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 .
Claims 1-15 are pending. Claims 1-15 are rejected herein.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. The priority date of this application is 23 March 2023.
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 1-15 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.
Claims 1 and 9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
The claim recites a method for training an artificial intelligence model to predict the chances of a solid tumor occurring in an individual and a method for predicting the chances of a solid tumor occurring in an individual, which are within a statutory category for subject matter eligibility analysis purposes.
Step 2A1
The limitations of (claim 1 being representative) receiving a first set of features, wherein the first set of features is related to individuals who have not been diagnosed with some type of solid tumor and comprises, as features for each individual, at least: age; at least one feature obtained from the red blood cell series of a blood count; and at least one feature obtained from the white blood cell series of a blood count; receiving a second set of features, wherein the second set of features is related to individuals diagnosed with some type of solid tumor and comprises, as features for each individual, at least: age; at least one feature obtained from the red blood cell series of a blood count; and at least one feature obtained from the white blood cell series of a blood count; and training the artificial intelligence model based on the first and second sets of features, as drafted, is a process that, under the broadest reasonable interpretation, covers performance of the limitation in the mind. Nothing in the claim precludes the step from practically being performed in the mind. For example, this claim encompasses a person thinking about training an artificial intelligence model in the manner described in the identified abstract idea, supra. If a claim limitation, under its broadest reasonable interpretation, covers practical performance of the limitation in the mind then it falls within the “Mental Processes” grouping of abstract ideas.
The claim further recites “training the artificial intelligence model based on the first and second sets of features.” Currently, as recited, given its broadest reasonable interpretation in light of the disclosure, this limitation in independent claim 1 is still practically categorizable as a mental process and is analyzed as part of the abstract idea. For example, practically, as recited, the training could be practically executed by logistic regression via mental processing with or without a computer or pen and paper.
Step 2A2
This judicial exception is not integrated into a practical application. The independent claim 1 does not currently recite computer part(s) to implement the identified abstract idea. The claim is directed to an abstract idea. Because a judicial exception is not eligible subject matter, Bilski, 561 U.S. at 601, 95 USPQ2d at 1005-06 (quoting Chakrabarty, 447 U.S. at 309, 206 USPQ at 197 (1980)), if there are no additional claim elements besides the judicial exception, or if the additional claim elements merely recite another judicial exception, that is insufficient to integrate the judicial exception into a practical application. For a claim reciting a judicial exception to be eligible, the additional elements (if any) in the claim must "transform the nature of the claim" into a patent-eligible application of the judicial exception, Alice Corp., 573 U.S. at 217, 110 USPQ2d at 1981, either at Prong Two or in Step 2B. If there are no additional elements in the claim, then it cannot be eligible. There are currently no additional elements in the claim. See MPEP 2106.04 Eligibility Step 2A: Whether a Claim is Directed to a Judicial Exception 2. Prong Two. Accordingly, even as additional elements, the recited limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the claim does not currently recite computer part(s) to implement the identified abstract idea. The claim is directed to an abstract idea. Because a judicial exception is not eligible subject matter, Bilski, 561 U.S. at 601, 95 USPQ2d at 1005-06 (quoting Chakrabarty, 447 U.S. at 309, 206 USPQ at 197 (1980)), if there are no additional claim elements besides the judicial exception, or if the additional claim elements merely recite another judicial exception, that is insufficient to integrate the judicial exception into a practical application. For a claim reciting a judicial exception to be eligible, the additional elements (if any) in the claim must "transform the nature of the claim" into a patent-eligible application of the judicial exception, Alice Corp., 573 U.S. at 217, 110 USPQ2d at 1981, either at Prong Two or in Step 2B. If there are no additional elements in the claim, then it cannot be eligible. There are currently no additional elements in the claim. Accordingly, even as additional elements, these steps do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and as such, cannot provide an inventive concept (“significantly more”).
Dependent Claims and Dependent Additional Elements
Claims 2-5, 8-10, and 12 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination.
