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 22, 2025.
2. Claim(s) 1-20 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 1-20 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:
The claims are directed to a process (claims 1–17), a machine (claims 18–19), and a computer-readable medium / program (claim 20), and therefore fall within statutory categories under 35 U.S.C. §101.
Independent Claims 1 and 20 are directed to an abstract idea consisting of mathematical concepts (formulas, calculations, scoring, model-based computations) and mental processes (evaluation and stratification of clinical risk).
Independent Claim 1 recites, in substance:
“An automated method for prognosticating risk of a clinical outcome for a patient, the method comprising:
receiving clinical data relating to the patient;
generating a histological index based on infrared absorption data gathered from a sample of the patient; and
generating a prognosticated risk score based on the histological index and the clinical data.”
Under its broadest reasonable interpretation, claim 1 covers:
Collecting data (clinical data and infrared absorption data).
Computing a histological index based on a ratio of energy/power measurements (see claims 9–12).
Computing a prognosticated risk score based on that index and clinical data, including via models, hazard ratios, and logistic regression (see claims 2–3).
Using the risk score to stratify the patient into risk classifications (claim 4).
These are mathematical concepts (calculations, formulas, model-based scoring, hazard ratios) and mental processes (prognostic evaluation and classification) that can be, and historically have been, performed mentally or using pen-and-paper statistical methods, but for the recitation of generic “automation.”
Independent Claim 20 recites, in substance:
“A computer program comprising computer code configured to cause one or more processors to perform the method of claim 1.”
Claim 20 merely recites a computer program that causes generic processors to perform the abstract method of claim 1; it does not add any substantive limitation beyond executing the same mental/mathematical steps on a conventional computer.
The limitations of Claims 1-20, as drafted, under their broadest reasonable interpretation, cover the performance of:
Mathematical concepts.
Claims 2–3 explicitly recite “incorporating the histological index and the clinical data in a model” and “determining a hazard ratio calculated using a logistic regression model for each of the histological index and the clinical data.” These are mathematical relationships and model-based computations.
Claims 9–12 recite computing a histological index PA using explicit formulas involving measures M(λn), ratios of amide and phosphate absorption, and scaling factors X1–X4. These are mathematical expressions and calculations.
Claims 1, 4-5, 7-8, and 13-17 rely on risk scoring, stratification, and index computation that are ultimately mathematical scoring and formula-based evaluations.
Mental processes.
Claim 1 recites “prognosticating risk of a clinical outcome for a patient” based on clinical data and histological index. Evaluating risk and stratifying into risk classes are mental processes involving observation, evaluation, judgment, and opinion.
Claim 4 recites using the risk score “to stratify the patient into one of at least two risk classifications with respect to the clinical outcome,” which covers mental classification and decision-making.
Claim 6 recites clinical outcomes such as death, disease-specific death, recurrence, and complete pathological response; using scores to assess likelihood of these outcomes is a mental evaluation performed by clinicians.
Claims 5, 7, and 8 recite determining clinicopathological factors (age, menopausal status, tumor size, grade, lymph node status) and using them to generate the risk score. This is clinical risk assessment that can be performed mentally given data and known prognostic factors.
Certain methods of organizing human activity (clinical decision-making).
Claims 1, 4, 5, 6, and 7 describe prognosticating clinical outcomes and stratifying patients into risk classifications, which are methods of organizing clinical workflows and treatment decisions based on predicted risk.
The claims recite additional elements such as:
Infrared absorption measurements and tissue samples (claims 9–17, 18–19).
A detector configured to obtain infrared absorption data at selected wavelengths (claim 18, 19).
A processing module configured to process infrared data and receive clinical data (claim 18).
A computer program with code executed by one or more processors (claim 20).
These elements, viewed individually and in combination, are recited at a high level of generality and merely invoke: Generic measurement equipment (detector, interferometer, Raman spectral imager, spectral detector, tunable light source) to gather input data. The claims do not recite any improvement to the functioning of the detectors or measurement architecture; they simply use known IR measurement techniques to obtain numerical values M(λn). Generic computing components (processing module, processors, computer program code) to perform the mathematical computations and risk scoring. The claims do not specify any non-conventional programming or computer architecture.
