DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Applicant Response
Applicant's response, filed 05/18/2026, has been fully considered. Rejections and/or objections not reiterated from previous Office Actions are hereby withdrawn. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Claim Status
Claims 1-20, 23-24, 30-31 and 37-39 are canceled.
Claims 44-47 are newly added.
Claims 21-22, 25-29, 32-36, and 40-47 are pending and under examination herein.
Claims 21-22, 25-29, 32-36, and 40-47 are rejected.
Priority
The instant application, filed 07/27/2020 is a Continuation of 15/354451 , filed 11/17/2016, which is a Divisional of 13/870910 , filed 04/25/2013, which claims priority from US Provisional Applications 61/694950 filed 08/30/2012, 61/694961 filed 08/30/2012, and 61/812874 filed 04/17/2013. As such, the effective filing date assigned to each of claims 21-22, 25-29, 32-36, and 40-47 is 08/30/2012.
Information Disclosure Statement
The Information Disclosure Statement filed 05/18/2026 is in compliance with the provisions of 37 CFR 1.97 and has therefore been considered. A signed copy of the IDS is included with this Office Action.
Claim Objections
The objection to claim 26 is withdrawn in view of claim amendments filed 05/18/2026.
Claim Rejections - 35 USC § 101
Claims 21-22, 25-29, 32-36, and 40-47 appear free of a rejection under 35 U.S.C. 101, as the claims integrate the judicial exception into practical application with the additional elements in the independent claims. Specifically, the limitations in the independent claims of “operating the insulin infusion device to deliver the insulin in a different mode based on the determination that the difference exceeds the threshold amount to lower the glucose level of the patient toward the target glucose range” integrate the recited judicial exceptions into practical application under Step 2A, Prong 2, because the additional elements apply or use the recited judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition (Step 2A, Prong 2: YES).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
The rejection of claims 25 and 32 under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, are withdrawn in view of claim amendments filed 05/18/2026.
Claim Rejections - 35 USC § 102
The rejection of claims 23 and 30 under pre-AIA 35 U.S.C. 102(e) are withdrawn in view of cancelation of the claims in the claim amendments filed 05/18/2026.
The rejection of claims 21-22, 26-39, 33-36 and 40-43 under pre-AIA 35 U.S.C. 102(e) as being anticipated by Sloan et al. US8597274B2; hereafter referred to as Sloan; previously cited) are withdrawn in view of cancelation of the claims in the claim amendments filed 05/18/2026, as Sloan does not appear to disclose wherein determining the predicted value is based on a prediction model generated using a plurality of previously measured glucose values obtained during a plurality of sampling periods comprising a training sampling period and a prediction sampling period.
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 pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under pre-AIA 35 U.S.C. 103(a) are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims under pre-AIA 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of pre-AIA 35 U.S.C. 103(c) and potential pre-AIA 35 U.S.C. 102(e), (f) or (g) prior art under pre-AIA 35 U.S.C. 103(a).
Claim 21-22, 25-29, 32-36, and 40-47 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Sloan et al. (US8597274B2; hereafter referred to as Sloan; previously cited), further in view of Daskalaki et al. (Diabetes technology & therapeutics 14, no. 2 (Feb 2012): 168-174, 10 pages; newly cited; hereafter referred to as Daskalaki). This rejection is newly cited and necessitated by claim amendments.
With respect to claims 21-22, 28-29 and 35-36, Sloan discloses systems and methods for management of a user's glucose level, including systems and methods for improving the usability and safety of such systems, and further discloses systems with devices such as computers, continuous glucose monitors and a drug delivery pumps (fig 1; col 2, lines 32-36; col 5, line56-col 6, line 29).
Sloan further discloses the system may be a closed-loop, semi closed-loop, or open loop system operable in a conventional manner to deliver insulin, as appropriate, based on glucose information provided thereto by the user (col 8, lines 1-23 and 60-64). Sloan discloses that in the system a controller is programmed to provide a “basal rate” of insulin delivery or administration, which is the rate of continuous supply of insulin by an insulin delivery device such as a pump that is used to maintain a desired glucose level in the user, and in a typical situation, an insulin pump normally delivers insulin without user intervention when delivering the insulin at the basal insulin delivery rate (i.e. an automatic mode) (col 13, lines 45-49; col 14, lines 1-3).
