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 .
Application Status
This is the first non-final action on the merits. Claims 1-20 as originally filed on June 20, 2024 are currently pending and considered below.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on June 20, 2024 is being considered by the examiner. The submission is in compliance with the provisions of 37 CFR 1.97.
Claim Objections
Claim 16 is objected to because of the following informalities: “an indication that the patient should contract the clinician” should read “an indication that the patient should cont
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 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.
Claims 1-8 recite a computer-implemented method for determining that the patient will likely experience the cytokine release syndrome after treatment, which is within the statutory category of a process. Claims 9-16 recite a computer-implemented method for determining that the patient will likely experience the adverse health condition, which is within the statutory category of a process. Claims 17-20 recite a system for determining that the patient will likely experience the adverse health condition, which is within the statutory category of a machine.
Step 2A - Prong One:
Regarding Prong One of Step 2A, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation, they "recite" a judicial exception or in other words whether a judicial exception is "set forth" or "described" in the claims. An "abstract idea" judicial exception is subject matter that falls within at least one of the following groupings: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes. Representative independent claim 1 includes limitations that recite at least one abstract idea.
Specifically, independent claim 1 recites: A computer-implemented method comprising:
retrieving, prior to a treatment, first health data entered by a patient on a prescribed application being executed by a patient computing device;
retrieving, prior to the treatment, second health data passively collected by a prescribed wearable device;
deploying a machine learning model on the first health data and the second health data to determine whether the patient will likely experience cytokine release syndrome after the treatment; and
in response to the determination that the patient will likely experience the cytokine release syndrome, triggering a notification on a clinician dashboard.
Specifically, independent claim 9 recites: A computer-implemented method comprising:
retrieving, after a treatment, a first health data entered by a patient on a prescribed application being executed by a patient computing device;
retrieving, after the treatment, a second health data passively collected by a prescribed wearable device;
deploying a machine learning model on the first health data and the second health data to determine whether the patient will likely experience an adverse health condition; and
in response to the determination that the patient will likely experience the adverse health condition, triggering a notification on a clinician dashboard.
Specifically, independent claim 17 recites: A system comprising:
one or more processors; and
a non-transitory storage medium storing computer program instructions that when executed by the one or more processors cause the system to perform operations comprising:
retrieving a first health data entered by a patient, undergoing bispecific antibody treatment, on a prescribed application being executed by a patient computing device;
retrieving a second health data passively collected by a prescribed wearable device worn by the patient;
deploying a machine learning model on the first health data and the second health data to determine whether the patient will likely experience an adverse health condition; and
in response to the determination that the patient will likely experience the adverse health condition, triggering one or more notifications.
The underlined limitations are directed to methods of organizing human activity. The claim recites steps of retrieving first health data and second health data, deploying a model, determining that the patient will likely experience cytokine release syndrome or an adverse reaction and triggering a notification. These steps, under its broadest reasonable interpretation, are categorized as methods of organizing human activity, specifically associated with managing personal behavior or relationships or interactions between people (e.g. predicting cytokine release syndrome in a patient after treatment). The claim encompasses a person following rules or instructions to receive and process data in the manner described in the abstract idea. If the claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. See MPEP § 2106.04(a). The Examiner further notes that “Certain Methods of Organizing Human Activity” includes a person's interaction with a computer (see October 2019 Update: Subject Matter Eligibility at Pg. 5). Any limitation not identified above as part of methods of organizing human activity, are deemed “additional elements” and will be discussed further in detail below. Accordingly, claims 1, 9 and 17 recite at least one abstract idea.
Similarly, dependent claims 4-7, 11, 13-16 and 20 further narrow the abstract idea described in the independent claims. Claims 4, 6 and 7 further describe the first health data and/or the second health data. Claim 5 further describes the determination that the patient will likely experience cytokine release syndrome after the treatment. Claim 11 describes the adverse health condition. Claim 13 describes retrieving bloodwork data for the patient. Claims 14 and 20 describe the notification. Claims 15 and 16 describe a second notification. These limitations only serve to further limit the abstract idea and hence, are directed toward fundamentally the same abstract ideas as independent claims 1, 9 and 17, even when considered individually and as an ordered combination.
Step 2A - Prong Two:
Regarding Prong Two of Step 2A, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. It must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a "practical application."