For example:
Claims 13–17 recite gathering infrared absorption data at selected wavelengths, using standard instruments (interferometer, spectral imager, spectral detector, tunable source), and operating on tissue samples of typical thickness. These are conventional pre-solution data-gathering steps that simply provide numerical inputs to the mathematical PA formulas and risk models.
Claim 18 recites a detector and processing module configured to obtain infrared data and receive clinical data, and “carried out” to perform the method of claim 14; this is a generic data-acquisition and processing system.
Claim 19 specifies that the detector “is comprised by an interferometer,” which is a well-known component in transform infrared systems.
Claim 20 recites a computer program causing processors to perform the steps of claim 1.
These additional elements do not improve the functioning of a computer, network, model architecture, image-processing architecture, or any other technology. They merely:
Implement the abstract idea (clinical outcome risk prediction and stratification via mathematical formulas and models) on conventional detectors and computers, and
Link the abstract idea to a field of use (clinical prognosis based on histology and clinicopathological factors).
The additional elements do not integrate the abstract idea into a practical application for the following reasons:
The detector, interferometer, Raman spectral imager, spectral detector, and tunable source (claims 14, 18–19) are used in a standard way to collect infrared absorption data at selected wavelengths. The claims do not recite any new or improved measurement technique, calibration, or hardware configuration that changes how infrared imaging works.
The tissue sample and thickness limitations (claims 15–16) reflect routine histology practices and do not contribute any technical improvement beyond preparing samples for standard IR or microscopic analysis.
The “processing module” and “computer program” (claims 18–20) simply execute the abstract mathematical computations (PA formulas, hazard ratios, logistic regression, risk scores) on generic processors. The claims do not recite any novel data structure, memory organization, or algorithmic optimization that improves computer performance or IR-processing architecture.
Thus, the claims “merely apply the abstract idea in a conventional computing environment and link it to a field of use” (clinical outcome prognostication), without integrating the judicial exception into a technological improvement.
The ordered combination of claim elements does not add significantly more than the abstract idea itself. Any storage, display, transmission, or reporting of the risk scores or classifications is insignificant extra-solution activity (post-solution output) and does not transform the nature of the claims.
Accordingly, taken as a whole, the claimed invention amounts to an abstract idea of mathematical risk prediction and mental clinical classification, implemented with conventional measurement and computing technology, and does not include an “inventive concept” that is sufficient to transform the judicial exception into a patent-eligible application.
Therefore, Claims 1–20 are directed to an abstract idea without significantly more and are not patent-eligible under 35 U.S.C. §101.
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 1–20 are rejected under 35 U.S.C. §103 as unpatentable over Phillips et al. (US 20180306710 A1) in view of Saillard et al. (US 20230282362 A1) and further in view of Cohen et al. (US 20180068083 A1).
Claim 1:
“An automated method for prognosticating risk of a clinical outcome for a patient, the method comprising:
receiving clinical data relating to the patient;
generating a histological index based on infrared absorption data gathered from a sample of the patient; and
generating a prognosticated risk score based on the histological index and the clinical data.”
Phillips et al. teaches:
“Generating a histological index based on infrared absorption data gathered from a sample of the patient.” Phillips et al. teaches “a method of mapping a tissue characteristic in a tissue sample comprising gathering infrared absorption data from the sample at selected wavelengths, and determining, from the infrared absorption data, a first measure of the amount of energy or power absorbed attributable to an amide moiety and a second measure … attributable to a phosphate moiety, and determining a ratio of the first measure and the second measure to establish a histological index,” see paragraphs [0006–0010], [0042–0047].
“Using the histological index as a prognostic biomarker for malignancy grade and recurrence.” Phillips et al. teaches that “the histological index PA may serve as a metric biomarker to assess malignancy grade of tumours” and describes an apparatus that “may comprise an e-pathology diagnostic tool to grade cancer biopsies and score patients on their chances of recurrence of a cancer,” see paragraphs [0081–0083].
Phillips et al. fails to explicitly teach:
“Receiving clinical data relating to the patient.”
“Generating a prognosticated risk score based on the histological index and the clinical data.”
