DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Application
This action is in reply to the correspondence received through March 12, 2026.
Claims 1-20 are pending.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Claims 17-20: This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: the various units in claims 17-20.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 U.S.C. § 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 non-statutory subject matter. Claims 1-20 are directed to an abstract idea without significantly more as required by the Alice test as discussed below.
Step 1
Claims 1-20 are directed to a process, machine, manufacture, or composition of matter.
Step 2A
Claims 1-20 are directed to abstract ideas, as explained below.
Prong one of the Step 2A analysis requires identifying the specific limitation(s) in the claim under examination that the examiner believes recites an abstract idea; and determining whether the identified limitation(s) falls within at least one of the groupings of abstract ideas of mathematical concepts, mental processes, and certain methods of organizing human activity.
The claims recite the following limitations that are directed to abstract ideas. Claim 1 recites a step (A): establishing a prediction model using a computational data through an algorithm, wherein the computational data comprises an autoimmune disease medication, a variation of a clinical index, and a proportion of a computational immune cell population; and a step (B): entering a proportion of a diagnostic immune cell population from a diagnostic data into the prediction model, thereby obtaining a prediction result of the autoimmune disease medication. Claim 17 recites similar features as claim 1. Claims 2-16 and 18-20 further specify features of the algorithm in the independent claims or characteristics of the data used thereby.
These limitations describe abstract ideas that correspond to concepts identified as abstract ideas by the courts as mathematical concepts—such as mathematical relationships, mathematical formulas or equations, and mathematical calculations—because the claimed use of the algorithm includes several mathematical techniques (see, e.g., claim 2), which are mathematical relationships, mathematical formulas or equations, and mathematical calculations.
These limitations describe abstract ideas that correspond to concepts identified as abstract ideas by the courts as mental processes—such as concepts performed in the human mind (including an observation, evaluation, judgment, or opinion)—because the claimed features identified above are concepts performed in the human mind (including an observation, evaluation, judgment, or opinion).
These limitations describe abstract ideas that correspond to concepts identified as abstract ideas by the courts as certain methods of organizing human activity—such as fundamental economic principles or practices (including hedging, insurance, mitigating risk), commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations), managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)—because the claim features manage personal behavior or relationships or interactions between people including social following rules or instructions.
Thus, the concepts set forth in claims 1-20 recite abstract ideas.
Prong two of the Step 2A requires identifying whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluating those additional elements to determine whether they integrate the exception into a practical application of the exception. “Integration into a practical application” requires an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. Further, “integration into a practical application” uses the considerations laid out by the Supreme Court and the Federal Circuit to evaluate whether the judicial exception is integrated into a practical application, such as considerations discussed in M.P.E.P. § 2106.05(a)-(h).
The claims recite the following additional elements beyond those identified above as being directed to an abstract idea. Claim 17 recites a processing unit. Claim 20 recites an input unit, an output unit, and a storage unit.
The identified judicial exception(s) are not integrated into a practical application for the following reasons.
First, evaluated individually, the additional elements do not integrate the identified abstract ideas into a practical application. The additional computer elements identified above—the various units—are recited at a high level of generality. Inclusion of these elements amounts to mere instructions to implement the identified abstract ideas on a computer. See M.P.E.P. § 2106.05(f). The use of conventional computer elements to input or output information is the insignificant, extra-solution activity of mere data gathering or outputting in conjunction with a law of nature or abstract idea. See M.P.E.P. § 2106.05(g). To the extent that the claims transform data, the mere manipulation of data is not a transformation. See M.P.E.P. § 2106.05(c). Inclusion of computing system in the claims amounts to generally linking the use of the judicial exception to a particular technological environment or field of use. See M.P.E.P. § 2106.05(h). Thus, taken alone, the additional elements do not amount to significantly more than a judicial exception.
Second, evaluating the claim limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. See M.P.E.P. § 2106.05(a). Their collective functions merely provide an implementation of the identified abstract ideas on a computer system in the general field of use of predicting therapeutic effectiveness. See M.P.E.P. § 2106.05(h).
Thus, claims 1-20 recite mathematical concepts, mental processes, or certain methods of organizing human activity without including additional elements that integrate the exception into a practical application of the exception.
Accordingly, claims 1-20 are directed to abstract ideas.
Step 2B
Claims 1-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, when considered both individually and as an ordered combination, do not amount to significantly more than the abstract idea.
The analysis above describes how the claims recite the additional elements beyond those identified above as being directed to an abstract idea, as well as why identified judicial exception(s) are not integrated into a practical application. These findings are hereby incorporated into the analysis of the additional elements when considered both individually and in combination. Additional features of these analyses are discussed below.
Evaluated individually, the additional elements do not amount to significantly more than a judicial exception. In addition to the factors discussed regarding Step 2A, prong two, these additional computer elements also provide conventional computer functions that do not add meaningful limits to practicing the abstract idea. Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system. The use of generic computer components to input or output information is the well-understood, routine, and conventional computer functions of receiving or transmitting data over a network, e.g., the Internet, and does not impose any meaningful limit on the computer implementation of the identified abstract ideas. See M.P.E.P. § 2106.05(d)(II). Thus, taken alone, the additional elements do not amount to significantly more than a judicial exception.
Evaluating the claim limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. In addition to the factors discussed regarding Step 2A, prong two, there is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely amount to mere instructions to implement the identified abstract ideas on a computer.
Thus, claims 1-20, taken individually and as an ordered combination of elements, are not directed to eligible subject matter since they are directed to an abstract idea without significantly more.
Claim Rejections - 35 U.S.C. § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. § 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 3, 4, 6, 7, 9, and 17 are rejected under 35 U.S.C. § 102(a)(1)-(2) as being anticipated by Steinberg-Koch et al. (U.S. Pub. No. 2022/0223293 A1) (hereafter “Steinberg-Koch”).
