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 .
Claims 1-20 are pending and are provided to be examined upon their merits.
Drawings
The drawings are objected to because Fig. 2, ref. 236 should be fp(v,x,u) and not fg(v,x,u). Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities: in paragraph [0047], the “identify matrix 530” where 530 should be 536, and “control-variable vector 542” where 542 should be 540.
Appropriate correction is required.
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 an abstract idea without significantly more.
Claims 1-20 are directed to a method or system, which are statutory categories of invention. (Step 1: YES).
The Examiner has identified method Claim 1 as the claim that represents the claimed invention for analysis and is similar to system Claim 10, method Claim 11, and system Claim 20.
Claim 1 recites the limitations of:
A treatment method that provides a personalized medical treatment or therapy to a new patient, the treatment method comprising:
receiving the new patient;
acquiring patient information from the new patient;
using the acquired patient information to determine a treatment type and to construct a patient-treatment-information instance;
using the determined treatment type to determine a first treatment plan for the new patient by optimizing a set of control variables representing the treatment plan using a generic efficacy-estimation function that receives, as inputs, the set of control variables and the patient- treatment-information instance;
carrying out a number of experiments by
for each experiment,
carrying out a next experimental treatment using a next treatment plan, wherein the next treatment plan is the first treatment plan for the first experiment and a most recently generated next treatment plan for subsequent experiments,
determining a treatment efficacy,
storing the determined treatment efficacy and the next treatment plan,
generating a new patient-specific efficacy-estimation function as a deformation of the generic efficacy-estimation function, and
using the new patient-specific efficacy-estimation function to optimize a set of control variables representing a treatment plan to generate a new next treatment plan; and
selecting a treatment plan from among the next treatment plan generated following a final treatment experiment and a stored treatment plan; and
applying a treatment of the determined treatment type and specified by the selected treatment plan to the new patient.
These above limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity. The claim recites elements, highlighted in bold above, which covers performance of the limitation as managing personal behavior or interactions between people by following rules or instructions and teaching. Receiving patient data and acquiring patient information, using the information to determine a first treatment plan (following rules or instructions), carrying out a next experimental treatment using a next treatment plan (following rules and instructions), generating new patient-specific efficacy-estimation function to optimize a set of control variables (following rules or instructions), selecting a treatment plan and applying a treatment to the new patient (following rules or instructions and teaching). If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a managing personal behavior or interactions between people, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Claims 10, 11, and 20 are also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract)
In as much as a person in their mind, with pen and paper, can perform the steps, the claims are also abstract as mental processes. A person can receive and acquire new patient information, determine by analyzing a treatment type and construct with pen and paper a patient-treatment-information instance, determine a first treatment plan by optimizing control variables using a function that receives control variables and patient-treatment information (analyze with pen and paper to determine and optimize a function), determine by analyzing a treatment efficacy, generating a new patient-specific estimation function (analyze and write down with pen and paper a new function), optimize the control variables to generate a new next treatment plan, and select a treatment plan. Further, no computer is claimed therefore the steps are performed without a computer. Even if a computer were claimed, It has been shown the using a computer for a judicial exception may not be enough to make abstract claims statutory, see MPEP 2106.04(a)(2) III C.
Giving the claim it’s broadest reasonable interpretation in light of the specification, the claims are also abstract as reciting mathematical concepts. Claim 1 recites “generating a new patient-specific efficacy-estimation function as a deformation of the generic efficacy-estimation function” where the generic function is deformed (changed or modified) to generate a patient function. Generating a function based on deforming another function is abstract as a mathematical concept as the function is created by changing (deforming) another function (see MPEP 2106.04(a)(2) B) and equations in text format).
This judicial exception is not integrated into a practical application. In particular, the claims only recite: local computer systems, remote computer systems (Claim 10); local computer systems, remote computer systems (Claim 20). The computer hardware is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore claims 1, 10, 11, and 20 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer hardware amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Steps such as receiving and storing are steps that are considered insignificant extra solution activity and mere instructions to apply the exception using general computer components (see MPEP 2106.05(d), II). Thus claims 1, 10, 11, and 20 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims 2-10 and 12-20 further define the abstract idea that is present in their respective independent claims 1 and 11 and thus correspond to Certain Methods of Organizing Human Activity, Mental Processes, and Mathematical Concepts and hence are abstract for the reasons presented above. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Claims 5 and 15 recite a function comprising a generic efficacy-estimation function, control-variable transform, and an efficacy-estimation modifier, which is using text to describe a mathematical algorithm, therefore abstract as a mathematical concept. Claims 6 and 16 recites steps including transforming a set of control variables and using an efficacy-estimation modifier to modify an output, which is text describing a mathematical algorithm, therefore abstract as a mathematical concept. Claims 7 and 17 recite “adds a constant value to the efficacy estimate output by the generic efficacy-estimation function” where adds is abstract a mathematical concept. Claims 8 and 18 recite “a patient-specific efficacy-estimation function is generated as a deformation of the generic efficacy-estimation function using a constrained optimization process that optimizes the control-variable transform and the efficacy-estimation modifier” where there a function depends on another deformation of another function, therefore, abstract as a mathematical concept. Claims 10 and 20 also recite deformation of a function, therefore abstract also as a mathematical concept. Therefore, the claims 2-10 and 12-20 are directed to an abstract idea. Thus, the claims 1-20 are not patent-eligible.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites “using the determined treatment type to determine a first treatment plan for the new patient by optimizing a set of control variables representing the treatment plan using a generic efficacy-estimation function that receives, as inputs, the set of control variables and the patient- treatment-information instance;…” where there is no antecedence for “the treatment plan” only.” Claim 11 has a similar problem.
Claim 1 recites “carrying out a next experimental treatment using a next treatment plan, wherein the next treatment plan is the first treatment plan for the first experiment and a most recently generated next treatment plan for subsequent experiments,…” where there is no antecedence for “the first experiment.” Claim 11 has a similar problem.
Claims 2-10 and 12-20 are rejected as they depend from their respective independent claim.
Examiner Request
The Applicant is requested to indicate where in the specification there is support for amendments to claims should Applicant amend. The purpose of this is to reduce potential 35 U.S.C. §112(a) or §112 1st paragraph issues that can arise when claims are amended without support in the specification. The Examiner thanks the Applicant in advance.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 2, 10-12, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Pub. No. US 2006/0173663 to Langheier et al. in view of Pub. No. US 2022/0328198 to Wasan et al. and in view of Pub. No. US 2017/0140109 to Kheifetz et al.
Regarding claim 1
A treatment method that provides a personalized medical treatment or therapy to a new patient, the treatment method comprising:
receiving the new patient;
Langheier et al. teaches:
New patient enter (receiving new patient) …
“Predictive modeler 100 may utilize clinical data that is in non-standardized formats as well as data in standardized formats to generate predictive models. Older datasets stored in databases which lack terminology standards or XML exportation, excel spreadsheets, and paper records must still be reviewed for data quality, consistency and standardized terminology and formatting for incorporation into predictive modeler 100 or any other type of software. However, some datasets contain data with standard terminology according to the Unified Medical Language System (UMLS) inclusive of SNOMED, and transmission of secure encrypted data in Predictive Model Markup Language (PMML; based on XML), and in Extensible Markup Language (XML). Tagging of transported data in this manner allows for the automation recalculating models based on new factors (i.e. if blood sample from the patient cohort are then analyzed for SNPs) or new patient data (10 new patients enter the cohort over the timeframe of 2005 to 2010).” [0039]
acquiring patient information from the new patient;
Data collection (acquiring information) on new patients…
“In one exemplary implementation, predictive modeler 100 may generate models from clinical and molecular data sequestered in data warehouse 112 regarding a population of individuals, thus linking predictive factors (predictors) in the population to clinical outcomes. In parallel, biomarker causality identification system 102 may validate additional biomarkers measured as part of the data collection process on new patients, that are true predictors even after considering confounding or collinearity with other factors. Newly validated biomarkers can then be used to generate better predictive models and decision support modules. Predictive model library 114 may store predictive models either generated by predictive modeler 100 or imported via model import wizard 116 for manual entry of models from the literature or exported from other applications in Predictive Model Markup Language. Sets of models can be bundled to address a key clinical decision that depends on multiple outcomes and requires stages of testing and screening for optimal cost-effectiveness.” [0031]
using the acquired patient information to determine a treatment type and to construct a patient-treatment-information instance;
{
From Applicant’s specification on treatment types…
“There are many different types of treatments and therapies provided to patients suffering from many different types of diseases, pathologies, and disorders. Therapies and treatments may include application of heat and cold, electromagnetic radiation, mechanical forces, and other forces to all or portions of patients' bodies, provision of information and feedback to patients through various means of communication, provision of pharmaceuticals that are ingested, received by injection, inhaled, or delivered to patients by various additional means, surgical interventions, and many other types of therapies. Medical therapies and treatments, including pharmaceuticals, are often thoroughly tested for efficacy and safety before they are allowed to be administered to patients…” [0003]
Therefore, heat/cold, radiation, drugs, surgery etc. are treatment types.
