DETAILED ACTION
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 the Claims
The office action is in response to the claims filed on September 19, 2024 for the application filed September 19, 2024 which claims priority to a provisional application filed on September 20, 2023. Claims 1-30 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.
Eligibility Step 1:
Under step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance, claims 1-23 are directed towards a method (i.e. a process), which is a statutory category. Claims 24-30 are directed towards a system (i.e. a machine), which is a statutory category. Claims XXX are directed towards a XXX (i.e. a manufacture), which is a statutory category. Since the claims are directed toward statutory categories, it must be determined if the claims are directed towards a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea). In the instant application, the claims are directed towards an abstract idea. Modify as appropriate. System claims correspond to machine, method to process and CRM to manufacture.
Eligibility Step 2A, Prong One:
Under step 2A, prong one of the 2019 Revised Patent Subject Matter Eligibility Guidance, independent claims 1 and 24 are determined to be directed to an judicial exception because an abstract idea is recited in the claims which fall within the subject matter groupings of abstract ideas. The abstract idea (identified in bold) recited in the representative claim 24 is identified as:
A system for performing a clinical trial or biomarker validation study, the system comprising one or more computer processors programmed to perform operations comprising:
obtaining access to a computer-implemented model that was trained using training data comprising a plurality of records for a plurality of individuals, each record comprising (i) values for a plurality of features for a respective individual from the plurality of individuals and (ii) a label providing an indication of a disease state for the respective individual;
providing, to the trained computer-implemented model, a plurality of sets of values for the plurality of features, each set of values corresponding to a candidate individual from a set of candidate individuals;
receiving, from the trained computer-implemented model, a prediction of a disease state for each set of values;
identifying, from the predictions of the disease state, a group of participants from the set of candidate individuals; and
facilitating at least one of a clinical trial or a biomarker validation study involving the group of participants.
The identified limitations of the abstract idea of claims “obtaining.. a model”, “receiving, from the… model, predictions…” and “identifying, from the predictions.., a group of participants…” fall within the subject matter grouping of mathematical concepts, such as mathematical relationships, mathematical formulas or equations, and mathematical calculations. Articulate how the claims recite a mathematical concept by mapping the limitations to one of mathematical relationships, mathematical formulas or equations, or mathematical calculations). See MPEP §2106.04(a)(2)(I).
The identified limitation of “obtaining.. a model”, “receiving, from the… model, predictions…” and “identifying, from the predictions.., a group of participants…” fall within the subject matter grouping of certain methods of organizing human activity related and the sub grouping of managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions). Using a model to obtain prediction of individuals and identify a group individuals for clinical trials is managing personal behavior for the human activity of clinical trial enrollment.
The identified limitation of “facilitating at least one of a clinical trial or a biomarker validation study involving the group of participants” fall within the subject matter grouping of certain methods of organizing human activity related and the sub grouping of managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions), commercial or legal interactions (including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations). Facilitating a clinical trial is including in all three subgroupings as it managing of behavior of subjects and professionals involved in the clinical trial, a commercial interaction and a fundamental between organizations, professionals and subjects.
The identified limitations of “receiving, from the… model, predictions…” and “identifying, from the predictions.., a group of participants…” fall within the subject matter grouping of mental processes. Receciving prediction and identifying indivuals based on the predicitons can practically be performed in the human mind using observations (i.e. receiving predictions), evaluations, judgments and opinions (identifying). If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea.
Accordingly, claims 1 and 24 recite an abstract idea under step 2A, prong one.
Eligibility Step 2A, Prong Two:
Under step 2A, prong two of the 2019 Revised Patent Subject Matter Eligibility Guidance, it must be determined whether the identified abstract ideas are integrated into a practical application. After evaluation, there is no indication that any additional elements or combination of elements integrate the abstract idea into a practical application, such as through: an additional element that reflects an improvement to the functioning of a computer, or an improvements to any other technology or technical field; an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition; an additional element that implements the judicial exception with, or uses the judicial exception in connection with, a particular machine or manufacture that is integral to the claim; an additional element that effects a transformation or reduction of a particular article to a different state or thing; or an additional element that applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. As shown below, the additional elements, other than the abstract idea per se, when considered both individually and as an ordered combination, amount to no more than a recitation of: generally linking the abstract idea to a particular technological environment or field of use; insignificant extra-solution activity to the judicial exception; and/or adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea as evidenced below.
The additional elements recited in representative claim 24 are identified in italics as:
A system for performing a clinical trial or biomarker validation study, the system comprising one or more computer processors programmed to perform operations comprising:
obtaining access to a computer-implemented model that was trained using training data comprising a plurality of records for a plurality of individuals, each record comprising (i) values for a plurality of features for a respective individual from the plurality of individuals and (ii) a label providing an indication of a disease state for the respective individual;
providing, to the trained computer-implemented model, a plurality of sets of values for the plurality of features, each set of values corresponding to a candidate individual from a set of candidate individuals;
receiving, from the trained computer-implemented model, a prediction of a disease state for each set of values;
identifying, from the predictions of the disease state, a group of participants from the set of candidate individuals; and
facilitating at least one of a clinical trial or a biomarker validation study involving the group of participants.
