DETAILED ACTION
This action is in reference to the communication filed on 11 MARCH 2025.
Amendments to claims 1, 3, 5, 8, 9, 11, 13, 14, as well as the addition of claims 16-20 have been entered and considered.
Claims 1-20 are present and have been examined.
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 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. As explained below, the claim(s) are directed to an abstract idea without significantly more.
Step One: Is the Claim directed to a process, machine, manufacture or composition of matter? YES
With respect to claim(s) 1-20 the independent claim(s) 1, 11 recite(s) a method and a system, each of which is a statutory category of invention.
Step 2A – Prong One: Is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? YES
With respect to claim(s) 1-20 the independent claim(s) (claims 1, 11) is/are directed, in part, to:
Claim 1: A method for predicting a likelihood of intensive care unit (ICU) mortality for a patient the method comprising:
obtaining, from an
extracting, from the obtained plurality of records, a plurality of different defined ICU prediction features for the patient;
analyzing the extracted plurality of different defined ICU prediction features using a trained ICU mortality prediction model; and
predicting, by the trained ICU mortality prediction model, a likelihood of ICU mortality for the patient;
These claim elements are considered to be abstract ideas because they are directed to mental process, in that the claims ensconce concepts performed in the human mind including observation, evaluation, judgment, and opinion functions. Obtaining records, extracting prediction features, analyzing the extracted features, and predicting the mortality for a patient are all examples of concepts performed in the human mind. The claims are further directed to mathematical concepts – i.e. mathematical relationships, formulas, equations, and/or calculations. Using a trained ICU prediction model to analyze features is an example of a mathematical concept.
If a claim limitation, under its broadest reasonable interpretation, covers a concept performed in the human mind, then it/they falls/ fall into the “mental processes” category. If a claim limitation, under its broadest reasonable interpretation, covers mathematical relationships, formulas, equations, and/or calculations, then it/they falls/ fall into the “mathematical processes” category. Accordingly, the claim recites an abstract idea.
Step 2A – Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? NO.
This judicial exception is not integrated into a practical application. In particular, the claim(s) recite(s) additional elements: Claims 1, 11 recites an electronic medical records database, and claim 11 recites the use of a processor to perform the claim steps. The database, and the processor, are both recited at a high level of generality and as such amount to no more than adding the words “apply it” to the judicial exception, or mere instructions to implement the abstract idea on a computer, or merely uses the computer as a tool to perform the abstract idea (see MPEP 2106.05f), or generally links the use of the judicial exception to a particular technological field of use/computing environment (see MPEP 2106.05h). recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component. Examiner further notes that storing data is generally considered to be adding insignificant extra solution activity to the judicial exception(s) identified (see MPEP 2106.05g). Examiner finds no improvement to the functioning of the computer or any other technology or technical field in the above identified elements as claimed (see MPEP 2106.05a), nor any other application or use of the judicial exception in some meaningful way beyond a general like between the use of the judicial exception to a particular technological environment (see MPEP 2106.05e).
Accordingly, this/these additional element(s) do(es) not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? NO.
The independent claim(s) is/are additionally directed to claim elements such as: Claims 1, 11 recites an electronic medical records database, and claim 11 recites the use of a processor to perform the claim steps. When considered individually, the above identified claim elements only contribute generic recitations of technical elements to the claims. It is readily apparent, for example, that the claim is not directed to any specific improvements of these elements. Examiner looks to Applicant’s specification in:
[0041] In further embodiments, the plurality of records for the patient may be obtained from an electronic medical records database 270A, 270B. For example, the electronic medical records database 270A, 270B may include patient unit stays admitted to ICUs where physiologic, diagnosis, and treatment information (collectively, "medical information" or "medical data") are captured, charted, or otherwise recorded. That is, the electronic medical records database 270A, 270B can comprise a plurality of healthcare-related records for a plurality of patients, including historical patients and/or patients of current ICU stays.