Claim 1: Steinberg-Koch, as shown, discloses the following limitations:
A method for predicting the medication for autoimmune disease, comprising:
step (A): establishing a prediction model using a computational data through an algorithm, wherein the computational data comprises an autoimmune disease medication, a variation of a clinical index, and a proportion of a computational immune cell population (see at least ¶ [0129]: reference is first made to FIG. 1, which illustrates schematically the overall structure of an exemplary implementation of the disclosed invention. A method detects individuals having characteristics that indicate a specific disease process. In a first phase of the method, historical patient data from electronic medical records (EMR), electronic health records (EHR), claims data or data from other sources are collected, followed by application of machine/deep learning, natural language processing (NLP), or other individual or combined machine learning techniques to train an algorithm of the method to identify subjects with the autoimmune conditions which are to be diagnosed based on known cases of such disease in the historic population data. In a second phase, new patient data are input to the algorithm to enable determination of the probability and risk that a given individual in the new population has an autoimmune condition; see also at least ¶ [0143]: example of parameters or features from the patient’s data file, used in the machine learning algorithm may fall into the following categories: demographics including family history of CD or other gastrointestinal conditions, symptoms, concurrent diagnoses, lab tests, medications, procedure and current and past measurements such as height, weight, and BMI. A large number of parameters may be used in training the algorithm; over time, additional, different, or fewer parameters may be incorporated to improve the diagnostic accuracy of the method; see also at least ¶ [0130]: a historic database of insurer medical claims and/or EMR data for a large population, representing the target population for this algorithm, is accessed to provide examples for training the models of the system. This data is augmented with additional sources, such as IOT sensor data, subject provided information, and aggregated statistics relevant to target subjects collected either from research datasets, or via use of the proposed system. This information is used in subsequent steps 103 and 106 a to generate processed and filtered training information, ultimately for use in step 109; see also at least ¶ [0131]: the large population data from block 101 is used in combination with rules derived from medical experts or known medical protocols, here referred to as “expert medical logic” 102, to generate tagged or labeled training data of subjects; see also at least ¶ [0135]: In step 108 a, a multi-output classifier model is trained using supervised learning of the tagged training data (107 a). The steps 101 to 108 a, shown in FIG. 1 within the dotted line 100, are steps used for the periodic training of the artificial intelligence models using the large historic population data. Steps 106 b to 108 b, on the other hand, are steps in which the feature embedding and classifying of the subject data are applied to the data of the currently analyzed patients, whose diagnoses are being resolved; see also at least ¶¶ [0034] and [0147]); and
step (B): entering a proportion of a diagnostic immune cell population from a diagnostic data into the prediction model, thereby obtaining a prediction result of the autoimmune disease medication (see at least ¶ [0139]: step 109 uses the output from step 108 b to generate a corresponding diagnosis probability vector with multiple values associated with a patient's file, that provides a probability that the current subject has each condition analyzed, such that further diagnosis recommendations and treatment recommendations can be derived; see at least ¶ [0168]: reference is now made to FIG. 4, a schematic representation of an implementation of the method for interventional recommendations. The steps within the dotted line 400 represent periodic training of artificial intelligence models. In block 403, an intervention recommendation model is developed, using supervised learning by examples. The training inputs for this model are examples generated from the population medical record database 401 using medical guidelines 402, and by collecting patients' response to specific treatments and scoring them accordingly. The information in steps 401 and 402 may be the same or different as that in FIG. 1 steps 101 and 102. These scores are used as target results to train the algorithm. After the model 400 is developed through machine learning or other form of artificial intelligence, the recommendation model parameters are input into the intervention recommendation model 406. Other inputs to the model 406 are the patient diagnosis probability vector from step 110 in FIG. 1, and patient historical data 405, comprised of previous tests and procedures, which may be the same data as provided in FIG. 1, step 105. The output of the intervention recommendation model is a ranked list of follow-up and/or treatment recommendations in step 407. Additionally, to the routine output in step 407, in step 408, the doctor or other health care provider can input retrospective feedback on the diagnostic accuracy of the output generated by the system. This information is used to improve the expert medical logic in step 402; see also at least ¶ [0172]: the system provides initial guidelines for intervention selection among a group of available treatment options, and based on prior training of the algorithm for optimal outcomes. Such intervention may be based on novel therapies developed by third parties, which are expected to be developed over time. Thus, the system may be updated on a regular basis to incorporate the current standard of treatment for CD. Thus, the outcomes should continually improve over time. In step 507, the system provides guidelines for chronic disease supervision based on algorithm training. Such guidelines may provide short- or long-term follow-up recommendations, goals for exercise, diet, medical treatment, and other advice for successful long-term management of the condition and minimization of secondary complications; see also at least ¶¶ [0034], [0147], and [0151]).
Claim 3: Steinberg-Koch discloses the limitations as shown in the rejections above. Further, Steinberg-Koch, as shown, discloses the following limitations:
wherein the autoimmune disease medication comprises: symptom-relieving medication, immunomodulator, immunosuppressant, biologic, or any combination thereof (see at least ¶ [0148]: medications specifically included in the algorithm may include antibiotics (IV and PO); H2 receptor antagonists, which block histamines and remove acidity in the stomach; NSAIDs (nonsteroidal anti-inflammatory drugs); paracetamol; PPI (proton pump inhibitors, which inhibit acid secretion in the stomach); and steroids (IV and PO), which may damage the GI tract lining. Further medications and other routes of administration may be included over time as the algorithm improves its specificity and accuracy, and is able to incorporate additional findings and correlate them with the diagnosis of CD).
Claim 4: Steinberg-Koch discloses the limitations as shown in the rejections above. Further, Steinberg-Koch, as shown, discloses the following limitations:
wherein the autoimmune disease medication comprises: a conventional synthetic disease modifying anti-rheumatic drug, (csDMARD), a biologic disease modifying anti-rheumatic drug (bDMARD), a targeted synthetic disease modifying anti-rheumatic drug (tsDMARD), or any combination thereof (see at least ¶ [0148]: medications specifically included in the algorithm may include antibiotics (IV and PO); H2 receptor antagonists, which block histamines and remove acidity in the stomach; NSAIDs (nonsteroidal anti-inflammatory drugs); paracetamol; PPI (proton pump inhibitors, which inhibit acid secretion in the stomach); and steroids (IV and PO), which may damage the GI tract lining. Further medications and other routes of administration may be included over time as the algorithm improves its specificity and accuracy, and is able to incorporate additional findings and correlate them with the diagnosis of CD).
