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 the Application
Claims 1-3, 7-8, and 10-20 are currently pending in this case and have been examined and addressed below. This communication is a Final Rejection in response to the Amendments to the Claims and Remarks filed on 11/11/2025.
Claims 1, 7-8, 10-12 and 19-20 are currently amended.
Claim 4-6 and 9 are cancelled.
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 1-3, 7-8, and 10-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Step 1: Claims 1-3, 7-8, and 10-18 are drawn to a process. Claims 19-20 are drawn to a machine. As such, claims 1-3, 7-8, and 10-20 are drawn to one of the statutory categories of invention (Step 1: YES).
Step 2A - Prong One: In prong one of step 2A, the claim(s) is/are analyzed to evaluate whether it/they recite(s) a judicial exception.
Independent Claim 1: A method for facilitating medical care of patients, the method comprising:
using one or more processors to perform:
obtaining one or more electronic patient health datasets related to a plurality of patients;
generating a plurality of patient profiles from the one or more electronic patient health datasets, wherein each patient profile is stored using at least one data structure, and wherein a first patient profile of the plurality of patient profiles is generated by:
analyzing data from the one or more electronic patient health datasets to identify electronic patient health data associated with a first patient;
determining a severity score for the first patient profile by:
analyzing electronic patient health data associated with the first patient to identify electronic patient health data associated with one or more patient features of the first patient;
and processing the electronic patient health data associated with one or more patient features of the first patient to determine the severity score for the first patient;
determining an urgency score for the first patient profile by:
analyzing the electronic patient health data associated with the first patient to identify electronic patient health data associated with one or more medical events of the first patient;
determining, from the electronic patient health data associated with the one or more medical events of the first patient, multiple signal values associated with the first patient, wherein:
the multiple signal values including at least one predictive signal value determined by processing, using at least one trained ML model, the electronic patient health data associated with the one or more medical events of the first patient, the one or more patient features;
processing one or more of the multiple signal values to determine whether the first patient has experienced one or more admission, discharge, or transfer medical events;
and determining the urgency score for the first patient profile based on one or more of the multiple signal values and whether the first patient has experienced one or more admission, discharge, or transfer medical events;
determining one or more recommended provider actions to be performed based on the electronic patient health data associated with the one or more medical events of the first patient and/or the multiple signal values associated with the first patient;
populating a respective first data structure with the severity score, the urgency score, and the one or more recommended provider actions determined for the first patient;
generating an interactive graphical user interface (GUI) to assist in prioritizing patients for clinician attention, the GUI having: ordered representations of profiles of the plurality of patient profiles, wherein the representations are ordered based at least in part on the severity score of the respective patient profile and provide an indication of the urgency score for the respective patient profile;
and displaying the interactive GUI.
Independent Claim 19: A system for facilitating medical care of patients, the system comprising:
at least one computer hardware processor;
a display;
and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform a method, the method comprising:
obtaining one or more electronic patient health datasets related to a plurality of patients;
generating a plurality of patient profiles from the one or more electronic patient health datasets, wherein each patient profile is stored using at least one data structure, and wherein a first patient profile of the plurality of patient profiles is generated by:
analyzing data from the one or more electronic patient health datasets to identify electronic patient health data associated with a first patient;
determining a severity score for the first patient profile by:
analyzing electronic patient health data associated with the first patient to identify electronic patient health data associated with one or more patient features of the first patient;
and processing the electronic patient health data associated with one or more patient features of the first patient to determine the severity score for the first patient;
determining an urgency score for the first patient by:
analyzing the electronic patient health data associated with the first patient to identify electronic patient health data associated with one or more medical events of the first patient;
determining, from the electronic patient health data associated with the one or more medical events of the first patient, multiple signal values associated with the first patient, wherein:
the multiple signal values including at least one predictive signal value determined by processing, using at least one trained ML model, the electronic patient health data associated with the one or more medical events of the first patient, the one or more patient features;
processing one or more of the multiple signal values to determine whether the first patient has experienced one or more admission, discharge, or transfer medical events;
and determining the urgency score for the first patient profile based on one or more of the multiple signal values events and whether the first patient has experienced one or more admission, discharge, or transfer medical events;
and determining one or more recommended provider actions to be performed based on the electronic patient health data associated with the one or more medical events of the first patient and/or the multiple signal values associated with the first patient;
populating a respective first data structure with the severity score, the urgency score, and the one or more recommended provider actions determined for the first patient profile;
generating an interactive graphical user interface (GUI) to assist in prioritizing patients for clinician attention, the GUI having:
ordered representations of profiles of the plurality of patient profiles, wherein the representations are ordered based at least in part on the severity score of the respective patient profile and provide an indication of the urgency score for the respective patient profile;
and displaying the interactive GUI on the display.
Independent Claim 20: At least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform a method, the method comprising:
obtaining one or more electronic patient health datasets related to a plurality of patients;
generating a plurality of patient profiles from the one or more electronic patient health datasets, wherein each patient profile is stored using at least one data structure, and wherein a first patient profile of the plurality of patient profiles is generated by:
analyzing data from the one or more electronic patient health datasets to identify electronic patient health data associated with a first patient;
determining a severity score for the first patient profile by:
analyzing electronic patient health data associated with the first patient to identify electronic patient health data associated with one or more patient features of the first patient;
and processing the electronic patient health data associated with one or more patient features of the first patient to determine the severity score for the first patient;
determining an urgency score for the first patient profile by:
analyzing the electronic patient health data associated with the first patient to identify electronic patient health data associated with one or more medical events of the first patient;
determining, from the electronic patient health data associated with the one or more medical events of the first patient, multiple signal values associated with the first patient, wherein:
the multiple signal values including at least one predictive signal value determined by processing, using at least one trained ML model, the electronic patient health data associated with the one or more medical events of the first patient, the one or more patient features;
processing one or more of the multiple signal values to determine whether the first patient has experienced one or more admission, discharge, or transfer medical events;
and determining the urgency score for the first patient profile based on one or more of the multiple signal values and whether the first patient has experienced one or more admission, discharge, or transfer medical events;
determining one or more recommended provider actions to be performed based on the electronic patient health data associated with the one or more medical events of the first patient and/or the multiple signal values associated with the first patient;
populating a respective first data structure with the severity score, the urgency score, and the one or more recommended provider actions determined for the first patient;
generating an interactive graphical user interface (GUI) to assist in prioritizing patients for clinician attention, the GUI having:
ordered representations of profiles of the plurality of patient profiles ,wherein the representations are ordered based at least in part on the severity score of the respective patient profile and provide an indication of the urgency score for the respective patient profile;
and displaying the interactive GUI.
