DETAILED CORRESPONDENCE
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This non-final office action on merits is in response to the Patent Application filed on 5 December 2022. Claims 1-75 are cancelled. Claims 76-106 are pending and considered below.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, or 365(c) is acknowledged. Examiner acknowledges the instant invention is a continuation of International Application No. PCT/US2021/035759, filed June 3, 2021, which claims the benefit of U.S. Provisional Patent Application No. 63/034,578, filed June 4, 2020, and U.S. Provisional Patent Application No. 63/094,478, filed October 21, 2020. Examiner notes that the provisional application filed June 4, 2020 does not include significant technical aspects however the provisional filed October 21, 2020 is technically descriptive in accord with the instant application. Therefore the priority date of the instant invention is given as October 21, 2020.
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 76-106 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
The claimed limitations, as per independent system, method and storage medium Claims 76, 91, and 106 include the steps of:
A system comprising a computer processor and a storage device having instructions stored thereon that are operable, when executed by the computer processor, to cause the computer processor to
(i) receive clinical data of a subject and a set of treatment options for a disease or disorder of the subject, wherein the set of treatment options corresponds to clinical outcomes having future uncertainty;
(ii) access a prediction module comprising a trained machine learning model that determines probabilistic predictions of clinical outcomes of the set of treatment options based at least in part on clinical data of test subjects; and
(iii) apply the prediction module to at least the clinical data of the subject to determine probabilistic predictions of clinical outcomes of the set of treatment options for the disease or disorder of the subject.
Examiner Note: underlined elements indicate additional elements of the claimed invention identified as performing the steps of the claimed invention.
Under Step One of the analysis under the Mayo framework, claims 91-105 is/are drawn to methods (i.e., a process), claims 76-90 is/are drawn to a system (i.e., a machine/manufacture), and claim 106 is/are drawn to a storage medium (i.e., a machine/manufacture). As such, claims 76-106 is/are drawn to one of the statutory categories of invention.
Under Revised Step 2A Prong One and MPEP 2106 of the analysis under the Mayo framework the claim(s) are determined to recite(s) the judicial exception of receive clinical data of a subject and a set of treatment options for a disease or disorder of the subject, wherein the set of treatment options corresponds to clinical outcomes having future uncertainty; determines probabilistic predictions of clinical outcomes of the set of treatment options based at least in part on clinical data of test subjects; and apply the prediction module to at least the clinical data of the subject to determine probabilistic predictions of clinical outcomes of the set of treatment options for the disease or disorder of the subject.
This judicial exception is similar to abstract ideas related to certain methods of organizing human activity such as managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions because the claimed invention analyzes data and provides treatment options determined by predicted clinical outcomes.
Under Step Revised Step 2A Prong Two and MPEP 2106 of the analysis under the Mayo Framework, the judicial exception expressed as the steps of the instant claims is not integrated into a practical application because the claims only recite one additional element, the element of using a processor or computing system including a local registry or memory to perform the steps of the claimed abstract idea. The processor is recited at a high-level of generality (i.e., as a generic processor performing generic computer functions to perform the claimed steps of the invention), and therefore the abstract idea amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element of performing the inventive steps with a generic computer does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus the claimed invention is directed to an abstract idea without a practical application.