Claim 6: Steinberg-Koch discloses the limitations as shown in the rejections above. Further, Steinberg-Koch, as shown, discloses the following limitations:
wherein the clinical index comprises: disease activity score by 28 joints (DAS28), erythrocyte sedimentation rate (ESR), rheumatoid factor (RF), anti-cyclic citrullinated peptide antibody (anti-CCP), C- reactive protein (CRP), or any combination thereof (see at least ¶ [0145]: the selected laboratory blood tests with relevance for diagnosis of CD are shown below, ALT (alanine aminotransferase, an indicator of liver damage); AST (aspartate aminotransferase, an indicator of liver damage); GGT (gamma-glutamyl transpeptidase); CRP (C-reactive protein); ESR (erythrocyte sedimentation rate); ferritin (a protein that stores iron in cells); folic acid; Hb (hemoglobin); MCV (mean corpuscular volume); RDW (red cell distribution width); HLA DQ2 and/or HLA DQ8. Also included in the category of laboratory tests are identification of anemia; size and volume of red blood cells; measuring enzymes responsible for liver function; and levels of vitamins in the blood, at least vitamin A; vitamin B12, and vitamin D. Further laboratory tests of the blood or other body fluids may be included over time as the algorithm improves its specificity and accuracy, and is able to incorporate additional lab values and correlate them with the diagnosis of CD).
Claim 7: Steinberg-Koch discloses the limitations as shown in the rejections above. Further, Steinberg-Koch, as shown, discloses the following limitations:
wherein the step (A) further comprises: obtaining the proportion of the computational immune cell population of a sample by an immunophenotyping using a marker (see at least ¶ [0158]: the algorithm details sub-steps specifically for determining the probability of a given individual to have a positive transglutaminase antibody result indicating CD autoimmunity. It is to be understood that the same process may be applied to other medical data with predictive value for a given autoimmune disease, such as lab values, genetic biomarkers, or imaging studies; see also at least ¶¶ [0025]-[0026], [0029], and [0034]).
Claim 9: Steinberg-Koch discloses the limitations as shown in the rejections above. Further, Steinberg-Koch, as shown, discloses the following limitations:
wherein the sample comprises a blood, a sweat, a spinal fluid, a saliva, tissue fluid, or any combination thereof (see at least ¶ [0145]: the selected laboratory blood tests with relevance for diagnosis of CD are shown below, ALT (alanine aminotransferase, an indicator of liver damage); AST (aspartate aminotransferase, an indicator of liver damage); GGT (gamma-glutamyl transpeptidase); CRP (C-reactive protein); ESR (erythrocyte sedimentation rate); ferritin (a protein that stores iron in cells); folic acid; Hb (hemoglobin); MCV (mean corpuscular volume); RDW (red cell distribution width); HLA DQ2 and/or HLA DQ8. Also included in the category of laboratory tests are identification of anemia; size and volume of red blood cells; measuring enzymes responsible for liver function; and levels of vitamins in the blood, at least vitamin A; vitamin B12, and vitamin D. Further laboratory tests of the blood or other body fluids may be included over time as the algorithm improves its specificity and accuracy, and is able to incorporate additional lab values and correlate them with the diagnosis of CD).
Claim 17: Steinberg-Koch, as shown, discloses the following limitations:
a processing unit (see at least ¶ [0180]: reference is now made to FIG. 7, showing a schematic representation of the system structure 700 used to perform the methods described herewithin above. In this disclosure, the term system may refer to, be part of, or include an Application Specific Integrated Circuit (ASIC); a digital, analog, or mixed analog/digital discrete circuit; a digital, analog, or mixed analog/digital integrated circuit; a combinational logic circuit; a field programmable gate array; at least one processor 702 (shared, dedicated, or group) that executes code; memory 701 (shared, dedicated, or group) that stores code executed by a processor 702; other suitable hardware components, such as optical, magnetic, or solid state drives, that provide the described functionality; or a combination of some or all of the above, such as in a system-on-chip; see also at least ¶¶ [0181]-[0184]), configured
to receive a proportion of a diagnostic immune cell population from a diagnostic data (see at least ¶ [0129]: reference is first made to FIG. 1, which illustrates schematically the overall structure of an exemplary implementation of the disclosed invention. A method detects individuals having characteristics that indicate a specific disease process. In a first phase of the method, historical patient data from electronic medical records (EMR), electronic health records (EHR), claims data or data from other sources are collected, followed by application of machine/deep learning, natural language processing (NLP), or other individual or combined machine learning techniques to train an algorithm of the method to identify subjects with the autoimmune conditions which are to be diagnosed based on known cases of such disease in the historic population data. In a second phase, new patient data are input to the algorithm to enable determination of the probability and risk that a given individual in the new population has an autoimmune condition; see also at least ¶ [0143]: example of parameters or features from the patient’s data file, used in the machine learning algorithm may fall into the following categories: demographics including family history of CD or other gastrointestinal conditions, symptoms, concurrent diagnoses, lab tests, medications, procedure and current and past measurements such as height, weight, and BMI. A large number of parameters may be used in training the algorithm; over time, additional, different, or fewer parameters may be incorporated to improve the diagnostic accuracy of the method; see also at least ¶ [0130]: a historic database of insurer medical claims and/or EMR data for a large population, representing the target population for this algorithm, is accessed to provide examples for training the models of the system. This data is augmented with additional sources, such as IOT sensor data, subject provided information, and aggregated statistics relevant to target subjects collected either from research datasets, or via use of the proposed system. This information is used in subsequent steps 103 and 106 a to generate processed and filtered training information, ultimately for use in step 109; see also at least ¶ [0131]: the large population data from block 101 is used in combination with rules derived from medical experts or known medical protocols, here referred to as “expert medical logic” 102, to generate tagged or labeled training data of subjects; see also at least ¶ [0135]: In step 108 a, a multi-output classifier model is trained using supervised learning of the tagged training data (107 a). The steps 101 to 108 a, shown in FIG. 1 within the dotted line 100, are steps used for the periodic training of the artificial intelligence models using the large historic population data. Steps 106 b to 108 b, on the other hand, are steps in which the feature embedding and classifying of the subject data are applied to the data of the currently analyzed patients, whose diagnoses are being resolved; see also at least ¶¶ [0034] and [0147]), and
