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 Claims
Claims 1-11, 13, 14, 16 and 21 are amended
Claim 23 is newly presented.
Claims 1-11, 13-18 and 21-23 as presented December 17, 2025 are currently pending and considered below.
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 1-11, 13-18 and 21-23 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 1-11, 21 and 23 recite a system for improved monitoring heart failure in a patient to improve monitoring resources, which is within the statutory category of a machine. Claims 13-18 and 22 recite method for improved monitoring heart failure in a patient to improve monitoring resources, which is within the statutory category of a process.
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 medical device system for improved heart failure (HF) monitoring in a patient to improve monitoring resources, the medical device system comprising:
a HF predictor circuit configured to:
generate a monitored sensor trend using HF sensor data including cardiac, electrical or mechanical data collected up to a prediction time from the patient;
generate a projected sensor trend for the patient over a forecast period of time in future beyond the prediction time using at least the monitored sensor trend of the patient, and a confidence interval about the projected sensor trend based at least in part on the HF sensor data; and
based at least in part on the projected sensor trend and the confidence interval, generate and provide to a user or process a forecast of future HF status of the patient to adjust or control a process of the medical device system.
The underlined limitations are directed to methods of organizing human activity and mathematical concepts. The claim recites steps of generating a sensor trend and generating and providing a future HF status. 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. steps for monitoring heart failure in a patient). The claim encompasses a person following rules or instructions to store and process data in the manner described in the abstract idea. The examiner further notes that “methods of organizing human activity” includes a person's interaction with a computer (see October 2019 Update: Subject Matter Eligibility at Pg. 5). 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). In addition, the claim encompasses an abstract idea that falls under the mathematical concepts grouping because generating a projected sensor trend over a forecast period and a confidence interval, under its broadest reasonable interpretation, represents mathematical calculations (see MPEP 2106.04(a)(2)). Any limitation not identified above as part of methods of organizing human activity or mathematical concepts, are deemed “additional elements” and will be discussed further in detail below. Accordingly, claims 1 and 13 recite at least one abstract idea.
Similarly, dependent claims 2-11, 14-18 and 21-23 further narrow the abstract idea described in the independent claims. Claims 2-10 and 14-18 further describe the monitored sensor trends and/or the projected sensor trend. Claims 11 describes the prediction time. Claims 21 and 22 describe generating an alert. Claim 23 describes initiating or adjusting a therapy. These limitations only serve to further limit the abstract idea and hence, are directed toward fundamentally the same abstract ideas as independent claims 1 and 13, even when considered individually and as an ordered combination. Claims 2-3, 6, 9, 14-16 and 23 partially narrow the abstract idea as described above, and also introduce additional element(s) which will be discussed in Step 2A Prong 2 and Step 2B.
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 1-11, 13-18 and 21-23 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 medical device system for improved heart failure (HF) monitoring in a patient to improve monitoring resources, the medical device system comprising:
a HF predictor circuit configured to:
generate a monitored sensor trend using HF sensor data including cardiac, electrical or mechanical data collected up to a prediction time from the patient;
generate a projected sensor trend for the patient over a forecast period of time in future beyond the prediction time using at least the monitored sensor trend of the patient, and a confidence interval about the projected sensor trend based at least in part on the HF sensor data; and
based at least in part on the projected sensor trend and the confidence interval, generate and provide to a user or process a forecast of future HF status of the patient to adjust or control a process of the medical device system.
The claim recites the additional elements of a medical device system, HF predictor circuit and adjusting or controlling a process of the medical device system that implement the identified abstract idea. The medical device system, HF predictor circuit and adjusting or controlling a process of the medical device system 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 (i.e., the system and circuit merely invoking the computer structure as a tool used to execute the limitations; the “adjust or control a process” merely adds a generic instruction to apply it with a medical device system, MPEP 2106.05(f)).
