Detailed Notice
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 1-20 are currently pending.
Claim 1 is amended.
Claims 2-20 are new.
Claims 1-20 are rejected.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-2, 5, 8-10, 13, 15-17, are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 6, 11, and 13-14 of U.S. Patent No. 11,335,461 in view of Cohen et al. (US 20190131016 A1). Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1-2, 5, 8-10, 13, 15-17 are similar to claims 1, 6, 11, and 13-14 of U.S. Patent No. 11,335,461 as shown in the table below:
Application No. 19/026,034
U.S. Patent No. 11,335,461
1. (Currently Amended) A computer-implemented method comprising:
determining a set of physiological variables associated with an individual based on content received as input data;
determining, at one or more hardware processors, a probability of a clinically significant glycogen storage disease for the individual based on the set of physiological variables and based further on a machine-learning electronic model, wherein:
(a) the machine-learning electronic model is trained based on (i) data associated with physiological variable instances and (ii) one or more decision elements associated with the physiological variable instances, and
(b) applying the machine-learning electronic model to data associated with the set of physiological variables generates information indicating the probability of the clinically significant glycogen storage disease;
and based on the probability of the clinically significant glycogen storage disease, automatically initiating via the one or more hardware processors an intervention action selected from a group comprising modifying treatment of the individual, ordering additional diagnostics for the individual, scheduling treatment or diagnostics for the individual, and issuing a notification to a caregiver associated with the individual.
1. A computerized method of initiating an electronic health record (EHR) intervention action for glycogen storage disease, the method comprising:
determining a multi-variable biomarker based on a set of physiological variables associated with an individual that is received as input data, the multi-variable biomarker comprising a plurality of: attribution of myalgia, attribution of a plurality of comorbid conditions, elevated creatine kinase, increased red blood cell size distribution width (RDW), elevated aminotransferases, elevated alanine aminotransferase to aspartate aminotransferase ratio, erythrocyte microcytosis, increased anion gap, or laboratory indicia of hypothyroidism;
training a multi-variable logistic regression statistical model to determine a probability of a clinically significant glycogen storage disease, the multi-variable logistic regression statistical model being trained with data from an anonymized data warehouse of electronic medical record (EMR) data that is collected via an automated process;
utilizing the trained multi-variable logistic regression statistical model, determining the probability of the clinically significant glycogen storage disease for the individual based on the multi-variable biomarker; and
based on the probability of the clinically significant glycogen storage disease for the individual, automatically initiating the EHR intervention action, wherein the intervention action comprises one or more of modifying treatment of the patient, ordering additional diagnostics for the patient, scheduling treatment or diagnostics for the patient, and issuing a notification to a caregiver associated with the patient.
2. (New) The computer-implemented method of claim 1, wherein the machine-learning electronic model comprises a multi-variable logistic regression statistical model.
1… training a multi-variable logistic regression statistical model to determine a probability of a clinically significant glycogen storage disease, the multi-variable logistic regression statistical model being trained with data from an anonymized data warehouse of electronic medical record (EMR) data that is collected via an automated process
3. (New) The computer-implemented method of claim 1, further comprising modifying electronic health record (EHR) associated with the individual according to the probability of the clinically significant glycogen storage disease to include data indicating that the individual is a candidate for receiving additional treatment or diagnostic procedures associated with the clinically significant glycogen storage disease.
14. The one or more computer-readable storage devices of claim 13, further comprising modifying the EMR according to the determined probability to include data indicating that the individual associated with the EMR is a candidate for receiving additional treatment or diagnostic procedures associated with the clinically significant glycogen storage disease.
It would be obvious for the functionality of the computer readable storage device of claim 13 to be applied to the computer-implemented method of claim 1 because a person skilled in the applied art would know to modify the EMR/HER in claim 3 based on the probability of clinically significant glycogen disease to indicate the individual/patient is eligible to receive additional treatment or diagnostic procedures.
4. (New) The computer-implemented method of claim 1, further comprising training the machine-learning electronic model based on first data from an electronic data store to determine the probability of the clinically significant glycogen storage disease.
X
5. (New) The computer-implemented method of claim 1, further comprising collecting the first data from an electronic data store via an automated process, wherein the electronic data store comprises an anonymized data warehouse of electronic medical record data.
1… training a multi-variable logistic regression statistical model to determine a probability of a clinically significant glycogen storage disease, the multi-variable logistic regression statistical model being trained with data from an anonymized data warehouse of electronic medical record (EMR) data that is collected via an automated process…
6. (New) The computer-implemented method of claim 1, wherein the intervention action comprises a particular notification that indicates the probability of the clinically significant glycogen storage disease.
11. The one or more computer-readable storage devices of claim 6, further comprising determining a risk level of the clinically significant glycogen storage disease based on comparing the probability to the diagnostic threshold, and communicating the risk level as part of the notification.
It would be obvious for the functionality of the computer readable storage device of claim 11 to be applied to the computer-implemented method of claim 1 because a person skilled in the applied art would know to provide a notification with a particular intervening action based on the probability the individual has the clinically significant glycogen storage disease.
7. (New) The computer-implemented method of claim 1, wherein: based on the scheduling of the treatment or diagnostics for the individual, a particular course of treatment is administered by a clinician to the individual to treat a disease or a medical condition of the individual.
X
8. (New) One or more non-transitory media having instructions that, when executed by one or more hardware processors, cause the one or more hardware processors to facilitate a plurality of operations, the operations comprising:
determining a set of physiological variables, associated with an individual, based on content received as input data;
determining, at the one or more hardware processors, a probability of a clinically significant glycogen storage disease for the individual based on the set of physiological variables and based further on a machine-learning electronic model, wherein:
(a) the machine-learning electronic model is trained based on (i) data associated with physiological variable instances and (ii) one or more decision elements associated with the physiological variable instances, and
(b) applying the machine-learning electronic model to data associated with the set of physiological variables generates information indicating the probability of the clinically significant glycogen storage disease;
and based on the probability of the clinically significant glycogen storage disease, automatically initiating via the one or more hardware processors an intervention action selected from a group comprising modifying treatment of the individual, ordering additional diagnostics for the individual, scheduling treatment or diagnostics for the individual, and issuing a notification to a caregiver associated with the individual.
6. One or more computer-readable storage devices storing computer-useable instructions that, when used by one or more computing devices, cause the one or more computing devices to perform a method of initiating an electronic health record (EHR) intervention action for glycogen storage disease, the method comprising:
determining a multi-variable biomarker based on a set of physiological variables associated with an individual that is received as input data, the multi-variable biomarker comprising a plurality of: attribution of myalgia, attribution of a plurality of comorbid conditions, elevated creatine kinase, increased red blood cell size distribution width (RDW), elevated aminotransferases, elevated alanine aminotransferase to aspartate aminotransferase ratio, erythrocyte microcytosis, increased anion gap, or laboratory indicia of hypothyroidism;
training a multi-variable logistic regression statistical model to determine a probability of a clinically significant glycogen storage disease, the multi-variable logistic regression statistical model being trained with data from an anonymized data warehouse of electronic medical record (EMR) data that is collected via an automated process;
utilizing the trained multi-variable logistic regression statistical model, determining the probability of the clinically significant glycogen storage disease for the individual based on the multi-variable biomarker; and
based on the probability of the clinically significant glycogen storage disease for the individual, automatically initiating the EHR intervention action, wherein the intervention action comprises one or more of modifying treatment of the patient, ordering additional diagnostics for the patient, scheduling treatment or diagnostics for the patient, and issuing a notification to a caregiver associated with the patient.
9. (New) The one or more non-transitory media of claim 8, wherein the machine-learning electronic model comprises a multi-variable logistic regression statistical model.
6… training a multi-variable logistic regression statistical model to determine a probability of a clinically significant glycogen storage disease, the multi-variable logistic regression statistical model being trained with data from an anonymized data warehouse of electronic medical record (EMR) data that is collected via an automated process
10. (New) The one or more non-transitory media of claim 8, wherein the operations further comprise modifying electronic health record (EHR) associated with the individual according to the probability of the clinically significant glycogen storage disease to include data indicating that the individual is a candidate for receiving additional treatment or diagnostic procedures associated with the clinically significant glycogen storage disease.
14. The one or more computer-readable storage devices of claim 13, further comprising modifying the EMR according to the determined probability to include data indicating that the individual associated with the EMR is a candidate for receiving additional treatment or diagnostic procedures associated with the clinically significant glycogen storage disease.
11. (New) The one or more non-transitory media of claim 8, wherein the operations further comprise training the machine-learning electronic model based on first data from an electronic data store to determine the probability of the clinically significant glycogen storage disease.
X
12. (New) The one or more non-transitory media of claim 8, wherein the operations further comprise collecting the first data from an electronic data store via an automated process, wherein the electronic data store comprises an anonymized data warehouse of electronic medical record data.
X
13. (New) The one or more non-transitory media of claim 8, wherein the intervention action comprises a particular notification that indicates the probability of the clinically significant glycogen storage disease.