[0059] In some examples, the one or more processors 220 may include a high-speed data processor adequate to execute the program components described herein and/or various specialized processing units as may be known in the art. In some examples, the one or more processors 220 may be a single processor, multiple processors, or multiple processor cores on a single die.
[0063] Generally, the memory 260 is configured to store data / information and instructions 215 that, when executed by the one or more processors 220, causes the ICU mortality prediction system 200 to perform one or more tasks. In particular examples, the memory 260 includes an ICU mortality prediction package 230 that causes the ICU mortality prediction system 200 to perform one or more steps of the methods described herein.
[0109] The present disclosure can be implemented as a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product can include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present disclosure.
These passages, as well as others, makes it clear that the invention is not directed to a technical improvement. When the claims are considered individually and as a whole, the additional elements noted above, appear to merely apply the abstract concept to a technical environment in a very general sense – i.e. a generic computer receives information from another generic computer, processes the information and then sends information back. The most significant elements of the claims, that is the elements that really outline the inventive elements of the claims, are set forth in the elements identified as an abstract idea. The fact that the generic computing devices are facilitating the abstract concept is not enough to confer statutory subject matter eligibility.
As per dependent claims 2-10, 12-20:
Dependent claims 2-10, 12-16, 18, 19 are not directed any additional abstract ideas and are also not directed to any additional non-abstract claim elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims and addressed above – such as additional considerations for the timing of the prediction, information about the subject collected via different means of monitoring, and additional information about the mathematical calculations/relationships for the patient. While these descriptive elements may provide further helpful context for the claimed invention these elements do not serve to confer subject matter eligibility to the invention since their individual and combined significance is still not heavier than the abstract concepts at the core of the claimed invention.
Dependent claims 17, 20 do not recite any additional abstract ideas beyond those identified above with respect to the independent claims, however they do recite non abstract elements such as a user interface. Examiner notes that displaying information on a display is generally found to be analogous to adding insignificant extra solution activity to the judicial exception(s) identified (see MPEP 2106.05g). Examiner finds no improvement to the functioning of the computer or any other technology or technical field in the display as claimed (see MPEP 2106.05a), nor any other application or use of the judicial exception in some meaningful way beyond a general like between the use of the judicial exception to a particular technological environment (see MPEP 2106.05e). As such, Examiner does not find a practical application present in the display itself. Similarly as per a finding of significantly more, Examiner looks to Applicant’s specification:
[0073] “In particular, the display component 265 can be configured operate a user interface 240 in order to present the generated likelihood of ICU mortality for the patient, as described herein. In some embodiments, the display component 265 can include a programmable processor, also referred to as a graphics progressing units (GPU), which is specialized for rendering images on a monitor or display screen of a user interface 240. In other words, the user interface 240 may be configured, via a display component 265, to provide or otherwise present a likelihood of ICU mortality generated for one or more patients.” This paragraph as well as others make it clear there is no improvement to the functioning of the display as the disclosure is in functional terms only. As such, the claim(s) is/are directed to an abstract idea without significantly more.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-9, 11-14, 19 is/are rejected under 35 U.S.C. 102a1 as being anticipated by Bihorac et al (US 20220044809 A1, hereinafter Bihorac).
