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 .
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, 15, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Bengtsson et al. (US 2010/0145262 A1; hereinafter “Bengtsson”) in view of Weinert et al. (US 2007/0179434 A1; hereinafter “Weinert”) and Palerm et al. (US 10,105,488 B2; published as US 2015/0165117 A1; hereinafter “Palerm”).
In relation to claim 1, Bengtsson discloses a closed loop control system for insulin delivery that uses historical data to predict future events and adjust insulin delivery accordingly.
Element: "one or more processors".
Bengtsson discloses: "The system comprises processing means for calculating a near-future
desired and prudent insulin delivery profile" (paragraph [0014]). The "processing means"
constitutes one or more processors.
Element: "one or more processor-readable media storing instructions".
Bengtsson's "processing means for calculating" inherently includes processor-readable media storing instructions to perform the calculations described. The system performs complex calculations including pattern recognition and prediction algorithms, which necessarily require stored instructions.
Element: "determine a predicted physiological condition of a patient in response to a
future activity based at least in part on historical data".
Bengtsson discloses: "The present invention provides a system for logging historical data concerning a user's behavioural patterns, insulin delivery profile (to the user), his/her blood glucose profile and possibly further physiological data. The system comprises processing means for calculating a near-future desired and prudent insulin delivery profile based on these logged data and also based on expected future critical disturbances influencing the insulin demand (mainly food intake and exercise as mentioned before as well as sleep)" (paragraph [0014], emphasis added).
Bengtsson further discloses: "For a user who has a stable daily or weekly rhythm of food intake size, food intake time of day, wake-up time, bed time, pattern of exercise and amount of exercise etc., the control system will then be able to estimate a close to true behavioural pattern and on this basis further calculate the near-future desired and prudent insulin delivery profile" (paragraph [0014], emphasis added).
Bengtsson also discloses: "The control system can present predicted critical disturbances, an 'event forecast' to the user, based on the logged historical data" (paragraph [0014], emphasis added).
Bengtsson explicitly teaches using historical data (logged behavioral patterns, blood glucose profiles, physiological data) to predict future activities (food intake, exercise, sleep) and calculating the expected insulin demand (which is directly correlated to predicted blood glucose levels, i.e., the predicted physiological condition) in response to those future activities.
Element: "determine an adjustment to fluid delivery by a medical device to
prospectively account for the future activity".
Bengtsson discloses: "on this basis further calculate the near-future desired and prudent insulin delivery profile" (paragraph [0014], emphasis added).
Bengtsson further discloses: "the control system of the present invention is pro-active, and presents the expected critical disturbance in due time, where 'in due time' in this context means in time for the control system to send the related correct output to the insulin pump considering the vast time-delay" (paragraph [0014], emphasis added).
This explicitly teaches determining an adjustment to insulin delivery (fluid delivery) to prospectively account for future activities, specifically accounting for the pharmacokinetic and pharmacodynamic delays inherent in insulin therapy.
Element: "operate the medical device to deliver fluid to the patient in accordance with
the adjustment".
Bengtsson discloses: "Via output means comprised in the system, this calculated insulin delivery profile can be communicated from the control system to the insulin pump" (paragraph [0014], emphasis added).
After reconsideration, while Bengtsson appears to teach all elements of claim 1, to the extent that the Applicant argues that Bengtsson does not explicitly teach "determining a predicted physiological condition," the following secondary references are provided to demonstrate that this concept was well-known and conventional in the art before the effective filing date.
Weinert (US 2007/0179434 A1) explicitly teaches modifying drug administration based on
anticipated future activities:
"The input device may be configured to provide for user input of additional feed forward
information in the form of exercise information. In such cases, the data storage device may have stored therein an additional map correlating the exercise information to modification information, and the processor may be responsive to user input of the exercise information to modify the corresponding drug administration information according to the modification information determined via the additional map." (paragraph [0011], emphasis added)
Weinert further teaches using maps to correlate anticipated activities to drug administration modifications, including exercise (paragraph [0011]), stress (paragraph [0012]), illness (paragraph [0013]), and menstrual cycle (paragraph [0014]).
