system and corresponding method of operation wherein the computing system comprises a computing processor adapted to execute said method of operation; abstract; fig 11; paragraphs [0224-0225]), comprising: determining, within an image of a biological sample, a plurality of tiles (images are captured of biological samples on a slide which contains a plurality of tiles breaking the sample image up into a grid of said tiles; figs 3-4; paragraphs [0016-0019], [0044], [0106-0109], [0116], [0221-0222]), each tile of the plurality of tiles depicting a portion of the biological sample (wherein each tile acts as a representative slice of the tumor tissue sample being images and separated into said tiles for analysis; figs 3-4; paragraphs [0016-0019], [0044], [0106-0109], [0116], [0221-0222]); applying a first machine learning model to determine a molecular subtype for the portion of the biological sample depicted in each tile of the plurality of tiles (the machine learning CNN model is designed for tumor finding in a digital pathology histological image, e.g. to classify each image pixel/tile into either a nontumor class or one of a plurality of tumor classes and this is a determination of molecular sub type; figs 6A-6B, 7A-7B, and 8-9; paragraphs [0006], [0022], [0093-0094], [0116-0118] [0211-0212]); and determining, based at least on the molecular subtype of each tile of the plurality of tiles, an overall molecular subtype for the biological sample (determining and producing based on the classification of tumor tissue containing tiles a heat map of the image in order to identify invasive tumors and noninvasive tumor areas based on the culmination of each individual tile test showing an overall output image of the medical image with the tissues types color labeled as desired by the user; figs 6A-6B, 7A-7B, and 8-9; paragraphs [0006], [0022], [0093-0094], [0116-0118] [0211-0212]).
As per claim 17, GEORGESCU discloses the method of claim 16, wherein the second machine learning model is trained to determine the overall molecular subtype by at least determining a representational encoding of the plurality of tiles (the model is adapted to include encoder and decoder steps and assist in determining molecular subtype by encoding information into a bitstream/signal to be decoded at the step of analyzation; paragraphs [0248], [0289-0291]).
As per claim 18, GEORGESCU discloses the method of claim 16, wherein the second machine learning model comprises a multiple instance learning (MIL) model (the model is a machine learning model having multiple training stages and training parameters which is substantially a multiple instance machine learning model; figs 6A-6B, 7A-7B, and 8-9; paragraphs [0006], [0022], [0093-0094], [0116-0118] [0211-0212]).
As per claim 19, GEORGESCU discloses the method of claim 15, further comprising: generating a first visual representation of a reduced dimension representation of the plurality of tiles (each visual representation slice within a representative tile is a height dimension and a width dimension of the tile and is broken apart using a deconvolutional layer, and further using a coordinate mapping between different whole slide/tile images s of a set comprising differently stained adjacent sections, the tile images can be merged into a single composite Image of multiple tiles/slides from which composite patches may be extracted for processing by the CNN, where such composite patches would have dimensions NxNx3 m, where 'm' is the number of composited tile/slide images forming the composite; paragraphs [0148], [0158-0164], [0169-0170]).
As per claim 20, GEORGESCU discloses the method of claim 15, wherein the overall molecular subtype of the biological sample is determined based at least on a quantity of each molecular subtype present within the plurality of cells (the overall region imaged of the patients anatomy is assigned color coded tissue /tumor type representations and the overall determination is based on the amount of tumor containing tissue is observed/analyzed; paragraphs [0200-0201], [0208-0214], [0221-0222], [0286-0287]; claim 43).
As per claim 22, GEORGESCU discloses the method of claim 15, further comprising: generating a visual representation depicting a first tile of the plurality of tiles having a first subtype along with a second tile of the first subtype from a same biological sample or a different biological sample (the whole image slides acting as tiles are images as shown in fig 7b and includes virtual tile/slide representations of tissues of the following types producing/generating a tumor probability heatmap by the CNN for the heatmap, different arbitrarily chosen colors indicate different classes, namely green for nontumor, reddish-brown for invasive tumor, and blue for in situ tumor, it can be seen how the approach of pixel-level prediction produces areas with smooth perimeter outline; fig 7B; paragraphs [0164-0170]).
