DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Amendments
Claims 1-16 and 18-20 are currently pending in this case and have been examined and addressed below. This communication is a Final Rejection in response to the Amendment to the Claims and Remarks filed on 08/12/2025.
Claims 1, 18, 19, and 20 are amended claims.
Claims 7 and 14-16 are original claims.
Claims 2-6 and 8-13 are previously presented.
Claims 17 have been cancelled and will not be considered at this time.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/22/2025 and 10/10/2025, is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-16 and 18-20 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 – Statutory Categories of Invention:
Claims 1 and 18-20 are drawn to methods, a system, and an article of manufacture, which are statutory categories of invention.
Step 2A – Judicial Exception Analysis, Prong 1:
Independent claim 1 recite a method comprising: forming an individual patient model based on the first set of individual parameters; determining a matching level between the individual patient model and each of a plurality of different predefined generic patient models, each of the plurality of different predefined generic patient models having a predefined patient risk score; selecting at least one generic patient model of the plurality of different predefined generic patient models having the matching level above a predetermined threshold; determining a risk score for the patient based on the selected at least one generic patient model; assigning , based on the risk score, a risk category selected from a plurality of predefined risk categories each associated with a range of risk scores; generating, based on the risk category, record that includes a treatment recommendation specific to the assigned risk category; calculating, based on the at least one generic patient model of the plurality of different predefined generic patient models and based on the first and second set of individual parameters, a patient health progression.
Independent claim 18 recite a method comprising forming an individual patient model based on the first set of individual parameters; determining a matching level between the individual patient model and each of a plurality of different predefined generic patient models, each of the plurality of different predefined generic patient models having a predefined patient risk score; selecting at least one generic patient model of the plurality of different predefined generic patient models having the matching level above a predetermined threshold; determining a risk score for the patient based on the selected at least one generic patient model; assigning, based on the risk score, a risk category to the patient by comparing the determined risk score for the patient with predefined risk score ranges for each of a low risk category, a medium risk category, and a high-risk category; generating, based on the risk category, record that includes a treatment recommendation specific to the assigned risk category; calculating, based on the at least one generic patient model of the plurality of different predefined generic patient models and based on the first and second set of individual parameters, a patient health progression.
Independent claim 19 recite a system comprising forming an individual patient model based on the first set of individual parameters; determining a matching level between the individual patient model and each of a plurality of different predefined generic patient models, each of the plurality of different predefined generic patient models having a predefined patient risk score; selecting at least one generic patient model of the plurality of different predefined generic patient models having the matching level above a predetermined threshold; determining a risk score for the patient based on the selected at least one generic patient model; assigning , based on the risk score, a risk category selected from a plurality of predefined risk categories each associated with a range of risk scores; generating, based on the risk category, record that includes a treatment recommendation specific to the assigned risk category; calculating, based on the at least one generic patient model of the plurality of different predefined generic patient models and based on the first and second set of individual parameters, a patient health progression.
Independent claim 20 recite a method comprising forming an individual patient model based on the first set of individual parameters; determining a matching level between the individual patient model and each of a plurality of different predefined generic patient models, each of the plurality of different predefined generic patient models having a predefined patient risk score; selecting at least one generic patient model of the plurality of different predefined generic patient models having the matching level above a predetermined threshold; determining a risk score for the patient based on the selected at least one generic patient model; assigning , based on the risk score, a risk category selected from a plurality of predefined risk categories each associated with a range of risk scores; generating, based on the risk category, record that includes a treatment recommendation specific to the assigned risk category; calculating, based on the at least one generic patient model of the plurality of different predefined generic patient models and based on the first and second set of individual parameters, a patient health progression.
These steps amount to certain methods of organizing human activity which includes functions relating to managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (MPEP § 2106.04(a)(2)(II)(C) citing the abstract idea grouping for methods of organizing human activity for managing personal behavior or relationships or interactions between people – also note MPEP § 2106.04(a)(2)(II) stating certain activity between a person and a computer may fall within the “certain methods of organizing human activity” grouping).
Step 2A – Judicial Exception Analysis, Prong 2:
This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to instructions to implement the judicial exception using a computer [MPEP 2106.05(f)].
The claims recite a computing device, stored in the memory, a digital record, computer system, computer program product, and the non-transitory computer readable medium.