Under step 2B of the Mayo analysis framework the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional limitations of performing the steps with a computer processor, a display module, and a memory storing machine executable instructions represents insignificant data gathering and data processing steps requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry. Applicant’s published written description paragraph [187] recites “computer system 1201 includes a central processing unit (CPU, also “processor” and “computer processor” herein) 1205, which can be a single core or multi core processor, or a plurality of processors for parallel processing. The computer system 1201 also includes memory or memory location 1210 (e.g., random-access memory, read-only memory, flash memory), electronic storage unit 1215 (e.g., hard disk), communication interface 1220 (e.g., network adapter) for communicating with one or more other systems, and peripheral devices 1225, such as cache, other memory, data storage and/or electronic display adapters,” written description paragraph [188] recites “network 1230 can include one or more computer servers, which can enable distributed computing, such as cloud computing. For example, one or more computer servers may enable cloud computing over the network 1230 (“the cloud”) to perform various aspects of analysis, calculation, and generation of the present disclosure, such as, for example, (i) receiving clinical data of a subject and a set of treatment options for a disease or disorder of the subject, (ii) accessing a prediction module comprising a trained machine learning model that determines probabilistic predictions of clinical outcomes of the set of treatment options based at least in part on clinical data of subjects, and (iii) applying the prediction module to clinical data of the subject, treatment features, and/or interaction terms to determine probabilistic predictions of clinical outcomes of the set of treatment options for the disease or disorder of the subject,” written description paragraph [189] recites “CPU 1205 may comprise one or more computer processors and/or one or more graphics processing units (GPUs). The CPU 1205 can execute a sequence of machine-readable instructions, which can be embodied in a program or software. The instructions may be stored in a memory location, such as the memory 1210. The instructions can be directed to the CPU 1205, which can subsequently program or otherwise configure the CPU 1205 to implement methods of the present disclosure,” written description paragraph [191] recites “storage unit 1215 can store files, such as drivers, libraries and saved programs. The storage unit 1215 can store user data, e.g., user preferences and user programs. The computer system 1201 in some cases can include one or more additional data storage units that may be external to the computer system 1201, such as located on a remote server that may be in communication with the computer system 1201 through an intranet or the Internet,” and written description paragraph [192] recites “computer system 1201 can communicate with one or more remote computer systems through the network 1230. For instance, the computer system 1201 can communicate with a remote computer system of a user. Examples of remote computer systems include personal computers (e.g., portable PC), slate or tablet PC’s (e.g., Apple® iPad, Samsung® Galaxy Tab), telephones, Smart phones (e.g., Apple® iPhone, Android-enabled device, Blackberry®), or personal digital assistants. The user can access the computer system 1201 via the network.” Thus the claimed inventive steps are performed by generic or general purpose computing systems executing well known and understood instructions and processes which do not comprise significantly more than a known computing system, or comprise improvements to another technological field.
Further, as per MPEP 2106, and TLI Communications LLC v. AV Automotive LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) ("It is well-settled that mere recitation of concrete, tangible components is insufficient to confer patent eligibility to an otherwise abstract idea") and as per Intellectual Ventures I LLC v. Capital One Bank (USA), N.A., 792 F.3d 1363, 1366, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015) ("An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment, such as the Internet [or] a computer") simply performing the steps of an abstract idea by a computing apparatus does not make an inventive concept statutorily eligible. Therefore, it is clear from Applicants’ specification that the elements and modules in the claims require no more than a generic computer (e.g., a general-purpose computing device) to perform generic computer functions (e.g., accessing, transmitting/receiving, sorting, and storing data) that are well-understood, routine and conventional activities previously known in the industry. None of the limitations, considered as a whole and as an ordered combination provide eligibility, because the steps of the claims simply instruct the practitioner to implement the abstract idea with routine, conventional activity.
Viewing the additional limitations in combination also shows that they fail to ensure the claims amount to significantly more than the abstract idea. When considered as an ordered combination, the additional components of the claims add nothing that is not already present when considered separately, and thus simply append the abstract idea with words equivalent to “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer, generally link the abstract idea to a particular technological environment or field of use, and append the abstract idea with insignificant extra solution activity associated with the implementation of the judicial exception, (e.g., mere data gathering).
Dependent claims 77-90 and 92-105 are directed to the judicial exception as explained above for Claims 76 and 91 and are further directed to limitations directed to the collection of clinical data as related to mutations and other related variables, cancer, the determination of a wide range of treatment options, the use of machine learning processes to determine clinical outcome predictions, the generation of electronic reports and the determination of probabilistic predictions of clinical outcomes as related to a variety of treatment options. These limitations or processes are considered to be executed by the general purpose computing system as explained above, and therefore do not result in the claimed invention being directed to a practical application or comprise significantly more than the identified abstract idea.
Dependent claims 77-90 and 92-105 do not add more to the abstract idea of independent Claims 76 and 91 and therefore are rejected as ineligible subject matter under 35 U.S.C. 101 based on a rationale similar to the claims from which they depend.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 76-88, 91-103, and 106 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lipsky et al. (20210104321) in view of Spetzler et al. (20200024669).
Claims 76, 91, and 106: Lipsky discloses a system comprising a computer processor and a storage device having instructions stored thereon that are operable, when executed by the computer processor ([156, 400-406]), to cause the computer processor to:
(iii) apply the prediction module to at least the clinical data of the subject to determine probabilistic predictions of clinical outcomes of the set of treatment options for the disease or disorder of the subject ([452 “diagnose” or “diagnosis” of a status or outcome includes predicting or diagnosing the status or outcome, determining predisposition to a status or outcome, monitoring treatment of patient, diagnosing a therapeutic response of a patient, and prognosis of status or outcome, progression,” 477, 480 “plurality of input variables or features may comprise one or more datasets indicative of the presence (e.g., positive test result) or absence (e.g., negative test result) of one or more conditions (e.g., a disease or disorder,” 487, 488, 607 “identifying the subject as having one or more conditions (e.g., a disease or disorder, such as a lupus condition), the subject may be optionally provided with a therapeutic intervention (e.g., prescribing an appropriate course of treatment to treat the one or more conditions of the subject). The therapeutic intervention may comprise a prescription of an effective dose of a drug, a further testing or evaluation of the condition, a further monitoring of the condition, or a combination thereof. If the subject is currently being treated for the condition with a course of treatment, the therapeutic intervention may comprise a subsequent different course of treatment (e.g., to increase treatment efficacy due to non-efficacy of the current course of treatment),”]).