Claim 2 merely describes wherein the solid tumor to be predicted is selected from the group consisting of: breast cancer, lung cancer, colorectal cancer, prostate cancer, and ovarian cancer. Claim(s) 3 merely describe(s) wherein the solid tumor is breast cancer, which further defines the abstract idea. Claim 4 merely describes wherein the first and second sets of features additionally comprise at least one feature obtained from the platelet series of a blood count. Claim 5 merely describes the first and second sets of features additionally comprise at least one feature obtained from the individual’s phenotypic characteristics and/or from imaging tests or routine medical examination. Claim 7 merely describes additional elements, dealt with below. Claim 8 merely describes additional elements, dealt with below. Claim 10 merely describes wherein the solid tumor to be predicted is selected from the group consisting of: breast cancer, lung cancer, colorectal cancer, prostate cancer, and ovarian cancer. Claim 11 merely describes wherein the solid tumor is breast cancer. Clam 12 merely describes wherein the first and second sets of features additionally comprise at least one feature obtained from the platelet series of a blood count. Claim 13 merely describes wherein the first and second sets of features additionally comprise at least one feature obtained from the individual’s phenotypic characteristics and/or from imaging tests or routine medical examinations. Claim 14 merely describes additional elements. Claim 15 merely describes additional elements. Besides the additional elements listed, the remaining dependent claims further refine or describe the independent claims.
Claims 7,8 recite instructions to execute various methods on a processor and 14 and 15 recite a computer readable medium comprising instructions to execute various methods via a processor. As additional elements, these served as instructions to apply the exception using generic computer parts. Mere instructions to apply an exception using a generic computer component cannot provide a practical application, nor an inventive concept (“significantly more”). Claim 6 merely describes wherein the model is trained using a supervised or unsupervised machine learning algorithm, or comprises the combination of models trained using a supervised and/or unsupervised learning algorithm. This use of the trained machine learning algorithm amounts to application of machine learning to new data. See, e.g., Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 10 (Fed. Cir. April 18, 2025) (finding that claims that do no more than apply established methods of machine learning to a new data environment are ineligible).
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.
Claim(s) 1-8 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by WO 2023/147472 A1 (hereafter Bourgon).
Regarding Claim 1
Bourgon teaches:
A method for training an artificial intelligence model to predict a solid tumor occurring in an individual, the method comprising at least the steps of: receiving a first set of features, wherein the first set of features is related to individuals who have not been diagnosed with some type of solid tumor and comprises, as features for each individual, at least: age; [Bourgon teaches at para. [0161] in certain embodiments, training data (and therefore training features) are collected at many points in a patient’s health history and not limited to single data points collected shortly before a patient’s CRC (colorectal cancer) diagnosis. Bourgon teaches in various embodiments, variables measured at least 2 time points provide trend data that will be featurized and input to train a classification model. Bourgon teaches at para. [0006] in an aspect, the present disclosure provides a classifier for evaluation of colorectal cancer risk of a target individual, wherein the classifier is trained on at least one training data set that comprises a plurality of features based on demographic, physiological, and clinical variable from the target individual, wherein the classifier is generated based at least in part on an analysis of a plurality of respective demographic, physiological, and clinical features from a plurality of sampled individuals, wherein at least one of the plurality of features is derived from at least data of the demographic, physiological, and clinical variables obtained at 2 or more time points. The clinical variables comprising a plurality of features obtained at 2 or more time points is receiving a first set of features, wherein the first set of features is related to individuals who have not been diagnosed with some type of solid tumor. Bourgon teaches at para. [0007] in some embodiments, the demographic, physiological, and clinical features comprise at least two features obtained from demographic, symptomatic, lifestyle, diagnosis, or biomedical variables. This teaches at least at least two features consisting of sets which are clinical variable obtained at 2 or more time points. Bourgon teaches in some embodiments the demographic variables are selected from age, gender, weight, height, BMI, race, country, and geographically determined data.]
at least one feature obtained from the red blood cell series of a blood count; [Bourgon teaches at para. [0017] in another aspect, the present disclosure provides a classifier for evaluation colorectal cancer risk of a target individual, wherein the classifier is trained on at least one training data set that comprises (i) a plurality of features based on two or more demographic, physiological, and clinical variables from the target individual, and (ii) 9 or less blood test features based on a plurality of current blood test results of the target individual, wherein each one of the 9 or less different blood test features is based on a blood test value of one of the plurality of current blood test results of the target individual, wherein at least one of the plurality of features is based on data of the two or more demographic, physiological, and clinical variables obtained at 2 or more time points. Bourgon teaches at para. [0018] in some embodiments, the plurality of blood test results comprises (i) 9 or less of the following blood tests: red blood cells (RBC), hemoglobin (HGB), hemocrit (HCT) and (ii) at least one result of the following blood tests: hemoglobin (MCH) and mean corpuscular hemoglobin concentration (MCHC).]