to generate a prediction result of an autoimmune disease medication using a prediction model (see at least ¶ [0139]: step 109 uses the output from step 108 b to generate a corresponding diagnosis probability vector with multiple values associated with a patient's file, that provides a probability that the current subject has each condition analyzed, such that further diagnosis recommendations and treatment recommendations can be derived; see at least ¶ [0168]: reference is now made to FIG. 4, a schematic representation of an implementation of the method for interventional recommendations. The steps within the dotted line 400 represent periodic training of artificial intelligence models. In block 403, an intervention recommendation model is developed, using supervised learning by examples. The training inputs for this model are examples generated from the population medical record database 401 using medical guidelines 402, and by collecting patients' response to specific treatments and scoring them accordingly. The information in steps 401 and 402 may be the same or different as that in FIG. 1 steps 101 and 102. These scores are used as target results to train the algorithm. After the model 400 is developed through machine learning or other form of artificial intelligence, the recommendation model parameters are input into the intervention recommendation model 406. Other inputs to the model 406 are the patient diagnosis probability vector from step 110 in FIG. 1, and patient historical data 405, comprised of previous tests and procedures, which may be the same data as provided in FIG. 1, step 105. The output of the intervention recommendation model is a ranked list of follow-up and/or treatment recommendations in step 407. Additionally, to the routine output in step 407, in step 408, the doctor or other health care provider can input retrospective feedback on the diagnostic accuracy of the output generated by the system. This information is used to improve the expert medical logic in step 402; see also at least ¶ [0172]: the system provides initial guidelines for intervention selection among a group of available treatment options, and based on prior training of the algorithm for optimal outcomes. Such intervention may be based on novel therapies developed by third parties, which are expected to be developed over time. Thus, the system may be updated on a regular basis to incorporate the current standard of treatment for CD. Thus, the outcomes should continually improve over time. In step 507, the system provides guidelines for chronic disease supervision based on algorithm training. Such guidelines may provide short- or long-term follow-up recommendations, goals for exercise, diet, medical treatment, and other advice for successful long-term management of the condition and minimization of secondary complications; see also at least ¶¶ [0034], [0147], and [0151]).
Claim Rejections - 35 U.S.C. § 103
The following is a quotation of 35 U.S.C. § 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. § 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 2, 8, 11-16, and 18-20 are rejected under AIA 35 U.S.C. § 103 as being unpatentable over Steinberg-Koch et al. (U.S. Pub. No. 2022/0223293 A1) (hereafter “Steinberg-Koch”) in view of McCullough et al. (U.S. Pub. No. 2020/0087710 A1) (hereinafter “McCullough”).
Claim 2: Steinberg-Koch discloses the limitations as shown in the rejections above.
Steinberg-Koch does not explicitly disclose, but McCullough, as shown, discloses the following limitations:
wherein the algorithm comprises: a Pearson correlation analysis, a Spearman rank correlation analysis, a principal component analysis, a multiple linear regression analysis, a min-max scaling, a ROC curve analysis, a Mann-Whitney U-test, a Kruskal Wallis test, or any combination thereof (see at least ¶ [0274]: a regression determines the presence or absence of a correlation (e.g., a linear correlation), for example between counts and a measure of GC content. In some embodiments a regression (e.g., a linear regression) is generated and a correlation coefficient is determined. In some embodiments a suitable correlation coefficient is determined, non-limiting examples of which include a coefficient of determination, an R2 value, a Pearson's correlation coefficient, or the like; see also at least ¶¶ [0133], [0267]-[0268], [0273], and [0295]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the techniques for genetic and medical analyses taught by McCullough with the systems for evaluating autoimmune disease risk and treatment selection disclosed by Steinberg-Koch, because McCullough teaches at ¶ [0243] that “Any suitable statistical algorithm, alone or in combination, may be used to analyze and/or manipulate a data set described herein” and at ¶ [0133] that “A weighting factor can be any suitable coefficient, estimated coefficient or constant derived from a suitable relation (e.g., a suitable mathematical relation, an algebraic relation, a fitted relation, a regression, a regression analysis, a regression model).” See M.P.E.P. § 2143(I)(G).
Moreover, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the techniques for genetic and medical analyses taught by McCullough with the systems for evaluating autoimmune disease risk and treatment selection disclosed by Steinberg-Koch, because the claimed invention is merely a combination of old elements (the techniques for genetic and medical analyses taught by McCullough and the systems for evaluating autoimmune disease risk and treatment selection disclosed by Steinberg-Koch), 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
Claim 8: Steinberg-Koch discloses the limitations as shown in the rejections above.
Steinberg-Koch does not explicitly disclose, but McCullough, as shown, discloses the following limitations:
wherein the immunophenotyping is performed using a flow cytometry (see at least ¶ [0158]: Detection and/or quantification of an identifier can be performed by a suitable method, apparatus or machine, non-limiting examples of which include flow cytometry, quantitative polymerase chain reaction (qPCR), gel electrophoresis, a luminometer, a fluorometer, a spectrophotometer, a suitable gene-chip or microarray analysis, Western blot, mass spectrometry, chromatography, cytofluorimetric analysis, fluorescence microscopy, a suitable fluorescence or digital imaging method, confocal laser scanning microscopy, laser scanning cytometry, affinity chromatography, manual batch mode separation, electric field suspension, a suitable nucleic acid sequencing method and/or nucleic acid sequencing apparatus, the like and combinations thereof).
The rationales to modify/combine the teachings of Steinberg-Koch to include the teachings of McCullough are presented above regarding claim 2 and incorporated herein.
Claim 11: Steinberg-Koch discloses the limitations as shown in the rejections above.
Steinberg-Koch does not explicitly disclose, but McCullough, as shown, discloses the following limitations:
wherein the computational immune cell population comprises: T cell, B cell, basophil, neutrophil, eosinophil, dendritic cell, macrophage, natural killer cell, or any subset thereof (see at least ¶ [0077]: a biological sample may be blood, plasma or serum. The term “blood” encompasses whole blood, blood product or any fraction of blood, such as serum, plasma, buffy coat, or the like as conventionally defined. Blood or fractions thereof often comprise nucleosomes. Nucleosomes comprise nucleic acids and are sometimes cell-free or intracellular. Blood also comprises buffy coats. Buffy coats are sometimes isolated by utilizing a ficoll gradient. Buffy coats can comprise white blood cells (e.g., leukocytes, T-cells, B-cells, platelets, and the like); see also at least ¶ [0076]).