(ii) access a prediction module comprising a trained machine learning model that determines probabilistic predictions of clinical outcomes of the set of treatment options ([452, 477, 480, 487, 488, 607]) based at least in part on clinical data of test subjects ([170, 171, 346 “disease may comprise an acute disease, a chronic disease, a clinical disease, a flare-up disease, a progressive disease, a refractory disease, a subclinical disease, or a terminal,” 470, 481 “plurality of input variables or features may also include clinical information of a subject, such as health data. For example, the health data of a subject may comprise one or more of: a diagnosis of one or more conditions (e.g., a disease or disorder, such as a lupus condition), a prognosis of one or more conditions (e.g., a disease or disorder, such as a lupus condition), a risk of having one or more conditions (e.g., a disease or disorder, such as a lupus condition), a treatment history of one or more conditions,” 482, 483, 489 “independent training samples may comprise a sample from a subject, associated datasets obtained by assaying the sample (as described elsewhere herein), and one or more known output values or classes of individuals corresponding to the sample (e.g., a clinical diagnosis, prognosis, absence, or treatment efficacy of a condition of the subject),” 498, 613-618]);
Lipsky does not explicitly disclose however Spetzler discloses:
(i) receive clinical data of a subject and a set of treatment options for a disease or disorder of the subject, wherein the set of treatment options corresponds to clinical outcomes having future uncertainty ([235 “clinical citations are assessed for their relevance to the methods of the invention using a hierarchy derived from the evidence grading system used by the United States Preventive Services Taskforce. The “best evidence” can be used as the basis for a rule. The simplest rules are constructed in the format of “if biomarker positive then treatment option one, else treatment option two.” Treatment options comprise no treatment with a specific drug, treatment with a specific drug or treatment with a combination of drugs. In some embodiments, more complex rules are constructed that involve the interaction of two or more biomarkers,” 328 “comprehensive profile may be used to assist in treatment selection for highly aggressive or rare tumors with uncertain treatment regimens. For example, a comprehensive profile can be used to identify a candidate treatment for a newly diagnosed case or when the patient has exhausted standard of care therapies or has an aggressive disease,” 359 “clinical trials that are matched may be identified based on results of “pathogenic,” “presumed pathogenic,” or variant of uncertain (or unknown) significance (“VUS”). In some embodiments, the decision to incorporate/associate a drug class with a biomarker mutation can further depend on one or more of the following: 1) Clinical evidence; 2) Preclinical evidence; 3) Understanding of the biological pathway affected by the biomarker; and 4) expert analysis,” 419 “displays a summary of therapies associated with potential benefit, therapies associated with uncertain benefit, and therapies associated with potential lack of benefit….potential benefit for treating the patient's breast cancer because the sample was determined to be MSI high based on analysis with NGS. FIG. 27H illustrates more detailed information for biomarker profiling used to associate agents with uncertain benefit. The report notes that therapies are placed in the uncertain benefit category when a result suggests only a decreased likelihood of response,”]).
Therefore it would be obvious for Lipsky wherein the caregivers receive clinical data of a subject and a set of treatment options for a disease or disorder of the subject, wherein the set of treatment options corresponds to clinical outcomes having future uncertainty as per the steps to Spetzler in order to determine treatment options which include levels of determined future uncertainty in order to provide patients and caregivers with a set of options to enable the optimal selection of treatment options to account for levels of certainty and uncertainty and thereby result in the optimization of treatments provided to patients.