and at least one feature obtained from the white blood cell series of a blood count; [Bourgon teaches at para. [0017] in another aspect, the present disclosure provides a classifier for evaluation colorectal cancer risk of a target individual, wherein the classifier is trained on at least one training data set that comprises (i) a plurality of features based on two or more demographic, physiological, and clinical variables from the target individual, and (ii) 9 or less blood test features based on a plurality of current blood test results of the target individual, wherein each one of the 9 or less different blood test features is based on a blood test value of one of the plurality of current blood test results of the target individual, wherein at least one of the plurality of features is based on data of the two or more demographic, physiological, and clinical variables obtained at 2 or more time points. Bourgon teaches at para. [0019] in some embodiments, the blood test results comprises 9 or less results of the following blood tests: white blood cell count (WBC); mean platelet volume (MPV); mean cell; platelet count (CBC); eosinophils count; neutrophils percentage; monocytes percentage; eosinophils percentage; basophils percentage; lymphocytes percentage; and neutrophils count; monocytes count, lymphocytes count; neutrophil-lymphocyte ratio (NLR).]
receiving a second set of features, wherein the second set of features is related to individuals diagnosed with some type of solid tumor and comprise, as features for each individual, at least: age; [Bourgon teaches at para. [0161] in certain embodiments, training data (and therefore training features) are collected at many points in a patient’s health history and not limited to single data points collected shortly before a patient’s CRC (colorectal cancer) diagnosis. Bourgon teaches in various embodiments, variables measured at least 2 time points provide trend data that will be featurized and input to train a classification model. Bourgon teaches at para. [0006] in an aspect, the present disclosure provides a classifier for evaluation of colorectal cancer risk of a target individual, wherein the classifier is trained on at least one training data set that comprises a plurality of features based on demographic, physiological, and clinical variable from the target individual, wherein the classifier is generated based at least in part on an analysis of a plurality of respective demographic, physiological, and clinical features from a plurality of sampled individuals, wherein at least one of the plurality of features is derived from at least data of the demographic, physiological, and clinical variables obtained at 2 or more time points. The clinical variables comprising a plurality of features obtained at 2 or more time points is receiving a first set of features, wherein the first set of features is related to individuals who have not been diagnosed with some type of solid tumor. Bourgon teaches at para. [0007] in some embodiments, the demographic, physiological, and clinical features comprise at least two features obtained from demographic, symptomatic, lifestyle, diagnosis, or biomedical variables. This teaches at least at least two features consisting of sets which are clinical variable obtained at 2 or more time points. Bourgon teaches in some embodiments the demographic variables are selected from age, gender, weight, height, BMI, race, country, and geographically determined data.]
at least one feature obtained from the red blood cell series of a blood count; [Bourgon teaches at para. [0161] in certain embodiments, training data (and therefore training features) are collected at many points in a patient’s health history and not limited to single data points collected shortly before a patient’s CRC diagnosis. CRC is colorectal cancer. Bourgon teaches at para. [0018] in some embodiments, the plurality of blood test results comprises (i) 9 or less of the following blood tests: red blood cells (RBC), hemoglobin (HGB), hemocrit (HCT) and (ii) at least one result of the following blood tests: hemoglobin (MCH) and mean corpuscular hemoglobin concentration (MCHC). Bourgon teaches in various embodiments, variables measured at least 2 time points provide trend data that will be featurized and input to train a classification model. Bourgon teaches at para. [0006] in an aspect, the present disclosure provides a classifier for evaluation of colorectal cancer risk of a target individual, wherein the classifier is trained on at least one training data set that comprises a plurality of features based on demographic, physiological, and clinical variable from the target individual, wherein the classifier is generated based at least in part on an analysis of a plurality of respective demographic, physiological, and clinical features from a plurality of sampled individuals, wherein at least one of the plurality of features is derived from at least data of the demographic, physiological, and clinical variables obtained at 2 or more time points. The clinical variables comprising a plurality of features obtained at 2 or more time points is receiving a first set of features, wherein the first set of features is related to individuals who have not been diagnosed with some type of solid tumor. Bourgon teaches at para. [0007] in some embodiments, the demographic, physiological, and clinical features comprise at least two features obtained from demographic, symptomatic, lifestyle, diagnosis, or biomedical variables. This teaches at least at least two features consisting of sets which are clinical variable obtained at 2 or more time points. Collectively, Bourgon teaches at least one feature obtained from the red blood cell series of a blood count.]