11. The one or more computer-readable storage devices of claim 6, further comprising determining a risk level of the clinically significant glycogen storage disease based on comparing the probability to the diagnostic threshold, and communicating the risk level as part of the notification.
14. (New) The one or more non-transitory media of claim 8, wherein: based on the scheduling of the treatment or diagnostics for the individual, a particular course of treatment is administered by a clinician to the individual to treat a disease or a medical condition of the individual.
X
15. (New) A system having one or more hardware processors configured to facilitate a plurality of operations, the operations comprising, comprising:
determining a set of physiological variables, associated with an individual, based on content received as input data;
determining, at the one or more hardware processors, a probability of a clinically significant glycogen storage disease for the individual based on the set of physiological variables and based further on a machine-learning electronic model, wherein:
(a) the machine-learning electronic model is trained based on (i) data associated with physiological variable instances and (ii) one or more decision elements associated with the physiological variable instances, and
(b) applying the machine-learning electronic model to data associated with the set of physiological variables generates information indicating the probability of the clinically significant glycogen storage disease;
and based on the probability of the clinically significant glycogen storage disease, automatically initiating via the one or more hardware processors an intervention action selected from a group comprising modifying treatment of the individual, ordering additional diagnostics for the individual, scheduling treatment or diagnostics for the individual, and issuing a notification to a caregiver associated with the individual.
13. One or more computer-readable storage devices storing computer-useable instructions that, when used by one or more computing devices, cause the one or more computing devices to perform a method of initiating an electronic health record (EHR) intervention action for glycogen storage disease, the method comprising:
identifying an EMR associated with an individual;
training a multi-variable logistic regression statistical model to determine a probability of a clinically significant glycogen storage disease, the multi-variable logistic regression statistical model being trained with data from an anonymized data warehouse of electronic medical record (EMR) data that is collected via an automated process;
receiving input data from the EMR associated with the individual;
determining a multi-variable biomarker based on a set of physiological variables in the received input data, the multi-variable biomarker comprising a plurality of: attribution of myalgia, attribution of a plurality of comorbid conditions, elevated creatine kinase, increased red blood cell size distribution width (RDW), elevated aminotransferases, elevated alanine aminotransferase to aspartate aminotransferase ratio, erythrocyte microcytosis, increased anion gap, or laboratory indicia of hypothyroidism;
utilizing the trained multi-variable logistic regression statistical model, determining the probability of the clinically significant glycogen storage disease for the individual based on the multi-variable biomarker; and
based on the probability of the clinically significant glycogen storage disease for the individual, automatically initiating the EHR intervention action, wherein the intervention action comprises one or more of modifying treatment of the patient, ordering additional diagnostics for the patient, scheduling treatment or diagnostics for the patient, and issuing a notification to a caregiver associated with the patient.
16. (New) The system of claim 15, wherein the machine-learning electronic model comprises a multi-variable logistic regression statistical model.
13. training a multi-variable logistic regression statistical model to determine a probability of a clinically significant glycogen storage disease, the multi-variable logistic regression statistical model being trained with data from an anonymized data warehouse of electronic medical record (EMR) data that is collected via an automated process
17. (New) The system of claim 15, wherein the operations further comprise modifying electronic health record (EHR) associated with the individual according to the probability of the clinically significant glycogen storage disease to include data indicating that the individual is a candidate for receiving additional treatment or diagnostic procedures associated with the clinically significant glycogen storage disease.
14. The one or more computer-readable storage devices of claim 13, further comprising modifying the EMR according to the determined probability to include data indicating that the individual associated with the EMR is a candidate for receiving additional treatment or diagnostic procedures associated with the clinically significant glycogen storage disease.
18. (New) The system of claim 15, wherein the operations further comprise training the machine-learning electronic model based on first data from an electronic data store to determine the probability of the clinically significant glycogen storage disease.
X
19. (New) The system of claim 15, wherein the operations further comprise collecting the first data from an electronic data store via an automated process, wherein the electronic data store comprises an anonymized data warehouse of electronic medical record data.
13… training a multi-variable logistic regression statistical model to determine a probability of a clinically significant glycogen storage disease, the multi-variable logistic regression statistical model being trained with data from an anonymized data warehouse of electronic medical record (EMR) data that is collected via an automated process…
20. (New) The system of claim 15, wherein the intervention action comprises a particular notification that indicates the probability of the clinically significant glycogen storage disease.
11. The one or more computer-readable storage devices of claim 6, further comprising determining a risk level of the clinically significant glycogen storage disease based on comparing the probability to the diagnostic threshold, and communicating the risk level as part of the notification.
It would be obvious for the functionality of the computer readable storage device of claim 11 to be applied to the computer-implemented method of claim 1 because a person skilled in the applied art would know to provide a notification with a particular intervening action based on the probability the individual has the clinically significant glycogen storage disease.
Regarding claims 1, 8, and 15, “based further on a machine-learning electronic model, wherein: (a) the machine-learning electronic model is trained based on (i) data associated with physiological variable instances and (ii) one or more decision elements associated with the physiological variable instances, and (b) applying the machine-learning electronic model” is not taught by the U.S. Patent No. 11,335,461.
However, Cohen et al. (US 20190131016 A1), hereinafter Cohen, teaches the set of physiological variables and based further on a machine-learning electronic model (Cohen, [0119]: “as additional samples are tested and the presence of cancer is validated, this data is fed back into the machine learning system to generate more accurate predictions of a patient's risk for having cancer”, [0137]: “A machine learning system may be utilized to aggregate the normalized biomarker scores along with other information (e.g., medical information, publicly available information, etc.) to generate a master composite score”, and [0151]: “multivariate logistic regression analysis may be used to determine a probability value. That value is then either classified per a risk categorization table or compared to a threshold value wherein above a threshold the nodule is deemed malignant and below the threshold value the nodule is deemed benign… In other embodiments, machine learning software, or support vector machine (SVM) learning algorithms, neural networks, random forest or decision tree models are used to analyze the obtained biomarker and clinical parameter values wherein a composite or risk score is generated and classified per a risk categorization table or compared to a threshold value”), wherein: (a) the machine-learning electronic model is trained based on (i) data associated with physiological variable instances and (ii) one or more decision elements associated with the physiological variable instances (Cohen, [0078]: “The statistical analysis may be a multivariate logistic regression model, a neural network model, a random forest model, a decision tree model, or other well-known methods for analyzing multiple variables”, [0371]: “Each of those variables (biomarkers or clinical parameters) was analyzed in a univariate logistic regression model and together in a multivariate logistic regression model”, and [0393]), and (b) applying the machine-learning electronic model to data associated with the set of physiological variables generates information indicating the probability (Cohen, [0020]: “the probability value is calculated using a multivariate logistic regression model, a neural network model, a random forest model or a decision tree model” and [0143]: “the measured value of the biomarkers (which may or may not include normalized values) and numerical clinical parameter data for a patient are analyzed using multi variable statistical models well understood in the art to obtain or calculate a probability value, which is a composite value for the entire panel of measured variables. In embodiments, a probability value may be calculated using a multivariate logistic regression (MLR) model, a neural network model, a random forest model or a decision tree model. The models are developed using retrospective clinical samples from a cohort of patients having benign nodules and malignant nodules”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Nilsson to incorporate the teachings of Cohen and account for clinically relevant markers for non-invasive detection of lung disease including cancer, monitoring response to therapy, or detecting lung cancer recurrence. It is also clear that such assays must be highly specific with reasonable sensitivity, and be readily available at a reasonable cost. Circulating biomarkers offer an alternative to imaging with the following advantages: 1) they are found in a minimally-invasive, easy to collect specimen type (blood or blood-derived fluids), 2) they can be monitored frequently over time in a subject to establish an accurate baseline, making it easy to detect changes over time, 3) they can be provided at a reasonably low cost, 4) they may limit the number of patients undergoing repeated expensive and potentially harmful CT scans, and/or 5) unlike CT scans, biomarkers may potentially distinguish indolent from more aggressive lung lesions (Cohen, Abstract and [0011]).
Additionally, claims 1-20, are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-2, 7-8, 13-14, 19, 21, and 23-25 of U.S. Application No. 17/733,538. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1-20 are similar to claims 1-2, 7-8, 13-14, 19, 21, and 23-25 of U.S. Patent No. 17/733,538 as shown in the table below:
Application No. 19/026,034
U.S. Application No. 17/733,538
1. (Currently Amended) A computer-implemented method comprising:
determining a set of physiological variables associated with an individual based on content received as input data;
determining, at one or more hardware processors, a probability of a clinically significant glycogen storage disease for the individual based on the set of physiological variables and based further on a machine-learning electronic model, wherein:
(a) the machine-learning electronic model is trained based on (i) data associated with physiological variable instances and (ii) one or more decision elements associated with the physiological variable instances, and
(b) applying the machine-learning electronic model to data associated with the set of physiological variables generates information indicating the probability of the clinically significant glycogen storage disease;
and based on the probability of the clinically significant glycogen storage disease, automatically initiating via the one or more hardware processors an intervention action selected from a group comprising modifying treatment of the individual, ordering additional diagnostics for the individual, scheduling treatment or diagnostics for the individual, and issuing a notification to a caregiver associated with the individual.