The rationales to modify/combine the teachings of Steinberg-Koch to include the teachings of McCullough are presented above regarding claim 2 and incorporated herein.
Claim 12: Steinberg-Koch discloses the limitations as shown in the rejections above. Further, Steinberg-Koch, as shown, discloses the following limitations:
wherein the step (A) further comprises:
obtaining a first immune cell population from the computational immune cell population through a correlation analysis using the variation of the clinical index (see at least ¶ [0143]: Additional categories and additional parameters within each category may be included over time as the machine learning algorithm identifies and correlates other factors as having relevancy to the diagnosis of CD. Demographics includes gender, birth season, and age at the time of the test and, if known, age at the time of CD diagnosis; see also at least ¶¶ [0144]-[0149]);
Steinberg-Koch does not explicitly disclose, but McCullough, as shown, discloses the following limitations:
obtaining a model immune cell population from the first immune cell population through a principal component analysis (see at least ¶ [0268]: a principal component can be obtained from a PCA using any suitable sample or reference. In some embodiments principal components are obtained from a test sample (e.g., a test subject). In some embodiments principal components are obtained from one or more references (e.g., reference samples, reference sequences, a reference set). In certain instances, a PCA is performed on a median read density profile obtained from a training set comprising multiple samples resulting in the identification of a 1st principal component and a 2nd principal component. In some embodiments, principal components are obtained from a set of subjects devoid of a copy number alteration in question. In some embodiments, principal components are obtained from a set of known euploids. Principal component are often identified according to a PCA performed using one or more read density profiles of a reference (e.g., a training set). One or more principal components obtained from a reference are often subtracted from a read density profile of a test subject thereby providing an adjusted profile; see also at least ¶¶ [0133], [0267], and [0295]); and
obtaining a multiple regression equation through a multiple linear regression analysis using the model immune cell population, thereby establishing the prediction model (see at least ¶ [0133]: any suitable model and/or method of fitting a relationship (e.g., training a model to a training set) can be used. Non-limiting examples of a suitable model that can be used include a regression model, linear regression model, simple regression model, ordinary least squares regression model, multiple regression model, general multiple regression model, polynomial regression model, general linear model, generalized linear model, discrete choice regression model, logistic regression model, multinomial logit model, mixed logit model, probit model, multinomial probit model, ordered logit model, ordered probit model, Poisson model, multivariate response regression model, multilevel model, fixed effects model, random effects model, mixed model, nonlinear regression model, nonparametric model, semiparametric model, robust model, quantile model, isotonic model, principal components model, least angle model, local model, segmented model, and errors-in-variables model […] For example, for a linear least squared model, a general multiple regression model can be trained using fetal fraction values and a portion-specific parameter (e.g., coverage, e.g., see Example 4) resulting in a relationship described by Equation (1) where the weighting factor β is further defined in Equations (2), (3) and (4); see also at least ¶¶ [0261], [0265], and [0458]).
The rationales to modify/combine the teachings of Steinberg-Koch to include the teachings of McCullough are presented above regarding claim 2 and incorporated herein.
Claim 13: The combination of Steinberg-Koch and McCullough teaches the limitations as shown in the rejections above.
Steinberg-Koch does not explicitly disclose, but McCullough, as shown, discloses the following limitations:
wherein a dependent variable of the multiple regression equation comprises a predictive index (see at least ¶ [0238]: any suitable procedure can be utilized for processing data sets described herein. Non-limiting examples of procedures suitable for use for processing data sets include filtering, normalizing, weighting, monitoring peak heights, monitoring peak areas, monitoring peak edges, peak level analysis, peak width analysis, peak edge location analysis, peak lateral tolerances, determining area ratios, mathematical processing of data, statistical processing of data, application of statistical algorithms, analysis with fixed variables, analysis with optimized variables, plotting data to identify patterns or trends for additional processing, the like and combinations of the foregoing; see also at least ¶ [0241]: any suitable number of normalizations can be used. In some embodiments, data sets can be normalized 1 or more, 5 or more, 10 or more or even 20 or more times. Data sets can be normalized to values (e.g., normalizing value) representative of any suitable feature or variable (e.g., sample data, reference data, or both); see also at least ¶ [0270]: a processing step comprises a hybrid normalization method. A hybrid normalization method may reduce bias (e.g., GC bias), in certain instances. A hybrid normalization, in some embodiments, comprises (i) an analysis of a relationship of two variables (e.g., counts and GC content) and (ii) selection and application of a normalization method according to the analysis. A hybrid normalization, in certain embodiments, comprises (i) a regression (e.g., a regression analysis) and (ii) selection and application of a normalization method according to the regression; see also at least ¶ [0370]: a suitable feature or variable can be utilized to reduce data set complexity and/or dimensionality. Non-limiting examples of features that can be chosen for use as a target feature for data processing include GC content, fetal gender prediction, fragment size (e.g., length of CCF fragments, reads or a suitable representation thereof (e.g., FRS)), fragment sequence, identification of a copy number alteration, identification of chromosomal aneuploidy, identification of particular genes or proteins, identification of cancer, diseases, inherited genes/traits, chromosomal abnormalities, a biological category, a chemical category, a biochemical category, a category of genes or proteins, a gene ontology, a protein ontology, co-regulated genes, cell signaling genes, cell cycle genes, proteins pertaining to the foregoing genes, gene variants, protein variants, co-regulated genes, co-regulated proteins, amino acid sequence, nucleotide sequence, protein structure data and the like, and combinations of the foregoing; see also at least ¶¶ [0272] and [0340]).
The rationales to modify/combine the teachings of Steinberg-Koch to include the teachings of McCullough are presented above regarding claim 2 and incorporated herein.
Claim 14: The combination of Steinberg-Koch and McCullough teaches the limitations as shown in the rejections above.