Sloan discloses obtaining user glucose concentration and interstitial glucose level from the user and/or analyte monitors(col 8, line 10-13; col 13, lines 58-59; claim 1).
Sloan further discloses providing a glucose level and predicting a future glucose level in order to determine an appropriate insulin bolus value for a latest control command, and further discloses models are used to estimate current glucose level or to predict future glucose levels, using a model based state estimation to determine predicted future glucose level and assess the likelihood that a CGM measurement that exceeds a high or low threshold is due to a true event or sensor artifact and that the likelihood is determined by comparing the difference between the latest CGM measurement and interstitial glucose computed by the model prior to the latest CGM measurement (col 4, lines 10-27; col 13, lines 22-34).
Sloan further discloses using the likelihood to adjust a tiered alarm mechanism and changing the alarm threshold (i.e. the delay for sounding the hypoglycemia/hyperglycemia alarm), such as when the likelihood reaches a risk level (“The same types of mechanisms may be applied to hyperglycemia detection, with the tiered thresholds increasing in the order of threshold values. For example, where the CGM glucose level crosses a 180 mg/dL threshold the longest delay time is implemented; where the CGM glucose level crosses a 200 mg/dL threshold, a shorter delay time is implemented before sounding an alarm; and where the CGM glucose level crosses a 220 gm/dL threshold, an even shorter delay time is implemented, up to a maximum threshold with a zero delay time“)(col 24, like 13-col 25, line 14). As the methods and systems of Sloan perform an action after the determination that the likelihood reaches a risk level, it also suggests that the action is performed when the likelihood exceeds the risk level.
Sloan further discloses if the user has a system including a CGM and an insulin delivery pump, information from the devices can be pooled or shared, and a model-based monitoring system can be used to modify the alarm mechanism to more efficiently minimize false alarms without imposing unnecessary risk to the patient (col 24, lines 13-17). Sloan also discloses that at times, however, the pump may detect conditions that warrant providing an alarm or other signal to the user that intervention in the insulin delivery by the user is necessary, and deliver a manual correction bolus of insulin to bring their glucose level back within an acceptable range (col 14, line 1-33; col 21, lines 17-44).
With respect to claims 26, 33 and 40, Sloan discloses a Kalman framework may be used to determine the likelihood for a predicted future glucose level, given the user's current CGM glucose level and other insulin delivery history (col 24, lines 39-41). Sloan further discloses these models are used to estimate current glucose level or to predict future glucose levels and that such models may also take into account unused insulin remaining in the user (col 13, lines 33-38) Sloan further discloses the insulin pump is configured to wirelessly transmit information relating to insulin delivery to the handheld device, and that the system is able to track the IOB amount by keeping track of insulin delivery data corresponding to the actual delivery of insulin to the user (col 9, lines 12-17; col 16, lines 24-31).
With respect to claims 27, 34 and 42, Sloan discloses obtaining current values of glucose using a measurement from CGM device (i.e. a interstitial sensor), and further that the model based state estimation assesses the likelihood that a CGM measurement that exceeds a high or low threshold is due to a sensor artifact, such as sensor drop-out (col 2, lines 65-66; col 4, lines 21-23).
With respect to claim 41, Sloan discloses computing interstitial glucose by the model (col 4, lines 10-26).
With respect to claim 43, Sloan discloses for users with insulin pumps, open-loop operation typically includes a pre-programmed insulin basal rate, suggesting that while operating in manual mode, the insulin pump can deliver a pre-programmed insulin basal rate of insulin (col 15, lines 44-46).
With respect to claim 44, Sloan discloses reevaluating the time allowed before each alarm is sounded using newly received glucose level measurements, indicating that the predicted values used to determine how alarms are sounded consider a moving window of previously measured glucose values (col 23, line 26-col 24, line 32)
However, with respect to claims 21, 28 and 35, while Sloan discloses the model based state estimation is a Kalman filter (which use measurements observed over time), using information from CGM and insulin delivery pumps and further that the rule sets for algorithms are generally based on observations and clinical practices as well as mathematical models derived through or based on analysis of physiological mechanisms obtained from clinical studies, Sloan does not appear to disclose wherein determining the predicted value is based on a prediction model generated using a plurality of previously measured glucose values obtained during a plurality of sampling periods comprising a training sampling period and a prediction sampling period (col 4, lines 17-18; col 12, lines 38-65; col 24, lines 13-41).