}
Evaluate (determine) different treatment strategies (type)…
“Still another problem associated with conventional predictive modeling is the inability to simultaneously predict more than a single outcome, including the original medical problem, the efficacy of different treatments and adverse effects of different treatment strategies to resolve that problem. For example, conventional predictive modeling systems typically predict the likelihood that an individual will have a particular outcome, such as a disease. It may be desirable to generate multiple probabilities or likelihoods associated with different outcomes for an individual. In addition, it may be desirable to evaluate different treatment and testing strategies and the effects of these strategies on the likelihoods associated with the different outcomes, and recommend the optimal overall strategy or decision path. Current predictive modeling systems do not provide this flexibility.” [0008]
See Treatment Type below.
using the determined treatment type to determine a first treatment plan for the new patient by optimizing a set of control variables representing the treatment plan using a generic efficacy-estimation function that receives, as inputs, the set of control variables and the patient- treatment-information instance;
Generic model (efficacy-estimation function) for surgery, chemotherapy (different treatment types)…
“Biomarker causality identification system 102 may automatically extract biomarkers from clinical literature and store that data in clinical data warehouse 112 for use by predictive modeler 100. Decision support modules 104-110 may apply the models generated by predictive modeler 100 to predict clinical or medical outcomes for individuals. In the illustrated example, a coronary surgery solutions module 106 uses a model to predict outcomes relating to coronary surgery. A chemotherapy solutions module 108 predicts outcomes relating to chemotherapy. Decision support modules 104 and 110 are intended to be generic to indicate that the models generated by predictive modeler 100 may be applied to any appropriate clinical or medical solution. Modules 104-110 may be used by surgeons, physicians, and individuals to predict medical outcomes for a patient. Examples of decision support modules will be described in detail below.” [0030]
Variables (control variables) with high predictive power…
“The space of possible models linking a well-defined adverse outcome to the variables available in the dataset will be explored. The goal is to find models with high predictive power. Two different techniques will be used at this step, each paired with two different selection criteria. In one exemplary implementation, for a small enough number of possible predictive variables (up to 15), enumeration is used to compare all the 2.sup.P possible models. Predictive modeler 100 lists all possible models and computes the predictive score for each one of them. When the number of explanatory variables increases, enumerating all possible models is not feasible and search methods are required.” [0046]
Optimizing predictions of predictive models (therefore, control variables)…
“Predictive modeler 100 may automate processing of clinical data as an ongoing assembly line and dynamically update predictive models with a focus on optimizing predictions. Some of the components of setting up such a "factory line" of data analysis for the creation of predictive models have been carefully researched, such as gene-expression analysis, various model search and selection methods, Bayesian model fitting parameters, the validity and usefulness of model averaging, yet, no solution is available which:…” [0067]
Optimal treatment regimens (plans)…
“Like chemotherapy solutions module 106, coronary surgery solutions module 108 may display risk scores associated with different treatment regimens, receive input from a user to modify treatment regimens, and automatically update risk scores based on the modified treatment regimens. FIG. 10B is a computer screen shot illustrating an exemplary modify treatment plan and risk screen that may be displayed by coronary surgery solutions module 106. Referring to FIG. 10B, the screen includes risk scores and confidence intervals associated with a plurality of different outcomes associated with coronary bypass surgery and a given set of medications for the individual. As with the chemotherapy solutions module, the user can select different treatments, and coronary surgery solutions module 106 will automatically update the risk scores for the various outcomes. Such a tool allows both physicians and patients to select optimal treatment regimens based on risk tolerance of the patients.” [0123]
carrying out a number of experiments by
for each experiment,
Example of outcome may be clinical trial (experiment) outcome…
“The outcome predicted by the predictive model may be any suitable outcome relating to an individual, a population of individuals, or a healthcare provider. For example, the outcome may be a disease outcome, an adverse outcome, a clinical trials outcome, or a healthcare-related business outcome. An example of a disease outcome is an indication of whether or not an individual has a particular disease, is likely to develop the disease, and survival time given a treatment regimen. An example of an adverse outcome includes different complications relating to surgery, such a coronary surgery, or medical therapy, such as chemotherapy. An example of a clinical trial outcome includes the effectiveness or adverse reactions associated with taking a new drug. An example of a healthcare-related business outcome is cost of care for an individual.” [0036]
carrying out a next experimental treatment using a next treatment plan, wherein the next treatment plan is the first treatment plan for the first experiment and a most recently generated next treatment plan for subsequent experiments,
Modify (next) treatment plans
“From either the initial risk assessment or modify treatment plans screen, the user can select, "visualize your patient's risk score versus model population, learn more about model used to generate risk score" and chemotherapy solutions module 108 will display the individual's risk versus the model population and model details. FIG. 9E illustrates an example of such a comparison screen that may be displayed by chemotherapy solutions module 108. In FIG. 9E, the individual's risk of developing febrile or severe neutropenia versus the population is presented in graphical and text format. In addition, the source of the model used to generate the risk score is displayed.” [0120]
determining a treatment efficacy,
Evaluate (determining) efficacy of different treatment options…
“As stated above, the system illustrated in FIG. 1 may include decision support modules that apply predictive models, generate multiple outcomes, and that evaluate the efficacy of different treatment options on the outcomes. FIGS. 9A-9F are computer screen shots of exemplary user interfaces and functionality that may be provided by a decision support module according to an embodiment of the subject matter described herein. Referring to FIG. 9A, a computer screen shot of a patent information screen for chemotherapy solutions module 108 is presented. The purpose of the chemotherapy solutions module is to evaluate and present outcomes associated with particular chemotherapy regimens. In FIG. 9A age, demographic information, and lab test information is obtained for an individual. The individual is also prompted as to whether the individual is willing to participate in clinical research to assist in new biomarker validation. If the individual selects "Yes," then the individual will be presented with the appropriate consent forms for participating in biomarker validation and the appropriate orders will be sent to the lab that will conduct the tests required for biomarker validation.” [0115]
storing the determined treatment efficacy and the next treatment plan,
Model results, therefore efficacy, storage…
“Model Results Storage” [0060]
generating a new patient-specific efficacy-estimation function as a deformation of the generic efficacy-estimation function, and
{
From Applicant’s specification on deformation….
“FIG. 6 illustrates, using a 1-dimensional example, the deformation or modification of a generic efficacy-estimation function to produce a patient-specific efficacy-estimation function. A first plot 602 shows the generic efficacy-estimation-function curve 604 for a single control variable plotted with respect to a horizontal axis 606, with the efficacy estimate 608 plotted with respect to a vertical axis. In this simple example, the optimal value for the single control variable lies at the bottom 610 of the well-shaped efficacy-estimation-function curve. In a second plot 612, three experimentally derived data points for a particular patient 614-616 are plotted along with the generic efficacy-estimation-function curve. In other words, for example, for a control-variable value of 618, the generic efficacy-estimation function estimates an efficacy of 620 but an experimental treatment or therapy corresponding to the control-variable value of 618 produces a different observed efficacy 622. The transform discussed above with reference to FIG. 5B is then used, as illustrated in plot 624, to shift, deform, and align the patient-specific efficacy-estimation-function curve 626 with the experimentally derived data points 614-616. Thus, the transformation of the control variable produces a slightly modified or deformed patient-specific efficacy-estimation function that retains much of the information contained in the generic efficacy-estimation function from which it is produced. Simply trying to fit an arbitrary curve through a handful of experimentally derived data points, without the benefit of a generic efficacy-estimation function, would not be possible or, perhaps stated more accurately, would not sufficiently constrain the form of the curve to produce a patient-specific efficacy-estimation function that would be accurate over a reasonable range of possible control-variable vectors. The deformation retains a great deal of knowledge accumulated over many treatments of many different patients while adjusting the generic efficacy-estimation function to create a patient-specific efficacy-estimation function for a particular patient.” [0048]
Therefore, deformation is modification.
}
Generic models…
“Biomarker causality identification system 102 may automatically extract biomarkers from clinical literature and store that data in clinical data warehouse 112 for use by predictive modeler 100. Decision support modules 104-110 may apply the models generated by predictive modeler 100 to predict clinical or medical outcomes for individuals. In the illustrated example, a coronary surgery solutions module 106 uses a model to predict outcomes relating to coronary surgery. A chemotherapy solutions module 108 predicts outcomes relating to chemotherapy. Decision support modules 104 and 110 are intended to be generic to indicate that the models generated by predictive modeler 100 may be applied to any appropriate clinical or medical solution. Modules 104-110 may be used by surgeons, physicians, and individuals to predict medical outcomes for a patient. Examples of decision support modules will be described in detail below.” [0030]
Dynamically update of models…
“Predictive modeler 100 may automate processing of clinical data as an ongoing assembly line and dynamically update predictive models with a focus on optimizing predictions. Some of the components of setting up such a "factory line" of data analysis for the creation of predictive models have been carefully researched, such as gene-expression analysis, various model search and selection methods, Bayesian model fitting parameters, the validity and usefulness of model averaging, yet, no solution is available which:…” [0067]
Predictive modeling (efficacy estimation)…
“…Predictive modeling links to and powers a Decision Support system, which includes the following outputs: A set of outcomes being analyzed and predicted for the patient. List shows outcomes which have been analyzed in the past, new outcomes analyzed this time, and outcome not analyzed; organized by disease and therapeutic categories…” [0092] – [0094]
See Deformation below.
using the new patient-specific efficacy-estimation function to optimize a set of control variables representing a treatment plan to generate a new next treatment plan; and
Optimal intervention strategy (treatment plan)…
“As stated above, decision support module 104 may automatically incorporate scores from multiple models into a decision tree to enable an individual to select an optimal intervention strategy. FIG. 12 illustrates an example of such a decision tree. In FIG. 12, the decision tree includes branches that correspond to outcomes related to febrile neutropenia. The branches in FIG. 12 are only a portion of the total decision tree that relates to one approach of many approaches to using predictive modeling to evaluate treatment strategies…” [0146]
selecting a treatment plan from among the next treatment plan generated following a final treatment experiment and a stored treatment plan; and
Select optimal intervention strategy (treatment plan)…
“As stated above, decision support module 104 may automatically incorporate scores from multiple models into a decision tree to enable an individual to select an optimal intervention strategy. FIG. 12 illustrates an example of such a decision tree. In FIG. 12, the decision tree includes branches that correspond to outcomes related to febrile neutropenia. The branches in FIG. 12 are only a portion of the total decision tree that relates to one approach of many approaches to using predictive modeling to evaluate treatment strategies…” [0146]
applying a treatment of the determined treatment type and specified by the selected treatment plan to the new patient.
Treatment orders (applying a treatment)…
“The next screen that may be presented by chemotherapy solutions module 108 is the initial risk assessment screen, as illustrated in FIG. 9B. In FIG. 9B, the initial risk assessment screen displays lab data for the individual. In addition, the risk assessment screen includes a clinical decisions dashboard that indicates the individual's risk of developing febrile neutropenia as a result of a chemotherapy regimen. The dashboard displays the drugs involved in the chemotherapy regimen and the dosage amounts of each drug. The drugs and dosage amounts are modifiable by the user. If the user modifies the drugs or the dosage amounts, chemotherapy solutions module 108 will automatically recalculate the individual's risk of developing febrile neutropenia. In addition, the dashboard allows the user to modify treatment orders or add a G-CSF drug. In response to either of these actions, chemotherapy solutions module 108 will recalculate the individual's risk of febrile neutropenia. Thus, the dashboard illustrated in FIG. 9B provides a convenient method for a physician or a patient to evaluate different outcomes and treatment options.” [0117]
See Type below.
Treatment Type
Langheier et al. teaches prediction of treatment. They also teach different treatment strategies. They do not specifically teach various types of treatments.