Steinberg-Koch does not explicitly disclose, but McCullough, as shown, discloses the following limitations:
wherein an independent variable of the multiple regression equation comprises a proportion of the model immune cell population (see at least ¶ [0265]: for each point in a data set, the LOESS method fits a low-degree polynomial to a subset of the data, with explanatory variable values near the point whose response is being estimated. The polynomial is fitted using weighted least squares, giving more weight to points near the point whose response is being estimated and less weight to points further away. The value of the regression function for a point is then obtained by evaluating the local polynomial using the explanatory variable values for that data point. The LOESS fit is sometimes considered complete after regression function values have been computed for each of the data points. Many of the details of this method, such as the degree of the polynomial model and the weights, are flexible; see also at least ¶ [0373]: the presence or absence of one or more genetic variations or genetic alterations is determined according to an outcome provided by methods and apparatuses described herein. A genetic variation generally is a particular genetic phenotype present in certain individuals, and often a genetic variation is present in a statistically significant sub-population of individuals; see also at least ¶¶ [0133], [0267], [0295], [0408], and [0425]).
Claim 15: The combination of Steinberg-Koch and McCullough teaches the limitations as shown in the rejections above.
Steinberg-Koch does not explicitly disclose, but McCullough, as shown, discloses the following limitations:
wherein a cutoff value is used to establish the prediction model (see at least ¶ [0295]: a “measure of uncertainty” as used in Steinberg-Koch refers to a measure of significance (e.g., statistical significance), a measure of error, a measure of variance, a measure of confidence, the like or a combination thereof. A measure of uncertainty can be a value (e.g., a threshold) or a range of values (e.g., an interval, a confidence interval, a Bayesian confidence interval, a threshold range). Non-limiting examples of a measure of uncertainty include p-values, a suitable measure of deviation (e.g., standard deviation, sigma, absolute deviation, mean absolute deviation, the like), a suitable measure of error (e.g., standard error, mean squared error, root mean squared error, the like), a suitable measure of variance, a suitable standard score (e.g., standard deviations, cumulative percentages, percentile equivalents, Z-scores, T-scores, R-scores, standard nine (stanine), percent in stanine, the like), the like or combinations thereof. In some embodiments determining the level of significance comprises determining a measure of uncertainty (e.g., a p-value). In certain embodiments, two or more data sets, relationships and/or profiles can be analyzed and/or compared by utilizing multiple (e.g., 2 or more) statistical methods (e.g., least squares regression, principal component analysis, linear discriminant analysis, quadratic discriminant analysis, bagging, neural networks, support vector machine models, random forests, classification tree models, K-nearest neighbors, logistic regression and/or loss smoothing) and/or any suitable mathematical and/or statistical manipulations (e.g., referred to herein as manipulations); see also at least ¶ [0328]: An outcome and/or classification sometimes is a component of health care and/or treatment of a subject. An outcome and/or classification sometimes is utilized and/or assessed as part of providing a treatment for a subject from whom a test sample was obtained. For example, an outcome and/or classification indicative of presence or absence of a genotype, phenotype, genetic variation, and/or medical condition is a component of health care and/or treatment of a subject from whom a test sample was obtained. Medical care, treatment and or diagnosis can be in any suitable area of health, such as medical treatment of subjects for prenatal care, cell proliferative conditions, cancer and the like, for example. An outcome and/or classification determinative of presence or absence of a genotype, phenotype, genetic variation and/or medical condition, disease, syndrome or abnormality by methods described herein sometimes is independently verified by further testing. Any suitable type of further test to verify an outcome and/or classification can be utilized, non-limiting examples of which include blood level test (e.g., serum test), biopsy, scan (e.g., CT scan, MRI scan), invasive sampling (e.g., amniocentesis or chorionic villus sampling), karyotyping, microarray assay, ultrasound, sonogram, and the like, for example), and
the step (A) further comprises: deciding the cutoff value based on the comparison of the predictive index and a clinical result of the administration of the autoimmune disease medication (see at least ¶¶ [0295] and [0328] and the analysis above).
The rationales to modify/combine the teachings of Steinberg-Koch to include the teachings of McCullough are presented above regarding claim 2 and incorporated herein.
Claim 16: The combination of Steinberg-Koch and McCullough teaches the limitations as shown in the rejections above.
Steinberg-Koch does not explicitly disclose, but McCullough, as shown, discloses the following limitations:
wherein the prediction result of the autoimmune disease medication is determined by the cutoff value (see at least ¶ [0295]; see also at least ¶ [0328]: An outcome and/or classification sometimes is a component of health care and/or treatment of a subject. An outcome and/or classification sometimes is utilized and/or assessed as part of providing a treatment for a subject from whom a test sample was obtained. For example, an outcome and/or classification indicative of presence or absence of a genotype, phenotype, genetic variation, and/or medical condition is a component of health care and/or treatment of a subject from whom a test sample was obtained. Medical care, treatment and or diagnosis can be in any suitable area of health, such as medical treatment of subjects for prenatal care, cell proliferative conditions, cancer and the like, for example. An outcome and/or classification determinative of presence or absence of a genotype, phenotype, genetic variation and/or medical condition, disease, syndrome or abnormality by methods described herein sometimes is independently verified by further testing. Any suitable type of further test to verify an outcome and/or classification can be utilized, non-limiting examples of which include blood level test (e.g., serum test), biopsy, scan (e.g., CT scan, MRI scan), invasive sampling (e.g., amniocentesis or chorionic villus sampling), karyotyping, microarray assay, ultrasound, sonogram, and the like, for example).
The rationales to modify/combine the teachings of Steinberg-Koch to include the teachings of McCullough are presented above regarding claim 2 and incorporated herein.
