DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to an application filed on 03/04/2025. Claims 17-32 are currently pending and have been examined.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 32 is rejected under 35 U.S.C. 112(d) as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 32 recites “A computer program product comprising a non-transitory machine-readable data storage medium having stored thereon program instructions that, when accessed by at least one processor, cause the at least one processor to implement the method of claim 31 “. When, as here, an independent claim recites a particular method, a dependent claim drawn to a computer-readable medium capable of performing the method of the independent claim is not a proper dependent claim, since the dependent claim could conceivably be infringed by mere possession of the computer program on a computer-readable medium without performing any particular method steps at all, thereby infringing the dependent claim without necessarily infringing the independent claim, in violation of the infringement test for proper dependency of claims. See MPEP § 608.01(n)(III). Applicant may cancel the claim, amend the claim to place the claim in proper dependent form, rewrite the claim in independent form, or present a sufficient showing that the dependent claim complies with the statutory requirements.
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 17-32 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), and does not include additional elements that either: 1) integrate the abstract idea into a practical application, or 2) that provide an inventive concept — i.e. element that amount to significantly more than the abstract idea. The Claims are directed to an abstract idea because, when considered as a whole, the plain focus of the claims is on an abstract idea.
STEP 1
The claims are directed to a system and method which is included in the statutory categories of invention.
STEP 2A PRONG ONE
The claims recite the abstract idea (based on claim 31) of:
A method for assessing a risk of adverse medical events leading to unplanned healthcare and/or death, and for facilitating mitigation of the adverse medical events, the method comprising: - obtaining existing healthcare data from at least one data source, wherein the existing healthcare data comprises existing accident and emergency data, existing inpatient data, and existing outpatient data; - building a predictive model for estimating individual patients' risks for adverse medical events leading to unplanned healthcare and/or death, using the existing healthcare data and a pre- defined set of medical conditions and pre-defined patient characteristics that are likely to lead to the adverse medical events; - deploying the predictive model for use; - obtaining first healthcare data from the at least one data source, wherein the first healthcare data comprises first accident and emergency data, first inpatient data, and first outpatient data, the first healthcare data being generated later in time than the existing healthcare data; - processing the first healthcare data using the predictive model, for predicting a risk level of at least one adverse medical event leading to unplanned healthcare and/or death, for each patient amongst a plurality of patients indicated in the first healthcare data; - identifying a target set of patients, wherein each patient belonging to the target set is one whose risk level of the at least one adverse medical event is greater than a threshold risk level of the at least one adverse medical event; and - sending a communication indicative of the target set of patients to the at least one data source, for enabling determination of at least one healthcare intervention, wherein the at least one healthcare intervention, when provided to said patient, facilitates in at least partially mitigating the at least one adverse medical event which in turn reduces mortality of said patient.
The claims, as illustrated by the limitations of Claim 31 above, recite an abstract idea within the “certain methods of organizing human activity” grouping — managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions and additionally recite an abstract idea within the “mathematical concepts” grouping - mathematical relationships, mathematical formulas or equations, mathematical calculations.
The claims recite mitigating adverse medical events of patients by identifying patients as having greater than a threshold risk of having an adverse medical event from received patient data processed through a predictive model. Mitigating adverse medical events of patients by identifying patients as having greater than a threshold risk of having an adverse medical event from received patient data processed through a predictive model is a process that merely organizes human activity, as it involves following rules and instructions to obtain data, deploy predictive model, obtain data, process data, identify patients, send communication. The claims also recite building a predictive model. Building a predictive model is a mathematical concept that involves mathematical calculations. The claims also involve interaction between a person and an computer. Interaction between a person and computer qualifies as interaction under certain methods of organizing human activity. See MPEP 2106.04(a)(2)(II). As such, the claims recite an abstract idea within the categories of certain methods of organizing human activity and mathematical concepts.