Saillard et al. teaches:
“Receiving clinical data relating to the patient.” Saillard et al. teaches “obtaining clinical attributes derived from the subject,” including “subject age at the time of surgery, menopausal status, tumor stage, tumor size, number of positive nodes N, number of nodules, surgery type, treatment type, estrogen receptor ER status, progesterone receptor PR status, HER2 status, tumor grade, Ki67 expression, histological type, and presence or absence of one or more mutations in the BRCA gene or the TP53 gene,” see paragraphs [0078–0080], [0147–0149].
“Generating a prognosticated risk score based on the histological index and the clinical data.” Saillard et al. teaches “a computer-implemented method for predicting the likelihood that a subject having breast cancer will experience a relapse following treatment,” including steps of obtaining a histologic section image, obtaining subject attributes, computing an AI risk score using a machine-learning model, computing a clinical risk score using a clinical model, and computing “a final risk score for the subject from the AI risk score and the clinical risk score, wherein the final risk score represents the likelihood that the subject will experience a relapse following treatment,” see paragraphs [0057–0068], [0090–0093].
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to include the clinical-data and risk-score features taught by Saillard et al. within the histological-index method taught by Phillips et al. with the motivation of improving clinical outcome prediction by combining a quantitative histological biomarker (PA) with established clinical risk factors in a unified scoring framework, thereby providing more accurate and actionable prognostication of cancer recurrence and survival, see paragraphs [0090–0093] of Saillard et al.
Cohen et al. teaches:
Cohen et al. teaches a machine-learning system that “determines a quantifiable risk for the presence of cancer in patients … in terms of an increase over the population … in the form of a percentage risk score or a multiplier value, composite score or risk score for the likelihood of having cancer or an increased risk of having cancer,” see paragraphs [0028–0034].
Therefore, it would additionally have been obvious to present the prognosticated risk score in the claimed method using the risk-score formats taught by Cohen et al., with the motivation of using conventional ML-based risk scoring formats (percentage, multiplier, composite score) to communicate the prognosticated risk of a clinical outcome to clinicians in a quantitative, interpretable manner, see paragraphs [0028–0034] of Cohen et al.
As per Claim 2, Phillips et al., Saillard et al. and Cohen et al. teach:
“The method of claim 1, wherein generating the prognosticated risk score comprises incorporating the histological index and the clinical data in a model.”
Phillips et al. teaches:
“Generating the histological index PA from infrared absorption data.” Phillips et al. teaches computing PA from measures of amide and phosphate absorption at selected wavelengths, see paragraphs [0042–0048].
Phillips et al. fails to explicitly teach:
“Incorporating the histological index and the clinical data in a model” that generates the prognosticated risk score.
Saillard et al. teaches:
Saillard et al. teaches computing an AI risk score and a clinical risk score using models trained respectively on histologic images and clinical attributes, and then combining these scores into a final risk score. Specifically, Saillard et al. teaches receiving WSI and patient attributes, “generating risk score using the trained machine learning model,” “generating risk score using the trained clinical model,” and “computing a final risk score … from the AI risk score and the clinical risk score,” see FIG. 1 and paragraphs [0057–0068], [0094–0103].
Saillard et al. explains that the models (DeepMIL and Cox) incorporate histologic features and clinical attributes in a model-based framework to predict risk of relapse, see paragraphs [0061–0068], [0153–0155].
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the histological index (PA) and the clinical data in a predictive model as taught by Saillard et al., within the PA-based histological method taught by Phillips et al., with the motivation of leveraging established machine-learning and Cox-model frameworks to integrate PA and clinical variables into a single predictive model that generates a prognosticated risk score, thereby increasing accuracy and robustness of clinical outcome prediction, see paragraphs [0153–0155] of Saillard et al.
Cohen et al. teaches:
Cohen et al. teaches using classifiers (e.g., logistic regression, neural nets, random forests) that take biomarker values and clinical parameters as inputs and output risk categories or risk scores for cancer, see paragraphs [0061–0063], [0456–0463], [0209–0217].
Therefore, it would additionally have been obvious to adopt the model-based combination of PA and clinical data in such classifiers in the claimed method, with the motivation of applying well-known machine-learning architectures already used for multi-parameter cancer risk prediction to the PA index and clinical attributes, see paragraphs [0061–0063], [0456–0463], [0209–0217] of Cohen et al.
As per Claim 3, Phillips et al., Saillard et al. and Cohen et al. teach:
“The method of claim 2, comprises determining a hazard ratio calculated using a logistic regression model for each of the histological index and the clinical data.”