Claim 18: Steinberg-Koch discloses the limitations as shown in the rejections above. Further, Steinberg-Koch, as shown, discloses the following limitations:
wherein the processing unit is further configured to receive a computational data (see at least ¶ [0129]: reference is first made to FIG. 1, which illustrates schematically the overall structure of an exemplary implementation of the disclosed invention. A method detects individuals having characteristics that indicate a specific disease process. In a first phase of the method, historical patient data from electronic medical records (EMR), electronic health records (EHR), claims data or data from other sources are collected, followed by application of machine/deep learning, natural language processing (NLP), or other individual or combined machine learning techniques to train an algorithm of the method to identify subjects with the autoimmune conditions which are to be diagnosed based on known cases of such disease in the historic population data. In a second phase, new patient data are input to the algorithm to enable determination of the probability and risk that a given individual in the new population has an autoimmune condition; see also at least ¶ [0143]: example of parameters or features from the patient’s data file, used in the machine learning algorithm may fall into the following categories: demographics including family history of CD or other gastrointestinal conditions, symptoms, concurrent diagnoses, lab tests, medications, procedure and current and past measurements such as height, weight, and BMI. A large number of parameters may be used in training the algorithm; over time, additional, different, or fewer parameters may be incorporated to improve the diagnostic accuracy of the method; see also at least ¶ [0130]: a historic database of insurer medical claims and/or EMR data for a large population, representing the target population for this algorithm, is accessed to provide examples for training the models of the system. This data is augmented with additional sources, such as IOT sensor data, subject provided information, and aggregated statistics relevant to target subjects collected either from research datasets, or via use of the proposed system. This information is used in subsequent steps 103 and 106 a to generate processed and filtered training information, ultimately for use in step 109; see also at least ¶ [0131]: the large population data from block 101 is used in combination with rules derived from medical experts or known medical protocols, here referred to as “expert medical logic” 102, to generate tagged or labeled training data of subjects; see also at least ¶ [0135]: In step 108 a, a multi-output classifier model is trained using supervised learning of the tagged training data (107 a). The steps 101 to 108 a, shown in FIG. 1 within the dotted line 100, are steps used for the periodic training of the artificial intelligence models using the large historic population data. Steps 106 b to 108 b, on the other hand, are steps in which the feature embedding and classifying of the subject data are applied to the data of the currently analyzed patients, whose diagnoses are being resolved; see also at least ¶¶ [0034] and [0147]),
Steinberg-Koch does not explicitly disclose, but McCollough, as shown, discloses the following limitations:
and to establish the prediction model by processing the computational data using an algorithm comprising: a correlation analysis, a principal component analysis, a multiple linear regression analysis, a min-max scaling, a ROC curve analysis, a Mann-Whitney U-test, a Kruskal Wallis test, or any combination thereof (see at least ¶ [0274]: a regression determines the presence or absence of a correlation (e.g., a linear correlation), for example between counts and a measure of GC content. In some embodiments a regression (e.g., a linear regression) is generated and a correlation coefficient is determined. In some embodiments a suitable correlation coefficient is determined, non-limiting examples of which include a coefficient of determination, an R2 value, a Pearson's correlation coefficient, or the like; see also at least ¶¶ [0133], [0267]-[0268], [0273], and [0295]).
The rationales to modify/combine the teachings of Steinberg-Koch to include the teachings of McCullough are presented above regarding claim 2 and incorporated herein.
Claim 19: The combination of Steinberg-Koch and McCollough teaches the limitations as shown in the rejections above. Further, Steinberg-Koch, as shown, discloses the following limitations:
wherein the computational data comprises the autoimmune disease medication, a variation of a clinical index, and a proportion of a computational immune cell population (see at least ¶ [0129]: reference is first made to FIG. 1, which illustrates schematically the overall structure of an exemplary implementation of the disclosed invention. A method detects individuals having characteristics that indicate a specific disease process. In a first phase of the method, historical patient data from electronic medical records (EMR), electronic health records (EHR), claims data or data from other sources are collected, followed by application of machine/deep learning, natural language processing (NLP), or other individual or combined machine learning techniques to train an algorithm of the method to identify subjects with the autoimmune conditions which are to be diagnosed based on known cases of such disease in the historic population data. In a second phase, new patient data are input to the algorithm to enable determination of the probability and risk that a given individual in the new population has an autoimmune condition; see also at least ¶ [0143]: example of parameters or features from the patient’s data file, used in the machine learning algorithm may fall into the following categories: demographics including family history of CD or other gastrointestinal conditions, symptoms, concurrent diagnoses, lab tests, medications, procedure and current and past measurements such as height, weight, and BMI. A large number of parameters may be used in training the algorithm; over time, additional, different, or fewer parameters may be incorporated to improve the diagnostic accuracy of the method; see also at least ¶ [0130]: a historic database of insurer medical claims and/or EMR data for a large population, representing the target population for this algorithm, is accessed to provide examples for training the models of the system. This data is augmented with additional sources, such as IOT sensor data, subject provided information, and aggregated statistics relevant to target subjects collected either from research datasets, or via use of the proposed system. This information is used in subsequent steps 103 and 106 a to generate processed and filtered training information, ultimately for use in step 109; see also at least ¶ [0131]: the large population data from block 101 is used in combination with rules derived from medical experts or known medical protocols, here referred to as “expert medical logic” 102, to generate tagged or labeled training data of subjects; see also at least ¶ [0135]: In step 108 a, a multi-output classifier model is trained using supervised learning of the tagged training data (107 a). The steps 101 to 108 a, shown in FIG. 1 within the dotted line 100, are steps used for the periodic training of the artificial intelligence models using the large historic population data. Steps 106 b to 108 b, on the other hand, are steps in which the feature embedding and classifying of the subject data are applied to the data of the currently analyzed patients, whose diagnoses are being resolved; see also at least ¶¶ [0034] and [0147]).