(Examiner notes: The above claim terms underlined are additional elements that fall under Step 2A - Prong Two analysis section detailed below)
These steps amount to functions performable in the mind or with pen and paper and are only concepts relating to organizing or analyzing information in a way that can be performed mentally or is analogous to human mental work (MPEP § 2106.04(a)(2)(III)(c)(2) citing the abstract idea grouping for mental processes in a computer environment). Therefore, obtaining electronic patient health datasets, generating a plurality of patient profile from the electronic patient datasets, analyzing data from electronic patient health datasets to identify electronic patient health data associated with patient features and medical events, determining a severity score, determining an urgency score, determining multiple signal values from the electronic patient health data, processing multiple signal values to determine whether the patient has experienced an admission, discharge, or transfer medical event, determining an urgency score based on the multiple signal values and whether the patient has experienced an admission, discharge, or transfer event, determining recommended provider actions to be performed, and providing an indication of the urgency score are directed to managing personal interactions or personal behavior.
The dependent claim 2 is directed to the one or more patient features include at least one of: an age of the first patient, a gender of the first patient, and chronic conditions of the first patient.
The dependent claim 3 is directed to the patient features further include at least one of: an income associated with the first patient, a location associated with the first patient, an education level associated with the first patient, an employment status associated with the first patient, a family condition associated with the first patient, one or more behaviors associated with the first patient, a measure of health care access associated with the first patient, acute conditions of the first patient, and comorbidities associated with the first patient.
The dependent claim 7 is directed to the signals include signals selected from: time since admission to a medical facility, time since discharge from a medical facility, time until a recommended follow-up, time since a recommended follow-up, and time since last annual wellness visit.
The dependent claim 8 is directed to the signals further include signals selected from: a measure of prescription adherence, a probability of readmission, a measure of a need for advanced care planning and a measure of a need for specialist referral.
The dependent claim 10 is directed to determining the one or more recommended provider actions includes analyzing each of the signals associated with the first patient and adding at least one action associated with each signal to the one or more recommended provider actions.
The dependent claim 11 is directed to determining the one or more recommended provider actions includes analyzing each of the signals associated with the first patient and adding at least one action associated with two or more signals to the one or more recommended provider actions.
The dependent claim 12 is directed to the one or more recommended provider actions includes one or more actions selected from: contact the first patient via phone, contact the first patient in medical facility, schedule an appointment for the first patient, schedule an annual wellness visit for the first patient, refer the first patient to a specialist, and recommend a medical intervention for the first patient.
The dependent claim 13 is directed to the urgency score of the first patient profile increases in response to the time until a recommended follow-up signal crossing a threshold amount of time.
The dependent claim 14 is directed to the urgency score of the first patient profile increases in response to an increase in the time since admission signal, an increase in the time since discharge signal, and an increase in the time since transfer signal, until a signal indicating a recommended follow-up has occurred is received.
The dependent claim 15 is directed to determining one or more performance metrics based at least in part on changes in urgency scores associated with patient profiles, changes in value of the time until a recommended follow-up signal, changes in value of the time since a recommended follow-up signal, and changes in value of the time since last annual wellness visit signal.
The dependent claim 16 is directed to an increase in the urgency score of the first patient profile indicates the first patient has a greater predicted need for proactive attention from medical providers.
The dependent claim 17 is directed to an increase in the severity score of the first patient profile indicates the first patient has experienced a decrease in overall health.
The dependent claim 18 is directed to receiving data related to one or more events related to the first patient; and in response to receiving data related to one or more events related to the first patient, updating the respective urgency score for the first patient profile.
Each of these steps of the preceding dependent claims 2-3, 7-8, and 10-18 only serve to further limit or specify the features of independent claims 1 and 19-20 accordingly, and hence are nonetheless directed towards fundamentally the same abstract idea as the independent claim and utilize the additional elements analyzed below in the expected manner.
As such, the Examiner concludes that the preceding claims recite an abstract idea (Step 2A – Prong One: YES).
Step 2A - Prong Two: In prong two of step 2A, an evaluation is made whether a claim recites any additional element, or combination of additional elements, that integrate the exception into a practical application of that exception. An “additional element” is an element that is recited in the claim in addition to (beyond) the judicial exception (i.e., an element/limitation that sets forth an abstract idea is not an additional element). The phrase “integration into a practical application” is defined as requiring an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception.
Claim 1 recites the use of one or more processors, in this case to obtaining one or more electronic patient health datasets, generating a plurality of patient profiles from the one or more electronic patient health datasets, analyzing data from the one or more electronic patient health datasets to identify electronic patient health data associated with one or more patient features and one or more medical events of a first patient, determining a severity score for the first patient profile, determining an urgency score, processing one or more of the multiple signal values to determine whether the first patient has experienced one or more admission, discharge, or transfer medical events, only recites the one or more processors as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2)).
Claims 1 and 19-20 recite the use of each patient profile is stored using at least one data structure, populating a respective first data structure with the severity score, the urgency score, and the one or more recommended provider actions determined for the first patient, and generating an interactive graphical user interface (GUI) to assist in prioritizing patients for clinician attention, the GUI having: ordered representations of profiles of the plurality of patient profiles ,wherein the representations are ordered based at least in part on the severity score of the respective patient profile, and displaying the interactive GUI. The patient profile stored in at least a data structure, populating a respective first data structure, generating and displaying an interactive graphical user interface (GUI) are only recited as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2)). The claims further recite the use of at least one trained ML model, only as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2)) amounting to instruction to implement the abstract idea using a general purpose computer.
Claim 12 recites a phone only as being used in its ordinary capacity and is merely a tool to execute the abstract idea (MPEP § 2106.05(f)(2)).
Claim 19 recites the use of at least one computer hardware processor and a display, only as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2)).
Claims 19-20 recite the use of at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform a method, only as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2)).
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claim(s) is/are directed to an abstract idea (Step 2A – Prong two: NO).
Step 2B: In step 2B, the claims are analyzed to determine whether any additional element, or combination of additional elements, is/are sufficient to ensure that the claims amount to significantly more than the judicial exception.
As discussed above in “Step 2A – Prong 2”, the identified additional elements, such as one or more processors, patient profile stored in at least a data structure, one trained ML model, populating a respective first data structure, generating and displaying an interactive graphical user interface (GUI), phone, one computer hardware processor, display, and one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform a method in independent claims 1, 19, and 20 and dependent claims 2-3, 7-8, and 10-18 are equivalent to adding the words “apply it” on a generic computer. Each of these elements is only recited as a tool for performing steps of the abstract idea, such as the use of the computer and data processing devices to apply the algorithm. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”). Each additional element under Step 2A, Prong 2 is analyzed in light of the specification’s explanation of the additional element’s structure. The claimed invention’s additional elements are directed to generic computer component and functions being used to perform the abstract idea.
