DETAILED ACTION
Status of Claims
This is a Final Office Action in response to the arguments and/or amendments filed on 11 May 2026.
Claim(s) 1, 2, 6, and 16-21 is/are amended.
Claim(s) 1-6 and 8-21 is/are currently pending and have been examined.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-6 and 8-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 19, which is representative of claims 1 and 20, recites: a system for observing member behavior and managing a care gap associated with the member, comprising:
determine a current gap-in-care for the member by: receiving a
determine, for the current gap-in-care for the member, one or more actions for the member that, if followed, are capable of at least partially closing the current gap-in-care within a clinically-defined period of time for the member;
use
use the
provide, based on the probability of at least partially closing the current gap-in-care, a communication to the member that describes the one or more actions for the member via a communication channel.
The preceding recitation of the claim has had strikethroughs applied to the features beyond one abstract idea to more clearly demonstrate the limitations setting forth that abstract idea. The remaining limitations describe a concept of analyzing information regarding a gap in care and providing a recommendation to close that gap. This concept describes a mental process that a provider should follow to induce a patient to close a gap-in-care, similar to the “mental process that a neurologist should follow when testing a patient for nervous system malfunctions” given in MPEP 2106.04(a)(2)(II)(C) as an example of managing personal behavior in the methods of organizing human activity sub-grouping. As such, these limitations manage personal behavior and thus set forth a method of organizing human activity. Alternatively, the identified concept is analogous to the examples of “observations”, “evaluation”, “judgement”, and “opinion” given in MPEP2106.04(a)(2)(III) and can be practically performed in the human mind. As such, these limitations set forth a mental process.
The claims also recite Shapely Additive Explanations (SHAP) values. The broadest reasonable interpretation of “Shapley Additive Explanations” refers to a specific mathematical technique for quantifying variable importance. The specification does not define the term but uses it consistent with the identified BRI. Thus when given their broadest reasonable interpretation in light of the specification, Shapely Additive Explanations (SHAP) values describe mathematical calculations. As such, these limitations set forth a mathematical concept.
While the above limitations set forth concepts that fall within different groupings of abstract ideas, they all set forth abstract ideas. As such, per MPEP 2106.04(II)(B), these concepts are considered together as a single abstract idea for further analysis. Therefore the claims are determined to recite an abstract idea.
MPEP 2106, reflecting the 2019 PEG, directs examiners at Step 2A Prong Two to consider whether the additional elements of the claims integrate a recited abstract idea into a practical application.
Claim 19 recites a system comprising: a processor; and a memory. Claim 20 recites a non-transitory computer-readable medium. These additional elements are recited at an extremely high level of generality, and are interpreted as generic computing devices used to implement the abstract idea. Per MPEP 2106.05(f), implementing an abstract idea on a generic computing device does not integrate an abstract idea into a practical application in Step 2A Prong Two, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea on a generic computer. As such, these additional elements do not integrate the abstract idea into a practical application.
The claims further recite a gradient boosting machine learning model and using the machine learning model. This limitations amount to instructions to implement the abstract idea on a computing device. As such, these additional elements do not integrate the abstract idea into a practical application.
The claims further recite an additional element of receiving electronic records. This additional element does not reflect any improvement to any technology or technical field. Additionally, this additional element does not implement the judicial exception with or using a particular machine. Further, this additional element does not effect a transformation or reduction of a particular article. Finally, this additional element does not apply or use the abstract idea in some other meaningful way. Instead, this addition element only generally links the abstract idea to a technological environment of computing devices. As such, this additional element does not integrate the abstract idea into a practical application.
There are no further additional elements. When considered as a combination, the additional elements only generally link the abstract idea to a technological environment of a computing device. As such, the combination of additional elements does not integrate the abstract idea into a practical application. Therefore the claims are determined to be directed to an abstract idea.
At Step 2B of the Mayo/Alice analysis, examiners are to consider whether the additional elements amount to significantly more than the abstract idea.
As previously noted, the claims recite additional elements which may be interpreted as generic computing devices used to implement the abstract idea or instructions to implement the abstract idea with a computing device. However, per MPEP 2106.05(f), implementing an abstract idea on a generic computing does not add significantly more in Step 2B, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea on a generic computer. As such, these additional elements do not amount to significantly more.