Finally, Weinert also teaches predicting physiological responses: "expected speed of overall glucose absorption from the meal by the user" (paragraph [0007]).
In addition to Weinert, Palerm explicitly teaches predicting future physiological conditions:
"determining a predicted value for the physiological condition of the user" (Abstract, emphasis added)
"Predictive algorithms may be utilized to provide estimations of the future blood glucose levels as an aid in regulating the blood glucose level" (paragraph [0004])
"the predicted value is calculated based at least in part on the most recent (or current) measurement value and represents an estimate of the anticipated condition in the body of the user at a particular point of time in the future" (paragraph [0066])
A person of ordinary skill in the art before the effective filing date would have been motivated to combine the teachings of Bengtsson, Weinert, and Palerm because:
1. Same Field of Endeavor: All three references are directed to insulin delivery systems for diabetes management and address the same technical problem of optimizing insulin delivery based on patient activities and physiological responses.
2. Recognized Problem: All references recognize that patient activities (meals, exercise, sleep, stress) significantly affect blood glucose levels and insulin requirements, and that proactive/predictive approaches are necessary to compensate for the inherent time delays in insulin therapy.
3. Complementary Teachings: Weinert provides explicit teaching of using feed-forward information about anticipated activities to modify drug administration, while Palerm provides explicit teaching of predicting future physiological conditions using predictive algorithms. Combining these teachings with Bengtsson's historical data-based prediction system would result in a more comprehensive and accurate system for managing diabetes.
In relation to claim 15, Claim 15 is a method claim that recites essentially the same steps as the functions performed by the system of claim 1. For the same reasons set forth above with respect to claim 1, claim 15 is rejected as obvious over Bengtsson in view of Weinert and Palerm.
In relation to claim 19, Claim 19 is a computer-readable media claim that recites essentially the same functions as the system of claim 1. For the same reasons set forth above with respect to claim 1, claim 19 is rejected as obvious over Bengtsson in view of Weinert and Palerm.
Claims 2-7, 9-11, 13-14, 16-17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Bengtsson et al. (US 2010/0145262A1; hereinafter “Bengtsson”) in view of Weinert et al. (US 2007/0179434 A1; hereinafter “Weinert”) and Palerm et al. (US 10,105,488 B2; published as US 2015/0165117 A1; hereinafter “Palerm”), as discussed above, and in further view of Rankers et al. (US 2008/0300572A1; hereinafter “Rankers”), Pesach et al. (US 8,409,133B2; hereinafter “Pesach”), and Magni et al. (US 20100262117A1; hereinafter “Magni”).
In relation to claim 2, Bengtsson does not disclose a "selectable graphical user interface elements corresponding to different activities". However, Rankers demonstrates the conventionality of using a user interface in combination with an infusion pump [see Rankers; Abstract and paragraph 0058]. Accordingly, for an artisan skilled in the art, modifying the system disclosed by Bengtsson with a user interface, as taught by Rankers, would have been considered obvious in view of the demonstrated conventionality of this enhancement. Moreover, the artisan would have been motivated to make the modification to simplify user input and improve the user experience, especially given the reference's explicit disclosure of touch sensitive screens and the need to select different activities.
In relation to claim 3, Bengtsson does not disclose a specific disclosure of "postprandial period
after administrating a bolus" and an explicit disclosure of "automatically adjusting a calculated meal bolus amount". However, Pesach discloses in column 1, starting in line 63: "[i]n many instances, patients require insulin delivery around the clock to keep proper levels of glucose in their blood. Insulin can be delivered at a basal rate or in bolus doses. The basal rate represents insulin that is continuously delivered to the patient. Such a continuous delivery of insulin keeps the blood glucose level in the desired range between meals and overnight. The bolus dose is an amount of insulin delivered to the patient according to food intake at meals, particularly carbohydrates." Therefore, the combination of Bengtsson and Pesach would have been obvious to one skilled in the art to provide comprehensive insulin management that accounts for both meal intake and subsequent physical activity, as both references are concerned with optimizing insulin delivery based on patient activities and physiological responses.
