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 ,
Status of Claims
This action is in reply to the application filed on July 18, 2024.
2. Claim(s) 3-4, 6-7, 9-12, 14-22 and 24-28 are currently pending and have been examined.
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 3-4, 6-7, 9-12, 14-22 and 24-28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Based upon consideration of all of the relevant factors with respect to the claims as a whole, the claims are directed to non-statutory subject matter which do not include additional elements that are sufficient to amount to significantly more than the judicial exception because of the following analysis:
Independent Claim(s) 1 and 13-16 are directed to an abstract idea consisting of a system and method comprising discovering natural correlation or relationship between the spatial distribution pattern (fractal-geometric metric) of tumor-infiltrating leukocytes (TILs) in a resected tumor sample and clinical outcomes in breast cancer, including prognosis and likelihood of response to specific treatment modalities.
Independent Claim 3 recites “applying, a trained model to detect and quantify (a) cancer cells and (b) tumor-infiltrating leukocytes (TH-s) in a biomedical image of a resected tumor sample including a tumor stromal region, wherein the resected tumor sample is obtained from the breast cancer patient; computing a fractal -geometric metric based on anatomic distribution of the TH-s in the biomedical image of the resected tumor sample; and administering a chemotherapeutic agent to the breast cancer patient, wherein the fractal-geometric metric of the breast cancer patient falls below a predetermined threshold.”
Independent Claim 6 recites “applying, a trained model to detect and quantify (a) cancer cells and (b) tumor-infiltrating leukocytes (TILs) in a biomedical image of a resected tumor sample including a tumor stromal region, wherein the resected tumor sample is obtained from the breast cancer patient; computing a fractal-geometric metric based on anatomic distribution of the TILs in the biomedical image of the resected tumor sample; and administering surgery, radiation therapy, or immunotherapy to the breast cancer patient, wherein the fractal-geometric metric of the breast cancer patient is at or above a predetermined threshold.”
Independent Claim 17 recites “apply, a trained model to detect and quantify (a) cancer cells and (b) tumor-infiltrating leukocytes (TILs) in a biomedical image of a resected tumor sample including a tumor stromal region, wherein the resected tumor sample is obtained from the patient; compute a fractal-geometric metric based on anatomic distribution of the TILs in the biomedical image of the resected tumor sample; and determine that the breast cancer patient has a favorable prognosis when the fractal-geometric metric falls below a predetermined threshold, or determining that the breast cancer patient has a negative prognosis when the fractal-geometric metric is at or above a predetermined threshold.”
The limitations of Claims 1, 6 and 17, as drafted, under its broadest reasonable interpretation, covers the performance of a Law of Nature/Natural Phenomenon (specifically, a newly discovered biological correlation) combined with Mathematical Concepts (fractal-geometric analysis), but for the recitation of generic computer components. That is, other than reciting, “computing system comprising a processor and a memory” nothing in the claim element precludes the step from practically being performed within the grouping of Law of Nature/Natural Phenomenon combined with Mathematical Concepts. For example, but for the “computer system” language, “computing” in the context of this claim encompasses the user manually calculating a fractal-geometric metric. Similarly, the determining that the breast cancer patient has a negative prognosis when the fractal-geometric metric is at or above a predetermined threshold, under its broadest reasonable interpretation, covers performance within Law of Nature/Natural Phenomenon combined with Mathematical Concepts, but for the recitation of generic computer components. If a claim limitation, under its broadest reasonable interpretation, covers performance within the grouping of Law of Nature/Natural Phenomenon combined with Mathematical Concepts, but for the recitation of generic computer components, then it falls within the “Law of Nature/Natural Phenomenon and Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of using a “computing system comprising a processor and a memory” to perform all of the “obtaining, transforming, parsing, determining, transforming, selecting and storing” steps. The “computing system comprising a processor and a memory” is/are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) of executing computer-executable instructions for implementing the specified logical function(s) 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.
Claim 3 has the following additional elements (i.e., computing system comprising a processor). Claim 6 has the following additional elements (i.e., computing system comprising a processor). Claim 17 has the following additional elements (i.e., computing system comprising a processor and a memory). Looking to the specification, these components are described at a high level of generality (¶ 59; The computer system 100 can be any workstation, telephone, desktop computer, laptop or notebook computer, netbook, tablet, server, handheld computer, mobile telephone, smartphone or other portable telecommunications device, media playing device, a gaming system, mobile computing device, or any other type and/or form of computing, telecommunications or media device that is capable of communication). The use of a general-purpose computer, taken alone, does not impose any meaningful limitation on the computer implementation of the abstract idea, so it does not amount to significantly more than the abstract idea. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. The combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology and their collective functions merely provide a conventional computer implementation of the abstract idea. Furthermore, the additional elements or combination of elements in the claims, other than the abstract idea per se, amount to no more than a recitation of generally linking the abstract idea to a particular technological environment or field of use, as the courts have found in Parker v. Flook. Therefore, there are no limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception.