Claims 77 and 92: Lipsky in view of Spetzler disclose the system and method of claims 76 and 91 and Lipsky does not explicitly disclose however Spetzler discloses wherein the clinical data is selected from somatic genetic mutations ([Table 4, 356 “cancer genes disclosed in the COSMIC (Catalogue Of Somatic Mutations In Cancer) database,”]), germline genetic mutations ([Table 4, 391, 457 “Tumors are classified as MMR-deficient (dMMR) if they have somatic or germline mutations,”]), mutational burden ([6 “biomarkers include without limitation microsatellite instability (MSI), tumor mutational burden (TMB, also referred to as tumor mutation load,”]), protein levels ([19 “profiling can comprise any useful technique, including without limitation determining: i) a protein expression level, wherein optionally the protein expression level is determined using IHC, flow cytometry or an immunoassay;”]), transcriptome levels ([134 “expression levels of nearly all transcripts can be quantitatively determined; the abundance of signatures is representative of the expression level of the gene in the analyzed tissue,” 136]), metabolite levels ([102 “Circulating biomarkers according to the invention include any appropriate biomarker that can be detected in bodily fluid, including without limitation protein, nucleic acids, e.g., DNA, mRNA and microRNA, lipids, carbohydrates and metabolites,”]), tumor size or staging ([311 “performing tumor profiling on a tumor sample from a subject comprising the selected methods to determine the status of the characteristic of each of the biomarkers; and compiling the status in a report according to said priority list; thereby generating a report that identifies a tumor profile,” 312-323]), clinical symptoms ([76 “beneficial or desired clinical results include, but are not limited to, alleviation or amelioration of one or more symptoms, diminishment of extent of disease, stabilized (i.e., not worsening) state of disease, preventing spread of disease,” 80 “Samples can be associated with relevant information such as age, gender, and clinical symptoms present in the subject; source of the sample; and methods of collection and storage of the sample,”]), laboratory test results ([82, 296 “clinical information management system includes the laboratory information management system and the medical information contained in the data warehouses and databases includes medical information libraries,” 299, 310]), and clinical history ([47 “Rules of the invention aide prioritizing treatment, e.g., direct results of molecular profiling, anticipated efficacy of therapeutic agent, prior history with the same or other treatments, expected side effects, availability of therapeutic agent, cost of therapeutic agent, drug-drug interactions, and other factors considered by a treating physician,” 259, 293, 295]).
Therefore it would be obvious for Lipsky wherein the clinical data is selected from somatic genetic mutations, germline genetic mutations, mutational burden, protein levels, transcriptome levels, metabolite levels, tumor size or staging, clinical symptoms, laboratory test results, and clinical history as per the steps to Spetzler in order to determine treatment options which include levels of determined future uncertainty in order to provide patients and caregivers with a set of options to enable the optimal selection of treatment options to account for levels of certainty and uncertainty and thereby result in the optimization of treatments provided to patients.
Claims 78 and 93: Lipsky in view of Spetzler disclose the system and method of claims 76 and 91 and Lipsky further discloses wherein the disease or disorder comprises cancer ([346 “disease may comprise an immune disease, a cancer, a genetic disease, a metabolic disease, an endocrine disease, a neurological disease, a musculoskeletal disease, or a psychiatric disease,” 522, 749, 1422]).
Claims 79 and 94: Lipsky in view of Spetzler disclose the system and method of claims 76 and 91 and Lipsky does not explicitly disclose however Spetzler discloses wherein (iii) comprises applying the prediction module to at least treatment features of the set of treatment options or interaction terms between the clinical data of the subject and the treatment features of the set of treatment options, to determine the probabilistic predictions of the clinical outcomes of the set of treatment options ([248 “treatment options are presented in a prioritized list. In some embodiments, the treatment options are presented without prioritization information. In either case, an individual, e.g., the treating physician or similar caregiver may choose from the available options,” 296 “information management systems relating to particular patients and the medical information databases and data warehouses come together at a data junction center where diagnostic information and therapeutic options can be obtained,” 303, 305 “report can further comprise a list describing the expected benefit of the plurality of treatment options based on the assessed characteristics, thereby identifying candidate treatment options for the subject,” 307-309]).
Therefore it would be obvious for Lipsky wherein (iii) comprises applying the prediction module to at least treatment features of the set of treatment options or interaction terms between the clinical data of the subject and the treatment features of the set of treatment options, to determine the probabilistic predictions of the clinical outcomes of the set of treatment options as per the steps to Spetzler in order to determine treatment options which include levels of determined future uncertainty in order to provide patients and caregivers with a set of options to enable the optimal selection of treatment options to account for levels of certainty and uncertainty and thereby result in the optimization of treatments provided to patients.
