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
Applicant' s arguments, filed 12/29/2025, have been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Applicants have amended their claims, filed 12/29/2025, and therefore rejections newly made in the instant office action have been necessitated by amendment.
As directed by the amendment, claims 1, 7-8, 12, and 19-20 have been amended. Thus claims 1-20 remain pending.
Objections
Claim 20 is objected to because of the following informalities:
In claim 20, line 14: “predicting a next time” is grammatically incorrect with the remainder of the claim and should be “predict a next time”.
Appropriate correction is required.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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, and 9-18 are rejected under 35 U.S.C. 103 as being unpatentable over Davis et al. (US20170311903A1; hereinafter known as “Davis”; previously cited) in view of Agrawal et al. (US20130338630A1; hereinafter known as Agrawal; previously cited) in view of Cobelli et al. (US20140118138; hereinafter known as “Cobelli”), and further in view of Budiman (US20140121488A1; hereinafter “Budiman”).
Regarding claim 1, Davis teaches a device for assisting in therapy delivery (see Davis Figure 45), the device comprising:
a memory configured to store alert data (see Davis [0021], [0247]-[0249], program memory 216 and other memory 218); and
one or more processors configured to (see Davis Figure 45, part 490, analyte processor):
communicatively couple to a glucose sensor to obtain one or more analyte values (see Davis Figure 45, part 10 sensor with electronics which communicated with 490);
obtain projected levels of glucose in a patient over a time frame based on the one or more analyte values from the glucose sensor (see Davis [0030], identifying a current or future diabetic state warranting attention may include measuring a glucose signal signature and comparing the measured signature with a plurality of binned signatures);
determine whether the projected levels of glucose fall outside a prescribed range (see Davis [0006], [0030], [0106], glucose concentration is hovering within a range for a period, or the trace is depicting above or below range);
generate, when the projected levels of glucose in the patient fall outside the prescribed range during the time frame and based on the alert data, a graphical alert indicating that the projected levels of glucose will fall outside the prescribed range (see Davis Figures 7-10; also see Davis [0106], [0182]-[0186], user alerted diabetic state warranting attention and may further provide details of current glucose values, expected glucose values, e.g., expected within a certain timeframe, e.g., 20 minutes);
present the graphical alert to the patient (see Davis Figures 7-10; also see Davis [0106], [0182]-[0186], user alerted diabetic state warranting attention); and
automatically detect occurrence of a maintenance event that modifies the projected levels of glucose (see Davis [0160], [0114], [0105], if system detects insulin delivery, the smart alert can be suspended or delayed to account for the change in hyperglycemic state).
Davis further teaches automatically suppressing or not generating an alert when the user is having a typical glucose response to an event, such as exercising or eating, and teaches automatic suspension or delay of a smart alert upon detection of insulin delivery. However, Davis does not expressly determine revised projected glucose levels based on the maintenance event and then determine that the revised projected glucose levels do not fall outside the prescribed range (see Davis [0104]-[0106], [0160]).
Davis is silent with respect to determine revised projected levels of glucose based on the maintenance event and based on a determination that the revised projected levels of glucose do not fall outside the prescribed range.
Agrawal teaches determine revised projected levels of glucose based on the maintenance event by determining common event occurrences in glucose readings, analyzing glucose readings from the common event occurrence onward in time, determining a glucose level pattern, and adapting glucose level readings to the pattern to form an adapted glucose level pattern (see Agrawal [0006]-[0008], [0139]-[0141]; Figure 3). More specifically, Agrawal teaches determining a current event occurrence, analyzing average glucose level information beginning from a corresponding event occurrence, predicting a current notification event based on the time span from the event occurrence to the notification event, and initiating an action in advance of the predicted current notification event (see Agrawal [0142]-[0146]; Figure 4). Agrawal further teaches a time-shifted current-event example in which a current lunch event occurring later than a prior lunch event is used to predict a corresponding later glucose peak or notification event, thereby providing a prospective event-based glucose projection following the current event occurrence (see Agrawal [0145]-[0146]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to provide Davis with determine revised projected levels of glucose based on the maintenance event, as taught by Agrawal, to modify Davis’s device to determine event-based glucose patterns and corresponding predicted glucose behavior following events such as meals, exercise, and insulin delivery, thereby improving the smart-alert determination of whether the user is having a typical or atypical glucose response and assisting in the management of diabetes therapy (see Davis [0002], [0104]-[0106], [0160]; Agrawal [0006]-[0008], [0139]-[0146]).
Davis in view of Agrawal teaches based on a determination that the revised projected levels of glucose do not fall outside the prescribed range because Davis teaches automatically suppressing, delaying, or not generating a smart alert when event-responsive glucose behavior indicates a typical response or when detected insulin delivery changes the hyperglycemic state, and Agrawal teaches using a current event occurrence to predict corresponding current glucose behavior and a current notification event from event-based average glucose information. Thus, when Agrawal’s event-based prospective glucose projection is applied to Davis’s smart-alert system, the combination determines, after the maintenance event, whether the revised event-based projected glucose behavior still warrants the alert or instead corresponds to a non-alerting condition, such that the alert may be suppressed, delayed, disabled, or not re-generated when the revised projected glucose behavior does not fall outside the prescribed range (see Davis [0104]-[0106], [0160]; Agrawal [0142]-[0146]).
Davis in view of Agrawal is silent with respect to determine a duration of a temporary period of time to disable the previously presented graphical alert and disable the previously presented graphical alert for the temporary period of time having the determined duration without user input.
Cobelli teaches automatic post-alert monitoring and suppression or non-re-alerting after an alert has been provided, without requiring user input, by continuing to monitor sensor data and not sending the alert again until re-alert criteria are met (see Cobelli [0138]-[0143], the alert is sent again when threshold sensor data and/or other re-alert conditions are met, and the user is not re-alerted before the re-alert condition is met). Cobelli also teaches time-period-based active monitoring, including an active monitoring time period such as 20, 40, or 60 minutes, with or without a sub-state (see Cobelli [0150]). Cobelli further teaches post-alert acknowledged-state examples in which additional alerts are not provided for a set time period and in which a transition to an acknowledged state can be based on data analysis, thereby showing both time-period suppression and data-driven state transition embodiments (see Cobelli [0192]-[0193], [0197], [0199]). To the extent Cobelli’s acknowledged-state examples describe user acknowledgement, Davis independently teaches automatic detection of insulin delivery and automatic suspension or delay of a smart alert to account for the changed hyperglycemic state (see Davis [0160], [0114], [0105]).
Davis in view of Agrawal and Cobelli is silent with respect to determining the duration of the temporary disable period based on predictive glucose information.