The dependent claims 2-3, 6, 9, 14-16 and 23 recite additional element(s) that implement the identified abstract idea. Claims 2, 9, 14 and 16 recite a storage device. Claims 3, 6 and 15 recite sensors. Claim 23 recites a therapy circuit. However, these additional elements do not integrate the abstract idea into a practical application because, as stated above, they represent mere instructions to apply the abstract idea on a computer.
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 1-11, 13-18 and 21-23 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 a medical device system, HF predictor circuit and adjusting or controlling a process of the medical device system 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 dependent claims 2-3, 6, 9, 14-16 and 23 recite additional element(s) that implement the identified abstract idea. Claims 2, 9, 14 and 16 recite a storage device. Claims 3, 6 and 15 recite sensors. Claim 23 recites a therapy circuit. However, these functions are not deemed significantly more than the abstract idea because, as stated above, they 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).
Therefore, claims 1-11, 13-18 and 21-23 are rejected under 35 USC §101 as being directed to non-statutory subject matter.
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, 11, 13 and 21-23 are rejected under 35 U.S.C. 103 as being unpatentable over Chase (US 2019/0328966 A1) in further view of Gargaro (US 2022/0296100 A1).
Regarding claim 1, Chase teaches: A medical device system for improved […] monitoring in a patient to improve monitoring resources (e.g. see [0009], [0016]-[0017]), the medical device system comprising:
a […] predictor circuit configured to: generate a monitored sensor trend using […] sensor data […] collected up to a prediction time from the patient; (“a device for controlling the blood glucose levels of a patient, including: a processor programmed to forecast”, e.g. see [0016]-[0017]; determining “current insulin sensitivity” of the patient and “the % change in insulin sensitivity from a prior insulin sensitivity value” based on blood glucose values, e.g. see claim 10, claim 1; [0042] acknowledges the physiological data comes from a sensor by discussing how the system can be used to identify errors, including “glucose or any other sensor error”)
generate a projected sensor trend for the patient over a forecast period of time in future beyond the prediction time using at least the monitored sensor trend of the patient, and a confidence interval about the projected sensor trend based at least in part on the […] sensor data; and (the model “predicts future Sin+1 based on current Sin and the percentage change in SI from Sin-1 to Sin”; forecasting SI over “over each of the next 1, 2, and 3 hours” (i.e. generate a projected sensor trend), e.g. see [0045], [0051]; estimating “the adjustment needed to obtain a blood glucose level between a desired lower and upper confidence interval”, e.g. see [0013]; Fig. 4 illustrates the upper and lower bounds of the forecasted insulin sensitivity, “FIG. 4 shows the forecasted bands (90% CI) of SI at various future times”, e.g. see [0026])
based at least in part on the projected sensor trend and the confidence interval, generate and provide to a user or process a forecast […] of the patient to adjust or control a process of the medical device system. (“predict future distributions of SI” to “to assess an individual's health state and/or adjust treatment”, e.g. see [0009]; “estimating with a multi-dimensional model the adjustment needed to obtain a blood glucose level between a desired lower and upper confidence interval”, e.g. see [0013]; “administering the estimated adjustment to the patient”, e.g. see [0014])
Chase does not teach:
monitoring heart failure (HF)
HF sensor data including cardiac, electrical or mechanical data
future HF status of the patient
However, Gargaro in the analogous art of analyzing physiological data from a patient to predict adverse health outcomes (e.g. see [0011]) teaches:
monitoring heart failure (HF) (“determine a heart failure prediction index” to provide “a prognosis for a heart failure event”, e.g. see [0014], [0005])
HF sensor data including cardiac, electrical or mechanical data (“data is obtained and processed making use of an implantable medical device…the medical device is able to sense signals which relate to cardiac activity”, e.g. see [0014])
future HF status of the patient (provide a prediction index so “it may be decided whether a hospitalization of the patient is required”, e.g. see [0015])
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 Chase to include monitoring heart failure, HF sensor data including cardiac, electrical or mechanical data and the future HF status of the patient as taught by Gargaro, for the purposes of creating a prediction with “an increased sensitivity and an improved specificity” (Gargaro [0009]).
Regarding claim 11, Chase and Gargaro teach the medical device system of claim 1 as described above.