It is worth noting that the above analysis already encompasses each of the current dependent claims (i.e., claims 4, 9-12, 14, 16, 18-22 and 26-28). Particularly, each of the dependent claims also fails to amount to “significantly more’ than the abstract idea since each dependent claim is directed to a further abstract idea, and/or a further conventional computer element/function utilized to facilitate the abstract idea. Accordingly, none of the current claims implements an element—or a combination of elements—directed to an inventive concept (e.g., none of the current claims is reciting an element—or a combination of elements—that provides a technological improvement over the existing/conventional technology). These information characteristics do not change the fundamental analogy to the abstract idea grouping of “Law of Nature/Natural Phenomenon and Mathematical Concepts,” and, when viewed individually or as a whole, they do not add anything substantial beyond the abstract idea. Furthermore, the combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology. Therefore, the claims when taken as a whole are ineligible for the same reasons as the independent claims.
Claims 3-4, 9-12, 14, 16, 17-22 and 26-28 are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
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.
Claims 3, 6, 10, 14, 16-18, 20-21, 24 and 26-28 are rejected under 35 U.S.C. 103 as being unpatentable over WO 2021/142449 A1 to Nantcell, Inc. et al. (hereinafter Nantcell) in view of in view of the publication entitled "Geospatial immune variability illuminates differential evolution of lung adenocarcinoma" by AbdulJabbar et al. (hereinafter 'AbdulJabbar').
As per Claim 3, Nantcell teaches a method for selecting a breast cancer patient for treatment with a chemotherapeutic agent (para [0009]-[0010]; para [0015]; para [0087] "The clinical value and/or the output may be, for example, one or more of: a diagnosis for the individual, a prognosis for the individual, a suitability of the individual, a classification of the tumor, a diagnostic and/or treatment recommendation for the individual, or the like."; para [0090] "Some example embodiments may use the determination of the tumor analysis model to determine, and to display for a user, a treatment of the individual based on the clinical value (such as the prognosis) for the individual. For example, based on the tumor being classified as a low-risk class by the tumor analysis model, an apparatus may recommend less aggressive treatment of the tumor, such as less aggressive chemotherapy. Based on the tumor being classified as a high-risk class by the tumor analysis model, an apparatus may recommend more aggressive treatment of the tumor, such as more aggressive chemotherapy and/or surgical removal.") comprising applying, by a computing system having one or more processors (para [0009]-[0010]; para [0148]), a trained model to detect (a) cancer cells and detect and quantify (b) tumor-infiltrating leukocytes (TILs) in a biomedical image of a tumor sample including a tumor stromal region (para [0134]; para [0054]-[0055]), wherein the tumor sample is obtained from the breast cancer patient (para [0009]-[0010]; para [0088]); computing a spatial metric based on anatomic distribution of the TILs in the biomedical image of the tumor sample (para [0011]; para [0134]); and determining that the breast cancer patient has a favorable or negative prognosis (para [0088]).
Nantcell does not disclose a trained model to detect and quantify (a) cancer cells, wherein the resected tumor sample is obtained from the breast cancer patient; computing a fractal-geometric metric based on anatomic distribution of the TILs in the biomedical image of the resected tumor sample; and administering a chemotherapeutic agent to the breast cancer patient, wherein the fractal-geometric metric of the breast cancer patient falls below a predetermined threshold. Nantcell does disclose analyzing images from resected tumors from individuals (para [0081]), and informing chemotherapy start date, frequency, and dosage (para [0091] "For example, based on the tumor being classified as a low-risk class by the tumor analysis model, an apparatus may recommend chemotherapy with a lower frequency, at a later date, and/or with a lower dosage. Based on the tumor being classified as a high-risk class by the tumor analysis model, an apparatus may recommend more aggressive treatment of the tumor, such chemotherapy with a higher frequency, at an earlier date, and/or with a higher dosage.").
Since Nantcell discloses quantifying TILs in a biomedical image of a tumor and where dyes and labels may be used for detection of cells in a tumor image, analysis of resected tumors, classifying a tumor as low- or high- risk based on the trained model output, and where patients may be recommended chemotherapy, it would have been obvious to one of ordinary skill in the art to modify the method, as disclosed by Nantcell, to include quantifying cancer cells in the biomedical image, since this would simply allow counting cancer cells in various tissue regions, such as infiltration of stromal areas by using a cancer cell specific label, to include analysis of resected tumors, allowing evaluation of biopsies and/or tumors already removed from an individual, such as for determining nature of the tumor including response to a chemotherapeutic, and to administer a recommended chemotherapeutic, since this would simply allow acting upon the recommended treatment, thereby providing any improved or altered prognostic or diagnostic ability of the method of Nantcell and any associated improved patient clinical outcome when the method is used to inform a chemotherapy treatment.