In the present case, claims 1-20 as a whole do not integrate the abstract idea into a practical application because they do not impose meaningful limits on practicing the abstract idea. The additional elements or combination of additional elements, beyond the above-noted at least one abstract idea will be described as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the “abstract idea(s)”).
Specifically, independent claim 1 recites: A computer-implemented method comprising:
retrieving, prior to a treatment, first health data entered by a patient on a prescribed application being executed by a patient computing device;
retrieving, prior to the treatment, second health data passively collected by a prescribed wearable device;
deploying a machine learning model on the first health data and the second health data to determine whether the patient will likely experience cytokine release syndrome after the treatment; and
in response to the determination that the patient will likely experience the cytokine release syndrome, triggering a notification on a clinician dashboard.
Specifically, independent claim 9 recites: A computer-implemented method comprising:
retrieving, after a treatment, a first health data entered by a patient on a prescribed application being executed by a patient computing device;
retrieving, after the treatment, a second health data passively collected by a prescribed wearable device;
deploying a machine learning model on the first health data and the second health data to determine whether the patient will likely experience an adverse health condition; and
in response to the determination that the patient will likely experience the adverse health condition, triggering a notification on a clinician dashboard.
Specifically, independent claim 17 recites: A system comprising:
one or more processors; and
a non-transitory storage medium storing computer program instructions that when executed by the one or more processors cause the system to perform operations comprising:
retrieving a first health data entered by a patient, undergoing bispecific antibody treatment, on a prescribed application being executed by a patient computing device;
retrieving a second health data passively collected by a prescribed wearable device worn by the patient;
deploying a machine learning model on the first health data and the second health data to determine whether the patient will likely experience an adverse health condition; and
in response to the determination that the patient will likely experience the adverse health condition, triggering one or more notifications.
The independent claims recite the additional elements of a processor, non-transitory storage medium, prescribed application, computing device, prescribed wearable device, machine learning and clinician dashboard that implement the identified abstract idea. The processor, non-transitory storage medium, computing device and machine learning are not described by the applicant and are recited at a high-level of generality such that they amounts to no more than mere instructions to apply the exception using a generic computer component. The prescribed application, prescribed wearable device and clinician dashboard are recited at a high-level of generality such that they are generally linking the use of a judicial exception to a particular technological environment or field of use, and thus, do not integrate a judicial exception into a practical application.
The dependent claims 2, 3, 10, 12, 13, 18 and 19 recite additional element(s) beyond those already recited in the independent claims that implement the identified abstract idea. Claims 2, 10 and 18 describe training the machine learning model. Claims 3, 12 and 19 further describe the machine learning. Claim 13 recites an EHR system. However, these functions do not integrate a practical application more than the abstract idea because:
the machine learning and training represent mere instructions to apply the abstract idea on a computer (i.e., merely invoking the computer structure as a tool used to execute the limitations); and,
the EHR system generally links the use of a judicial exception to a particular technological environment or field of use.
Accordingly, the claims as a whole do not integrate the abstract idea into a practical application as they do not impose any meaningful limits on practicing the abstract idea.
Step 2B
Regarding Step 2B, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
When viewed as a whole, claims 1-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite processes that are routine and well-known in the art and simply implements the process on a computer(s) is not enough to qualify as "significantly more."
As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a processor, non-transitory storage medium, computing device and machine learning to perform the noted steps amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). The prescribed application, prescribed wearable device and clinician dashboard generally link the use of a judicial exception to a particular technological environment or field of use, and thus, do not amount to significantly more than the judicial exception.
The dependent claims 2, 3, 10, 12, 13, 18 and 19 recite additional element(s) beyond those already recited in the independent claims that implement the identified abstract idea. Claims 2, 10 and 18 describe training the machine learning model. Claims 3, 12 and 19 further describe the machine learning. Claim 13 recites an EHR system. However, these functions are not deemed significantly more than the abstract idea because:
the machine learning and training represent mere instructions to apply the abstract idea on a computer (i.e., merely invoking the computer structure as a tool used to execute the limitations); and,
the EHR system generally links the use of a judicial exception to a particular technological environment or field of use.