Chase further teaches:
wherein the prediction time includes at least one of: a time of detection of worsening heart failure (WHF) onset; a time of detection of WHF termination; or a periodic prediction time (the insulin sensitivity parameter is “identified hourly”, e.g. see [0047])
Claim 13 recites substantially similar limitations as those already addressed in claim 1, and, as such is rejected for similar reasons as given above.
Regarding claim 21, Chase and Gargaro teach the medical device system of claim 1 as described above.
Chase further teaches:
[…] in response to the projected sensor trend exceeding a first threshold and the confidence interval falling below a second threshold (the method is designed to find a treatment that keeps the projected target glucose within a target band while “ensuring a maximum 5% risk” (i.e. the confidence interval falling below a second threshold) of “predicted BG outcomes below a lower limit” (i.e. the projected sensor trend exceeding a first threshold), e.g. see [0049]; the output of the system to “suggest the determined combination [of insulin and nutrition] to the nurse, e.g. see [0080])
Chase does not teach:
wherein the HF predictor circuit is configured to generate an alert of the forecast of future HF status
However, Gargaro in the analogous art teaches:
wherein the HF predictor circuit is configured to generate an alert of the forecast of future HF status patient (“Based on the heart failure prediction index an alert may be triggered, for example in case the heart failure prediction index exceeds a predefined threshold.”, e.g. see [0015])
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 Chase to include generate an alert of the forecast of future HF status patient as taught by Gargaro, for the purposes of “controlling for inappropriate alert rate” (Gargaro [0008]).
Claim 22 recites substantially similar limitations as those already addressed in claim 21, and, as such is rejected for similar reasons as given above.
Regarding claim 23, Chase and Gargaro teach the medical device system of claim 1 as described above.
Chase further teaches:
comprising a therapy circuit configured to initiate or adjust a therapy to the patient based on the forecast of future HF status or the projected sensor trend (“A device for controlling the blood glucose levels of a patient, comprising: a processor programmed to forecast with a multi-dimensional model…the adjustment needed…after administration of the estimated adjustment to the patient”, e.g. see claim 11; “administering the estimated adjustment to the patient”, e.g. see [0014]).
Claims 2, 3, 10, 14 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Chase, and Gargaro in further view of Sharma (US 2016/0206250 A1).
Regarding claim 2, Chase and Gargaro teach the medical device system of claim 1 as described above.
Chase further teaches:
comprising a storage device configured to store sensor trends data collected from a plurality of patients, the stored sensor trends each including a first portion comprising […] sensor data prior to a prediction time and a subsequent second portion post the prediction time, and (a database is built from physiological data gathered from a “large multinational cohort” and a “plurality of patients”; the stored data consists of “trajectories of [SI] values” (i.e. stored sensor trends), which can be used to create relationships between a patient’s past/current [state] SI(n) (i.e. first portion) and their future [state] SI(n+dt) (i.e. subsequent second portion), e.g. see [0037]-[0038], [0040])
wherein the HF predictor circuit is configured to: based on the monitored sensor trend of the patient, select at least a subset of the stored sensor trends; and generate the projected sensor trend for the patient further using the selected subset of the stored sensor trends (the method involves using “observed future insulin sensitivity values for similar patients or subjects” to inform the prediction for a current patient; the clinical input data used by the model can be derived from a “sub-cohort of individuals, including male groups, or female groups,” e.g. see abstract, [0039]; this data is used to make predictions for the current patient, “predictions could be patient-specific or more generally related to a cohort or similar or otherwise grouped individuals”, e.g. see [0009])
Chase teaches a stored sensor trend of a first portion prior to a prediction time. The selection of a specific window for the first portion, e.g. 14 days, is a matter of design choice. A person of ordinary skill in the art would find it obvious to choose the most optimum window. See MPEP 21044.05. However, reference Sharma has been cited for the purposes of compact prosecution.