Claims 80 and 95: Lipsky in view of Spetzler disclose the system and method of claims 76 and 91 and Lipsky does not explicitly disclose however Spetzler discloses wherein the clinical outcomes having future uncertainty ([419 “summary of therapies associated with potential benefit, therapies associated with uncertain benefit, and therapies associated with potential lack of benefit…. detailed information for biomarker profiling used to associate agents with uncertain benefit. The report notes that therapies are placed in the uncertain benefit category when a result suggests only a decreased likelihood of response (vs. little to no likelihood of response),”]) comprise a change in tumor size ([81 “the size and type of the tumor (e.g., solid or suspended, blood or ascites), among other factors,” 254 “decrease in size or number of the lesions by 30% or more. Stable disease (SD) refers to a disease that has remained relatively unchanged in size and number of lesions. Generally, less than a 50% decrease or a slight increase in size would be described as stable disease. Progressive disease (PD) means that the disease has increased in size or number on treatment,”]), a change in patient functional status ([258 “other functional aspects of the systems (and components of the individual operating components of the systems) may not be described in detail herein but are part of the invention. Furthermore, the connecting lines shown in the various figures contained herein are intended to represent illustrative functional relationships and/or physical couplings between the various elements. It should be noted that many alternative or additional functional relationships or physical connections,” 280-282]), a time-to- disease progression ([4 “some patients have very limited options after their tumor has progressed in spite of front line, second line and sometimes third line and beyond) therapies,” 21]), a time-to-treatment failure ([250 “Progression-free survival rates are an indication of the effectiveness of a particular treatment. Similarly, disease-free survival (DFS) denotes the chances of staying free of disease after initiating a particular treatment for an individual or a group of individuals suffering from a cancer,” 251-254]), or a progression-free survival time ([76 “delay or slowing of disease progression, amelioration or palliation of the disease state, and remission (whether partial or total), whether detectable or undetectable. Treatment also includes prolonging survival as compared to expected survival if not receiving treatment or if receiving a different treatment,” 250, 251]).
Therefore it would be obvious for Lipsky wherein the clinical outcomes having future uncertainty comprise a change in tumor size, a change in patient functional status, a time-to- disease progression, a time-to-treatment failure, overall survival, or progression-free survival as per the steps to Spetzler in order to determine treatment options which include levels of determined future uncertainty in order to provide patients and caregivers with a set of options to enable the optimal selection of treatment options to account for levels of certainty and uncertainty and thereby result in the optimization of treatments provided to patients.
Claims 81 and 96: Lipsky in view of Spetzler disclose the system and method of claims 76 and 91 and Lipsky further discloses wherein the probabilistic predictions of clinical outcomes of the set of treatment options comprise statistical distributions of the clinical outcomes of the set of treatment options ([452 “term “diagnose” or “diagnosis” of a status or outcome includes predicting or diagnosing the status or outcome, determining predisposition to a status or outcome, monitoring treatment of patient, diagnosing a therapeutic response of a patient, and prognosis of status or outcome, progression, and response to particular treatment,” 477 “individuals not having the condition (e.g., healthy individuals, or individuals who do not have a lupus condition), in order to classify a subject as having the condition (e.g., positive test outcome) or not having the condition (e.g., negative test outcome),” 499]).
Claims 82 and 97: Lipsky in view of Spetzler disclose the system and method of claims 76 and 91 and Lipsky further discloses wherein the probabilistic predictions of clinical outcomes of the set of treatment options are explainable based on performing a query of the probabilistic predictions ([338 “predictive tool for evaluating patient at both the chemical and cellular levels to advance personalized treatment. Data analytical techniques such as machine learning enable proper correlation between genetic records and phenotypes,” 452 “monitoring treatment of patient, diagnosing a therapeutic response of a patient, and prognosis of status or outcome, progression, and response to particular treatment,” 454, 956 “machine learning approaches to integrate gene expression data from multiple SLE data sets and used it to predict active disease. Both raw whole blood gene expression data and informative gene modules generated by Weighted Gene Co-expression Network Analysis from purified leukocyte populations are employed by classification algorithms. SLE whole blood gene expression data from 156 patients across three data sets are used to classify patients as having active or inactive disease as characterized by standard clinical composite outcome measures,”]).