AbdulJabbar discloses a trained model for analyzing a tumor image comprising a stromal region and tumor infiltrating leukocytes (abstract; pg. 1, col 2, para 1; pg. 4, col 2, para 3; pg. 10, col 2, para 4), and analysis using fractal dimension to quantify cancer cell and stromal geometry and comparison to spatial tumor immune response (pg. 4, col 2, para 1; pg. 11, col 2, para 2). Since Nantcell discloses a trained model for analyzing a tumor image comprising cancer and stromal regions and presence of TILs, and AbdulJabbar discloses a trained model for analyzing a tumor image comprising cancer and stromal regions and presence of TILs, and where fractal dimension analysis, which uses a geometric pattern approach, may be used to determine cancer-stromal morphological patterns, it would have been obvious to one of ordinary skill in the art to modify the method, as disclosed by Nantcell, to include computing a fractal-geometric metric based on anatomic distribution of the TILs and determining that the breast cancer patient has prognosis indicating benefit of chemotherapy when the fractal-geometric metric falls below a threshold, since this would simply allow use of the fractal dimension analysis of AbdulJabbar as additional input into a trained tumor classification model, enabling profiling and consideration of geometric irregularity in cancer-stromal cell interface in conjunction with immune cell infiltration and providing a metric indicating need or benefit from chemotherapy, thereby providing any improved or altered prognostic or diagnostic ability of the method and model of Nantcell and therefore any improved patient clinical outcome.
As per Claim 6, Nantcell discloses a method for selecting a breast cancer patient for surgery, radiation therapy, or immunotherapy (para [0009]-[0010]; para [0015]; para [0087]; para [0090] Based on the tumor being classified as a high-risk class by the tumor analysis model, an apparatus may recommend more aggressive treatment of the tumor, such as more aggressive chemotherapy and/or surgical removal.") comprising applying, by a computing system having one or more processors (para [0009]-[0010]; para [0148]), a trained model to detect (a) cancer cells and detect and quantify (b) tumor-infiltrating leukocytes (TILs) in a biomedical image of a tumor sample including a tumor stromal region (para [0134]; para [0054]-[0055]), wherein the tumor sample is obtained from the breast cancer patient (para [0009]-[0010]; para [0088]); computing a fractal-geometric metric based on anatomic distribution of the TILs in the biomedical image of the tumor sample (para [0011]; para [0134]); and determining that the breast cancer patient has a favorable or negative prognosis (para [0088]).
Nantcell does not disclose a trained model to detect and quantify (a) cancer cells in a biomedical image of a resected tumor sample including a tumor stromal region, wherein the resected tumor sample is obtained from the breast cancer patient; computing a fractal-geometric metric based on anatomic distribution of the TILs in the biomedical image of the resected tumor sample; and administering surgery, radiation therapy, or immunotherapy to the breast cancer patient, wherein the fractal-geometric metric of the breast cancer patient is at or above a predetermined threshold.
Nantcell does disclose analyzing images from resected tumors from individuals (para [0081]).
Since Nantcell discloses quantifying TILs in a biomedical image of a tumor and where dyes and labels may be used for detection of cells in a tumor image, analysis of resected tumors, classifying a tumor as low- or high- risk based on the trained model output, and where patients may be recommended surgical removal of a tumor, it would have been obvious to one of ordinary skill in the art to modify the method, as disclosed by Nantcell, to include quantifying cancer cells in the biomedical image, since this would simply allow counting cancer cells in various tissue regions, such as infiltration of stromal areas by using a cancer cell specific label, to include analysis of resected tumors, allowing evaluation of biopsies, and to administer a recommended surgical intervention, since this would simply allow acting upon the recommended treatment, thereby providing any improved or altered prognostic or diagnostic ability of the method of Nantcell and any associated improved patient clinical outcome.
AbdulJabbar discloses a trained model for analyzing a tumor image comprising a stromal region and tumor infiltrating leukocytes (abstract; pg. 1, col 2, para 1; pg. 4, col 2, para 3; pg. 10, col 2, para 4), and analysis using fractal dimension to quantify cancer cell and stromal geometry and comparison to spatial tumor immune response (pg. 4, col 2, para 1; pg. 11, col 2, para 2).
Since Nantcell discloses a trained model for analyzing a tumor image comprising cancer and stromal regions and presence of TILs, and AbdulJabbar discloses a trained model for analyzing a tumor image comprising cancer and stromal regions and presence of TILs, and where fractal dimension analysis, which uses a geometric pattern approach, may be used to determine cancer-stromal morphological patterns, it would have been obvious to one of ordinary skill in the art to modify the method, as disclosed by Nantcell, to include computing a fractal-geometric metric based on anatomic distribution of the TILs and determining that the breast cancer patient has a prognosis indicating need or benefit from a surgery when the fractal-geometric metric is at or above a threshold, since this would simply allow use of the fractal dimension analysis of AbdulJabbar as additional input into a trained tumor classification model, enabling profiling and consideration of geometric irregularity in cancer-stromal cell interface in conjunction with immune cell infiltration and providing a metric which may indicate need or benefit from surgery, thereby providing any improved or altered prognostic or diagnostic ability of the method and model of Nantcell and therefore any improved patient clinical outcome.
As per Claim 10, Nantcell and AbdulJabbar, in combination, make obvious the method of claim 3, wherein the biomedical image is a hematoxylin and eosin (H&E)-stained image, a radiographic image or an immunohistochemical (IHC) image (see Nantcell para [0062]; the chart in FIG. 5 was developed based on diagnostic hematoxylin and eosin stain (H&E-stain) of pathology images of pancreatic adenocarcinoma patients who underwent chemotherapy.)
The obviousness of combining the teachings of Nantcell and AbdulJabbar are discussed in the rejection of claim 3, and incorporated herein.