Chase and Gargaro do not teach:
a portion comprising 14 days of sensor data prior to a prediction time
However, Sharma in the analogous art of predictive heart failure monitoring (e.g. see [0028]) teaches:
a portion comprising 14 days of sensor data prior to a prediction time (when accumulating and evaluating historical cardiac sensor data (heart rate variability, night heart rate and activity) to determine heart failure status, the accumulation of past data is bounded to “the last 14 days” prior to the evaluation time, e.g. see [0227]-[0229], [0231], [0235]-[0236])
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 Chase and Gargaro to include a portion comprising 14 days of sensor data prior to a prediction time as taught by Sharma, for the purposes of capturing sufficient physiological variance (Sharma [0228], [0231]).
Regarding claim 3, Chase, Gargaro and Sharma teach the medical device system of claim 2 as described above.
Chase further teaches:
the monitored sensor trend includes a composite sensor trend based on […] sensor data from two or more sensors associated with the patient; and the stored sensor trends include at least one composite sensor trend based on sensor data from two or more sensors associated with one of the plurality of patients (“forecast with a multidimensional model” based on clinical input data that includes “current SI and prior SI or % delta SI; current and prior blood glucose levels; current and prior HR, HRV or other metrics accounting for activity or exercise; current diagnosis… or severity score” (this is understood to teach combining data from different types of sensors, e.g. sensors used to measure glucose, ECG, heart rate, activity, etc. ), e.g. see [0017], [0039])
Chase does not teach:
HF sensor data from two or more sensors associated with the patient
However, Gargaro in the analogous art teaches:
HF sensor data from two or more sensors associated with the patient (“process a multiplicity of variables relating to different cardiac characteristics to obtain a multiplicity of processed variables, and to combine such processed variables”, e.g. see [0013]; “data is obtained and processed making use of an implantable medical device”, which “is able to sense signals which relate to cardiac activity”, e.g. see [0014])
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 Chase to include HF sensor data from two or more sensors as taught by Gargaro, for the purposes of “process[ing] a multiplicity of variables relating to different cardiac characteristics” (Gargaro [0013]).
Regarding claim 10, Chase, Gargaro and Sharma teach the medical device system of claim 2 as described above.
Chase further teaches:
wherein the HF predictor circuit is configured to generate, using the selected subset of the stored sensor trends, at least one of an upper bound of the projected sensor trend, a lower bound of the projected sensor trend, or a confidence interval about the projected sensor trend (forecasted bands of insulin sensitivity (SI) at various future times with 90% confidence interval, e.g. see [0025], [0051], Fig. 4; Fig. 4 illustrates the upper and lower bounds of the forecasted insulin sensitivity).
Claim 14 recites substantially similar limitations as those already addressed in claim 2, and, as such is rejected for similar reasons as given above.
Claim 15 recites substantially similar limitations as those already addressed in claim 3, and, as such is rejected for similar reasons as given above.
Claims 4-6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Chase, Gargaro and Sharma in further view of Hayeri (US 2020/0229772 A1).
Regarding claim 4, Chase, Gargaro and Sharma teach the medical device system of claim 2 as described above.