As per claim 24, GEORGESCU discloses the method of claim 15, wherein the first machine learning model is trained to determine the molecular subtype associated with each tile of the plurality of tiles based on a morphological pattern present within the portion of the biological sample depicted in each tile (the computing system is adapted to make its tissue molecular subtype tile by tile/pixel by pixel determination using morphological patterns of the tissues; paragraphs [0170-0183]).
As per claim 25, GEORGESCU discloses the method of claim 15, wherein the first machine learning model comprises an artificial neural network (ANN) (the CNN used in the computing system is a trained CNN acting substantially as an ANN since a CNN is a type of specialized ANN; figs 6A-6B, 7A-7B, and 8-9; paragraphs [0006], [0022], [0093-0094], [0116-0118] [0211-0212]).
As per claim 27, GEORGESCU discloses the method of claim 15, further comprising: identifying, based at least on transcriptome data associated with a plurality of tumor tissue samples, a plurality of molecular subtypes (as detailed in fig 7B and paragraph [0164] the algorithm/model is adapted to determine data on a plurality of tissue sample types and molecular subtypes of different tissues; figs 6A-6B, 7A-7B, and 8-9; paragraphs [0006], [0022], [0093-0094], [0116-0118], [0164], [0211-0212]).
As per claim 28, GEORGESCU discloses the method of claim 15, wherein the image depicts a plurality of cells comprising the biological sample, and wherein each tile of the plurality of tiles depict a portion of the plurality of cells comprising the biological sample (wherein as seen in figs 7A-7B the plurality of cells making up the sample can be observed individually and are color coated pixel by pixel/tile by tile to differentiate various tissue types by assigning arbitrary colors to each type observed in the image; figs 3-4, 7A-7B; paragraphs [0016-0019], [0044], [0106-0109], [0116], [0221-0222]).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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 non-obviousness.
Claims 7 and 21 are rejected under 35 § U.S.C. 103 as being obvious over US 2022/0084660 A1 to GEORGESCU et al. (hereinafter “GEORGESCU”) in view of WO 2020/014477 A1 to PEROU et al. (hereinafter “PEROU”).
As per claim 7, GEORGESCU discloses the system of claim 1. GEORGESCU fails to disclose wherein the operations further comprise: generating, based at least on the molecular subtype of each tile of the plurality of tiles, a visual representation depicting a spatial distribution of one or more molecular subtypes within the biological sample.
PEROU discloses wherein the operations further comprise: generating, based at least on the molecular subtype of each tile of the plurality of tiles, a visual representation depicting a spatial distribution of one or more molecular subtypes within the biological sample (the computing system generates a grid of instance predictions as a feature map of the tissue types in the image and includes a bag prediction function should be invariant to the number and spatial arrangement of instances; page 26 lines 14-32).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify GEORGESCU to have a visual representation depicting a spatial distribution of one or more molecular subtypes within the biological sample of PEROU reference. The Suggestion/motivation for doing so would have been to provide the ability to incorporate a foreground mask for the input image as suggested by page 26, lines 24-30 of PEROU. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine PEROU with GEORGESCU to obtain the invention as specified in claim 7.
As per claim 21, GEORGESCU discloses the method of claim 15. GEORGESCU fails to disclose further comprising: generating, based at least on the molecular subtype of each tile of the plurality of tiles, a visual representation depicting a spatial distribution of one or more molecular subtypes within the biological sample.
PEROU discloses further comprising: generating, based at least on the molecular subtype of each tile of the plurality of tiles, a visual representation depicting a spatial distribution of one or more molecular subtypes within the biological sample (the computing system generates a grid of instance predictions as a feature map of the tissue types in the image and includes a bag prediction function should be invariant to the number and spatial arrangement of instances; page 26 lines 14-32).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify GEORGESCU to have a visual representation depicting a spatial distribution of one or more molecular subtypes within the biological sample of PEROU reference. The Suggestion/motivation for doing so would have been to provide the ability to incorporate a foreground mask for the input image as suggested by page 26, lines 24-30 of PEROU. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine PEROU with GEORGESCU to obtain the invention as specified in claim 21.