Applicant’s own disclosure pages 11-12 acknowledges that the “one or more data structures which are accessible by a computer…data structures which are computer readable and writable”. Page 31 discloses “the machine learning models may be trained using one or more training techniques. In some examples, different training techniques may be used for training different machine learning models of the one or more machine learning models. Examples of techniques that may be used for training the one or more machine learning models include: gradient descent, regression analysis, and stochastic gradient descent, among other suitable training techniques. In some examples, historical patient health data may be organized into training data. The training data may include training input data and ground truth data". Additionally, page 36 acknowledges that the “system may display representations of the patient profiles using a Graphical User Interface (GUI) shown on a display. The display may be internal or external to the system". Furthermore pages 49-50 discloses “The technology described herein is operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well-known computing systems, environments, and/or configurations that may be suitable for use with the technology described herein include, but are not limited to, personal computers, server computers, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, set topboxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. The computing environment may execute computer-executable instructions, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types…Computer readable media can be any available media that can be accessed by computer 1410 and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer readable media may comprise computer storage media and communication media. Computer storage media includes volatile and nonvolatile, removable, and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by computer 1410”. The disclosure also acknowledges on pages 52-53 “processors may be implemented as integrated circuits, with one or more processors in an integrated circuit component, including commercially available integrated circuit components known in the art by names such as CPU chips, GPU chips, microprocessor, microcontroller, or co-processor. Alternatively, a processor may be implemented in custom circuit1y, such as an ASIC, or semicustom circuitry resulting from configuring a programmable logic device. As yet a further alternative, a processor may be a portion of a larger circuit or semiconductor device, whether commercially available, semi-custom or custom. As a specific example, some commercially available microprocessors have multiple cores such that one or a subset of those cores may constitute a processor. However, a processor may be implemented using circuitry in any suitable format”.
The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claim(s) amount to significantly more than the abstract idea identified above (Step 2B: NO).
Therefore, claims 1-3, 7-8, and 10-20 are not eligible subject matter under 35 USC 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 7-8, 16, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Rahman et al. (US-20230005603-A1)[hereinafter Rahman], in view of Agarwal (US-20200242566-A1)[hereinafter Agarwal].
As per Claim 1, Rahman discloses a method for facilitating medical care of patients in paragraphs [0020] and [0025] (operations for intelligently and accurately triaging patients), the method comprising: using one or more processors in paragraphs [0025] (a processor) to perform: obtaining one or more electronic patient health datasets related to a plurality of patients in paragraphs [0020] and [0025] and [0032] and [0047] (accessing one or more electronic health record (synonymous to one or more electronic patient health datasets) related to a plurality of patients); generating a plurality of patient profiles from the one or more electronic patient health datasets, wherein each patient profile is stored using at least one data structure in paragraphs [0020] and [0055-0056] and [0061-0062] and Figure 4 (generating electronic triage powerform charts from the electronic health records, wherein the electronic triage powerform chart is stored in a database (synonymous to at least one data structure) (Examiner notes that according to the 2021 version of ED LaunchPoint Training Manual, ED LaunchPoint creates, manages, and displays a plurality of patient profiles for a plurality of patients)), and wherein a first patient profile of the plurality of patient profiles is generated by: analyzing data from the one or more electronic patient health datasets to identify electronic patient health data associated with a first patient in paragraphs [0055-0058] (an electronic triage powerform chart for the patient is generated by analyzing data from one of the electronic health records to identify electronic patient health data associated with the patient); determining an urgency score for the first patient profile in paragraphs [0028] and [0058] (determining an acuity level (synonymous to an urgency score) for the electronic triage powerform chart) by: analyzing the electronic patient health data associated with the first patient to identify electronic patient health data associated with one or more medical events of the first patient in paragraph [0034] (analyzing the electronic health records associated with the patient to identify whether a patient is experiencing a risk of health deterioration (synonymous to one or more medical events)); determining, from the electronic patient health data associated with the one or more medical events of the first patient, multiple signal values associated with the first patient in paragraphs [0034] and [0061] and Figure 5 (determining, from the electronic patient health data associated with the patient experiencing a risk of health deterioration, multiple vital sign values (synonymous to signal values) associated with the patient), wherein: the multiple signal values determined by processing, using at least one trained ML model, the electronic patient health data associated with the one or more medical events of the first patient, the one or more patient features in paragraphs [0028] and [0041] and [0061] and Figure 5 (the multiple vital sign values determined by processing, using a machine learning algorithm (synonymous to a trained ML model), the electronic patient health data associated with the patient experiencing a risk of health deterioration, the input features (synonymous to patient features)); processing one or more of the multiple signal values to determine whether the first patient has experienced one or more admission, discharge, or transfer medical events in paragraphs [0060-0062] (processing the multiple vital sign values to determine whether the patient has a likelihood of ICU admission or inpatient hospitalization (Examiner notes that ICU admissions or inpatient hospitalization indicates the patient has experienced a admission medical event)); and determining the on one or more of the multiple signal values and whether the first patient has experienced one or more admission, discharge, or transfer medical events in paragraphs [0028] and [0041-0042] and [0060-0062] (determining an acuity level for the patient based on the multiple vital sign values and whether the patient has a likelihood of ICU admission or inpatient hospitalization); determining one or more recommended provider actions to be performed based on the electronic patient health data associated with the one or more medical events of the first patient and/or the multiple signal values associated with the first patient in paragraphs [0060] (determining a recommended treatment procedure (synonymous to one or more recommended provider actions to be performed) based on the electronic health record associated with a medical event); populating a respective first data structure with the urgency score, and the one or more recommended provider actions determined for the first patient profile in paragraphs [0025] and [0044-0045] and [0064] and [0067] and Figure 7 (mapping and binning into categorical features (synonymous to populating a respective first data structure) with the acuity level and the recommended treatment procedure, referred to as outcome predictions); generating an interactive graphical user interface (GUI) to assist in prioritizing patients for clinician attention in paragraphs [0001-0002] and [0055] and [0061-0062] and [0064] and Figures 5-7 (generating an emergency room launchpoint (synonymous to an interactive graphical user interface (GUI)) to assist in prioritizing patients for clinician attention), and displaying the interactive GUI in paragraphs [0061-0062] and Figures 5-7 (displaying the emergency room launchpoint).