Phillips et al. teaches:
Phillips et al. teaches that PA can be correlated with cancer grading and used to “score patients on their chances of recurrence of a cancer,” see paragraphs [0071–0077], [0081–0083].
Phillips et al. fails to explicitly teach:
“Determining a hazard ratio calculated using a logistic regression model for each of the histological index and the clinical data.”
Saillard et al. teaches:
Saillard et al. teaches training a “proportional hazards model” (e.g., a Cox model) using survival endpoints (overall survival, iDFS, dDFS, MFI) and clinical attributes, and using hazard ratios (HR) as metrics in risk stratification. For example, Saillard et al. reports that “the WSI-based machine learning model stratified high and low relapse risk groups with a hazard ratio HR of 5.25 CI 3.20–8.61,” see paragraphs [0150–0155], [0161–0173].
Saillard et al. uses Cox models and hazard ratios to compare different risk groups (high vs. low relapse risk) based on combined AI risk scores and clinical variables, see FIGS. 6A–6D and paragraphs [0161–0168].
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to determine hazard ratios using Cox/logistic regression models for both the histological index (PA) and the clinical data in the combined method taught by Phillips et al., with the motivation of quantifying relative risk and survival impact using standard proportional-hazard techniques commonly applied in oncology prognostics, see paragraphs [0150–0173] of Saillard et al.
Cohen et al. teaches:
Cohen et al. teaches using logistic regression models and other machine-learning approaches to build classifiers that output risk scores and ROC statistics, and compares deep learning neural networks to logistic regression, random forest, and other models for cancer risk prediction, see Table 19 and paragraphs [0456–0463], [0476–0479].
Therefore, it would additionally have been obvious to apply logistic regression modeling to PA and clinical variables as in Cohen et al., with the motivation of using well-known statistical modeling approaches that already derive hazard/odds ratios and risk scores in cancer screening to support the claimed hazard-ratio calculations, see paragraphs [0456–0463], [0476–0479] of Cohen et al.
As per Claim 4, Phillips et al., Saillard et al. and Cohen et al. teach:
“The method of claim 1, further comprising: using the prognosticated risk score to stratify the patient into one of at least two risk classifications with respect to the clinical outcome.”
Phillips et al. teaches:
Phillips et al. teaches that PA values can be used to segment tissue into histological grades and to “score patients on their chances of recurrence,” with PA ranges corresponding to different grades (e.g., grade 1, 2, 3), see FIG. 9 and paragraphs [0071–0077], [0081–0083].
Phillips et al. fails to explicitly teach :
“Using the prognosticated risk score to stratify the patient into one of at least two risk classifications with respect to the clinical outcome.”
Saillard et al. teaches:
Saillard et al. teaches stratifying subjects into high, medium, and low relapse-risk groups using AI risk scores and clinical models, and presents Kaplan–Meier survival curves for these risk classifications, see FIG. 11C and paragraphs [0116–0117].
Saillard et al. further teaches stratifying ERHER2- subjects into low-risk and high-risk groups based on AI risk score, with hazard ratios quantifying the difference between groups, see paragraphs [0170–0176].
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to use the prognosticated risk score (computed from PA and clinical data) to stratify the patient into at least two risk classifications (e.g., high vs. low relapse risk) as taught by Saillard et al., with the motivation of guiding treatment decisions, surveillance intensity, and counseling based on clearly defined risk groups in line with standard practice in breast cancer prognosis, see paragraphs [0116–0117], [0170–0176] of Saillard et al.
Cohen et al. teaches:
Cohen et al. teaches classifying patients into categories indicative of a likelihood of having cancer or not having cancer and further categorizing the risk into qualitative groups such as “low, medium, high,” and quantitative risk scores, see paragraphs [0022], [0060–0065].
Therefore, it would additionally have been obvious to adopt such stratified risk-categories for the prognosticated clinical outcome risk score in the claimed method, with the motivation of presenting risk results in familiar low/medium/high categories that are widely understood by clinicians and used to trigger diagnostic testing or treatment escalation, see paragraphs [0022], [0060–0065] of Cohen et al.
As per Claim 5, Phillips et al., Saillard et al. and Cohen et al. teach:
“The method of claim 1, further comprising: determining one or more clinicopathological factor values based on the clinical data, wherein the or each clinicopathological factor value is used to generate the prognosticated risk score.”