Wasan et al. also in the business of treatment prediction teaches:
Patient specific values (patient profile) to assess (determine) particular (type) of treatment, such as medication, injections, etc…
“This specification describes technologies for training and evaluating patient treatment prediction models to predict the likelihood of a patient realizing a clinically meaningful improvement in a medical/health condition responsive to a specified treatment modality. A patient treatment prediction model can be a machine-learning model configured to process as inputs values of features from a personalized patient treatment prediction profile that describes a range of presenting characteristics (e.g., a “phenotype”) of a patient. For example, patient-specific values of features related to demographic, mental and physical health, and pain characteristics of a patient can be processed to assess the probability of a patient responding to a particular treatment or combination of treatments (e.g., medications, injections, therapies, etc.)…” [0006]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of Langheier et al. the ability to determine a type of treatment as taught by Wasan et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Wasan et al. who teaches the importance of responding to treatment and the type of treatment would improve the response.
Deformation
The combined references teach generic and personalized models. They do not specifically teach deformation.
Kheifetz et al. also in the business of generic and personal models teaches:
Modifying (deforming) basic (generic) models by providing functions…
“The process of modifying the basic disease progression models, by training the models using the training data set(s) of medical data of a group of treated individuals, provides functions describing relations between the medical data of the group of individuals and variations of one or more components (parameters) in the modified disease progression models, each relating to a specific treatment plan of the specific medical condition. These functions form integral part of or define the modified disease progression model(s), enabling the personalization of the modified disease progression models for a specific individual/patient, as will be further described below.” [0015]
A personalized model based on generic model for treatment…
“The determination of the personalized disease progression models involved a Bayesian estimator that modifies the reference disease progression models and evaluates the personal model parameters based on the pre-treatment patient's data, by using functions constructed from the analysis of the training data. The Bayesian estimator produces a personalized model, based on the population generic NSCLC model, but whose parameters are particularized for the given patient. A Bayesian predictor was used in simulating the personalized disease progression model to predict the patient's short- and long-term effects, as materialized in tumor progression, survival, and response to endpoint(s). The simulation output of the Bayesian predictor was converted by a Report Generator into a descriptive graphical/textual report, providing definitive, clinically critical answers to the prognosis questions (progression-free probability at time X, survival time) and treatment queries (tumor size after a given therapy, response for the specific regimen, time to development of resistance to a drug, etc.). The above algorithms were developed using the training-designated part of the clinical datasets.
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to change (deform) a generic model as taught by Kheifetz et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Kheifetz et al. who teaches the benefits of changing (deforming) a generic model to a personalized model.
Regarding claim 2
The treatment method of claim 1 wherein both the generic efficacy-estimation function and each patient-specific efficacy-estimation function receive, as inputs, a set of control variables and a patient-treatment-information instance, and
{
From Applicant’s specification on “control variable”…
“… Control variables may include instructions and directions to treatment providers and therapists. A control-variable vector v 110 contains values for controlling or instructing devices, systems and personnel to apply a particular type of treatment to a particular patient, and thus represents a treatment plan or a therapy plan. In Figure 1, curved arrows, such as curved arrow 112, represent input of control variable-vector values to devices, systems, and personnel within a treatment facility to effect a treatment or therapy” [0031]
Therefore, just about any variable that is an input to devices, systems, or personnel to effect a treatment.
}
Langheier et al. teaches:
Factors (input control variables) to predict outcome and individual (patient) analyzed (patient information instance)….
“Predictive models are commonly used to predict medical outcomes. Such models are based on statistical data obtained from populations of individuals that are identified as having or not having a particular medical outcome. Data regarding the population of individuals is typically analyzed to identify factors that predict the outcome. The factors may be combined in a mathematical equation or used to generate a posterior distribution to predict the outcome. In order to predict whether an individual has a particular outcome, the individual may be analyzed to determine the presence of one or more factors (variables). The model may then be applied to the individual to determine a likelihood that the individual will have the particular medical outcome or survival time.” [0003]
Patient therapeutic (treatment) recommendations…
“As will be described in detail below, the model and the outcomes may be used to provide healthcare-related decision support. For example, decision support module 104 may output a set of potential outcomes associated with a proposed therapeutic regimen and probabilities or risk scores associated with each outcome. The set of potential outcomes may be sorted by disease or therapeutic category. Other outcomes that may be generated by decision support module 104 include outcomes and therapeutic recommendations analyzed for the patient in the past, new outcomes and recommendations, and outcomes not yet analyzed. In addition to using a final model to predict outcomes for an individual, decision support module 104 may generate statistics on risk of an aggregate subpopulation of people versus risk of the complete population for the outcome.” [0038]
output an estimate of the treatment efficacy that would obtain were the patient represented by the patient-treatment-information instance treated according to the control variables.
Generate (output) probabilities (estimate) of outcomes (treatment)…
“Still another problem associated with conventional predictive modeling is the inability to simultaneously predict more than a single outcome, including the original medical problem, the efficacy of different treatments and adverse effects of different treatment strategies to resolve that problem. For example, conventional predictive modeling systems typically predict the likelihood that an individual will have a particular outcome, such as a disease. It may be desirable to generate multiple probabilities or likelihoods associated with different outcomes for an individual. In addition, it may be desirable to evaluate different treatment and testing strategies and the effects of these strategies on the likelihoods associated with the different outcomes, and recommend the optimal overall strategy or decision path. Current predictive modeling systems do not provide this flexibility.” [0008]
Regarding claim 10
A system that implements the method of claim 1, the system comprising:
one or more of local computer systems and remote computer systems that execute an application the implements the method of claim 1; and
Langheier et al. teaches:
Fig. 2, ref. 204 and 208 as computers…
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one or more databases that store treatment data and patient data.
Model results, therefore efficacy, storage…
“Model Results Storage” [0060]
Example of databases….
“Biomarker causality validation system 102 searches medical literature (i.e., Medline) and genome-disease association databases (i.e., OMIM--Online Mendelian Inheritance in Man) for the outcome of interest (i.e., anemia, chemotherapy), collects additional data on the potential biomarkers found from molecular information databases (i.e., Gene, Genome, SNP, etc), and stores the data in the potential biomarkers section of the biomarker causality library. The following are examples of outcomes and potential biomarkers that may be identified by biomarker causality validation system 102:” [0129]
Regarding claim 11
A treatment method that provides a personalized medical treatment or therapy to a returning patient, the treatment method comprising:
receiving the returning patient;
Langheier et al. teaches:
Outcomes and therapeutic recommendations for patient in the past, therefore receiving returning patient…
“As will be described in detail below, the model and the outcomes may be used to provide healthcare-related decision support. For example, decision support module 104 may output a set of potential outcomes associated with a proposed therapeutic regimen and probabilities or risk scores associated with each outcome. The set of potential outcomes may be sorted by disease or therapeutic category. Other outcomes that may be generated by decision support module 104 include outcomes and therapeutic recommendations analyzed for the patient in the past, new outcomes and recommendations, and outcomes not yet analyzed. In addition to using a final model to predict outcomes for an individual, decision support module 104 may generate statistics on risk of an aggregate subpopulation of people versus risk of the complete population for the outcome.” [0038]
acquiring patient information from the returning patient;
Outcomes and recommendations (acquiring patient information) and patient in the past (returning patient)….
“As will be described in detail below, the model and the outcomes may be used to provide healthcare-related decision support. For example, decision support module 104 may output a set of potential outcomes associated with a proposed therapeutic regimen and probabilities or risk scores associated with each outcome. The set of potential outcomes may be sorted by disease or therapeutic category. Other outcomes that may be generated by decision support module 104 include outcomes and therapeutic recommendations analyzed for the patient in the past, new outcomes and recommendations, and outcomes not yet analyzed. In addition to using a final model to predict outcomes for an individual, decision support module 104 may generate statistics on risk of an aggregate subpopulation of people versus risk of the complete population for the outcome.” [0038]
using the acquired patient information to access stored patient information for the returning patient;
Patient criteria (information) from database (stored) queries (access patient information)…
“In the original setup of a predictive model project, the lead statistics system administrator or clinical researcher can choose factors and patient criteria to be selected in the ongoing dynamic modeling, and database queries will be automatically generated to extract this information from datasets 214-226. This user can choose if he/she wants to include patients who have missing data for certain factors in data analysis matrices 206, or not.” [0040]
determining a treatment type and constructing a patient-treatment-information instance using the acquired patient information and stored patient information;
{
From Applicant’s specification on treatment types…
“There are many different types of treatments and therapies provided to patients suffering from many different types of diseases, pathologies, and disorders. Therapies and treatments may include application of heat and cold, electromagnetic radiation, mechanical forces, and other forces to all or portions of patients' bodies, provision of information and feedback to patients through various means of communication, provision of pharmaceuticals that are ingested, received by injection, inhaled, or delivered to patients by various additional means, surgical interventions, and many other types of therapies. Medical therapies and treatments, including pharmaceuticals, are often thoroughly tested for efficacy and safety before they are allowed to be administered to patients…” [0003]
Therefore, heat/cold, radiation, drugs, surgery etc. are treatment types.