Claims 83 and 98: Lipsky in view of Spetzler disclose the system and method of claims 76 and 91 and Lipsky further discloses wherein the instructions are operable, when executed by the computer processor, to cause the computer processor to further apply a training module that trains the trained machine learning model, wherein the training module updates the trained machine learning model using the probabilistic predictions of the clinical outcomes of the set of treatment options generated in (iii) ([477 “Feature sets may be generated from datasets obtained using one or more assays of a biological sample, and a trained algorithm may be used to process one or more of the feature sets to identify or assess the condition (e.g., a disease or disorder, such as a lupus condition). For example, the trained algorithm may be used to apply a machine learning classifier to a plurality of lupus condition-associated or interferon-associated genomic loci that are associated with two or more classes of individuals inputted into a machine learning model, in order to classify a subject into one of the two or more classes of individuals. For example, the trained algorithm may be used to apply a machine learning classifier to a plurality of lupus condition-associated or interferon-associated genomic loci that are associated with individuals with known conditions (e.g., a disease or disorder, such as a lupus condition) and individuals not having the condition (e.g., healthy individuals, or individuals who do not have a lupus condition), in order to classify a subject as having the condition (e.g., positive test outcome) or not having the condition (e.g., negative test outcome),” 478-483, 489-491, 498 “Classifiers of the trained algorithm may be adjusted or tuned to improve or optimize one or more performance metrics, such as accuracy, PPV, NPV, clinical sensitivity, clinical specificity, AUC, or a combination thereof (e.g., a performance index incorporating a plurality of such performance metrics, such as by calculating a weight sum therefrom), of identifying the presence (e.g., positive test result) or absence (e.g., negative test result) of the condition. The classifiers may be adjusted or tuned by adjusting parameters of the classifiers (e.g., a set of cutoff values used to classify a sample as described elsewhere herein, or weights of a neural network) to improve or optimize the performance metrics,”]).
Claims 84 and 99: Lipsky in view of Spetzler disclose the system and method of claims 76 and 91 and Lipsky further discloses wherein the trained machine learning model is selected from the group consisting of a Bayesian model, a support vector machine (SVM), a linear regression, a logistic regression, a random forest, and a neural network ([358 “normalizing is performed by Robust Multi-Array Analysis (RMA), Guanine Cytosine Robust Multi-Array Analysis (GCRMA), Linear Models for Microarray Data, variance stabilizing transformation (VST), normal-exponential quantile correction (NEQC),” 359 “variance correction comprises employing a local empirical Bayesian shrinkage, adjusting the p-values for multiple hypothesis testing using the Benjamini-Hochberg correction,” 479 “trained algorithm may comprise a machine learning algorithm, such as a supervised machine learning algorithm. The supervised machine learning algorithm may comprise, for example, a Random Forest, a support vector machine (SVM), a neural network, or a deep learning algorithm. The trained algorithm may comprise a classification and regression tree (CART) algorithm. The trained algorithm may comprise an unsupervised machine learning algorithm,” 480-483]).
Claims 85 and 100: Lipsky in view of Spetzler disclose the system and method of claims 76 and 91 and Lipsky does not explicitly disclose however Spetzler discloses wherein the trained machine learning model comprises a multilevel statistical model that accounts for variation at a plurality of distinct levels of analysis or correlation of subject-level effects across the plurality of distinct levels of analysis ([46 “identifying targets for drugs that may be effective for a given cancer. For example, the candidate treatment can be a treatment known to have an effect on cells that differentially express genes as identified by molecular profiling techniques, an experimental drug, a government or regulatory approved drug or any combination of such drugs,” 47, 76 “treatment can include administration of a therapeutic agent, which can be an agent that exerts a cytotoxic, cytostatic, or immunomodulatory effect on diseased cells, e.g., cancer cells, or other cells that may promote a diseased state, e.g., activated immune cells. Therapeutic agents selected by the methods of the invention are not limited. Any therapeutic agent can be selected where a link can be made between molecular profiling and potential efficacy of the agent. Therapeutic agents include without limitation drugs, pharmaceuticals, small molecules, protein therapies, antibody therapies, viral therapies, gene therapies, and the like. Cancer treatments or therapies include apoptosis-mediated and non-apoptosis mediated cancer therapies including, without limitation, chemotherapy, hormonal therapy, radiotherapy, immunotherapy,” 236, 250 “Progression-free survival rates are an indication of the effectiveness of a particular treatment. Similarly, disease-free survival (DFS) denotes the chances of staying free of disease after initiating a particular treatment for an individual or a group of individuals suffering from a cancer. It can refer to the percentage of individuals in a group who are likely to be free of disease after a specified duration of time. Disease-free survival rates are an indication of the effectiveness of a particular treatment,” 254 “The effectiveness of a treatment can be monitored by other measures. A complete response (CR) comprises a complete disappearance of the disease: no disease is evident on examination, scans or other tests. A partial response (PR) refers to some disease remaining in the body, but there has been a decrease in size or number of the lesions by 30% or more,”]).
