DETAILED ACTION
Priority
The current application claims benefit of continuation in app 17/751,339, filed on October 26th, 2021 with the priority date of July 20th, 2018. Examiner acknowledges the applicant’s claim for priority.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on January 16th, 2025 is being considered by the examiner.
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 .
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 22-42 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more
Step 1
The claims recite subject matter within a statutory category as a process, machine, and/or article of manufacture. However, it will be shown in the following steps, that claims 22-42 are nonetheless unpatentable under 35 U.S.C. 101.
Step 2A Prong One
A method for estimating, in real-time or near-real-time, a risk of acute myocardial infarctions in at least one patient, the method comprising:
extracting batch information at a pre-defined time interval for one or more past patients from an electronic medical records (EMR) database into one or more databases operably connected to a machine learning model;
calculating, using the machine learning model, a risk level for the one or more past patients in the one or more databases, wherein the one or more past patients includes the at least one patient, and the risk level for the at least one patient is calculated using one or more medical feature coefficients obtained from training the machine learning model;
parsing at least one stream of real-time clinical administrative data associated with the at least one patient in a current medical encounter, in real-time or near real-time to identify and extract specified EMR data comprising patient clinical data obtained during the current medical encounter into the one or more databases;
predicting, in real-time or near-real-time, the risk of acute myocardial infarction in the at least one patient, wherein a real-time risk prediction value is estimated by adjusting the calculated risk level for the at least one patient by applying a linear model, different from the machine learning model, to one or more of the medical feature coefficients obtained from the machine learning model and to the specified real-time EMR data from the at least one patient;
and displaying a risk prediction based on the real-time risk prediction value and comprising a recommendation to admit or discharge the at least one patient into or from a medical facility.
The broadest reasonable interpretation of these steps includes mathematical concepts and/or mental processes because each bolded component can practically be performed by the human mind or with pen and paper. Other than reciting generic computer terms, nothing in the claims precludes the bold-font portions from practically being performed in the mind. For example, “calculating, using the machine learning model, a risk level for the one or more past patients in the one or more databases, wherein the one or more past patients includes the at least one patient, and the risk level for the at least one patient is calculated using one or more medical feature coefficients obtained from training the machine learning model;” in the context of this claim encompasses calculating out the concepts of weighted averages in a decision matrix for cardiac risk assessments. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” or “Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Therefore, the steps of:
A method for estimating, in real-time or near-real-time, a risk of acute myocardial infarctions in at least one patient, the method comprising:
calculating, using the machine learning model, a risk level for the one or more past patients in the one or more databases, wherein the one or more past patients includes the at least one patient, and the risk level for the at least one patient is calculated using one or more medical feature coefficients obtained from training the machine learning model;
predicting, in real-time or near-real-time, the risk of acute myocardial infarction in the at least one patient, wherein a real-time risk prediction value is estimated by adjusting the calculated risk level for the at least one patient by applying a linear model, different from the machine learning model, to one or more of the medical feature coefficients obtained from the machine learning model and to the specified real-time EMR data from the at least one patient;
as drafted, could lay out the mathematical calculations of using a myocardial infarction risk assessment calculator throughout the patient’s care and assessing any trends that occur amidst active symptoms using linear models and weight balancing optimization techniques. Therefore, under the broadest reasonable interpretation, these steps as drafted, under the broadest reasonable interpretation, includes multiple abstract ideas that will be identified as a single abstract idea moving forward.
Independent claims 29 and 36 cover similar steps of receiving information regarding a patient, calculating a risk level of a myocardial infarction associated with this patient, and predicting a risk associated with the symptoms of a presenting patient. These claims fall under the same category of an abstract idea and follows the same rationale as claim 1.
Dependent claims recite additional subject matter which further narrows or defines an abstract idea embodied in the claims (such as claim 27, reciting particular aspects of how “wherein the machine learning model comprises a gradient boosting machine” recites mathematic equations but for recitation of the machine learning model’s generic computer components).
Dependent claims 25, 31, 32, 37, 38 and 39 add additional elements to their parent claims which will be further inspected in the following steps for a practical application to their abstract idea.