As previously noted, the claims recite an additional element of receiving electronic records. Per MPEP 2106.05(d), the courts have recognizing receiving data and electronic recordkeeping as well understood, routine, and conventional computer functions. As such, this additional element, individually and in combination with the prior computing devices, does not amount to significantly more than the abstract idea.
There are no further additional elements. When considered as a combination at Step 2B, the additional elements only generally link the abstract idea to a technological environment of a computing device. As such, the combination of additional elements does not amount to significantly more than the abstract idea. Therefore, when considered individually and as an ordered combination, the additional elements of the independent claims do not amount to significantly more than the judicial exception. Thus the independent claims are not patent eligible.
Dependent claims 2-6, 8-18, and 21, further describe the abstract idea, but the claims continue to recite an abstract idea, albeit a narrowed one. Dependent claims 2-6, 9-18, and 21 do not recite any further additional elements. The previously identified additional elements, individually and as a combination, fail to integrate the narrowed abstract idea into a practical application or amount to significantly more than the narrowed abstract idea for the same reasons articulated above. Dependent claim 8 recites the additional element of a channel comprising email. This additional element, when considered individually and in combination with the previously identified additional elements, only generally links the abstract idea to a technological environment involving computers. As such, this additional element does not integrate the abstract idea into a practical application. Further, Baji et al. (US 5027400) demonstrates (“conventional electronic mail” See at least Column 24, Line 24) that this additional element was a conventional computer function long before the priority date of the claimed invention. As such, this additional element does not amount to significantly more than the abstract idea. Further, this additional element, when considered individually and in combination with the previously identified additional elements, only generally links the abstract idea to a technological environment involving computers. As such, the combination of additional elements does not amount to significantly more. Thus as the dependent claims remain directed to a judicial exception, and as the additional elements of the claims do not amount to significantly more, the dependent claims are not patent eligible.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-6, 8-13, and 15-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Reisman et al. (US 2009/0216558 A1) in view of Holub et al. (US 2021/0313063 A1) and Rho et al. (US 2022/0067580 A1).
Regarding Claim 1, 19, and 20: Reisman discloses a method of observing member behavior and managing a care gap associated with the member, the method comprising:
determining a current gap-in-care for the member by: receiving an electronic record associated with the member that describes a current health-related behavior of the member (In steps 200-202, the health care organization 100 collects a wide spectrum of medical care information 114, 122, 124, 128 and aggregates it in the medical database 118 for subsequent analysis. See at least [0046]. Also: Clinical data 114 originates from medical services claims, pharmacy data, as well as from lab results, and includes information associated with the patient-health care provider interaction, including information related to the patient's diagnosis and treatment, medical procedures, drug prescription information, in-patient information and health care provider notes. See at least [0038]).
receiving guideline behavior for the member supported by a professional clinical recommendation (In step 204, the health care organization 100 establishes a set of clinical rules 120 for a plurality of conditions, such as by having an on-site medical professional team continuously review collected health reference information 122, including evidence-based medical literature. See at least [0046]. Also: An on-staff team of medical professionals within the health care organization 100 consults various sources of health reference information 122, including evidence-based preventive health data, to establish and continuously or periodically revise a set of clinical rules 120 that reflect best evidence-based medical standards of care for a plurality of conditions. The clinical rules 120 are stored in the medical database 118. See at least [0039]); and
determining a difference between the current health-related behavior of the member and the guideline behavior for the member, wherein the difference defines, at least in part, the current gap-in-care for the member (In steps 210 and 212, the rules engine module 126 applies the latest evidence-based medical standards of care included within the clinical rules 120 to the patient's actual care, as evidenced from the claims, pharmacy, lab and patient-entered clinical data, to identify at least one instance where the patient's actual care is inconsistent with the expected care embodied by the clinical rules 120. See at least [0046]. Also: the rules engine module 126 instantiates a patient-specific rule processing session 772 (FIG. 18) and generates one or more clinical alerts 104 when the incoming data, as well as previously stored patient data, indicates a deviation from the best evidence-based best medical standards of care in light of the clinical rules 120. See at least [0075]).
determining, for the current gap-in-care for the member, one or more actions for the member that, if followed, are capable of at least partially closing the current gap-in-care within a clinically-defined period of time for the member (when the rules engine module identifies an instance of actual care inconsistent with the established, best evidence-based medical standards of care, the patient is presented with a clinical alert via the PHR. In embodiments, the clinical alerts include notifications to contact the health care provider in order to start or stop a specific medication and/or to undergo a specific examination or test procedure associated with one or more conditions and co-morbidities specific to the patient. See at least [0009]. Also: RTRecommendationList--a list of real-time alerts 104, 106 generated by the rules engine module 126, including an alert number, alert name, instructional text, severity code, creation date, and a completion status indicator (e.g., open, completed, ignore) for each generated alert. See at least [0065]).