1. (Currently Amended) A method, comprising:
determining via one or more hardware processors (OOMHPs) a multi-variable (MV) biomarker based on a set of physiological variables associated with an individual, wherein the set of physiological variables is received via the OOMHPs as input data at a medical records computer system associated with an electronic memory;
determining, at the OOMHPs, a probability of a clinically significant glycogen storage (CSGS) disease for the individual based on the MV biomarker and based further on a machine-learning electronic model, wherein:
(a) the machine-learning electronic model is trained based on (i) data associated with MV biomarker instances comprising a plurality of: myalgia, comorbidity, creatine kinase, red blood cell size distribution width, aminotransferases, alanine aminotransferase to aspartate aminotransferase ratio, erythrocyte microcytosis, anion gap, or hypothyroidism and (ii) one or more decision elements associated with the MV biomarker instances, and
(b) applying the machine-learning electronic model to data associated with the MV biomarker generates information indicating the probability of the CSGS disease;
electronically writing, via the OOMHPs, electronic encoded data to the electronic memory at the medical records computer system, wherein the electronic encoded data indicates the probability of the CSGS disease;
and automatically initiating via the OOMHPs an intervention action, wherein automatically initiating via the OOMHPs the intervention action comprises transmitting to an electronic device associated with the medical records computer system information identifying the probability of the CSGS disease.
2. (New) The computer-implemented method of claim 1, wherein the machine-learning electronic model comprises a multi-variable logistic regression statistical model.
2. (Currently Amended) The method of claim 1, wherein the set of physiological variables comprises (i) attribution of myalgia and (ii) attribution of three or more of comorbid conditions, and wherein the machine-learning electronic model comprises a multi-variable logistic regression statistical model.
3. (New) The computer-implemented method of claim 1, further comprising modifying electronic health record (EHR) associated with the individual according to the probability of the clinically significant glycogen storage disease to include data indicating that the individual is a candidate for receiving additional treatment or diagnostic procedures associated with the clinically significant glycogen storage disease.
19. (Currently Amended) The system of claim 13,wherein the operations further comprise: identifying an electronic medical record (EMR) associated with the individual; and modifying the EMR according to the probability of the CSGS disease to include data indicating that the individual associated with the EMR is a candidate for receiving additional treatment or diagnostic procedures associated with the CSGS disease.
It would be obvious for the functionality of system of claim 19 to be applied to the computer-implemented method of claim 1 because a person skilled in the applied art would know to modify the EMR/HER in claim 3 based on the probability of clinically significant glycogen disease to indicate the individual/patient is eligible to receive additional treatment or diagnostic procedures.
4. (New) The computer-implemented method of claim 1, further comprising training the machine-learning electronic model based on first data from an electronic data store to determine the probability of the clinically significant glycogen storage disease.
21. (Currently Amended) The method of claim 1, further comprising training the machine- learning electronic model based on first data from an electronic data store to determine the probability of the CSGS disease.
5. (New) The computer-implemented method of claim 1, further comprising collecting the first data from an electronic data store via an automated process, wherein the electronic data store comprises an anonymized data warehouse of electronic medical record data.
23. (Currently Amended) The method of claim 1, further comprising: collecting the first data from an electronic data storage via an automated process, wherein the electronic data storage comprises an anonymized data warehouse of electronic medical record (EMR) data; and training the machine-learning electronic model based on the first data from the electronic data storage to determine the probability of the CSGS disease.
6. (New) The computer-implemented method of claim 1, wherein the intervention action comprises a particular notification that indicates the probability of the clinically significant glycogen storage disease.
24. (Currently Amended) The method of claim 1, further comprising presenting via an electronic user interface a particular notification that indicates the probability of the CSGS disease.
7. (New) The computer-implemented method of claim 1, wherein: based on the scheduling of the treatment or diagnostics for the individual, a particular course of treatment is administered by a clinician to the individual to treat a disease or a medical condition of the individual.
25. (Currently Amended) The one or more non-transitory media of claim 7, wherein: based on the probability of the CSGS disease, a particular course of treatment comprising a glycogen storage disease medication is used for the individual to treat a glycogen storage disease condition of the individual.
It would be obvious for the functionality of non-transitory media of claim 25 to administer a scheduled treatment or order more diagnostics for a patient to treat a disease/condition based on the probability of the clinically significant glycogen storage disease.
8. (New) One or more non-transitory media having instructions that, when executed by one or more hardware processors, cause the one or more hardware processors to facilitate a plurality of operations, the operations comprising:
determining a set of physiological variables, associated with an individual, based on content received as input data;
determining, at the one or more hardware processors, a probability of a clinically significant glycogen storage disease for the individual based on the set of physiological variables and based further on a machine-learning electronic model, wherein:
(a) the machine-learning electronic model is trained based on (i) data associated with physiological variable instances and (ii) one or more decision elements associated with the physiological variable instances, and
(b) applying the machine-learning electronic model to data associated with the set of physiological variables generates information indicating the probability of the clinically significant glycogen storage disease;
and based on the probability of the clinically significant glycogen storage disease, automatically initiating via the one or more hardware processors an intervention action selected from a group comprising modifying treatment of the individual, ordering additional diagnostics for the individual, scheduling treatment or diagnostics for the individual, and issuing a notification to a caregiver associated with the individual.
7. (Currently Amended) One or more non-transitory media having computer-readable instructions that, when executed by one or more hardware processors (OOMHPs), cause the OOMHPs to facilitate a plurality of operations, the operations comprising:
determining via the one or more hardware processors (OOMHPs) a multi-variable (MV) biomarker based on a set of physiological variables associated with an individual, wherein the set of physiological variables is received via the OOMHPs as input data at a medical records computer system associated with an electronic memory;
determining, at the OOMHPs, a probability of a clinically significant glycogen storage (CSGS) disease for the individual based on the MV biomarker and based further on a machine-learning electronic model, wherein:
(a) the machine-learning electronic model is trained based on (i) data associated with MV biomarker instances comprising a plurality of: myalgia, comorbidity, creatine kinase, red blood cell size distribution width, aminotransferases, alanine aminotransferase to aspartate aminotransferase ratio, erythrocyte microcytosis, anion gap, or hypothyroidism and (ii) one or more decision elements associated with the MV biomarker instances, and
(b) applying the machine-learning electronic model to data associated with the MV biomarker generates information indicating the probability of the CSGS disease;
electronically writing, via the OOMHPs, electronic encoded data to the electronic memory at the medical records computer system, wherein the electronic encoded data indicates the probability of the CSGS disease;
and automatically initiating via the OOMHPs an intervention action, wherein automatically initiating via the OOMHPs the intervention action comprises transmitting to an electronic device associated with the medical records computer system information identifying the probability of the CSGS disease.
9. (New) The one or more non-transitory media of claim 8, wherein the machine-learning electronic model comprises a multi-variable logistic regression statistical model.
8. (Currently Amended) The one or more non-transitory media of claim 7, wherein the machine-learning electronic model comprises a multi-variable logistic regression statistical model trained to determine the probability of the CSGS disease.
10. (New) The one or more non-transitory media of claim 8, wherein the operations further comprise modifying electronic health record (EHR) associated with the individual according to the probability of the clinically significant glycogen storage disease to include data indicating that the individual is a candidate for receiving additional treatment or diagnostic procedures associated with the clinically significant glycogen storage disease.
19. (Currently Amended) The system of claim 13,wherein the operations further comprise: identifying an electronic medical record (EMR) associated with the individual; and modifying the EMR according to the probability of the CSGS disease to include data indicating that the individual associated with the EMR is a candidate for receiving additional treatment or diagnostic procedures associated with the CSGS disease.
It would be obvious for the functionality of system of claim 19 to be applied to the computer-implemented method of claim 1 because a person skilled in the applied art would know to modify the EMR/HER in claim 3 based on the probability of clinically significant glycogen disease to indicate the individual/patient is eligible to receive additional treatment or diagnostic procedures.
11. (New) The one or more non-transitory media of claim 8, wherein the operations further comprise training the machine-learning electronic model based on first data from an electronic data store to determine the probability of the clinically significant glycogen storage disease.
21. (Currently Amended) The method of claim 1, further comprising training the machine-learning electronic model based on first data from an electronic data store to determine the probability of the CSGS disease.
It would be obvious for the functionality of method of claim 21 to be applied to the computer-implemented method of claim 1 because a person skilled in the applied art would know to train the machine-learning electronic model to determine the probability of the clinically significant glycogen storage disease.
12. (New) The one or more non-transitory media of claim 8, wherein the operations further comprise collecting the first data from an electronic data store via an automated process, wherein the electronic data store comprises an anonymized data warehouse of electronic medical record data.
23. (Currently Amended) The method of claim 1, further comprising: collecting the first data from an electronic data storage via an automated process, wherein the electronic data storage comprises an anonymized data warehouse of electronic medical record (EMR) data; and training the machine-learning electronic model based on the first data from the electronic data storage to determine the probability of the CSGS disease.