In reference to claim 1, 11:
Bihorac teaches: A method for predicting a likelihood of intensive care unit (ICU) mortality for a patient the method comprising:
obtaining, from an electronic medical records database, a plurality of records for a patient in an ICU covering at least a first time period (at least [fig 8 and related text] “At step/operation 802, an assessment computing entity 100 receives an indication that a patient interaction, admission, medical event, and/or the like has been initiated… In an example embodiment, the initiation of the patient interaction, admission, and/or the like is the admission of the patient to the ICU. For example, the medical event could be an ICU stay. In an example embodiment, to initiate the deep learning model, the assessment computing entity 100 may access (e.g., request and receive) information/data from the patient's EHR, such as biometric and/or medical information/data, patient history information/data, and/or the like. ;
extracting, from the obtained plurality of records, a plurality of different defined ICU prediction features for the patient (at least [fig 8 and related text] “For example, the static and/or background information/data may be captured up to a year prior to the current medical event, in an example embodiment. For example, the static and/or background information/data may comprise a plurality of clinical descriptors of health. In various embodiments, the static and/or background information/data may comprise demographic and/or socioeconomic indicators; planned procedure and/or provider information; admission diagnoses; Charlson comorbidities; summary statistics of select medications, laboratory tests, physiological measurements, and/or the like; and/or other information/data corresponding to the patient's health. In an example embodiment, raw the time series of measurements captured during the medical event include a plurality of measurements comprising one or more of systolic blood pressure, diastolic blood pressure, mean arterial pressure (MAP), end-tidal carbon dioxide (EtCO2), fraction of inspired oxygen (FiO2), heart rate, minimum alveolar concentration (MAC), oxygen flow rate, positive end-expiratory pressure (PEEP), peak inspiratory pressure (PIP), respiratory rate, blood oxygen saturation (SpO2), temperature, urine output, and blood loss. For example, step/operation 804 may comprise accessing (e.g., requesting and receiving) static and/or background information/data, processing of static and/or background information/data (e.g., via the static module of the deep learning model), and/or the like.”);
analyzing the extracted plurality of different defined ICU prediction features using a trained ICU mortality prediction model (at least [fig 8 and related text including [080-083] deep learning model as applied to the static/background features; “In various embodiments, the deep learning model has been previously trained. “; at [068-070] model training for real time prediction of ICU mortality, and at [079] “In an example embodiment, the acuity score, mortality prediction, one or more self-attention parameters and/or distributions and/or the like is provided for user review in (near) real-time (e.g., with respect to the biometric and/or medical information/data for the more recent time step). In an example embodiment, the one or more self-attention parameter and/or distributions may indicate and/or flag which parameters represent therapeutic targets such that the acuity score and/or mortality prediction may be improved effectively through clinical therapies targeting such therapeutic targets.“); and
predicting, by the trained ICU mortality prediction model, a likelihood of ICU mortality for the patient (at least [fig 8 and related text] “ At step/operation 810, a prediction for the patient is generated. For example, the assessment computing entity 100 generates a prediction for the patient. In an example embodiment, the prediction may comprise an acuity score, a mortality prediction, one or more self-attention parameters and/or distributions, and/or the like. For example, the deep learning model corresponding to the patient may be updated based at least in part on the biometric and/or medical information/data corresponding to the patient for the most recent time step (e.g., using Equations S1, S3-S6, and/or the like).”)
In reference to claim 2, 12:
Bihorac teaches: wherein the first time period is at least 24 hours in the ICU (at least [004] “One of the most commonly used tools for assessing ICU patient acuity is the Sequential Organ Failure Assessment (SOFA) score. SOFA considers variables representing six different organ systems (cardiovascular, respiratory, nervous, liver, coagulation, and renal) and uses their worst measurements over a given interval (typically 24 hours) in conjunction with static value thresholds to assign numerical scores for each component.”