Budiman teaches determining a duration of a temporary period of time for an alarm delay or suppression period based on glucose-related predictive information. Budiman teaches delaying annunciation of a CGM-based hypoglycemic alarm and determining whether or not the alarm should persist based on glucose level measurements, CGM signal artifact characteristics, and best-estimate physiological states such as plasma glucose, interstitial glucose, insulin onboard, and effective insulin (see Budiman [0088]). Budiman further teaches modifying the length of a delay timer based on prior knowledge of various factors such as glucose level, CGM value, insulin onboard, and the like (see Budiman [0103]). Budiman also teaches that a variable time can be added to delay return into a periodic CGM-based hypoglycemic detection state, and that the duration of the timer is dependent upon a determination of the likelihood of glucose value changes based on the future glucose level profile determined by the control model and the latest finger-stick glucose level value (see Budiman [0103]-[0105]). Budiman further teaches determining timer delay based on glucose concentration, including delay values of 30 minutes, 15 minutes, or 0 minutes (see Budiman [0107]-[0108]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to provide Davis in view of Agrawal and Cobelli with determining the duration of the temporary disable period based on predictive glucose information, as taught by Budiman, in the post-alert suppression framework taught by Cobelli. In particular, Davis and Agrawal teach that therapy-related events, such as insulin delivery, eating, or exercise, can modify projected glucose levels and thereby affect whether an alert should continue to issue. Davis also teaches automatic detection of insulin delivery and automatic suspension or delay of a smart alert to account for the changed glycemic state. Agrawal teaches determining a prospective event-based glucose projection following a current event occurrence. Cobelli teaches automatic post-alert monitoring and suppressing or not re-alerting a user until re-alert criteria are met, as well as time-period-based active monitoring and post-alert suppression examples. Budiman teaches determining the duration of an alarm delay timer based on predictive glucose information, including a future glucose level profile, glucose level, CGM value, and insulin onboard. Thus, one of ordinary skill in the art would have had reason to apply Budiman’s variable timer-duration technique to the alert suppression framework of Davis, Agrawal, and Cobelli so that the system disables or suppresses a previously presented glucose alert, without requiring user input, for a determined duration based on the revised projected glucose condition, thereby reducing nuisance alerts while maintaining clinically safe alerting when the predicted glucose condition indicates that an alert remains warranted.
Regarding claim 2, Davis teaches one or more processors are configured (See Davis Figure 45-part 490 analyte processor) to:
determine based on the one or more analyte values obtained from the glucose sensor a current level of glucose in the patient (See Davis [0030], current diabetic state by measuring a glucose signal signature); and
obtain, based on the current level of glucose, the projected levels of glucose in the patient over the time frame (See Davis [0030], identifying a current or future diabetic state warranting attention may include measuring a glucose signal signature and comparing the measured signature with a plurality of binned signatures, also se Figure 7 and 8).
Regarding claim 3, Davis teaches the one or more processors are (See Davis Figure 45-part 490 analyte processor), when determining the projected levels of glucose (See Davis [0105-106], if system detects events smart alert can be suspended or delayed to account for the change in hyperglycemic state), configured to: automatically detect a meal event indicating that the patient is currently eating a meal (See Davis [0024] [0269], determine if user is eating meal);
obtain, based on the meal event, the projected levels of glucose; and determine that the projected levels of glucose do not fall outside the prescribed range (See Davis [0024] [0105-0106][0127-0128], glucose response to events such as eating determined by machine learning to generate an alerts also see [0030]).
Davis is silent to revised projected levels of glucose based on a maintenance event.
Agarwal teaches revised projected levels of glucose based on maintenance event (See Agarwal Figure 3, the determine common event occurrence in glucose level and analyze the readings to determine a pattern and then calculate the dosage needed).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to provide Davis with revised projected levels of glucose based on maintenance event as taught by Agarwal to modify Davis device to determine glucose patterns based on common events to assist in the management of diabetes therapy (See Davis [0002]).
Regarding claim 4, Davis teaches the one or more processors (See Davis Figure 45-part 490 analyte processor) are configured to:
interface with a wearable computing device worn by the patient to identify movements performed by the patient (See Davis [0214] Figure 45 mobile 18 can be connected to smart watch also see [0214] identify movements using GPS [0132]); and
automatically detect, based on the movements, the meal event indicating that the patient is currently eating a meal (See Davis [0132], GPS determines user location and likely to consume meal).
Regarding claim 5, Davis teaches the one or more processors (See Davis Figure 45-part 490 analyte processor) are, when determining the projected levels of glucose, configured to:
automatically detect an insulin delivery event indicating that the patient has injected insulin; obtain, based on the insulin delivery event, the projected levels of glucose; and determine that the projected levels of glucose do not fall outside the prescribed range (See Davis [0030][0160][0114][0105], if system detects insulin delivery smart alert can be suspended or delayed to account for the change in hyperglycemic state, also see [0104] uses machine learning to suppress or delay the issuance of an alert).
Davis is silent to revised projected levels of glucose based on a maintenance event.
Agarwal teaches revised projected levels of glucose based on maintenance event (See Agarwal Figure 3, the determine common event occurrence in glucose level and analyze the readings to determine a pattern and then calculate the dosage needed).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to provide Davis with revised projected levels of glucose based on maintenance event as taught by Agarwal to modify Davis device to determine glucose patterns based on common events to ensure consistency in the diabetes therapy (See Davis [0002]).
Regarding claim 6, Davis teaches the maintenance event is a first maintenance event (See Davis [0104], when user is having a typical response such as exercising or eating the system uses machine learning to suppress the issuance of an alert), and wherein the one or more processors (See Davis Figure 45-part 490 analyte processor) are further configured to: detect initiation of a second maintenance event (See Davis [0024] [0269], determine if user is eating meal);
determine an amount associated with the second maintenance event; and
determine, an amount of carbohydrates consumed by patient associated with the second maintenance event, is insufficient to keep that the projected levels of glucose within prescribed range (See Davis [See Davis [0024] [0105-0106][0127-0128], glucose response to events such as eating determined by machine learning to generate an alerts also see [0030]).
Davis is silent to second revised projected levels.
Agarwal teaches second revised projected levels of glucose based on maintenance event (See Agarwal Figure 3, the determine common event occurrence in glucose level and analyze the readings to determine a pattern and then calculate the dosage needed).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to provide Davis with second revised projected levels of glucose based on maintenance event as taught by Agarwal to modify Davis device to determine glucose patterns based on common events to ensure consistency in the diabetes therapy (See Davis [0002]). Regarding claim 7, Davis in view of Agrawal, Cobelli, and Budiman as applied to claim 6 teaches wherein the one or more processors are further configured to: present the graphical alert along with an audible alert to the patient prior to detecting initiation of the second maintenance event (see Davis [0176], visual or audible display; see also Davis [0251], the user interface may include an LCD, vibrator, and audio transducer, and audible signals may be provided in response to present and/or predicted hyperglycemic and hypoglycemic conditions).
Davis in view of Agrawal teaches responsive to determining that the second revised projected levels of glucose fall outside the prescribed range despite consumption of the amount of carbohydrates associated with the second maintenance event. As discussed above with respect to claim 6, Davis teaches smart-alert logic based on current or predicted diabetic states warranting attention and automatic alert management based on detected therapy-related or glucose-affecting events. Agrawal teaches event-based glucose pattern prediction after a current event occurrence, including using time-shifted glucose patterns following a current event to predict a future notification event. Accordingly, Davis in view of Agrawal teaches determining, after the second maintenance event, whether the second revised projected glucose levels remain outside the prescribed range despite the amount associated with the second maintenance event.