With respect to claims 25 and 32, Sloan does not appear to disclose that a baseline glucose value is obtained during the training sampling period.
Sloan further does not appear to disclose the limitation so newly added claims 45-47.
However, with respect to claims 21, 28 and 35, the prior at to Daskalaki, in the same field of endeavor, discloses real-time adaptive models for the personalized prediction of glycemic profile in type 1 diabetes patients, including an artificial neural network (ANN) using both glucose and insulin information, and shows that the ANN appears to be more appropriate for the prediction of glucose profile based on glucose and insulin data and that the nonlinear architecture of the ANN allows for an accurate and universal approach to total glucose range prediction and it has the ability to be personalized to account for inter- and intra-subject variability (title; abstract; p 3, para 3; p 8, para 1-4). Daskalaki further discloses determining a future glucose value using an online adaptive ANN-based model that was generated on sampling periods, with the data from the first four days being used for training (i.e. training sampling period), whereas the remaining data were used for evaluation (i.e. prediction sampling period) (p 4, para 6-p 5 para 2).
With respect to claims 25 and 32, and 46, Daskalaki discloses that the determination of the mathematical regulation equation used in the non-linear function used by the ANN includes the next and the current glucose values predicted by the model, and further discloses that teacher-forced version of the algorithm, replacement of predicted values with real values permits more efficient computation of the future activity of the ANN, and that the state variable was initialized as the first glucose value available, indicating that a baseline glucose value was determined during the sampling period (i.e. a current values used as an initial value to determine a future value, as described in the instant specification para 00499) (p 4, para 6-p 5, para 1).
With respect to claim 44, Daskalaki discloses using a prediction horizons (PHs) of 30 and 45 minutes, and comparing the model predictions to the reference glucose for a time period, indicating a moving window was used (abstract; p 4, para 6-p 5, para 2; fig 1).
With respect to claim 45, Daskalaki discloses in the teacher-forced version of the algorithm, predicted values were replaced with real values, if they are available, and further that the ANN does not need full access to the training set, suggesting one or more measured glucose values could be inhibited from being considered in the prediction model (p 4, para 6-p 5, para 2; fig 1).
With respect to claim 47, Daskalaki discloses a teacher-forced, real-time, recurrent learning algorithm was used to train the ANN, and that during training, the weights were online-updated for every new input of the ANN, and that the number of hidden layers and neurons was determined after trial-and-error processing during the ANN training based on minimization of the (a root mean square error ) RMSE, indicating generating the prediction model comprises selecting a best-fit solution from a plurality of candidate solutions (abstract; p 4, para 6-p 5, para 2)
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method for systems and methods for management of a user's glucose level as disclosed by Sloan, by the ANN with a training and prediction sampling period using previously measured glucose values, which can be used to determine a predicted future value for glucose, as disclosed by Daskalaki, because using an ANN is appropriate for the prediction of glucose profile based on glucose and insulin data as the nonlinear architecture of the ANN allows for an accurate and universal approach to total glucose range prediction and it has the ability to be personalized to account for inter- and intra-subject variability, as disclosed by Daskalaki. There would be a reasonable expectation of success because using an ANN would not impede the analysis steps of Sloan, as Sloan discloses that the ule sets for algorithms are generally based on observations and clinical practices as well as mathematical models derived through or based on analysis of physiological mechanisms obtained from clinical studies and Daskalaki’s ANN uses rules and algorithms based on observations and clinical practices as well as mathematical models derived through or based on analysis of physiological mechanisms obtained from clinical studies (Sloan: col 12, lines 38-65; Daskalaki: p 3, para 3-p 5, para 2). Therefore, the invention is prima facie obvious.
Conclusion
No claims allowed.
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.
Inquiries
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NIDHI DHARITHREESAN whose telephone number is (571)272-5486. The examiner can normally be reached Monday - Friday 9:00 - 5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Larry D Riggs II can be reached on (571) 270-3062. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/N.D./ Examiner, Art Unit 1686
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
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