It would be obvious for the functionality of method of claim 23 to be applied to the computer-implemented method of claim 1 because a person skilled in the applied art would know to collect anonymized data from an electronic data store via an automated process.
13. (New) The one or more non-transitory media of claim 8, wherein the intervention action comprises a particular notification that indicates the probability of the clinically significant glycogen storage disease.
24. (Currently Amended) The method of claim 1, further comprising presenting via an electronic user interface a particular notification that indicates the probability of the CSGS disease.
It would be obvious for the functionality of method of claim 24 to be applied to the computer-implemented method of claim 1 because a person skilled in the applied art would know to the intervention action comprises a particular notification that indicates the probability of the clinically significant glycogen storage disease.
14. (New) The one or more non-transitory media of claim 8, wherein: based on the scheduling of the treatment or diagnostics for the individual, a particular course of treatment is administered by a clinician to the individual to treat a disease or a medical condition of the individual.
25. (Currently Amended) The one or more non-transitory media of claim 7, wherein: based on the probability of the CSGS disease, a particular course of treatment comprising a glycogen storage disease medication is used for the individual to treat a glycogen storage disease condition of the individual.
It would be obvious for the functionality of non-transitory media of claim 25 to administer a scheduled treatment or order more diagnostics for a patient to treat a disease/condition based on the probability of the clinically significant glycogen storage disease.
15. (New) A system having one or more hardware processors configured to facilitate a plurality of operations, the operations comprising, comprising:
determining a set of physiological variables, associated with an individual, based on content received as input data;
determining, at the one or more hardware processors, a probability of a clinically significant glycogen storage disease for the individual based on the set of physiological variables and based further on a machine-learning electronic model, wherein:
(a) the machine-learning electronic model is trained based on (i) data associated with physiological variable instances and (ii) one or more decision elements associated with the physiological variable instances, and
(b) applying the machine-learning electronic model to data associated with the set of physiological variables generates information indicating the probability of the clinically significant glycogen storage disease;
and based on the probability of the clinically significant glycogen storage disease, automatically initiating via the one or more hardware processors an intervention action selected from a group comprising modifying treatment of the individual, ordering additional diagnostics for the individual, scheduling treatment or diagnostics for the individual, and issuing a notification to a caregiver associated with the individual.
13. (Currently Amended) A system having one or more hardware processors (OOMHPs) configured to facilitate a plurality of operations, the operations comprising: determining via the one or more hardware processors (OOMHPs) a multi-variable (MV) biomarker based on a set of physiological variables associated with an individual, wherein the set of physiological variables is received via the OOMHPs as input data at a medical records computer system associated with an electronic memory;
determining, at the OOMHPs, a probability of a clinically significant glycogen storage (CSGS) disease for the individual based on the MV biomarker and based further on a machine-learning electronic model, wherein:
(a) the machine-learning electronic model is trained based on (i) data associated with MV biomarker instances comprising a plurality of: myalgia, comorbidity, creatine kinase, red blood cell size distribution width, aminotransferases, alanine aminotransferase to aspartate aminotransferase ratio, erythrocyte microcytosis, anion gap, or hypothyroidism and (ii) one or more decision elements associated with the MV biomarker instances, and
(b) applying the machine-learning electronic model to data associated with the MV biomarker generates information indicating the probability of the CSGS disease;
electronically writing, via the OOMHPs, electronic encoded data to the electronic memory at the medical records computer system, wherein the electronic encoded data indicates the probability of the CSGS disease;
and automatically initiating via the OOMHPs an intervention action, wherein automatically initiating via the OOMHPs the intervention action comprises transmitting to an electronic device associated with the medical records computer system information identifying the probability of the CSGS disease.
16. (New) The system of claim 15, wherein the machine-learning electronic model comprises a multi-variable logistic regression statistical model.
14. (Currently Amended) The system of claim 13, wherein the machine-learning electronic model comprises a multi-variable logistic regression statistical model trained to determine the probability of the CSGS disease.
17. (New) The system of claim 15, wherein the operations further comprise modifying electronic health record (EHR) associated with the individual according to the probability of the clinically significant glycogen storage disease to include data indicating that the individual is a candidate for receiving additional treatment or diagnostic procedures associated with the clinically significant glycogen storage disease.
19. (Currently Amended) The system of claim 13, wherein the operations further comprise: identifying an electronic medical record (EMR) associated with the individual; and modifying the EMR according to the probability of the CSGS disease to include data indicating that the individual associated with the EMR is a candidate for receiving additional treatment or diagnostic procedures associated with the CSGS disease.
18. (New) The system of claim 15, wherein the operations further comprise training the machine-learning electronic model based on first data from an electronic data store to determine the probability of the clinically significant glycogen storage disease.
21. (Currently Amended) The method of claim 1, further comprising training the machine-learning electronic model based on first data from an electronic data store to determine the probability of the CSGS disease.
It would be obvious for the functionality of method of claim 21 to be applied to the computer-implemented method of claim 1 because a person skilled in the applied art would know to train the machine-learning electronic model to determine the probability of the clinically significant glycogen storage disease.
19. (New) The system of claim 15, wherein the operations further comprise collecting the first data from an electronic data store via an automated process, wherein the electronic data store comprises an anonymized data warehouse of electronic medical record data.
23. (Currently Amended) The method of claim 1, further comprising: collecting the first data from an electronic data storage via an automated process, wherein the electronic data storage comprises an anonymized data warehouse of electronic medical record (EMR) data; and training the machine-learning electronic model based on the first data from the electronic data storage to determine the probability of the CSGS disease.
It would be obvious for the functionality of method of claim 23 to be applied to the computer-implemented method of claim 1 because a person skilled in the applied art would know to collect anonymized data from an electronic data store via an automated process.
20. (New) The system of claim 15, wherein the intervention action comprises a particular notification that indicates the probability of the clinically significant glycogen storage disease.
24. (Currently Amended) The method of claim 1, further comprising presenting via an electronic user interface a particular notification that indicates the probability of the CSGS disease.
It would be obvious for the functionality of method of claim 24 to be applied to the computer-implemented method of claim 1 because a person skilled in the applied art would know to the intervention action comprises a particular notification that indicates the probability of the clinically significant glycogen storage disease.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 7-8, and 14-15 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1, 8, and 15 recite “decision element” and “physiological variable instances” which is not disclosed by the specification. The closest paragraphs in the specification disclose [0004] “A diagnostic and decision support technology is provided for determining the presence, identity, and/or severity of an inherited lysosomal storage disorder. In particular, a mechanism is provided to detect and classify a lysosomal storage disorder in a human patient, which utilizes a logistic regression classifier determined based on a multi-variable composite-biomarker comprising a specific set of physiological variables of the patient”, [0018] “a decision support tool is provided, which may be a component in an electronic health records (EHR) system for detecting presence, identity, and/or severity of a glycogen storage disorder in patients, for notifying caregivers, and/or generating a recommendation or automatically performing additional actions such as scheduling diagnostic testing, treatments, modification to care plans, or other intervening actions”, [0019] “The decision support tool may utilize a multi- variable composite biomarker pattern and predictive model. In one embodiment, the biomarker patterns comprise a set or pattern of physiological variables (which may also include clinical variables comprising conditions or clinical events) associated with a particular patient, which operate as independent variables in the model”, [0024] “The method comprises: determining a multi-variable biomarker based on a set of physiological variables associated with an individual that is received as input data”, [0037] “Example operating environment 100 further includes a user/clinician interface 142 and decision support application 140, each communicatively coupled through network 175 to an EHR system 160… In some embodiments, application 140 includes or is incorporated into a computerized decision support tool”. None of these paragraphs explain or recite “decision element” and “physiological variable instances”.
Claims 7 and 14 recite “a particular course of treatment is administered by a clinician to the individual to treat a disease or a medical condition of the individual”, which is not disclosed by the specification. The closest paragraphs in the specification disclose [0004] “A diagnostic and decision support technology is provided for determining the presence, identity, and/or severity of an inherited lysosomal storage disorder”, [0005] “some embodiments further comprise technologies for scoring or ascertaining the severity of a previously-diagnosed glycogen storage disease in human patients, such as late-onset Pompe disease, to assist in optimizing the medical treatment of individual patients and as a biomarker to follow the efficacy of treatment in animal models and in patients”, [0018] “a decision support tool is provided, which may be a component in an electronic health records (EHR) system for detecting presence, identity, and/or severity of a glycogen storage disorder in patients, for notifying caregivers, and/or generating a recommendation or automatically performing additional actions such as scheduling diagnostic testing, treatments, modification to care plans, or other intervening actions”, [0019] “these systems or methods are incorporated into a decision support tool used for screening, monitoring, and/or treating a patient”, [0024]-[0026] “based on the probability of the clinically significant glycogen storage disease for the individual, initiating the intervention action, the intervention action comprising one or more of modifying treatment of the patient, ordering additional diagnostics for the patient, scheduling treatment or diagnostics for the patient, and issuing a notification to a caregiver associated with the patient”, and [0069] “generating and providing a specific recommendation regarding the treatment or care of the patient (including recommending diagnostics, courses or care, or additional screenings), and/or automatically performing additional actions such as scheduling diagnostic testing, treatments, modification to care plans, or other intervening actions”, but is not similar because it does not recite any type of “administration” of a particular treatment by a clinician to the individual.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1:
In the instant case, claims 1-7 are directed toward a computer-implemented method (i.e. a process), claims 8-14 are directed towards a non-transitory media (i.e., manufacture), and claims 15-20 are directed toward a system (i.e. machine). Thus, each of the claims falls within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
Step 2A—Prong 1:
Independent claims 1, 8, and 15 recites steps that, under their broadest reasonable interpretations, cover performance of the limitations of a certain method of organizing human activity but for the recitation of generic computer components.