Phillips et al. teaches:
Phillips et al. focuses on the PA index from IR absorption and its correlation with histological grade and malignancy, but does not explicitly enumerate clinicopathological factor values such as age, stage, nodal status, etc., see paragraphs [0071–0077].
Phillips et al. fails to explicitly teach:
“Determining one or more clinicopathological factor values based on the clinical data.”
“Using each clinicopathological factor value to generate the prognosticated risk score.”
Saillard et al. teaches:
Saillard et al. teaches obtaining clinicopathological factors including “subject age at the time of surgery, menopausal status, tumor stage, tumor size, number of positive nodes N, number of nodules, surgery type, treatment type, ER status, PR status, HER2 status, tumor grade, Ki67 expression, histological type, and genetic abnormalities (BRCA, TP53)” and using these features in clinical models (e.g., Cox proportional hazards) to compute clinical risk scores and final risk scores, see paragraphs [0078–0080], [0147–0153], [0161–0173].
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to determine clinicopathological factor values from the clinical data and use each factor as an input to the risk-prediction model and prognosticated risk score as taught by Saillard et al., with the motivation of capturing known prognostic variables that materially affect relapse risk and survival and thus improving the accuracy and clinical utility of the risk score, see paragraphs [0078–0080], [0147–0153] of Saillard et al.
As per Claim 6, Phillips et al., Saillard et al. and Cohen et al. teach:
“The method of claim 1, wherein the clinical outcome comprises one or more from a list comprising:
death;
disease-specific death;
recurrence; or
complete pathological response.”
Phillips et al. teaches:
Phillips et al. teaches that PA correlates with malignancy grade and recurrence risk and may be used to “score patients on their chances of recurrence,” see paragraphs [0081–0083].
Phillips et al. fails to explicitly teach:
“Clinical outcome comprises death, disease-specific death, recurrence, or complete pathological response” explicitly as a list.
Saillard et al. teaches:
Saillard et al. defines several survival endpoints that correspond to the claimed clinical outcomes, including “overall survival” (death from any cause), “invasive disease-free survival (iDFS), distant disease-free survival (dDFS), and metastasis-free interval (MFI)” and uses these endpoints to assess recurrence and disease-specific outcomes, see paragraphs [0150–0151], [0080–0084].
Saillard et al. further evaluates “risk of relapse” (recurrence) and clinical response based on risk scores and survival metrics, see paragraphs [0153–0155], [0161–0173].
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to treat death, disease-specific death, recurrence, and complete pathological response as clinical outcomes for the prognosticated risk score, with the motivation of aligning the claimed clinical outcomes of Saillard et al. with standard survival and response endpoints already used in breast cancer prognostic modeling (overall survival, iDFS, dDFS, MFI, relapse, response), see paragraphs [0080–0084] of Saillard et al.
As per Claim 7, Phillips et al., Saillard et al. and Cohen et al. teach:
“The method of claim 1, wherein the patient is a patient previously diagnosed with a cancer, wherein the clinical data is an attribute of the patient associated with prognosis for the previously diagnosed cancer.”
Phillips et al. teaches:
Phillips et al. teaches applying PA-based analysis to breast tissue samples obtained from cancer patients and grading cancer biopsies and scoring recurrence chances, see paragraphs [0081–0083].
Phillips et al. fails to explicitly teach:
“Patient previously diagnosed with a cancer” and that “clinical data is an attribute … associated with prognosis for the previously diagnosed cancer” in those explicit words.
Saillard et al. teaches:
Saillard et al. teaches a cohort of “subjects diagnosed with early breast cancer,” who have undergone surgical resection and follow-up, and collects clinical and pathological data (age, stage, grade, nodal status, histologic type, biomarkers, treatments) that are explicitly used to predict prognosis and relapse risk for these previously diagnosed cancer patients, see paragraphs [0147–0149], [0150–0155].
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to apply the PA-based prognostication method of Phillips et al. to patients previously diagnosed with cancer using clinical attributes associated with prognosis as taught by Saillard et al., with the motivation of leveraging established prognostic factors and survival endpoints from cancer cohorts to inform the prognosticated risk for already diagnosed patients, see paragraphs [0147–0155] of Saillard et al.