Claim 20: The combination of Steinberg-Koch and McCollough teaches the limitations as shown in the rejections above. Further, Steinberg-Koch, as shown, discloses the following limitations:
an input unit, connected to the processing unit and configured to provide the diagnostic data (see at least ¶ [0129]: historical patient data from electronic medical records (EMR), electronic health records (EHR), claims data or data from other sources are collected, followed by application of machine/deep learning, natural language processing (NLP), or other individual or combined machine learning techniques to train an algorithm of the method to identify subjects with the autoimmune conditions which are to be diagnosed based on known cases of such disease in the historic population data; see also at least ¶ [0130]: a historic database of insurer medical claims and/or EMR data for a large population, representing the target population for this algorithm, is accessed to provide examples for training the models of the system. This data is augmented with additional sources, such as IOT sensor data, subject provided information, and aggregated statistics relevant to target subjects collected either from research datasets, or via use of the proposed system. This information is used in subsequent steps 103 and 106 a to generate processed and filtered training information, ultimately for use in step 109);
an output unit, connected to the processing unit and configured to present the prediction result of the autoimmune disease medication (see at least ¶ [0176]: reference is now made to FIG. 6, showing a visualization of the embedding space, to illustrate the clustering of subjects with respect to lab values or other exemplary indicators of autoimmune disease; see also at least ¶ [0184]: the user interface is configured to communicate with other systems and share information via the IoT and other tools. The system may be configured to provide alerts to doctor or to insurer system or even to the subject via health app or other patient interface. Furthermore, the system may be configured to receive feedback from the user or a doctor regarding the accuracy of the classifier model results. Such human feedback regarding diagnosis or treatment/follow-up recommendations may be incorporated in order to influence future training cycles of its models, such as is shown in step 110 of FIGS. 1 and 408 of FIG. 4; see also at least ¶ [0177]); and
a storage unit, connected to the processing unit and configured to provide the computational data (see at least ¶ [0183]: the system comprises a memory 701, processors and graphic processing units 702, cloud application program interface or storage 703, other storage and databases 704, and a user interface 705. The components of the system 700 are further delineated below, with reference to the steps of the exemplary method in FIG. 1 to which they correspond. The memory 701 may comprise data relating to patient feature vectors 706 (FIG. 1, steps 106 a, 106 b), patient diagnosis probability vectors 707 (FIG. 1, step 109), and expert medical logic 708 (FIG. 1, step 102). The processing unit 702 may comprise algorithms of artificial intelligence, machine learning, and deep learning 709, a controller 710, and supervised and self-supervised training and inference 711 (FIG. 1, steps 103, 104, 106 a, 106 b, 108 a, 108 b). The cloud storage 703 may comprise historic population medical data (FIG. 1, step 101, 105). The at least one database 704 may comprise the data incorporating classifier model parameters 715 and embedding model parameters 716. The user interface 705 communicates with the medical staff or other professionals using the system, and provides the output of the system, such as a diagnosis or list of possible diagnoses, ranked in order of likelihood 712, referrals to specialists and follow-up guidelines 713, and in some implementations, treatment recommendations or guidelines 714).
Claims 5 and 10 are rejected under AIA 35 U.S.C. § 103 as being unpatentable over Steinberg-Koch et al. (U.S. Pub. No. 2022/0223293 A1) (hereafter “Steinberg-Koch”) in view of Ghiassian et al. (U.S. Pub. No. 2026/0057960 A1) (hereinafter “Ghiassian”).
Claim 5: Steinberg-Koch discloses the limitations as shown in the rejections above.
Steinberg-Koch does not explicitly disclose, but Ghiassian, as shown, discloses the following limitations:
wherein the tsDMARD comprises: Janus kinase inhibitor (JAKi) (see at least ¶ [0256]: one treatment goal in UC is to induce remission and maintain a corticosteroid-free remission, which often requires use of a targeted therapy. […] Approved targeted therapies include anti-integrin α.sub.4β.sub.7 (e.g., vedolizumab), anti-interleukin-12 of 23 (e.g., ustekinumab), tumor necrosis factor inhibitor (TNFi; e.g., adalimumab, infliximab and golimumab) and Janus kinase inhibitor (JAKi; e.g., tofacitinib) therapies).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the techniques for predicting therapeutic responses taught by Ghiassian with the systems for evaluating autoimmune disease risk and treatment selection disclosed by Steinberg-Koch, because Ghiassian teaches at ¶ [0256] that its techniques achieve the goal to “induce remission and maintain a corticosteroid-free remission” and at ¶ [0007] that its “methods and compositions described herein permit care providers to distinguish subjects likely to benefit from anti-TNF therapy from those who are not, reduce risks to patients, increase timing and quality of care for non-responder patient populations, increase efficiency of drug development, and avoid costs associated with administering ineffective therapy to non-responder patients or with treating side effects such patients experience upon receiving anti-TNF therapy.” See M.P.E.P. § 2143(I)(G).
Moreover, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the techniques for predicting therapeutic responses taught by Ghiassian with the systems for evaluating autoimmune disease risk and treatment selection disclosed by Steinberg-Koch, because the claimed invention is merely a combination of old elements (the techniques for predicting therapeutic responses taught by Ghiassian and the systems for evaluating autoimmune disease risk and treatment selection disclosed by Steinberg-Koch), 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
Claim 10: Steinberg-Koch discloses the limitations as shown in the rejections above.
Steinberg-Koch does not explicitly disclose, but Ghiassian, as shown, discloses the following limitations:
wherein the marker comprises: CD95, CD366, HLA-DR, CD62L, CD127, CD8, KLRG-1, CD3, CD4, CD45RA, CCR7, PD-1, CD27, CD28, CD25, FOXP3, CD39, CD19, IgM, IgD, CD38, CD21, or any combination thereof (see at least ¶ [0045]: an antibody utilized in accordance with the present disclosure is in a comprising intact IgA, IgG, IgE or IgM antibodies; bi- or multi-specific antibodies (e.g., Zybodies®, etc); antibody fragments such as Fab fragments, Fab′ fragments, F(ab′)2 fragments, Fd′ fragments, Fd fragments, and isolated CDRs or sets thereof; see also at least ¶ [0277]).
The rationales to modify/combine the teachings of Steinberg-Koch to include the teachings of Ghiassian are presented above regarding claim 5 and incorporated herein.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. The following references have been cited to further show the state of the art with respect to predicting responses to autoimmune therapies.
Faham et al. (U.S. Pub. No. 2011/0207134 A1) (monitoring health and disease status using clonotype profiles); and
Chen et al. (“The tumor necrosis factor receptor superfamily member 1B polymorphisms predict response to anti-TNF therapy in patients with autoimmune disease: A meta-analysis.” International immunopharmacology 28.1 (2015): 146-153).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Christopher Tokarczyk, whose telephone number is 571-272-9594. The examiner can normally be reached Monday-Thursday between 6:00 AM and 4:00 PM Eastern.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mamon Obeid, can be reached at 571-270-1813. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHRISTOPHER B TOKARCZYK/Primary Examiner, Art Unit 3687
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