providing a communication to the member that describes the one or more actions for the member via a communication channel (in steps 214-216, the rules engine module 126 stores an alert indicator in the patient's 102 medical data file within the medical database 118, including the associated alert detail, and presents the patient with one or more clinical alerts 104 and/or personalized wellness alerts 106 via the appropriate interface of the PHR 108. See at least [0046]. Also: the alerts list 402 includes a number of clinical alerts 104 suggesting specific tests related to patient's diabetes and recommending use of statins (e.g., to lower cholesterol levels). In one embodiment, the list 402 includes one or more personalized wellness alerts 106, such a recommendation to undergo periodic breast cancer screenings for female patients of predetermined age range that have not had a recent screening. See at least [0048]).
Reisman does not disclose a gradient boosting machine learning model or using the machine learning model to generate a member-level prediction for the member by determining a probability of at least partially closing the current gap-in-care for each of the one or more actions. However, Holub teaches a gradient boosting machine learning model (the process 400 may include generating one or more machine learning models. See at least [0052]. Also: the machine learning models may also comprise, for example, … gradient-boosting machines (GBM). See at least [0041]) and using the machine learning model to generate a member-level prediction for the member by determining a probability of at least partially closing the current gap-in-care for each of the one or more action (At block 420, the process 400 may include generating actions for addressing one or more gaps in care and generating a probability of successfully addressing the one or more gaps in care. See at least [0061]. Also: FIG. 2 also depicts output data 220 that may be generated from the machine learning models 218. The output data 220 may include data regarding gap(s) in care (e.g., colonoscopy, mammogram, and/or untreated diabetes), as well as a probability. See at least [0042]. Also: At block 1010, the process 1000 may include generating, utilizing the machine learning models, second data indicating identified gaps in care for the medical patient. In examples, the machine learning models may be configured to surface recommended action(s), wherein individual machine learning models are trained to identify potential issues with the medication, recommend an action, and generate a probability that the recommended action will address the issue with the medication. See at least [0106]), and providing, based on the probability of at least partially closing the current gap-in-care, a communication (sending the notification comprises sending the notification based at least in part on the probability satisfying a threshold probability for successfully addressing the at least one of the haps in medical care or the one or more actions. See at least [0023]).
Reisman provides a system which identifies gaps in care, identifies actions to address a gap in care, and provides recommendations to perform identified actions, upon which the claimed invention’s determination of a probability of an action to address a gap in care can be seen as an improvement. However, Holub demonstrates that the prior art already knew of identifying probabilities of an action addressing a gap in care and using that probability to determine whether to send a notification. One of ordinary skill in the art could have trivially applied the techniques of Holub to the system of Reisman. Further, one of ordinary skill in the art would have recognized that the application of Holub would have resulted in an improved system which would limit recommended actions to those sufficiently likely to address gaps in care. As such, the application of Holub would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the disclosures of Reisman and the teachings of Holub.
Reisman does not disclose using Shapley Additive Explanations (SHAP) values to determine an impact of each of a plurality of features; assigning an importance value to each of the plurality of features based on the impact of each of the plurality of features determining using SHAP values.
Rho teaches using Shapley Additive Explanations (SHAP) values to determine an impact of each of a plurality of features; assigning an importance value to each of the plurality of features based on the impact of each of the plurality of features determining using SHAP values (in some examples, executed analytical engine 156 may also perform operations that generate, for each of the analyst-specified features, a feature value contribution that characterizes a contribution of the analyst-specified features to the outcome of the selected machine learning or artificial intelligence process. For example, executed analytical engine 156 may compute a Shapley value feature contribution for each of the analyst-specified features based on the elements of predicted output data 324A, 324B, and 324C and corresponding ones of the modified feature vectors 314. In some instances, executed analytical engine 156 may calculate one or more of the Shapley value feature contributions in accordance with a Shapley Additive exPlanations (SNAP) algorithm (e.g., when the selected machine learning or artificial intelligence process corresponds to a gradient-boosted decision tree algorithm). See at least [0100]).