Claim 1 recites: “A computerized method of initiating an electronic health record (EHR) intervention action for glycogen storage disease, the computer-implemented method comprising: determining a multi-variable biomarker based on a set of physiological variables associated with an individual based on content received as input data; determining, at one or more hardware processors, a probability of a clinically significant glycogen storage disease for the individual based on the multi-variable biomarker the set of physiological variables and based further on a machine-learning electronic model, wherein:(a) the machine-learning electronic model is trained based on (i) data associated with physiological variable instances and (ii) one or more decision elements associated with the physiological variable instances, and(b) applying the machine-learning electronic model to data associated with the set of physiological variables generates information indicating the probability of the clinically significant glycogen storage disease; and based on the probability of the clinically significant glycogen storage disease for the individual, automatically initiating via the one or more hardware processors an intervention action selected from a group comprising modifying treatment of the patient individual, ordering additional diagnostics for the patient individual, scheduling treatment or diagnostics for the patient individual, and issuing a notification to a caregiver associated with the patient individual”.
The limitations of determining a multi-variable biomarker based on a set of physiological variables associated with an individual based on content received as input data; determining, a probability of a clinically significant glycogen storage disease for the individual based on the multi-variable biomarker the set of physiological variables, wherein: (a) the model is trained based on (i) data associated with physiological variable instances and (ii) one or more decision elements associated with the physiological variable instances, and (b) applying the model to data associated with the set of physiological variables generates information indicating the probability of the clinically significant glycogen storage disease; and based on the probability of the clinically significant glycogen storage disease for the individual, automatically initiating an intervention action selected from a group comprising modifying treatment of the patient individual, ordering additional diagnostics for the patient individual, scheduling treatment or diagnostics for the patient individual, and issuing a notification to a caregiver associated with the patient individual, given the broadest reasonable interpretation, cover the abstract idea of a certain method of organizing human activity because they recite managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions—in this case the aforementioned steps recite a process of determining, trained, applying, initiating,, modifying, ordering, scheduling, and issuing, which is properly interpreted as a “personal behavior”), but instead automates the process via a computer model, e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea are deemed “additional elements”, and will be discussed in further detail below.
Further, the abstract idea of claims 8 and 15 are identical as the abstract idea of claim 1. This limitation, given the broadest reasonable interpretation, also falls under the abstract idea of a certain method of organizing human activity because it recites managing personal behavior or relationships or interactions between people.
Dependent claims 2-7, 9-14, and 16-20 include other limitations, as well as specific step of data to be processed, received, and applied, but these only serve to further limit the abstract idea and do not add and additional elements, and hence are nonetheless directed towards fundamentally the same abstract idea as independent claims 1, 8, and 15. However, recitation of an abstract idea is not the end of the 35 U.S.C. 101 analysis. Each of the claims must be analyzed for additional elements that indicate the abstract idea is integrated into a practical application to determine whether the claim is considered to be “directed to” an abstract idea.
Step 2A—Prong 2:
Claims 1-20 are not integrated into a practical application because the additional elements (i.e. any limitations that are not identified as part of the abstract idea) amount to no more than limitations which:
Amount to mere instructions to apply an exception—for example, the recitation of “multi-variable logistic regression statistical model”, “hardware processors”, “machine-learning electronic model”, and “non-transitory media” , which amount to merely invoking a computer as a tool to perform the abstract idea, e.g. see FIG. 1A, FIG. 1B, and [0013]-[0017], of the present specification, and see further MPEP 2106.05(f);
Generally linking the abstract idea to a particular technological environment or field of use, for example, “at one or more hardware processors”, “and based further on a machine-learning electronic model”, “machine-learning electronic model”, and “via the one or more hardware processors”, which amounts to limiting the abstract idea to the field of technology/the environment of computers, see MPEP 2106.05(h); and/or
Merely acquiring information for further analysis by the system and the particular manner of acquisition is not described or shown to be important, for example, “received as input data”, which amounts to insignificant extra-solution activity in the form of mere data gathering because it merely functions tangentially to the main idea of the invention and serves only to bring in the data necessary for the inventions main analysis, see MPEP 2106.05(g).
Additionally, dependent claims 2-7, 9-14, and 16-20 include other limitations, but as stated above, the limitations recited by these claims do not include any additional elements beyond those already recited in independent claims 1, 8, and 15, and hence also do not integrate the aforementioned abstract idea into a practical application.
Step 2B:
The claims do not include additional elements (i.e., “multi-variable logistic regression statistical model”, “hardware processors”, “machine-learning electronic model”, and “non-transitory media”) that are sufficient to amount to “significantly more” than the judicial exception because the additional elements (i.e. the elements other than the abstract idea), as stated above, are directed towards no more than limitations that amount to mere instructions to apply the exception, and/or generally link the abstract idea to a particular technological environment or field of use, which even when reevaluated under the considerations of Step 2B of the analysis, do not amount to “significantly more” than the abstract idea.
Dependent claims 2-7, 9-14, and 16-20 include other limitations, but none of these limitations are deemed significantly more than the abstract idea because, as stated above, the aforementioned dependent claims do not recite any additional elements not already recited in independent claims 1, 8, and 15, and hence do not amount to “significantly more” than the abstract idea.
Additionally, the additional elements (i.e., “received as input data”), add extra solution activity, which comprises limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in a particular field as demonstrated by:
Relevant court decisions (See MPEP 2106.05(d)(II)):
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) (“Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink.” (emphasis added)).
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, claims 1-20 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-5, 7-12, and 14-20 are rejected under 35 U.S.C. 103 as being unpatentable over Nilsson et al. (US 20060172429 A1), hereinafter Nilsson, in view of Cohen et al. (US 20190131016 A1), hereinafter Cohen.
Regarding claim 1 Nilsson teaches a computer-implemented method (Nilsson, [0004]: “The present invention provides methods for identifying biological states, in particular for the diagnosis, prognosis, and prediction of diseases” and [0092]: “Metabolic diseases include but not limited to… Pompe… Glycogen Storage Disease”) comprising: determining a set of physiological variables associated with an individual based on content received as input data (Nilsson, [0005]: “identifying a biomarker pattern for a biological state comprising obtaining a biological sample, said biological sample obtained from a subject in a first biological state”); determining, at one or more hardware processors, a probability of a clinically significant glycogen storage disease for the individual (Nilsson, [0045]: “Clinical applications include, for example, detection of disease; distinguishing disease states to inform prognosis, selection of therapy, and/or prediction of therapeutic response; disease staging; identification of disease processes; prediction of efficacy of therapy; monitoring of patients trajectories (e.g., prior to onset of disease); prediction of adverse response; monitoring of therapy associated efficacy and toxicity; prediction of probability of occurrence; recommendation for prophylactic measures; and detection of recurrence” and [0092]: “Metabolic diseases include but not limited to… Pompe… Glycogen Storage Disease”); clinically significant glycogen storage disease (Nilsson, [0092]: “Metabolic diseases include but not limited to… Pompe… Glycogen Storage Disease”); and based on the probability of the clinically significant glycogen storage disease (Nilsson, [0043]: “the term diagnosis of disease or disease states as used herein is intended to include identifying the presence of a disease, prediction of the possible future occurrence of a disease, prognosis of a disease, potential seriousness of a disease, predicting the outcome of a disease, predicting the possible response to a therapeutic intervention, predict the recurrence of a disease, and determining whether an individual is responding to an ongoing therapeutic intervention”, [0205], and [0273]: “The model can be used to predict disease and treatment response, and may be useful in staging patients, measuring progression, and measuring treatment response” and [0092]: “Metabolic diseases include but not limited to… Pompe… Glycogen Storage Disease”), automatically initiating via the one or more hardware processors an intervention action selected from a group comprising modifying treatment of the individual, ordering additional diagnostics for the individual (Nilsson, [0215]: “Preferably, the agents used for therapeutic and/or prophylactic benefit can be administered per se or in the form of a pharmaceutical composition. The pharmaceutical compositions comprise the therapeutic agents, one or more pharmaceutically acceptable carriers, diluents or excipients, and optionally additional therapeutic agents”), scheduling treatment (Nilsson, [0045]: “Clinical applications include, for example, detection of disease; distinguishing disease states to inform prognosis, selection of therapy, and/or prediction of therapeutic response”) or diagnostics for the individual (Nilsson, [0043]: “predicting the possible response to a therapeutic intervention, predict the recurrence of a disease, and determining whether an individual is responding to an ongoing therapeutic intervention”, [0045]: “Clinical applications include, for example, detection of disease; distinguishing disease states to inform prognosis, selection of therapy, and/or prediction of therapeutic response; disease staging; identification of disease processes; prediction of efficacy of therapy; monitoring of patients trajectories (e.g., prior to onset of disease); prediction of adverse response; monitoring of therapy associated efficacy and toxicity; prediction of probability of occurrence; recommendation for prophylactic measures; and detection of recurrence”, [0205]: “the methods described herein can be used to compare the efficacies of different therapies and/or responses to one or more treatments in different populations (e.g., different age groups, ethnicities, family histories, etc.)”, and [0273]: “The model can be used to predict disease and treatment response, and may be useful in staging patients, measuring progression, and measuring treatment response”)., and issuing a notification to a caregiver associated with the individual (Nilsson, [0204]: “In certain embodiments, patients, health care providers, such as doctors and nurses, or health care managers, use the patterns of markers to make a diagnosis, prognosis, and/or select treatment options”).