As per Claim 14, Nantcell and AbdulJabbar, in combination, make obvious the method of claim 3, wherein the trained model is a machine learning model generated using a machine learning technique, optionally wherein the machine learning technique is a random forest technique, and wherein the machine learning model is a random forest model (see Nantcell para [0008] Machine learning techniques may be applied to identify clusters of tumors within the feature space that share a similar prognosis, such as a first cluster representing high- risk tumors and second cluster representing low-risk tumors, where each cluster is represented as a collection of Gaussian probability distributions of the respective features within the feature space.).
The obviousness of combining the teachings of Nantcell and AbdulJabbar are discussed in the rejection of claim 3, and incorporated herein.
As per Claim 16, Nantcell and AbdulJabbar, in combination, make obvious the method of claim 3, wherein the tumor stromal region includes basement membrane, fibroblasts, extracellular matrix, immune cells, and mesenchymal stromal cells (see AbdulJabbar pg. 1, col 2, para 1; pg. 7, col 1, para 3-4).
The obviousness of combining the teachings of Nantcell and AbdulJabbar are discussed in the rejection of claim 3, and incorporated herein.
As per Claim 17, Nantcell teaches a computer system for predicting prognosis in a breast cancer patient, the computing system comprising a processor and a memory with instructions which, when executed by the processor, cause the processor to: apply, by a computing system having one or more processors (para [0009]-[0010]; para [0015]; para [0148]; para [0152] "In some example embodiments, the storage 1806 may be configured to store other computer readable instructions to implement an operating system, an application program, and the like. Computer-readable instructions may be loaded in memory 1804 for execution by processing circuitry 1502, for example."), a trained model to detect and quantify (a) cancer cells and (b) tumor-infiltrating leukocytes (TILs) in a biomedical image of a resected tumor sample including a tumor stromal region (para [0134]; para [0054]-[0055]), wherein the resected tumor sample is obtained from the patient (para [0009]-[0010]; para [0088]): compute a fractal-geometric metric based on anatomic distribution of the TILs in the biomedical image of the resected tumor sample (para [0011]; para [0134]); and determine that the breast cancer patient has a favorable prognosis when the fractal-geometric metric falls below a predetermined threshold, or determining that the breast cancer patient has a negative prognosis when the fractal-geometric metric is at or above a predetermined threshold (para [0088]).
Nantcell does not disclose a trained model to detect and quantify (a) cancer cells in a biomedical image of a resected tumor sample, wherein the resected tumor sample is obtained from the patient; compute a fractal-geometric metric based on anatomic distribution of the TILs in the biomedical image of the resected tumor sample; and determine that the breast cancer patient has a favorable prognosis when the fractal-geometric metric falls below a predetermined threshold, or determining that the breast cancer patient has a negative prognosis when the fractal-geometric metric is at or above a predetermined threshold.
Nantcell does disclose analyzing images from resected tumors from individuals (para [0081]).
Since Nantcell discloses quantifying TILs in a biomedical image of a tumor and where dyes and labels may be used for detection of cells in a tumor image, analysis of resected tumors, and classifying a tumor as low- or high- risk based on the trained model output, it would have been obvious to one of ordinary skill in the art to modify the system, as disclosed by Nantcell, to include quantifying cancer cells in the image, since this would simply allow counting cancer cells in various tissue regions, such as infiltration of stromal areas by using a cancer cell specific label, and to include analysis of resected tumors, allowing evaluation of biopsies and/or tumors already removed from an individual, such as for determining nature of the tumor including, for instance, likelihood of recurrence, response to a therapeutic, or likelihood of metastasis, thereby providing any improved or altered prognostic or diagnostic ability of the method of Nantcell and therefore any improved patient clinical outcome when the method is used, for instance, to inform selection of a therapeutic intervention.
AbdulJabbar discloses a trained model for analyzing a tumor image comprising a stromal region and tumor infiltrating leukocytes (abstract; pg. 1, col 2, para 1; pg. 4, col 2, para 3; pg. 10, col 2, para 4), and analysis using fractal dimension to quantify cancer cell and stromal geometry and comparison to spatial tumor immune response (pg. 4, col 2, para 1; pg. 11, col 2, para 2).
Since Nantcell discloses a trained model for analyzing a tumor image comprising cancer and stromal regions and presence of TILs, and AbdulJabbar discloses a trained model for analyzing a tumor image comprising cancer and stromal regions and presence of TILs, and where fractal dimension analysis, which uses a geometric pattern approach, may be used to determine cancer-stromal morphological patterns, it would have been obvious to one of ordinary skill in the art to modify the system, as disclosed by Nantcell, to include computing a fractal-geometric metric based on anatomic distribution of the TILs and determining that the breast cancer patient has a favorable or negative prognosis when the fractal-geometric metric falls below or above a predetermined threshold, since this would simply allow use of the fractal dimension analysis of AbdulJabbar as additional input into a trained tumor classification model, enabling profiling and consideration of geometric irregularity in cancer-stromal cell interface in conjunction with immune cell infiltration and providing a metric, such as a metric indicating a favorable prognosis when below a threshold and a negative prognosis when above a threshold, thereby providing any improved or altered prognostic or diagnostic ability of the system and model of Nantcell and therefore any improved patient clinical outcome when the systems and/or model is used, for instance, to inform selection of a therapeutic intervention
As per Claim 18, Nantcell and AbdulJabbar, in combination, make obvious the computer system of claim 17, but Nantcell does not disclose wherein negative prognosis comprises recurrent disease in breast tissue, bone tissue, or brain tissue.