and training the artificial intelligence model based on the first and second sets of features. [Bourgon teaches in various embodiments, variables measured at least 2 time points provide trend data that will be featurized and input to train a classification model. Bourgon teaches at para. [0006] in an aspect, the present disclosure provides a classifier for evaluation of colorectal cancer risk of a target individual, wherein the classifier is trained on at least one training data set that comprises a plurality of features based on demographic, physiological, and clinical variable from the target individual, wherein the classifier is generated based at least in part on an analysis of a plurality of respective demographic, physiological, and clinical features from a plurality of sampled individuals, wherein at least one of the plurality of features is derived from at least data of the demographic, physiological, and clinical variables obtained at 2 or more time points. The clinical variables comprising a plurality of features obtained at 2 or more time points is receiving a first set of features, wherein the first set of features is related to individuals who have not been diagnosed with some type of solid tumor. Bourgon teaches at para. [0007] in some embodiments, the demographic, physiological, and clinical features comprise at least two features obtained from demographic, symptomatic, lifestyle, diagnosis, or biomedical variables. This teaches at least at least two features consisting of sets which are clinical variables obtained at 2 or more time points. Collectively, Bourgon teaches and training the artificial intelligence model based on the first and second sets of features.]
Regarding Claim 8
Due to its similarity to Claim 1, Claim 8 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 1.
Regarding Claim 2
Bourgon teaches the method of claim 1. Bourgon further teaches:
wherein the solid tumor to be predicted is selected from the group consisting of: breast cancer, lung cancer, colorectal cancer, prostate cancer, and ovarian cancer. [Bourgon teaches at para. [0094] the system inputs the feature vector into the machine learning model and obtains an output classification of whether the individual has increased risk of cancer. Bourgon teaches at para. [0117] in some embodiments, the classification model is indicative of an elevated risk of colorectal cancer at a positive predictive value (PPV) of at least 99%.]
Regarding Claim 4
Bourgon teaches the method of claim 1. Bourgon further teaches:
wherein the first and second sets of features additionally comprise at least one feature obtained from the platelet series of a blood count. [Bourgon teaches at para. [0019] in some embodiments, the blood test results comprises 9 or less results of the following blood tests: white blood cell count (WBC); mean platelet volume (MPV); mean cell; platelet count (CBC); eosinophils count; neutrophils percentage; monocytes percentage; eosinophils percentage; basophils percentage; lymphocytes percentage; and neutrophils count; monocytes count, lymphocytes count; neutrophil-lymphocyte ratio (NLR).]
Regarding Claim 5
Bourgon teaches the method of claim 1. Bourgon further teaches:
wherein the first and second sets of features additionally comprise at least one feature obtained from the individual’s phenotypic characteristics and/or from imaging tests or routine medical examinations. [Bourgon teaches at para. [0007] in some embodiments, the demographic, physiological, and clinical features comprise at least two features obtained from demographic, symptomatic, lifestyle, diagnosis, or biomedical variables. Bourgon teaches at para. [0120] a data analysis module will perform probabilistic and statistical analysis to identify abnormal patterns related to a disease, pathology, state, risk, condition, or phenotype. Collectively, Bourgon teaches wherein the first and second sets of features additionally comprise at least one feature obtained from the individual’s phenotypic characteristics.]
Regarding Claim 6
Bourgon teaches the method of claim 1. Bourgon further teaches:
wherein the model is trained using a supervised or unsupervised machine learning algorithm, or comprises the combination of models trained using a supervised and/or unsupervised learning algorithm. [Bourgon teaches at para. [0151] in some examples, the computer processing method is a supervised machine learning method including, for example, a regression, support vector machine, tree-based method, and network. Bourgon teaches at para. [0151] in some examples the computer processing method is an unsupervised machine learning method including, for example, clustering, network, principal component analysis, and matrix factorization.]
Regarding Claim 7
Bourgon teaches the method of claim 1. Bourgon further teaches:
A system for training an artificial intelligence model to predict the changes of a solid tumor occurring in an individual, the system comprising at least one processor, wherein the processor is configured to perform the method of claim 1. [Bourgon teaches at para. [0156] in an aspect, the present disclosure provides a non-transitory computer-readable medium comprising instructions that direct a processor to carry out a method disclosed therein.]