The additional limitations of “system comprising one or more computer processors programmed” and “trained computer-implemented” model are determined to be mere instructions to apply an abstract idea under MPEP §2106.05(f). The processors and trained computer-implemented model are recited at a high level of generality and merely used in their ordinary capacity to perform the abstract idea. Therefore, these additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or no more than mere instructions to implement an abstract idea or other exception on a computer or no more than merely using a computer as a tool to perform an abstract idea.
Accordingly, claims 1 and 24 do not recite additional elements which integrate the abstract idea into a practical application.
Eligibility Step 2B:
Under step 2B of the 2019 Revised Patent Subject Matter Eligibility Guidance, it must be determined whether provide an inventive concept by determining if the claims include additional elements or a combination of elements that are sufficient to amount to significantly more than the judicial exception. After evaluation, there is no indication that an additional element or combination of elements are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional limitations amount to mere instructions to apply an abstract idea under MPEP §2106.05(f), which does not amount to significantly more than the abstract idea.
Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements amounts to an inventive concept.
Dependent Claims:
The dependent claims merely present additional abstract information in tandem with further details regarding the elements from the independent claims and are, therefore, directed to an abstract idea for similar reasons as given above. None of these limitations are deemed to integrate the claims into a practical application or to amount to significantly more than the abstract idea as detailed below.
The abstract idea (identified in bold) and additional elements (identified in italics) recited in the dependent claims 2-23 and 25-30 are identified as:
2. The method of claim 1, wherein the values for the plurality of features describe at least one of a demographic, a medical encounter, a physical examination, a diagnosis, a medication, a medical procedure, a vital measurement, a vaccination, a lab result, a serum, a urine sample, a bio-sample, a gene expression level, a medical image, a clinical note, a radiology report, a genetic test, a biomarker, a pathology report, health information, a social determinant of health, financial data, consumer data, or any combination thereof.
Merely defining the values used for prediction and training is encompassed by the abstract idea as no details are presented as to how the prediction is made and the claims merely require access the trained model and receiving the predictions. This could also be considered insignificant pre-solution activity.
3. The method of claim 1, wherein the model comprises at least one of a regression model, a classifier, a linear model, a non-linear model, a random forest, a kernel method, a Bayesian model, a decision tree, or a neural network.
Merely defining the model is encompassed by the abstract idea as the claims merely require accessing the trained model and receiving the predictions.
4. The method of claim 1, wherein the prediction of the disease state comprises a probability distribution.
Merely defining the prediction is encompassed by the abstract idea as the claims merely require receiving the probability distribution prediction and identifying a group of individuals based on the probability distribution prediction.
5. The method of claim 1, wherein the prediction of the disease state is on a primary stratification axis comprising a health-to-disease axis or a disease risk axis.
Merely defining the prediction is encompassed by the abstract idea as the claims merely require receiving the prediction and identifying a group of individuals based on the prediction.
6. The method of claim 1, wherein identifying the group of participants comprises stratifying each candidate individual from the plurality of candidate individuals according to one or more primary stratification axes.
Determined to be both a mental process and method of organizing human activity.
7. The method of claim 6, wherein identifying the group of participants further comprises stratifying each candidate individual from the plurality of candidate individuals according to one or more secondary stratification axes, and wherein the one or more secondary stratification axes represent one or more properties of the plurality of individuals, the properties comprising at least one of a physiological comorbidity, a demographic, or a socioeconomic variable.
Determined to be both a mental process and method of organizing human activity.
8. The method of claim 7, wherein at least one stratification axis from the one or more primary stratification axes or the one or more secondary stratification axes represents at least one of a continuous dimension or an ordinal dimension.
Determined to be both a mental process and method of organizing human activity.
9. The method of claim 7, wherein a position along at least one stratification axis from the one or more primary stratification axes or the one or more secondary stratification axes is represented by a point, a point estimate, or a probability distribution.
Determined to be both a mental process and method of organizing human activity.
10. The method of claim 1, wherein the clinical trial or the biomarker validation study comprises studying at least one of a progression of transitions in health, validity of a biomarker, validity of a panel of biomarkers, validity of a target therapeutic, efficacy of a therapeutic intervention, efficacy of a digital intervention, efficacy of a behavioral intervention, or any combination thereof.
Determined to be encompassed by the abstract idea of certain methods of organizing human activity.
11. The method of claim 1, wherein the group of participants is enriched in one or more properties relative to the set of candidate individuals, the one or more properties comprising at least one of reduced heterogeneity in physiology, increased probability of outcome events, increased propensity to respond to an intervention, increased likelihood of observing specific physiological or pathological signs, or any combination thereof.