The dependent claims 18, 19, 23-28 recite further abstract ideas within the category of certain methods of organizing human activity, such as 18 receive a plurality of first inputs provided by the at least one healthcare professional, the plurality of first inputs pertaining to a selection of a first subset of patients from amongst the target set such that a portion of the first healthcare data that is associated with each patient selected to belong to the first subset complies with at least one inclusion criteria; 19 receive a plurality of second inputs provided by the at least one healthcare professional, the plurality of second inputs pertaining to a selection of a second subset of patients from amongst the first subset of patients such that the portion of the first healthcare data that is associated with each patient selected to belong to the second subset is non-compliant with at least one exclusion criteria; 23 - identify a set of vulnerable patients, based on the patient characteristics, wherein a vulnerable patient is one who belongs to one or more of: a vulnerable age group, a vulnerable gender, a vulnerable ethnicity, a deprived group; - determine a vulnerability score for each patient in the set of vulnerable patients, based on data associated with patient characteristics of said patient; and - enhance the risk level of the at least one adverse medical event for each vulnerable patient having a vulnerability score higher than a threshold vulnerability score, by a predetermined level; 24 process the existing healthcare data to extract an additional feature set, wherein the additional feature set comprises time-dependent variables indicative of patient condition and activity, wherein when training the predictive model, the at least one processor is configured to also use the additional feature set; 25 - receive, from the at least one data source, feedback pertaining to accuracy of the target set of patients and suitability of the target set of patients to receive the at least one healthcare intervention; - determine a performance metric of the predictive model, based on the feedback; and - initiate re-training of the predictive model based on the feedback and the performance metric; 26 receive, for each patient belonging to the target set, an input indicative of the at least one healthcare intervention, from the at least one data source; 27 - obtain second healthcare data from the at least one data source, wherein the second healthcare data comprises second accident and emergency data, second inpatient data, and second outpatient data, the second healthcare data being generated later in time than the first healthcare data; - process the second healthcare data to predict an updated risk level of the at least one adverse medical event for: each patient not belonging to the target set, each patient belonging to the target set who received the at least one healthcare intervention; - update the target set of patients, based on the updated risk levels; and - send a communication indicative of the updated target set of patients, to the at least one data source; 28 - deploy a pseudonymization engine at the at least one data source, wherein the pseudonymization engine employs a hashing algorithm to pseudonymize a given healthcare data; and enable at least one of: sending of real-time updates of the given healthcare data, sending of the given healthcare data only upon pseudonymization, re-identification of the given healthcare data of a subset of patients within the target set of patients at a target device wherein the subset of patients includes patients with risk levels greater than a threshold high-risk level of the at least one adverse medical event. The dependent claims 20-22 recite further abstract ideas within the category of mathematical concepts, such as 20 when building the predictive model, - normalize the existing healthcare data into a unified feature set, wherein the unified feature set comprises features pertaining to patient characteristics, medical diagnosis, and healthcare provider activity; - execute data quality and imputation checks on the unified feature set; - train and validate the predictive model using a first portion of the unified feature set and at least one learning algorithm to build weights against each medical condition in the pre- defined set of medical conditions; and - test the predictive model that is trained, using a second portion of the unified feature set; 21 the predictive model is trained and validated using k-fold cross validation, and wherein prior to testing the predictive model that is trained - generate model evaluation scores for the k folds; and - fine tune the predictive model by adjusting the weights, based on the model evaluation scores, for improving an accuracy of the predictive model; 22 the weights are built against each medical condition in the pre-defined set of medical conditions using pre-defined weights.
STEP 2A PRONG TWO
The claims recite additional elements beyond those that encompass the abstract idea above including:
Independent claim 17:
at least one processor configured to
and/or at least one first device associated with at least one healthcare professional
Dependent claim 18:
the at least one first device is configured to
Dependent claim 19:
the at least one first device is further configured to
Dependent claim 20:
the at least one processor is configured to:
machine
Dependent claim 21:
the at least one processor is further configured to:
Independent claim 23:
the at least one processor is further configured to:
Dependent claim 24:
the at least one processor is further configured to
Dependent claim 25:
the at least one processor is further configured to:
and/or the at least one first device
Dependent claim 26:
the at least one processor is further configured to
and/or the at least one first device
Dependent claim 27:
the at least one processor is further configured to:
and/or the at least one first device
Dependent claim 28:
the at least one processor is further configured to:
- deploy communication interfaces connecting the at least one data source with at least the at least one processor and the at least one first device to
However, these additional elements do not integrate the abstract idea into a practical application of that idea in accordance with considerations laid out by the Supreme Court or the Federal Circuit. (see MPEP 2106.05 a-c and e) The additional elements integrate the abstract idea into a practical application when they: improve the functioning of a computer or improving any other technology, apply or use a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, apply the judicial exception with, or by use of, a particular machine, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. The additional limitations do not integrate the abstract idea into a practical application when they merely serve to link the use of the abstract idea to a particular technological environment or field of use — i.e. merely uses the computer as a tool to perform the abstract idea; or recite insignificant extra-solution activity (see MPEP 2106.05 f - h).