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 nonobviousness.
Claim(s) 3,9-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over WO 2023/147472 A1 (hereafter Bourgon) in view of WO 2005/091203 A2 (hereafter Saidi).
Regarding Claim 3
Bourgon teaches the method of claim 2. Bourgon may not explicitly teach:
wherein the solid tumor is breast cancer
Saidi teaches:
wherein the solid tumor is breast cancer. [Saidi teaches at pg. 9 for example, other histologic disease-specific features/manifestations will include regions of necrosis (E.g. ductal carcinoma in situ for the breast), size, shape and regional pattern/distribution of epithelial cells (e.g. breast, lung), degree of differentiation (e.g., squamous differentiation with non-small cell lung cancer (NSCLC, mucin production as seen with various adenocarcinomas seen in both breast and colon), morphological/microscopic distribution of the cells (e.g., lining ducts in breast cancer, lining bronchioles in NSCLC), and degree and type of inflammation (e.g., having different characteristics for breast and NSCLC in comparison to prostate). Collectively, this teaches wherein the solid tumor is breast cancer.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the methods and systems for risk stratification of colorectal cancer of Bourgon to the systems and methods for treating, diagnosing and predicting the occurrence of a medical condition of Saidi with the motivation of determining an appropriate course of treatment for a patient, which may increase the patient’s chances for, for example, survival and/or recovery (Saidi at pg. 3).
Regarding Claim 9
Bourgon teaches:
A method for predicting the chances of a solid tumor occurring in an individual, comprising the steps of: receiving, as features for the individual, at least: age; [Bourgon teaches at para. [0161] in certain embodiments, training data (and therefore training features) are collected at many points in a patient’s health history and not limited to single data points collected shortly before a patient’s CRC (colorectal cancer) diagnosis. Bourgon teaches in various embodiments, variables measured at least 2 time points provide trend data that will be featurized and input to train a classification model. Bourgon teaches at para. [0006] in an aspect, the present disclosure provides a classifier for evaluation of colorectal cancer risk of a target individual, wherein the classifier is trained on at least one training data set that comprises a plurality of features based on demographic, physiological, and clinical variable from the target individual, wherein the classifier is generated based at least in part on an analysis of a plurality of respective demographic, physiological, and clinical features from a plurality of sampled individuals, wherein at least one of the plurality of features is derived from at least data of the demographic, physiological, and clinical variables obtained at 2 or more time points. The clinical variables comprising a plurality of features obtained at 2 or more time points is receiving a first set of features, wherein the first set of features is related to individuals who have not been diagnosed with some type of solid tumor. Bourgon teaches at para. [0007] in some embodiments, the demographic, physiological, and clinical features comprise at least two features obtained from demographic, symptomatic, lifestyle, diagnosis, or biomedical variables. This teaches at least at least two features consisting of sets which are clinical variable obtained at 2 or more time points. Bourgon teaches in some embodiments the demographic variables are selected from age, gender, weight, height, BMI, race, country, and geographically determined data.]
at least one feature obtained from the red blood cell series of a blood count; [Bourgon teaches at para. [0017] in another aspect, the present disclosure provides a classifier for evaluation colorectal cancer risk of a target individual, wherein the classifier is trained on at least one training data set that comprises (i) a plurality of features based on two or more demographic, physiological, and clinical variables from the target individual, and (ii) 9 or less blood test features based on a plurality of current blood test results of the target individual, wherein each one of the 9 or less different blood test features is based on a blood test value of one of the plurality of current blood test results of the target individual, wherein at least one of the plurality of features is based on data of the two or more demographic, physiological, and clinical variables obtained at 2 or more time points. Bourgon teaches at para. [0018] in some embodiments, the plurality of blood test results comprises (i) 9 or less of the following blood tests: red blood cells (RBC), hemoglobin (HGB), hemocrit (HCT) and (ii) at least one result of the following blood tests: hemoglobin (MCH) and mean corpuscular hemoglobin concentration (MCHC).]
and at least one feature obtained from the white blood cell series of a blood count; [Bourgon teaches at para. [0017] in another aspect, the present disclosure provides a classifier for evaluation colorectal cancer risk of a target individual, wherein the classifier is trained on at least one training data set that comprises (i) a plurality of features based on two or more demographic, physiological, and clinical variables from the target individual, and (ii) 9 or less blood test features based on a plurality of current blood test results of the target individual, wherein each one of the 9 or less different blood test features is based on a blood test value of one of the plurality of current blood test results of the target individual, wherein at least one of the plurality of features is based on data of the two or more demographic, physiological, and clinical variables obtained at 2 or more time points. Bourgon teaches at para. [0019] in some embodiments, the blood test results comprises 9 or less results of the following blood tests: white blood cell count (WBC); mean platelet volume (MPV); mean cell; platelet count (CBC); eosinophils count; neutrophils percentage; monocytes percentage; eosinophils percentage; basophils percentage; lymphocytes percentage; and neutrophils count; monocytes count, lymphocytes count; neutrophil-lymphocyte ratio (NLR).]