These elements are recited at a high-level of generality such that it amounts to mere instructions to apply the exception because this is an example of applying the abstract idea by use of general-purpose computer which does not integrate the abstract idea into a practical application.
Claims 1 and 18-20 recite receiving, at a computing device, a first set of
individual parameters indicative of a present or a previous state of a patient and
receiving, at the computing device, a second set of individual parameters indicative of a
state of the patient after administration of the recommended treatment. The limitations
are only recited as a tool which only serves to input data for use by the abstract idea
(MPEP § 2106.05(g) - insignificant pre/post-solution activity that amounts to mere data
gathering to obtain input) and is therefore not a practical application of the recited
judicial exception.
Claims 1 and 18-20 recite updating, using a neural network-based machine learning model, based on the patient health progression, at least one of the at least one generic patient model of the plurality of different predefined generic patient models. This limitation amounts to mere instructions to apply the exception because a mathematical algorithm applied on a general-purpose computer has been found by the courts to be mere instructions to apply as in MPEP 2106.05(f)(2).
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B – Additional Elements that Amount to Significantly More:
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of instructions to implement the abstract idea on a computer.
As discussed above with the respect to integration of the abstract idea into a
practical application, the additional elements of a computing device, stored in the memory, updating, using a neural network-based machine learning model, based on the patient health progression, at least one of the at least one generic patient model of the plurality of different predefined generic patient models. computer system, computer program product, and the non-transitory computer readable medium amounts to no more than mere instructions to apply the exception using a generic computing component.
Claims 1 and 18-20 recite receiving, at a computing device, a first set of
individual parameters indicative of a present or a previous state of a patient and
receiving, at the computing device, a second set of individual parameters indicative of a
state of the patient after administration of the recommended treatment. The courts have decided that receiving or transmitting data over a network as well-understood, routine, conventional activity when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (MPEP § 2106.05(d)(II) other types of activities example i. receiving or transmitting data over a network, OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network).
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Their collective functions merely provide conventional computer implementation to amount to significantly more than the recited judicial exception.
For the reasons stated, these claims are consequently rejected under 35 U.S.C. § 101.
Analysis of Dependent Claims
Dependent claim 2 merely describes wherein selecting the at least one generic patient model of the plurality of different predefined generic patient models having the matching level above the predetermined threshold comprises selecting the at least one generic patient model having a highest matching level.
Dependent claim 3 merely describes wherein the risk score is determined based on a combination of at least two selected generic patient models of the plurality of different predefined generic patient models.
Dependent claim 4 merely describes where each of the at least two selected generic patient models have a weight to be applied when determining the risk score.
Dependent claim 5 merely describes wherein the first set of individual parameters comprise a plurality of the patient's clinical data collected over a predetermined time period.
Dependent claim 6 merely describes wherein the plurality of the patient's clinical data comprises at least patient vitals, number of hospitalizations, laboratory results, and prescribed medications.
Dependent claim 7 merely describes wherein the patient vitals comprise at least one of heart rate data, electrocardiograph (EKG/ECG) data, respiration rate data, patient temperature data, pulse oximetry data, and blood pressure data.
Dependent claim 10 recites wherein the recommended treatment for the patient is only formed if the patient has been assigned the high-risk category.
Claim 12 merely describes updating a predefined generic patient model of the plurality of different predefined generic patient models based on a combination of the determined individual patient model and a result of the predefined health progression comparison.
Each of these steps of the preceding dependent claims 2-12 only serve to further limit or specify the features of independent claims 1 accordingly, and hence are nonetheless directed towards fundamentally the same abstract idea as the independent claim.
Dependent claim 8 merely describes defining, using the computing device, a low-risk category, a medium risk category, and a high-risk category, and determining, using the computing device, the risk category by comparing the determined risk score for the patient with predefined risk score ranges for the each of the low-risk category, the medium risk category and the high-risk category. The computing device is an additional element, which is mere instructions to apply the exception and does not provide a practical application or significantly more for the same reasons.
Dependent claim 9 recites forming, using the computing device, the recommended treatment for the patient, wherein the recommended treatment is different for the each of the low risk category, the medium risk category and the high-risk category. The computing device is an additional element, which is mere instructions to apply the exception and does not provide a practical application or significantly more for the same reasons.