}
Evaluate (determine) different treatment strategies (type)…
“Still another problem associated with conventional predictive modeling is the inability to simultaneously predict more than a single outcome, including the original medical problem, the efficacy of different treatments and adverse effects of different treatment strategies to resolve that problem. For example, conventional predictive modeling systems typically predict the likelihood that an individual will have a particular outcome, such as a disease. It may be desirable to generate multiple probabilities or likelihoods associated with different outcomes for an individual. In addition, it may be desirable to evaluate different treatment and testing strategies and the effects of these strategies on the likelihoods associated with the different outcomes, and recommend the optimal overall strategy or decision path. Current predictive modeling systems do not provide this flexibility.” [0008]
See Type below.
using the determined treatment type to determine a first treatment plan for the returning patient by optimizing a set of control variables representing the first treatment plan using one of a generic efficacy-estimation function or a patient-specific efficacy-estimation function previously generated for the returning patient that receives, as inputs, a set of control variables and the patient-treatment-information instance;
Generic model (efficacy-estimation function) for surgery, chemotherapy (different treatment types)…
“Biomarker causality identification system 102 may automatically extract biomarkers from clinical literature and store that data in clinical data warehouse 112 for use by predictive modeler 100. Decision support modules 104-110 may apply the models generated by predictive modeler 100 to predict clinical or medical outcomes for individuals. In the illustrated example, a coronary surgery solutions module 106 uses a model to predict outcomes relating to coronary surgery. A chemotherapy solutions module 108 predicts outcomes relating to chemotherapy. Decision support modules 104 and 110 are intended to be generic to indicate that the models generated by predictive modeler 100 may be applied to any appropriate clinical or medical solution. Modules 104-110 may be used by surgeons, physicians, and individuals to predict medical outcomes for a patient. Examples of decision support modules will be described in detail below.” [0030]
Variables (control variables) with high predictive power…
“The space of possible models linking a well-defined adverse outcome to the variables available in the dataset will be explored. The goal is to find models with high predictive power. Two different techniques will be used at this step, each paired with two different selection criteria. In one exemplary implementation, for a small enough number of possible predictive variables (up to 15), enumeration is used to compare all the 2.sup.P possible models. Predictive modeler 100 lists all possible models and computes the predictive score for each one of them. When the number of explanatory variables increases, enumerating all possible models is not feasible and search methods are required.” [0046]
Optimizing predictions of predictive models (therefore, control variables)…
“Predictive modeler 100 may automate processing of clinical data as an ongoing assembly line and dynamically update predictive models with a focus on optimizing predictions. Some of the components of setting up such a "factory line" of data analysis for the creation of predictive models have been carefully researched, such as gene-expression analysis, various model search and selection methods, Bayesian model fitting parameters, the validity and usefulness of model averaging, yet, no solution is available which:…” [0067]
Optimal treatment regimens…
“Like chemotherapy solutions module 106, coronary surgery solutions module 108 may display risk scores associated with different treatment regimens, receive input from a user to modify treatment regimens, and automatically update risk scores based on the modified treatment regimens. FIG. 10B is a computer screen shot illustrating an exemplary modify treatment plan and risk screen that may be displayed by coronary surgery solutions module 106. Referring to FIG. 10B, the screen includes risk scores and confidence intervals associated with a plurality of different outcomes associated with coronary bypass surgery and a given set of medications for the individual. As with the chemotherapy solutions module, the user can select different treatments, and coronary surgery solutions module 106 will automatically update the risk scores for the various outcomes. Such a tool allows both physicians and patients to select optimal treatment regimens based on risk tolerance of the patients.” [0123]
carrying out a number of experiments by
for each experiment,
Example of outcome may be clinical trial (experiment) outcome…
“The outcome predicted by the predictive model may be any suitable outcome relating to an individual, a population of individuals, or a healthcare provider. For example, the outcome may be a disease outcome, an adverse outcome, a clinical trials outcome, or a healthcare-related business outcome. An example of a disease outcome is an indication of whether or not an individual has a particular disease, is likely to develop the disease, and survival time given a treatment regimen. An example of an adverse outcome includes different complications relating to surgery, such a coronary surgery, or medical therapy, such as chemotherapy. An example of a clinical trial outcome includes the effectiveness or adverse reactions associated with taking a new drug. An example of a healthcare-related business outcome is cost of care for an individual.” [0036]
carrying out a next experimental treatment using a next treatment plan, wherein the next treatment plan is the first treatment plan for the first experiment and a most recently generated next treatment plan for subsequent experiments,
Modify (next) treatment plans
“From either the initial risk assessment or modify treatment plans screen, the user can select, "visualize your patient's risk score versus model population, learn more about model used to generate risk score" and chemotherapy solutions module 108 will display the individual's risk versus the model population and model details. FIG. 9E illustrates an example of such a comparison screen that may be displayed by chemotherapy solutions module 108. In FIG. 9E, the individual's risk of developing febrile or severe neutropenia versus the population is presented in graphical and text format. In addition, the source of the model used to generate the risk score is displayed.” [0120]
determining a treatment efficacy,
Evaluate (determining) efficacy of different treatment options…
“As stated above, the system illustrated in FIG. 1 may include decision support modules that apply predictive models, generate multiple outcomes, and that evaluate the efficacy of different treatment options on the outcomes. FIGS. 9A-9F are computer screen shots of exemplary user interfaces and functionality that may be provided by a decision support module according to an embodiment of the subject matter described herein. Referring to FIG. 9A, a computer screen shot of a patent information screen for chemotherapy solutions module 108 is presented. The purpose of the chemotherapy solutions module is to evaluate and present outcomes associated with particular chemotherapy regimens. In FIG. 9A age, demographic information, and lab test information is obtained for an individual. The individual is also prompted as to whether the individual is willing to participate in clinical research to assist in new biomarker validation. If the individual selects "Yes," then the individual will be presented with the appropriate consent forms for participating in biomarker validation and the appropriate orders will be sent to the lab that will conduct the tests required for biomarker validation.” [0115]
storing the determined treatment efficacy and the next treatment plan,
Model results, therefore efficacy, storage…
“Model Results Storage” [0060]
generating a new patient-specific efficacy-estimation function as a deformation of the generic efficacy-estimation function, and
{
From Applicant’s specification on deformation….
“FIG. 6 illustrates, using a 1-dimensional example, the deformation or modification of a generic efficacy-estimation function to produce a patient-specific efficacy-estimation function. A first plot 602 shows the generic efficacy-estimation-function curve 604 for a single control variable plotted with respect to a horizontal axis 606, with the efficacy estimate 608 plotted with respect to a vertical axis. In this simple example, the optimal value for the single control variable lies at the bottom 610 of the well-shaped efficacy-estimation-function curve. In a second plot 612, three experimentally derived data points for a particular patient 614-616 are plotted along with the generic efficacy-estimation-function curve. In other words, for example, for a control-variable value of 618, the generic efficacy-estimation function estimates an efficacy of 620 but an experimental treatment or therapy corresponding to the control-variable value of 618 produces a different observed efficacy 622. The transform discussed above with reference to FIG. 5B is then used, as illustrated in plot 624, to shift, deform, and align the patient-specific efficacy-estimation-function curve 626 with the experimentally derived data points 614-616. Thus, the transformation of the control variable produces a slightly modified or deformed patient-specific efficacy-estimation function that retains much of the information contained in the generic efficacy-estimation function from which it is produced. Simply trying to fit an arbitrary curve through a handful of experimentally derived data points, without the benefit of a generic efficacy-estimation function, would not be possible or, perhaps stated more accurately, would not sufficiently constrain the form of the curve to produce a patient-specific efficacy-estimation function that would be accurate over a reasonable range of possible control-variable vectors. The deformation retains a great deal of knowledge accumulated over many treatments of many different patients while adjusting the generic efficacy-estimation function to create a patient-specific efficacy-estimation function for a particular patient.” [0048]
Therefore, deformation is modification.
}
Generic models…
“Biomarker causality identification system 102 may automatically extract biomarkers from clinical literature and store that data in clinical data warehouse 112 for use by predictive modeler 100. Decision support modules 104-110 may apply the models generated by predictive modeler 100 to predict clinical or medical outcomes for individuals. In the illustrated example, a coronary surgery solutions module 106 uses a model to predict outcomes relating to coronary surgery. A chemotherapy solutions module 108 predicts outcomes relating to chemotherapy. Decision support modules 104 and 110 are intended to be generic to indicate that the models generated by predictive modeler 100 may be applied to any appropriate clinical or medical solution. Modules 104-110 may be used by surgeons, physicians, and individuals to predict medical outcomes for a patient. Examples of decision support modules will be described in detail below.” [0030]
Dynamically update of models…
“Predictive modeler 100 may automate processing of clinical data as an ongoing assembly line and dynamically update predictive models with a focus on optimizing predictions. Some of the components of setting up such a "factory line" of data analysis for the creation of predictive models have been carefully researched, such as gene-expression analysis, various model search and selection methods, Bayesian model fitting parameters, the validity and usefulness of model averaging, yet, no solution is available which:…” [0067]
Predictive modeling (efficacy estimation)…
“…Predictive modeling links to and powers a Decision Support system, which includes the following outputs: A set of outcomes being analyzed and predicted for the patient. List shows outcomes which have been analyzed in the past, new outcomes analyzed this time, and outcome not analyzed; organized by disease and therapeutic categories…” [0092] – [0094]
See Deformation below.
using the new patient-specific efficacy-estimation function to optimize a set of control variables representing a treatment plan to generate a new next treatment plan;and
Optimal intervention strategy (treatment plan)…
“As stated above, decision support module 104 may automatically incorporate scores from multiple models into a decision tree to enable an individual to select an optimal intervention strategy. FIG. 12 illustrates an example of such a decision tree. In FIG. 12, the decision tree includes branches that correspond to outcomes related to febrile neutropenia. The branches in FIG. 12 are only a portion of the total decision tree that relates to one approach of many approaches to using predictive modeling to evaluate treatment strategies…” [0146]
selecting a treatment plan from among the next treatment plan generated following a final treatment experiment and a stored treatment plan; and
Select optimal intervention strategy (treatment plan)…
“As stated above, decision support module 104 may automatically incorporate scores from multiple models into a decision tree to enable an individual to select an optimal intervention strategy. FIG. 12 illustrates an example of such a decision tree. In FIG. 12, the decision tree includes branches that correspond to outcomes related to febrile neutropenia. The branches in FIG. 12 are only a portion of the total decision tree that relates to one approach of many approaches to using predictive modeling to evaluate treatment strategies…” [0146]
applying a treatment of the determined treatment type and specified by the selected treatment plan to the returning patient.
Treatment orders (applying a treatment)…
“The next screen that may be presented by chemotherapy solutions module 108 is the initial risk assessment screen, as illustrated in FIG. 9B. In FIG. 9B, the initial risk assessment screen displays lab data for the individual. In addition, the risk assessment screen includes a clinical decisions dashboard that indicates the individual's risk of developing febrile neutropenia as a result of a chemotherapy regimen. The dashboard displays the drugs involved in the chemotherapy regimen and the dosage amounts of each drug. The drugs and dosage amounts are modifiable by the user. If the user modifies the drugs or the dosage amounts, chemotherapy solutions module 108 will automatically recalculate the individual's risk of developing febrile neutropenia. In addition, the dashboard allows the user to modify treatment orders or add a G-CSF drug. In response to either of these actions, chemotherapy solutions module 108 will recalculate the individual's risk of febrile neutropenia. Thus, the dashboard illustrated in FIG. 9B provides a convenient method for a physician or a patient to evaluate different outcomes and treatment options.” [0117]
See Type below.
Type
Langheier et al. teaches prediction of treatment. They also teach different treatment strategies. They do not specifically teach various types of treatments.