Rahman discloses analyzing the electronic patient health data to identify patient features, multiple signal values being determined by processing the electronic patient health data associated with a medical event, and populating a data structure with the urgency score and a recommended provider action. Rahman does not disclose analyzing the electronic patient health data to identify patient features to generate a severity score, the multiple signal values including a predictive signal value determined using a trained ML model and populating a data structure with the severity score. However, Agarwal discloses determining a severity score for the first patient profile in paragraphs [0035] and [0043] and [0045] and [0047] (determining a risk score (synonymous to a severity score) for the patient profile (Examiner notes that the patient's historical data which includes previous PAP data, current or previous risk estimates, patient co-morbidities, demographics, and medication changes indicate a patient profile)) by: analyzing electronic patient health data associated with the first patient to identify electronic patient health data associated with one or more patient features of the first patient in paragraphs [0035-0036] and [0043] and [0047-0048] (analyzing patient data (synonymous to electronic patient health data) to identify patient data associated with patient variables, referred to as patient parameters (synonymous to one or more patient features)); and processing the electronic patient health data associated with one or more patient features of the first patient to determine the severity score for the first patient in paragraphs [0043-0046] (processing the patient data associated with patient variables to determine the risk score for the patient); wherein: the multiple signal values including at least one predictive signal value determined by processing, using at least one trained ML model, the electronic patient health data associated with the one or more medical events of the first patient, the one or more patient features in paragraphs [0020] and [0046] and [0048] and [0054] and [0087] (the PAP waveforms (synonymous to multiple signal values) including predictive signal values determined by processing, using a generalizable estimator (synonymous to at least one trained ML model), the patient data associated with the predetermined health event and the patient parameters); populating a respective first data structure with the severity score in paragraphs [0056] and [0064-0065] (organizing a category list (synonymous to a respective first data structure) with the risk score); the GUI having: ordered representations of profiles of the plurality of patient profiles, wherein the representations are ordered based at least in part on the severity score of the respective patient profile and provide an indication of the urgency score for the respective patient profile in paragraphs [0040] and [0046] and [0049-0050] (patient prioritization ranking (synonymous to ordered representations of profiles of the plurality of patient profiles) based on the patient risk score (synonymous to the severity score of the respective patient profile) and provide an indication of the priority score for the patient).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a method for facilitating medical care of patients, as disclosed by Rahman, to be combined with determining a severity score by analyzing electronic patient health data to identify patient features, multiple signal values including a predictive signal value, populating a data structure with the severity score, having an ordered representations of profiles, and providing an indication of the urgency score for the patient profile, as disclosed by Agarwal, for the purpose of accommodating high volume patient intake, increasing accuracy, improving patient care, and reducing costs [0004 and 0045].
As per Claim 2, Rahman and Agarwal disclose the method of claim 1, Rahman also discloses wherein the one or more patient features comprise at least one of: an age of the first patient, a gender of the first patient, and chronic conditions of the first patient in paragraphs [0041-0042] and [0057] (the input features include age, gender, and clinical conditions (synonymous to chronic conditions)).
As per Claim 3, Rahman and Agarwal disclose the method of claim 2, Rahman also discloses wherein the patient features further comprise at least one of: an income associated with the first patient, a location associated with the first patient, an education level associated with the first patient, an employment status associated with the first patient, a family condition associated with the first patient, one or more behaviors associated with the first patient, a measure of health care access associated with the first patient, acute conditions of the first patient, and comorbidities associated with the first patient in paragraph [0041] (the input features include home address (synonymous to a location associated with the first patient), work address (synonymous to an employment status associated with the first patient), and an insurance provider (synonymous to a measure of health care access associated with the first patient) (Examiner notes that a patient's work address indicates the patient's employment status and the insurance provider indicates a measure of healthcare access. Also, the home address, work address, and insurance provider meet the "at least one of" limitation)).
As per Claim 7, Rahman and Agarwal disclose the method of claim 1.
Rahman discloses signal values but does not disclose the signal values being selected from time since admission to a medical facility, time since discharge from a medical facility, time until a recommended follow-up, time since a recommended follow-up, and time since last annual wellness visit. However, Agarwal discloses wherein the signals include signals selected from: time since admission to a medical facility, time since discharge from a medical facility, time until a recommended follow-up, time since a recommended follow-up, and time since last annual wellness visit in paragraphs [0036] and [0044] and [0087] (the PAP waveforms include time since last appointment (synonymous to an annual wellness visit)).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a method for facilitating medical care of patients, as disclosed by Rahman, to be combined with the signals being selected from time since admission to a medical facility, time since discharge from a medical facility, time until a recommended follow-up, time since a recommended follow-up, or time since last annual wellness visit, as disclosed by Agarwal, for the purpose of accommodating high volume patient intake, increasing accuracy, improving patient care, and reducing costs [0004 and 0045].
As per Claim 8, Rahman and Agarwal disclose the method of claim 7.
Rahman discloses signal values but does not disclose the signal values being selected from a measure of prescription adherence, a probability of readmission, or a measure of a need for advanced care planning and a measure of a need for specialist referral. However, Agarwal discloses wherein the signals further include signals selected from: a measure of prescription adherence, a probability of readmission, a measure of a need for advanced care planning and a measure of a need for specialist referral in paragraphs [0035] and [0044] and [0087] and [0093-0094] (the PAP waveforms include the probability of hospitalization (synonymous to probability of readmission)).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a method for facilitating medical care of patients, as disclosed by Rahman, to be combined with the signal values being selected from a measure of prescription adherence, a probability of readmission, or a measure of a need for advanced care planning and a measure of a need for specialist referral, as disclosed by Agarwal, for the purpose of accommodating high volume patient intake, increasing accuracy, improving patient care, and reducing costs [0004 and 0045].
As per Claim 16, Rahman and Agarwal disclose the method of claim 1, Rahman also discloses wherein an increase in the urgency score of the first patient profile indicates the first patient has a greater predicted need for proactive attention from medical providers in paragraph [0029] and [0060] and [0064] and Table 1 (an increase in the acuity level of the electronic triage powerform chart indicates the patient has a greater predicted need for immediate assistance from medical providers).