Nilsson does not teach the set of physiological variables and based further on a machine-learning electronic model, wherein: (a) the machine-learning electronic model is trained based on (i) data associated with physiological variable instances and (ii) one or more decision elements associated with the physiological variable instances, and (b) applying the machine-learning electronic model to data associated with the set of physiological variables generates information indicating the probability.
However, Cohen teaches the set of physiological variables and based further on a machine-learning electronic model (Cohen, [0119]: “as additional samples are tested and the presence of cancer is validated, this data is fed back into the machine learning system to generate more accurate predictions of a patient's risk for having cancer”, [0137]: “A machine learning system may be utilized to aggregate the normalized biomarker scores along with other information (e.g., medical information, publicly available information, etc.) to generate a master composite score”, and [0151]: “multivariate logistic regression analysis may be used to determine a probability value. That value is then either classified per a risk categorization table or compared to a threshold value wherein above a threshold the nodule is deemed malignant and below the threshold value the nodule is deemed benign… In other embodiments, machine learning software, or support vector machine (SVM) learning algorithms, neural networks, random forest or decision tree models are used to analyze the obtained biomarker and clinical parameter values wherein a composite or risk score is generated and classified per a risk categorization table or compared to a threshold value”), wherein: (a) the machine-learning electronic model is trained based on (i) data associated with physiological variable instances and (ii) one or more decision elements associated with the physiological variable instances (Cohen, [0078]: “The statistical analysis may be a multivariate logistic regression model, a neural network model, a random forest model, a decision tree model, or other well-known methods for analyzing multiple variables”, [0371]: “Each of those variables (biomarkers or clinical parameters) was analyzed in a univariate logistic regression model and together in a multivariate logistic regression model”, and [0393]), and (b) applying the machine-learning electronic model to data associated with the set of physiological variables generates information indicating the probability (Cohen, [0020]: “the probability value is calculated using a multivariate logistic regression model, a neural network model, a random forest model or a decision tree model” and [0143]: “the measured value of the biomarkers (which may or may not include normalized values) and numerical clinical parameter data for a patient are analyzed using multi variable statistical models well understood in the art to obtain or calculate a probability value, which is a composite value for the entire panel of measured variables. In embodiments, a probability value may be calculated using a multivariate logistic regression (MLR) model, a neural network model, a random forest model or a decision tree model. The models are developed using retrospective clinical samples from a cohort of patients having benign nodules and malignant nodules”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Nilsson to incorporate the teachings of Cohen and account for clinically relevant markers for non-invasive detection of lung disease including cancer, monitoring response to therapy, or detecting lung cancer recurrence. It is also clear that such assays must be highly specific with reasonable sensitivity, and be readily available at a reasonable cost. Circulating biomarkers offer an alternative to imaging with the following advantages: 1) they are found in a minimally-invasive, easy to collect specimen type (blood or blood-derived fluids), 2) they can be monitored frequently over time in a subject to establish an accurate baseline, making it easy to detect changes over time, 3) they can be provided at a reasonably low cost, 4) they may limit the number of patients undergoing repeated expensive and potentially harmful CT scans, and/or 5) unlike CT scans, biomarkers may potentially distinguish indolent from more aggressive lung lesions (Cohen, Abstract and [0011]).
Regarding claims 2, 9, and 16 Nilsson does not teach the machine-learning electronic model comprises a multi-variable logistic regression statistical model.
However, Cohen teaches the machine-learning electronic model comprises a multi-variable logistic regression statistical model (Cohen, [0020]: “the probability value is calculated using a multivariate logistic regression model, a neural network model, a random forest model or a decision tree model”, [0045]: “the inclusion of at least two lung cancer biomarkers and at least two clinical parameters, when analyzed as a panel using a statistical model such as multivariate logistic regression, neural networks or random forest, are used to predict whether or not a patient is positive for malignant pulmonary nodules”, [0078], [0143], and [0151]: “multivariate logistic regression analysis may be used to determine a probability value. That value is then either classified per a risk categorization table or compared to a threshold value wherein above a threshold the nodule is deemed malignant and below the threshold value the nodule is deemed benign”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Nilsson to incorporate the teachings of Cohen and account for clinically relevant markers for non-invasive detection of lung disease including cancer, monitoring response to therapy, or detecting lung cancer recurrence. It is also clear that such assays must be highly specific with reasonable sensitivity, and be readily available at a reasonable cost. Circulating biomarkers offer an alternative to imaging with the following advantages: 1) they are found in a minimally-invasive, easy to collect specimen type (blood or blood-derived fluids), 2) they can be monitored frequently over time in a subject to establish an accurate baseline, making it easy to detect changes over time, 3) they can be provided at a reasonably low cost, 4) they may limit the number of patients undergoing repeated expensive and potentially harmful CT scans, and/or 5) unlike CT scans, biomarkers may potentially distinguish indolent from more aggressive lung lesions (Cohen, Abstract and [0011]).
Regarding claims 3, 10, and 17 Nilsson does not teach modifying electronic health record (EHR) associated with the individual according to the probability of the clinically significant glycogen storage disease to include data indicating that the individual is a candidate for receiving additional treatment or diagnostic procedures associated with the clinically significant glycogen storage disease.
However, Cohen teaches modifying electronic health record (EHR) associated with the individual according to the probability of the clinically significant glycogen storage disease to include data indicating that the individual is a candidate for receiving additional treatment or diagnostic procedures associated with the clinically significant glycogen storage disease (Cohen, [0261]: “”the NACS system 100 could search information in databases 30-60 to reevaluate its determined risk and provide an updated risk to a patient or physician. For example, a question could be generated and stored in public KS 110, which would be asked to dbs 30-60 at predefined intervals (e.g., monthly, quarterly, annually, etc.), and the risk determination could be updated periodically”, [0307]: “The embodiments herein may automatically and continuously update the risk scores, the corresponding confidence values/margin of error, based on evolving data (e.g., medical patient data) in order to provide the highest confidence answers and recommendations. Rather than providing static calculations that always provide the same answers when given the same input, the embodiments herein continually update as new data is received, thereby, providing the physician and patient with the best most up-to-date information”, and [0308]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Nilsson to incorporate the teachings of Cohen and account for clinically relevant markers for non-invasive detection of lung disease including cancer, monitoring response to therapy, or detecting lung cancer recurrence. It is also clear that such assays must be highly specific with reasonable sensitivity, and be readily available at a reasonable cost. Circulating biomarkers offer an alternative to imaging with the following advantages: 1) they are found in a minimally-invasive, easy to collect specimen type (blood or blood-derived fluids), 2) they can be monitored frequently over time in a subject to establish an accurate baseline, making it easy to detect changes over time, 3) they can be provided at a reasonably low cost, 4) they may limit the number of patients undergoing repeated expensive and potentially harmful CT scans, and/or 5) unlike CT scans, biomarkers may potentially distinguish indolent from more aggressive lung lesions (Cohen, Abstract and [0011]).
Regarding claims 4, 11, and 18 Nilsson further teaches training the machine-learning electronic model based on first data from an electronic data store to determine the probability of the clinically significant glycogen storage disease (Nilsson, [0092]: “Metabolic diseases include but not limited to… Pompe… Glycogen Storage Disease”, [0165]: “Diagnostic tests can be developed for model systems, clinical trials, or the routine clinical setting”, [0172]: “his apparatus may be specially constructed for the required purposes, or it may comprise a general-purpose computer, selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a computer readable storage medium, such as, but not limited to, any type of disk including floppy disks, hard disks, optical disks, compact disk-read only memories (CD-ROMs), and magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs), electrically programmable read-only memories (EPROM)s, electrically erasable programmable read-only memories (EEPROMs), FLASH memories, magnetic or optical cards, etc., or any type of media suitable for storing electronic instructions either local to the computer or remote to the computer”, [0200]: “the memory 504 of the computer 500 stores test 505 and reference 506 biomarker patterns. The memory 504 also stores a comparison module 507”, and [0201]: “The memory 504 also stores a decision module 508. The decision module 508 includes a set of executable instructions to process data created by the comparison module 507”).