Determined to be encompassed by the abstract idea of mental processes and certain methods of organizing human activity.
12. The method of claim 1, wherein identifying the group of participants comprises: determining a target distribution for the set of candidate individuals according to a goal of the clinical trial or the biomarker validation study, the target distribution defining a probability distribution; and
selecting the group of participants from the set of candidate individuals to achieve a collective distribution for the group of participants that resembles the target distribution.
Determined to be encompassed by the abstract idea of mental processes and certain methods of organizing human activity.
13. The method of claim 12, wherein the target distribution is determined based on at least one parameter to be evaluated in the clinical trial or the biomarker validation study, the at least one parameter comprising at least one of a biological property, a biomarker, a panel of biomarkers, a therapeutic target, or a pharmacologic intervention.
Determined to be encompassed by the abstract idea of mental processes and certain methods of organizing human activity.
14. The method of claim 12, wherein the target distribution is uniform with respect to the disease state.
Determined to be encompassed by the abstract idea of mental processes and certain methods of organizing human activity.
15. The method of claim 12, wherein the target distribution comprises a risk probability distribution.
Determined to be encompassed by the abstract idea of mental processes and certain methods of organizing human activity.
16. The method of claim 12, wherein the target distribution is enriched with respect to a region of a stratification axis associated with the disease state.
Determined to be encompassed by the abstract idea of mental processes and certain methods of organizing human activity.
17. The method of claim 12, wherein selecting the group of participants comprises minimizing a number of individuals in the group of participants.
Determined to be encompassed by the abstract idea of mental processes and certain methods of organizing human activity.
18. The method of claim 12, wherein selecting the group of participants comprises: identifying multiple groups of individuals, wherein each group of individuals from the multiple groups of individuals has a similar probability distribution with respect to the disease state; and
determining a number of individuals from each group of individuals to include in the group of participants to satisfy the target distribution.
Determined to be encompassed by the abstract idea of mental processes and certain methods of organizing human activity.
19. The method of claim 1, wherein the method comprises facilitating the clinical trial, and wherein the clinical trial comprises evaluating the efficacy of a treatment.
Determined to be encompassed by the abstract idea of certain methods of organizing human activity.
20. The method of claim 1, wherein the method comprises facilitating the clinical trial, and wherein facilitating the clinical trial comprises registering members from the group of participants to the clinical trial, such that the members are administered a medication, a digital intervention, or a behavioral intervention.
Determined to be encompassed by the abstract idea of certain methods of organizing human activity.
21. The method of claim 1, wherein the method comprises facilitating the biomarker validation study, and wherein the biomarker validation study comprises validating a biomarker or panel of biomarkers.
Determined to be encompassed by the abstract idea of certain methods of organizing human activity.
22. The method of claim 1, wherein identifying the group of participants comprises achieving a desired distribution of at least one covariate, the at least one covariate comprising at least one of a demographic, a comorbidity, a medication usage, a medical history, a socioeconomic property, or a biological property.
Determined to be encompassed by the abstract idea of mental processes and certain methods of organizing human activity.
23. The method of claim 1, further comprising training the computer-implemented model using the training data.
Determining to be mere instructions to apply an abstract idea under MPEP §2106.05(f) and insignificant pre-solution activity.
Claims 25-30 are rejected using the same rationale as claims 2-23.
Therefore, whether taken individually or as an ordered combination, 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-6, 10-14, 16-17, 19-27 and 30 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Sutton et al. (U.S. Pub. No. 2022/0208376).
Regarding claim 1, Sutton discloses a method of performing a clinical trial or biomarker validation study, the method (Abstract)comprising:
obtaining access to a computer-implemented model (Paragraph [0082], providing, to a generalized biomarker model, a first biomarker associated with a cohort.) that was trained using training data comprising a plurality of records for a plurality of individuals, each record comprising (i) values for a plurality of features for a respective individual from the plurality of individuals and (ii) a label providing an indication of a disease state for the respective individual (Paragraph [0039], Training of model 330 may involve the use of a training data set 310, which may be input into training algorithm 320 to develop the model. Training data 310 may include a plurality of patient medical records 312 (e.g., “Medical Record 1” Medical Record 2”, etc.) for which results associated with various training biomarkers 311 may already be known. Paragraph [0031], determined to have a certain type of disease, or more specifically, having been tested for certain biomarkers associated with that disease may be identified.);
providing, to the trained computer-implemented model, a plurality of sets of values for the plurality of features, each set of values corresponding to a candidate individual from a set of candidate individuals (Paragraph [0082], providing, to a generalized biomarker model, a first biomarker associated with a cohort. Paragraph [0085], the generalized biomarker may access information about a plurality of individuals, which may be analyzed to generate the output.);