Therefore, claims 1-20 are rejected under 35 USC §101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraph of 35 U.S.C. 102 that forms the basis for the rejections under this section set forth in this Office action:
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 9 and 12-15 rejected under 35 U.S.C. 102(a)(2) as being anticipated by Knuff (US 2022/0188654 A1).
Regarding claim 9, Knuff teaches: A computer-implemented method comprising:
retrieving, after a treatment, a first health data entered by a patient on a prescribed application being executed by a patient computing device; (the system includes a “software application running on an edge device” and the application is used for “receiv[ing] patient data” including “self-reported AEs (adverse events)”, e.g. see [0019], [0077]; “As data is input into the mobile application via clinicians and patients, some of that data may flow to the cloud-based machine learning model”, e.g. see [0190]; “analyzing clinical trial data from inception to completion” (i.e. after a treatment), e.g. see [0018])
retrieving, after the treatment, a second health data passively collected by a prescribed wearable device; (“The system takes in biomarkers from a variety of sources” including IoT devices, smart wearables, Internet-connected medical devices, i.e., glucose meters, heart monitors, etc.; data is collected in “real-time” throughout the trial “from inception to completion”, e.g. see [0072], [0018])
deploying a machine learning model on the first health data and the second health data to determine whether the patient will likely experience an adverse health condition; and (the system uses “edge-based and cloud-based machine learning for analyzing clinical trial data” to “predict adverse effects in a trial patient” and identify “indications of sever[e] adverse effects or adverse effects”, e.g. see [0018]-[0019], [0072]; the data analyzed by the machine learning system comes from a variety of sources including smart wearables and “input into the mobile application via…patients”, e.g. see [0072], [0190])
in response to the determination that the patient will likely experience the adverse health condition, triggering a notification on a clinician dashboard. (the system is designed to “issue an alert to the software application if one of the adverse effects is predicted”, e.g. see [0019]; “From those predictions, the machine learning model 4300 may produce…alerts 4350 to sponsors and clinicians”, e.g. see [0241])
Regarding claim 12, Knuff teaches the computer-implemented method of claim 9 as described above.
Knuff further teaches:
wherein the machine learning model comprises at least one of a regression model, a gradient boosted regression model, a logistic regression model, a random forest regression model, an ensemble model, a classification model, a deep learning neural network, a recurrent neural network for deep learning, or a convolutional neural network for deep learning (the machine learning model can be a “classifier”, “3D convolutional neural network (3D CNN)”, “long short term memory (LSTM) neural network”, “graph-based neural network”, e.g. see [0184], [0115], [0199], [0196])
Regarding claim 13, Knuff teaches the computer-implemented method of claim 9 as described above.
Knuff further teaches:
retrieving, after the treatment, bloodwork data for the patient; and (“The system takes in biomarkers from a variety of sources” including “lab results, i.e., blood, urine, etc.”; data is collected in “real-time” throughout the completion of the trial (i.e. after the treatment), e.g. see [0072], [0018])
deploying the machine learning model on the first health data, the second health data, and the bloodwork data to determine whether the patient will likely experience the adverse health condition; (the system uses “edge-based and cloud-based machine learning for analyzing clinical trial data” to “predict adverse effects in a trial patient” and identify “indications of sever[e] adverse effects or adverse effects”, e.g. see [0018]-[0019], [0072]; the data analyzed by the machine learning system comes from a variety of sources including smart wearables, lab results and “input into the mobile application via…patients”, e.g. see [0072], [0190])
in response to determining that the patient will likely experience the adverse health condition, triggering the one or more notifications (the system is designed to “issue an alert to the software application if one of the adverse effects is predicted”, e.g. see [0019])
Regarding claim 14, Knuff teaches the computer-implemented method of claim 9 as described above.
Knuff further teaches:
wherein the notification on a clinician dashboard comprises at least one of an indication that the clinician should contact the patient, an indication that dosage of a prescription medication is to be adjusted, or indication that the patient should be admitted to the hospital (“the alert comprises a warning to stop the drug treatment for one or more of the trial patients”, e.g. see claim 2)
Regarding claim 15, Knuff teaches the computer-implemented method of claim 9 as described above.