Reisman and Holub suggests a systems which uses a gradient boosted machine learning model, upon which the claimed invention’s use of Shapley Additive Explanations to evaluate a model can be seen as an improvement. However, Rho demonstrates that the prior art already knew of applying the SHAP technique to evaluate gradient boosted machine learning models. One of ordinary skill in the art could have trivially applied the SHAP techniques of Rho to evaluate the model of Reisman and Holub, and one of ordinary skill in the art would have recognized that such an application of Rho would have resulted in a system using a more interpretable machine learning model. As such, the application of Rho, and the claimed invention, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the disclosures of Reisman and the teachings of Holub and Rho.
Regarding Claim 2 and 21: Reisman in view of Holub and Rho makes obvious the above limitations. Additionally, Rho teaches wherein the determined impact of each of the plurality of features comprises a clinical impact for each of the plurality of features (in some examples, executed analytical engine 156 may also perform operations that generate, for each of the analyst-specified features, a feature value contribution that characterizes a contribution of the analyst-specified features to the outcome of the selected machine learning or artificial intelligence process. For example, executed analytical engine 156 may compute a Shapley value feature contribution for each of the analyst-specified features based on the elements of predicted output data 324A, 324B, and 324C and corresponding ones of the modified feature vectors 314. In some instances, executed analytical engine 156 may calculate one or more of the Shapley value feature contributions in accordance with a Shapley Additive exPlanations (SNAP) algorithm (e.g., when the selected machine learning or artificial intelligence process corresponds to a gradient-boosted decision tree algorithm). See at least [0100]. Examiner’s note: The description of the impact of the features as comprising “a clinical impact” is considered non-functional descriptive material and is given limited patentable weight).
Regarding Claim 3: Reisman in view of Holub and Rho makes obvious the above limitations. Additionally, Reisman discloses wherein the clinical impact is measured by a health biomarker (Specifically, the rules engine module 126 processes the newly-received data point 744 in light of the previously stored health profile (e.g., prior health indicator readings, patient's chronic conditions, age, and sex) and the best evidence-based medical standards of care 120 to generate in real-time a normal or target range 748, as well as a high risk indicator 750, which provide context for the updated readings. For health indicators, such as blood pressure, which need to stay within a given target range 748, the high risk indicator 750 is demarcated via a high range and a low range. In addition to providing the target range and the health risk indicator, the rules engine provides specific messaging to the member to alert them if the health indicator like blood pressure is critically high to seek urgent medical care. In embodiments, the health indicator includes cholesterol levels, blood pressure readings, HbA1c test results, and body mass index (BMI) readings. See at least [0080]).
Regarding Claim 4: Reisman in view of Holub and Rho makes obvious the above limitations. Additionally, Reisman discloses wherein the health biomarker comprises at least one of HbAlc, blood pressure, and health complications (Specifically, the rules engine module 126 processes the newly-received data point 744 in light of the previously stored health profile (e.g., prior health indicator readings, patient's chronic conditions, age, and sex) and the best evidence-based medical standards of care 120 to generate in real-time a normal or target range 748, as well as a high risk indicator 750, which provide context for the updated readings. For health indicators, such as blood pressure, which need to stay within a given target range 748, the high risk indicator 750 is demarcated via a high range and a low range. In addition to providing the target range and the health risk indicator, the rules engine provides specific messaging to the member to alert them if the health indicator like blood pressure is critically high to seek urgent medical care. In embodiments, the health indicator includes cholesterol levels, blood pressure readings, HbA1c test results, and body mass index (BMI) readings. See at least [0080]).
Regarding Claim 5: Reisman in view of Holub and Rho makes obvious the above limitations. Additionally, Reisman discloses wherein the health complications comprise at least one of a stroke, myocardial infraction, in-member admission, and emergency room admission (the rules engine module 126 applies clinical data 114 and clinical components of the patient-entered data 128 to generate a real-time risk score 105 for various medical conditions (e.g., points are assigned to various clinical factors that increase the risk for heart disease and based on the member's conditions and lifestyle behaviors, a percentage score is calculated to identify the member's risk for future heart disease). The risk score 105 quantifies the severity of existing medical conditions and assesses the risk for future conditions in light of evaluating multiple risk factors in accordance with the clinical rules 120. For example, the risk score 105 may identify high risk diabetics or patients subject to a risk of future stroke. See at least [0067]. Also: The rules engine module 126 identifies relevant health reference information 122 and medical news 124 based on a real-time analysis of the clinical data 114, patient-entered data 128, risk score 105. See at least [0077]).