Wasan et al. also in the business of treatment prediction teaches:
Patient specific values (patient profile) to assess (determine) particular (type) of treatment, such as medication, injections, etc…
“This specification describes technologies for training and evaluating patient treatment prediction models to predict the likelihood of a patient realizing a clinically meaningful improvement in a medical/health condition responsive to a specified treatment modality. A patient treatment prediction model can be a machine-learning model configured to process as inputs values of features from a personalized patient treatment prediction profile that describes a range of presenting characteristics (e.g., a “phenotype”) of a patient. For example, patient-specific values of features related to demographic, mental and physical health, and pain characteristics of a patient can be processed to assess the probability of a patient responding to a particular treatment or combination of treatments (e.g., medications, injections, therapies, etc.)…” [0006]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of Langheier et al. the ability to determine a type of treatment as taught by Wasan et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Wasan et al. who teaches the importance of responding to treatment and the type of treatment would improve the response.
Deformation
The combined references teach generic and personalized models. They do not specifically teach deformation.
Kheifetz et al. also in the business of generic and personal models teaches:
Modifying (deforming) basic (generic) models by providing functions…
“The process of modifying the basic disease progression models, by training the models using the training data set(s) of medical data of a group of treated individuals, provides functions describing relations between the medical data of the group of individuals and variations of one or more components (parameters) in the modified disease progression models, each relating to a specific treatment plan of the specific medical condition. These functions form integral part of or define the modified disease progression model(s), enabling the personalization of the modified disease progression models for a specific individual/patient, as will be further described below.” [0015]
A personalized model based on generic model for treatment…
“The determination of the personalized disease progression models involved a Bayesian estimator that modifies the reference disease progression models and evaluates the personal model parameters based on the pre-treatment patient's data, by using functions constructed from the analysis of the training data. The Bayesian estimator produces a personalized model, based on the population generic NSCLC model, but whose parameters are particularized for the given patient. A Bayesian predictor was used in simulating the personalized disease progression model to predict the patient's short- and long-term effects, as materialized in tumor progression, survival, and response to endpoint(s). The simulation output of the Bayesian predictor was converted by a Report Generator into a descriptive graphical/textual report, providing definitive, clinically critical answers to the prognosis questions (progression-free probability at time X, survival time) and treatment queries (tumor size after a given therapy, response for the specific regimen, time to development of resistance to a drug, etc.). The above algorithms were developed using the training-designated part of the clinical datasets.
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to change (deform) a generic model as taught by Kheifetz et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Kheifetz et al. who teaches the benefits of changing (deforming) a generic model to a personalized model.
Regarding claim 12
The treatment method of claim 11 wherein both the generic efficacy-estimation function and each patient-specific efficacy-estimation function receive, as inputs, a set of control variables and a patient-treatment-information instance, and
{
From Applicant’s specification on “control variable”…
“… Control variables may include instructions and directions to treatment providers and therapists. A control-variable vector v 110 contains values for controlling or instructing devices, systems and personnel to apply a particular type of treatment to a particular patient, and thus represents a treatment plan or a therapy plan. In Figure 1, curved arrows, such as curved arrow 112, represent input of control variable-vector values to devices, systems, and personnel within a treatment facility to effect a treatment or therapy” [0031]
Therefore, just about any variable that is an input to devices, systems, or personnel to effect a treatment.
}
Langheier et al. teaches:
Factors (input control variables) to predict outcome and individual (patient) analyzed (patient information instance)….
“Predictive models are commonly used to predict medical outcomes. Such models are based on statistical data obtained from populations of individuals that are identified as having or not having a particular medical outcome. Data regarding the population of individuals is typically analyzed to identify factors that predict the outcome. The factors may be combined in a mathematical equation or used to generate a posterior distribution to predict the outcome. In order to predict whether an individual has a particular outcome, the individual may be analyzed to determine the presence of one or more factors (variables). The model may then be applied to the individual to determine a likelihood that the individual will have the particular medical outcome or survival time.” [0003]
Patient therapeutic (treatment) recommendations…
“As will be described in detail below, the model and the outcomes may be used to provide healthcare-related decision support. For example, decision support module 104 may output a set of potential outcomes associated with a proposed therapeutic regimen and probabilities or risk scores associated with each outcome. The set of potential outcomes may be sorted by disease or therapeutic category. Other outcomes that may be generated by decision support module 104 include outcomes and therapeutic recommendations analyzed for the patient in the past, new outcomes and recommendations, and outcomes not yet analyzed. In addition to using a final model to predict outcomes for an individual, decision support module 104 may generate statistics on risk of an aggregate subpopulation of people versus risk of the complete population for the outcome.” [0038]
output an estimate of the treatment efficacy that would obtain were the patient represented by the patient-treatment- information instance treated according to the control variables.
Generate (output) probabilities (estimate) of outcomes (treatment)…
“Still another problem associated with conventional predictive modeling is the inability to simultaneously predict more than a single outcome, including the original medical problem, the efficacy of different treatments and adverse effects of different treatment strategies to resolve that problem. For example, conventional predictive modeling systems typically predict the likelihood that an individual will have a particular outcome, such as a disease. It may be desirable to generate multiple probabilities or likelihoods associated with different outcomes for an individual. In addition, it may be desirable to evaluate different treatment and testing strategies and the effects of these strategies on the likelihoods associated with the different outcomes, and recommend the optimal overall strategy or decision path. Current predictive modeling systems do not provide this flexibility.” [0008]
Regarding claim 20
A system that implements the method of claim 1, the system comprising:
one or more of local computer systems and remote computer systems that execute an application the implements the method of claim 1; and
Langheier et al. teaches:
Fig. 2, ref. 204 and 208 as computers…
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one or more databases that store treatment data and patient data.
Model results, therefore efficacy, storage…
“Model Results Storage” [0060]
Example of databases….
“Biomarker causality validation system 102 searches medical literature (i.e., Medline) and genome-disease association databases (i.e., OMIM--Online Mendelian Inheritance in Man) for the outcome of interest (i.e., anemia, chemotherapy), collects additional data on the potential biomarkers found from molecular information databases (i.e., Gene, Genome, SNP, etc), and stores the data in the potential biomarkers section of the biomarker causality library. The following are examples of outcomes and potential biomarkers that may be identified by biomarker causality validation system 102:” [0129]
Claims 3-9 and 13-19 are rejected under 35 U.S.C. 103 as being unpatentable over the combined references in section (9) above in further view of Pub. No. US 2025/0292904 to Wolber et al.
Regarding claim 3
The treatment method of claim 2
wherein the generic efficacy-estimation function is implemented as a neural network, by another type of trainable computational entity, or by a combination of trainable computational entities; and
Langheier et al. teaches:
Example of another type with training sets
“Leave-one-out cross-validation, testing and training sets and bootstrapping are used to check the predictive performance of each of the selected models. In each step one or parts of the sample are held out of the estimation and are predicted after the model is fitted. The predictive algorithm can then be evaluated by generating a Receiver Operating Curve and by calculating the concordance index (c-index). The highest sensitivity (low false negatives) and highest specificity (high true positives) predictive models possible are identified.” [0059]
See Neural Network and Training below.
wherein the generic efficacy-estimation function is trained to produce an estimate of the treatment efficacy that would obtain were the patient represented by the patient-treatment- information instance treated according to the control variables.
Check (produce) predictive performance (treatment efficacy) of the models…
“Leave-one-out cross-validation, testing and training sets and bootstrapping are used to check the predictive performance of each of the selected models. In each step one or parts of the sample are held out of the estimation and are predicted after the model is fitted. The predictive algorithm can then be evaluated by generating a Receiver Operating Curve and by calculating the concordance index (c-index). The highest sensitivity (low false negatives) and highest specificity (high true positives) predictive models possible are identified.” [0059]
See Neural Network and Training below.
Neural Network and Training
The combined references teach training. They do not teach neural network.
Wolber et al. also in the business of training teaches:
Neural network and training…
“The term “deep learning” is a machine learning technique that utilizes multiple data processing layers to recognize various structures in data sets and classify the data sets with high accuracy. A deep learning network (DLN), also referred to as a deep neural network (DNN), can be a training network (e.g., a training network model or device) that learns patterns based on a plurality of inputs and outputs. A deep learning network/deep neural network can be a deployed network (e.g., a deployed network model or device) that is generated from the training network and provides an output in response to an input.” [0032]
Training and prediction (estimate) to determine treatment…
“Using the dataset (e.g., resampled or otherwise), a model can be trained and tested to generate a prediction. For example, when recall maximization is desired as the model selection criterion, the dataset can be used to train an RF model. When F1-score maximization is desired as the model selection criterion, the dataset can be used to train a GB model, for example. As such, the model selection criterion can be used to determine a desired goal, target, or focus, such as recall maximization, F1-score maximization, precision maximization, etc. For example, a model generated to configure a cohort of patients for a clinical trial may be selected or otherwise set to admit many patients into the trial. As another example, a model generated to determine a treatment plan for a patient can be selected to proceed cautiously (or alternatively, aggressively) with treatment and associated risk based on a likelihood of toxicity balanced with a likelihood of efficacy. For example, a high risk of toxicity combined with a low chance of efficacy may keep a patient from an immunotherapy treatment, while a low risk of toxicity paired with a chance of efficacy encourages the patient to begin (or continue) the immunotherapy treatment. A relatively even balance of toxicity risk and efficacy probability can leave selection to the interests, preferences, and/or objectives of the patient and/or their physician, for example. Additionally, safety monitoring, blood tests, and/or other measures (e.g., periodic reevaluation, check-in, etc.) may be ordered depending on the likelihood of toxicity, in order to detect any toxicities early and be able to manage them while continuing treatment. In certain examples, the trained model can be validated, such as with Leave-One-Out Cross-Validation, where each sample is predicted individually with the rest as the training set.” [0047]
Model trained on broader population (generic model) and outputs for a given patient…
“Using the precision 2030 and recall 2040 characteristics of the model represented in FIG. 20, the patient selection criterion threshold 2010 can be selected to drive clinical trial cohort selection, patient treatment determination, etc. The model, trained on a broader population of prior healthcare data, outputs a probability of survival for a given patient by processing that particular patient's healthcare data to generate a predictive output representative of efficacy (using overall survivability as a surrogate or substitute for efficacy of the immunotherapy treatment) for that patient. The model output for that patient is evaluated according to the determined threshold 2010 to determine whether or not the patient is an acceptable candidate for clinical trial cohort, immunotherapy treatment regime, etc. Based on clinical trial motivations and constraints, a patient's personal circumstances and desires, etc., the patient selection threshold 2010 can be adjusted to be more or less inclusive/exclusive to include or exclude the particular patient from the clinical trial cohort, treatment plan, etc., taking on an associated risk of inaccuracy/accuracy as the threshold is moved according to the precision 2030 and recall 2040 curves for the model.” [0200]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to use a neural network with training as taught by Wolber et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Wolber et al. who teaches the benefits of using neural network for model analysis.