Knuff further teaches:
in response to determining that the patient will likely experience the adverse healthcare condition, triggering a second notification to the prescribed application (“Edge devices in the possession of….patients” and “the alert triggers a notification on the edge device”, e.g. see [0184], [0021])
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-7, 10, 11 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Knuff (US 2022/0188654 A1) in further view of Komanduri (“Development of a Predictive Model for Cytokine Release Syndrome to Inform Risk Stratification and CRS Management Following Immunotherapy”, The American Society of Hematology, Nov. 2021).
Regarding claim 1, Knuff teaches: A computer-implemented method comprising:
retrieving, prior to a treatment, first health data entered by a patient on a prescribed application being executed by a patient computing device; (the system includes a “software application running on an edge device” and the application is used for “receiv[ing] patient data” including “self-reported AEs (adverse events)”, e.g. see [0019], [0077]; “As data is input into the mobile application via clinicians and patients, some of that data may flow to the cloud-based machine learning model”, e.g. see [0190]; “Prior to the initialization of a clinical trial, pharma companies 4160 and sponsors 4140 may use a pharmaceutical research system 4100 to de-risk investment decisions…Data from preclinical analytics could assist in the defining the patient populations that would be best suited for a specific clinical trial” (i.e. prior to a treatment), e.g. see [0189])
retrieving, prior to the treatment, second health data passively collected by a prescribed wearable device; (“The system takes in biomarkers from a variety of sources” including IoT devices, smart wearables, Internet-connected medical devices, i.e., glucose meters, heart monitors, etc., e.g. see [0072])
deploying a machine learning model on the first health data and the second health data to determine whether the patient will likely experience […] after the treatment; and (the system uses “edge-based and cloud-based machine learning for analyzing clinical trial data” to “predict adverse effects in a trial patient”, e.g. see [0018]-[0019]; the data analyzed by the machine learning system comes from a variety of sources including smart wearables and “input into the mobile application via clinicians and patients”, e.g. see [0072], [0190])
in response to the determination […], triggering a notification on a clinician dashboard. (the system is designed to “issue an alert to the software application if one of the adverse effects is predicted”, e.g. see [0019]; “From those predictions, the machine learning model 4300 may produce…alerts 4350 to sponsors and clinicians”, e.g. see [0241])
Knuff does not teach:
the patient will likely experience cytokine release syndrome
However, Komanduri in the analogous art of a predictive model for cytokine release syndrome (e.g. see “Background”) teaches:
the patient will likely experience cytokine release syndrome (“predict the occurrence of Grade ≥ 2 CRS after the first glofitamab dose”, e.g. see “Background”); the prediction is based on a “CRS risk score” (CRSRS) which is calculated from “binarized risk factor values at baseline”, e.g. see “Results”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Knuff to include the patient will likely experience cytokine release syndrome as taught by Komanduri for the purposes of an “accurate classification of risk for Grade ≥ 2 CRS” (Komanduri, see “Conclusions”).
Regarding claim 2, Knuff and Komanduri teach the computer-implemented method of claim 1 as described above.
Knuff further teaches:
training the machine learning model […] (“train a machine learning model according to the clinical trial parameters”, e.g. see [0019])
Knuff does not teach:
training the model using a supervised approach by passing through labeled data of a cohort of patients through the model
However, Komanduri in the analogous art teaches:
training the model using a supervised approach by passing through labeled data of a cohort of patients through the model (developing the model using a “training data set” which “included patients with aggressive (n=165) or indolent NHL (n=31)” (i.e. cohort of patients) where the “primary outcome was defined as Grade ≥ 2 CRS” (i.e. labeled data), e.g. see “Methods”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Knuff to include training the model using a supervised approach by passing through labeled data of a cohort of patients through the model as taught by Komanduri for the purposes of “develop[ing] a model to predict the occurrence of Grade ≥ 2 CRS…to enable stratification of patients according to risk of CRS” (Komanduri, see “Background”).
Regarding claim 3, Knuff and Komanduri teach the computer-implemented method of claim 1 as described above.
Knuff further teaches:
wherein the machine learning model comprises at least one of a regression model, a gradient boosted regression model, a logistic regression model, a random forest regression model, an ensemble model, a classification model, a deep learning neural network, a recurrent neural network for deep learning, or a convolutional neural network for deep learning (the machine learning model can be a “classifier”, “3D convolutional neural network (3D CNN)”, “long short term memory (LSTM) neural network”, “graph-based neural network”, e.g. see [0184], [0115], [0199], [0196])
Regarding claim 4, Knuff and Komanduri teach the computer-implemented method of claim 1 as described above.