As per Claim 19, Rahman discloses a system for facilitating medical care of patients in paragraphs [0025-0026] (a system for triaging patients), the system comprising: at least one computer hardware processor in paragraphs [0025-0026] (a processor); a display in paragraphs [0061-0062] and [0085] and Figures 5-7 (a display); and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform a method in paragraphs [0025-0026] (a non-transitory computer storage media storing computer-executable instructions, when executed by the processor, cause the processor to perform operations), the method comprising: obtaining one or more electronic patient health datasets related to a plurality of patients in paragraphs [0020] and [0025] and [0032] and [0047] (accessing one or more electronic health record (synonymous to one or more electronic patient health datasets) related to a plurality of patients); generating a plurality of patient profiles from the one or more electronic patient health datasets, wherein each patient profile is stored using at least one data structure in paragraphs [0020] and [0055-0056] and [0061-0062] and Figure 4 (generating electronic triage powerform charts from the electronic health records, wherein the electronic triage powerform chart is stored in a database (synonymous to at least one data structure) (Examiner notes that according to the 2021 version of ED LaunchPoint Training Manual, ED LaunchPoint creates, manages, and displays a plurality of patient profiles for a plurality of patients)), and wherein a first patient profile of the plurality of patient profiles is generated by: analyzing data from the one or more electronic patient health datasets to identify electronic patient health data associated with a first patient in paragraphs [0055-0058] (an electronic triage powerform chart for the patient is generated by analyzing data from one of the electronic health records to identify electronic patient health data associated with the patient); determining an urgency score for the first patient in paragraphs [0028] and [0058] (determining an acuity level (synonymous to an urgency score) for the electronic triage powerform chart) by: analyzing the electronic patient health data associated with the first patient to identify electronic patient health data associated with one or more medical events of the first patient in paragraphs [0034] (analyzing the electronic health records associated with the patient to identify whether a patient is experiencing a risk of health deterioration (synonymous to one or more medical events)); determining, from the electronic patient health data associated with the one or more medical events of the first patient, multiple signal values associated with the first patient in paragraphs [0034] and [0061] and Figure 5 (determining, from the electronic patient health data associated with the patient experiencing a risk of health deterioration, multiple vital sign values (synonymous to signal values) associated with the patient), wherein: the multiple signal values determined by processing, using at least one trained ML model, the electronic patient health data associated with the one or more medical events of the first patient, the one or more patient features in paragraphs [0028] and [0041] and [0061] and Figure 5 (the multiple vital sign values determined by processing, using a machine learning algorithm (synonymous to a trained ML model), the electronic patient health data associated with the patient experiencing a risk of health deterioration, the input features (synonymous to patient features)); processing one or more of the multiple signal values to determine whether the first patient has experienced one or more admission, discharge, or transfer medical events in paragraphs [0060-0062] (processing the multiple vital sign values to determine whether the patient has a likelihood of ICU admission or inpatient hospitalization (Examiner notes that ICU admissions or inpatient hospitalization indicates the patient has experienced a admission medical event)); and determining the urgency score for the first patient profile based on one or more of the multiple signal values events and whether the first patient has experienced one or more admission, discharge, or transfer medical events in paragraphs [0028] and [0041-0042] and [0060-0062] (determining an acuity level for the patient based on the multiple vital sign values and whether the patient has a likelihood of ICU admission or inpatient hospitalization); and determining one or more recommended provider actions to be performed based on the electronic patient health data associated with the one or more medical events of the first patient and/or the multiple signal values associated with the first patient in paragraph [0060] (determining a recommended treatment procedure (synonymous to one or more recommended provider actions to be performed) based on the electronic health record associated with a medical event); populating a respective first data structure with the urgency score, and the one or more recommended provider actions determined for the first patient profile in paragraphs [0025] and [0044-0045] and [0064] and [0067] and Figure 7 (mapping and binning into categorical features (synonymous to populating a respective first data structure) with the acuity level and the recommended treatment procedure, referred to as outcome predictions); generating an interactive graphical user interface (GUI) to assist in prioritizing patients for clinician attention in paragraphs [0001-0002] and [0055] and [0061-0062] and [0064] and Figures 5-7 (generating an emergency room launchpoint (synonymous to an interactive graphical user interface (GUI)) to assist in prioritizing patients for clinician attention), and displaying the interactive GUI on the display in paragraphs [0061-0062] and Figures 5-7 (displaying the emergency room launchpoint).
Rahman discloses analyzing the electronic patient health data to identify patient features, multiple signal values being determined by processing the electronic patient health data associated with a medical event, and populating a data structure with the urgency score and a recommended provider action. Rahman does not disclose analyzing the electronic patient health data to identify patient features to generate a severity score, the multiple signal values including a predictive signal value determined using a trained ML model and populating a data structure with the severity score. However, Agarwal discloses determining a severity score for the first patient profile in paragraphs [0035] and [0043] and [0045] and [0047] (determining a risk score (synonymous to a severity score) for the patient profile (Examiner notes that the patient's historical data which includes previous PAP data, current or previous risk estimates, patient co-morbidities, demographics, and medication changes indicate a patient profile)) by: analyzing electronic patient health data associated with the first patient to identify electronic patient health data associated with one or more patient features of the first patient in paragraphs [0035-0036] and [0043] and [0047-0048] (analyzing patient data (synonymous to electronic patient health data) to identify patient data associated with patient variables, referred to as patient parameters (synonymous to one or more patient features)); and processing the electronic patient health data associated with one or more patient features of the first patient to determine the severity score for the first patient in paragraphs [0043-0046] (processing the patient data associated with patient variables to determine the risk score for the patient); the multiple signal values including at least one predictive signal value determined by processing, using at least one trained ML model, the electronic patient health data associated with the one or more medical events of the first patient, the one or more patient features in paragraphs [0020] and [0046] and [0048] and [0054] and [0087] (the PAP waveforms (synonymous to multiple signal values) including predictive signal values determined by processing, using a generalizable estimator (synonymous to at least one trained ML model), the patient data associated with the predetermined health event and the patient parameters); populating a respective first data structure with the severity score in paragraphs [0056] and [0064-0065] (organizing a category list (synonymous to a respective first data structure) with the risk score); the GUI having: ordered representations of profiles of the plurality of patient profiles, wherein the representations are ordered based at least in part on the severity score of the respective patient profile and provide an indication of the urgency score for the respective patient profile in paragraphs [0040] and [0046] and [0049-0050] (patient prioritization ranking (synonymous to ordered representations of profiles of the plurality of patient profiles) based on the patient risk score (synonymous to the severity score of the respective patient profile) and provide an indication of the priority score for the patient).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a system for facilitating medical care of patients, as disclosed by Rahman, to be combined with determining a severity score by analyzing electronic patient health data to identify patient features, multiple signal values including a predictive signal value, populating a data structure with the severity score, having an ordered representations of profiles, and providing an indication of the urgency score for the patient profile, as disclosed by Agarwal, for the purpose of accommodating high volume patient intake, increasing accuracy, improving patient care, and reducing costs [0004 and 0045].