Regarding claim 4
The treatment method of claim 3 wherein the generic efficacy-estimation function is trained using treatment information collected from many different patients over time periods of days, weeks, months, or years.
Langheier et al. teaches:
Update based on day…
“Updated model parameters and clinical factors after the addition of new patients on a particular day; highlighting of new factors as potential contributors to disease physiology or health protection” [0110]
Regarding claim 5
The treatment method of claim 4 wherein a patient-specific efficacy-estimation function comprises:
the generic efficacy-estimation function;
Langheier et al. teaches:
Example of equation (function) to predict (estimate) outcome….
“Predictive models are commonly used to predict medical outcomes. Such models are based on statistical data obtained from populations of individuals that are identified as having or not having a particular medical outcome. Data regarding the population of individuals is typically analyzed to identify factors that predict the outcome. The factors may be combined in a mathematical equation or used to generate a posterior distribution to predict the outcome. In order to predict whether an individual has a particular outcome, the individual may be analyzed to determine the presence of one or more factors (variables). The model may then be applied to the individual to determine a likelihood that the individual will have the particular medical outcome or survival time.” [0003]
a control-variable transform; and
One example of datasets (variable) transformation…
“High-volume molecular datasets such as Affymetrix microarray data are prepared using the MAS 5.0 method, log base 2 transformation and quantile normalization, followed by the removal of low expressing and non-varying genes. Data reduction to allow for effective model searching is achieved through k-means clustering followed by principal component analysis (PCA). These composite factors are then compared alongside other potential predictors of a given outcome as part of model development.” [0076]
an efficacy-estimation modifier.
Example of modifies medications and percent change…
“FIG. 9C illustrates an exemplary modify treatment plan screen that may be displayed by chemotherapy solutions module 108 if the user modifies any of the medications illustrated in FIG. 9C. In FIG. 9C, it can be seen that the individual's risk of febrile neutropenia has decreased from 27% to 10% as a result in changes of dosage amounts of some of the drugs displayed by the dashboard.” [0118]
Regarding claim 6
The treatment method of claim 5 wherein the patient-specific efficacy-estimation function:
receives, as inputs, a set of control variables and a patient-treatment-information instance;
Langheier et al. teaches:
Factors (input control variables) to predict outcome and individual (patient) analyzed (patient information instance)….
“Predictive models are commonly used to predict medical outcomes. Such models are based on statistical data obtained from populations of individuals that are identified as having or not having a particular medical outcome. Data regarding the population of individuals is typically analyzed to identify factors that predict the outcome. The factors may be combined in a mathematical equation or used to generate a posterior distribution to predict the outcome. In order to predict whether an individual has a particular outcome, the individual may be analyzed to determine the presence of one or more factors (variables). The model may then be applied to the individual to determine a likelihood that the individual will have the particular medical outcome or survival time.” [0003]
Patient therapeutic (treatment) recommendations…
“As will be described in detail below, the model and the outcomes may be used to provide healthcare-related decision support. For example, decision support module 104 may output a set of potential outcomes associated with a proposed therapeutic regimen and probabilities or risk scores associated with each outcome. The set of potential outcomes may be sorted by disease or therapeutic category. Other outcomes that may be generated by decision support module 104 include outcomes and therapeutic recommendations analyzed for the patient in the past, new outcomes and recommendations, and outcomes not yet analyzed. In addition to using a final model to predict outcomes for an individual, decision support module 104 may generate statistics on risk of an aggregate subpopulation of people versus risk of the complete population for the outcome.” [0038]
uses the control-variable transform to transform the received set of control variables;
One example of datasets (variable) transformation…
“High-volume molecular datasets such as Affymetrix microarray data are prepared using the MAS 5.0 method, log base 2 transformation and quantile normalization, followed by the removal of low expressing and non-varying genes. Data reduction to allow for effective model searching is achieved through k-means clustering followed by principal component analysis (PCA). These composite factors are then compared alongside other potential predictors of a given outcome as part of model development.” [0076]
inputs the transformed set of control variables to the generic efficacy-estimation function;
“Yet another problem associated with conventional predictive modeling include the inability to validate biomarkers and to update predictive models based on newly validated biomarkers. As described above, new factor identification requires lengthy peer review and dissemination through traditional channels. There is no ability in current predictive modeling systems to rapidly validate new biomarkers and to automatically update predictive models based on newly validated biomarkers” [0007]
“Biomarker causality identification system 102 may automatically extract biomarkers from clinical literature and store that data in clinical data warehouse 112 for use by predictive modeler 100. Decision support modules 104-110 may apply the models generated by predictive modeler 100 to predict clinical or medical outcomes for individuals. In the illustrated example, a coronary surgery solutions module 106 uses a model to predict outcomes relating to coronary surgery. A chemotherapy solutions module 108 predicts outcomes relating to chemotherapy. Decision support modules 104 and 110 are intended to be generic to indicate that the models generated by predictive modeler 100 may be applied to any appropriate clinical or medical solution. Modules 104-110 may be used by surgeons, physicians, and individuals to predict medical outcomes for a patient. Examples of decision support modules will be described in detail below.” [0030]
receives an efficacy estimate output by the generic efficacy-estimation function;
Generic models to predict (estimate) appropriate solution (efficacy)…
“Biomarker causality identification system 102 may automatically extract biomarkers from clinical literature and store that data in clinical data warehouse 112 for use by predictive modeler 100. Decision support modules 104-110 may apply the models generated by predictive modeler 100 to predict clinical or medical outcomes for individuals. In the illustrated example, a coronary surgery solutions module 106 uses a model to predict outcomes relating to coronary surgery. A chemotherapy solutions module 108 predicts outcomes relating to chemotherapy. Decision support modules 104 and 110 are intended to be generic to indicate that the models generated by predictive modeler 100 may be applied to any appropriate clinical or medical solution. Modules 104-110 may be used by surgeons, physicians, and individuals to predict medical outcomes for a patient. Examples of decision support modules will be described in detail below.” [0030]
uses the efficacy-estimation modifier to modify the efficacy estimate output by the generic efficacy-estimation function; and
Dynamically update (modify) of models (functions)…
“Predictive modeler 100 may automate processing of clinical data as an ongoing assembly line and dynamically update predictive models with a focus on optimizing predictions. Some of the components of setting up such a "factory line" of data analysis for the creation of predictive models have been carefully researched, such as gene-expression analysis, various model search and selection methods, Bayesian model fitting parameters, the validity and usefulness of model averaging, yet, no solution is available which:…” [0067]
Predictive modeling (efficacy estimation)…
“…Predictive modeling links to and powers a Decision Support system, which includes the following outputs: A set of outcomes being analyzed and predicted for the patient. List shows outcomes which have been analyzed in the past, new outcomes analyzed this time, and outcome not analyzed; organized by disease and therapeutic categories…” [0092] – [0094]
returns the modified efficacy estimate as output from the patient-specific efficacy- estimation function.
Predictive modeling (efficacy estimation)…
“…Predictive modeling links to and powers a Decision Support system, which includes the following outputs: A set of outcomes being analyzed and predicted for the patient. List shows outcomes which have been analyzed in the past, new outcomes analyzed this time, and outcome not analyzed; organized by disease and therapeutic categories…” [0092] – [0094]
Regarding claim 7
The treatment method of claim 6 wherein the efficacy-estimation modifier adds a constant value to the efficacy estimate output by the generic efficacy-estimation function.
Langheier et al. teaches:
Additional metric applied to the model such as cost or risk (constant) of a test….
“…In step 506, the models are arranged in a hierarchical manner based on relative predictive value and at least one additional metric associated with applying each model to an individual. The additional metric may be monetary cost to the individual or to an organization of determining whether the individual possesses a particular factor. In another example, the additional metric may be risk to the individual associated with performing a test to determine whether or not the individual possesses the factor. The additional metric may be any suitable factor other than predictive value for arranging and applying predictive models in a hierarchical manner.” [0111]
Regarding claim 8
The treatment method of claim 7 wherein a patient-specific efficacy-estimation function is generated as a deformation of the generic efficacy-estimation function using a constrained optimization process that optimizes the control-variable transform and the efficacy-estimation modifier to align the output of the patient-specific efficacy-estimation function with the stored treatment efficacy or efficacies and treatment plan or plans.
Langheier et al. teaches:
Dynamically update (deformation) of models…
“Predictive modeler 100 may automate processing of clinical data as an ongoing assembly line and dynamically update predictive models with a focus on optimizing predictions. Some of the components of setting up such a "factory line" of data analysis for the creation of predictive models have been carefully researched, such as gene-expression analysis, various model search and selection methods, Bayesian model fitting parameters, the validity and usefulness of model averaging, yet, no solution is available which:…” [0067]
Predictive modeling (efficacy estimation)…
“…Predictive modeling links to and powers a Decision Support system, which includes the following outputs: A set of outcomes being analyzed and predicted for the patient. List shows outcomes which have been analyzed in the past, new outcomes analyzed this time, and outcome not analyzed; organized by disease and therapeutic categories…” [0092] – [0094]
Cost/benefit ratio (constrained optimization process) and determine optimal intervention strategy (treatment plan)….
“As stated above, decision support module 104 may automatically incorporate scores from multiple models into a decision tree to enable an individual to select an optimal intervention strategy. FIG. 12 illustrates an example of such a decision tree. In FIG. 12, the decision tree includes branches that correspond to outcomes related to febrile neutropenia. The branches in FIG. 12 are only a portion of the total decision tree that relates to one approach of many approaches to using predictive modeling to evaluate treatment strategies. Other branches, such as not testing and not treating or not testing and treating the patient are not shown for simplicity. The % symbols on each branch correspond to probabilities associated with each branch. The # symbols represent quality adjusted life years. In order to assess the summary benefit and cost for each branch, the probabilities for each branch are multiplied by the total cost and total benefit. The circles in each branch mean that the values being calculated for the sub-branches should be added. A cost/benefit ratio can be calculated for each branch by dividing the total cost by the total benefit. Branches can then be compared to determine the optimal intervention strategy. The probabilities output from a predictive model used by decision support module 104 may be automatically incorporated into a decision tree, such as that illustrated in FIG. 12, to evaluate different outcomes and treatment strategies.” [0146]
Regarding claim 9
The treatment method of claim I wherein selecting a treatment plan from among the next treatment plan generated following a final treatment experiment and a stored treatment plan further comprises:
selecting the next treatment plan generated following the final treatment experiment when an aggressive approach is indicated; and
See Aggressive and Conservative below.
selecting a most effective stored treatment plan from among the stored treatment plans when a conservative approach is indicated.