Therefore it would be obvious for Lipsky wherein the trained machine learning model comprises a multilevel statistical model that accounts for variation at a plurality of distinct levels of analysis or correlation of subject-level effects across the plurality of distinct levels of analysis as per the steps to Spetzler in order to determine treatment options which include levels of determined future uncertainty in order to provide patients and caregivers with a set of options to enable the optimal selection of treatment options to account for levels of certainty and uncertainty and thereby result in the optimization of treatments provided to patients.
Claims 86 and 101: Lipsky in view of Spetzler disclose the system and method of claims 85 and 100 and Lipsky further discloses wherein the multilevel statistical model comprises a generalized linear model ([4 “the classifier comprises an elastic generalized linear model classifier, a k-nearest neighbors classifier, a random forest classifier, 5, 17]).
Claims 87 and 102: Lipsky in view of Spetzler disclose the system and method of claims 86 and 101 and Lipsky further discloses wherein the generalized linear model comprises use of the expression:
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wherein q is a linear response, X is a vector of predictors for treatment effects fixed across subjects, # is a vector of fixed effects, Z is a vector of predictors for subject-level treatment effects, and u is a vector of subject-level effects ([40 “supervised machine learning algorithm comprises a deep learning algorithm, a support vector machine (SVM), a neural network, or a Random Forest,” 348 “biased algorithm may comprise Gene Set Enrichment Analysis (GSVA) enrichment of phenotype-associated cell-specific modules. The unbiased approach may employ all available phenotypic data. The machine learning algorithm may comprise an elastic generalized linear model (GLM), a k-nearest neighbors classifier (KNN), a random forest (RF) classifier, or any combination thereof. GLM, KNN, and RF machine learning algorithms may be performed using the glmnet, caret, and randomForest R packages,” 351 “GLM algorithm may carry out logistic regression with a tunable elastic penalty term to find a balance between an L1 (LASSO) and an L2 (ridge), whereby penalties facilitate variable selection in order to generate sparse solutions. Least Absolute Shrinkage and Selection Operator (LASSO) is a regularization feature selection technique to reduce overfitting in regression problems. Ridge regression employs a penalty term is to shrink the LASSO coefficient values,”353, 358-360, 479 “trained algorithm may comprise a machine learning algorithm, such as a supervised machine learning algorithm. The supervised machine learning algorithm may comprise, for example, a Random Forest, a support vector machine (SVM), a neural network, or a deep learning algorithm. The trained algorithm may comprise a classification and regression tree (CART) algorithm,”]). Examiner Note: Examiner as cited to above with respect to the disclosures of Lipsky interprets the implementation of a wide range of trained machine learning algorithms which are implemented in detail throughout the disclosures of Lipsky to detail, as would be understood by a person of skill in the art to disclose the implementation of generalized linear model executions. Therefore Examiner interprets the disclosures of Lipsky to detail the claimed formula as would be understood by a person of skill in the art.
Claims 88 and 103: Lipsky in view of Spetzler disclose the system and method of claims 86 and 101 and Lipsky further discloses wherein the generalized linear model comprises use of the expression:
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17
82
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wherein 17 is a linear response, g is an appropriately chosen link function from observed data to the linear response, and y is an outcome variable of interest ([40 “supervised machine learning algorithm comprises a deep learning algorithm, a support vector machine (SVM), a neural network, or a Random Forest,” 348 “biased algorithm may comprise Gene Set Enrichment Analysis (GSVA) enrichment of phenotype-associated cell-specific modules. The unbiased approach may employ all available phenotypic data. The machine learning algorithm may comprise an elastic generalized linear model (GLM), a k-nearest neighbors classifier (KNN), a random forest (RF) classifier, or any combination thereof. GLM, KNN, and RF machine learning algorithms may be performed using the glmnet, caret, and randomForest R packages,” 351 “GLM algorithm may carry out logistic regression with a tunable elastic penalty term to find a balance between an L1 (LASSO) and an L2 (ridge), whereby penalties facilitate variable selection in order to generate sparse solutions. Least Absolute Shrinkage and Selection Operator (LASSO) is a regularization feature selection technique to reduce overfitting in regression problems. Ridge regression employs a penalty term is to shrink the LASSO coefficient values,”353, 358-360, 479 “trained algorithm may comprise a machine learning algorithm, such as a supervised machine learning algorithm. The supervised machine learning algorithm may comprise, for example, a Random Forest, a support vector machine (SVM), a neural network, or a deep learning algorithm. The trained algorithm may comprise a classification and regression tree (CART) algorithm,”]). Examiner Note: Examiner as cited to above with respect to the disclosures of Lipsky interprets the implementation of a wide range of trained machine learning algorithms which are implemented in detail throughout the disclosures of Lipsky to detail, as would be understood by a person of skill in the art to disclose the implementation of generalized linear model executions. Therefore Examiner interprets the disclosures of Lipsky to detail the claimed formula as would be understood by a person of skill in the art..