Step 2A Prong Two
This judicial exception of “Mathematical Concepts” or “Mental Process” is not integrated into a practical application. Independent claim 36’s computer related product recites additional elements such as a processor, machine learning model, a user interface, a device and a non-transitory computer readable medium. In addition to the generic components and additional elements listed above, independent claim 29’s system also includes various servers. The processor, device and non-transitory computer readable medium, and servers will be treated as a generic computer components. In particular, these additional elements do not integrate the abstract idea into a practical application because the additional elements:
add insignificant extra-solution activity to the abstract idea (such as recitation of “extracting batch information at a pre-defined time interval for one or more past patients from an electronic medical records (EMR) database into one or more databases operably connected to a machine learning model” amounts to mere data gathering, recitation of “parsing at least one stream of real-time clinical administrative data associated with the at least one patient in a current medical encounter, in real-time or near real-time to identify and extract specified EMR data comprising patient clinical data obtained during the current medical encounter into the one or more databases” amounts to selecting a particular data source or type of data to be manipulated, recitation of “and displaying a risk prediction based on the real-time risk prediction value and comprising a recommendation to admit or discharge the at least one patient into or from a medical facility.” amounts to insignificant application, see MPEP 2106.05(g))
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. For instance, dependent claims 6, 13, and 20 add additional elements of a gradient booster machine to their parent claims. Additionally, claim 25, 32, and 39 “further comprising storing the risk prediction value in the real-time database”, claim 30 and 38 ““wherein the at least one batch server further performs preprocessing the extracted updated batch information from the one or more past patients prior to storing the updated batch information in the one or more databases.” and claim 31 and 37 “wherein the at least one batch server further performs mapping covariates in the EMR database to EMR vendor agnostic categories”, add insignificant extra-solution activity to the abstract idea which amounts to mere data gathering, see MPEP 2106.05(g)). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
The remaining dependent claims (22-24, 26-28, 33-36, 38, 40-42) do not recite additional elements or activity but further narrow or define the abstract idea embodied in the claims and hence also do not integrate the aforementioned abstract idea into a practical application.
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field of use. Additionally, the additional limitations, amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
To elaborate:
“extracting batch information at a pre-defined time interval for one or more past patients from an electronic medical records (EMR) database into one or more databases operably connected to a machine learning model;” , is equivalently, storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv);
“parsing at least one stream of real-time clinical administrative data associated with the at least one patient in a current medical encounter, in real-time or near real-time to identify and extract specified EMR data comprising patient clinical data obtained during the current medical encounter into the one or more databases” , is equivalently, Arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price, Versata Dev. Group, Inc. v. SAP Am., Inc., MPEP 2106.05(d)(II)(ii);
“and displaying a risk prediction based on the real-time risk prediction value and comprising a recommendation to admit or discharge the at least one patient into or from a medical facility.” is equivalently, Presenting offers and gathering statistics, OIP Techs., MPEP 2106.05(d)(II)(iv);
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. These additional limitations amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
To elaborate:
Claim 25 “further comprising storing the risk prediction value in the real-time database”, is equivalently, storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv);
Claim 30 “wherein the at least one batch server further performs preprocessing the extracted updated batch information from the one or more past patients prior to storing the updated batch information in the one or more databases.” , is equivalently, storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv);
Claim 32 “wherein the risk prediction value is stored in the one or more databases.”, is equivalently, storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv);
Claim 38 “wherein the instructions further perform preprocessing the extracted updated batch information from the one or more past patients prior to storing in the one or more databases.” , is equivalently, Arranging a hierarchy of groups, sorting information, Versata Dev. Group, Inc. v. SAP Am., Inc., MPEP 2106.05(d)(II)(ii);
Claim 39 “wherein the instructions upon execution by a processor further perform the step of storing the real-time risk prediction value in the one or more databases.”, is equivalently, storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv);
claim 31 and 37 “wherein the at least one batch server further performs mapping covariates in the EMR database to EMR vendor agnostic categories”, is equivalently, Arranging a hierarchy of groups, sorting information, Versata Dev. Group, Inc. v. SAP Am., Inc., MPEP 2106.05(d)(II)(ii);
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Double Patenting
A rejection based on double patenting of the “same invention” type finds its support in the language of 35 U.S.C. 101 which states that “whoever invents or discovers any new and useful process... may obtain a patent therefor...” (Emphasis added). Thus, the term “same invention,” in this context, means an invention drawn to identical subject matter. See Miller v. Eagle Mfg. Co., 151 U.S. 186 (1894); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Ockert, 245 F.2d 467, 114 USPQ 330 (CCPA 1957).