Chase does not teach:
the sensor trend comprising 14 days of sensor data up to the prediction time
However, Sharma in the analogous art teaches:
the sensor trend comprising 14 days of sensor data up to the prediction time (when accumulating and evaluating historical cardiac sensor data (heart rate variability, night heart rate and activity) to determine heart failure status, the accumulation of past data is bounded to “the last 14 days” prior to the evaluation time, e.g. see [0227]-[0229], [0231], [0235]-[0236])
Chase, Gargaro and Sharma do not teach:
determine similarity indices between (1) the monitored sensor trend of the patient comprising […] sensor data up to the prediction time and (2) respective first portions of the stored sensor trends; select at least the subset of the stored sensor trends based on the similarity indices; and
generate the projected sensor trend for the patient using a central tendency of respective second portions of the selected subset of the stored sensor trends
However, Hayeri in the analogous art of physiological property forecasting for detecting disease complications (e.g. see [0003]) teaches:
determine similarity indices between (1) the monitored sensor trend of the patient comprising […] sensor data up to the prediction time and (2) respective first portions of the stored sensor trends; select at least the subset of the stored sensor trends based on the similarity indices; and (comparing a current sensor trend to stored trends to find the best fit; calculating a “comparison value” for each stored trend; the comparison value is a “sum of differences” (i.e. similarity index) “between the blood glucose levels of the considered blood glucose trend and the blood glucose values of the recently sensed blood glucose record”, e.g. see [0117], [0120]; “select at least one of the sets of blood glucose levels based on the comparing performed”; “select the 50 blood glucose trend records that are associated with the lowest comparison values”, e.g. see [0141]-[0142])
generate the projected sensor trend for the patient using a central tendency of respective second portions of the selected subset of the stored sensor trends (generating a function “by averaging changes in blood glucose values over time, as represented by the selected blood glucose trend records”, e.g. see [0146]; calculating a weighted average (i.e. central tendency) of the changes from the selected trends, “The number of blood glucose trend records selected may be chosen such that a weighted average of changes represented by the selected blood glucose trend records can be used as a function to forecast future blood glucose levels.” [0142])
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 Chase, Gargaro and Sharma to include determining similarity indices, selecting the subset of the stored sensor trends based on the similarity indices and generating the projected sensor trend for the patient using a central tendency of respective second portions of the selected subset of the stored sensor trends as taught by Hayeri, for the purposes of “improv[ing] accuracy in forecasting future physiological properties” (Hayeri [0045]).
Regarding claim 5, Chase, Gargaro, Sharma and Hayeri teach the medical device system of claim 4 as described above.
Chase does not teach:
determine the similarity indices using distance metrics or correlation metrics between the monitored sensor trend of the patient and the respective first portions of the stored sensor trends
However, Hayeri in the analogous art teaches:
determine the similarity indices using distance metrics or correlation metrics between the monitored sensor trend of the patient and the respective first portions of the stored sensor trends (the comparison value is a “sum of differences” (i.e. similarity index) “between the blood glucose levels of the considered blood glucose trend and the blood glucose values of the recently sensed blood glucose record”, e.g. see [0117], [0120]; “determine the sum of differences by determining a Euclidean distance” (i.e. distance metrics), e.g. see [0122])
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 Chase and Gargaro to include determine the similarity indices using distance metrics or correlation metrics between the monitored sensor trend of the patient and the respective first portions of the stored sensor trends as taught by Hayeri, for the purposes of “improv[ing] accuracy in forecasting future physiological properties” (Hayeri [0045]).
Regarding claim 6, Chase, Gargaro, Sharma and Hayeri teach the medical device system of claim 4 as described above.
Chase and Gargaro teach the stored sensor trends include a composite sensor trend generated using sensor data from two or more sensors associated with one of the plurality of patients as described above.
Chase does not teach:
identify, from the two or more sensors used for generating the composite sensor trend, one or more sensors with respective sensor trends that are substantially similar to the composite sensor trend;
However, Gargaro in the analogous art teaches:
identify, from the two or more sensors used for generating the composite sensor trend, one or more sensors with respective sensor trends that are substantially similar to the composite sensor trend; (“process a multiplicity of variables relating to different cardiac characteristics to obtain a multiplicity of processed variables, and to combine such processed variables”, e.g. see [0013]; the individual variables do not contribute equally, “nearly 50% of the numerical value of the index…depended on the 24-hour and nocturnal heart rates”, a contribution that was “higher than ventricular extrasystoles, patient activity and AF”, e.g. see [0177]; “the presence of…very high level of the pulmonary fluid index in a month was associated to 4.8-fold higher risk of hospitalization in the subsequent 30 days”, e.g. see [0175]).
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 Chase to include identify, from the two or more sensors used for generating the composite sensor trend, one or more sensors with respective sensor trends that are substantially similar to the composite sensor trend as taught by Gargaro, for the purposes of “a prediction with an increased sensitivity and specificity” (Gargaro [0011]).