Regarding Claim 6: Reisman in view of Holub and Rho makes obvious the above limitations. Additionally, Rho teaches wherein the determined impact of each of the plurality of features comprises a cost impact for each of the plurality of factors (in some examples, executed analytical engine 156 may also perform operations that generate, for each of the analyst-specified features, a feature value contribution that characterizes a contribution of the analyst-specified features to the outcome of the selected machine learning or artificial intelligence process. For example, executed analytical engine 156 may compute a Shapley value feature contribution for each of the analyst-specified features based on the elements of predicted output data 324A, 324B, and 324C and corresponding ones of the modified feature vectors 314. In some instances, executed analytical engine 156 may calculate one or more of the Shapley value feature contributions in accordance with a Shapley Additive exPlanations (SNAP) algorithm (e.g., when the selected machine learning or artificial intelligence process corresponds to a gradient-boosted decision tree algorithm). See at least [0100]. Examiner’s note: The description of the impact of the features as comprising “a cost impact” is considered non-functional descriptive material and is given limited patentable weight).
Regarding Claim 8: Reisman in view of Holub and Rho makes obvious the above limitations. Reisman separately discloses a communication channel comprises at least one of email, direct mail, SMS, and an automated outbound calling campaign (via phone, mail, email or other communications. See at least [0044]).
Reisman, Holub, and Rho suggests a systems which provides a communication to a user regarding a gap-in-care, which differs from the claimed invention by the substitution of an unstated communication channel for one of particular channels. However, Reisman separate demonstrates that those particular channels were already known by the prior art. One of ordinary skill in the art could have trivially substituted email into the system of Reisman, Holub, and Rho to provide a notification. Further, one of ordinary skill in the art would have recognized that such a substitution would have predictably resulted in a system which contacts patients regarding gaps in car via email. As such, the identified substitution and the claimed invention would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the disclosures of Reisman and the teachings of Holub and Rho.
Regarding Claim 9: Reisman in view of Holub and Rho makes obvious the above limitations. Additionally, Reisman discloses wherein the electronic record associated with the member comprises claims-based electronic data (The medical care information collected by the health care organization comprises patient-specific clinical data (e.g., based on claims, health care provider, and patient-entered input), as well as health reference information, including evidence-based literature relating to a plurality of medical conditions. See at least [0004]. Also: A medical insurance carrier collects clinical information originating from medical services claims, performed procedures, pharmacy data, lab results, and provides it to the health care organization for storage in a medical database. See at least [0005]).
Regarding Claim 10: Reisman in view of Holub and Rho makes obvious the above limitations. Additionally, Reisman discloses wherein the electronic record further comprises electronic medical record (EMR) data (In steps 200-202, the health care organization 100 collects a wide spectrum of medical care information 114, 122, 124, 128 and aggregates it in the medical database 118 for subsequent analysis. See at least [0046]. Also: Clinical data 114 originates from medical services claims, pharmacy data, as well as from lab results, and includes information associated with the patient-health care provider interaction, including information related to the patient's diagnosis and treatment, medical procedures, drug prescription information, in-patient information and health care provider notes. See at least [0038]).
Regarding Claim 11: Reisman in view of Holub and Rho makes obvious the above limitations. Additionally, Reisman discloses wherein the claims-based electronic data comprises data describing at least one insurance medical and/or insurance claim made by at least one of the member and a provider (The medical care information collected by the health care organization comprises patient-specific clinical data (e.g., based on claims, health care provider, and patient-entered input), as well as health reference information, including evidence-based literature relating to a plurality of medical conditions. See at least [0004]. Also: A medical insurance carrier collects clinical information originating from medical services claims, performed procedures, pharmacy data, lab results, and provides it to the health care organization for storage in a medical database. See at least [0005]).
Regarding Claim 12: Reisman in view of Holub and Rho makes obvious the above limitations. Additionally, Reisman discloses wherein the electronic record associated with the member comprises device data obtained from at least one device associated with the member (Exemplary patient-entered data 128 includes additional clinical data, such as patient's family history, use of non-prescription drugs, known allergies, unreported and/or untreated conditions (e.g., chronic low back pain, migraines, etc.), as well as results of self-administered medical tests (e.g., periodic blood pressure and/or blood sugar readings). See at least [0040]).