As per Claim 20, Rahman discloses at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform a method in paragraphs [0025-0026] (a non-transitory computer storage media storing computer-executable instructions, when executed by the processor, cause the processor to perform operations), the method comprising: obtaining one or more electronic patient health datasets related to a plurality of patients in paragraphs [0020] and [0025] and [0032] and [0047] (accessing one or more electronic health record (synonymous to one or more electronic patient health datasets) related to a plurality of patients); generating a plurality of patient profiles from the one or more electronic patient health datasets, wherein each patient profile is stored using at least one data structure in paragraphs [0020] and [0055-0056] and [0061-0062] and Figure 4 (generating electronic triage powerform charts from the electronic health records, wherein the electronic triage powerform chart is stored in a database (synonymous to at least one data structure) (Examiner notes that according to the 2021 version of ED LaunchPoint Training Manual, ED LaunchPoint creates, manages, and displays a plurality of patient profiles for a plurality of patients)), and wherein a first patient profile of the plurality of patient profiles is generated by: analyzing data from the one or more electronic patient health datasets to identify electronic patient health data associated with a first patient in paragraphs [0055-0058] (an electronic triage powerform chart for the patient is generated by analyzing data from one of the electronic health records to identify electronic patient health data associated with the patient); determining an urgency score for the first patient profile in paragraphs [0028] and [0058] (determining an acuity level (synonymous to an urgency score) for the electronic triage powerform chart) by: analyzing the electronic patient health data associated with the first patient to identify electronic patient health data associated with one or more medical events of the first patient in paragraph [0034] (analyzing the electronic health records associated with the patient to identify whether a patient is experiencing a risk of health deterioration (synonymous to one or more medical events)); determining, from the electronic patient health data associated with the one or more medical events of the first patient, multiple signal values associated with the first patient in paragraphs [0034] and [0061] and Figure 5 (determining, from the electronic patient health data associated with the patient experiencing a risk of health deterioration, multiple vital sign values (synonymous to signal values) associated with the patient), wherein: the multiple signal values determined by processing, using at least one trained ML model, the electronic patient health data associated with the one or more medical events of the first patient, the one or more patient features in paragraphs [0028] and [0041] and [0061] and Figure 5 (the multiple vital sign values determined by processing, using a machine learning algorithm (synonymous to a trained ML model), the electronic patient health data associated with the patient experiencing a risk of health deterioration, the input features (synonymous to patient features)); processing one or more of the multiple signal values to determine whether the first patient has experienced one or more admission, discharge, or transfer medical events in paragraphs [0060-0062] (processing the multiple vital sign values to determine whether the patient has a likelihood of ICU admission or inpatient hospitalization (Examiner notes that ICU admissions or inpatient hospitalization indicates the patient has experienced a admission medical event)); and determining the urgency score for the first patient profile based on one or more of the multiple signal values and whether the first patient has experienced one or more admission, discharge, or transfer medical events in paragraphs [0028] and [0041-0042] and [0060-0062] (determining an acuity level for the patient based on the multiple vital sign values and whether the patient has a likelihood of ICU admission or inpatient hospitalization); determining one or more recommended provider actions to be performed based on the electronic patient health data associated with the one or more medical events of the first patient and/or the multiple signal values associated with the first patient in paragraphs [0060] (determining a recommended treatment procedure (synonymous to one or more recommended provider actions to be performed) based on the electronic health record associated with a medical event); populating a respective first data structure with the urgency score, and the one or more recommended provider actions determined for the first patient profile in paragraphs [0025] and [0044-0045] and [0064] and [0067] and Figure 7 (mapping and binning into categorical features (synonymous to populating a respective first data structure) with the acuity level and the recommended treatment procedure, referred to as outcome predictions); generating an interactive graphical user interface (GUI) to assist in prioritizing patients for clinician attention in paragraphs [0001-0002] and [0055] and [0061-0062] and [0064] and Figures 5-7 (generating an emergency room launchpoint (synonymous to an interactive graphical user interface (GUI)) to assist in prioritizing patients for clinician attention), and displaying the interactive GUI in paragraphs [0061-0062] and Figures 5-7 (displaying the emergency room launchpoint).
Rahman discloses analyzing the electronic patient health data to identify patient features, multiple signal values being determined by processing the electronic patient health data associated with a medical event, and populating a data structure with the urgency score and a recommended provider action. Rahman does not disclose analyzing the electronic patient health data to identify patient features to generate a severity score, the multiple signal values including a predictive signal value determined using a trained ML model and populating a data structure with the severity score. However, Agarwal discloses determining a severity score for the first patient profile in paragraphs [0035] and [0043] and [0045] and [0047] (determining a risk score (synonymous to a severity score) for the patient profile (Examiner notes that the patient's historical data which includes previous PAP data, current or previous risk estimates, patient co-morbidities, demographics, and medication changes indicate a patient profile)) by: analyzing electronic patient health data associated with the first patient to identify electronic patient health data associated with one or more patient features of the first patient in paragraphs [0035-0036] and [0043] and [0047-0048] (analyzing patient data (synonymous to electronic patient health data) to identify patient data associated with patient variables, referred to as patient parameters (synonymous to one or more patient features)); and processing the electronic patient health data associated with one or more patient features of the first patient to determine the severity score for the first patient in paragraphs [0043-0046] (processing the patient data associated with patient variables to determine the risk score for the patient); the multiple signal values including at least one predictive signal value determined by processing, using at least one trained ML model, the electronic patient health data associated with the one or more medical events of the first patient, the one or more patient features in paragraphs [0020] and [0046] and [0048] and [0054] and [0087] (the PAP waveforms (synonymous to multiple signal values) including predictive signal values determined by processing, using a generalizable estimator (synonymous to at least one trained ML model), the patient data associated with the predetermined health event and the patient parameters); populating a respective first data structure with the severity score in paragraphs [0056] and [0064-0065] (organizing a category list (synonymous to a respective first data structure) with the risk score); the GUI having: ordered representations of profiles of the plurality of patient profiles, wherein the representations are ordered based at least in part on the severity score of the respective patient profile and provide an indication of the urgency score for the respective patient profile in paragraphs [0040] and [0046] and [0049-0050] (patient prioritization ranking (synonymous to ordered representations of profiles of the plurality of patient profiles) based on the patient risk score (synonymous to the severity score of the respective patient profile) and provide an indication of the priority score for the patient).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a non-transitory computer-readable storage medium executed by a processor to execute a method of facilitating medical care of patients, as disclosed by Rahman, to be combined with determining a severity score by analyzing electronic patient health data to identify patient features, multiple signal values including a predictive signal value, populating a data structure with the severity score, having an ordered representations of profiles, and providing an indication of the urgency score for the patient profile, as disclosed by Agarwal, for the purpose of accommodating high volume patient intake, increasing accuracy, improving patient care, and reducing costs [0004 and 0045].
Claims 10-12, 15, and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Rahman et al. (US-20230005603-A1)[hereinafter Rahman], in view of Agarwal (US-20200242566-A1)[hereinafter Agarwal], in view PAUWS et al. (US-20170337345-A1)[hereinafter Pauws].
As per Claim 10, Rahman and Agarwal disclose the method of claim 1.
Rahman and Agarwal do not disclose the following limitations. However, Pauws discloses wherein determining one or more recommended provider actions comprises analyzing each of the signals associated with the first patient and adding at least one action associated with each signal to the one or more recommended provider actions in paragraphs [0003-0004] and [0010] and [0023] and [0086] and [0103-0105] (a medical intervention plan (synonymous to one or more recommended provider actions) includes analyzing each of the signals and selecting actions associated with the signals to the medical intervention plan).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a method for facilitating medical care of patients, as disclosed by Rahman and Agarwal, to be combined with analyzing the signals and adding an action associated with the signal to the recommended provider actions to determine the recommended provider actions, as disclosed by Pauws, for the purpose of providing appropriate healthcare to patients efficiently and effectively [0003].