Dependent claim 11 recites comparing, using the computing device, the determined patient health progression with a predefined health progression being defined for the selected at least one generic patient model. The computing device is an additional element, which is mere instructions to apply the exception and does not provide a practical application or significantly more for the same reasons.
Dependent claim 13 merely describes updating the predefined generic patient model of the plurality of different predefined generic patient models comprises applying a machine learning process. The applying a machine learning process is an additional element, which is mere instructions to apply the exception and does not provide a practical application or significantly more for the same reasons.
Dependent claim 14 merely describes wherein the machine learning process is an unsupervised machine learning process. The unsupervised machine learning process is an additional element, which is mere instructions to apply the exception and does not provide a practical application or significantly more for the same reasons.
Dependent claim 15 merely describes wherein the machine learning process is a supervised machine learning process. The supervised machine learning process is an additional element, which is mere instructions to apply the exception and does not provide a practical application or significantly more for the same reasons.
Dependent claim 16 merely describes wherein the machine learning process is based on a convolutional neural network (CNN) or a recurrent neural network (RNN). The convolutional neural network (CNN) or a recurrent neural network (RNN) is an additional element, which is mere instructions to apply the exception and does not provide a practical application or significantly more for the same reasons.
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.
Claim(s) 1-2, 5, 8, and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Swartz (US 20180165418 A1) in view of Yu (US 20200126636 A1) in view of Schmidt (US 20180075207 A1).
As per Claim 1, Swartz teaches a method comprising:
receiving, at a computing device, a first set of individual parameters indicative of a present or a previous state of a patient, ([Para. 0012] Certain data collected by the system relate to factors that directly characterize the current or past health of an individual. For example, the collected data may be objective measures of the individual's heart rate, blood pressure, blood sugar level, length of sleep, etc. [Para. 0044] system can be embodied in a special purpose computer or data processor that is specifically programmed, configured, or constructed to perform one or more of the computer-executable instructions. Examiner interprets the data processor to be indicative of the control unit. [Para. 0046] The environment may include one or more client computing devices 205.)
forming, using the computing device, an individual patient model based on the first set of individual parameters, ([Para. 0012] Data about the different types of factors monitored by the system, whether direct or contextual, are captured by the system over time and used to generate the health vector that characterizes the individual. Examiner interprets health vector to be indicative of individual patient model. [Para. 0044] system can be embodied in a special purpose computer or data processor that is specifically programmed, configured, or constructed to perform one or more of the computer-executable instructions. [Para. 0046] The environment may include one or more client computing devices 205.)
determining, using the computing device, a matching level between the individual patient model and each of a plurality of different predefined generic patient models stored in a memory, each of the plurality of different predefined generic patient models having a predefined patient risk score, ([Para. 0055] the system retrieves population cohorts. Each cohort is characterized by a cohort health vector and includes health vectors and health score trends of the population members of the cohort. [Para. 0056] the system identifies the cohorts for which the associated cohort health vectors are within a certain proximity to the received individual health vector. Such a comparison may be made, for example, by calculating a sum total of the squares of the distance or difference between each of the factors making up the health vector. In making such calculation, each of the factor ranges may be normalized to a scale that allows a comparison between factors. [Para. 0058] For the evaluated factors, the proximity check determines whether the aggregate distance between each of the cohort health vectors and the individual health vector falls within a threshold distance. The system may identify a cohort health vector as satisfying the proximity check when all of its evaluated factors, when summed, are within a threshold distance from the evaluated factors of the individual health vector. [Para. 0060] every population cohort includes the health score trends (i.e. predefined patient risk score) of the cohort members. [Para. 0044] system can be embodied in a special purpose computer or data processor that is specifically programmed, configured, or constructed to perform one or more of the computer-executable instructions. [Para. 0046] The environment may include one or more client computing devices 205.)
selecting, using the computing device, at least one generic patient model of the plurality of different predefined generic patient models having the matching level above a predetermined threshold, ([Para. 0058] For the evaluated factors, the proximity check determines whether the aggregate distance between each of the cohort health vectors and the individual health vector falls within a threshold distance. The system may identify a cohort health vector as satisfying the proximity check when all of its evaluated factors, when summed, are within a threshold distance from the evaluated factors of the individual health vector. [Para. 0044] system can be embodied in a special purpose computer or data processor that is specifically programmed, configured, or constructed to perform one or more of the computer-executable instructions. [Para. 0046] The environment may include one or more client computing devices 205.)