As per Claim 8, Phillips et al., Saillard et al. and Cohen et al. teach:
“The method of claim 1, wherein the clinical data comprise one or more from a list comprising:
age when diagnosed with cancer;
menopausal status;
tumor size;
tumor grade; and
lymph node status.”
Phillips et al. teaches:
Phillips et al. focuses on IR-derived PA and histological grading but does not explicitly list age, menopausal status, tumour size, tumour grade, and lymph node status as clinical data, see paragraphs [0071–0077].
Phillips et al. fails to explicitly teach:
The specific clinical data list: “age when diagnosed with cancer; menopausal status; tumour size; tumour grade; lymph node status.”
Saillard et al. teaches:
Saillard et al. teaches collecting and using “subject age at the time of surgery, menopausal status, tumor stage, tumor size, number of positive nodes N, tumor grade” and related clinical attributes in the clinical model, see paragraphs [0078–0080], [0147–0153].
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to include clinical data such as age at diagnosis, menopausal status, tumour size, tumour grade, and lymph node status in the claimed method, with the motivation of employing standard, well-known clinicopathologic factors that are recognized in the art as strong predictors of prognosis and relapse in breast cancer and are routinely used in Cox and ML models, see paragraphs [0078–0080] of Saillard et al.
As per Claim 9, Phillips et al., Saillard et al. and Cohen et al. teach:
“The method of claim 1, wherein generating the histological index based on infrared absorption data gathered from the sample comprises:
gathering infrared absorption data from the sample at selected wavelengths;
determining, from the infrared absorption data, a first measure of an amount of energy or power absorbed attributable to an amide moiety and a second measure of the amount of energy or power absorbed attributable to a phosphate moiety; and
determining a ratio of the first measure and the second measure to establish the histological index.”
Phillips et al. teaches:
Phillips et al. teaches gathering infrared absorption data from a tissue sample at selected wavelengths (e.g., around 6.0 - 10.0 microns), determining from the absorption data “a first measure of the amount of energy or power absorbed attributable to an amide moiety and a second measure … attributable to a phosphate moiety,” and “determining a ratio of the first measure and the second measure to establish a histological index,” see paragraphs [0006–0010], [0042–0047].
As per Claim 10, Phillips et al., Saillard et al. and Cohen et al. teach:
“The method of claim 9, wherein the histological index comprises a numeric value obtained by dividing the first measure by the second measure.”
Phillips et al. teaches:
Phillips et al. teaches that “the histological index may comprise a numeric value obtained by dividing the first measure by the second measure,” and provides explicit expressions for PA such as PA = M3–M4 / (M1–M2), see paragraphs [0042–0048].
As per Claim 11, Phillips et al., Saillard et al. and Cohen et al. teach:
“The method of claim 9, wherein the histological index, PA, is derived according to expression PA=[|M(λ3)−M(λ4)|]/[|M(λ1)−M(λ2)|] where: M(λn) is a measure of the absorbed energy or power at λn; λ1 is a wavelength corresponding to a peak absorption value attributable to an amide moiety; λ2 is a wavelength corresponding to a baseline absorption value attributable to an amide moiety; λ3 is a wavelength corresponding to a peak absorption value attributable to a phosphate moiety; λ4 is a wavelength corresponding to a baseline absorption value attributable to a phosphate moiety.”
Phillips et al. teaches:
Phillips et al. teaches deriving PA according to expressions involving measures M(λn) of absorbed energy or power at peak and baseline wavelengths for amide and phosphate moieties. Phillips et al. describes PA = M23–M24 / (M21–M22) where M21 and M22 correspond to amide peak and baseline and M23 and M24 correspond to phosphate peak and baseline, see paragraphs [0042–0048].
As per Claim 12, Phillips et al., Saillard et al. and Cohen et al. teach:
“The method of claim 9, wherein the histological index, PA, is derived according to expression PA=[X3 M(λ3)−X4 M(λ4)]/[X1 M(λ1)−X2 M(λ2)] where: M(λn) is a measure of the absorbed energy or power at λn; λ1 is a wavelength corresponding to a peak absorption value attributable to an amide moiety; λ2 is a wavelength corresponding to a baseline absorption value attributable to an amide moiety; λ3 is a wavelength corresponding to a peak absorption value attributable to a phosphate moiety; λ4 is a wavelength corresponding to a baseline absorption value attributable to a phosphate moiety; and X1 to X4 are numerical factors ≥1 which are set to values sufficient to ensure that measure M for a peak absorption values λ3 and λ1 is always greater than the measure M for the corresponding baseline absorption values λ4 and λ2 for all measurements.”