See Aggressive and Conservative below.
Aggressive and Conservative
The combined references teach treatment prediction. They also teach risk. They do not teach conservative and aggressive treatment.
Wobler et al. also in the business of treatment prediction teaches:
Cautiously (conservative) and aggressively treatment…
“Using the dataset (e.g., resampled or otherwise), a model can be trained and tested to generate a prediction. For example, when recall maximization is desired as the model selection criterion, the dataset can be used to train an RF model. When F1-score maximization is desired as the model selection criterion, the dataset can be used to train a GB model, for example. As such, the model selection criterion can be used to determine a desired goal, target, or focus, such as recall maximization, F1-score maximization, precision maximization, etc. For example, a model generated to configure a cohort of patients for a clinical trial may be selected or otherwise set to admit many patients into the trial. As another example, a model generated to determine a treatment plan for a patient can be selected to proceed cautiously (or alternatively, aggressively) with treatment and associated risk based on a likelihood of toxicity balanced with a likelihood of efficacy. For example, a high risk of toxicity combined with a low chance of efficacy may keep a patient from an immunotherapy treatment, while a low risk of toxicity paired with a chance of efficacy encourages the patient to begin (or continue) the immunotherapy treatment. A relatively even balance of toxicity risk and efficacy probability can leave selection to the interests, preferences, and/or objectives of the patient and/or their physician, for example. Additionally, safety monitoring, blood tests, and/or other measures (e.g., periodic reevaluation, check-in, etc.) may be ordered depending on the likelihood of toxicity, in order to detect any toxicities early and be able to manage them while continuing treatment. In certain examples, the trained model can be validated, such as with Leave-One-Out Cross-Validation, where each sample is predicted individually with the rest as the training set.” [0047]
“In certain examples, the objective or motivation can change over time (e.g., treatment starts cautiously for a patient and becomes more aggressive, the patient's circumstances change to warrant a treatment with less toxicity and/or greater efficacy, a drug company does an initial screen and then wants to switch to a broader confirmation, etc.). As such, model loading can be repeated with different model selection criterion over time.” [0174]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to have conservative and aggressive treatment as taught by Wolber et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Wolber et al. who teaches the benefits of both conservative and aggressive treatment and consideration of tradeoffs for patients between risk and benefit of treatment.
Regarding claim 13
The treatment method of claim 12
wherein the generic efficacy-estimation function is implemented as a neural network, by another type of trainable computational entity, or by a combination of trainable computational entities; and
Langheier et al. teaches:
Example of another type with training sets
“Leave-one-out cross-validation, testing and training sets and bootstrapping are used to check the predictive performance of each of the selected models. In each step one or parts of the sample are held out of the estimation and are predicted after the model is fitted. The predictive algorithm can then be evaluated by generating a Receiver Operating Curve and by calculating the concordance index (c-index). The highest sensitivity (low false negatives) and highest specificity (high true positives) predictive models possible are identified.” [0059]
See Neural Network and Training below.
wherein the generic efficacy-estimation function is trained to produce an estimate of the treatment efficacy that would obtain were the patient represented by the patient-treatment- information instance treated according to the control variables.
Check (produce) predictive performance (treatment efficacy) of the models…
“Leave-one-out cross-validation, testing and training sets and bootstrapping are used to check the predictive performance of each of the selected models. In each step one or parts of the sample are held out of the estimation and are predicted after the model is fitted. The predictive algorithm can then be evaluated by generating a Receiver Operating Curve and by calculating the concordance index (c-index). The highest sensitivity (low false negatives) and highest specificity (high true positives) predictive models possible are identified.” [0059]
See Neural Network and Training below.
Neural Network and Training
The combined references teach training. They do not teach neural network.
Wolber et al. also in the business of training teaches:
Neural network and training…
“The term “deep learning” is a machine learning technique that utilizes multiple data processing layers to recognize various structures in data sets and classify the data sets with high accuracy. A deep learning network (DLN), also referred to as a deep neural network (DNN), can be a training network (e.g., a training network model or device) that learns patterns based on a plurality of inputs and outputs. A deep learning network/deep neural network can be a deployed network (e.g., a deployed network model or device) that is generated from the training network and provides an output in response to an input.” [0032]
Training and prediction (estimate) to determine treatment…
“Using the dataset (e.g., resampled or otherwise), a model can be trained and tested to generate a prediction. For example, when recall maximization is desired as the model selection criterion, the dataset can be used to train an RF model. When F1-score maximization is desired as the model selection criterion, the dataset can be used to train a GB model, for example. As such, the model selection criterion can be used to determine a desired goal, target, or focus, such as recall maximization, F1-score maximization, precision maximization, etc. For example, a model generated to configure a cohort of patients for a clinical trial may be selected or otherwise set to admit many patients into the trial. As another example, a model generated to determine a treatment plan for a patient can be selected to proceed cautiously (or alternatively, aggressively) with treatment and associated risk based on a likelihood of toxicity balanced with a likelihood of efficacy. For example, a high risk of toxicity combined with a low chance of efficacy may keep a patient from an immunotherapy treatment, while a low risk of toxicity paired with a chance of efficacy encourages the patient to begin (or continue) the immunotherapy treatment. A relatively even balance of toxicity risk and efficacy probability can leave selection to the interests, preferences, and/or objectives of the patient and/or their physician, for example. Additionally, safety monitoring, blood tests, and/or other measures (e.g., periodic reevaluation, check-in, etc.) may be ordered depending on the likelihood of toxicity, in order to detect any toxicities early and be able to manage them while continuing treatment. In certain examples, the trained model can be validated, such as with Leave-One-Out Cross-Validation, where each sample is predicted individually with the rest as the training set.” [0047]
Model trained on broader population (generic model) and outputs for a given patient…
“Using the precision 2030 and recall 2040 characteristics of the model represented in FIG. 20, the patient selection criterion threshold 2010 can be selected to drive clinical trial cohort selection, patient treatment determination, etc. The model, trained on a broader population of prior healthcare data, outputs a probability of survival for a given patient by processing that particular patient's healthcare data to generate a predictive output representative of efficacy (using overall survivability as a surrogate or substitute for efficacy of the immunotherapy treatment) for that patient. The model output for that patient is evaluated according to the determined threshold 2010 to determine whether or not the patient is an acceptable candidate for clinical trial cohort, immunotherapy treatment regime, etc. Based on clinical trial motivations and constraints, a patient's personal circumstances and desires, etc., the patient selection threshold 2010 can be adjusted to be more or less inclusive/exclusive to include or exclude the particular patient from the clinical trial cohort, treatment plan, etc., taking on an associated risk of inaccuracy/accuracy as the threshold is moved according to the precision 2030 and recall 2040 curves for the model.” [0200]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to use a neural network with training as taught by Wolber et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Wolber et al. who teaches the benefits of using neural network for model analysis.
Regarding claim 14
The treatment method of claim 13 wherein the generic efficacy-estimation function is trained using treatment information collected from many different patients over time periods of days, weeks, months, or years.
Langheier et al. teaches:
Update based on day…
“Updated model parameters and clinical factors after the addition of new patients on a particular day; highlighting of new factors as potential contributors to disease physiology or health protection” [0110]
Regarding claim 15
The treatment method of claim 14 wherein a patient-specific efficacy-estimation function comprises:
the generic efficacy-estimation function;
Langheier et al. teaches:
Example of equation (function) to predict (estimate) outcome….
“Predictive models are commonly used to predict medical outcomes. Such models are based on statistical data obtained from populations of individuals that are identified as having or not having a particular medical outcome. Data regarding the population of individuals is typically analyzed to identify factors that predict the outcome. The factors may be combined in a mathematical equation or used to generate a posterior distribution to predict the outcome. In order to predict whether an individual has a particular outcome, the individual may be analyzed to determine the presence of one or more factors (variables). The model may then be applied to the individual to determine a likelihood that the individual will have the particular medical outcome or survival time.” [0003]
a control-variable transform; and
One example of datasets (variable) transformation…
“High-volume molecular datasets such as Affymetrix microarray data are prepared using the MAS 5.0 method, log base 2 transformation and quantile normalization, followed by the removal of low expressing and non-varying genes. Data reduction to allow for effective model searching is achieved through k-means clustering followed by principal component analysis (PCA). These composite factors are then compared alongside other potential predictors of a given outcome as part of model development.” [0076]
an efficacy-estimation modifier.
Example of modifies medications and percent change…
“FIG. 9C illustrates an exemplary modify treatment plan screen that may be displayed by chemotherapy solutions module 108 if the user modifies any of the medications illustrated in FIG. 9C. In FIG. 9C, it can be seen that the individual's risk of febrile neutropenia has decreased from 27% to 10% as a result in changes of dosage amounts of some of the drugs displayed by the dashboard.” [0118]
Regarding claim 16
The treatment method of claim 15 wherein the patient-specific efficacy-estimation function:
receives, as inputs, a set of control variables and a patient-treatment-information instance;
Langheier et al. teaches:
Factors (input control variables) to predict outcome and individual (patient) analyzed (patient information instance)….