Davis further teaches automatically silencing the previously presented audible alert. Davis teaches that an audible signal may be silenced while alert information remains available, thereby informing the user of the glucose condition while avoiding the annoyance of the audible alert (see Davis [0251]). Davis also teaches that the user interface may include multiple alert-output components, including an LCD, vibrator, and audio transducer, and that other alerting mechanisms may be used, including audible, tactile, or visual alerts for a predetermined period of time (see Davis [0251], [0253]). Davis further teaches that distinctive haptic or vibratory patterns may be rendered according to relative urgency or safety concerns (see Davis [0254]-[0255]). Thus, Davis teaches or suggests silencing the audible component of a previously presented alert while providing a haptic or vibratory alert in its place. In the Davis and Agrawal system, the silencing is automatic because the change in alert output is responsive to the system's automatic determination that the second revised projected glucose levels remain outside the prescribed range despite the second maintenance event, rather than responsive to a user request to silence the alert.
Budiman further teaches for a second temporary period of time. As discussed above with respect to claim 1, Budiman teaches determining timer durations for alarm delay or suppression based on predicted glucose behavior, including modifying the length of a delay timer based on glucose level, CGM value, insulin onboard, and a future glucose level profile determined by a control model (see Budiman [0103]-[0105]). Although claim 1 applies the temporary period to disabling or suppressing a previously presented graphical alert when the revised projected glucose levels do not fall outside the prescribed range, claim 7 applies a second temporary period to a downgraded alert mode when the second revised projected glucose levels still fall outside the prescribed range. Budiman's teaching of determining a timer duration based on predicted glucose behavior is applicable to both contexts because both involve determining how long an alert state or alarm-output state should be modified based on the predicted glucose profile.
Accordingly, Davis in view of Agrawal, Cobelli, and Budiman teaches or suggests automatically silencing the previously presented audible alert for a second temporary period of time by replacing the previously presented audible alert with a haptic alert. Davis teaches silencing an audible alert and using haptic or vibratory alert modalities. Budiman teaches determining the duration of an alarm delay or suppression timer based on predicted glucose behavior. Cobelli teaches post-alert monitoring and reducing unnecessary repeat alerts while maintaining sufficient alerting when the glycemic condition warrants further attention. Thus, one of ordinary skill in the art would have had reason to automatically silence the previously presented audible alert for a second temporary period and replace the audible alert with a haptic alert when the system determines that the second revised projected glucose levels remain outside the prescribed range despite the second maintenance event. Such a modification would reduce audible-alert annoyance while maintaining a non-audible warning, and Davis's urgency-based haptic or vibratory patterns would have provided an appropriate replacement modality for continued alerting when the glucose condition remains outside the prescribed range despite the corrective carbohydrate event.
Regarding claim 9, Davis teaches the graphical alert further includes a prompt for a user to clear the graphical alert for the temporary period of time via user input (See Davis Figure 31).
Regarding claim 10, Davis teaches the one or more processors (See Davis Figure 45-part 490 analyte processor) are configured to:
determine a duration until the projected levels of glucose are projected to fall outside the prescribed range; and determine, based on the duration, the temporary period of time by which to automatically clear the graphical alert (See Davis [0160], if a hyperglycemic diabetic state warranting attention occurs, but the smart alerts functionality app uses data from an insulin sensor to detect that there is a degree of insulin on board, then the smart alerts functionality may suppress an alert until such time as it is determined that the current insulin is no longer able to control the hyperglycemia and that the user is not cognitively aware of a need for more.).
Davis is silent to revised projected levels.
Agarwal teaches revised projected levels of glucose (See Agarwal Figure 3, the determine common event occurrence in glucose level and analyze the readings to determine a pattern and then calculate the dosage needed).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to provide Davis with revised projected levels of as taught by Agarwal to modify Davis device to determine glucose patterns based on common events to ensure consistency in the diabetes therapy (See Davis [0002]). Regarding claim 11, Davis teaches the prescribed range (See Davis [0006]) includes values between a lower threshold identifying a hypoglycemic condition for the patient and an upper threshold identifying a hyperglycemic condition for the patient (See Davis [0006][0087][0162][0168][0186], alerted when a patient above 200mg/dl and below 80mg/dl, . For example, a user's high alert threshold may be set at 180 mg/dL low threshold 55md/dl).
Regarding claim 12, Davis teaches a method for assisting in therapy delivery (see Davis Figure 45), the method comprising:
communicatively couple to a glucose sensor to obtain one or more analyte values (see Davis Figure 45, part 10 sensor with electronics which communicated with 490);
obtaining, by one or more processors, projected levels of glucose in a patient over a time frame based on the one or more analyte values from the glucose sensor (see Davis [0030], identifying a current or future diabetic state warranting attention may include measuring a glucose signal signature and comparing the measured signature with a plurality of binned signatures);
determining, by the one or more processors, whether the projected levels of glucose fall outside a prescribed range (see Davis [0006], [0030], [0106], glucose concentration is hovering within a range for a period, or the trace is depicting above or below range);
generating, by the one or more processors, when the projected levels of glucose in the patient fall outside the prescribed range, and based on alert data, a graphical alert indicating that the projected levels of glucose will fall outside the prescribed range (see Davis Figures 7-10; also see Davis [0106], [0182]-[0186], user alerted diabetic state warranting attention and may further provide details of current glucose values, expected glucose values, e.g., expected within a certain timeframe, e.g., 20 minutes);
causing presentation of the graphical alert to the patient (see Davis Figures 7-10; also see Davis [0106], [0182]-[0186], user alerted diabetic state warranting attention); and
automatically detecting occurrence of a maintenance event that modifies the projected levels of glucose (see Davis [0160], [0114], [0105], if system detects insulin delivery, the smart alert can be suspended or delayed to account for the change in hyperglycemic state).
Davis further teaches automatically suppressing or not generating an alert when the user is having a typical glucose response to an event, such as exercising or eating, and teaches automatic suspension or delay of a smart alert upon detection of insulin delivery. However, Davis does not expressly determine revised projected glucose levels based on the maintenance event (see Davis [0104]-[0106], [0160]).
Davis is silent with respect to determining, by the one or more processors, revised projected levels of glucose based on the maintenance event.
Agrawal teaches determining, by the one or more processors, revised projected levels of glucose based on the maintenance event by determining common event occurrences in glucose readings, analyzing glucose readings from the common event occurrence onward in time, determining a glucose level pattern, and adapting glucose level readings to the pattern to form an adapted glucose level pattern (see Agrawal [0006]-[0008], [0139]-[0141]; Figure 3). More specifically, Agrawal teaches determining a current event occurrence, analyzing average glucose level information beginning from a corresponding event occurrence, predicting a current notification event based on the time span from the event occurrence to the notification event, and initiating an action in advance of the predicted current notification event (see Agrawal [0142]-[0146]; Figure 4). Agrawal further teaches a time-shifted current-event example in which a current lunch event occurring later than a prior lunch event is used to predict a corresponding later glucose peak or notification event, thereby providing a prospective event-based glucose projection following the current event occurrence (see Agrawal [0145]-[0146]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to provide Davis with determining revised projected levels of glucose based on the maintenance event, as taught by Agrawal, to modify Davis's method to determine event-based glucose patterns and corresponding predicted glucose behavior following events such as meals, exercise, and insulin delivery, thereby improving the smart-alert determination of whether the user is having a typical or atypical glucose response and assisting in the management of diabetes therapy (see Davis [0002], [0104]-[0106], [0160]; Agrawal [0006]-[0008], [0139]-[0146]).