Phillips et al. teaches:
Phillips et al. teaches a scaled expression for PA like PA = X(M3–M4) / X(M1–M2) and explains that X is “set to a value sufficient to ensure that the measure M for peak absorption values 3 and 1 is always greater than the measure M for the corresponding baseline absorption values 4 and 2 for all measurements,” see paragraphs [0055–0057].
As per Claim 13, Phillips et al., Saillard et al. and Cohen et al. teach:
“The method of claim 1, wherein:
the method further comprises gathering infrared absorption data from the sample at selected wavelengths; and
generating a histological index based on infrared absorption data gathered from a sample of the patient comprises generating a histological index based on the gathered infrared absorption data.”
Phillips et al. teaches:
Phillips et al. teaches “gathering infrared absorption data from the sample at selected wavelengths” and generating PA from those gathered data, see FIG. 1 and paragraphs [0032–0039], [0042–0051].
As per Claim 14, Phillips et al., Saillard et al. and Cohen et al. teach:
“The method of claim 13, wherein the infrared absorption data are gathered using an interferometer, Raman spectroscopy spectral imager, spectral detector and/or a wavelength-tuneable light source.”
Phillips et al. teaches:
Phillips et al. teaches Fourier transform infrared spectroscopic imaging (FTIR) as an established method, which uses an interferometer as the core acquisition element, and mentions tunable infrared sources and detector arrays, see paragraphs [0003], [0031–0035].
Therefore, it would have been obvious to implement the IR data gathering using interferometer-based FTIR, spectral detectors, Raman spectral imaging, or tunable sources as claimed, with the motivation of using standard, well-known IR imaging architectures for tissue spectroscopy that provide robust spectra at the desired wavelengths, see paragraphs [0031–0035] of Phillips et al.
As per Claim 15, Phillips et al., Saillard et al. and Cohen et al. teach:
“The method of claim 13, wherein the sample is a tissue sample.”
Phillips et al. teaches:
Phillips et al. repeatedly refers to “tissue samples,” including breast tissue, oesophageal epithelium, and other biological tissue specimens, see paragraphs [0001–0004], [0070–0083].
As per Claim 16, Phillips et al., Saillard et al. and Cohen et al. teach:
“The method of claim 13, wherein the sample is from 1 microns to 10 microns thick, preferably about 4 microns thick.”
Phillips et al. teaches:
Phillips et al. teaches preparing tissue sections of “2–3 microns, and 6–7 microns,” and describes an example where two adjacent sections are microtomed to 5 microns thickness, with thicknesses in the 1–10 microns range being suitable for IR imaging, see paragraphs [0083–0084].
Therefore, it would have been obvious to use tissue sample thicknesses in the range of 1–10 microns, including about 4 microns, with the motivation of optimizing IR penetration and spatial resolution based on conventional microtomy practices in histopathology, see paragraphs [0083–0084] of Phillips et al.
As per Claim 17, Phillips et al., Saillard et al. and Cohen et al. teach:
“The method of claim 13, wherein the obtained infrared absorption data relates to a single spatial position on the sample.”
Phillips et al. teaches:
Phillips et al. teaches acquiring IR absorption measurements for each pixel in a two-dimensional array and indicates that measurements can be per pixel or per region, implying that single spatial positions (individual pixels) are measured, see paragraphs [0032–0037], [0051–0054].
As per Claim 18, Phillips et al., Saillard et al. and Cohen et al. teach:
“An apparatus for prognosticating risk of a clinical outcome based on a sample of a patient, comprising:
a detector configured to obtain infrared absorption data from a tissue at selected wavelengths; and
a processing module configured to process said infrared absorption data and receive clinical data related to the patient,
wherein the apparatus is configured to carry out the method of any one of claim 14.”