In relation to claim 4, Bengtsson discloses a “control system seeks to discover a main trend in the historical data” [see Bengtsson; paragraph 0014]; “the control system will then be able to estimate a close to true behavioural pattern and on this basis further calculate the near future desired and prudent insulin delivery profile" [see Bengtsson; paragraph 0014]. Bengtsson does not disclose an explicit disclosure of "automatically adjusting a bolus delivery configuration" and the specific purpose of "reducing likelihood of postprandial hypoglycemia or hyperglycemia". However, Pesach discloses in column 2, starting in line 5, "[s]ome conventional pump mechanisms are configured to react upon command, or by way of an automated procedure, to the increase in glucose levels by delivering a bolus dose of insulin that matches the rise in the level of glucose and prevents large fluctuations in glucose levels." Moreover, Pesach states in column 2, lines 34-35, that the invention is for "preventing and/or reducing occurrence of hyperglycemic and hypoglycemic events in a subject." Therefore, for an artisan skilled in the art, the combination of Bengtsson and Pesach would have been obvious to provide improved glycemic control and reduce dangerous blood glucose fluctuations, which is a primary goal of insulin therapy.
In relation to claims 5 and 16, Bengtsson discloses "[t]he system comprises processing means for calculating a near-future desired and prudent insulin delivery profile based on these logged data and also based on expected future critical disturbances influencing the insulin demand" [see Bengtsson; paragraph 0014]. Bengtsson does not disclose explicit disclosure of "identifying a current operational context" and specific disclosure of "a first correlation between historical data and current operational context". However, Magni discloses "[t]he system of claim 14, wherein, for a given stage corresponding to a discrete time period, said first insulin rate is determined by considering a set of parameters, said set of parameters comprising one or more of the following: a state vector, target glucose concentration, and future glucose disturbances." Therefore, for an artisan skilled in the art, the combination of Bengtsson and Magni would have been obvious to provide more accurate predictions by incorporating current operational context with historical data, resulting in more personalized insulin delivery.
In relation to claim 6, Bengtsson discloses "[t]he present invention provides a system for logging historical data concerning a user's behavioural patterns, insulin delivery profile (to the user), his/her blood glucose profile and possibly further physiological data" [see Bengtsson; paragraph 0014]. Bengtsson does not disclose the explicit disclosure of "determining a model characterizing the physiological condition", "a second correlation between historical measurement data and historical operational context data" and "applying the model to the current operational context". However, Magni discloses in claim 3, “[t]he system of claim 2, wherein said control law is derived from said discrete-time model of glucose insulin dynamics by linearizing a model about an equilibrium point that is
associated with the average basal values of a population model." The reference further states in claim 12: "[t]he system of claim 2, wherein said discrete-time model of glucose insulin dynamics describes deviations from the subject's fasting glucose concentration and basal insulin rate." Therefore, for an artisan skilled in the art, the combination of Bengtsson and Magni would have been obvious to provide more sophisticated and accurate prediction models by incorporating both historical data and current context, resulting in more effective insulin delivery.
In relation to claim 7, Bengtsson discloses "[t]he user can at any time manually input an event not foreseen by the predictive system, which enables the control system to adjust the insulin delivery profile accordingly." Bengtsson does not disclose explicit disclosure of "obtaining user input indicating a characteristic of a meal", "determining a meal bolus dosage based on the characteristic of the meal", and "adjusting the meal bolus dosage based on predicted physiological condition.” However, Pesach discloses "[t]he bolus dose is an amount of insulin delivered to the patient according to food intake at meals, particularly carbohydrates. When patient consumes food, his or her levels of glucose rise. Some conventional pump mechanisms are configured to react upon command, or by way of an automated procedure, to the increase in glucose levels by delivering a bolus dose of insulin that matches the rise in the level of glucose and prevents large fluctuations in glucose levels" [see Pesach; col. 2, lines 2-9]. Therefore, for an artisan skilled in the art, the combination of Bengtsson and Pesach would have been obvious to provide comprehensive meal and activity management for optimal insulin delivery, addressing both immediate meal-related insulin needs and anticipated activity effects.