Nantcell does disclose where patient history, including cancer medical history, and tumor metastatic condition may be included in the model (para [0014]), and where the model output may be diagnostic, prognostic, or indicate survivability (para [0087]).
Since Nantcell discloses analyzing a tumor image from an individual and determining if a tumor is low or high risk, where a history of cancer in the patient and metastatic nature of the tumor may be considered, and where the model may output may indicate diagnosis of disease and survivability, it would have been obvious to one of ordinary skill in the art to modify the system, as disclosed by Nantcell, to include or instead consider where a diagnosis based on a model output metric indicates recurrent disease in breast tissue, such as when analyzing a tumor from a patient with a previous history of breast cancer or when predicting likelihood of recurrence or metastasis, since this would allow profiling of additional subsets of patients, such as for training a model, and predicting or indicating a specific patient outcome, thereby providing any improved or altered prognostic or diagnostic ability of the method and model of Nantcell and therefore any improved patient clinical outcome when the system and/or model is used, for instance, to inform selection of a therapeutic intervention.
As per Claim 20, Nantcell and AbdulJabbar, in combination, make obvious the system of claim 17, wherein the biomedical image is a hematoxylin and eosin (H&E)-stained image, a radiographic image or an immunohistochemical (IHC) image (see AbdulJabbar; pg. 10, col 2, para 3; Altogether, this pipeline enabled spatial mapping of four cell types from H&E images: cancer (malignant epithelial) cells, lymphocytes (including plasma cells), noninflammatory stromal cells (fibroblasts and endothelial cells) and an ‘other’ cell type that included nonidentifiable cells, less abundant cells such as macrophages and chondrocytes and ‘normal’ pneumocytes and bronchial epithelial cells.).
The obviousness of combining the teachings of Nantcell and AbdulJabbar are discussed in the rejection of claim 17, and incorporated herein.
As per Claim 21, Nantcell and AbdulJabbar, in combination, make obvious the computer system of claim 17, wherein the instructions further cause the processor to compute an additional metric based on quantity and anatomic distribution of HER2-expressing cancer cells, and/or Trop2-expressing cancer cells in the biomedical image of the resected tumor sample (see Nantcell para [0015] In some example embodiments, the instructions may further cause the apparatus to display a visualization of a clinical value (such as a prognosis) for the individual. In some example embodiments, the visualization is a Kaplan Meier survivability projection of the tumor. In some example embodiments, the instructions may further cause the apparatus to determine a diagnostic test for the tumor based on the clinical value (such as the prognosis) for the individual. In some example embodiments, the instructions may further cause the apparatus to determine a treatment of the individual based on the clinical value (such as the prognosis) for the individual. In some example embodiments, the instructions may further cause the apparatus to determine a schedule of a therapeutic agent for treating the tumor based on the clinical value (such as the prognosis) for the individual.).
The obviousness of combining the teachings of Nantcell and AbdulJabbar are discussed in the rejection of claim 17, and incorporated herein.
As per Claim 24, Nantcell and AbdulJabbar, in combination, make obvious the computer system of claim 17, wherein the trained model is a machine learning model generated using a machine learning technique, optionally wherein the machine learning technique is a random forest technique, and wherein the machine learning model is a random forest mode (see Nantcell para [0008] Machine learning techniques may be applied to identify clusters of tumors within the feature space that share a similar prognosis, such as a first cluster representing high- risk tumors and second cluster representing low-risk tumors, where each cluster is represented as a collection of Gaussian probability distributions of the respective features within the feature space.).
The obviousness of combining the teachings of Nantcell and AbdulJabbar are discussed in the rejection of claim 17, and incorporated herein.
As per Claim 26, Nantcell and AbdulJabbar, in combination, make obvious the computer system of claim 17, wherein the tumor stromal region includes basement membrane, fibroblasts, extracellular matrix, immune cells, and mesenchymal stromal cells (see AbdulJabbar pg. 1, col 2, para 1; pg. 7, col 1, para 3-4).
The obviousness of combining the teachings of Nantcell and AbdulJabbar are discussed in the rejection of claim 17, and incorporated herein.
As per Claim 27, Nantcell and AbdulJabbar, in combination, make obvious the computer system of claim 17, wherein the instructions further cause the processor to provide a cancer therapy recommendation based on the fractal-geometric metric (see Nantcell para [0015] In some example embodiments, the instructions may further cause the apparatus to display a visualization of a clinical value (such as a prognosis) for the individual. In some example embodiments, the visualization is a Kaplan Meier survivability projection of the tumor. In some example embodiments, the instructions may further cause the apparatus to determine a diagnostic test for the tumor based on the clinical value (such as the prognosis) for the individual. In some example embodiments, the instructions may further cause the apparatus to determine a treatment of the individual based on the clinical value (such as the prognosis) for the individual. In some example embodiments, the instructions may further cause the apparatus to determine a schedule of a therapeutic agent for treating the tumor based on the clinical value (such as the prognosis) for the individual.).