Davis in view of Agrawal teaches determining that the revised projected levels of glucose are within the prescribed range because Davis teaches automatically suppressing, delaying, or not generating a smart alert when event-responsive glucose behavior indicates a typical response or when detected insulin delivery changes the hyperglycemic state, and Agrawal teaches using a current event occurrence to predict corresponding current glucose behavior and a current notification event from event-based average glucose information. Thus, when Agrawal's event-based prospective glucose projection is applied to Davis's smart-alert method, the combination determines, after the maintenance event, whether the revised event-based projected glucose behavior still warrants the alert or instead corresponds to a non-alerting condition, such that the alert may be suppressed, delayed, disabled, or not re-generated when the revised projected glucose behavior is within the prescribed range (see Davis [0104]-[0106], [0160]; Agrawal [0142]-[0146]).
Davis in view of Agrawal is silent with respect to determining a duration of a temporary period of time to disable the previously presented graphical alert and automatically disabling the previously presented graphical alert for the temporary period of time having the determined duration.
Cobelli teaches automatic post-alert monitoring and suppression or non-re-alerting after an alert has been provided, without requiring user input, by continuing to monitor sensor data and not sending the alert again until re-alert criteria are met (see Cobelli [0138]-[0143], the alert is sent again when threshold sensor data and/or other re-alert conditions are met, and the user is not re-alerted before the re-alert condition is met). Cobelli also teaches time-period-based active monitoring, including an active monitoring time period such as 20, 40, or 60 minutes, with or without a sub-state (see Cobelli [0150]). Cobelli further teaches post-alert acknowledged-state examples in which additional alerts are not provided for a set time period and in which a transition to an acknowledged state can be based on data analysis, thereby showing both time-period suppression and data-driven state transition embodiments (see Cobelli [0192]-[0193], [0197], [0199]). To the extent Cobelli's acknowledged-state examples describe user acknowledgement, Davis independently teaches automatic detection of insulin delivery and automatic suspension or delay of a smart alert to account for the changed hyperglycemic state (see Davis [0160], [0114], [0105]).
Davis in view of Agrawal and Cobelli is silent with respect to determining the duration of the temporary disable period based on predictive glucose information.
Budiman teaches determining a duration of a temporary period of time for an alarm delay or suppression period based on glucose-related predictive information. Budiman teaches delaying annunciation of a CGM-based hypoglycemic alarm and determining whether or not the alarm should persist based on glucose level measurements, CGM signal artifact characteristics, and best-estimate physiological states such as plasma glucose, interstitial glucose, insulin onboard, and effective insulin (see Budiman [0088]). Budiman further teaches modifying the length of a delay timer based on prior knowledge of various factors such as glucose level, CGM value, insulin onboard, and the like (see Budiman [0103]). Budiman also teaches that a variable time can be added to delay return into a periodic CGM-based hypoglycemic detection state, and that the duration of the timer is dependent upon a determination of the likelihood of glucose value changes based on the future glucose level profile determined by the control model and the latest finger-stick glucose level value (see Budiman [0103]-[0105]). Budiman further teaches determining timer delay based on glucose concentration, including delay values of 30 minutes, 15 minutes, or 0 minutes (see Budiman [0107]-[0108]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to provide Davis in view of Agrawal and Cobelli with determining the duration of the temporary disable period based on predictive glucose information, as taught by Budiman, in the post-alert suppression framework taught by Cobelli. In particular, Davis and Agrawal teach that therapy-related events, such as insulin delivery, eating, or exercise, can modify projected glucose levels and thereby affect whether an alert should continue to issue. Davis also teaches automatic detection of insulin delivery and automatic suspension or delay of a smart alert to account for the changed glycemic state. Agrawal teaches determining a prospective event-based glucose projection following a current event occurrence. Cobelli teaches automatic post-alert monitoring and suppressing or not re-alerting a user until re-alert criteria are met, as well as time-period-based active monitoring and post-alert suppression examples. Budiman teaches determining the duration of an alarm delay timer based on predictive glucose information, including a future glucose level profile, glucose level, CGM value, and insulin onboard. Thus, one of ordinary skill in the art would have had reason to apply Budiman's variable timer-duration technique to the alert suppression framework of Davis, Agrawal, and Cobelli so that the method automatically disables or suppresses a previously presented glucose alert, without requiring user input, for a determined duration based on the revised projected glucose condition, thereby reducing nuisance alerts while maintaining clinically safe alerting when the predicted glucose condition indicates that an alert remains warranted.
Regarding claim 13, Davis teaches obtaining the projected levels of glucose comprises: obtaining, from the glucose sensor, a current level of glucose in the patient (See Davis [0030], current diabetic state by measuring a glucose signal signature); and
obtaining, based on the current level of glucose, the projected levels of glucose in the patient over the time frame (See Davis [0030], identifying a future diabetic state warranting attention may include measuring a glucose signal signature and comparing the measured signature with a plurality of binned signatures, also se Figure 7 and 8).
Regarding claim 14, Davis teaches determining that the maintenance event alters the projected levels of glucose (See Davis [0105-106], if system detects events smart alert can be suspended or delayed to account for the change in hyperglycemic state) comprises: automatically detecting a meal event indicating that the patient is currently eating a meal (See Davis [0024] [0269], determine if user is eating meal;);
obtaining, based on the meal event, a version of the projected levels of glucose; and
determining that the projected levels of glucose do not fall outside the prescribed range (See Davis [0024] [0105-0106][0127-0128], glucose response to events such as eating determined by machine learning to generate an alerts also see [0030]).
Davis is silent to revised projected levels of glucose based on a maintenance event.
Agarwal teaches revised projected levels of glucose based on maintenance event (See Agarwal Figure 3, the determine common event occurrence in glucose level and analyze the readings to determine a pattern and then calculate the dosage needed).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to provide Davis with revised projected levels of glucose based on maintenance event as taught by Agarwal to modify Davis device to determine glucose patterns based on common events to ensure consistency in the diabetes therapy(See Davis [0002]).
Regarding claim 15, Davis teaches automatically detecting the meal event comprises: interfacing with a wearable computing device worn by the patient (See Davis [0214] Figure 45 mobile 18 can be connected to smart watch also see [0214] identify movements using GPS [0132]) to identify movements performed by the patient; and
automatically detecting, based on the movements, the meal event indicating that the patient is currently eating a meal (See Davis [0132], GPS determines user location and likely to consume meal).
Regarding claim 16, Davis teaches determining the projected levels of glucose comprises: automatically detecting an insulin delivery event indicating that the patient has injected insulin; obtaining, based on the insulin delivery event, a revised version of the projected levels of glucose; and determining that the projected levels of glucose do not fall outside the prescribed range (See Davis [0030][0160][0114][0105], if system detects insulin delivery smart alert can be suspended or delayed to account for the change in hyperglycemic state, also see [0104] uses machine learning to suppress or delay the issuance of an alert).
Davis is silent to revised projected levels of glucose based on a maintenance event.
Agarwal teaches revised projected levels of glucose based on maintenance event (See Agarwal Figure 3, the determine common event occurrence in glucose level and analyze the readings to determine a pattern and then calculate the dosage needed).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to provide Davis with revised projected levels of glucose based on maintenance event as taught by Agarwal to modify Davis device to determine glucose patterns based on common events to ensure consistency in the diabetes therapy(See Davis [0002]).