Claim(s) 89, 90, 104, 105 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lipsky et al. (20210104321) in view of Spetzler et al. (20200024669) and McNutt et al. (20170083682).
Claims 89 and 104: Lipsky in view of Spetzler disclose the system and method of claims 85 and 100 and Lipsky does not explicitly disclose, however McNutt discloses wherein the instructions are operable, when executed by the computer processor, to cause the computer processor to further generate an electronic report comprising the probabilistic predictions of clinical outcomes of the set of treatment options, and wherein the electronic report is used to select a treatment option from among the set of treatment options based at least in part on the probabilistic predictions of clinical outcomes of the set of treatment options ([45, 46 “output device may include, e.g., but not limited to, display, and display interface, including displays, printers, speakers, cathode ray tubes (CRTs), plasma displays, light-emitting diode (LED) displays, liquid crystal displays (LCDs), printers, vacuum florescent displays (VFDs), surface-conduction electron-emitter displays (SEDs), field emission displays (FEDs),” 50 “risk evaluation may be made based on the proximity of critical structures to target volumes with all the clinical and demographic information to provide input to the automated treatment planning,” 71 “embodiment of the current invention can establish a data-mining framework in which treatment planning data and normal tissue complication effects in an integrated, analytic oncology database can be efficiently and automatically formulated into meaningful clinical recommendations,” 77-83, 304 “data integrity, tools are being developed to assist with identifying possible errant data in the system. These tools evaluate data for consistency and completeness. As with any clinical information, the data can be improperly recorded. Integrity checks offer a way to systematically look for errant data to report and correct,” Fig. 29]). Examiner Note: Examiner interprets McNutt extensive implementation of treatment plans across a wide range of situations as well as the selection of relevant features to disclose the implementation of treatment plans with respect to a wide variety of options and predictions of outcomes associated with the implementation of the treatment plans and therefore as referenced above the planning of the treatments is determined to be disclosed by McNutt.
Therefore it would be obvious for Lipsky wherein the instructions are operable, when executed by the computer processor, to cause the computer processor to further generate an electronic report comprising the probabilistic predictions of clinical outcomes of the set of treatment options, and wherein the electronic report is used to select a treatment option from among the set of treatment options based at least in part on the probabilistic predictions of clinical outcomes of the set of treatment options as per the steps to McNutt in order to determine treatment options which include levels of determined future uncertainty in order to provide patients and caregivers with a set of options to enable the optimal selection of treatment options to account for levels of certainty and uncertainty and thereby result in the optimization of treatments provided to patients.
Claims 90 and 105: Lipsky in view of Spetzler disclose the system and method of claims 89 and 104 and Lipsky does not explicitly disclose, however McNutt discloses wherein the selected treatment option is administered to the subject, and wherein the prediction module is further applied to outcome data of the subject that is obtained subsequent to administering the selected treatment option to the subject, to determine updated probabilistic predictions of the clinical outcomes of the set of treatment options ([308 “knowledge discovery in databases (KDD) [80]. Typically the vast majority of data analyzed was not collected for that purpose, but rather in the course of an institution conducting its general activities. In the case of health-care, data is generally from electronic health records (EHR), or other components within hospital information system,” 309 “divides KDD into nine steps: (1) understanding the problem domain and the previous work in the area; (2) selecting a target dataset; (3) data cleaning and preprocessing; (4) data reduction and projection; (5) matching the knowledge discovery goals with a data mining approach; (6) exploratory analysis with hypothesis and model testing; (7) data mining; (8) interpreting results; and (9) acting on discovered knowledge,” 340 “set of best features is selected using information gain. Complications are modeled using the following machine learning algorithms: linear regression (LR), random forest (RF) [113], and naïve Bayes (NB),” 373 “one embodiment includes a system that can demonstrate individualized medicine for cancer patients by substantially improving predictions of treatment related toxicities and enabling clinicians the ability to adjust their radiation doses or their symptom management regimens to improve care for their patients,” Fig. 29]). Examiner Note: Examiner interprets McNutt extensive implementation of treatment plans across a wide range of situations as well as the selection of relevant features to disclose the implementation of treatment plans with respect to a wide variety of options an