The obviousness of combining the teachings of Nantcell and AbdulJabbar are discussed in the rejection of claim 17, and incorporated herein.
As per Claim 28, Nantcell and AbdulJabbar, in combination, make obvious the computer system of claim 27, wherein the cancer therapy may comprise one or more of surgery, chemotherapy, immunotherapy and radiation therapy (see Nantcell para [0015] In some example embodiments, the instructions may further cause the apparatus to display a visualization of a clinical value (such as a prognosis) for the individual. In some example embodiments, the visualization is a Kaplan Meier survivability projection of the tumor. In some example embodiments, the instructions may further cause the apparatus to determine a diagnostic test for the tumor based on the clinical value (such as the prognosis) for the individual. In some example embodiments, the instructions may further cause the apparatus to determine a treatment of the individual based on the clinical value (such as the prognosis) for the individual. In some example embodiments, the instructions may further cause the apparatus to determine a schedule of a therapeutic agent for treating the tumor based on the clinical value (such as the prognosis) for the individual.).
The obviousness of combining the teachings of Nantcell and AbdulJabbar are discussed in the rejection of claim 17, and incorporated herein.
Claims 4, 7, 9, 11-12, 19 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Nantcell AbdulJabbar et al. as applied to claims 3, 6, 10, 14, 16-18, 20-21, 24 and 26-28 above, and further in view of WO 2021/055517 A1 to the Board of Trustees of the Leland Stanford Junior University (hereinafter "Stanford").
As per Claim 4, Nantcell and AbdulJabbar, in combination, make obvious the method of claim 3, but Nantcell does not teach wherein the chemotherapeutic agent comprises one or more agents selected from the group consisting of alkylating agents, platinum agents, taxanes, vinca agents, anti-estrogen drugs, aromatase inhibitors, ovarian suppression agents, VEGF/VEGFR inhibitors, EGF/EGFR inhibitors, PARP inhibitors, cytostatic alkaloids, cytotoxic antibiotics, antimetabolites, and endocrine/hormonal agents or wherein the chemotherapeutic agent is selected from the group consisting of cyclophosphamide, fluorouracil (or 5-fluorouracil or 5-FU), methotrexate, edatrexate (10- ethyl-i0-deaza-aminopterin), thiotepa, carboplatin, cisplatin, taxanes, paclitaxel, protein- bound paclitaxel, docetaxel, vinorelbine, tamoxifen, raloxifene, Toremifene, Fulvestrant, gemcitabine, irinotecan, ixabepilone, Temozolmide, Topotecan, Vincristine, Vinblastine, eribulin, mutamycin, capecitabine, anastrozole, Exemestane, letrozole, leuprolide, abarelix, buserlin, goserelin, megestrol acetate, risedronate, pamidronate, ibandronate, alendronate, denosumab, zoledronate, trastuzumab, tykerb, anthracyclines (e.g.,,daunorubicin and doxorubicin), bevacizumab, oxaliplatin, melphalan, etoposide, mechlorethamine, bleomycin, microtubule poisons, annonaceous, acetogenins, or combinations thereof.
Stanford discloses using a trained model to classify breast cancer including those with high immune infiltration (para [0166]-[0167] "In a number of embodiments, classification of breast cancer is performed utilizing a computational model.. A number of embodiments utilize statistical computation to stratify breast cancer recurrence risk (e.g., high, intermediate, low). In various embodiments, statistical computation models include, Cox Proportional Hazards models In some embodiments, thresholds are utilized to separate higher risk scores from lower risk scores."; para [0231] "An emerging option for TNBC is treatment with checkpoint inhibitors such as pembrolizumab or nivolumab and/or immunotherapies that target the protein PD-L1 or PD1 such as atezolizumab (Tecentriq). In some embodiments, TNBCs that classify within IntClust4ER- are treated with atezolizumab, as cancers within this classification have a high degree of immune infiltration and a persistent risk of recurrence."), and treatment with chemotherapeutics (para [0207]-[0208] "A number of treatments and medications are available to treat breast cancer including (but not limited to) radiotherapy, chemotherapy, targeted (molecular) therapy, endocrine therapy, and immunotherapy. Classes of anti-cancer or chemotherapeutic agents can include alkylating agents, platinum agents, taxanes, vinca agents, anti-estrogen drugs, aromatase inhibitors, ovarian suppression agents, endocrine/hormonal agents, bisphosphonate therapy agents and targeted biological therapy agents.").
Since Nantcell discloses a trained model to classify breast cancer and where the model may recommend a chemotherapy treatment, and Stanford discloses using a trained model to classify breast cancer and treatment with chemotherapeutics including alkylating agents, platinum agents, taxanes, vinca agents, anti-estrogen drugs, aromatase inhibitors, ovarian suppression agents, and endocrine/hormonal agents, it would have been obvious to one of ordinary skill in the art to modify the method, as disclosed by Nantcell, to include or instead consider treatment of breast cancer with a chemotherapeutic comprising one or more of alkylating agents, platinum agents, taxanes, vinca agents, anti-estrogen drugs, aromatase inhibitors, ovarian suppression agents, and endocrine/hormonal agents, since this would simply allow the treatment of breast cancer patients identified as needing or benefiting from chemotherapy with one or more of several known anti-cancer chemotherapies, providing any improved or altered patient clinical outcome and therefore any improved value of the method of Nantcell.