The processor, communication interface, device, and data source are recited at a high level of generality such that it amounts to no more than instructions to apply the abstract idea using generic computer components. These elements merely add instructions to implement the abstract idea on a computer, and generally link the abstract idea to a particular technological environment. Nothing in the claim recites specific limitations directed to an improved processor, communication interface, device, and data source. Similarly, the specification is silent with respect to these kinds of improvements. A general purpose computer that applies a judicial exception to computer functions, as is the case here, does not qualify as a particular machine, nor does the recitation of a basic computer impose meaningful limits in the claimed process. (see Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 716-17 (Fed. Cir. 2014)). As such, the additional elements recited in the claims do not integrate the abstract medical risk assessment process into a practical application of that process.
STEP 2B
The additional elements identified above do not amount to significantly more than the abstract medical risk assessment process. The additional structural elements or combination of elements in the claims, other than the abstract idea per se, amount to no more than a recitation of generic computer structure. Because the specification describes these additional elements in general terms, without describing particulars, Examiner concludes that the claim limitations may be broadly, but reasonably construed, as reciting basic computer components and techniques. The specification describes the elements in a manner that indicates that they are sufficiently straightforward such that the specification does not need to describe the particulars in order to satisfy U.S.C. 112. Considered as an ordered combination, the limitations recited in the claims add nothing that is not already present when the steps are considered individually.
The limitations recited in the dependent claims, in combination with those recited in the independent claims add nothing that integrates the abstract idea into a practical application, or that amounts to significantly more. For example, dependent claim limitations 18 receive a plurality of first inputs provided by the at least one healthcare professional, the plurality of first inputs pertaining to a selection of a first subset of patients from amongst the target set such that a portion of the first healthcare data that is associated with each patient selected to belong to the first subset complies with at least one inclusion criteria; 19 receive a plurality of second inputs provided by the at least one healthcare professional, the plurality of second inputs pertaining to a selection of a second subset of patients from amongst the first subset of patients such that the portion of the first healthcare data that is associated with each patient selected to belong to the second subset is non-compliant with at least one exclusion criteria; 23 - identify a set of vulnerable patients, based on the patient characteristics, wherein a vulnerable patient is one who belongs to one or more of: a vulnerable age group, a vulnerable gender, a vulnerable ethnicity, a deprived group; - determine a vulnerability score for each patient in the set of vulnerable patients, based on data associated with patient characteristics of said patient; and - enhance the risk level of the at least one adverse medical event for each vulnerable patient having a vulnerability score higher than a threshold vulnerability score, by a predetermined level; 24 process the existing healthcare data to extract an additional feature set, wherein the additional feature set comprises time-dependent variables indicative of patient condition and activity, wherein when training the predictive model, the at least one processor is configured to also use the additional feature set; 25 - receive, from the at least one data source, feedback pertaining to accuracy of the target set of patients and suitability of the target set of patients to receive the at least one healthcare intervention; - determine a performance metric of the predictive model, based on the feedback; and - initiate re-training of the predictive model based on the feedback and the performance metric; 26 receive, for each patient belonging to the target set, an input indicative of the at least one healthcare intervention, from the at least one data source; 27 - obtain second healthcare data from the at least one data source, wherein the second healthcare data comprises second accident and emergency data, second inpatient data, and second outpatient data, the second healthcare data being generated later in time than the first healthcare data; - process the second healthcare data to predict an updated risk level of the at least one adverse medical event for: each patient not belonging to the target set, each patient belonging to the target set who received the at least one healthcare intervention; - update the target set of patients, based on the updated risk levels; and - send a communication indicative of the updated target set of patients, to the at least one data source; 28 - deploy a pseudonymization engine at the at least one data source, wherein the pseudonymization engine employs a hashing algorithm to pseudonymize a given healthcare data; and enable at least one of: sending of real-time updates of the given healthcare data, sending of the given healthcare data only upon pseudonymization, re-identification of the given healthcare data of a subset of patients within the target set of patients at a target device wherein the subset of patients includes patients with risk levels greater than a threshold high-risk level of the at least one adverse medical event are directed to the abstract idea of