As per Claim 11, Rahman, Agarwal, and Pauws disclose the method of claim 10.
Rahman and Agarwal do not disclose the following limitations. However, Pauws discloses wherein determining the one or more recommended provider actions comprises analyzing each of the signals associated with the first patient and adding at least one action associated with two or more signals to the one or more recommended provider actions in paragraphs [0003-0004] and [0010] and [0023-0024] and [0086] and [0103-0105] (the medical intervention plan includes analyzing each of the signals and selecting actions associated with two or more signals to the medical intervention plan).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a method for facilitating medical care of patients, as disclosed by Rahman and Agarwal, to be combined with performance metrics, as disclosed by Pauws, for the purpose of providing appropriate healthcare to patients efficiently and effectively [0003].
As per Claim 12, Rahman, Agarwal, and Pauws disclose the method of claim 10.
Rahman and Agarwal do not disclose the following limitations. However, Pauws discloses wherein the one or more recommended provider actions comprises one or more, selected from: contact the first patient via phone, contact the first patient in medical facility, schedule an appointment for the first patient, schedule an annual wellness visit for the first patient, refer the first patient to a specialist, and recommend a medical intervention for the first patient in paragraph [0029] (the medical intervention plan includes contacting the subject via phone (Examiner notes that contacting the subject via phone meets the “one or more” limitations).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a method for facilitating medical care of patients, as disclosed by Rahman and Agarwal, to be combined with the recommended provider actions, as disclosed by Pauws, for the purpose of providing appropriate healthcare to patients efficiently and effectively [0003].
As per Claim 15, Rahman and Agarwal disclose the method of claim 7.
Rahman and Agarwal do not disclose the following limitations. However, Pauws discloses further comprising determining one or more performance metrics based at least in part on changes in urgency scores associated with patient profiles, changes in value of the time until a recommended follow-up signal, changes in value of the time since a recommended follow-up signal, and changes in value of the time since last annual wellness visit signal in paragraphs [0101] and [0103-0105] (performing retrospective event analysis scores (synonymous to performance metrics) based on differences in time before a recommended follow-up intervention signal).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a method for facilitating medical care of patients, as disclosed by Rahman and Agarwal, to be combined with receiving data related to the medical events and in response updating the urgency score, as disclosed by Pauws, for the purpose of providing appropriate healthcare to patients efficiently and effectively [0003].
As per Claim 17, Rahman and Agarwal disclose the method of claim 1.
Rahman does not disclose the following limitations. However, Agarwal discloses wherein an increase in the severity score of the first patient profile indicates the first patient has experienced a decrease in overall health in paragraph [0056] (an increase in the risk score indicates the patient is at a higher risk of having heart failure (Examiner notes that being at a higher risk of having heart failure indicates that the patient's health has decreased).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a method for facilitating medical care of patients, as disclosed by Rahman, to be combined with an increase in the severity score of the patient profile indicates has experienced a decrease in health, as disclosed by Agarwal, for the purpose of accommodating high volume patient intake, increasing accuracy, improving patient care, and reducing costs [0004 and 0045].
The combination of Rahman and Agarwal discloses an increase in the risk score indicating the patient having a higher risk of having heart failure, but does not explicitly disclose the decrease of the patient’s overall health when the risk score increase. However, Pauws discloses wherein an increase in the severity score of the first patient profile indicates the first patient has experienced a decrease in overall health in paragraphs [0014-0016] and [0039] and [0085-0086] (an increase in the severity score of the risk profile indicates the patient has experienced a detrimental deterioration of the well-being of the patient (synonymous to overall health)).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a method for facilitating medical care of patients, as disclosed by Rahman and Agarwal, to be combined with an increase in the severity score of the patient profile indicates has experienced a decrease in health, as disclosed by Pauws, for the purpose of providing appropriate healthcare to patients efficiently and effectively [0003].
As per Claim 18, Rahman and Agarwal disclose the method of claim 7.
Rahman and Agarwal do not disclose the following limitations. However, Pauws discloses further comprising receiving data related to one or more events related to the first patient in paragraphs [0074-0076] and [0092-0094] and [0101] (obtaining data related to medical events of the patient); and in response to receiving data related to one or more events related to the first patient, updating the respective urgency score for the first patient profile in paragraphs [0010-0011] and [0080] and [0092-0094] and [0101-0106] (in response to obtaining data related to medical events related to the patient, changing the score trajectory for the risk profile).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a method for facilitating medical care of patients, as disclosed by Rahman and Agarwal, to be combined receiving data related to events related to the patient, as disclosed by Pauws, for the purpose of providing appropriate healthcare to patients efficiently and effectively [0003].
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Rahman et al. (US-20230005603-A1)[hereinafter Rahman], in view of Agarwal (US-20200242566-A1)[hereinafter Agarwal], in view PAUWS et al. (US-20170337345-A1)[hereinafter Pauws], in view of Lyman (US-20220061746-A1)[hereinafter Lyman].
As per Claim 13, Rahman and Agarwal disclose the method of claim 7.
Rahman and Agarwal do not disclose the following limitations. However, Pauws discloses wherein the urgency score of the first patient profile increases in response to the time until a recommended follow-up signal crossing a threshold amount of time in paragraphs [0101] and [0103-0105] (the risk score of the risk profile increase in response to the time until a recommended follow-up intervention signal).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a method for facilitating medical care of patients, as disclosed by Rahman and Agarwal, to be combined with the urgency score increasing until a recommended follow-up signal crosses a threshold, as disclosed by Pauws, for the purpose of providing appropriate healthcare to patients efficiently and effectively [0003].
The combination of Rahman, Agarwal, and Pauws discloses the risk score of the profile increasing in response to the time until a recommended follow-up signal. The combination does not disclose the recommended follow-up signal crossing a threshold amount of time. However, Lyman discloses, wherein the urgency score of the first patient profile increases in response to the time until a recommended follow-up signal crossing a threshold amount of time in paragraphs [0113] and [0249] and [0356-0359] and [0376] (the time sensitivity score (synonymous to an urgency score) increases in response to the time until an urgent review recommendation signal crossing a timespan threshold).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a method for facilitating medical care of patients, as disclosed by Rahman, Agarwal, and Pauws, to be combined with the urgency score increasing until a recommended follow-up signal crosses a threshold, as disclosed by Lyman, for the purpose of improving the technology of patient care [0294].
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Rahman et al. (US-20230005603-A1)[hereinafter Rahman], in view of Agarwal (US-20200242566-A1)[hereinafter Agarwal], in view PAUWS et al. (US-20170337345-A1)[hereinafter Pauws], in view of Vasudevan (US-20210375437-A1)[hereinafter Vasudevan].
