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 .
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.
Claim(s) 1, 2, 4-6, 8, 10-12, 14-16, 18 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Barnes et al. (US 20170270666 A1) and GILL et al. (US 20200166523 A1).
As to claim 1, Barnes discloses a method of determining risk stratification for subjects [providing reliable risk stratification for early- stage cancer patients; Figs. 1, 2A, 2B, 3, 4, 5A, 5B, 6, 7, Abstract & para [0037]], comprising:
identifying, by a computing system, a first feature set for a first subject at risk of a condition [providing reliable risk stratification for early-stage cancer patients; risk stratification 115 are dependent on the type of biomarker (feature) being used; an IHC3 score for 3 markers (ER, PR, Ki67) may be computed; (para [0049], [0060])], the first feature set [a whole tumor region may be annotated on an H&E slide; an IHC3 score for 3 markers (feature set) may be computed (para [0037], [0059], [0060])] comprising:
(ii) a first histologic feature acquired using a whole slide image of a sample having the condition from the first subject [risk stratification for early-stage breast cancer patients; a risk stratification system may be trained using training cohort that includes histopathological (H&E and IHC) tissue slides from several patients; the tissue slides may be processed by a whole slide scanner, and analyzed using automated image analysis algorithms to quantify stains or biomarker expressions in the tissue slides (para [0006])];
applying, by the computing system, the first feature set to a model [fitting the Cox model with existing survival data for a specific tissue sample data and workflow enables building of a prognostic model to be applied to new patient slides in a clinical context (para [0062])], wherein the model is established using a plurality of second feature sets [fitting the Cox model with existing survival data for a specific tissue sample data and workflow enables building of a prognostic model to be applied to new patient slides in a clinical context (para [0062])] and a plurality of expected risk scores for a corresponding plurality of second subjects [apply the optimal cutoff point for determining accurate survival for that particular workflow to stratify the scores into low-risk and high-risk groups for cancer recurrence (para [0051])];
determining, by the computing system, from applying the first feature set to the model, a predicted risk score of the condition for the first subject [output by the thresholding module is binary whereby for example logical '0' indicates that the patient from which the biopsy tissue sample has been obtained belongs to a low-risk group of patients, whereas the logical value of '1' of the signal may indicate that the patient belongs to a high-risk group; (para [0049], [0111])]; and
storing, by the computing system, using one or more data structures, an association between the predicted risk score and the first feature set for the first subject [the operations of scoring, COX modeling and risk stratification are dependent on the type of biomarker being used; the parameters for each workflow may be stored in COX model parameters database; the statistical model is then fitted using these data entries which provides a set of model parameters that may be stored on a database; para [0047], [0098])].
Barnes does not disclose, (i) a first radiological feature derived from a tomogram of a section associated with the condition within the first subject; and (iii) a first genomic feature obtained from gene sequencing of the first subject for genes associated with the condition.
GILL discloses an imaging techniques include magnetic resonance imaging, computed tomography scanning (coronary calcium score), positron emission tomography (PET); imaging techniques to obtain information about the targeted tissue and the biomarker ((i) a first radiological feature derived from a tomogram of a section associated with the condition within the first subject) (para [0173], [0174])]; and genetic marker correlating with a higher risk of cardiovascular disease in said individual; (para [0051]) ((iii) a first genomic feature obtained from gene sequencing of the first subject for genes associated with the condition)
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention was made to use the teachings of GILL to modify the system of Barnes by including (i) a first radiological feature derived from a tomogram of a section associated with the condition within the first subject; and (iii) a first genomic feature obtained from gene sequencing of the first subject for genes associated with the condition as taught by GILL in order to providing the evaluation of risk of an Event within 5 years or a predefined time period by detecting, in a biological sample from an individual, at least one biomarker value corresponding to at least one biomarker selected from the group of biomarkers.
Claim 11 is a system analogous to method claim 1, ground of rejection analogous to those applied to claim 1 are applicable to claim 11. Barnes further discloses comprising: a computing system having one or more processors coupled with memory, configured to [the system includes a processor; and a memory coupled to the processor, the memory configured to store computer-readable instructions that, when executed by the processor, cause the processor to perform operations; (para [0010])]
As to claim 2 Barnes further discloses further comprising classifying, by the computing system, the first subject into one of a plurality of risk level groups [determining accurate survival for that particular workflow to stratify the scores into low-risk and high-risk groups for cancer recurrence; (para [0053])] based on a comparison between the predicted risk score indicating a likelihood of an occurrence of an event due to the condition in the first subject [comparing the biomarker/IHC scores for individual slides with survival (event) data comprising populations of high and low risks to determine whole-slide scoring algorithms; using the scoring protocol may be analyzed using the risk scoring algorithm generated during the training process, and stratified using the generated cutoff point to determine the survival probability (likelihood of an occurrence of an event) and/or prognosis for the single patient; (para [0048])] and a threshold for each of the plurality of risk level groups [output by the thresholding module is binary whereby for example logical '0' indicates that the patient from which the biopsy tissue sample has been obtained belongs to a low-risk group of patients, whereas the logical value of '1' of the signal may indicate that the patient belongs to a high-risk group (para [0049], [0111])].