In relation to claims 9, 17, and 20, Bengtsson discloses: "[t]he present invention provides a system for logging historical data concerning a user's behavioural patterns, insulin delivery profile (to the user), his/her blood glucose profile and possibly further physiological data" [Bengtsson; paragraph 0014]. "The control system seeks to discover a main trend in the historical data. For a user who has a stable daily or weekly rhythm of food intake size, food intake time of day, wake-up time, bed time, pattern of exercise and amount of exercise etc., the control system will then be able to estimate a close to true behavioural pattern and on this basis further calculate the near-future desired and prudent insulin delivery profile" [see Bengtsson; paragraph 0014]. Bengtsson does not disclose the steps of determining a model for a physiological response based on correlation between historical measurement data and historical context data and applying the model to a current operational context. However, Magni discloses in claim 2, “[t]he system of claim 2, wherein said control law is derived from a discrete-time model of glucose insulin dynamics and an aggressiveness parameter", in claim 12, "[t]he system of claim 2, wherein said discrete-time model of glucose insulin dynamics describes deviations from the subject's fasting glucose concentration and basal insulin rate", and in claim 15, "[t]he system of claim 15, wherein, for a given stage corresponding to a discrete time period, said first insulin rate is determined by considering a set of parameters, said set of parameters comprising one or more of the following: a state vector, target glucose concentration, and future glucose disturbances." Based on the above observations, for an artisan skilled in the art, the combination of Bengtsson and Magni would have been obvious to provide more sophisticated and accurate prediction models by incorporating both historical data and current context, resulting in more effective insulin delivery.
In relation to claim 10, Bengtsson discloses "[f]or a user who has a stable daily or weekly rhythm of food intake size, food intake time of day, wake-up time, bed time, pattern of exercise and amount of exercise etc., the control system will then be able to estimate a close to true behavioural pattern and on this basis further calculate the near future desired and prudent insulin delivery profile" [see Bengtsson; paragraph 0014]. This clearly indicates that the system logs timestamps (time of day) associated with
previous events. Bengtsson does not disclose “using a current time of day as part of the current operational context”. However, Magni discloses in claim 15: "[t]he system of claim 14, wherein, for a given stage corresponding to a discrete time period, said first insulin rate is determined by considering a set of parameters, said set of parameters comprising one or more of the following: a state vector, target glucose concentration, and future glucose disturbances”. Based on the above observations, for an artisan skilled in the art, the combination of Bengtsson and Magni would have been obvious to provide time-sensitive predictions that account for daily patterns in patient physiology and behavior, resulting
in more accurate insulin delivery.
In relation to claim 11, Bengtsson discloses in paragraph 0014: "[t]he present invention provides a system for logging historical data concerning a user's behavioural patterns, insulin delivery profile (to the user), his/her blood glucose profile and possibly further physiological data. The system comprises processing means for calculating a near-future desired and prudent insulin delivery profile based on these logged data and also based on expected future critical disturbances influencing the insulin demand (mainly food intake and exercise as mentioned before as well as sleep)." Bengtsson does not disclose the steps of "identifying previous exercise events specifically", "obtaining historical context data associated with previous exercise events", and "determining a model for physiological response based on correlation between historical measurement data and historical context data associated with previous exercise events". However, Pesach discloses in claim 3, "[t]he apparatus according to claim 1, wherein the at least one sensor is configured to measure at least one of the following: a temperature of the patient at a location proximate to the predetermined location on the body of the patient, a level of the drug in the body of the patient, a level of physical activity of the patient, a blood perfusion at a tissue region proximate to the predetermined location on the body of the patient, a quality of the
drug, and a blood parameter level of the patient" and in claim 7, "[t]he apparatus according to claim 6, wherein the adjustment factor is configured to decrease the drug delivery rate when the measured level of physical activity is greater than a pre-defined physical activity threshold value; wherein, when the measured level of physical activity is higher than the pre-defined threshold value, the adjustment factor is configured to adjust the drug delivery rate such that the measured concentration of the drug in the blood of the patient is substantially close to a pre-defined concentration of the drug in the blood of the patient obtain without physical activity by the patient." Based on the above comments, for an artisan skilled in the art, the combination of Bengtsson and Pesach would have been obvious to provide exercise-specific predictions and adjustments to insulin delivery, as exercise is known to significantly affect glucose metabolism and insulin requirements.