Regarding claims 5, 12, and 19 Nilsson does not teach collecting the first data from an electronic data store via an automated process, wherein the electronic data store comprises an anonymized data warehouse of electronic medical record data.
However, Cohen teaches collecting the first data from an electronic data store via an automated process, wherein the electronic data store comprises an anonymized data warehouse of electronic medical record data (Cohen, [0245]: “Clean data is sent to HIPPA Redaction and Anonymizer module 75, which anonymizes data to comply with regulatory and other legal requirements. Unless otherwise authorized by the individual, individual health care records are usually anonymized in order to comply with privacy and other regulations. In some embodiments, the individual records are anonymized by replacing patient specific identification information (e.g., a name, social security number, etc.) with a unique identifier, providing a way to identify the individual after the risk score has been determined”, [0246], and [0296]: “the machine learning system determines whether the information needs to be anonymized, and if so, the information is anonymized. Otherwise, the process may continue to operation 350. At operation 350, the anonymized (or corrected) information is stored in clean data knowledge store (KS) 80, where it is ready for extraction, e.g., by NN3 “EMR Extractor””).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Nilsson to incorporate the teachings of Cohen and account for clinically relevant markers for non-invasive detection of lung disease including cancer, monitoring response to therapy, or detecting lung cancer recurrence. It is also clear that such assays must be highly specific with reasonable sensitivity, and be readily available at a reasonable cost. Circulating biomarkers offer an alternative to imaging with the following advantages: 1) they are found in a minimally-invasive, easy to collect specimen type (blood or blood-derived fluids), 2) they can be monitored frequently over time in a subject to establish an accurate baseline, making it easy to detect changes over time, 3) they can be provided at a reasonably low cost, 4) they may limit the number of patients undergoing repeated expensive and potentially harmful CT scans, and/or 5) unlike CT scans, biomarkers may potentially distinguish indolent from more aggressive lung lesions (Cohen, Abstract and [0011]).
Regarding claims 7 and 14 Nilsson further teaches based on the scheduling of the treatment or diagnostics for the individual, a particular course of treatment is administered by a clinician to the individual to treat a disease or a medical condition of the individual (Nilsson, [0210]: “These agents may be administered alone or in combination with other types of treatments known and available to those skilled in the art” and [0212]-[0215]: “The compositions can be administered by injection, topically, orally, transdermally, rectally, or via inhalation”).
Regarding claim 8 Nilsson teaches one or more non-transitory media having instructions that, when executed by one or more hardware processors, cause the one or more hardware processors to facilitate a plurality of operations (Nilsson, [0004]: “The present invention provides methods for identifying biological states, in particular for the diagnosis, prognosis, and prediction of diseases” and [0092]: “Metabolic diseases include but not limited to… Pompe… Glycogen Storage Disease”), the operations comprising: determining a set of physiological variables, associated with an individual, based on content received as input data (Nilsson, [0005]: “identifying a biomarker pattern for a biological state comprising obtaining a biological sample, said biological sample obtained from a subject in a first biological state”); determining, at the one or more hardware processors, a probability of a clinically significant glycogen storage disease for the individual (Nilsson, [0045]: “Clinical applications include, for example, detection of disease; distinguishing disease states to inform prognosis, selection of therapy, and/or prediction of therapeutic response; disease staging; identification of disease processes; prediction of efficacy of therapy; monitoring of patients trajectories (e.g., prior to onset of disease); prediction of adverse response; monitoring of therapy associated efficacy and toxicity; prediction of probability of occurrence; recommendation for prophylactic measures; and detection of recurrence” and [0092]: “Metabolic diseases include but not limited to… Pompe… Glycogen Storage Disease”); of the clinically significant glycogen storage disease (Nilsson, [0092]: “Metabolic diseases include but not limited to… Pompe… Glycogen Storage Disease”); and based on the probability of the clinically significant glycogen storage disease (Nilsson, [0043]: “the term diagnosis of disease or disease states as used herein is intended to include identifying the presence of a disease, prediction of the possible future occurrence of a disease, prognosis of a disease, potential seriousness of a disease, predicting the outcome of a disease, predicting the possible response to a therapeutic intervention, predict the recurrence of a disease, and determining whether an individual is responding to an ongoing therapeutic intervention”, [0205], and [0273]: “The model can be used to predict disease and treatment response, and may be useful in staging patients, measuring progression, and measuring treatment response” and [0092]: “Metabolic diseases include but not limited to… Pompe… Glycogen Storage Disease”), automatically initiating via the one or more hardware processors an intervention action selected from a group comprising modifying treatment of the individual, ordering additional diagnostics for the individual (Nilsson, [0215]: “Preferably, the agents used for therapeutic and/or prophylactic benefit can be administered per se or in the form of a pharmaceutical composition. The pharmaceutical compositions comprise the therapeutic agents, one or more pharmaceutically acceptable carriers, diluents or excipients, and optionally additional therapeutic agents”), scheduling treatment (Nilsson, [0045]: “Clinical applications include, for example, detection of disease; distinguishing disease states to inform prognosis, selection of therapy, and/or prediction of therapeutic response”) or diagnostics for the individual (Nilsson, [0043]: “predicting the possible response to a therapeutic intervention, predict the recurrence of a disease, and determining whether an individual is responding to an ongoing therapeutic intervention”, [0045]: “Clinical applications include, for example, detection of disease; distinguishing disease states to inform prognosis, selection of therapy, and/or prediction of therapeutic response; disease staging; identification of disease processes; prediction of efficacy of therapy; monitoring of patients trajectories (e.g., prior to onset of disease); prediction of adverse response; monitoring of therapy associated efficacy and toxicity; prediction of probability of occurrence; recommendation for prophylactic measures; and detection of recurrence”, [0205]: “the methods described herein can be used to compare the efficacies of different therapies and/or responses to one or more treatments in different populations (e.g., different age groups, ethnicities, family histories, etc.)”, and [0273]: “The model can be used to predict disease and treatment response, and may be useful in staging patients, measuring progression, and measuring treatment response”)., and issuing a notification to a caregiver associated with the individual (Nilsson, [0204]: “In certain embodiments, patients, health care providers, such as doctors and nurses, or health care managers, use the patterns of markers to make a diagnosis, prognosis, and/or select treatment options”).
Nilsson does not teach based on the set of physiological variables and based further on a machine-learning electronic model, wherein: (a) the machine-learning electronic model is trained based on (i) data associated with physiological variable instances and (ii) one or more decision elements associated with the physiological variable instances, and (b) applying the machine-learning electronic model to data associated with the set of physiological variables generates information indicating the probability.
However, Cohen teaches based on the set of physiological variables and based further on a machine-learning electronic model (Cohen, [0119]: “as additional samples are tested and the presence of cancer is validated, this data is fed back into the machine learning system to generate more accurate predictions of a patient's risk for having cancer”, [0137]: “A machine learning system may be utilized to aggregate the normalized biomarker scores along with other information (e.g., medical information, publicly available information, etc.) to generate a master composite score”, and [0151]: “multivariate logistic regression analysis may be used to determine a probability value. That value is then either classified per a risk categorization table or compared to a threshold value wherein above a threshold the nodule is deemed malignant and below the threshold value the nodule is deemed benign… In other embodiments, machine learning software, or support vector machine (SVM) learning algorithms, neural networks, random forest or decision tree models are used to analyze the obtained biomarker and clinical parameter values wherein a composite or risk score is generated and classified per a risk categorization table or compared to a threshold value”), wherein: (a) the machine-learning electronic model is trained based on (i) data associated with physiological variable instances and (ii) one or more decision elements associated with the physiological variable instances (Cohen, [0078]: “The statistical analysis may be a multivariate logistic regression model, a neural network model, a random forest model, a decision tree model, or other well-known methods for analyzing multiple variables”, [0371]: “Each of those variables (biomarkers or clinical parameters) was analyzed in a univariate logistic regression model and together in a multivariate logistic regression model”, and [0393]), and (b) applying the machine-learning electronic model to data associated with the set of physiological variables generates information indicating the probability (Cohen, [0020]: “the probability value is calculated using a multivariate logistic regression model, a neural network model, a random forest model or a decision tree model” and [0143]: “the measured value of the biomarkers (which may or may not include normalized values) and numerical clinical parameter data for a patient are analyzed using multi variable statistical models well understood in the art to obtain or calculate a probability value, which is a composite value for the entire panel of measured variables. In embodiments, a probability value may be calculated using a multivariate logistic regression (MLR) model, a neural network model, a random forest model or a decision tree model. The models are developed using retrospective clinical samples from a cohort of patients having benign nodules and malignant nodules”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Nilsson to incorporate the teachings of Cohen and account for clinically relevant markers for non-invasive detection of lung disease including cancer, monitoring response to therapy, or detecting lung cancer recurrence. It is also clear that such assays must be highly specific with reasonable sensitivity, and be readily available at a reasonable cost. Circulating biomarkers offer an alternative to imaging with the following advantages: 1) they are found in a minimally-invasive, easy to collect specimen type (blood or blood-derived fluids), 2) they can be monitored frequently over time in a subject to establish an accurate baseline, making it easy to detect changes over time, 3) they can be provided at a reasonably low cost, 4) they may limit the number of patients undergoing repeated expensive and potentially harmful CT scans, and/or 5) unlike CT scans, biomarkers may potentially distinguish indolent from more aggressive lung lesions (Cohen, Abstract and [0011]).