As to claim 12 refer to claim 2 rejection.
As to claim 4, Barnes further discloses wherein determining the predicted risk score further comprises determining a survival function identifying the predicted risk score for the first subject over a period of time [any single patient's tissue slides that are processed using scores generated may be combined and analyzed using the risk stratification scoring algorithm generated during the training process, and stratified using the generated cutoff point to predict a survival probability and/or prognosis for the single patient; models the impact of explanatory variables (such as individual marker whole slide scores) on the survival probability time to distant recurrence by taking two linear variables and finding the best logistic regression model of the two to predict time to distant recurrence; (para [0008], [0048])].
As to claim 14 refer to claim 4 rejection.
As to claim 5, Barnes further discloses wherein identifying the first feature set further comprises selecting, from a plurality of radiological features [a computer system can be programmed to automatically identify features in an image of a specimen based at least in part on one or more selection criteria, including criteria based at least in part on color characteristics, sample morphology, tissue characteristics (e.g., density, composition), spatial parameters (e.g., arrangement of tissue structures, relative positions between tissue structures, etc.), image characteristic parameters (radiological features);(para [0078])], the first radiological feature based on a hazard ratio of each of the plurality of radiological features determined using a univariate model for radiological features [the Cox proportional (ratio) hazards regression method models the impact of explanatory variables, such as individual marker (feature) whole slide scores, on the survival probability time to distant recurrence by taking two linear variables and finding the best logistic regression model of the two to predict time to distant (univriate model) recurrence; generation of a statistical model, such as a Cox Proportional Hazard Model; the model is fitted using patient data from a cohort of cancer patients, such as breast cancer patients.; para [0048])].
As to claim 15 refer to claim 5 rejection.
As to claim 6, Barnes further discloses wherein identifying the first feature set further comprises selecting, from a plurality of histological features [a whole tumor region annotated on a Hematoxylin and Eosin (H&E) slide from among the plurality of serial slides may be selected automatically; (para [0044])], the first histological feature based on a hazard ratio of each of the plurality of histological features determined using a univariate model for histological features [the Cox proportional (ratio) hazards regression method models the impact of explanatory variables (such as individual marker (feature) whole slide scores) on the survival probability time to distant recurrence by taking two linear variables and finding the best logistic regression model of the two to predict time to distant (univariate model) recurrence; (para [0048])].
As to claim 16 refer to claim 6 rejection.
As to claim 8, Barnes further discloses wherein the first histologic feature [automatically interpreting and scoring tissue specimen slides, for example, specimens stained with an immunohistochemical (IHC) assay; the feature computation can use information from the whole slide; (para [0073])] further comprises at least one of: (i) a tissue type of the sample from which the whole slide image is derived [whole-slide interpretation includes identifying portions of a digitized whole slide image corresponding to tissue; for each of the extracted tissue objects, characteristics of the extracted object are identified; different trained classifiers can be used to analyze different types of tissues and markers ;(para [0074], [0075], [0077])], (ii) an area of cell nuclei corresponding to the condition within the sample [tissue nuclei objects are extracted from the identified regions; for each of the extracted tissue objects, characteristics of the extracted object are identified; (para [0075])], or (iii) a length of a portion of the sample corresponding to the tissue type [if the features are nuclei, the selection criteria can include, color characteristics, nuclei morphology (e.g., shape, dimensions (length); (para [0078])].
As to claim 18 refer to claim 8 rejection.
AS to claim 10, Barnes discloses further comprising providing, by the computing system, information based on the association between the predicted risk score and the first feature set for the first subject [providing reliable risk stratification for early-stage breast cancer patients; a risk stratification system may be trained using training data that includes tissue slides from several patients; the tissue slides may be processed according to a specific staining protocol and stains or biomarkers may be scored using a specific scoring protocol; the risk stratification system may include a proportional hazards regression module that is used to combine the individual whole slide scores from chosen subset of all the analyzed whole slides to generate a particular risk stratification scoring algorithm; (para [0037], [0039])].
As to claim 20 refer to claim 10 rejection.
Claim(s) 3 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Barnes et al. (US 20170270666 A1) and GILL et al. (US 20200166523 A1) as applied to claim 1 and 11 and further in view Snider et al. (US 20210255200 A1).
AS to claim 3, neither Barnes nor GILL discloses further comprising establishing, by the computing system, the model comprising a multivariate model using one or more features selected from the plurality of second feature set using one or more corresponding univariate models.