Gargaro teaches the identified one or more sensors. Chase and Gargaro do no teach:
determine similarity indices between (1) the monitored sensor trend of the patient and (2) respective first portions of the sensor trends; select a subset of the sensor trends with respective similarity indices exceeding a threshold; and for the patient, generate the projected sensor trend using a central tendency of respective second portions of the selected subset of the sensor trends
However, Hayeri in the analogous art teaches:
determine similarity indices between (1) the monitored sensor trend of the patient and (2) respective first portions of the sensor trends; select a subset of the sensor trends with respective similarity indices exceeding a threshold; and for the patient, generate the projected sensor trend using a central tendency of respective second portions of the selected subset of the sensor trends (comparing a current sensor trend to stored trends to find the best fit; calculating a “comparison value” for each stored trend; the comparison value is a “sum of differences” (i.e. similarity index) “between the blood glucose levels of the considered blood glucose trend and the blood glucose values of the recently sensed blood glucose record”, e.g. see [0117], [0120]; “select at least one of the sets of blood glucose levels based on the comparing performed”; “select the 50 blood glucose trend records that are associated with the lowest comparison values”, e.g. see [0141]-[0142]; generating a function “by averaging changes in blood glucose values over time, as represented by the selected blood glucose trend records”, e.g. see [0146]; calculating a weighted average (i.e. central tendency) of the changes from the selected trends, “The number of blood glucose trend records selected may be chosen such that a weighted average of changes represented by the selected blood glucose trend records can be used as a function to forecast future blood glucose levels.” [0142]).
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 Chase and Gargaro to include determine similarity indices between (1) the monitored sensor trend of the patient and (2) respective first portions of the sensor trends; select a subset of the sensor trends with respective similarity indices exceeding a threshold; and for the patient, generate the projected sensor trend using a central tendency of respective second portions of the selected subset of the sensor trends as taught by Hayeri, for the purposes of “improv[ing] accuracy in forecasting future physiological properties” (Hayeri [0045]).
Claim 16 recites substantially similar limitations as those already addressed in claim 4, and, as such is rejected for similar reasons as given above.
Claims 7-9, 17 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Chase, Gargaro and Sharma in further view of McMahon (US 2018/0272064 A1).
Regarding claim 7, Chase, Gargaro and Sharma teach the medical device system of claim 2 as described above.
Chase, Gargaro and Sharma do not teach:
generate a parametric predictive model representing a relationship between (1) respective first portions of the selected subset of the sensor trends and (2) respective second portions of the selected subset of the sensor trends; and
apply the monitored sensor trend to the parametric predictive model to generate the projected sensor trend for the patient
However, McMahon in the analogous art of identifying risks associated with a medical condition (e.g. see [0005]) teaches:
generate a parametric predictive model representing a relationship between (1) respective first portions of the selected subset of the sensor trends and (2) respective second portions of the selected subset of the sensor trends; and (determining a risk model for a particular condition based on relationships between the measurement data and medical records data across the patient population, using stepwise feature selection (stepwise regression is a feature selection technique, which is an example of parametric predictive model as disclosed in the Specification [0072]) to identify which fields or attributes of the patient measurement data and medical records data are most correlative to or predictive of the occurrence of a particular condition within the patient population, e.g. see [0162])
apply the monitored sensor trend to the parametric predictive model to generate the projected sensor trend for the patient (after identifying the sensor glucose measurement variables (i.e. the selected subset) and medical record variables correlative or predictive of occurrence of a medical condition, determining an equation, function or model for calculating the probability of likelihood of the occurrence of the medical condition of interest for the patient within a limited future prediction horizon, e.g. a risk prediction model; obtaining a patient’s sensor glucose measurement data and electronic medical records data for a patient and applying one or more risk models associated with the particular medical condition to determine the patient’s risk of experiencing the condition, e.g. see [0164]-[0165]; for example, the patient’s risk score indicates a risk of a severe hypoglycemic event (i.e. projected sensor trend), e.g. see [0168])
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 Chase, Gargaro and Sharma to include generating a parametric predictive model and applying the monitored sensor trend to the parametric predictive model as taught by McMahon, for the purposes of identifying which fields or attributes of the patient measurement data and medical records data are most correlative to or predictive of the occurrence of a particular condition within the patient population (McMahon [0162]).