In reference to claim 3, 13:
Bihorac teaches: Wherein the extracted plurality of different defined ICU prediction features for the subject comprises one or more of BMI; age; gender; pre-ICU admission lead time; ventilation status at hour 24 of ICU admission; whether the subject was admitted with elective surgery status; mean blood pressure; systolic blood pressure; diastolic blood pressure; heart rate; respiratory rate; oxygen saturation; blood glucose; white blood cell count; blood sodium; blood potassium; blood creatinine; blood hemoglobin; blood albumin; blood lactate; arterial blood gas, pH; arterial blood gas, PaCO2; admission diagnosis; and Total Glasgow Coma Scale score (at least [0081] “For example, the static and/or background information/data may be captured up to a year prior to the current medical event, in an example embodiment. For example, the static and/or background information/data may comprise a plurality of clinical descriptors of health. In various embodiments, the static and/or background information/data may comprise demographic and/or socioeconomic indicators; planned procedure and/or provider information; admission diagnoses; Charlson comorbidities; summary statistics of select medications, laboratory tests, physiological measurements, and/or the like; and/or other information/data corresponding to the patient's health. In an example embodiment, raw the time series of measurements captured during the medical event include a plurality of measurements comprising one or more of systolic blood pressure, diastolic blood pressure, mean arterial pressure (MAP), end-tidal carbon dioxide (EtCO2), fraction of inspired oxygen (FiO2), heart rate, minimum alveolar concentration (MAC), oxygen flow rate, positive end-expiratory pressure (PEEP), peak inspiratory pressure (PIP), respiratory rate, blood oxygen saturation (SpO2), temperature, urine output, and blood loss. For example, step/operation 804 may comprise accessing (e.g., requesting and receiving) static and/or background information/data, processing of static and/or background information/data (e.g., via the static module of the deep learning model), and/or the like.”)
In reference to claim 4:
Bihorac further teaches: wherein the likelihood of ICU mortality for the patient further comprises a mortality timeline (at least [047-049, 054-056, 062-70] etc. all disclose time steps for the mortality prediction, i.e. a timeline, see also [fig 5 and related text] for examples of mortality timeline).
In reference to claim 5, 14:
Bihorac further teaches: wherein the predicting of the likelihood of ICU mortality is performed by a ICU mortality prediction system that is a component of, a patient data management systems (PDMS) or a patient monitoring system (at least [066-067] “Since the deep learning model is centered on patient monitoring (e.g., real-time ICU patient monitoring), at each time step t during a medical event, patient interaction, admission, and/or the like (e.g., a patient's ICU stay, medical/surgical procedure, and/or the like) this self-attention mechanism provides (e.g., to a clinician operating a user computing entity 110) with information regarding which prior time steps were most influential in generating the current representation h.sub.t and current prediction y.sub.t. Thus, in various embodiments, the self-attention features of the deep learning model enable clinicians to understand the interactions between changes in patient biometric and/or medical information/data in real-time… At each time step t during a medical event, patient interaction, admission, and/or the like (e.g., a patient's ICU stay, a medical/surgical procedure), attention values α.sub.i ∀i=1, . . . , t are recalculated to present a current, updated view on the most important time steps influencing the mortality prediction provided by the deep learning model. “)
In reference to claim 6:
Bihorac further teaches: wherein the patient is a historical patient (at least [020] “For example, background information/data captured prior to the medical procedure, surgical procedure, and/or the like may be provided to a first module and/or static information/data module of a deep learning model. In an example embodiment, the background information/data is captured up to one year prior to the medical procedure, surgical procedure, and/or the like. In an example embodiment, at least some of the background information/data is extracted from the patient's electronic health record. “ at [075] “n an example embodiment, to initiate the deep learning model, the assessment computing entity 100 may access (e.g., request and receive) information/data from the patient's EHR, such as biometric and/or medical information/data, patient history information/data, and/or the like. “)
In reference to claim 7:
Bihorac further teaches: wherein the patient is in the ICU during the presentation of the generated likelihood of ICU mortality (at least [0048] “At each time step (e.g., each hour during a real-time ICU stay, in an example embodiment), the self-attention mechanism focuses on salient deep representations of all previous time points, assigning relevance scores to every preceding time step that determine the magnitude of each time step's contribution to the DeepSOFA model's overall mortality prediction. “ at [0062] “At each time step during a patient interaction, admission, and/or the like (e.g., at each hour during a patient ICU stay, each minute during a medical/surgical procedure), the deep learning model (e.g., operating on the assessment computing entity 100) makes a mortality probability calculation based at least in part on the sequence of biometric and/or medical information/data corresponding to the patient available through the current time step.”)