determining, using the computing device, a risk score for the patient based on the selected at least one generic patient model; ([Para. 0033] The health assessment model generates an indication of an individual's level of healthiness or unhealthiness (e.g., on a spectrum from very healthy to very unhealthy) based on one or more of the individual's health vector, the individual's health vector change and the health vectors of the members in the cohorts associated with the individual. The health score of an individual may be represented by a value, such as from +100 to −100, that corresponds to strongly healthy and strongly unhealthy, respectively. [Para. 0044] system can be embodied in a special purpose computer or data processor that is specifically programmed, configured, or constructed to perform one or more of the computer-executable instructions. [Para. 0046] The environment may include one or more client computing devices 205.)
Swartz does not explicitly teach, however Yu teaches
receiving, at the computing device, a second set of individual parameters indicative of a state of the patient after administration of the recommended treatment; ([Para. 0022] determining second concentrations of multiple clinicopathological markers (i.e. second set of individual parameters) of the patient. [Para. 0029] execution by a computing device.)
calculating, using the computing device, based on the at least one generic patient model of the plurality of different predefined generic patient models and based on the first and the second set of individual parameters, a patient health progression. ([Para. 0022] providing, to a gradient boosting machine learning model, the determined second concentrations of the multiple clinicopathological markers; g) receiving, from the gradient boosting machine learning model, a second prediction of whether the patient has poor immune fitness; and h) administering an anticancer therapeutic to the patient if the prediction indicates that the patient does not have poor immune fitness. [Para. 0029] execution by a computing device. Examiner interprets the patient health progression to be indicated by the administering of the anticancer therapeutic because it is only given when the patient does not have poor immune fitness.)
updating, using a neural network-based machine learning model, based on the patient health progression, at least one of the at least one generic patient model of the plurality of different predefined generic patient models; ([Para. 0167] a system is contemplated that includes one or more testing devices that is directly or indirectly linked to one or more computing devices such that upon determination of concentrations of one or more clinicopathological markers, the data be sent to a computing device to be provided to a gradient boosting machine learning model (i.e. neural network-based machine learning model) within the computing device. In turn, the gradient boosting machine learning model can output a prediction regarding patient EM and/or immune fitness or a prediction of effectiveness of an ICI therapeutic, such as durvalumab, to another component of the system. [Para. 0213] The updated F1 model predicted patients with high risk of early mortality (FIG. 27). Overall survival of the patients in the chemotherapy and durva schemes tested with the updated F1 model is shown in FIG. 28. Overall survival of patients in the chemotherapy and Durva schemes with a predicted high risk of early mortality excluded is shown in FIG. 29. Overall survival of all PD-L1 high patients is shown in FIG. 30, and the data without the patients predicted to have a high risk of early mortality is shown in FIG. 31. Overall survival of all PD-L1 low patients is shown in FIG. 32, and the data without the patients predicted to have a high risk of early mortality is shown in FIG. 33. Overall survival of all PD-L1 negative patients is shown in FIG. 34, and the data without the predicted patients with high risk of early mortality is shown in FIG. 35.Example 8—Training and Testing of an Updated F1 Model Using the EAGLE Trial and Neutrophils (NEUT), Lymphocyte Percentage (LYM %), Albumin (ALB), and Lactate Dehydrogenase (LDH) as Clinicopathological Markers.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the health recommendations system as taught by Swartz and incorporate the methods for determining treatments for cancer patients as taught by Yu, with the motivation of better informing the appropriate treatment for their individual clinical state (Yu Para. 0005).