As per Claim 7, Nantcell and AbdulJabbar, in combination, make obvious the method of claim 6, but Nantcell does not teach wherein the immunotherapy comprises one or more of an anti-PD-1 antibody, an anti-PD-L1 antibody, an anti-PD-L2 antibody, an anti-CTLA-4 antibody, an anti-TIM3 antibody, an anti-TIGIT antibody, an anti- VISTA antibody, an anti-B7-H3 antibody, an anti- BTLA antibody, an anti-CD73 antibody, or an anti-LAG-3 antibody; or wherein the immunotherapy comprises one or more agents selected from the group consisting of pembrolizumab, nivolumab, cemiplimab, atezolizumab, avelumab, durvalumab, ipilimumab, tremelimumab, ticlimumab, JTX-4014, Spartalizumab (PDR001), Camrelizumab (SHR1210), Sintilimab (IBI308), Tislelizumab (BGB-A317), Toripalimab (JS 001), Dostarlimab (TSR-042, WBP-285), INCMGA00012 (MGA012),AMP-224, AMP-514, KN035, CK-301, AUNP12, CA-170, or BMS-986189.
Stanford discloses using a trained model to classify breast cancer including those with high immune infiltration (para [0166] - [0167]; para [0231]), and treatment with anti-PDL1 and anti-PD1 Immunotherapy (para [0230] - [0231] "Treatments for Triple Negative Breast Cancer. When a breast cancer lacks amplification of PR, ER, or HER2 (i.e., PR-, ER-, and HER2-), it is considered a triple negative breast cancer. For triple negative breast cancer (TNBC), therapies that target hormones or HER2 do not work. An emerging option for TNBC is treatment with checkpoint inhibitors such as pembrolizumab or nivolumab and/or immunotherapies that target the protein PD-L1 or PD1 such as atezolizumab (Tecentriq). in some embodiments, TNBCs that classify within IntClust4ER- are treated with atezolizumab, as cancers within this classification have a high degree of immune infiltration and a persistent risk of recurrence. In some embodiments, TNBCs that classify within IntClustiO are treated with atezolizumab after or potentially in combination with radiation or chemotherapy to better stimulate the immune system and thus more sensitive to the atezolizumab treatment.").
Since Nantcell discloses a trained model to classify breast cancer and where the model may recommend a therapy, and Stanford discloses using a trained model to classify breast cancer and treatment with immunotherapies including anti-PD-L1 and anti-PD-1 therapies, it would have been obvious to one of ordinary skill in the art to modify the method, as disclosed by Nantcell, to include or instead consider treatment of breast cancer with an anti-PD-L1 and/or anti-PD-1 immunotherapy, since this would simply allow the treatment of breast cancer patients identified as needing or benefiting from therapeutic intervention with one or more of several known anti-cancer immunotherapies, such as those specifically beneficial to certain types of breast cancer, providing any improved or altered patient clinical outcome and therefore any improved value of the method of Nantcell.
As per Claim 9, Nantcell and AbdulJabbar, in combination, make obvious the method of claim 3, but Nantcell does not teach wherein the breast cancer is triple negative breast cancer, HER2-positive breast cancer, Estrogen-Receptor (ER) positive breast cancer, or Progesterone-Receptor (PR) positive breast cancer or wherein the breast cancer is metastatic or primary (see Stanford para [0211] Any appropriate breast cancer can be treated, including Stage I, II, III, and IV breast cancer. Breast cancer with positive and/or negative status for estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor 2 (Her2) can also be treated in accordance with various embodiments of the invention).
Since Nantcell discloses a trained model to classify breast cancer and where the model may recommend a therapy, and Stanford discloses using a trained model to classify breast cancer and treatment with immunotherapies including anti-PD-L1 and anti-PD-1 therapies, it would have been obvious to one of ordinary skill in the art to modify the method, as disclosed by Nantcell, to include or instead consider treatment of breast cancer with an anti-PD-L1 and/or anti-PD-1 immunotherapy, since this would simply allow the treatment of breast cancer patients identified as needing or benefiting from therapeutic intervention with one or more of several known anti-cancer immunotherapies, such as those specifically beneficial to certain types of breast cancer, providing any improved or altered patient clinical outcome and therefore any improved value of the method of Nantcell.
As per Claim 11, Nantcell and AbdulJabbar, in combination, make obvious the method of claim 3, but Nantcell and AbdulJabbar, in combination, does not teach wherein further comprising computing an additional metric based on quantity and anatomic distribution of HER2-expressing cancer cells, and/or Trop2-expressing cancer cells in the biomedical image of the resected tumor sample (see Stanford para [0122] – [0123]).
Since Nantcell discloses a trained model to classify breast cancer and where the model may recommend a therapy, and Stanford discloses using a trained model to classify breast cancer and treatment with immunotherapies including anti-PD-L1 and anti-PD-1 therapies, it would have been obvious to one of ordinary skill in the art to modify the method, as disclosed by Nantcell and AbdulJabbar, to include or instead consider treatment of breast cancer with an anti-PD-L1 and/or anti-PD-1 immunotherapy, since this would simply allow the treatment of breast cancer patients identified as needing or benefiting from therapeutic intervention with one or more of several known anti-cancer immunotherapies, such as those specifically beneficial to certain types of breast cancer, providing any improved or altered patient clinical outcome and therefore any improved value of the method of Nantcell.