Regarding claim 17, Davis teaches the maintenance event is a first maintenance event (See Davis [0104], when user is having a typical response such as exercising or eating the system uses machine learning to suppress the issuance of an alert), and wherein the one or more processors (See Davis Figure 45-part 490 analyte processor) are further configured to: detect initiation of a second maintenance event (See Davis [0024] [0269], determine if user is eating meal);
determine an amount carbohydrates consumed by the patient associated with the second maintenance event; and
determine, based on the amount of carbohydrates associated with the second maintenance event, that the projected levels of glucose will fall outside the prescribed range (See Davis [See Davis [0024] [0105-0106][0127-0128], glucose response to events such as eating determined by machine learning to generate an alerts also see [0030]).
Davis is silent to second revised projected levels.
Agarwal teaches second revised projected levels of glucose based on maintenance event (See Agarwal Figure 3, the determine common event occurrence in glucose level and analyze the readings to determine a pattern and then calculate the dosage needed).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to provide Davis with revised projected levels of glucose based on maintenance event as taught by Agarwal to modify Davis device to determine glucose patterns based on common events to ensure consistency in the diabetes therapy (See Davis [0002]).
Regarding claim 18, Davis teaches wherein the one or more processors (See Davis Figure 45-part 490 analyte processor) are further configured to:
present the graphical alert along with an audible alert to the patient (See Davis [0176], can be visual or audible display),
present, responsive to determining that the projected levels of glucose will fall outside the prescribed range (See Davis Figure 7-10, also see [0182-0186], user alerted diabetic state warranting attention and may further provide details of current glucose values, expected glucose values, e.g., expected within a certain timeframe, e.g., 20 minutes), a haptic alert in place of the audible alert such that the audible alert is cleared for the temporary period of time (See Davis [0215]).
Davis is silent to second revised projected levels.
Agarwal teaches second revised projected levels of glucose based on maintenance event (See Agarwal Figure 3, the determine common event occurrence in glucose level and analyze the readings to determine a pattern and then calculate the dosage needed).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to provide Davis with revised projected levels of glucose based on maintenance event as taught by Agarwal to modify Davis device to determine glucose patterns based on common events to assist in the management of diabetes therapy (See Davis [0002]).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Davis et al. (US20170311903A1; hereinafter known as “Davis”; previously cited) in view of Agrawal et al. (US20130338630A1; hereinafter known as Agrawal; previously cited) in view of Cobelli et al. (US20140118138; hereinafter known as “Cobelli”), and further in view of Budiman (US20140121488A1; hereinafter “Budiman”), and further in view of Rankers (US20080300572A1; hereinafter “Rankers”).
Davis in view of Agrawal, Cobelli, and Budiman teach claims 1 as shown above.
Regarding claim 8, Davis in view of Agrawal, Cobelli, and Budiman as applied to claim 1 does not expressly teach wherein the one or more processors are further configured to: predict a next time a hyperglycemic event or a hypoglycemic event will occur based on the revised projected levels of glucose and determine the temporary period of time based on the predicted next time that the hyperglycemic event or the hypoglycemic event will occur such that the previously presented graphical alert is presented again prior to the predicted next time that the hyperglycemic event or the hypoglycemic event will occur.
Rankers et al. (US 2008/0300572 A1; hereinafter “Rankers”) teaches predicting a next time a glucose event will occur. Rankers teaches receiving sensor data corresponding to empirical blood glucose measurements, estimating future blood glucose measurements based on the empirical blood glucose measurements, generating a predictive blood glucose graph indicating the estimated future blood glucose measurements, determining whether there is a glucose alarm prediction, obtaining an estimated alarm time, and generating an indicator of the estimated alarm time for display with the predictive blood glucose graph (see Rankers Figure 20; see also Rankers [0194]-[0195]). Rankers further teaches that the predictive graph display process predicts whether future glucose measurements will leave the patient’s target glucose zone within a designated period of time in the future and obtains an estimated time corresponding to when the future glucose measurement will leave the target zone (see Rankers [0194]). Because leaving the target glucose zone includes crossing either an upper glucose boundary or a lower glucose boundary, Rankers teaches predicting the next time a hyperglycemic or hypoglycemic event will occur by determining an estimated time at which future glucose will leave the target zone.
In the modified Davis and Agrawal system, the future glucose estimates used to determine whether and when the glucose will leave the target zone correspond to the revised projected glucose levels determined after the maintenance event. As discussed above with respect to claim 1, Davis teaches smart-alert logic based on current or predicted diabetic states warranting attention and automatic alert management based on detected therapy-related or glucose-affecting events, and Agrawal teaches event-based glucose pattern prediction after a current event occurrence, including using time-shifted glucose patterns following a current event to predict a future notification event. Accordingly, Davis in view of Agrawal and Rankers teaches predict a next time a hyperglycemic event or a hypoglycemic event will occur based on the revised projected levels of glucose.
Rankers teaches presenting an alarm or warning before the predicted glucose event occurs. Rankers teaches that, if the predictive graph display process predicts an alarm condition, then the monitor may generate and display an appropriate alarm screen, and that the alarm screen may serve as a warning to the user to expect a glucose alarm in the near future (see Rankers [0195]). Thus, Rankers teaches presenting an alarm or warning before the future glucose measurement actually leaves the target zone.
Budiman teaches determining a duration of an alarm delay or suppression timer based on predicted future glucose behavior. As discussed above with respect to claim 1, Budiman teaches modifying the length of a delay timer based on glucose level, CGM value, insulin onboard, and the like (see Budiman [0103]), and teaches that the duration of the timer is dependent upon a determination of the likelihood of glucose value changes based on the future glucose level profile determined by the control model and the latest finger-stick glucose level value (see Budiman [0103]-[0105]). Thus, Budiman teaches determining an alarm delay or suppression duration based on a predicted future glucose profile.
Cobelli teaches post-alert monitoring and re-alerting after an alert has already been presented. As discussed above with respect to claim 1, Cobelli teaches dynamically and intelligently monitoring a host’s glycemic condition after an alert is triggered, including determining when to re-alert and/or determining a state change, and reactivating an alert state during an acknowledgement or post-alert monitoring period when reactivation criteria are met (see Cobelli Figures 8-10; see also Cobelli [0150], [0192]-[0193], [0197], [0199]). Thus, Cobelli teaches the re-presentation or re-alerting framework for a previously presented alert after suppression or post-alert monitoring.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the present application to provide Davis in view of Agrawal, Cobelli, and Budiman with predict a next time a hyperglycemic event or a hypoglycemic event will occur based on the revised projected levels of glucose and determine the temporary period of time based on the predicted next time that the hyperglycemic event or the hypoglycemic event will occur such that the previously presented graphical alert is presented again prior to the predicted next time that the hyperglycemic event or the hypoglycemic event will occur, as taught or suggested by Rankers. Davis and Agrawal teach automatically managing glucose alerts based on projected and revised projected glucose behavior following glucose-affecting events. Cobelli teaches suppressing or not re-alerting a user after an alert has been triggered while continuing to monitor whether re-alerting is warranted. Budiman teaches determining the duration of an alarm delay or suppression timer based on predicted future glucose behavior. Rankers teaches determining an estimated alarm time corresponding to when future glucose will leave a target zone and presenting a warning before the future glucose measurement leaves the target zone. Thus, one of ordinary skill in the art would have had reason to use Rankers’s estimated alarm time in the Davis, Agrawal, Cobelli, and Budiman system as a timing constraint for the temporary alert-disable period taught by Budiman and Cobelli, so that the previously presented graphical alert remains suppressed while the predicted event is not yet imminent, but is presented again before the predicted hyperglycemic or hypoglycemic event. Such a modification would reduce nuisance alerts while preserving timely warning of the predicted glycemic event.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Davis et al. (US20170311903A1; hereinafter known as “Davis”; previously cited) in view of Agrawal et al. (US20130338630A1; hereinafter known as Agrawal; previously cited) in view of Cobelli et al. (US20140118138; hereinafter known as “Cobelli”), and further in view of Budiman (US20140121488A1; hereinafter “Budiman”), and further in view of Rankers (US20080300572A1; hereinafter “Rankers”), and further in view of Manetta et al. (US0170039319A1; hereinafter “Manetta”).