Knuff further teaches:
wherein the first and the second health data is periodically received for a predetermined period of time prior to the treatment (the system analyzes data collected from inception of the clinical trial (i.e. predetermined period of time prior to the treatment) and e.g. see [0018], [0072]; the biomarkers may be measured “continuously throughout a clinical trial for each patient” and the data collected in analyzed in “real-time”, e.g. see [0084], [0072])
Regarding claim 5, Knuff and Komanduri teach the computer-implemented method of claim 1 as described above.
Knuff does not teach:
wherein the determination that the patient will likely experience the cytokine release syndrome is for a predetermined period of time after receiving the treatment
However, Komanduri in the analogous art teaches:
wherein the determination that the patient will likely experience the cytokine release syndrome is for a predetermined period of time after receiving the treatment (the model is developed to determine the “primary outcome” which is “defined as Grade ≥ 2 CRS in the week after the first glofitamab dose”, e.g. see “Methods”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Knuff to include the determination that the patient will likely experience the cytokine release syndrome is for a predetermined period of time after receiving the treatment as taught by Komanduri for the purposes of managing the “potentially life-threatening toxicity caused by immune activation” (Komanduri, see “Background”).
Regarding claim 6, Knuff and Komanduri teach the computer-implemented method of claim 1 as described above.
Knuff further teaches:
wherein the first data comprises at least one of medical history, laboratory results, or patient profile (data may be input into the application via patients, e.g. see [0190]; “the patient data comprises data selected from the group consisting of biometrics, biomarkers, medical history, and vital signs”, e.g. see [0021])
Regarding claim 7, Knuff and Komanduri teach the computer-implemented method of claim 1 as described above.
Knuff further teaches:
wherein the second data comprises at least one of temperature, heart rate, blood pressure, or oxygen saturation (data collected from “smart wearables” include “vital signs”, e.g. see [0072])
Claims 10 and 18 recite substantially similar limitations as those already addressed in claim 2, and, as such are rejected for similar reasons as given above.
Regarding claim 11, Knuff teaches the computer-implemented method of claim 9 as described above.
Knuff does not teach:
wherein the adverse health condition comprises at least one of cytokine release syndrome, infection, neurotoxicity, or cytopenia
However, Komanduri in the analogous art teaches:
wherein the adverse health condition comprises at least one of cytokine release syndrome, infection, neurotoxicity, or cytopenia (“predict the occurrence of Grade ≥ 2 CRS after the first glofitamab dose”, e.g. see “Background”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Knuff to include the adverse health condition comprises at least one of cytokine release syndrome, infection, neurotoxicity, or cytopenia as taught by Komanduri for the purposes of monitoring for a “potentially life-threatening toxicity caused by immune activation” (Komanduri, see “Background”).
Regarding claim 17, Knuff teaches: A system comprising:
one or more processors; and a non-transitory storage medium storing computer program instructions that when executed by the one or more processors cause the system to perform operations comprising: (a computer server comprising a memory and a processor; a clinical trials module, comprising a first plurality of programming instructions stored in the memory and operating on the processor, e.g. see claim 1)
retrieving a first health data entered by a patient, undergoing […] treatment, on a prescribed application being executed by a patient computing device; (the system includes a “software application running on an edge device” and the application is used for “receiv[ing] patient data” including “self-reported AEs (adverse events)”, e.g. see [0019], [0077]; “As data is input into the mobile application via clinicians and patients, some of that data may flow to the cloud-based machine learning model”, e.g. see [0190]; “analyzing clinical trial data from inception to completion” of a trial drug (i.e. undergoing treatment), e.g. see [0018], [0087])
retrieving a second health data passively collected by a prescribed wearable device worn by the patient; (“The system takes in biomarkers from a variety of sources” including IoT devices, smart wearables, Internet-connected medical devices, i.e., glucose meters, heart monitors, etc.; data is collected in “real-time” throughout the trial “from inception to completion”, e.g. see [0072], [0018])
deploying a machine learning model on the first health data and the second health data to determine whether the patient will likely experience an adverse health condition; (the system uses “edge-based and cloud-based machine learning for analyzing clinical trial data” to “predict adverse effects in a trial patient” and identify “indications of sever[e] adverse effects or adverse effects”, e.g. see [0018]-[0019], [0072]; the data analyzed by the machine learning system comes from a variety of sources including smart wearables and “input into the mobile application via…patients”, e.g. see [0072], [0190])
and in response to the determination that the patient will likely experience the adverse health condition, triggering one or more notifications. (the system is designed to “issue an alert to the software application if one of the adverse effects is predicted”, e.g. see [0019]; “From those predictions, the machine learning model 4300 may produce…alerts 4350 to sponsors and clinicians”, e.g. see [0241])
Knuff does not teach:
the patient undergoing bispecific antibody treatment
However, Komanduri in the analogous art teaches:
the patient undergoing bispecific antibody treatment (“evaluating glofitamab in patients” which is a “T-cell engaging bispecific antibody”, e.g. see “Background”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Knuff to include the patient undergoing bispecific antibody treatment as taught by Komanduri for the purposes of monitoring for a “potentially life-threatening toxicity caused by immune activation” (Komanduri, see “Background”).