In relation to claim 13, Bengtsson discloses in paragraph 0014: "[t]he user can at any time manually input an event not foreseen by the predictive system, which enables the control system
to adjust the insulin delivery profile accordingly as fast as possible with respect to the time delay factors" and "[i]f the event forecast is not in accordance with the actual planned near-future activities
of the user, he/she has the choice of delaying the event, adjust the event time, length, size, intensity or the like, cancelling the event or inputting an alternative event." Bengtsson does not disclose the steps of "determining a model for physiological response based on correlation between historical measurement data and historical characteristics of previous activity instances" and "applying the model to the anticipated characteristic of the future activity". However, Pesach discloses in claim 7: “[t]he apparatus according to claim 6, wherein the adjustment factor is configured to decrease the drug
delivery rate when the measured level of physical activity is greater than a pre-defined physical activity threshold value; wherein, when the measured level of physical activity is higher than the pre-defined threshold value, the adjustment factor is configured to adjust the drug delivery rate such that the measured concentration of the drug in the blood of the patient is substantially close to a pre-defined concentration of the drug in the blood of the patient obtain without physical activity by the patient." Accordingly, for an artisan skilled in the art the combination of Bengtsson and Pesach would have been obvious to provide more personalized and accurate predictions based on specific characteristics of planned activities, resulting in more effective insulin delivery.
In relation to claim 14, Bengtsson discloses in paragraph 0014: "[i]f the event forecast is not in
accordance with the actual planned near-future activities of the user, he/she has the choice of delaying the event, adjust the event time, length, size, intensity or the like, cancelling the event or inputting an alternative event." This explicitly mentions adjusting the "length" (duration) and "intensity" of an event,
indicating that these characteristics are part of the event data. Bengtsson does not disclose the use of activity intensity to make adjustments to drug delivery. However, Pesach discloses in claim 5, "[t]he apparatus according to claim 6, wherein the adjustment factor is configured to decrease the drug
delivery rate when the measured level of physical activity is greater than a pre-defined physical activity threshold value." This shows that it is well-known to use activity intensity as a factor in determining adjustments to drug delivery. Therefore, for an artisan skilled in the art, the combination of Bengtsson and Pesach would have been obvious to provide more accurate predictions based on the specific duration and intensity of planned activities, which are known to affect insulin requirements.
Allowable Subject Matter
Claims 8, 12, and 18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is an examiner’s statement of reasons for allowance:
In relation to claims 8 and 18, the prior art of record does not disclose or suggest, in combination, the steps of:
determining the adjustment comprises adjusting a target value of a physiological condition of the patient based on a relationship between the predicted physiological condition and the target value to obtain an adjusted target value; and
operating the medical device to deliver the fluid to the patient in accordance with the adjustment comprises:
determining a dosage command based at least in part on a difference between a current measurement value of the physiological condition and the adjusted target value of the physiological condition; and
operating an actuation arrangement of the medical device to deliver an amount of the fluid corresponding to the dosage command.
In relation to claim 12, the prior art of record does not disclose or suggest, the future activity comprises sleep; the historical data includes historical measurement data corresponding to a plurality of preceding overnight periods, wherein the historical measurement data is indicative of a physiological condition of the patient during respective ones of the plurality of preceding overnight periods; and determining the predicted physiological condition of the patient comprises determining the predicted physiological condition of the patient based on the historical measurement data corresponding to the plurality of preceding overnight periods.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MANUEL A MENDEZ whose telephone number is (571)272-4962. The examiner can normally be reached Mon-Fri 7:00 AM-5:00 PM.
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Respectfully submitted,
/MANUEL A MENDEZ/ Primary Examiner, Art Unit 3783