Bourgon may not explicitly teach:
and calculating, using a model trained according to the method of claim 1 and based on the received features, the chances of cancer occurring in the individual.
Saidi teaches:
and calculating, using a model trained according to the method of claim 1 and based on the received features, the chances of cancer occurring in the individual. [The definition of chance used here is the possibility of a particular outcome in an uncertain situation. Saidi teaches at pg. 3/pg.4 in one embodiment, systems and methods are provided for generating a predictive model based on one or more computer-generated morphometric features related to stroma, cytoplasm, epithelial nuclei, stroma nuclei, lumen, red blood cells, tissue artifacts or tissue background, or a combination thereof. Saidi teaches at pg. 4 the predictive model will be generated based on the computer generated morphometric features alone or in combination with one or more of the clinical features listed in Table 4 and/or one or more of the molecular features listed in Table 6. Saidi teaches at pg. 11 the results will include a diagnostic “score” (e.g., an indication of the likelihood that the patient will experience one or more outcomes related to the medical condition such as the predicted time to recurrence of the event), information indicating one or more features analyzed by predictive model as being correlated with the medical condition, information indicating the sensitivity and/or specificity of the predictive mode, or other suitable diagnostic information or a combination thereof. Saidi teaches at pg. 11 as shown, the report maps the patient’s probability of outcome (e.g. recurrence of prostate cancer; i.e., y-axis to time in months, x-axis).]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the methods and systems for risk stratification of colorectal cancer of Bourgon to the systems and methods for treating, diagnosing and predicting the occurrence of a medical condition of Saidi with the motivation of determining an appropriate course of treatment for a patient, which may increase the patient’s chances for, for example, survival and/or recovery (Saidi at pg. 3).
Regarding Claim 10
Bourgon/Saidi teach the method of claim 9. Bourgon/Saidi further teach:
wherein the solid tumor to be predicted is selected from the group consisting of: breast cancer, lung cancer, colorectal cancer, prostate cancer, and ovarian cancer. [Bourgon teaches at para. [0094] the system inputs the feature vector into the machine learning model and obtains an output classification of whether the individual has increased risk of cancer. Bourgon teaches at para. [0117] in some embodiments, the classification model is indicative of an elevated risk of colorectal cancer at a positive predictive value (PPV) of at least 99%.]
Regarding Claim 11
Bourgon/Saidi teach the method of claim 10. Bourgon/Saidi further teach:
wherein the solid tumor is breast cancer. [Saidi teaches at pg. 9 for example, other histologic disease-specific features/manifestations will include regions of necrosis (E.g. ductal carcinoma in situ for the breast), size, shape and regional pattern/distribution of epithelial cells (e.g. breast, lung), degree of differentiation (e.g., squamous differentiation with non-small cell lung cancer (NSCLC, mucin production as seen with various adenocarcinomas seen in both breast and colon), morphological/microscopic distribution of the cells (e.g., lining ducts in breast cancer, lining bronchioles in NSCLC), and degree and type of inflammation (e.g., having different characteristics for breast and NSCLC in comparison to prostate). Collectively, this teaches wherein the solid tumor is breast cancer.]
Regarding Claim 12
Bourgon/Saidi teach the method of claim 9. Bourgon/Saidi further teach:
wherein the first and second sets of features additionally comprise at least one feature obtained from the platelet series of a blood count. [Bourgon teaches at para. [0017] in another aspect, the present disclosure provides a classifier for evaluation colorectal cancer risk of a target individual, wherein the classifier is trained on at least one training data set that comprises (i) a plurality of features based on two or more demographic, physiological, and clinical variables from the target individual, and (ii) 9 or less blood test features based on a plurality of current blood test results of the target individual, wherein each one of the 9 or less different blood test features is based on a blood test value of one of the plurality of current blood test results of the target individual, wherein at least one of the plurality of features is based on data of the two or more demographic, physiological, and clinical variables obtained at 2 or more time points. Bourgon teaches at para. [0019] in some embodiments, the blood test results comprises 9 or less results of the following blood tests: white blood cell count (WBC); mean platelet volume (MPV); mean cell; platelet count (CBC); eosinophils count; neutrophils percentage; monocytes percentage; eosinophils percentage; basophils percentage; lymphocytes percentage; and neutrophils count; monocytes count, lymphocytes count; neutrophil-lymphocyte ratio (NLR).]