A statutory type (35 U.S.C. 101) double patenting rejection can be overcome by canceling or amending the claims that are directed to the same invention so they are no longer coextensive in scope. The filing of a terminal disclaimer cannot overcome a double patenting rejection based upon 35 U.S.C. 101.
Claims 22-42 are rejected under 35 U.S.C. 101 as claiming the same invention as that of claims 1-21 of prior U.S. Patent No. 11967433. This is a statutory double patenting rejection.
Application 18/641,310
Patent No. 11,967,433
Claim 22:
A method for estimating, in real-time or near-real-time, a risk of acute myocardial infarctions in at least one patient, the method comprising:
extracting batch information at a pre-defined time interval for one or more past patients from an electronic medical records (EMR) database into one or more databases operably connected to a machine learning model;
calculating, using the machine learning model, a risk level for the one or more past patients in the one or more databases, wherein the one or more past patients includes the at least one patient, and the risk level for the at least one patient is calculated using one or more medical feature coefficients obtained from training the machine learning model;
parsing at least one stream of real-time clinical administrative data associated with the at least one patient in a current medical encounter, in real-time or near real-time to identify and extract specified EMR data comprising patient clinical data obtained during the current medical encounter into the one or more databases;
predicting, in real-time or near-real-time, the risk of acute myocardial infarction in the at least one patient, wherein a real-time risk prediction value is estimated by adjusting the calculated risk level for the at least one patient by applying a linear model, different from the machine learning model, to one or more of the medical feature coefficients obtained from the machine learning model and to the specified real-time EMR data from the at least one patient;
and displaying a risk prediction based on the real-time risk prediction value and comprising a recommendation to admit or discharge the at least one patient into or from a medical facility.
Claim 1:
A method for estimating, in real-time or near-real-time, a risk of acute myocardial infarctions in at least one patient, the method comprising:
extracting batch information at a pre-defined time interval for one or more past patients from an electronic medical records (EMR) database into one or more databases operably connected to a machine learning model;
calculating, using the machine learning model, a risk level for the one or more past patients in the one or more databases, wherein the one or more past patients includes the at least one patient, and the risk level for the at least one patient is calculated using one or more medical feature coefficients obtained from training the machine learning model;
parsing at least one stream of real-time clinical administrative data associated with the at least one patient in a current medical encounter, in real-time or near real-time to identify and extract specified EMR data comprising patient clinical data obtained during the current medical encounter into the one or more databases;
predicting, in real-time or near-real-time, the risk of acute myocardial infarction in the at least one patient, wherein a real-time risk prediction value is estimated by adjusting the calculated risk level for the at least one patient by applying a linear model, different from the machine learning model, to one or more of the medical feature coefficients obtained from the machine learning model and to the specified real-time EMR data from the at least one patient;
and displaying a risk prediction based on the real-time risk prediction value and comprising a recommendation to admit or discharge the at least one patient into or from the medical facility.
Claim 23:
wherein the one or more past patients includes the at least one patient in a current medical encounter.
Claim 2:
wherein the one or more past patients includes the at least one patient in a current medical encounter.
Claim 24:
wherein the at least one patient in a current medical encounter is a new patient.
Claim 3:
wherein the at least one patient in a current medical encounter is a new patient.
Claim 25:
further comprising storing the risk prediction value in the real-time database.
Claim 4:
further comprising storing the risk prediction value in the real-time database.
Claim 25:
wherein the real-time database further comprises one or more derived medical features of a real-time patient encounter.
Claim 5:
wherein the real-time database further comprises one or more derived medical features of a real-time patient encounter.
Claim 27:
wherein the machine learning model comprises a gradient boosting machine.
Claim 6:
wherein the machine learning model comprises a gradient boosting machine.
Claim 28:
wherein the pre-defined time interval comprises 24 hours.
Claim 7:
wherein the pre-defined time interval comprises 24 hours.