certain methods of organizing human activity without integrating into a practical application or amounting to significantly more. Dependent claim limitations 20 when building the predictive model, - normalize the existing healthcare data into a unified feature set, wherein the unified feature set comprises features pertaining to patient characteristics, medical diagnosis, and healthcare provider activity; - execute data quality and imputation checks on the unified feature set; - train and validate the predictive model using a first portion of the unified feature set and at least one learning algorithm to build weights against each medical condition in the pre- defined set of medical conditions; and - test the predictive model that is trained, using a second portion of the unified feature set; 21 the predictive model is trained and validated using k-fold cross validation, and wherein prior to testing the predictive model that is trained - generate model evaluation scores for the k folds; and - fine tune the predictive model by adjusting the weights, based on the model evaluation scores, for improving an accuracy of the predictive model; 22 the weights are built against each medical condition in the pre-defined set of medical conditions using pre-defined weights are directed to the abstract idea of mathematical concepts without integrating into a practical application or amounting to significantly more. Dependent claim limitations 23 the patient characteristics comprise one or more of: age, gender, ethnicity, social and economic deprivation; 29 the at least one data source is at least one of a device associated with a healthcare facility, a device associated with a primary care provider, an out of hours(OOH) service, a device associated with a health trust, an ambulance service, a device associated with a mental health and community facility; 30 the at least one adverse medical event is at least one of: unplanned hospitalization, unplanned outpatient visit, requirement of emergency services, readmission to a healthcare facility, stranding in a healthcare facility, serious fall, frailty progression, worsening of existing medical conditions, emergence of new medical conditions, pediatric response exacerbation, high-intensity primary care usage; 32 a computer program product comprising a non-transitory machine-readable data storage medium having stored thereon program instructions that, when accessed by at least one processor, cause the at least one processor to implement the method of claim 31 merely serve to further narrow the abstract idea above. As such, the additional elements do not integrate the abstract idea into a practical application, or provide an inventive concept that transforms the claims into a patent eligible invention. Therefore, the claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 17- are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Zhang, et al. (US 2020/0402665 A1).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 17-27 and 29-32 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang, et al. (US 2020/0402665 A1) in view of Montijo, et al. (US 2011/0071363 A1).
With regards to claim 17, Zhang teaches a system for assessing a risk of adverse medical events leading to unplanned healthcare and/or death, and for facilitating mitigation of the adverse medical events, the system comprising at least one processor configured to: -obtain existing healthcare data from at least one data source, wherein the existing healthcare data comprises existing accident and emergency data, existing inpatient data, and existing outpatient data (see at least figure 1 (102), ¶ 0020, admission due to car accident; ¶ 0054, patient data includes medical history data that can be used as input to the readmission risk forecasting model 106 can include but are not limited to: comorbidities, ongoing illnesses (including mental illnesses), past diagnoses, past hospital stays/admissions and associated information regarding past courses of care and length of stay (LOS), past intensive care unit (ICU) stays, past surgeries, regular and acute medications taken, and whether the patient has any implanted medical devices (IMDs) and if so, the type and location of the IMDs, exacerbation conditions associated to heart failure in last 6 months, historical total inpatient expenditure for the patient, historical total medical expenditures of the patient and the like; ¶ 0056, emergency room data; ¶ 0078-0079, outpatient data); - build a predictive model for estimating individual patients' risks for adverse medical events leading to unplanned healthcare and/or death, using the existing healthcare data and a pre-defined set of medical conditions and pre-defined patient characteristics that are likely to lead to the adverse medical events (see at least ¶ 0054, patient data includes medical history data that can be used as input to the readmission risk forecasting model can include but are not limited to: comorbidities, ongoing illnesses (including mental illnesses), past diagnoses, past hospital stays/admissions and associated information regarding past courses of care and length of stay (LOS), past intensive care unit (ICU) stays, past surgeries, regular and acute medications taken, and whether the patient has any implanted medical devices (IMDs) and if so, the type and location of the IMDs, exacerbation conditions associated to heart failure in last 6 months, historical total inpatient expenditure for the patient, historical total medical expenditures of the patient and the like; ¶ 0093-0102, risk factors from last admission; [all interpreted as existing healthcare data and a pre-defined set of medical conditions and pre-defined patient characteristics that are likely to lead to the adverse medical events]; ¶ 0104, historical past patient data used to train and develop readmission risk forecasting model [build predictive