Claims 2, 9, 12, 16, 23, and 26 are rejected under 35 § U.S.C. 103 as being obvious over US 2022/0084660 A1 to GEORGESCU et al. (hereinafter “GEORGESCU”) in view of US 2019/0100809 A1 to KENNEDY et al. (hereinafter “KENNEDY”)
As per claim 2, GEORGESCU discloses the system of claim 1. GEORGESCU fails to disclose wherein the overall molecular subtype of the biological sample is determined by applying a second machine learning model.
KENNEDY discloses wherein the overall molecular subtype of the biological sample is determined by applying a second machine learning model (two models are used as described in paragraph [0270] a LIMMA model for feature extraction and a SVM for classification; paragraph [0270]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify GEORGESCU to have overall molecular subtype of the biological sample is determined by applying a second machine learning model of KENNEDY reference. The Suggestion/motivation for doing so would have been to provide specific models trained for specific purposes/functions within the computing system as suggested by paragraph [0270] of KENNEDY. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine KENNEDY with GEORGESCU to obtain the invention as specified in claim 2.
As per claim 9, GEORGESCU discloses the system of claim 1. GEORGESCU fails to disclose wherein the plurality of tiles exclude one or more tiles in the image with an above-threshold proportion of a background of the image or a below- threshold mean color channel variance.
KENNEDY discloses wherein the plurality of tiles exclude one or more tiles in the image with an above-threshold proportion of a background of the image or a below- threshold mean color channel variance (common quality control metrics used to pass or fail a sample in order to enter it into the molecular classifier are Intron/Exon separation AUC and pDABG or pDET, and the threshold for pDET (percentage of genes or probe sets that are detected above background) can be adjusted for different data sets as learning continues during marker discover; paragraphs [0041], [0159-0160]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify GEORGESCU to have an above-threshold proportion of a background of the image or a below- threshold mean color channel variance of KENNEDY reference. The Suggestion/motivation for doing so would have been to provide an adjustable threshold value for the pass/fail testing adjusted for different data sets as learning continues during marker discovery as suggested by paragraph [0041] of KENNEDY. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine KENNEDY with GEORGESCU to obtain the invention as specified in claim 9.
As per claim 12, GEORGESCU discloses the system of claim 1. GEORGESCU fails to disclose wherein the biological sample comprises a hepatocellular carcinoma (HCC) tissue sample, wherein each tile of the plurality of tiles is assigned a molecular subtype comprising one of a cholangio-like subtype, a hepatocyte-like subtype, or a progenitor-like subtype, and wherein the overall molecular subtype of the plurality of cells depicted in the image of the biological sample comprises one of the cholangio-like subtype, the hepatocyte-like subtype, or the progenitor-like subtype.
KENNEDY discloses wherein the biological sample comprises a hepatocellular carcinoma (HCC) tissue sample (the biological sample being classified is hepatocellular carcinoma HCC tissues; paragraphs [0250-0252]), wherein each tile of the plurality of tiles is assigned a molecular subtype comprising one of a cholangio-like subtype, a hepatocyte-like subtype, or a progenitor-like subtype (the molecular subtype of each tile/image frame of the overall image includes and is determined to be a heptacyte subtype such as HBV OR HCV determined by classifying the slides; paragraphs [0036-0037], [0250-0252], [0254]), and wherein the overall molecular subtype of the plurality of cells depicted in the image of the biological sample comprises one of the cholangio-like subtype, the hepatocyte-like subtype, or the progenitor-like subtype (the molecular subtype of the classified tissue sample includes and is determined to be a heptacyte subtype such as HBV OR HCV determined by classifying the slides; paragraphs [0036-0037], [0250-0252], [0254]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify GEORGESCU to have wherein the biological sample comprises a hepatocellular carcinoma (HCC) tissue of KENNEDY reference. The Suggestion/motivation for doing so would have been to provide the ability to classify based on identified subtypes such as benign or malignant tumor tissues type for patient/medical diagnostic purposes as suggested by paragraph [0070] of KENNEDY. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine KENNEDY with GEORGESCU to obtain the invention as specified in claim 12.
As per claim 16, GEORGESCU discloses the method of claim 15. GEORGESCU fails to disclose wherein the overall molecular subtype of the biological sample is determined by applying a second machine learning model.