Regarding claim 8, Chase, Gargaro, Sharma and McMahon teach the medical device system of claim 7 as described above.
Chase does not teach:
wherein the parametric predictive model is a regression model
However, McMahon in the analogous art teaches:
wherein the parametric predictive model is a regression model (determining a risk model for a particular condition based on relationships between the measurement data and medical records data across the patient population, using stepwise feature selection (stepwise regression is a feature selection technique, which is an example of parametric predictive model as disclosed in the Specification [0072]) to identify which fields or attributes of the patient measurement data and medical records data are most correlative to or predictive of the occurrence of a particular condition within the patient population, e.g. see [0162]).
Regarding claim 9, Chase, Gargaro and Sharma teach the medical device system of claim 2 as described above.
Chase, Gargaro and Sharma do not teach:
identify, from the plurality of patients with sensor trends stored in the storage device, one or more matching patients with respective demographic or medical history information substantially similar to that of the patient; and select the subset of the stored sensor trends from the one or more matching patients
However, McMahon in the analogous art teaches:
identify, from the plurality of patients with sensor trends stored in the storage device, one or more matching patients with respective demographic or medical history information substantially similar to that of the patient; and select the subset of the stored sensor trends from the one or more matching patients (retrieving from the database a subset of historical patient data for a population of patients and corresponding subset of electronic medical records data for the patient population; the patient population may be tailored for a particular demographic (construed to match the patient) or combination of demographic attributes; for a given medical diagnosis code of interest (construed to be the medical diagnosis of the patient and therefore similar medical history information of the patient), identifying from the patient population the subset of sensor glucose measurements correlative or predictive of occurrence of that medical condition’s diagnostic code, e.g. see [0163], [0161])
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 Chase, Gargaro and Sharma to include identifying one or more matching patients with respective demographic or medical history information substantially similar to that of the patient and selecting the subset of the stored sensor trends from the one or more matching patients as taught by McMahon, for the purposes of tailoring the patient population data based on data of the most interest (McMahon [0161], [0163]).
Claim 17 recites substantially similar limitations as those already addressed in claim 7, and, as such is rejected for similar reasons as given above.
Claim 18 recites substantially similar limitations as those already addressed in claim 9, and, as such is rejected for similar reasons as given above.
Response to Arguments
Regarding the rejection under 35 U.S.C. § 101 of Claims 1-11, 13-18 and 21-23, the Examiner has considered the Applicant’s arguments; however, the arguments are not persuasive.
Applicant argues the “present claims similarly improve the technological field of device-based HF monitoring”.
The Examiner respectfully disagrees. Applicant’s reliance on CardioNet is misplaced. The claims of CardioNet were directed to a specific, improved cardiac monitoring device that was structurally configured to filter out special electrical noise to accurately distinguish between atrial and ventricular fibrillation. It improved the machine’s intrinsic ability to measure a physical phenomenon. In contrast, the present claims do not improve the physical measuring capabilities of the sensors. Instead, they gathered sensor data and apply a mathematical algorithm (calculating a projected trend and confidence interval) to predict a future medical state. This is an abstract process, regardless of whether it is performed in the field of cardiac monitoring.
Applicant argues the claims recite a special “dual-metric forecasting architecture” that provides a concrete improvement.
The Examiner respectfully disagrees. Re-labeling a mathematical concept as an “architecture” does not impart patent eligibility. Calculating a trend line and a statistical confidence interval based on historical data are “Mathematical Concepts” under MPEP 2106.04(a)(2) as described above. The claims merely recite the steps of gathering data, performing standard statistical calculations and outputting the result. There is no specific hardware interaction claimed. It is purely data analysis. 1. Additionally, in response to applicant's argument that the claims recite improvements to medical device functionality, it is noted that the features upon which applicant relies (e.g., efficient memory usage, extended battery life and optimized therapy titration) are not recited in the claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Applicant argues claims 21-22 use dual-threshold logic to differentiate a genuine deterioration, providing concrete clinical decision support.