Davis in view of Agrawal, Cobelli, and Budiman teach claims 12 as shown above.
Regarding claim 19, Davis in view of Agrawal, Cobelli, and Budiman as applied to claim 12 does not expressly teach wherein predicting a time a next hyperglycemic event or hypoglycemic event will occur is based on the revised projected levels of glucose and determining the temporary period of time to disable the previously presented graphical alert is as a predetermined percentage of a duration of time until the predicted time.
Rankers teaches predicting a time a next hyperglycemic event or hypoglycemic event will occur. Rankers teaches receiving sensor data corresponding to empirical blood glucose measurements, estimating future blood glucose measurements based on the empirical blood glucose measurements, generating a predictive blood glucose graph indicating the estimated future blood glucose measurements, determining whether there is a glucose alarm prediction, obtaining an estimated alarm time, and generating an indicator of the estimated alarm time for display with the predictive blood glucose graph (see Rankers Figure 20; see also Rankers [0194]-[0195]). Rankers further teaches that the predictive graph display process predicts whether future glucose measurements will leave the patient’s target glucose zone within a designated period of time in the future and obtains an estimated time corresponding to when the future glucose measurement will leave the target zone (see Rankers [0194]). Because leaving the target glucose zone includes crossing either an upper glucose boundary or a lower glucose boundary, Rankers teaches predicting a time a next hyperglycemic event or hypoglycemic event will occur by determining an estimated time at which future glucose will leave the target zone.
In the modified Davis and Agrawal system, the future glucose estimates used to determine whether and when the glucose will leave the target zone correspond to the revised projected glucose levels determined after the maintenance event. As discussed above with respect to claim 12, Davis teaches smart-alert logic based on current or predicted diabetic states warranting attention and automatic alert management based on detected therapy-related or glucose-affecting events, and Agrawal teaches event-based glucose pattern prediction after a current event occurrence, including using time-shifted glucose patterns following a current event to predict a future notification event. Accordingly, Davis in view of Agrawal and Rankers teaches predicting a time a next hyperglycemic event or hypoglycemic event will occur based on the revised projected levels of glucose.
Rankers further teaches determining a duration of time until the predicted time. Rankers teaches that the monitor may obtain an estimated time corresponding to when the patient’s future glucose level will leave the target zone, that the predictive glucose graph may include an indicator of the estimated time, and that the monitor may generate, maintain, and display a countdown timer corresponding to the estimated time (see Rankers [0194]). Thus, Rankers teaches determining the duration of time remaining until the predicted time at which the hyperglycemic event or hypoglycemic event will occur.
Budiman teaches determining alarm-delay or suppression timing using predetermined percentage-based timing adjustment. Budiman teaches determining timer delay based on glucose concentration, including delay values of 30 minutes, 15 minutes, or 0 minutes (see Budiman [0107]-[0108]). Budiman further teaches that, when there is agreement between the user’s blood glucose concentration determined by the CGM sensor and the best-estimate prediction that a hypoglycemic event is likely, and as the likelihood of a hypoglycemic event increases to a higher risk, the alarm mechanism can be implemented with a 50% shortening of the alarm delay (see Budiman [0109]; Table 2). Budiman also teaches corresponding percentage-based shortening for hyperglycemia, including delay values of 15 minutes, 7.5 minutes, and 3.75 minutes (see Budiman Table 5). Thus, Budiman teaches using a predetermined percentage to modify an alarm-delay duration based on predicted glucose risk.
Manetta teaches using a predetermined percentage of a medically relevant time interval to determine a patient-monitoring status. Manetta teaches displaying the status of medical lines for a patient and determining a status that characterizes a time until a target usable time of the medical line will be reached or whether the target usable time has been reached (see Manetta Abstract; [0004], [0034]). Manetta further teaches that the status can indicate that the target usable time is approaching when the medical line has been used on the patient for more than a predefined portion of the target usable time, for example, greater than 80% of the target usable time (see Manetta [0034]). Manetta also teaches dynamically determining and updating the status over time, including displaying green until 80% of the target usable time has elapsed and yellow until 100% has elapsed (see Manetta [0040]). Thus, Manetta is relied upon to show that using a predetermined percentage of a clinically relevant time interval to determine a medical monitoring timing/status state was a known timing technique. Manetta is reasonably pertinent because it addresses automatic patient-monitoring GUI status updates based on a clinically relevant time interval, which is the same type of timing-control problem presented by determining how long a patient alert should remain in a modified state before re-presentation.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the present application to provide Davis in view of Agrawal, Cobelli, Budiman, and Rankers with determining the temporary period of time to disable the previously presented graphical alert as a predetermined percentage of a duration of time until the predicted time, as taught or suggested by Rankers, Budiman, and Manetta. Davis and Agrawal teach automatically managing glucose alerts based on projected and revised projected glucose behavior following glucose-affecting events. Cobelli teaches automatic post-alert monitoring and suppressing or not re-alerting a user after an alert has been triggered while continuing to monitor whether re-alerting is warranted. Rankers teaches determining an estimated time at which the future glucose level will leave the target zone and generating a countdown timer corresponding to the estimated time. Budiman teaches determining alarm-delay timing based on predicted glucose behavior and modifying an alarm-delay duration using a predetermined percentage, such as a 50% shortening of the alarm delay when glucose risk increases (see Budiman [0109]; Table 2). Manetta confirms that predetermined percentages of clinically relevant time intervals were known for medical monitoring status timing.
Thus, one of ordinary skill in the art would have had reason to use Rankers’s time remaining until the predicted glucose event as the clinically relevant time interval for determining the temporary alert-disable period, and to apply the known percentage-based timing techniques taught by Budiman and Manetta to that interval. Rankers already provides the time remaining until the predicted glucose event, Budiman teaches percentage-based shortening of a glucose alarm delay when predicted glucose risk increases, and Manetta confirms that predetermined percentages of clinically relevant time intervals were known for medical monitoring status timing. The modification would have predictably caused the graphical alert to remain disabled for only a predetermined fractional portion of the time before the predicted hyperglycemic or hypoglycemic event, thereby reducing nuisance re-alerts while ensuring that the graphical alert can be re-presented before the predicted event occurs.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Davis et al. (US20170311903A1; hereinafter known as “Davis”; previously cited) in view of Agrawal et al. (US20130338630A1; hereinafter known as Agrawal; previously cited) in view of Rankers (US20080300572A1; hereinafter “Rankers”) in view of Cobelli et al. (US20140118138; hereinafter known as “Cobelli”) in view of Budiman (US20140121488A1; hereinafter “Budiman”).