In reference to claim 8:
Bihorac further teaches: wherein the trained ICU mortality prediction model is configured to analyze the extracted plurality of different defined ICU prediction features and predict the likelihood of ICU mortality for the patient when some of the plurality of different defined ICU prediction features are missing from the obtained plurality of records (at least [004] “ Although SOFA provides a reasonably accurate assessment of a patient's overall condition and mortality risk, its accuracy is hindered by fixed cutoff points for each component score, and SOFA variables are often infrequent or missing in electronic health records. In particular, Glasgow Coma Scale scores and measurements of serum bilirubin and partial pressure of arterial oxygen are often sparse. Additionally, the complexity of determining a patient's SOFA score hinders the (near) real time determination of a SOFA scores for patients.” At [005-008] real time modeling is described accounting for said missing elements; at [062-067] “Rather than relying on the most recent hidden state of the GRU for making a prediction, various embodiments of the deep learning model instead provide a weighted average of all prior hidden states to the final classification layer…. Thus, various embodiments of the deep learning model a framework to consider real-time self-attention distributions that are updated on-the-fly and only consider currently available patient biometric and/or medical information/data for immediate clinician interpretability.”)
In reference to claim 9:
Bihorac teaches: wherein the extracted plurality of different defined ICU prediction features comprises a total Glasgow Coma Scale score (GCS), wherein the GCS is assessed most recently during the first time period (at least [fig 5 and related text] “Shown also are variable time series at each time step (one hour, in this example embodiment) of the ICU stay, with initial and final measurement values shown on the left and right respectively. Some of the shown values correspond to mean arterial pressure (MAP), fraction of inspired oxygen (FiO2), partial pressure of oxygen (SpO2), and Glasgow Coma Scale (GCS). “ [004] GCS measured during first 24 hours).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 10, 15, 16, 18, 17, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bihorac in view of Bihorac et al (US 20200161000 A1, hereinafter second named inventor Li).
In reference to claim 10, 15:
Bihorac as cited teaches all the limitations above, but does not specifically teach a GAM. Li however does teach: wherein the ICU mortality prediction models a generalized additive model (GAM) (at least [045-047, 075] “In some example embodiments, patient-level risk scores representing the probability for each complication during hospitalization after index surgery are calculated using a generalized additive model (GAM) with logistic link function. “). Bihorac and Li are analogous references as both disclose a means of predicting outcomes in the ICU. One of ordinary skill in the art would have found the use of a GAM as taught by Li to be obvious in the risk prediction of Bihorac, as Li teaches “The most important features contributing to the risk for an individual patient were derived based on how different she or he is from the patient with an “average” risk. “ As such, one would be motivated to use a GAM in order to more accurately predict patient risk.
In reference to claim 16, 18:
Bihorac teaches: wherein the ICU mortality prediction model is trained (at least [068] patient training) by:
obtaining, from an electronic medical records database, a plurality of historical records for each of a plurality of historical patients in an ICU (at least [0046] ;
and storing the trained ICU mortality prediction model (at least [fig 1, 3, and related text]).