Phillips et al. teaches:
Phillips et al. teaches “apparatus for mapping a tissue characteristic in a tissue sample comprising a detector configured to obtain infrared absorption data from a tissue sample at selected wavelengths and a processing module configured to process said infrared absorption data,” see paragraphs [0020–0021], [0032–0038].
Phillips et al. fails to explicitly teach:
“Processing module configured to receive clinical data related to the patient” and “apparatus for prognosticating risk of a clinical outcome” explicitly.
Saillard et al. teaches:
Saillard et al. teaches a computer system with processor, memory, display, and IO devices that “receives WSI and patient attributes,” computes AI risk scores and clinical risk scores, and outputs a final risk score representing the likelihood of relapse, see FIG. 1 and paragraphs [0094–0103], [0185–0186].
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to configure the processing module of Phillips et al. to also receive clinical data related to the patient and to perform prognostic risk calculations as in Saillard et al., with the motivation of combining the IR-based PA apparatus with established clinical risk-prediction software to create a unified apparatus for prognosticating clinical outcome risk from both tissue and clinical data, see paragraphs [0185–0186] of Saillard et al.
As per Claim 19, Phillips et al., Saillard et al. and Cohen et al. teach:
“The apparatus of claim 18, wherein the detector is comprised by an interferometer.”
Phillips et al. teaches:
Phillips et al. teaches Fourier transform infrared spectroscopic imaging (FTIR) as an established technique, which relies on an interferometer to generate spectra, and references prior FTIR implementations, see paragraph [0003].
Therefore, it would have been obvious to implement the detector of the apparatus of claim 18 using an interferometer as in FTIR systems, with the motivation of using standard FTIR hardware that is widely known to efficiently acquire mid-infrared absorption spectra of tissue samples, see paragraph [0003] of Phillips et al.
As per Claim 20, Phillips et al., Saillard et al. and Cohen et al. teach:
“A computer program comprising computer code configured to cause one or more processors to perform the method of claim 1.”
Phillips et al. teaches:
Phillips et al. teaches that the apparatus may include a processor and software routines (flow diagrams) to compute PA and map tissue characteristics but does not explicitly claim a computer program for performing the entire method of claim 1, see FIGS. 3–6 and paragraphs [0038–0067].
Phillips et al. fails to explicitly teach:
“Computer program comprising computer code configured to cause one or more processors to perform the method of claim 1.”
Saillard et al. teaches:
Saillard et al. teaches “machine readable medium having executable instructions to cause one or more processing units to perform a method of predicting the likelihood that a subject having breast cancer will experience a relapse following treatment,” which includes obtaining histologic images and subject attributes and computing AI and clinical risk scores and a final risk score, see paragraphs [0005–0006].
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to provide the claimed method in the form of a computer program comprising executable code as taught by Saillard et al., with the motivation of delivering the PA-based and clinical-data-based prognostication method in a standard software package that can be executed on general-purpose processors, see paragraphs [0005–0006] of Saillard et al.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
WO 2015157577 A2: A holistic hospital patient care and management system comprises a data store operable to receive and store patient data including clinical and non-clinical data; at least one video camera focused on a patient; presence detection sensors configured to detect presence of physicians to enable real-time tracking and location; at least one predictive model in consideration of the clinical and non-clinical data to identify at least one medical condition of the patient; a risk logic module to apply the predictive model to the clinical and non-clinical data to determine at least one risk score for the patient; and a telemedicine logic module to receive physician real-time location and status information, identify and alert a physician available to consult with the medical personnel regarding the patient, enable two-way audio and video communication between the physician and the medical personnel, and provide the patient's medical condition and stratified risk to the physician.
US 20210118136 A1: Techniques performed by a data processing system for operating a personalized oncology system herein include accessing a first histopathological image of a histopathological slide of a sample taken from a first patient; analyzing the first histopathological image using a first machine learning model configured to extract first features from the first histopathological image; searching a histological database that includes a plurality of second histopathological images and corresponding clinical data for a plurality of second patients to generate search results; analyzing the plurality of third histopathological images and the corresponding clinical data associated with the plurality of third histopathological images using statistical analysis techniques to generate associated statistics and metrics associated with mortality, morbidity, time-to-event, or a combination thereof for the plurality of third patients associated with the third histopathological images; and presenting an interactive visual representation of the associated statistics and metrics including information for the personalized therapeutic plan for treating the first patient.
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
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