Swartz/ Yu do not explicitly teach, however Schmidt teaches
assigning, using the computing device based on the risk score, a risk category selected from a plurality of predefined risk categories each associated with a range of risk scores; ([Para. 0025] a processor (i.e. computing device). [Para. 0037] an assignment of a wellness score for each new candidate in the group, and classifying the candidates into a hierarchy of risk levels based on the wellness scores. [Para. 0114] patient risk stratification can include processing a group of clinical factors, lifestyle factors, and medication compliance factors, for each patient of a population; classifying the population of patients into a hierarchy of risk levels; and assigning a health risk status (a wellness score) to each patient of the population, which is based on the group of factors.[Para. 0145] The integrator computer module 908 can be configured to perform triage on a large group of new candidates, which can include the steps of performing a health risk assessment for each new candidate in the group, which results in an assignment of a wellness score for each new candidate in the group; and classifying the group of new candidates into a hierarchy of risk levels based on the wellness score for each candidate.)
generating, based on the risk category, a digital record that includes a treatment recommendation specific to the assigned risk category; ([Para. 0032] determining an optimum DPP and/or DPP provider for which the candidate is likely to succeed, from among many DPP providers with essentially the same content, based on matching a candidate's success metrics with ideal participant profiles associated with various DPP providers and programs. [Para. 0145] The integrator computer module 908 can be configured to perform triage on a large group of new candidates, which can include the steps of performing a health risk assessment for each new candidate in the group, which results in an assignment of a wellness score for each new candidate in the group; and classifying the group of new candidates into a hierarchy of risk levels based on the wellness score for each candidate. [Para. 0176-177] The method can include accessing the wellness score of the patient; accessing medical records of the patient; combining the wellness score and the medical record with demographic and socioeconomic characteristics for the patient; and creating a comprehensive patient profile. The method can include mapping the comprehensive patient profile over a group of disease prevention program providers qualified to deliver the disease prevention program; determining the optimal disease prevention program provider for the patient; and enrolling the patient with the optimal disease prevention program provider.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the health recommendations system as taught by Swartz and incorporate the methods for determining treatments for cancer patients as taught by Yu, and incorporate calculating wellness scores for individual patents and assigning a risk level to each patient as taught by Schmidt, with the motivation of provides a risk stratification system to prioritize chronic disease and lifestyle interventions for patients with multiple risk factors (Schmidt Para. 0009).
As per Claim 2, Swartz/ Yu/ Schmidt teach the method according to claim 1, Swartz further teaches wherein selecting the at least one generic patient model of the plurality of different predefined generic patient models having the matching level above the predetermined threshold comprises selecting the at least one generic patient model having a highest matching level. ([Para. 0015] The health recommendations system uses the health vector of an individual to identify cohorts of similar people. On a periodic or continuous basis the system constructs population cohorts of individuals monitored by the system. The different population cohorts may be constructed, for example, based on unsupervised clustering of the individual health vectors of the population. [Para. 0037] the system constructs population cohorts periodically, and separately identifies the cohorts most closely associated with the individual when the individual's health vector is extended.)
As per Claim 5, Swartz/ Yu/ Schmidt teach the method according to claim 1, Swartz further teaches wherein the first set of individual parameters comprise a plurality of the patient's clinical data collected over a predetermined time period. ([Para. 0012] Certain data collected by the system relate to factors that directly characterize the current or past health of an individual. For example, the collected data may be objective measures of the individual's heart rate, blood pressure, blood sugar level, length of sleep, etc. Data about the different types of factors monitored by the system, whether direct or contextual, are captured by the system over time and used to generate the health vector that characterizes the individual. The size of the health vector increases over time, as additional data characterizing the user is continuously obtained by the system.)
As per Claim 8, Swartz/ Yu/ Schmidt teach the method according to claim 1, Schmidt further teaches further comprising defining, using the computing device, a low-risk category, a medium risk category, and a high-risk category, ([Para. 0025] a processor (i.e. computing device) [Para. 0037] The hierarchy of risk levels can include a high health risk level, a medium health risk, and a low health risk. [Para. 0040] Using the wellness score having a range of 1-150 points, the health risk levels of a population can be stratified by risk as follows: high risk level is in the range of 101 to 150 points, medium risk level is in the range of 51 to 100, and a low risk level is in the range of 0 to 50. A wellness score of 150 is the highest possible score and indicates the highest health risk for a patient.)