Regarding claim 20, Davis teaches a non-transitory computer-readable storage medium having instructions stored thereon that, when executed, cause one or more processors to perform alert-related therapy delivery functions (see Davis Figure 45, part 490, analyte processor; see also Davis [0021], [0247]-[0249], program memory 216 and other memory 218), including:
communicatively couple to a glucose sensor to obtain one or more analyte values (see Davis Figure 45, part 10 sensor with electronics which communicated with 490);
obtain projected levels of glucose in a patient over a time frame based on the one or more analyte values from the glucose sensor (see Davis [0030], identifying a current or future diabetic state warranting attention may include measuring a glucose signal signature and comparing the measured signature with a plurality of binned signatures);
determine whether the projected levels of glucose fall outside a prescribed range (see Davis [0006], [0030], [0106], glucose concentration is hovering within a range for a period, or the trace is depicting above or below range);
generate, when the projected levels of glucose in the patient fall outside the prescribed range and based on an alert template, a graphical alert indicating that the projected levels of glucose will fall outside the prescribed range (see Davis Figures 7-10; also see Davis [0106], [0182]-[0186], user alerted diabetic state warranting attention and may further provide details of current glucose values, expected glucose values, e.g., expected within a certain timeframe, e.g., 20 minutes; see also Davis [0021], [0247]-[0249], program memory 216 and other memory 218). Davis's stored alert data and recurring graphical alert display formats teach or suggest the claimed alert template.
cause presentation of the graphical alert to the patient (see Davis Figures 7-10; also see Davis [0106], [0182]-[0186], user alerted diabetic state warranting attention); and
automatically detect occurrence of a maintenance event that modifies the projected levels of glucose (see Davis [0160], [0114], [0105], if system detects insulin delivery, the smart alert can be suspended or delayed to account for the change in hyperglycemic state).
Davis further teaches automatically suppressing or not generating an alert when the user is having a typical glucose response to an event, such as exercising or eating, and teaches automatic suspension or delay of a smart alert upon detection of insulin delivery. However, Davis does not expressly determine revised projected glucose levels based on the maintenance event and then determine the timing of a next hyperglycemic or hypoglycemic event based on the revised projected glucose levels (see Davis [0104]-[0106], [0160]).
Davis is silent with respect to determine revised projected levels of glucose based on the maintenance event.
Agrawal teaches determining revised projected levels of glucose based on the maintenance event by determining common event occurrences in glucose readings, analyzing glucose readings from the common event occurrence onward in time, determining a glucose level pattern, and adapting glucose level readings to the pattern to form an adapted glucose level pattern (see Agrawal [0006]-[0008], [0139]-[0141]; Figure 3). More specifically, Agrawal teaches determining a current event occurrence, analyzing average glucose level information beginning from a corresponding event occurrence, predicting a current notification event based on the time span from the event occurrence to the notification event, and initiating an action in advance of the predicted current notification event (see Agrawal [0142]-[0146]; Figure 4). Agrawal further teaches a time-shifted current-event example in which a current lunch event occurring later than a prior lunch event is used to predict a corresponding later glucose peak or notification event, thereby providing a prospective event-based glucose projection following the current event occurrence (see Agrawal [0145]-[0146]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to provide Davis with determining revised projected levels of glucose based on the maintenance event, as taught by Agrawal, to modify Davis's computer-readable storage medium instructions to determine event-based glucose patterns and corresponding predicted glucose behavior following events such as meals, exercise, and insulin delivery, thereby improving the smart-alert determination of whether the user is having a typical or atypical glucose response and assisting in the management of diabetes therapy (see Davis [0002], [0104]-[0106], [0160]; Agrawal [0006]-[0008], [0139]-[0146]).
Davis in view of Agrawal is silent with respect to predicting a next time a hyperglycemic event or a hypoglycemic event will occur based on the revised projected levels of glucose.
Rankers et al. (US 2008/0300572 A1; hereinafter "Rankers") teaches predicting a next time a glucose event will occur. Rankers teaches receiving sensor data corresponding to empirical blood glucose measurements, estimating future blood glucose measurements based on the empirical blood glucose measurements, generating a predictive blood glucose graph indicating the estimated future blood glucose measurements, determining whether there is a glucose alarm prediction, obtaining an estimated alarm time, and generating an indicator of the estimated alarm time for display with the predictive blood glucose graph (see Rankers Figure 20; see also Rankers [0194]-[0195]). Rankers further teaches that the predictive graph display process predicts whether future glucose measurements will leave the patient's target glucose zone within a designated period of time in the future and obtains an estimated time corresponding to when the future glucose measurement will leave the target zone (see Rankers [0194]). Because leaving the target glucose zone includes crossing either an upper glucose boundary or a lower glucose boundary, Rankers teaches predicting a next time a hyperglycemic event or a hypoglycemic event will occur by determining an estimated time at which future glucose will leave the target zone.
In the modified Davis and Agrawal system, the future glucose estimates used to determine whether and when the glucose will leave the target zone correspond to the revised projected glucose levels determined after the maintenance event. As discussed above, Davis teaches smart-alert logic based on current or predicted diabetic states warranting attention and automatic alert management based on detected therapy-related or glucose-affecting events, and Agrawal teaches event-based glucose pattern prediction after a current event occurrence, including using time-shifted glucose patterns following a current event to predict a future notification event. Accordingly, Davis in view of Agrawal and Rankers teaches predicting a next time a hyperglycemic event or a hypoglycemic event will occur based on the revised projected levels of glucose.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to provide Davis in view of Agrawal with predicting a next time a hyperglycemic event or a hypoglycemic event will occur based on the revised projected levels of glucose, as taught by Rankers, because Davis and Agrawal already use predicted glucose behavior to manage alerts after glucose-affecting events, and Rankers teaches determining an estimated alarm time corresponding to when future glucose will leave a target glucose zone. Thus, one of ordinary skill in the art would have had reason to apply Rankers's estimated alarm time technique to the event-based revised glucose projection of Davis and Agrawal to identify when the revised projected glucose behavior is expected to become clinically significant, thereby improving the timing of alert management and preserving timely warning of predicted hyperglycemic or hypoglycemic events.
Davis in view of Agrawal and Rankers teaches predicting the next time a hyperglycemic event or a hypoglycemic event will occur based on the revised projected levels of glucose, but does not expressly teach determine a temporary period of time to disable the previously presented graphical alert based on the predicted next time and automatically disable the previously presented graphical alert for the temporary period of time having the determined duration without user input.
Cobelli teaches automatic post-alert monitoring and suppression or non-re-alerting after an alert has been provided, without requiring user input, by continuing to monitor sensor data and not sending the alert again until re-alert criteria are met (see Cobelli [0138]-[0143], the alert is sent again when threshold sensor data and/or other re-alert conditions are met, and the user is not re-alerted before the re-alert condition is met). Cobelli also teaches time-period-based active monitoring, including an active monitoring time period such as 20, 40, or 60 minutes, with or without a sub-state (see Cobelli [0150]). Cobelli further teaches post-alert acknowledged-state examples in which additional alerts are not provided for a set time period and in which a transition to an acknowledged state can be based on data analysis, thereby showing both time-period suppression and data-driven state transition embodiments (see Cobelli [0192]-[0193], [0197], [0199]). To the extent Cobelli's acknowledged-state examples describe user acknowledgement, Davis independently teaches automatic detection of insulin delivery and automatic suspension or delay of a smart alert to account for the changed hyperglycemic state (see Davis [0160], [0114], [0105]).