receiving, from the trained computer-implemented model, a prediction of a disease state for each set of values (Paragraph [0085], receiving, from the generalized biomarker model, an output indicating a plurality of individuals with associated likelihoods of at least one of: having an attribute associated with the first biomarker or having been tested for the attribute associated with the first biomarker. Paragraph [0031], determined to have a certain type of disease, or more specifically, having been tested for certain biomarkers associated with that disease may be identified. Paragraph [0043], generalized biomarker model 330 may be used to identify patients that have been tested for test biomarker 331, that have tested positive for test biomarker 331, or the other attributes. Accordingly, further analysis may determine whether the patients are candidates for the cohort. The likelihood of a individual having an attribute associated with a biomarker is construed as a likelihood of the individual having a disease. Also see paragraph [0041]. output 350 may indicate that patients have tested positive for test biomarker 331, tested negative for test biomarker 331, are diagnosed with a certain condition based on biomarker 331, prescribed a particular treatment based on test biomarker 331, etc.);
identifying, from the predictions of the disease state, a group of participants from the set of candidate individuals (Paragraph [0090], In step 840, process 800 may include identifying, based on the output, a group of the plurality of individuals for inclusion in a cohort. Each individual in the group of the plurality of individuals may be associated with a likelihood received from the generalized biomarker model that satisfies the likelihood threshold.); and
facilitating at least one of a clinical trial or a biomarker validation study involving the group of participants (Paragraph [0069], a group of patients exhibiting a particular trait may be selected based on his or her associated confidence score, which may be compared to a likelihood threshold. Identified patients with confidence scores exceeding the likelihood threshold may then be surfaced as potential candidates for inclusion in a cohort. For example, the group of patients may be identified to a trial coordinator, who may review medical records for each patient for suitability for inclusion in a patient trial. Selected patients may then be identified to a medical practice (e.g., a healthcare facility) for treatment, including in a clinical trial, or the like. Also see paragraph [0077] and fig. 6.).
Regarding claim 2, Sutton further discloses wherein the values for the plurality of features describe at least one of a demographic, a medical encounter, a physical examination, a diagnosis, a medication, a medical procedure, a vital measurement, a vaccination, a lab result, a serum, a urine sample, a bio-sample, a gene expression level, a medical image, a clinical note, a radiology report, a genetic test, a biomarker, a pathology report, health information, a social determinant of health, financial data, consumer data, or any combination thereof (Paragraph [0080], , the model may be tuned to account for differences between the trial-specific data set and the training data. This may include adjusting the model based on patient demographic information, patient treatment information, types of biomarkers being tested for, a number of patients included in the trial-specific data, or various other factors. Paragraph [0026], system 130 may be configured to identify patients based on whether they have been tested for a specific biomarker, specific test results or other attributes associated with the biomarker (having been tested positive, negative, etc. Paragraph [0058], the structured information may include a gender, a birth date, a race, a weight, a lab result, a vital sign, a diagnosis date, a visit date, a medication order, a diagnosis code, a procedure code, a drug code, a prior therapy, or a medication administration.),.
Regarding claim 3, Sutton further discloses wherein the model comprises at least one of a regression model, a classifier, a linear model, a non-linear model, a random forest, a kernel method, a Bayesian model, a decision tree, or a neural network (Paragraph [0052], training algorithm 320 may include logistic regression 416. , training algorithm 320 may include one or more neural networks. machine learning techniques may also be used, either in combination with or separate from logistic regression 416, such as a linear regression model, a lasso regression analysis, a random forest model, a K-Nearest Neighbor (KNN) model, a K-Means model, a decision tree, a cox proportional hazards regression model, a Naïve Bayes model, a Support Vector Machines (SVM) model, or gradient boosting algorithms.).
Regarding claim 4, Sutton further discloses wherein the prediction of the disease state comprises a probability distribution (Paragraph [0052], For example, training algorithm 320 may include logistic regression 416 to determine scores based on feature vectors. The scores may be correlated with or otherwise indicate whether the patient associated with the medical record has been tested for the biomarker, etc. Additionally, or alternatively, training algorithm 320 may include one or more neural networks that adjust weights of one or more nodes such that an input layer of features is run through one or more hidden layers and then through an output layer of patient results (with associated probabilities.).
Regarding claim 5, Sutton further discloses wherein the prediction of the disease state is on a primary stratification axis comprising a health-to-disease axis or a disease risk axis (Paragraph [0085], At step 820, process 800 may include receiving, from the generalized biomarker model, an output indicating a plurality of individuals with associated likelihoods of at least one of: having an attribute associated with the first biomarker or having been tested for the attribute associated with the first biomarker. Paragraph [0052], For example, training algorithm 320 may include logistic regression 416 to determine scores based on feature vectors. Paragraph [0051], The score for each phrase and/or portion of structured data may represent a magnitude along a dimension associated with the corresponding phrase and/or portion. This is the prediction of likelihood of a biomarker associated with an attribute (i.e. disease state) represented as a score along a dimension associated with the attribute is construed as being the primary stratification of a health-to-disease or disease risk.).