KENNEDY discloses wherein the overall molecular subtype of the biological sample is determined by applying a second machine learning model (two models are used as described in paragraph [0270] a LIMMA model for feature extraction and a SVM for classification; paragraph [0270]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify GEORGESCU to have overall molecular subtype of the biological sample is determined by applying a second machine learning model of KENNEDY reference. The Suggestion/motivation for doing so would have been to provide specific models trained for specific purposes/functions within the computing system as suggested by paragraph [0270] of KENNEDY. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine KENNEDY with GEORGESCU to obtain the invention as specified in claim 16.
As per claim 23, GEORGESCU discloses the method of claim 15. GEORGESCU fails to disclose wherein the plurality of tiles exclude one or more tiles in the image with an above-threshold proportion of a background of the image or a below-threshold mean color channel variance.
KENNEDY discloses wherein the plurality of tiles exclude one or more tiles in the image with an above-threshold proportion of a background of the image or a below-threshold mean color channel variance (common quality control metrics used to pass or fail a sample in order to enter it into the molecular classifier are Intron/Exon separation AUC and pDABG or pDET, and the threshold for pDET (percentage of genes or probe sets that are detected above background) can be adjusted for different data sets as learning continues during marker discover; paragraphs [0041], [0159-0160]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify GEORGESCU to have an above-threshold proportion of a background of the image or a below- threshold mean color channel variance of KENNEDY reference. The Suggestion/motivation for doing so would have been to provide an adjustable threshold value for the pass/fail testing adjusted for different data sets as learning continues during marker discovery as suggested by paragraph [0041] of KENNEDY. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine KENNEDY with GEORGESCU to obtain the invention as specified in claim 23.
As per claim 26, GEORGESCU discloses the method of claim 15. GEORGESCU fails to disclose wherein the biological sample comprises a hepatocellular carcinoma (HCC) tissue sample, wherein each tile of the plurality of tiles is assigned a molecular subtype comprising one of a cholangio-like subtype, a hepatocyte-like subtype, or a progenitor-like subtype, and wherein the overall molecular subtype of the plurality of cells depicted in the image of the biological sample comprises one of the cholangio-like subtype, the hepatocyte-like subtype, or the progenitor-like subtype.
KENNEDY discloses wherein the biological sample comprises a hepatocellular carcinoma (HCC) tissue sample (the biological sample being classified is hepatocellular carcinoma HCC tissues; paragraphs [0250-0252]), wherein each tile of the plurality of tiles is assigned a molecular subtype comprising one of a cholangio-like subtype, a hepatocyte-like subtype, or a progenitor-like subtype (the molecular subtype of each tile/image frame of the overall image includes and is determined to be a heptacyte subtype such as HBV OR HCV determined by classifying the slides; paragraphs [0036-0037], [0250-0252], [0254]), and wherein the overall molecular subtype of the plurality of cells depicted in the image of the biological sample comprises one of the cholangio-like subtype, the hepatocyte-like subtype, or the progenitor-like subtype (the molecular subtype of the classified tissue sample includes and is determined to be a heptacyte subtype such as HBV OR HCV determined by classifying the slides; paragraphs [0036-0037], [0250-0252], [0254]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify GEORGESCU to have wherein the biological sample comprises a hepatocellular carcinoma (HCC) tissue of KENNEDY reference. The Suggestion/motivation for doing so would have been to provide the ability to classify based on identified subtypes such as benign or malignant tumor tissues type for patient/medical diagnostic purposes as suggested by paragraph [0070] of KENNEDY. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine KENNEDY with GEORGESCU to obtain the invention as specified in claim 26.
Conclusion
Examiner's Note: Examiner has cited figures, and paragraphs in the references as applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested for the applicant, in preparing the responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. Examiner has also cited references in PTO892 but not relied on, which are relevant and pertinent to the applicant’s disclosure, and may also be reading (anticipatory/obvious) on the claims and claimed limitations. Applicant is advised to consider the references in preparing the response/amendments in-order to expedite the prosecution.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. These prior arts include the following:
US 2020/0272864 A1
US 2024/0221159 A1
US 2023/0296584 A1
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/D J DHOOGE/Examiner, Art Unit 2677
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