Claim 29:
A system for estimating, in real-time or near-real-time, a risk of acute coronary syndrome in at least one patient, the system comprising:
at least one health system server comprising at least one electronic medical records (EMR) database;
a plurality of data servers comprising:
at least one batch server connected to one or more databases, extracting updated batch information at a pre-defined time interval for one or more past patients from the at least one electronic medical records (EMR) database into one or more databases operably connected to a machine learning model, and
at least one real-time server connected to the one or more databases, receiving streams of real-time or near real-time clinical administrative data, wherein at least one stream of real-time clinical administrative data is associated with the at least one patient in a current medical encounter, wherein the at least one stream of real-time clinical administrative data is parsed in real-time or near real-time to identify and extract specified EMR data comprising patient clinical data obtained during the current medical encounter;
a modeling server calculating a risk level for the one or more past patients in the one or more databases using the machine learning model wherein the risk level for the one or more past patients is calculated using one or more medical feature coefficients obtained from the machine learning model, and predicting, in real-time or near-real-time, the risk of acute coronary syndrome in the at least one patient in a current medical encounter, wherein a risk prediction value is estimated by applying a linear model, different from the machine learning model, to one or more of the medical feature coefficients obtained from the machine learning model and to the specified real-time EMR data, to adjust the calculated risk level for the at least one patient; and
a business integration server configuring and transmitting a display of the risk prediction of acute coronary syndrome, the risk prediction based on the risk prediction value and comprising a recommendation to admit or discharge the at least one patient into or from the medical facility.
Claim 8:
A system for estimating, in real-time or near-real-time, a risk of acute coronary syndrome in at least one patient, the system comprising:
at least one health system server comprising at least one electronic medical records (EMR) database;
a plurality of data servers comprising: at least one batch server connected to one or more databases, extracting updated batch information at a pre-defined time interval for one or more past patients from the at least one electronic medical records (EMR) database into one or more databases operably connected to a machine learning model, and
at least one real-time server connected to the one or more databases, receiving streams of real-time or near real-time clinical administrative data, wherein at least one stream of real-time clinical administrative data is associated with the at least one patient in a current medical encounter, wherein the at least one stream of real-time clinical administrative data is parsed in real-time or near real-time to identify and extract specified EMR data comprising patient clinical data obtained during the current medical encounter;
a modeling server calculating a risk level for the one or more past patients in the one or more databases using the machine learning model wherein the risk level for the one or more past patients is calculated using one or more medical feature coefficients obtained from the machine learning model, and predicting, in real-time or near-real-time, the risk of acute coronary syndrome in the at least one patient in a current medical encounter, wherein a risk prediction value is estimated by applying a linear model, different from the machine learning model, to one or more of the medical feature coefficients obtained from the machine learning model and to the specified real-time EMR data, to adjust the calculated risk level for the at least one patient; and
a business integration server configuring and transmitting a display of the risk prediction of acute coronary syndrome, the risk prediction based on the risk prediction value and comprising a recommendation to admit or discharge the at least one patient into or from the medical facility.
Claim 30:
wherein the at least one batch server further performs preprocessing the extracted updated batch information from the one or more past patients prior to storing the updated batch information in the one or more databases.
Claim 9:
wherein the at least one batch server further performs preprocessing the extracted updated batch information from the one or more past patients prior to storing the updated batch information in the one or more databases.
Claim 31:
wherein the at least one batch server further performs mapping covariates in the EMR database to EMR vendor agnostic categories.
Claim 10:
wherein the at least one batch server further performs mapping covariates in the EMR database to EMR vendor agnostic categories.
Claim 32:
wherein the risk prediction value is stored in the one or more databases.
Claim 11:
wherein the risk prediction value is stored in the one or more databases.
Claim 33:
wherein the one or more real-time databases comprise one or more derived features of a real-time patient encounter.
Claim 12:
wherein the one or more real- time databases comprise one or more derived features of a real-time patient encounter.
Claim 34:
wherein the machine learning model comprises a gradient boosting machine.
Claim 13:
wherein the machine learning model comprises a gradient boosting machine.
Claim 35:
wherein the clinical administrative data comprises Health Level 7 (HL7) data.
Claim 14:
wherein the clinical administrative data comprises Health Level 7 (HL7) data.