Regarding claim 6, Sutton further discloses wherein identifying the group of participants comprises stratifying each candidate individual from the plurality of candidate individuals according to one or more primary stratification axes (Paragraph [0090], In step 840, process 800 may include identifying, based on the output, a group of the plurality of individuals for inclusion in a cohort. Each individual in the group of the plurality of individuals may be associated with a likelihood received from the generalized biomarker model that satisfies the likelihood threshold. Paragraph [0052], For example, training algorithm 320 may include logistic regression 416 to determine scores based on feature vectors. Paragraph [0051], The score for each phrase and/or portion of structured data may represent a magnitude along a dimension associated with the corresponding phrase and/or portion. The prediction of likelihood of a attribute associated with a biomarker (disease state) is construed as being the primary stratification axis/dimension.).
Regarding claim 10, Sutton further discloses wherein the clinical trial or the biomarker validation study comprises studying at least one of a progression of transitions in health, validity of a biomarker, validity of a panel of biomarkers, validity of a target therapeutic, efficacy of a therapeutic intervention, efficacy of a digital intervention, efficacy of a behavioral intervention, or any combination thereof (Paragraph [0031], In some instances, cohorts may be assembled to form groups used to analyze the characteristics of certain diseases, such as their epidemiology, treatment approaches, how outcomes such as mortality or progression of disease depend on certain variables, or the like.).
Regarding claim 11, Sutton further discloses wherein the group of participants is enriched in one or more properties relative to the set of candidate individuals, the one or more properties comprising at least one of reduced heterogeneity in physiology, increased probability of outcome events, increased propensity to respond to an intervention, increased likelihood of observing specific physiological or pathological signs, or any combination thereof (Paragraph [0085], receiving, from the generalized biomarker model, an output indicating a plurality of individuals with associated likelihoods of at least one of: having an attribute associated with the first biomarker or having been tested for the attribute associated with the first biomarker. Paragraph [0090], In step 840, process 800 may include identifying, based on the output, a group of the plurality of individuals for inclusion in a cohort. Each individual in the group of the plurality of individuals may be associated with a likelihood received from the generalized biomarker model that satisfies the likelihood threshold. Selecting a cohort with the likelihood of having attribute associated with a biomarker is construed as enriching the cohort by reducing heterogeneity in physiology.)
Regarding claim 12, Sutton further discloses wherein identifying the group of participants comprises: determining a target distribution for the set of candidate individuals according to a goal of the clinical trial or the biomarker validation study, the target distribution defining a probability distribution (Paragraph [0072], a generalized biomarker model may output confidence scores indicating a degree of certainty that a patient is associated with a particular trait (e.g., having been tested for a biomarker, testing positive for a particular biomarker, etc.). The likelihood threshold may be adjusted such that only patients with confidence scores exceeding a value set by the likelihood threshold are surfaced by the system. For example, a likelihood threshold set at 95% for a particular biomarker may result in patients with confidence scores greater than 95% being surfaced. Thus, a lower likelihood threshold may be more inclusive and may result in more patients being surfaced. In some embodiments, these thresholds may be set manually by users. For example, medical practices or system administrators may adjust one or more thresholds through a user interface. The thresholds may be adjusted directly, for example, by adjusting a likelihood threshold value, or indirectly, for example, by setting a patient number limit, providing feedback that a practice is receiving too many patients, etc. In some embodiments, the thresholds may be adjusted automatically. Paragraph [0052], Additionally, or alternatively, training algorithm 320 may include one or more neural networks that adjust weights of one or more nodes such that an input layer of features is run through one or more hidden layers and then through an output layer of patient results (with associated probabilities). Also see paragraphs [0073]-[0076] and [0086]-[0089]. Likelihood/probability thresholds are construed as defining a probability distribution.); and
selecting the group of participants from the set of candidate individuals to achieve a collective distribution for the group of participants that resembles the target distribution (Paragraph [0090], In step 840, process 800 may include identifying, based on the output, a group of the plurality of individuals for inclusion in a cohort. Each individual in the group of the plurality of individuals may be associated with a likelihood received from the generalized biomarker model that satisfies the likelihood threshold.).
Regarding claim 13, Sutton further discloses wherein the target distribution is determined based on at least one parameter to be evaluated in the clinical trial or the biomarker validation study, the at least one parameter comprising at least one of a biological property, a biomarker, a panel of biomarkers, a therapeutic target, or a pharmacologic intervention (Paragraph [0073], Various factors may be considered when determining the likelihood threshold for each biomarker. In some embodiments, a rarity or relative prevalence of the biomarker being exhibited in patients compared to other biomarkers may be used to determine a likelihood threshold for each biomarker. For example, a particular biomarker such as neorotrophic tropomyosin receptor kinase (“NTRK”) may be relatively rare and may only be tested for (or tested positive for) in a relatively small number of patients, but may still be important to practices. Accordingly, the likelihood threshold associated with the NTRK biomarker may be set lower (e.g., more inclusive of patients) as compared to other biomarkers. For example, there may be less concern about overwhelming a practice with patients exhibiting the NTRK biomarker traits because of the relative rarity associated with the biomarker. Conversely, for more commonly tested or exhibited biomarkers, such as the human epidermal growth factor receptor 2 (“HER2”), the threshold may be set higher to avoid overwhelming the practice. Paragraph [0031], Cohorts may be assembled for various purposes. In some instances, cohorts may be assembled to form groups used to analyze the characteristics of certain diseases, such as their epidemiology, treatment approaches, how outcomes such as mortality or progression of disease depend on certain variables, or the like.).