model]; - deploy the predictive model for use (see at least figure 1 (106, 108, 110, 112)); -obtain first healthcare data from the at least one data source, wherein the first healthcare data comprises first accident and emergency data, first inpatient data, and first outpatient data, the first healthcare data being generated later in time than the existing healthcare data (see at least ¶ 0020, admission due to car accident; ¶ 0055, obtain patient data from current admission from time of admission to time of discharge [first inpatient data, first outpatient data, first healthcare data being generated later in time than existing healthcare data]; ¶ 0056, emergency room data); - process the first healthcare data using the predictive model, to predict a risk level of at least one adverse medical event leading to unplanned healthcare and/or death, for each patient …indicated in the first healthcare data (see at least ¶ 0003-0004, using risk model with patient data to predict unplanned readmissions for patients); - identify a target set of patients, wherein each patient belonging to the target set is one whose risk level of the at least one adverse medical event is greater than a first threshold risk level of the at least one adverse medical event (see at least ¶ 0110, 0112, patients whose readmission risk score is above a threshold [target set of patients] indicating they are at a high likelihood of being readmitted); - send a communication indicative of the target set of patients to the at least one data source and/or at least one first device associated with at least one healthcare professional, for enabling determination of at least one healthcare intervention, wherein the at least one healthcare intervention, when provided to said patient, facilitates in at least partially mitigating the at least one adverse medical event which in turn reduces mortality of said patient (see at least ¶ 0125, system determines actionable care plan for preventing or minimizing the occurrence/risks of the unplanned readmission for patients whose readmission risk is high and presents the actionable care plan via a GUI which can be access and reviewed by a clinician).
Zhang does not explicitly teach …amongst a plurality of patients. Montijo teaches …amongst a plurality of patients (see at least ¶ 0006). It would have been obvious to one of ordinary skill in the art at the time of invention to combine the level of healthcare determination method of Montijo with the readmission prediction system of Zhang with the motivation of providing increased healthcare to patients who need it (Montijo, ¶ 0005, 0023).
Claim 31 recites similar limitations regarding the method of the system and is rejected for the same reasons.
With regards to claim 18, Zhang teaches the system according to claim 17, wherein the at least one first device is configured to receive a plurality of first inputs provided by the at least one healthcare professional, the plurality of first inputs pertaining to a selection of a first subset of patients from amongst the target set such that a portion of the first healthcare data that is associated with each patient selected to belong to the first subset complies with at least one inclusion criteria (see at least ¶ 0121, 0123, 0125, system receives data about patients and determines some patients are not outliers and their readmission risk is high).
With regards to claim 19, Zhang teaches the system according to claim 18, wherein the at least one first device is further configured to receive a plurality of second inputs provided by the at least one healthcare professional, the plurality of second inputs pertaining to a selection of a second subset of patients from amongst the first subset of patients such that the portion of the first healthcare data that is associated with each patient selected to belong to the second subset is non-compliant with at least one exclusion criteria (see at least ¶ 0121, 0122, 0124, system receives data about patients and determines some patients are outliers and their readmission risk is not high).
With regards to claim 20, Zhang teaches the system according to claim 17 , wherein when building the predictive model, the at least one processor is configured to: - normalize the existing healthcare data into a unified feature set, wherein the unified feature set comprises features pertaining to patient characteristics, medical diagnosis, and healthcare provider activity (see at least ¶ 0121, initially perform data cleaning and data pre-processing on patient data. For example, the data cleaning and pre-processing can involve identifying and extracting the relevant features/factors (and values) included in the patient data that can be used as input to the readmission risk forecasting model 106 (and optionally the care plan model 118). In this regard, the data cleaning and preprocessing at 502 builds an indexed list of features and feature values); - execute data quality and imputation checks on the unified feature set (see at least ¶ 0135, patient's claim data will be cleaned, processed (removing/filling the missing data, removing outliers/extreme data, standardization etc.) to generate the datasets used for training); - train and validate the predictive model using a first portion of the unified feature set and at least one machine learning algorithm to build weights against each medical condition in the pre- defined set of medical conditions (see at least ¶ 0142, the system can periodically retrain the models discussed herein based on the new data and the feedback collected and annotated, with more weights added to the cases in the failure case database); and - test the predictive model that is trained, using a second portion of the unified feature set (see at least ¶ 0135, the care plan model can be or comprise one or more machine learning models trained on the historical care plan data).