As per Claim 14, Rahman and Agarwal disclose the method of claim 7.
Rahman and Agarwal do not disclose the following limitations. However, Pauws discloses wherein the urgency score of the first patient profile increases in response to an increase in the time since admission signal in paragraphs [0080] and [0101] and [0103-0105] (the risk score of the risk profile increases in response to an increase in time since a medical event (synonymous to an increase in time since admission signal) until a recommended follow-up intervention indication).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a method for facilitating medical care of patients, as disclosed by Rahman and Agarwal, to be combined with the urgency score increases in response to an increase in the time since admission signal, as disclosed by Pauws, for the purpose of providing appropriate healthcare to patients efficiently and effectively [0003].
The combination of Rahman, Agarwal, and Pauws discloses the urgency score of the patient profile increasing in response to an increase in the time since admission signal but does not disclose the urgency score increasing due to an increase in the time since discharge signal, and an increase in the time since transfer signal, until a signal indicating a recommended follow-up has occurred is received. However, Vasudevan discloses wherein the urgency score of the first patient profile increases in response to an increase in the time since admission signal, an increase in the time since discharge signal, and an increase in the time since transfer signal, until a signal indicating a recommended follow-up has occurred is received in paragraphs [0085] and [0095-0096] and [0149] and [0166-0170] (the time critical prioritization score (synonymous to the urgency score) for the patient increase in response to an increase in time of admission insights or indicators (synonymous to signals), discharge indicators, and transition indicators to provide expert intervention recommendation decisions (synonymous to a recommended follow-up signal)).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a method for facilitating medical care of patients, as disclosed by Rahman, Agarwal, and Pauws, to be combined with the urgency score increasing in response to an increase in time, as disclosed by Vasudevan, for the purpose of providing well-informed, high quality, and cost-effective decisions for a patient's optimal health [0001].
Response to Arguments
Applicant's arguments, see Pages 11-13, “REJECTIONS UNDER 35 U.S.C. 102” filed 11/11/2025 with respect to claims 1-4, 16, and 19-20 have been fully considered.
With regards to claims 1 and 19-20, Applicant argues Finn, Kelly and Vasudevan do not describe the recited limitations of the amended claims. Examiner finds this persuasive. Therefore, the rejection of 08/11/2025 has been withdrawn. However, upon further consideration a new grounds of rejection is made over Rahman in view of Agarwal.
Applicant's arguments, see Pages 13-16, “REJECTIONS UNDER 35 U.S.C. 101”, filed 11/11/2025 with respect to claims 1, 19 and 20 have been fully considered but they are not persuasive.
Applicant argues that amended claims do not recite mental processes nor any other abstract idea. Examiner respectfully disagrees. The claim limitations describe obtaining electronic patient health datasets, generating a plurality of patient profile from the electronic patient datasets, analyzing data from electronic patient health datasets to identify electronic patient health data associated with patient features and medical events, determining a severity score, determining an urgency score, determining multiple signal values from the electronic patient health data, processing multiple signal values to determine whether the patient has experienced an admission, discharge, or transfer medical event, determining an urgency score based on the multiple signal values and whether the patient has experienced an admission, discharge, or transfer event, determining recommended provider actions to be performed, and providing an indication of the urgency score are directed to managing personal interactions or personal behavior, which are activities that can be performed in the human mind or by a human using a pen and paper (see MPEP § 2106.05(a)(III) stating “claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions.”) The courts indicated in Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016), that “collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind. The use of one or more processors, patient profile stored in at least a data structure, one trained ML model, populating a respective first data structure, generating and displaying an interactive graphical user interface (GUI), phone, one computer hardware processor, display, and one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to carry out the steps of the abstract idea is merely applying the abstract idea to general purpose computer components which amounts to mere instructions to apply the exception, as per MPEP 2106.05(f)(2).
Applicant argues that the amended limitations integrate the abstract idea into a practical application by contributing to an improvement of managing patient populations health data and prioritizing patients for clinician attention through an interactive GUI. Examiner respectfully disagrees. The claims do not recite an improvement to interactive GUIs technology. The claims merely recite obtaining electronic patient health datasets, generating a plurality of patient profile from the electronic patient datasets, analyzing data from electronic patient health datasets to identify electronic patient health data associated with patient features and medical events, determining a severity score, determining an urgency score, determining multiple signal values from the electronic patient health data, processing multiple signal values to determine whether the patient has experienced an admission, discharge, or transfer medical event, determining an urgency score based on the multiple signal values and whether the patient has experienced an admission, discharge, or transfer event, determining recommended provider actions to be performed, and providing an indication of the urgency score are directed to managing personal interactions or personal behavior, which are a part of the abstract idea. An improvement to the abstract ideas of obtaining electronic patient health datasets, generating a plurality of patient profile from the electronic patient datasets, analyzing data from electronic patient health datasets to identify electronic patient health data associated with patient features and medical events, determining a severity score, determining an urgency score, determining multiple signal values from the electronic patient health data, processing multiple signal values to determine whether the patient has experienced an admission, discharge, or transfer medical event, determining an urgency score based on the multiple signal values and whether the patient has experienced an admission, discharge, or transfer event, determining recommended provider actions to be performed, and providing an indication of the urgency score are directed to managing personal interactions or personal behavior does not amount to an improvement to technology or a technical field (see MPEP § 2106.05(a)(II) stating “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology."). The courts indicated in TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48, that gathering and analyzing information using conventional techniques and providing the output is not sufficient to show an improvement to technology. The claim language and instant application fails to provide details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Here, the improvement is to obtaining electronic patient health datasets, generating a plurality of patient profile from the electronic patient datasets, analyzing data from electronic patient health datasets to identify electronic patient health data associated with patient features and medical events, determining a severity score, determining an urgency score, determining multiple signal values from the electronic patient health data, processing multiple signal values to determine whether the patient has experienced an admission, discharge, or transfer medical event, determining an urgency score based on the multiple signal values and whether the patient has experienced an admission, discharge, or transfer event, determining recommended provider actions to be performed, and providing an indication of the urgency score are directed to managing personal interactions or personal behavior. There is no indication in the disclosure that the involvement of a computer assists in improving the technology for the outlined problem statement. Merely adding generic computer components to perform the method is not sufficient.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ahmed, Zeeshan et al. “Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine” teaches on implementing artificial intelligence in healthcare to manage patient healthcare
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 KRYSTEN N WRIGHT whose telephone number is (571)272-5116. The examiner can normally be reached Monday thru Friday 8 - 5 pm, ET.
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, Fonya Long can be reached on (571)270-5096. 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.
/K.N.W./
Examiner, Art Unit 3682
/FONYA M LONG/Supervisory Patent Examiner, Art Unit 3682