Regarding claim 14, Sutton further discloses wherein the target distribution is uniform with respect to the disease state (Paragraph [0090], In step 840, process 800 may include identifying, based on the output, a group of the plurality of individuals for inclusion in a cohort. Each individual in the group of the plurality of individuals may be associated with a likelihood received from the generalized biomarker model that satisfies the likelihood threshold. Including when satisfying a threshold is construed as a uniform target distribution.).
Regarding claim 16, Sutton further discloses wherein the target distribution is enriched with respect to a region of a stratification axis associated with the disease state (Paragraph [0090], In step 840, process 800 may include identifying, based on the output, a group of the plurality of individuals for inclusion in a cohort. Each individual in the group of the plurality of individuals may be associated with a likelihood received from the generalized biomarker model that satisfies the likelihood threshold. Including when satisfying a threshold is construed as a target distribution. Paragraph [0051], the phrases extracted, as well as any structured data considered, may be converted into a multi-dimensional vector that correlates a score to the phrases and other structured data. The score for each phrase and/or portion of structured data may represent a magnitude along a dimension associated with the corresponding phrase and/or portion. This is the prediction of likelihood of a biomarker associated with an attribute (i.e. disease state) represented as a score along a dimension associated with the attribute to identify patients with a likelihood of a biomarker associated with an attribute (i.e. disease state) above a likelihood threshold is construed as enriching the target cohort to a region above threshold and a likelihood stratification axis associated with the disease state.).
Regarding claim 17, Sutton further discloses wherein selecting the group of participants comprises minimizing a number of individuals in the group of participants (Paragraph [0076], the likelihood threshold may be informed based on a predetermined cohort size (e.g., based on capacity of a practice) in addition to determined likelihood values for the patients. For example, if a practice has a capacity of 30 patients, a predetermined cohort size may be set at 30 to limit the number of individuals that are surfaced. Also see paragraph [0070], the likelihood threshold may be tuned to be as inclusive as possible while minimizing a number of false positive identifications of patients as testing positive for a particular biomarker.).
Regarding claim 19, Sutton further discloses wherein the method comprises facilitating the clinical trial (Paragraphs [0069] and [0077] and fig. 6), and wherein the clinical trial comprises evaluating the efficacy of a treatment (Paragraph [0031], In some instances, cohorts may be assembled to form groups used to analyze the characteristics of certain diseases, such as their epidemiology, treatment approaches, how outcomes such as mortality or progression of disease depend on certain variables, or the like. Paragraph [0003], provide a more effective treatment. Clinical trials to analyze treatment approaches and provide effective treatment is construed as including evaluating the efficacy of a treatment.).
Regarding claim 20, Sutton further discloses wherein the method comprises facilitating the clinical trial, and wherein facilitating the clinical trial comprises registering members from the group of participants to the clinical trial, such that the members are administered a medication, a digital intervention, or a behavioral intervention (Paragraph [0069], a group of patients exhibiting a particular trait may be selected based on his or her associated confidence score, which may be compared to a likelihood threshold. Identified patients with confidence scores exceeding the likelihood threshold may then be surfaced as potential candidates for inclusion in a cohort. For example, the group of patients may be identified to a trial coordinator, who may review medical records for each patient for suitability for inclusion in a patient trial. Selected patients may then be identified to a medical practice (e.g., a healthcare facility) for treatment, including in a clinical trial, or the like. Paragraph [0031], In some instances, cohorts may be assembled to form groups used to analyze the characteristics of certain diseases, such as their epidemiology, treatment approaches, how outcomes such as mortality or progression of disease depend on certain variables, or the like. Also see paragraph [0077] and fig. 6.).
Regarding claim 21, Sutton further discloses wherein the method comprises facilitating the biomarker validation study, and wherein the biomarker validation study comprises validating a biomarker or panel of biomarkers (In some instances, cohorts may be assembled to form groups used to analyze the characteristics of certain diseases, such as their epidemiology, treatment approaches, how outcomes such as mortality or progression of disease depend on certain variables, or the like. Fig. 7 shows that variable include biomarkers. Clinical trials/studies for analyzing how outcomes such as mortality or progression of disease depend on certain variables, such as biomarkers, is construed as a biomarker validation study for validating a biomarker.).