Regarding claim 15 Nilsson teaches a system having one or more hardware processors configured to facilitate a plurality of operations, the operations comprising (Nilsson, [0004]: “The present invention provides methods for identifying biological states, in particular for the diagnosis, prognosis, and prediction of diseases” and [0092]: “Metabolic diseases include but not limited to… Pompe… Glycogen Storage Disease”), comprising: determining a set of physiological variables, associated with an individual, based on content received as input data (Nilsson, [0005]: “identifying a biomarker pattern for a biological state comprising obtaining a biological sample, said biological sample obtained from a subject in a first biological state”); determining, at the one or more hardware processors, a probability of a clinically significant glycogen storage disease for the individual (Nilsson, [0045]: “Clinical applications include, for example, detection of disease; distinguishing disease states to inform prognosis, selection of therapy, and/or prediction of therapeutic response; disease staging; identification of disease processes; prediction of efficacy of therapy; monitoring of patients trajectories (e.g., prior to onset of disease); prediction of adverse response; monitoring of therapy associated efficacy and toxicity; prediction of probability of occurrence; recommendation for prophylactic measures; and detection of recurrence” and [0092]: “Metabolic diseases include but not limited to… Pompe… Glycogen Storage Disease”); of the clinically significant glycogen storage disease (Nilsson, [0092]: “Metabolic diseases include but not limited to… Pompe… Glycogen Storage Disease”); and based on the probability of the clinically significant glycogen storage disease (Nilsson, [0043]: “the term diagnosis of disease or disease states as used herein is intended to include identifying the presence of a disease, prediction of the possible future occurrence of a disease, prognosis of a disease, potential seriousness of a disease, predicting the outcome of a disease, predicting the possible response to a therapeutic intervention, predict the recurrence of a disease, and determining whether an individual is responding to an ongoing therapeutic intervention”, [0205], and [0273]: “The model can be used to predict disease and treatment response, and may be useful in staging patients, measuring progression, and measuring treatment response” and [0092]: “Metabolic diseases include but not limited to… Pompe… Glycogen Storage Disease”), automatically initiating via the one or more hardware processors an intervention action selected from a group comprising modifying treatment of the individual, ordering additional diagnostics for the individual (Nilsson, [0215]: “Preferably, the agents used for therapeutic and/or prophylactic benefit can be administered per se or in the form of a pharmaceutical composition. The pharmaceutical compositions comprise the therapeutic agents, one or more pharmaceutically acceptable carriers, diluents or excipients, and optionally additional therapeutic agents”), scheduling treatment (Nilsson, [0045]: “Clinical applications include, for example, detection of disease; distinguishing disease states to inform prognosis, selection of therapy, and/or prediction of therapeutic response”) or diagnostics for the individual (Nilsson, [0043]: “predicting the possible response to a therapeutic intervention, predict the recurrence of a disease, and determining whether an individual is responding to an ongoing therapeutic intervention”, [0045]: “Clinical applications include, for example, detection of disease; distinguishing disease states to inform prognosis, selection of therapy, and/or prediction of therapeutic response; disease staging; identification of disease processes; prediction of efficacy of therapy; monitoring of patients trajectories (e.g., prior to onset of disease); prediction of adverse response; monitoring of therapy associated efficacy and toxicity; prediction of probability of occurrence; recommendation for prophylactic measures; and detection of recurrence”, [0205]: “the methods described herein can be used to compare the efficacies of different therapies and/or responses to one or more treatments in different populations (e.g., different age groups, ethnicities, family histories, etc.)”, and [0273]: “The model can be used to predict disease and treatment response, and may be useful in staging patients, measuring progression, and measuring treatment response”)., and issuing a notification to a caregiver associated with the individual (Nilsson, [0204]: “In certain embodiments, patients, health care providers, such as doctors and nurses, or health care managers, use the patterns of markers to make a diagnosis, prognosis, and/or select treatment options”).
Nilsson does not teach based on the set of physiological variables and based further on a machine-learning electronic model, wherein: (a) the machine-learning electronic model is trained based on (i) data associated with physiological variable instances and (ii) one or more decision elements associated with the physiological variable instances, and (b) applying the machine-learning electronic model to data associated with the set of physiological variables generates information indicating the probability.
However, Cohen teaches based on the set of physiological variables and based further on a machine-learning electronic model (Cohen, [0119]: “as additional samples are tested and the presence of cancer is validated, this data is fed back into the machine learning system to generate more accurate predictions of a patient's risk for having cancer”, [0137]: “A machine learning system may be utilized to aggregate the normalized biomarker scores along with other information (e.g., medical information, publicly available information, etc.) to generate a master composite score”, and [0151]: “multivariate logistic regression analysis may be used to determine a probability value. That value is then either classified per a risk categorization table or compared to a threshold value wherein above a threshold the nodule is deemed malignant and below the threshold value the nodule is deemed benign… In other embodiments, machine learning software, or support vector machine (SVM) learning algorithms, neural networks, random forest or decision tree models are used to analyze the obtained biomarker and clinical parameter values wherein a composite or risk score is generated and classified per a risk categorization table or compared to a threshold value”), wherein: (a) the machine-learning electronic model is trained based on (i) data associated with physiological variable instances and (ii) one or more decision elements associated with the physiological variable instances (Cohen, [0078]: “The statistical analysis may be a multivariate logistic regression model, a neural network model, a random forest model, a decision tree model, or other well-known methods for analyzing multiple variables”, [0371]: “Each of those variables (biomarkers or clinical parameters) was analyzed in a univariate logistic regression model and together in a multivariate logistic regression model”, and [0393]), and (b) applying the machine-learning electronic model to data associated with the set of physiological variables generates information indicating the probability (Cohen, [0020]: “the probability value is calculated using a multivariate logistic regression model, a neural network model, a random forest model or a decision tree model” and [0143]: “the measured value of the biomarkers (which may or may not include normalized values) and numerical clinical parameter data for a patient are analyzed using multi variable statistical models well understood in the art to obtain or calculate a probability value, which is a composite value for the entire panel of measured variables. In embodiments, a probability value may be calculated using a multivariate logistic regression (MLR) model, a neural network model, a random forest model or a decision tree model. The models are developed using retrospective clinical samples from a cohort of patients having benign nodules and malignant nodules”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Nilsson to incorporate the teachings of Cohen and account for clinically relevant markers for non-invasive detection of lung disease including cancer, monitoring response to therapy, or detecting lung cancer recurrence. It is also clear that such assays must be highly specific with reasonable sensitivity, and be readily available at a reasonable cost. Circulating biomarkers offer an alternative to imaging with the following advantages: 1) they are found in a minimally-invasive, easy to collect specimen type (blood or blood-derived fluids), 2) they can be monitored frequently over time in a subject to establish an accurate baseline, making it easy to detect changes over time, 3) they can be provided at a reasonably low cost, 4) they may limit the number of patients undergoing repeated expensive and potentially harmful CT scans, and/or 5) unlike CT scans, biomarkers may potentially distinguish indolent from more aggressive lung lesions (Cohen, Abstract and [0011]).
Claims 6, 13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Nilsson and Cohen, in view of Dala et al. (US 20130054467 A1), hereinafter Dala.
Regarding claims 6, 13, and 20 Nilsson teaches indicates the probability of the clinically significant glycogen storage disease (Nilsson, [0043]: “A biological state of interest also includes the state of various patient populations, prediction of treatment outcomes, and predisposition to diseases”, [0092]: “Metabolic diseases include but not limited to… Pompe… Glycogen Storage Disease”, [0192] : “the invention includes the use of modified forms of the markers of Tables 1 and/or 2 to diagnose cardiovascular diseases”).
Nilsson and Cohen do not teach the intervention action comprises a particular notification.
However, Dala teaches the intervention action comprises a particular notification (Dala, FIG. 14, FIG. 21, [0113]: “notifying the physician that a message is available for download, 331”, and [0114]: “the data server 131 may transmit the message and image data directly to the physician's mobile handheld device 150 without sending an SMS notification message or waiting for the request an authentication message”). It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Nilsson and Cohen to incorporate the teachings of Dala and account for data server that may further include a resident messaging system that communicates to the mobile handheld device such that the user is notified of messages or data awaiting review along with the urgency of the required review (Dala, Abstract and [0007]-[0009]).
Conclusion
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/R.S.S./Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681
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