Regarding Claim 13: Reisman in view of Holub and Rho makes obvious the above limitations. Additionally, Reisman discloses wherein the device data comprises at least one of gyroscopic data, accelerometer data, beacon data, glucose readings, heart rate data, blood pressure data, blood oxygen data, temperature data, kinetics data, location data, motion data, a device identifier, and a timestamp (Exemplary patient-entered data 128 includes additional clinical data, such as patient's family history, use of non-prescription drugs, known allergies, unreported and/or untreated conditions (e.g., chronic low back pain, migraines, etc.), as well as results of self-administered medical tests (e.g., periodic blood pressure and/or blood sugar readings). See at least [0040]).
Regarding Claim 15: Reisman in view of Holub and Rho makes obvious the above limitations. Additionally, Reisman discloses wherein the guideline behavior for the member supported by the professional clinical recommendation comprises guidance based on at least one of medical history, demographics, social indices, biomarkers, behavior data, engagement data, historical gap-in-care data, and a machine learning model-derived output (In step 204, the health care organization 100 establishes a set of clinical rules 120 for a plurality of conditions, such as by having an on-site medical professional team continuously review collected health reference information 122, including evidence-based medical literature. See at least [0046]. Also: An on-staff team of medical professionals within the health care organization 100 consults various sources of health reference information 122, including evidence-based preventive health data, to establish and continuously or periodically revise a set of clinical rules 120 that reflect best evidence-based medical standards of care for a plurality of conditions. The clinical rules 120 are stored in the medical database 118. See at least [0039]. Also: based on a history of a heart attack and the patient's drug regimen compliance information (e.g., as entered by a health care provider), the rules engine module 126 presents relevant drug-related educational materials 122, 124 relating to the importance of taking medications for heart attacks. See at least [0077]).
Regarding Claim 16: Reisman in view of Holub and Rho makes obvious the above limitations. Additionally, Reisman discloses adjusting the determined impact by a baseline biomarker of the member (Specifically, the rules engine module 126 processes the newly-received data point 744 in light of the previously stored health profile (e.g., prior health indicator readings, patient's chronic conditions, age, and sex) and the best evidence-based medical standards of care 120 to generate in real-time a normal or target range 748, as well as a high risk indicator 750, which provide context for the updated readings. For health indicators, such as blood pressure, which need to stay within a given target range 748, the high risk indicator 750 is demarcated via a high range and a low range. In addition to providing the target range and the health risk indicator, the rules engine provides specific messaging to the member to alert them if the health indicator like blood pressure is critically high to seek urgent medical care. In embodiments, the health indicator includes cholesterol levels, blood pressure readings, HbA1c test results, and body mass index (BMI) readings. See at least [0080]).
Regarding Claim 17: Reisman in view of Holub and Rho makes obvious the above limitations. Additionally, Reisman discloses adjusting the determined impact by a degree of member management of the member’s condition (Specifically, the rules engine module 126 processes the newly-received data point 744 in light of the previously stored health profile (e.g., prior health indicator readings, patient's chronic conditions, age, and sex) and the best evidence-based medical standards of care 120 to generate in real-time a normal or target range 748, as well as a high risk indicator 750, which provide context for the updated readings. For health indicators, such as blood pressure, which need to stay within a given target range 748, the high risk indicator 750 is demarcated via a high range and a low range. In addition to providing the target range and the health risk indicator, the rules engine provides specific messaging to the member to alert them if the health indicator like blood pressure is critically high to seek urgent medical care. In embodiments, the health indicator includes cholesterol levels, blood pressure readings, HbA1c test results, and body mass index (BMI) readings. See at least [0080]).
Regarding Claim 18: Reisman in view of Holub and Rho makes obvious the above limitations. Additionally, Reisman discloses wherein the determined impact of each of the plurality of features comprises at least partially closing one or more additional current gaps-in-care for the member (in some examples, executed analytical engine 156 may also perform operations that generate, for each of the analyst-specified features, a feature value contribution that characterizes a contribution of the analyst-specified features to the outcome of the selected machine learning or artificial intelligence process. For example, executed analytical engine 156 may compute a Shapley value feature contribution for each of the analyst-specified features based on the elements of predicted output data 324A, 324B, and 324C and corresponding ones of the modified feature vectors 314. In some instances, executed analytical engine 156 may calculate one or more of the Shapley value feature contributions in accordance with a Shapley Additive exPlanations (SNAP) algorithm (e.g., when the selected machine learning or artificial intelligence process corresponds to a gradient-boosted decision tree algorithm). See at least [0100]. Examiner’s note: The description of the impact of the features as comprising “a clinical impact” is considered non-functional descriptive material and is given limited patentable weight).