and determining , using the computing device, the risk category to the patient by comparing the determined risk score for the patient with predefined risk score ranges for the each of the low-risk category, the medium risk category and the high-risk category. ([Para. 0025] a processor (i.e. computing device) [Para. 0114] patient risk stratification can include processing a group of clinical factors, lifestyle factors, and medication compliance factors, for each patient of a population; classifying the population of patients into a hierarchy of risk levels; and assigning a health risk status (a wellness score) to each patient of the population, which is based on the group of factors.[Para. 0145] The integrator computer module 908 can be configured to perform triage on a large group of new candidates, which can include the steps of performing a health risk assessment for each new candidate in the group, which results in an assignment of a wellness score for each new candidate in the group; and classifying the group of new candidates into a hierarchy of risk levels based on the wellness score for each candidate. [Para. 0025] an integrator system including an integrator computer module having a processor. Examiner interprets a processor to be indicative of a control unit.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the health recommendations system as taught by Swartz and incorporate the methods for determining treatments for cancer patients as taught by Yu, and incorporate calculating wellness scores for individual patents and assigning a risk level to each patient as taught by Schmidt, with the motivation of provides a risk stratification system to prioritize chronic disease and lifestyle interventions for patients with multiple risk factors (Schmidt Para. 0009).
As per Claim 18, Swartz teaches a method comprising:
receiving, at a computing device, a first set of individual parameters indicative of a present or a previous state of a patient, ([Para. 0012] Certain data collected by the system relate to factors that directly characterize the current or past health of an individual. For example, the collected data may be objective measures of the individual's heart rate, blood pressure, blood sugar level, length of sleep, etc. [Para. 0044] system can be embodied in a special purpose computer or data processor that is specifically programmed, configured, or constructed to perform one or more of the computer-executable instructions. Examiner interprets the data processor to be indicative of the control unit. [Para. 0046] The environment may include one or more client computing devices 205.)
forming, using the computing device, an individual patient model based on the first set of individual parameters, ([Para. 0012] Data about the different types of factors monitored by the system, whether direct or contextual, are captured by the system over time and used to generate the health vector that characterizes the individual. Examiner interprets health vector to be indicative of individual patient model. [Para. 0044] system can be embodied in a special purpose computer or data processor that is specifically programmed, configured, or constructed to perform one or more of the computer-executable instructions. [Para. 0046] The environment may include one or more client computing devices 205.)
determining, using the computing device, a matching level between the individual patient model and each of a plurality of different predefined generic patient models, each of the plurality of different predefined generic patient models having a predefined patient risk score, ([Para. 0055] the system retrieves population cohorts. Each cohort is characterized by a cohort health vector and includes health vectors and health score trends of the population members of the cohort. [Para. 0056] the system identifies the cohorts for which the associated cohort health vectors are within a certain proximity to the received individual health vector. Such a comparison may be made, for example, by calculating a sum total of the squares of the distance or difference between each of the factors making up the health vector. In making such calculation, each of the factor ranges may be normalized to a scale that allows a comparison between factors. [Para. 0058] For the evaluated factors, the proximity check determines whether the aggregate distance between each of the cohort health vectors and the individual health vector falls within a threshold distance. The system may identify a cohort health vector as satisfying the proximity check when all of its evaluated factors, when summed, are within a threshold distance from the evaluated factors of the individual health vector. [Para. 0060] every population cohort includes the health score trends (i.e. predefined patient risk score) of the cohort members. [Para. 0044] system can be embodied in a special purpose computer or data processor that is specifically programmed, configured, or constructed to perform one or more of the computer-executable instructions. [Para. 0046] The environment may include one or more client computing devices 205.)
selecting, using the computing device, at least one generic patient model of the plurality of different predefined generic patient models having the matching level above a predetermined threshold, ([Para. 0058] For the evaluated factors, the proximity check determines whether the aggregate distance between each of the cohort health vectors and the individual health vector falls within a threshold distance. The system may identify a cohort health vector as satisfying the proximity check when all of its evaluated factors, when summed, are within a threshold distance from the evaluated factors of the individual health vector. [Para. 0044] system can be embodied in a special purpose computer or data processor that is specifically programmed, configured, or constructed to perform one or more of the computer-executable instructions. [Para. 0046] The environment may include one or more client computing devices 205.)
determining, using the computing device, a risk score for the patient based on the selected at least one generic patient model, ([Para. 0033] The health assessment model generates an indication of an individual's level of healthiness or unhealthiness (e.g., on a spectrum from very healthy to very unhealthy) based on one or more of the individual's health vector, the individual's health vector change and the health vectors of the members in the cohorts associated with the individual. The health score of an individual may be represented by a value, such as from +100 to −100, that corresponds to strongly healthy and strongly unhealthy, respectively. [Para. 0044] system can be embodied in a special purpose computer or data processor that is specifically programmed, configured, or constructed to perform one or more of the computer-executable instructions. [Para. 0046] The environment may include one or more client computing devices 205.)