With regards to claim 21, Zhang teaches the system according to claim 20, wherein the predictive model is trained and validated using k-fold cross validation, and wherein prior to testing the predictive model that is trained, the at least one processor is further configured to: - generate model evaluation scores for the k folds; and - fine tune the predictive model by adjusting the weights, based on the model evaluation scores, for improving an accuracy of the predictive model (see at least ¶ 0033, 0136).
With regards to claim 22, Zhang teaches the system according to any of claim 20, wherein the weights are built against each medical condition in the pre-defined set of medical conditions using pre-defined weights (see at least ¶ 0062, 0105).
With regards to claim 23, Zhang teaches the system according to any of claim 20, wherein the patient characteristics comprise one or more of: age, gender, ethnicity, social and economic deprivation (see at least ¶ 0062), and wherein the at least one processor is further configured to: - identify a set of vulnerable patients, based on the patient characteristics, wherein a vulnerable patient is one who belongs to one or more of: a vulnerable age group, a vulnerable gender, a vulnerable ethnicity, a deprived group; - determine a vulnerability score for each patient in the set of vulnerable patients, based on data associated with patient characteristics of said patient; and - enhance the risk level of the at least one adverse medical event for each vulnerable patient having a vulnerability score higher than a threshold vulnerability score, by a predetermined level (see at least ¶ 0119).
With regards to claim 24, Zhang teaches the system according to claim 17, wherein the at least one processor is further configured to process the existing healthcare data to extract an additional feature set, wherein the additional feature set comprises time-dependent variables indicative of patient condition and activity, wherein when training the predictive model, the at least one processor is configured to also use the additional feature set (see at least ¶ 0107).
With regards to claim 25, Zhang teaches the system according to claim 17, wherein the at least one processor is further configured to: - receive, from the at least one data source and/or the at least one first device, feedback pertaining to accuracy of the target set of patients and suitability of the target set of patients to receive the at least one healthcare intervention; - determine a performance metric of the predictive model, based on the feedback; and - initiate re-training of the predictive model based on the feedback and the performance metric (see at least ¶ 0126).
With regards to claim 26, Zhang teaches the system according to claim 17,wherein the at least one processor is further configured to receive, for each patient belonging to the target set, an input indicative of the at least one healthcare intervention, from the at least one data source and/or the at least one first device (see at least ¶ 0057).
With regards to claim 27, Zhang teaches the system according to claim 17,wherein the at least one processor is further configured to: - obtain …healthcare data from the at least one data source, wherein the …healthcare data comprises …accident and emergency data, …inpatient data, and …outpatient data (see at least figure 1 (102), ¶ 0020, admission due to car accident; ¶ 0054, patient data includes medical history data that can be used as input to the readmission risk forecasting model 106 can include but are not limited to: comorbidities, ongoing illnesses (including mental illnesses), past diagnoses, past hospital stays/admissions and associated information regarding past courses of care and length of stay (LOS), past intensive care unit (ICU) stays, past surgeries, regular and acute medications taken, and whether the patient has any implanted medical devices (IMDs) and if so, the type and location of the IMDs, exacerbation conditions associated to heart failure in last 6 months, historical total inpatient expenditure for the patient, historical total medical expenditures of the patient and the like; ¶ 0056, emergency room data; ¶ 0078-0079, outpatient data).
Furthermore, Montijo teaches …second …the second healthcare data being generated later in time than the first healthcare data; - process the second healthcare data to predict an updated risk level of the at least one adverse medical event for: each patient not belonging to the target set, each patient belonging to the target set who received the at least one healthcare intervention; - update the target set of patients, based on the updated risk levels; and - send a communication indicative of the updated target set of patients, to the at least one data source and/or the at least one first device (see at least ¶ 0073). It would have been obvious to one of ordinary skill in the art at the time of invention to combine the level of healthcare determination method of Montijo with the readmission prediction system of Zhang with the motivation of providing increased healthcare to patients who need it (Montijo, ¶ 0005, 0023).
With regards to claim 29, Zhang teaches the system according to claim 17,wherein the at least one data source is at least one of a device associated with a healthcare facility, a device associated with a primary care provider, an out of hours(OOH) service, a device associated with a health trust, an ambulance service, a device associated with a mental health and community facility (see at least ¶ 0054).