Claim 19 recites substantially similar limitations as those already addressed in claim 3, and, as such is rejected for similar reasons as given above.
Regarding claim 20, Knuff and Komanduri teach the system of claim 17 as described above.
Knuff further teaches:
wherein the one or more notifications comprise at least one of a patient notification on the prescribed application and a clinician notification on a clinician dashboard (the system is designed to “issue an alert to the software application if one of the adverse effects is predicted”, e.g. see [0019]; “From those predictions, the machine learning model 4300 may produce…alerts 4350 to sponsors and clinicians”, e.g. see [0241])
Claims 8 is rejected under 35 U.S.C. 103 as being unpatentable over Knuff and Komanduri in further view of Luellen (US 2018/0308569 A1).
Regarding claim 8, Knuff and Komanduri teach the computer-implemented method of claim 1 as described above.
Knuff and Komanduri teach triggering the notification on the clinician dashboard as described above.
Knuff and Komanduri do not teach:
providing the notification to an electronic health record (EHR) system
However, Luellen in the analogous art of a digital health intervention platform (e.g. see [0018]) teaches:
providing the notification to an electronic health record (EHR) system (“the system generates automated alerts or push notifications” where “Electronic Health or Medical Records (EHR/EMR)” may act as an “Output”, e.g. see [0028], [0092])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Knuff and Komanduri to include providing the notification to an electronic health record (EHR) system as taught by Luellen for the purposes of providing coordination of care (Luellen [0007]).
Claims 16 is rejected under 35 U.S.C. 103 as being unpatentable over Knuff in further view of Berz (US 2021/0321957 A1).
Regarding claim 16, Knuff teaches the computer-implemented method of claim 15 as described above.
Knuff teaches the second notification as described above.
Knuff does not teach:
at least one of an indication that the patient should contract the clinician, an indication that the patient should pick up medication at the pharmacy, or an indication that the patient should contact emergency services
However, Berz in the analogous art of monitoring for “development of medication induced side effects in a patient” (e.g. see [1070]) teaches:
at least one of an indication that the patient should contract the clinician, an indication that the patient should pick up medication at the pharmacy, or an indication that the patient should contact emergency services (“to provide a communication to the patient computer…prompting the patient to perform at least one of: seeking professional medical care, and begin taking antibiotics”, e.g. see [1134], [0092])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Knuff to include at least one of an indication that the patient should contract the clinician, an indication that the patient should pick up medication at the pharmacy, or an indication that the patient should contact emergency services as taught by Berz for the purposes of providing “early intervention” and avoiding “hospitalization of the patient” (Berz [1181]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Reference Gannon (US 2021/0100454 A1) discloses a system and method for a body temperature logging patch. Reference Borthakur (US 2021/0151179 A1) discloses a wearable device and IOT network for prediction and management of chronic disorders. Reference Fokoue-Nkoutche (US 2018/0060508 A1) discloses personalized tolerance prediction of adverse drug events.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Aaisha Abdullah whose telephone number is (571)272-5668. The examiner can normally be reached Monday through Friday 8:00 am - 5:00 pm.
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/A.A./Examiner, Art Unit 3686
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681