Swartz does not explicitly teach, however Yu teaches
receiving, at the computing device, a second set of individual parameters indicative of a state of the patient after receiving the recommended treatment; ([Para. 0022] determining second concentrations of multiple clinicopathological markers (i.e. second set of individual parameters) of the patient. [Para. 0029] execution by a computing device.)
calculating, using the computing device, based on the at least one generic patient model of the plurality of different predefined generic patient models and based on the first and the second set of individual parameters, a patient health progression ([Para. 0022] providing, to a gradient boosting machine learning model, the determined second concentrations of the multiple clinicopathological markers; g) receiving, from the gradient boosting machine learning model, a second prediction of whether the patient has poor immune fitness; and h) administering an anticancer therapeutic to the patient if the prediction indicates that the patient does not have poor immune fitness. [Para. 0029] execution by a computing device. Examiner interprets the patient health progression to be indicated by the administering of the anticancer therapeutic because it is only given when the patient does not have poor immune fitness.)
updating, using a neural network-based machine learning model, based on the patient health progression, at least one of the at least one generic patient model of the plurality of different predefined generic patient models; ([Para. 0167] a system is contemplated that includes one or more testing devices that is directly or indirectly linked to one or more computing devices such that upon determination of concentrations of one or more clinicopathological markers, the data be sent to a computing device to be provided to a gradient boosting machine learning model (i.e. neural network-based machine learning model) within the computing device. In turn, the gradient boosting machine learning model can output a prediction regarding patient EM and/or immune fitness or a prediction of effectiveness of an ICI therapeutic, such as durvalumab, to another component of the system. [Para. 0213] The updated F1 model predicted patients with high risk of early mortality (FIG. 27). Overall survival of the patients in the chemotherapy and durva schemes tested with the updated F1 model is shown in FIG. 28. Overall survival of patients in the chemotherapy and Durva schemes with a predicted high risk of early mortality excluded is shown in FIG. 29. Overall survival of all PD-L1 high patients is shown in FIG. 30, and the data without the patients predicted to have a high risk of early mortality is shown in FIG. 31. Overall survival of all PD-L1 low patients is shown in FIG. 32, and the data without the patients predicted to have a high risk of early mortality is shown in FIG. 33. Overall survival of all PD-L1 negative patients is shown in FIG. 34, and the data without the predicted patients with high risk of early mortality is shown in FIG. 35.Example 8—Training and Testing of an Updated F1 Model Using the EAGLE Trial and Neutrophils (NEUT), Lymphocyte Percentage (LYM %), Albumin (ALB), and Lactate Dehydrogenase (LDH) as Clinicopathological Markers.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the health recommendations system as taught by Swartz and incorporate the methods for determining treatments for cancer patients as taught by Yu, with the motivation of better informing the appropriate treatment for their individual clinical state (Yu Para. 0005).
Swartz/ Yu do not explicitly teach, however Schmidt teaches
assigning, using the computing device based on the risk score, a risk category to the patient by comparing the determined risk score for the patient with predefined risk score ranges for the each of the low-risk category, the medium risk category and the high-risk category; ([Para. 0025] a processor (i.e. computing device) [Para. 0114] patient risk stratification can include processing a group of clinical factors, lifestyle factors, and medication compliance factors, for each patient of a population; classifying the population of patients into a hierarchy of risk levels; and assigning a health risk status (a wellness score) to each patient of the population, which is based on the group of factors.[Para. 0145] The integrator computer module 908 can be configured to perform triage on a large group of new candidates, which can include the steps of performing a health risk assessment for each new candidate in the group, which results in an assignment of a wellness score for each new candidate in the group; and classifying the group of new candidates into a hierarchy of risk levels based on the wellness score for each candidate. [Para. 0025] an integrator system including an integrator computer module having a processor.)