As per Claim 12, Nantcell and AbdulJabbar, in combination, make obvious the method of claim 3, but Nantcell and AbdulJabbar, in combination, does not teach wherein the breast cancer patient suffers from stage I cancer, stage II cancer, stage III cancer, or stage IV breast cancer (see Stanford para [0211] Any appropriate breast cancer can be treated, including Stage I, II, III, and IV breast cancer. Breast cancer with positive and/or negative status for estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor 2 (Her2) can also be treated in accordance with various embodiments of the invention).
Since Nantcell discloses a trained model to classify breast cancer, and Stanford discloses using a trained model to classify breast cancer including triple negative breast cancer, it would have been obvious to one of ordinary skill in the art to modify the system, as disclosed by Nantcell and AbdulJabbar, to include or instead consider prognosis of triple breast cancer, since this would simply allow the analysis of a particular breast cancer subtype, such as for recommending a treatment, such as atezolizumab which may still provide benefit to triple negative breast cancer patients, as taught by Stanford, thereby providing any improved or altered prognostic or diagnostic ability, any associated improvement in patient clinical outcome, and therefore any improved value of the method of Nantcell.
As per Claim 19, Nantcell and AbdulJabbar, in combination, make obvious the computer system of claim 17, but Nantcell does not teach, wherein the breast cancer is triple negative breast cancer, HER2-positive breast cancer, Estrogen-Receptor (ER) positive breast cancer, or Progesterone-Receptor (PR) positive breast cancer or wherein the breast cancer is metastatic or primary.
Stanford discloses using a trained model to classify breast cancer including those with high immune infiltration (para [0166]-[0167]; para [0231]), and where breast cancer may be triple negative (para [0231]).
Since Nantcell discloses a trained model to classify breast cancer, and Stanford discloses using a trained model to classify breast cancer including triple negative breast cancer, it would have been obvious to one of ordinary skill in the art to modify the system, as disclosed by Nantcell and AbdulJabbar, to include or instead consider prognosis of triple breast cancer, since this would simply allow the analysis of a particular breast cancer subtype, such as for recommending a treatment, such as atezolizumab which may still provide benefit to triple negative breast cancer patients, as taught by Stanford, thereby providing any improved or altered prognostic or diagnostic ability, any associated improvement in patient clinical outcome, and therefore any improved value of the method of Nantcell.
As per Claim 22, Nantcell and AbdulJabbar, in combination, make obvious the system of claim 17, but Nantcell and AbdulJabbar, in combination, does not teach wherein the breast cancer patient suffers from stage I cancer, stage II cancer, stage III cancer, or stage IV breast cancer (see Stanford para [0211] Any appropriate breast cancer can be treated, including Stage I, II, III, and IV breast cancer. Breast cancer with positive and/or negative status for estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor 2 (Her2) can also be treated in accordance with various embodiments of the invention).
Since Nantcell discloses a trained model to classify breast cancer, and Stanford discloses using a trained model to classify breast cancer including triple negative breast cancer, it would have been obvious to one of ordinary skill in the art to modify the system, as disclosed by Nantcell and AbdulJabbar, to include or instead consider prognosis of triple breast cancer, since this would simply allow the analysis of a particular breast cancer subtype, such as for recommending a treatment, such as atezolizumab which may still provide benefit to triple negative breast cancer patients, as taught by Stanford, thereby providing any improved or altered prognostic or diagnostic ability, any associated improvement in patient clinical outcome, and therefore any improved value of the method of Nantcell.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Pat. No.: US 11657505 B1; In some aspects, the described systems and methods provide for a method for training a model to predict survival time for a patient. The method includes accessing annotated pathology images associated with a first group of patients in a clinical trial. Each of the annotated pathology images is associated with survival data for a respective patient. Each of the annotated pathology images includes an annotation describing a tissue characteristic category for a portion of the image. Values for one or more features are extracted from each of the annotated pathology images. A model is trained based on the survival data and the extracted values for the features. The trained model is stored on a storage device.
Pat. No.: US 12073560 B2; Various embodiments of the present disclosure are directed towards a method for generating a risk group classification for an African American (AA) patient. The method includes extracting a first plurality of architectural features from a digitized H&E slide image of the AA patient. A risk score for the AA patient is generated based on the first plurality of architectural features, where the risk score is prognostic of overall survival (OS) of the AA patient. The risk group classification is generated for the AA patient, where generating the risk group classification includes classifying the AA patient into either a high risk group or a low risk group based on the risk score, where the high risk group indicates the AA patient will die before a threshold date and the low risk group indicates the AA patient will die after or on the threshold date.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EDWARD B WINSTON III whose telephone number is (571)270-7780. The examiner can normally be reached M-F 1030 to 1830.
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/E.B.W/Examiner, Art Unit 3683
/ROBERT W MORGAN/Supervisory Patent Examiner, Art Unit 3683