The Examiner respectfully disagrees. Comparing a calculated numerical value (the trend or the interval) against a predefine limit (a threshold) is a mathematical operation. Merely adding a second threshold (a dual threshold) does not transform the math into a technological solution. The dual-threshold comparison and the resulting clinical decision support (generating the alert) are part of the abstract idea and, by definition, cannot provide a practical application or significantly more.
Regarding the rejection under 35 U.S.C. § 103 of Claims 1-11, 13-18 and 21-23, the Examiner has considered the Applicant’s arguments; however the arguments are not persuasive.
Applicant argues Chase’s confidence intervals represent “statistical uncertainty in predicted SI parameter values” and not confidence intervals about a projected sensor trend.
The Examiner respectfully disagrees. Chase teaches projecting values over a series of future times (e.g. 1, 2, 3 hours in the future), resulting in “forecasted bands (90% CI) of SI at various future times” ([0026]). A time-series progression of forecasted data points constitutes a trend. In addition, insulin sensitivity (SI) is a physiologic metric derived directly from continuous sensor data. When combined with Gargaro’s teachings to process heart failure sensor data, the resulting projected time-series trend is a “projected sensor trend” of HF data, and the 90% confidence bands evaluated at each future step constitute the claimed “confidence interval about the projected sensor trend”.
Applicant argues that claim 1 requires specific logic wherein “a high alert may be generated if the projected sensor trend exceeds a threshold within a specific time period...optionally along with a confidence interval within the specific time period falling below a threshold." (specification, [0077]).
The Examiner respectfully disagrees. Applicant is reading limitations from the specification into the claims. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Claim 1 recites generating a forecast “based at least in part on the projected sensor trend and the confidence interval”. Claim 1 does not recite the “dual-metric threshold logic”.
Applicant argues the combination of Chase and Gargaro do not teach the claimed invention.
The Examiner respectfully disagrees. In response to Applicant's argument that the Examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971). Both Chase and Gargaro are directed to the same field of endeavor of predictive physiologic monitoring to forecast adverse patient events. A person of ordinary skill in the art would be motivated to apply the forecasting architecture of Chase to the heart failure data of Gargaro to improve the accuracy of the heart failure prediction.
Applicant argues Hayeri does not teach the specified “claimed windowed similarity-matching architecture-using pre-prediction and post-prediction temporal portions” and the 14 day window of claim 4.
The Examiner respectfully disagrees. Regarding the 14 day window of claim 4, any arguments directed to amended limitations are moot given the new grounds of rejection as necessitated by amendment. Hayeri teaches comparing the patient’s data to historical records (the “first portion” or “fit” blood glucose values) to find the lowest comparison values/similarity indices ([0141]). Hayeri then teaches generating the forecast using the data from selected records that occur after the fit values. See “averaging changes in blood glucose values over time, as represented by the selected blood glucose trend records…that are after the fit blood glucose values” ([0146]). Taking an average of the post-prediction portions of the selected subset maps to “generate the projected sensor trend for the patient using a central tendency of respective second portions”.
Applicant argues that McMahon does not teach “generating "a parametric predictive model representing a relationship between (1) respective first portions of the selected subset of the sensor trends and (2) respective second portions of the selected subset of the sensor trends" and applying "the monitored sensor trend to the parametric predictive model to generate the projected sensor trend."”
The Examiner respectfully disagrees. In response to Applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). The combination of Chase, Gargaro and Sharma, as cited in the claim that claim 7 depends from (claim 2), already teach the temporal windowing architecture. McMahon is cited to teach the generating and applying of the parametric predictive model of claim 7. McMahon’s output of a risk of a severe hypoglycemic event ([0168]) is the projection that the patient’s glucose sensor is trending downwards across the hypoglycemic threshold.
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 extension fee 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.
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/A.A./Examiner, Art Unit 3686
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681
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