Budiman teaches determining a duration of a temporary period of time for an alarm delay or suppression period based on glucose-related predictive information. Budiman teaches delaying annunciation of a CGM-based hypoglycemic alarm and determining whether or not the alarm should persist based on glucose level measurements, CGM signal artifact characteristics, and best-estimate physiological states such as plasma glucose, interstitial glucose, insulin onboard, and effective insulin (see Budiman [0088]). Budiman further teaches modifying the length of a delay timer based on prior knowledge of various factors such as glucose level, CGM value, insulin onboard, and the like (see Budiman [0103]). Budiman also teaches that a variable time can be added to delay return into a periodic CGM-based hypoglycemic detection state, and that the duration of the timer is dependent upon a determination of the likelihood of glucose value changes based on the future glucose level profile determined by the control model and the latest finger-stick glucose level value (see Budiman [0103]-[0105]). Budiman further teaches determining timer delay based on glucose concentration, including delay values of 30 minutes, 15 minutes, or 0 minutes (see Budiman [0107]-[0108]). Thus, Budiman teaches determining an alarm delay or suppression duration based on predicted future glucose behavior.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to provide Davis in view of Agrawal and Rankers with determining a temporary period of time to disable the previously presented graphical alert based on the predicted next time and automatically disabling the previously presented graphical alert for the temporary period of time having the determined duration without user input, as taught or suggested by Cobelli and Budiman. Davis and Agrawal teach automatically managing glucose alerts based on projected and revised projected glucose behavior following glucose-affecting events. Rankers teaches determining an estimated time at which future glucose will leave the target zone. Cobelli teaches suppressing or not re-alerting after an alert has already been presented while continuing post-alert monitoring. Budiman teaches varying the duration of an alarm delay or suppression timer based on predicted future glucose behavior. Thus, one of ordinary skill in the art would have had reason to use Rankers’s estimated alarm time as an input for determining the duration of the temporary alert-disable period in the Cobelli/Budiman post-alert suppression framework, so that the previously presented graphical alert remains disabled while the predicted event is not yet imminent but the temporary disable period ends before the estimated time at which the hyperglycemic or hypoglycemic event is predicted to occur. Such a modification would reduce nuisance alerts while preserving timely warning before the predicted glycemic event.
Response to Arguments
35 U.S.C. §101
Applicant's arguments filed 12/29/2025, page 9, regarding the previous 101 Rejections of claims 1-20 have been fully considered and are persuasive. The previous 101 rejections have been withdrawn.
35 U.S.C. §103
Applicant's arguments filed 12/29/2025, pages 10-11, regarding the previous 103 Rejections of claims 1-20 have been fully considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. That is, there are new grounds of rejection. Additionally, see the arguments addressed below.
Applicant’s arguments with respect to the rejection of claims 1-20 under 35 U.S.C. § 103 have been fully considered but are not persuasive.
Applicant argues that Cobelli does not disclose or suggest “determin[ing] a duration of a temporary period of time to disable the previously presented graphical alert” and “disabl[ing] the previously presented graphical alert for the temporary period of time having the determined duration,” as recited in the amended independent claims. Applicant further argues that Cobelli describes re-alerting based on glucose levels leaving a threshold range, rather than determining a duration of a temporary period of time to disable a previously presented graphical alert.
This argument is not persuasive with respect to the present rejection because the rejection, as modified in view of Applicant’s amendment, does not rely on Cobelli alone for the newly recited feature of determining a duration of the temporary disable period, but instead relies on Davis in view of Agrawal, in view of Cobelli, and further in view of Budiman. Cobelli is relied upon for teaching post-alert suppression and re-alerting after an alert has already been provided. Budiman is additionally relied upon for teaching determining a duration of an alarm delay or suppression period based on glucose-related predictive information, including modifying the length of a delay timer based on factors such as glucose level, CGM value, insulin onboard, and a future glucose level profile. Thus, Applicant’s argument that Cobelli alone does not determine the claimed duration does not address the combined teachings relied upon in the present rejection.
Applicant also argues that, during the telephonic interview, the Examiner agreed that the amendments would overcome the current rejection. To the extent Applicant characterizes the interview as indicating that the amendment would overcome the prior rejection, the amendment has been considered and the prior rejection has been updated accordingly. The present rejection, as modified in view of Applicant’s amendment, further relies on Budiman for determining the duration of the temporary disable period based on predictive glucose information. Accordingly, Applicant’s characterization of the interview does not establish patentability over the additional teachings applied in the present rejection.
Applicant further argues that claims 8 and 20 are allowable because Davis does not disclose “predicting a time a next hyperglycemic or hypoglycemic event will occur based on the revised projected levels of glucose” and does not disclose determining the temporary period of time based on the predicted time. Applicant also argues that Davis merely suppresses an alert until it is determined that current insulin is no longer able to control hyperglycemia.
This argument is not persuasive because the present rejection does not rely on Davis alone for predicting the time of the next hyperglycemic or hypoglycemic event or for determining the temporary disable period based on the predicted time, but instead relies on Davis in view of Agrawal, in view of Cobelli, in view of Budiman, and further in view of Rankers. Further, any prior identification that Davis alone does not expressly disclose clearing or disabling the previously presented graphical alert for a temporary period of time is not an admission that the combined teachings of Davis, Agrawal, Cobelli, and Budiman fail to teach the temporary disable period recited in the independent claims, nor is it an admission that the further combination with Rankers fails to teach the predicted-time basis recited in claims 8 and 20. As set forth in the rejection, Agrawal teaches event-based glucose pattern prediction after a current event occurrence, including using glucose information following an event occurrence to predict a future notification event. Rankers teaches receiving empirical blood glucose measurements, estimating future blood glucose measurements based on the empirical measurements, determining whether there is a glucose alarm prediction, obtaining an estimated alarm time, and displaying an indicator of the estimated alarm time with a predictive blood glucose graph. Rankers further teaches predicting whether future glucose measurements will leave the patient’s target glucose zone within a designated future period and obtaining an estimated time corresponding to when the future glucose measurement will leave the target zone. Budiman teaches determining a duration of an alarm delay or suppression timer based on predictive glucose information. Thus, the combination of Agrawal, Rankers, and Budiman addresses the claimed predicted-time-based determination of the temporary disable period.
Applicant’s argument also does not address the manner in which the references are combined in the present rejection. Davis teaches smart-alert logic based on current or predicted diabetic states warranting attention and automatic alert management based on detected therapy-related or glucose-affecting events. Agrawal teaches event-based revised glucose prediction after a current event occurrence. Cobelli teaches post-alert suppression and re-alerting after an alert has already been presented. Budiman teaches determining a duration of an alarm delay or suppression period based on predictive glucose information. Rankers teaches determining an estimated future alarm time based on predicted future glucose measurements. Therefore, the combined teachings support determining the temporary disable period based on a predicted future glucose event time and presenting the graphical alert again prior to the predicted time, as recited in claims 8 and 20.
Accordingly, Applicant’s arguments do not overcome the present rejection because the arguments are directed to the prior reliance on Davis, Agrawal, and Cobelli and do not address the additional teachings of Budiman and Rankers applied in the updated rejection.
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.
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/AARON MERRIAM/Examiner, Art Unit 3791
/MATTHEW KREMER/Primary Examiner, Art Unit 3791
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