With regards to claim 30, Zhang teaches the system according to claim 17, wherein the at least one adverse medical event is at least one of: unplanned hospitalization, unplanned outpatient visit, requirement of emergency services, readmission to a healthcare facility, stranding in a healthcare facility, serious fall, frailty progression, worsening of existing medical conditions, emergence of new medical conditions, pediatric response exacerbation, high-intensity primary care usage (see at least ¶ 0002).
With regards to claim 32, Zhang teaches the computer program product comprising a non-transitory machine-readable data storage medium having stored thereon program instructions that, when accessed by at least one processor, cause the at least one processor to implement the method of claim 31 (see at least ¶ 0158).
Claim 28 is rejected under 35 U.S.C. 103 as being unpatentable over Zhang, et al. (US 2020/0402665 A1) in view of Montijo, et al. (US 2011/0071363 A1) in further view of Gotthardt (US 2011/0225114 A1).
With regards to claim 28, Zhang teaches a system according to claim 17, wherein the at least one processor is further configured to: …and - deploy communication interfaces connecting the at least one data source with at least the at least one processor and the at least one first device to enable at least one of: sending of real-time updates of the given healthcare data (see at least ¶ 0057), sending of the given healthcare data only upon pseudonymization, re-identification of the given healthcare data of a subset of patients within the target set of patients at a target device wherein the subset of patients includes patients with risk levels greater than a threshold high-risk level of the at least one adverse medical event.
Zhang does not explicitly teach - deploy a pseudonymization engine at the at least one data source, wherein the pseudonymization engine employs a hashing algorithm to pseudonymize a given healthcare data. Gotthardt teaches - deploy a pseudonymization engine at the at least one data source, wherein the pseudonymization engine employs a hashing algorithm to pseudonymize a given healthcare data (see at least ¶ 0171, 0321). It would have been obvious to one of ordinary skill in the art at the time of invention to combine the medical data pseudonymization method of Gotthardt with the readmission prediction system of Zhang with the motivation of protection of user medical data (Gotthard, ¶ 0171).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Amarasingham, et al. (US 2014/0074509 A1) which discloses a dashboard user interface method comprises displaying a navigable list of at least one target disease, displaying a navigable list of patient identifiers associated with a target disease selected in the target disease list, displaying historic and current data associated with a patient in the patient list identified as being associated with the selected target disease, including clinician notes at admission, receiving, storing, and displaying review's comments, and displaying automatically-generated intervention and treatment recommendations.
Mossin, et al. (US 2019/0034591 A1) which discloses a system for predicting and summarizing medical events from electronic health records includes a computer memory storing aggregated electronic health records from a multitude of patients of diverse age, health conditions, and demographics including medications, laboratory values, diagnoses, vital signs, and medical notes. The aggregated electronic health records are converted into a single standardized data structure format and ordered arrangement per patient, e.g., into a chronological order. A computer (or computer system) executes one or more deep learning models trained on the aggregated health records to predict one or more future clinical events and summarize pertinent past medical events related to the predicted events on an input electronic health record of a patient having the standardized data structure format and ordered into a chronological order. An electronic device configured with a healthcare provider-facing interface displays the predicted one or more future clinical events and the pertinent past medical events of the patient.
Muralitharan S, Nelson W, Di S, McGillion M, Devereaux PJ, Barr NG, Petch J. Machine Learning-Based Early Warning Systems for Clinical Deterioration: Systematic Scoping Review. J Med Internet Res. 2021 Feb 4;23(2):e25187. doi: 10.2196/25187. PMID: 33538696; PMCID: PMC7892287 which timely identification of patients at a high risk of clinical deterioration is key to prioritizing care, allocating resources effectively, and preventing adverse outcomes. Vital signs–based, aggregate-weighted early warning systems are commonly used to predict the risk of outcomes related to cardiorespiratory instability and sepsis, which are strong predictors of poor outcomes and mortality. Machine learning models, which can incorporate trends and capture relationships among parameters that aggregate-weighted models cannot, have recently been showing promising results.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Joey Burgess whose telephone number is (571)270-5547. The examiner can normally be reached Monday through Friday 9-6.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kambiz Abdi can be reached on 571-272-6702 The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JOSEPH D BURGESS/ Primary Examiner, Art Unit 3685
Prosecution Insights