Bihorac as cited teaches all the limitations above, but does not specifically teach the steps as claimed. Li further teaches:
Obtaining from an electronic medical records database, a plurality of historical records for each of a plurality of historical patients (at least [0025-6, 037] “At least one purpose of the data transformer module 204 is to gather all data from different sources such as patients' EHR, US Bureau of Vital Statistics, Social Security Death Index, US Renal Data System, US Census Data, and the like. “)
extracting, from the obtained plurality of historical records, a plurality of different health features for each of the plurality of historical patients (at least [026] “ In particular, the perioperative learning system (e.g., a surgery risk analytics platform) is configured to identify one or more extracted features that are suggestive a particular result, such as a score.” At [039] “In some example embodiments the variable generator 212 is configured to determine and extract useful perioperative predictor features to be used in calculating risk from 285 available perioperative demographic, socio-economic, administrative, clinical, pharmacy and laboratory variables to be used for the patient. In some example embodiments, patient perioperative comorbidities were derived using up to fifty International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM).”);
curating the extracted plurality of different health features to identify a plurality of different historical prediction features, wherein a duration is configured to minimize outlier bias (at least [040-041, table 1 included, 072] “In some example embodiments normalizing the access health record data involves using a set of automatic rules to remove errors and outliers to aid in the generation of the personalized risk panel.” At [007] “ In some example embodiments, the method may include removing one or more outliers from the health record data, replacing one or more missing variables of the health record data with replacement data, and normalizing the health record data to generate a health record data set for the patient. In some example embodiments, the one or more features from the health record data set are at least one of perioperative demographic, socio-economic, administrative, clinical, pharmacy, and laboratory variables.”) , and
wherein admission diagnosis is one of the plurality of different historical prediction features and further wherein curation comprises grouping admission diagnoses into one or more groups using clinical knowledge to minimize misclassification (at least [027] “…the score may forecast patient-level probabilistic risk scores for eight major postoperative complications (i.e., acute kidney injury, sepsis, venous thromboembolism, intensive care unit admission>48 hours, mechanical ventilation>48 hours, wound, neurological and cardiovascular complications) and death up to 24 months after surgery. Alternatively or additionally, in some examples, the systems and methods are configured to output a personalized risk panel for the eight major complications and mortality risk at 1, 3, 6, 12, and 24 months after surgery together with a list of the top three features contributing to each of the calculated risk scores.”);
training the ICU mortality prediction model using the plurality of different historical prediction features (at least [026, 044] “In some examples, the surgery risk algorithm is built or otherwise instantiated using a perioperative learning system that is trained using a patient's electronic health records and public datasets from the United States Census data. In particular, the perioperative learning system (e.g., a surgery risk analytics platform) is configured to identify one or more extracted features that are suggestive a particular result, such as a score. “); and
storing the trained ICU mortality prediction model (at least fig 3 and related text] model stored). Bihorac and Li are analogous references as both disclose a means of predicting mortality/outcomes in a medical environment. One of ordinary skill in the art would have been motivated to include the training process as taught by Li in order to further refine the health data common to both references to produce a more accurate representation by removing outliers that would/could otherwise disproportionately affect the predictions.
In reference to claim 17, 20:
Bihorac further teaches: presenting, via a user interface, the predicted likelihood of ICU mortality for the patient (at least [fig 8 and related text] “For example, the assessment computing entity 100 provides (e.g., transmits) the prediction for the patient such that one or more user computing entities 110 receive the prediction and the user computing entities 110 will process the prediction and provide (e.g., via a user interface such as display 316) at least a portion of the prediction such that a user (e.g., clinician, patient, and/or the like) may review the at least a portion of the prediction. As noted above, the prediction may comprise an acuity score, mortality prediction, one or more self-attention parameters and/or distributions, and/or the like. In an example embodiment, the acuity score, mortality prediction, one or more self-attention parameters and/or distributions and/or the like is provided for user review in (near) real-time (e.g., with respect to the biometric and/or medical information/data for the more recent time step).”)
Non-Obvious Subject Matter
Claim 19, inclusive of claim 18, is believed to be not fairly taught by the prior art in that it requires both a random and slope intercept. This is believed to be a distinction over the prior art of record.
Relevant Prior Art
The following references are made of record:
US 20200227166 A1 to Rose discloses mapping mortality for a given condition.
US 20220351857 A1 to Yudkovicz discloses monitoring an acuity risk in an ICU environment.
US 20150154372 A1 to Soenksen discloses an acute incident risk in an ICU
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KATHERINE KOLOSOWSKI-GAGER whose telephone number is (571)270-5920. The examiner can normally be reached Monday - Friday.
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/KATHERINE . KOLOSOWSKI-GAGER/
Primary Examiner
Art Unit 3687
/KATHERINE KOLOSOWSKI-GAGER/Primary Examiner, Art Unit 3687