“Predictive models are commonly used to predict medical outcomes. Such models are based on statistical data obtained from populations of individuals that are identified as having or not having a particular medical outcome. Data regarding the population of individuals is typically analyzed to identify factors that predict the outcome. The factors may be combined in a mathematical equation or used to generate a posterior distribution to predict the outcome. In order to predict whether an individual has a particular outcome, the individual may be analyzed to determine the presence of one or more factors (variables). The model may then be applied to the individual to determine a likelihood that the individual will have the particular medical outcome or survival time.” [0003]
Patient therapeutic (treatment) recommendations…
“As will be described in detail below, the model and the outcomes may be used to provide healthcare-related decision support. For example, decision support module 104 may output a set of potential outcomes associated with a proposed therapeutic regimen and probabilities or risk scores associated with each outcome. The set of potential outcomes may be sorted by disease or therapeutic category. Other outcomes that may be generated by decision support module 104 include outcomes and therapeutic recommendations analyzed for the patient in the past, new outcomes and recommendations, and outcomes not yet analyzed. In addition to using a final model to predict outcomes for an individual, decision support module 104 may generate statistics on risk of an aggregate subpopulation of people versus risk of the complete population for the outcome.” [0038]
uses the control-variable transform to transform the received set of control variables;
One example of datasets (variable) transformation…
“High-volume molecular datasets such as Affymetrix microarray data are prepared using the MAS 5.0 method, log base 2 transformation and quantile normalization, followed by the removal of low expressing and non-varying genes. Data reduction to allow for effective model searching is achieved through k-means clustering followed by principal component analysis (PCA). These composite factors are then compared alongside other potential predictors of a given outcome as part of model development.” [0076]
inputs the transformed set of control variables to the generic efficacy-estimation function;
“Yet another problem associated with conventional predictive modeling include the inability to validate biomarkers and to update predictive models based on newly validated biomarkers. As described above, new factor identification requires lengthy peer review and dissemination through traditional channels. There is no ability in current predictive modeling systems to rapidly validate new biomarkers and to automatically update predictive models based on newly validated biomarkers” [0007]
“Biomarker causality identification system 102 may automatically extract biomarkers from clinical literature and store that data in clinical data warehouse 112 for use by predictive modeler 100. Decision support modules 104-110 may apply the models generated by predictive modeler 100 to predict clinical or medical outcomes for individuals. In the illustrated example, a coronary surgery solutions module 106 uses a model to predict outcomes relating to coronary surgery. A chemotherapy solutions module 108 predicts outcomes relating to chemotherapy. Decision support modules 104 and 110 are intended to be generic to indicate that the models generated by predictive modeler 100 may be applied to any appropriate clinical or medical solution. Modules 104-110 may be used by surgeons, physicians, and individuals to predict medical outcomes for a patient. Examples of decision support modules will be described in detail below.” [0030]
receives an efficacy estimate output by the generic efficacy-estimation function;
Generic models to predict (estimate) appropriate solution (efficacy)…
“Biomarker causality identification system 102 may automatically extract biomarkers from clinical literature and store that data in clinical data warehouse 112 for use by predictive modeler 100. Decision support modules 104-110 may apply the models generated by predictive modeler 100 to predict clinical or medical outcomes for individuals. In the illustrated example, a coronary surgery solutions module 106 uses a model to predict outcomes relating to coronary surgery. A chemotherapy solutions module 108 predicts outcomes relating to chemotherapy. Decision support modules 104 and 110 are intended to be generic to indicate that the models generated by predictive modeler 100 may be applied to any appropriate clinical or medical solution. Modules 104-110 may be used by surgeons, physicians, and individuals to predict medical outcomes for a patient. Examples of decision support modules will be described in detail below.” [0030]
uses the efficacy-estimation modifier to modify the efficacy estimate output by the generic efficacy-estimation function; and
Dynamically update (modify) of models (functions)…
“Predictive modeler 100 may automate processing of clinical data as an ongoing assembly line and dynamically update predictive models with a focus on optimizing predictions. Some of the components of setting up such a "factory line" of data analysis for the creation of predictive models have been carefully researched, such as gene-expression analysis, various model search and selection methods, Bayesian model fitting parameters, the validity and usefulness of model averaging, yet, no solution is available which:…” [0067]
Predictive modeling (efficacy estimation)…
“…Predictive modeling links to and powers a Decision Support system, which includes the following outputs: A set of outcomes being analyzed and predicted for the patient. List shows outcomes which have been analyzed in the past, new outcomes analyzed this time, and outcome not analyzed; organized by disease and therapeutic categories…” [0092] – [0094]
returns the modified efficacy estimate as output from the patient-specific efficacy- estimation function.
Predictive modeling (efficacy estimation)…
“…Predictive modeling links to and powers a Decision Support system, which includes the following outputs: A set of outcomes being analyzed and predicted for the patient. List shows outcomes which have been analyzed in the past, new outcomes analyzed this time, and outcome not analyzed; organized by disease and therapeutic categories…” [0092] – [0094]
Regarding claim 17
The treatment method of claim 16 wherein the efficacy-estimation modifier adds a constant value to the efficacy estimate output by the generic efficacy-estimation function.
Langheier et al. teaches:
Additional metric applied to the model such as cost or risk (constant) of a test….
“…In step 506, the models are arranged in a hierarchical manner based on relative predictive value and at least one additional metric associated with applying each model to an individual. The additional metric may be monetary cost to the individual or to an organization of determining whether the individual possesses a particular factor. In another example, the additional metric may be risk to the individual associated with performing a test to determine whether or not the individual possesses the factor. The additional metric may be any suitable factor other than predictive value for arranging and applying predictive models in a hierarchical manner.” [0111]
Regarding claim 18
The treatment method of claim 17 wherein a patient-specific efficacy-estimation function is generated as a deformation of the generic efficacy-estimation function using a constrained optimization process that optimizes the control-variable transform and the efficacy-estimation modifier to align the output of the patient-specific efficacy-estimation function with the stored treatment efficacy or efficacies and treatment plan or plans.
Langheier et al. teaches:
Dynamically update (deformation) of models…
“Predictive modeler 100 may automate processing of clinical data as an ongoing assembly line and dynamically update predictive models with a focus on optimizing predictions. Some of the components of setting up such a "factory line" of data analysis for the creation of predictive models have been carefully researched, such as gene-expression analysis, various model search and selection methods, Bayesian model fitting parameters, the validity and usefulness of model averaging, yet, no solution is available which:…” [0067]
Predictive modeling (efficacy estimation)…
“…Predictive modeling links to and powers a Decision Support system, which includes the following outputs: A set of outcomes being analyzed and predicted for the patient. List shows outcomes which have been analyzed in the past, new outcomes analyzed this time, and outcome not analyzed; organized by disease and therapeutic categories…” [0092] – [0094]
Cost/benefit ratio (constrained optimization process) and determine optimal intervention strategy (treatment plan)….
“As stated above, decision support module 104 may automatically incorporate scores from multiple models into a decision tree to enable an individual to select an optimal intervention strategy. FIG. 12 illustrates an example of such a decision tree. In FIG. 12, the decision tree includes branches that correspond to outcomes related to febrile neutropenia. The branches in FIG. 12 are only a portion of the total decision tree that relates to one approach of many approaches to using predictive modeling to evaluate treatment strategies. Other branches, such as not testing and not treating or not testing and treating the patient are not shown for simplicity. The % symbols on each branch correspond to probabilities associated with each branch. The # symbols represent quality adjusted life years. In order to assess the summary benefit and cost for each branch, the probabilities for each branch are multiplied by the total cost and total benefit. The circles in each branch mean that the values being calculated for the sub-branches should be added. A cost/benefit ratio can be calculated for each branch by dividing the total cost by the total benefit. Branches can then be compared to determine the optimal intervention strategy. The probabilities output from a predictive model used by decision support module 104 may be automatically incorporated into a decision tree, such as that illustrated in FIG. 12, to evaluate different outcomes and treatment strategies.” [0146]
Regarding claim 19
The treatment method of claim 11 wherein selecting a treatment plan from among the next treatment plan generated following a final treatment experiment and a stored treatment plan further comprises:
selecting the next treatment plan generated following the final treatment experiment when an aggressive approach is indicated; and
See Aggressive and Conservative below.
selecting a most effective stored treatment plan from among the stored treatment plans when a conservative approach is indicated.
See Aggressive and Conservative below.
Aggressive and Conservative
The combined references teach treatment prediction. They also teach risk. They do not teach conservative and aggressive treatment.
Wobler et al. also in the business of treatment prediction teaches:
Cautiously (conservative) and aggressively treatment…
“Using the dataset (e.g., resampled or otherwise), a model can be trained and tested to generate a prediction. For example, when recall maximization is desired as the model selection criterion, the dataset can be used to train an RF model. When F1-score maximization is desired as the model selection criterion, the dataset can be used to train a GB model, for example. As such, the model selection criterion can be used to determine a desired goal, target, or focus, such as recall maximization, F1-score maximization, precision maximization, etc. For example, a model generated to configure a cohort of patients for a clinical trial may be selected or otherwise set to admit many patients into the trial. As another example, a model generated to determine a treatment plan for a patient can be selected to proceed cautiously (or alternatively, aggressively) with treatment and associated risk based on a likelihood of toxicity balanced with a likelihood of efficacy. For example, a high risk of toxicity combined with a low chance of efficacy may keep a patient from an immunotherapy treatment, while a low risk of toxicity paired with a chance of efficacy encourages the patient to begin (or continue) the immunotherapy treatment. A relatively even balance of toxicity risk and efficacy probability can leave selection to the interests, preferences, and/or objectives of the patient and/or their physician, for example. Additionally, safety monitoring, blood tests, and/or other measures (e.g., periodic reevaluation, check-in, etc.) may be ordered depending on the likelihood of toxicity, in order to detect any toxicities early and be able to manage them while continuing treatment. In certain examples, the trained model can be validated, such as with Leave-One-Out Cross-Validation, where each sample is predicted individually with the rest as the training set.” [0047]
“In certain examples, the objective or motivation can change over time (e.g., treatment starts cautiously for a patient and becomes more aggressive, the patient's circumstances change to warrant a treatment with less toxicity and/or greater efficacy, a drug company does an initial screen and then wants to switch to a broader confirmation, etc.). As such, model loading can be repeated with different model selection criterion over time.” [0174]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to have conservative and aggressive treatment as taught by Wolber et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Wolber et al. who teaches the benefits of both conservative and aggressive treatment and consideration of tradeoffs for patients between risk and benefit of treatment.
Conclusion
The following prior art teaches estimating/predicting patient treatment plans…
WO-2016079506-A1; CA-2687562-A1; WO-2007124147-A1; WO-2017106686-A1; AU-2012308101-A1; CA-3215884-A1; US-11875903-B2; US-11386986-B2; US-11195616-B1; US-7266483-B2; US-20170351829-A1; US-20090006061-A1; US-20100332249-A1; US-20040122703-A1; US-20040122706-A1; US-20040122719-A1; US-20070118399-A1; US-20140052465-A1; US-20250127454-A1; US-20090070138-A1; US-20230187069-A1; US-20170199189-A1; US-20220059240-A1; US-20220172841-A1; US-20090131758-A1; US-20090299767-A1; US-20130024125-A1; US-20130024207-A1; US-20190333636-A1; US-20230099880-A1; US-20250356972-A1; US-20140279746-A1; US-20220375611-A1; US-20150019241-A1; US-20220044826-A1
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/KENNETH BARTLEY/Primary Examiner, Art Unit 3684