Regarding Claim 13
Bourgon/Saidi teach the method of claim 9. Bourgon/Saidi further teach:
wherein the first and second sets of features additionally comprise at least one feature obtained from the individual’s phenotypic characteristics and/or from imaging tests or routine medical examinations. [Bourgon teaches at pg. 26 a data analysis module will perform probabilistic and statistical analysis to identify abnormal patterns related to a disease, pathology, state, risk, condition, or phenotype. Bourgon teaches at para. [0017] in another aspect, the present disclosure provides a classifier for evaluation colorectal cancer risk of a target individual, wherein the classifier is trained on at least one training data set that comprises (i) a plurality of features based on two or more demographic, physiological, and clinical variables from the target individual, and (ii) 9 or less blood test features based on a plurality of current blood test results of the target individual, wherein each one of the 9 or less different blood test features is based on a blood test value of one of the plurality of current blood test results of the target individual, wherein at least one of the plurality of features is based on data of the two or more demographic, physiological, and clinical variables obtained at 2 or more time points. Bourgon teaches at para. [0019] in some embodiments, the blood test results comprises 9 or less results of the following blood tests: white blood cell count (WBC); mean platelet volume (MPV); mean cell; platelet count (CBC); eosinophils count; neutrophils percentage; monocytes percentage; eosinophils percentage; basophils percentage; lymphocytes percentage; and neutrophils count; monocytes count, lymphocytes count; neutrophil-lymphocyte ratio (NLR). Collectively, Bourgon teaches the first and second sets of features additionally comprise at least one feature obtained from the individual’s phenotypic characteristics and from routine medical examinations. MPV, CBC and WBC are interpreted as routine medical examinations.]
Regarding Claim 14
Bourgon/Saidi teach the method of claim 9. Bourgon/Saidi further teach:
A system for predicting the changes of a solid tumor occurring in an individual, the system comprising at least one processor, wherein the processor is configured to perform the method of claim 9. [Bourgon teaches at para. [0156] in an aspect, the present disclosure provides a non-transitory computer-readable medium comprising instructions that direct a processor to carry out a method disclosed therein.]
Regarding Claim 15
Bourgon/Saidi teach the method of claim 9. Bourgon/Saidi further teach:
A computer-readable medium comprising instructions that, when executed by at least one processor, cause the processor to perform the method of claim 9. [Bourgon teaches at para. [0156] in an aspect, the present disclosure provides a non-transitory computer-readable medium comprising instructions that direct a processor to carry out a method disclosed therein.]
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Botlagunta, M., Botlagunta, M.D., Myneni, M.B. et al. Classification and diagnostic prediction of breast cancer metastasis on clinical data using machine learning algorithms. Sci Rep 13, 485 (2023). Botlagunta teaches the application of artificial intelligence to breast cancer prediction and classification, which is tangentially related to the subject matter herein.
Rasool A, Bunterngchit C, Tiejian L, Islam MR, Qu Q, Jiang Q. Improved Machine Learning-Based Predictive Models for Breast Cancer Diagnosis. International Journal of Environmental Research and Public Health. 2022; 19(6):3211. Rasool teaches the application of machine learning to breast cancer diagnosis, which is tangentially related to the subject matter herein.
US 20250285754 A1 (hereafter Gleadall) teaches receiving CBC data from one or more sources, and generally teaches on anomaly detection, which is tangentially related to the subject matter herein.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TRISTAN ISAAC EVANS whose telephone number is (571)270-5972. The examiner can normally be reached Mon-Thurs 8:00am-12:00pm & 1:00pm-7:00pm, off Fridays.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Morgan can be reached at 571-272-6773. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/T.I.E./Examiner, Art Unit 3683
/CHRISTOPHER L GILLIGAN/Primary Examiner, Art Unit 3683
Prosecution Insights