generating, based on the risk category, a digital record that includes a treatment recommendation specific to the assigned risk category; ([Para. 0032] determining an optimum DPP and/or DPP provider for which the candidate is likely to succeed, from among many DPP providers with essentially the same content, based on matching a candidate's success metrics with ideal participant profiles associated with various DPP providers and programs. [Para. 0145] The integrator computer module 908 can be configured to perform triage on a large group of new candidates, which can include the steps of performing a health risk assessment for each new candidate in the group, which results in an assignment of a wellness score for each new candidate in the group; and classifying the group of new candidates into a hierarchy of risk levels based on the wellness score for each candidate. [Para. 0176-177] The method can include accessing the wellness score of the patient; accessing medical records of the patient; combining the wellness score and the medical record with demographic and socioeconomic characteristics for the patient; and creating a comprehensive patient profile. The method can include mapping the comprehensive patient profile over a group of disease prevention program providers qualified to deliver the disease prevention program; determining the optimal disease prevention program provider for the patient; and enrolling the patient with the optimal disease prevention program provider.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the health recommendations system as taught by Swartz and incorporate the methods for determining treatments for cancer patients as taught by Yu, and incorporate calculating wellness scores for individual patents and assigning a risk level to each patient as taught by Schmidt, with the motivation of provides a risk stratification system to prioritize chronic disease and lifestyle interventions for patients with multiple risk factors (Schmidt Para. 0009).
As per Claim 19, Swartz teaches a computer system adapted for determining a risk score for a patient, the computer system adapted to:
receive a first set of individual parameters indicative of a present or a previous state of the patient, ([Para. 0012] Certain data collected by the system relate to factors that directly characterize the current or past health of an individual. For example, the collected data may be objective measures of the individual's heart rate, blood pressure, blood sugar level, length of sleep, etc. [Para. 0044] system can be embodied in a special purpose computer or data processor that is specifically programmed, configured, or constructed to perform one or more of the computer-executable instructions. Examiner interprets the data processor to be indicative of the control unit. [Para. 0046] The environment may include one or more client computing devices 205.)
form an individual patient model based on the first set of individual parameters, ([Para. 0012] Data about the different types of factors monitored by the system, whether direct or contextual, are captured by the system over time and used to generate the health vector that characterizes the individual. Examiner interprets health vector to be indicative of individual patient model. [Para. 0044] system can be embodied in a special purpose computer or data processor that is specifically programmed, configured, or constructed to perform one or more of the computer-executable instructions. [Para. 0046] The environment may include one or more client computing devices 205.)
determine a matching level between the individual patient model and each of a plurality of different predefined generic patient models, each of the plurality of different predefined generic patient models having a predefined patient risk score, ([Para. 0055] the system retrieves population cohorts. Each cohort is characterized by a cohort health vector and includes health vectors and health score trends of the population members of the cohort. [Para. 0056] the system identifies the cohorts for which the associated cohort health vectors are within a certain proximity to the received individual health vector. Such a comparison may be made, for example, by calculating a sum total of the squares of the distance or difference between each of the factors making up the health vector. In making such calculation, each of the factor ranges may be normalized to a scale that allows a comparison between factors. [Para. 0058] For the evaluated factors, the proximity check determines whether the aggregate distance between each of the cohort health vectors and the individual health vector falls within a threshold distance. The system may identify a cohort health vector as satisfying the proximity check when all of its evaluated factors, when summed, are within a threshold distance from the evaluated factors of the individual health vector. [Para. 0060] every population cohort includes the health score trends (i.e. predefined patient risk score) of the cohort members. [Para. 0044] system can be embodied in a special purpose computer or data processor that is specifically programmed, configured, or constructed to perform one or more of the computer-executable instructions. [Para. 0046] The environment may include one or more client computing devices 205.)
select at least one generic patient model of the plurality of different predefined generic patient models having the matching level above a predetermined threshold, ([Para. 0058] For the evaluated factors, the proximity check determines whether the aggregate distance between each of the cohort health vectors and the individual health vector falls within a threshold distance. The system may identify a cohort health vector as satisfying the proximity check when all of its evaluated factors, when summed, are within a threshold distance from the evaluated factors of the individual health vector. [Para. 0044] system can be embodied in a special purpose computer or data processor that is specifically programmed, configured, or constructed to perform one or more of the computer-executable instructions. [Para. 0046] The environment may include one or more client computing de