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Reisman et al. (US 2009/0216558 A1) in view of Holub et al. (US 2021/0313063 A1) and Rho et al. (US 2022/0067580 A1), and further in view of Irish et al. (US 2020/0281542 A1).
Regarding Claim 14: Reisman in view of Holub and Rho makes obvious the above limitations. Reisman does not appear to disclose wherein the electronic record comprises an image of the member.
However, Irish teaches an electronic record comprises an image of the member (transmitting, by the imaging device, the captured image to the user interface system in response to generating the captured image, wherein the captured image is medically related to a patient; automatically associating, by the user interface system, the captured image to the patient upon receipt; and storing, by the user interface system, the received captured image in an electronic medical record (EMR) of the patient associated with the captured image. See at least [0006]).
Reisman, Holub, and Rho suggest a system which delivers health recommendations to users based on their electronic medical records, which differs from the claimed invention by the substitution of Reisman’s generic electronic medical record for a medical record containing an image of the patient. Irish demonstrates that the prior art already knew of electronic medical records containing images of a patient. One of ordinary skill in the art could have trivially substituted Irish’s EMR in for the EMR of Reisman, Holub, and Rho. Further, one of ordinary skill in the art would have recognized that such a substitution would have predictably resulted in a system which would analyze EMRs including patient images to determine health recommendations. As such, the identified substitution, and the claimed invention, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the disclosures of Reisman and the teachings of Holub, Rho, and Irish.
Response to Arguments
Examiner’s Note Regarding Applicant’s Arguments Regarding 101 Rejections of claims 1-6 and 8-21: Applicant’s arguments appear to be a word-for-word repeat of the arguments advanced by Applicant in the 19 December 2025 response. Applicant’s arguments were addressed in the Response to Arguments of the non-final action dated 10 February 2026, which is incorporated herein.
Applicant’s Argument Regarding 103 Rejections of claims 1-6 and 8-21:
Applicant again submits that the Driouich reference relied upon in the 103 rejection does not qualify as prior art based on the arguments presented in previous responses.
Holub teaches that machine learning models may be individually trained for different medications or actions for medication and may be trained to identify potential issues with a medication. See Holub at 105-106. Nothing in Holub discloses, teaches, or suggest using the machine learning model to generate a member-level prediction for the member by determining a probability of at least partially closing the current-gap-in-care for each of the one or more actions.
Applicant submits that Rho does not disclose or teach or suggest “using Shapley Additive Explanations (SHAP) values to determine an impact of each of a plurality of features associated with a gradient boosting machine learning model.”
Examiner’s Response: Applicant's arguments filed 11 May 2026 have been fully considered but they are not persuasive.
Examiner notes that Applicant’s arguments remain unpersuasive for the reasons articulated in the 10 February 2026 office action. Driouich is still considered prior art. However, due to the limitations of examining resources at this stage in prosecution, the alternative rejection based on Driouich is not maintained.
Contrary to Applicant’s assertion, Holub appears to teach the referenced material. For example, Holub at [0106] states: “At block 1010, the process 1000 may include generating, utilizing the machine learning models, second data indicating identified gaps in care for the medical patient. In examples, the machine learning models may be configured to surface recommended action(s), wherein individual machine learning models are trained to identify potential issues with the medication, recommend an action, and generate a probability that the recommended action will address the issue with the medication. See at least [0106].” It is unclear how applicant concludes, based on their consideration of this paragraph, that Holub does not teach the identified limitation.
Examiner notes that Rho expressly describes the application of SHAP values to a gradient boosting machine learning model. It is unclear how applicant concludes that Rho does not teach the identified limitation.
Additional Considerations
The prior art made of record and not relied upon that is considered pertinent to applicant’s disclosure can be found in the PTO-892 of the prior office actions dated 5 July 2024, 6 February 2025, 25 June 2025, 22 September 2025, 19 November 2025, and 10 February 2026.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 Bion A Shelden whose telephone number is (571)270-0515. The examiner can normally be reached M-F, 12pm-10pm EST.
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/Bion A Shelden/Primary Examiner, Art Unit 3685 2026-07-14
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