Regarding claim 22, Sutton further discloses wherein identifying the group of participants comprises achieving a desired distribution of at least one covariate, the at least one covariate comprising at least one of a demographic, a comorbidity, a medication usage, a medical history, a socioeconomic property, or a biological property (Paragraph [0085], At step 820, process 800 may include receiving, from the generalized biomarker model, an output indicating a plurality of individuals with associated likelihoods of at least one of: having an attribute associated with the first biomarker or having been tested for the attribute associated with the first biomarker. Paragraph [0090], In step 840, process 800 may include identifying, based on the output, a group of the plurality of individuals for inclusion in a cohort. Each individual in the group of the plurality of individuals may be associated with a likelihood received from the generalized biomarker model that satisfies the likelihood threshold. Paragraph [0072], In some embodiments, these thresholds may be set manually by users. For example, medical practices or system administrators may adjust one or more thresholds through a user interface. The thresholds may be adjusted directly, for example, by adjusting a likelihood threshold value, or indirectly, for example, by setting a patient number limit, providing feedback that a practice is receiving too many patients, etc. In some embodiments, the thresholds may be adjusted automatically. The likelihoods of at least one of: having an attribute associated with the first biomarker is construed as a biological property covariate and the threshold is construed as the desired distribution.).
Regarding claim 23, Sutton further discloses training the computer-implemented model using the training data (Paragraph [0039], Training of model 330 may involve the use of a training data set 310, which may be input into training algorithm 320 to develop the model. Training data 310 may include a plurality of patient medical records 312 (e.g., “Medical Record 1” Medical Record 2”, etc.) for which results associated with various training biomarkers 311 may already be known..
Regarding claims 24-27 and 30: all limitations as recited have been analyzed and rejected with respect to claims 1, 4-6, 12 and 23. Claims 24-27 and 30 pertain to a system, corresponding to the method of claims 1, 4-6, 12 and 23. Claims 24-27 and 30 do not teach or define any new limitations beyond claims 1, 4-6, 12 and 23 apart from the one or more processors to perform the method disclosed by Sutton in paragraph [0081]; therefore claims 24-27 and 30 are rejected under the same rationale.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 7-9, 15, 18 and 28-29 are rejected under 35 U.S.C. 103 as being unpatentable over Sutton et al. (U.S. Pub. No. 2022/0208376) in view of Dwyer (U.S. Pub. No. 2013/0211805).
Regarding claim 7, Sutton does not appear to explicitly disclose, but Dwyer teaches that it is old and well known in the art of clinical trials at the time of the filing, wherein identifying the group of participants further comprises stratifying each candidate individual from the plurality of candidate individuals according to one or more secondary stratification axes, and wherein the one or more secondary stratification axes represent one or more properties of the plurality of individuals, the properties comprising at least one of a physiological comorbidity, a demographic, or a socioeconomic variable (Dwyer, paragraph [0056], A “covariate” is a variable which may be predictive, in whole or in part, of the outcome in question in a study or trial. Covariates may be secondary characteristics which may interact with a primary variable of interest. Examples of covariates include the age, weight, level of physical activity, and known diseases or conditions of the subject. Because covariates may play a significant role in determining the outcome of a randomized study, it may be important to control for them. Paragraph [0065], A simulated population may be generated by specifying conditions (e.g., covariates) and the distribution of those conditions throughout the population. Other preferences, such as preferred size and/or number of cohorts into which the simulated population should be divided, may also be provided. Using the conditions, distribution, and preferences, a number of simulated subjects may be generated. Each simulated subject may have, for example, a covariate profile indicating the covariates with which the simulated subject is associated. Paragraph [0074], The population size 202 may represent the total number of individuals who are available for study, and from which randomized groups may be drawn. Paragraph [0076], The population may be divided into two or more subpopulations with varying characteristics of interest. Subpopulations may be defined, for example, based on one or more characteristics of the subjects (examples of such characteristics may include age, gender, race, etc.). Paragraph [0077[, A list 212 of one or more characteristics of interest in the population may be included in the population. For example, if the members of the population are associated with one or more covariates of interest, a covariate profile 214 may be prepared for the covariates. The covariate profile 214 may include a name 216 of the covariate, a distribution 218 of the covariate amongst members of the population or one or more subpopulations, and/or risk levels 220 for each subpopulation in having or contracting a conditions associated with the covariate of interest. Also see paragraphs [0079], [0112]-[0114].) to improve the quality and predictive value of a randomized study (Sutton, paragraph [0006]).
Therefore, it would have been obvious to one of ordinary skill in the art of clinical trials at the time of the filing to modify the method of Sutton such that identifying the group of participants further comprises stratifying each candidate individual from the plurality of candidate individuals according to one or more secondary stratification axes, and wherein the one or more secondary stratification axes represent one or more properties of the plurality of individuals, the properties comprising at least one of a physiological comorbidity, a demographic, or a socioeconomic variable, as taught by Dwyer, in order to improve the quality and predictive value of a randomized study.