Snider discloses methods for predicting mortality and detecting the presence of severe disease by measuring circulating levels of ST2 and/or IL-33, alone or in combination with other biomarkers (para [0002), a multivariate model was also constructed that included BNP, NT-ProBNP, BUN and creatinine clearance levels with change in ST2; predictor of mortality (para [0183]), e.g., within 90 days, in both univariate or multivariate predictive analysis; change in ST2 was shown to be the strongest univariate predictor of patient outcome; combining change in ST2 data with a measure of renal function provided additional predictive value, as did including an NT-proBNP measurement with change in ST2; para [0192]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention was made to use the teachings of Snider to modify the combined system of Barnes and Gill by establishing, by the computing system, the model comprising a multivariate model using one or more features selected from the plurality of second feature set using one or more corresponding univariate models for providing prognostic evaluation of subjects, in particular for the prediction of adverse clinical outcomes, e.g., mortality, and the detection of disease.
As to claim 13 refer to claim 3 rejection.
Claim(s) 7 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Barnes et al. (US 20170270666 A1) and GILL et al. (US 20200166523 A1) as applied to claims 1 and 11 and further in view " ERDEN et al. Gender Recognition Using Facial Images" 2013 International Conference on Agriculture and Biotechnology IPCBEE vol.60 (2013) IACSIT Press, Singapore DOI: 10.7763/IPCBEE. 2013. V60. 22, pages 112-117.
As to claim 7, Barnes nor GILL discloses wherein the first radiological feature is derived from the tomogram using a Coif-wavelet transform, and comprises at least one of: (i) a gray level cooccurrence matrix (GLCM), (ii) gray level dependence matrix (GLDM), (iii) a gray level run length matrix (GLRLM), (vi) a gray level size zone matrix (GLSZM), or (v) a neighboring gray tone difference matrix.
ERGEN discloses classifying data set which are created by 5 Coiflets wavelet filters (coif1-coif5 Coiflets Wavelets Filter; Abstract & page 115, para 5, generating 7 X 200 matrix which obtained from images that include 3 statistical values and 4 parameters of GLCM (Gray Level Co-occurrence Matrix); Abstract & page 114, para 4.1 and page 5, par 5.)
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention was made to use the teachings of ERFEN to modify the combined system of Barnes and Gill by deriving , the first radiological feature from the tomogram using a Coif-wavelet transform, and comprises at least one of: (I) a gray level cooccurrence matrix (GLCM), (ii) gray level dependence matrix (GLDM), (iii) a gray level run length matrix (GLRLM), (vi) a gray level size zone matrix (GLSZM), or (v) a neighboring gray tone difference matrix in order to providing classification accuracy rate achieved based on the generated image data set.
As to claim 17 refer to claim 7 rejection.
5. Claim(s) 9 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Barnes et al. (US 20170270666 A1) and GILL et al. (US 20200166523 A1) as applied to claims 1 and 11 and further in view Venkat (US 20200255909 A1).
As to claim 9, Neither Barnes nor GILL discloses wherein the first genomic feature identifies a status of Homologous recombination deficiency (HRD) or Homologous recombination proficiency (HRP) in the first subject, the status determined using at least one of: (i) variants in genes associated with HRD DNA damage response or (ii) subtypes for disjoint tandem duplicator and foldback inversion mutations. Tempus discloses methods, systems, and software are provided for determining a homologous recombination pathway status of a cancer in a test subject, e.g., to improve cancer treatment predictions and outcomes determining a homologous recombination pathway status of a cancer in a test subject; using one or more of (i) a heterozygosity status for DNA damage repair genes in a cancerous tissue; Abstract & para [0058]), determining a homologous recombination pathway status of a cancer in a test subject; using one or more of (i) a heterozygosity status for DNA damage repair genes in a cancerous tissue features of the genomes of the cancerous and non-cancerous tissues of the subject that can be inputted into a classifier trained to distinguish between cancers with homologous recombination pathway deficiencies and cancers includes for a first plurality of DNA damage repair genes, a heterozygosity status in the genome of the cancerous tissue of the subject; Abstract & para [0058]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention was made to use the teachings of ERFEN to modify the combined system of Barnes and Gill by identifying from the first genomic feature a status of Homologous recombination deficiency (HRD) or Homologous recombination proficiency (HRP) in the first subject, the status determined using at least one of: (i) variants in genes associated with HRD DNA damage response or (ii) subtypes for disjoint tandem duplicator and foldback inversion mutations in order to improving cancer treatment predictions and outcomes.
As to claim 19 refer to claim 9 rejection.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SAMIR ANWAR AHMED whose telephone number is (571)272-7413. The examiner can normally be reached flex.
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/SAMIR A AHMED/ Primary Examiner, Art Unit 2665
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