Claim 36:
A computer related product comprising a non-transitory computer readable medium storing instructions for estimating, in real-time or near-real-time, a risk of acute myocardial infarction in at least one patient, wherein the instructions upon execution by a processor perform:
extracting updated batch information at a pre-defined time interval for one or more past patients from an electronic medical records (EMR) database into one or more databases operably connected to a machine learning model;
calculating, using the machine learning model, a risk level for the one or more past patients in the one or more databases wherein the risk level for the at least one patient is calculated using one or more medical feature coefficients obtained from training the machine learning model;
parsing at least one stream of real-time clinical administrative data is associated with the at least one patient in a current medical encounter, in real-time or near real-time to identify and extract specified EMR data comprising patient clinical data obtained during the current medical encounter into the one or more databases;
predicting, in real-time or near-real-time, the risk of acute myocardial infarction in the at least one patient, wherein a real-time risk prediction value is estimated by adjusting the calculated risk level for the at least one patient by applying a linear model, different from the machine learning model, to one or more of the medical feature coefficients obtained from the machine learning model and to the specified EMR data from the at least one patient; and
displaying a risk prediction is based on the real-time risk prediction value and comprising a recommendation to admit or discharge the at least one patient into or from a medical facility.
Claim 15:
A computer related product comprising a non-transitory computer readable medium storing instructions for estimating, in real-time or near- real-time, a risk of acute myocardial infarction in at least one patient, wherein the instructions upon execution by a processor perform:
extracting updated batch information at a pre-defined time interval for one or more past patients from an electronic medical records (EMR) database into one or more databases operably connected to a machine learning model;
calculating, using the machine learning model, a risk level for the one or more past patients in the one or more databases wherein the risk level for the at least one patient is calculated using one or more medical feature coefficients obtained from training the machine learning model;
parsing at least one stream of real-time clinical administrative data is associated with the at least one patient in a current medical encounter, in real-time or near real-time to identify and extract specified EMR data comprising patient clinical data obtained during the current medical encounter into the one or more databases;
predicting, in real-time or near-real-time, the risk of acute myocardial infarction in the at least one patient, wherein a real-time risk prediction value is estimated by adjusting the calculated risk level for the at least one patient by applying a linear model, different from the machine learning model, to one or more of the medical feature coefficients obtained from the machine learning model and to the specified EMR data from the at least one patient; and
displaying a risk prediction is based on the real-time risk prediction value and comprising a recommendation to admit or discharge the at least one patient into or from the medical facility.
Claim 37:
wherein the instructions further perform mapping covariates in the EMR database to EMR vendor agnostic categories.
Claim 16:
wherein the instructions further perform mapping covariates in the EMR database to EMR vendor agnostic categories.
Claim 38:
wherein the instructions further perform preprocessing the extracted updated batch information from the one or more past patients prior to storing in the one or more databases.
Claim 17:
wherein the instructions further perform preprocessing the extracted updated batch information from the one or more past patients prior to storing in the one or more databases.
Claim 39:
wherein the instructions upon execution by a processor further perform the step of storing the real-time risk prediction value in the one or more databases.
Claim 18:
wherein the instructions upon execution by a processor further perform the step of storing the real-time risk prediction value in the one or more databases.
Claim 40:
wherein the real-time database further comprises one or more derived features of a real-time patient encounter.
Claim 19:
wherein the real-time database further comprises one or more derived features of a real-time patient encounter.
Claim 41:
wherein the machine learning model comprises a gradient boosting machine.
Claim 20:
wherein the machine learning model comprises a gradient boosting machine.
Claim 42:
wherein the clinical administrative data comprises Health Level 7 (HL7) data.
Claim 21:
wherein the clinical administrative data comprises Health Level 7 (HL7) data.
Response to Arguments
Regarding pages 8-10, Applicant’s arguments have been fully considered but are moot in view of the amended claim language.
Additional Considerations
The prior art made of record and not relied upon that is considered pertinent to applicant’s disclosure can be found on PTO-892 of the prior office action dated July 10th, 2025.
Gheorghita et al. (US20220093270) discloses a machine learning model that classifies cardiovascular diseases using functional or anatomical characteristics. The system is modeled to diagnose a patient or use clinical support using a wide range of machine learning models.
Amarasingham et al. (US 20130262357) discloses a system that describes a monitoring system that adapts artificial intelligence engines to automatically adjust predictive model parameters in response to trends in patient data. It tracks the healthcare data of patients to determine high risk patients in order to notify a team of the high risk patient.
Francois et al. (WO2014105752) discloses a system that assembles databases comprising electronic medical records for diagnosing patient diseases.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROBERT ANTHONY SKROBARCZYK whose telephone number is (571)272-3301. The examiner can normally be reached Monday thru Friday 7:30AM -5PM CST.
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/R.A.S/Examiner, Art Unit 3685
/KAMBIZ ABDI/Supervisory Patent Examiner, Art Unit 3685