Regarding claim 8, Sutton does not appear to explicitly disclose, but Dwyer teaches that it is old and well known in the art of clinical trials at the time of the filing, wherein at least one stratification axis from the one or more primary stratification axes or the one or more secondary stratification axes represents at least one of a continuous dimension or an ordinal dimension (Dwyer, paragraph [0108], In addition to categorical predictors such as sepsis, interval predictors (or interval covariates) may also be used in the trial design. An interval predictor is a predictor which may vary in a continuous manner, such as height or weight.) to improve the quality and predictive value of a randomized study (Sutton, paragraph [0006]).
Therefore, it would have been obvious to one of ordinary skill in the art of clinical trials at the time of the filing to modify the method of Sutton such that at least one stratification axis from the one or more primary stratification axes or the one or more secondary stratification axes represents at least one of a continuous dimension or an ordinal dimension, as taught by Dwyer, in order to improve the quality and predictive value of a randomized study.
Regarding claim 9, Sutton does not appear to explicitly disclose, but Dwyer teaches that it is old and well known in the art of clinical trials at the time of the filing, wherein a position along at least one stratification axis from the one or more primary stratification axes or the one or more secondary stratification axes is represented by a point, a point estimate, or a probability distribution (Dwyer, Fig. 2l shows the position along an axis as a probability distribution. Also see paragraph [0145], an output plot of the results may be generated and displayed, construed as a point or point estimate.) to improve the quality and predictive value of a randomized study (Sutton, paragraph [0006]).
Therefore, it would have been obvious to one of ordinary skill in the art of clinical trials at the time of the filing to modify the method of Sutton such that a position along at least one stratification axis from the one or more primary stratification axes or the one or more secondary stratification axes is represented by a point, a point estimate, or a probability distribution, as taught by Dwyer, in order to improve the quality and predictive value of a randomized study.
Regarding claim 15, Sutton does not appear to explicitly disclose, but Dwyer teaches that it is old and well known in the art of clinical trials at the time of the filing, wherein the target distribution comprises a risk probability distribution (Dwyer, Any given covariate may have multiple effects on other characteristics or outcomes, As a consequence, there are situations when it is beneficial to group predictors into dependent risk levels, or risk groups. For example, “High Risk,” “Normal Risk,” and “Low Risk” may be used as dependency groups. This allows predictors or subpredictors and exclusion percentages to be customized across several risk levels. Paragraph [0101], a distribution input 254 may be provided for specifying a distribution of the categorical covariate among the population. Paragraph [0109], a default interval information input 284 for describing details of the interval, such as the beginning and end of the interval, distribution of subjects within the interval, etc. may be provided.).
Therefore, it would have been obvious to one of ordinary skill in the art of clinical trials at the time of the filing to modify the method of Sutton such that a position along at least one stratification axis from the one or more primary stratification axes or the one or more secondary stratification axes is represented by a point, a point estimate, or a probability distribution, as taught by Dwyer, in order to account for covariates that have multiple effects on other characteristics or outcomes.
Regarding claim 18, Sutton does not appear to explicitly disclose, but Dwyer teaches that it is old and well known in the art of clinical trials at the time of the filing wherein selecting the group of participants comprises: identifying multiple groups of individuals, wherein each group of individuals from the multiple groups of individuals has a similar probability distribution with respect to the disease state; and determining a number of individuals from each group of individuals to include in the group of participants to satisfy the target distribution (Dwyer, paragraphs [0097]-[0114] discuss specifying a target population of individuals for including in a cohort by specifying covariates, cohort size and distribution of those covariates throughout the population/cohort. By specifying what percentage of patients in the population/cohort come from each covariant, the system identifies multiple groups of individuals which may each satisfy a first covariant (e.g. Covariant 1 distribution set to 100%) and have different other covariants (e.g. covariants 2 distribution set to 20% and covariant 3 distribution set to 45%.) to improve the quality and predictive value of a randomized study (Sutton, paragraph [0006]).
Therefore, it would have been obvious to one of ordinary skill in the art of clinical trials at the time of the filing to modify the method of Sutton such that selecting the group of participants comprises: identifying multiple groups of individuals, wherein each group of individuals from the multiple groups of individuals has a similar probability distribution with respect to the disease state; and determining a number of individuals from each group of individuals to include in the group of participants to satisfy the target distribution, as taught by Dwyer, in order to improve the quality and predictive value of a randomized study.
For example, identifying the group of Sutton would include identifying patients with a likelihood/probability distribution satisfying a threshold (Paragraphs [0052] and [0090]) and include 50% from a male group satisfying the threshold and 50% from a female group satisfying the threshold to achieve a target distribution of 100% above the threshold, 50% male and 50% female.
Regarding claims 28-29: all limitations as recited have been analyzed and rejected with respect to claims 15 and 18. Claims 28-29 pertain to a system, corresponding to the method of claims 15 and 18. Claims 28-29 do not teach or define any new limitations beyond claims 15 and 18; therefore claims 28-29 are rejected under the same rationale.
Conclusion
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/DEVIN C HEIN/Examiner, Art Unit 3686
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