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 .
Claims 73, 78-82, 93-99, 104-108 and 119 have been presented for examination.
Claims 1-72, 74-77, 83-92, 100-103 and 109-118 have been cancelled.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 73, 82, 94-99 and 108 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kamath et al [Kamath] PGPUB 2021/0260289 in view of Rosinko PGPUB 2015/0182693.
Referring to claim 73, Kamath teaches the subcutaneous medicament delivery system comprising:
A pump [Fig. 3 0058].
A cannula attached to the pump [0058].
A controller [0037, 0271], the controller configured to:
Automatically calculate a medicament dosage protocol based upon a blood glucose level [0272-0273].
Use machine learning calculations utilizing calculated error data to modify the medicament dosage protocol [0028, 0271-0273]. Machine learning is known to use error data in the learning process.
In summary, Kamath teaches a controller that works in coordination with an insulin pump and a continuous glucose monitor to determine insulin dosages based on received glucose measurements. Adjustments are made as necessary to improve the administration of insulin to the patient.
While Kamath does not explicitly teach the use of neural networks, it is taught to use machine learning. Since neural networks are a type of machine learning, it would have been obvious to one of ordinary skill in the art before the effective filing date to try using a neural network in Kamath as a person of ordinary skill has good reason to pursue the known options within his or her technical grasp.
Lastly, while Kamath teaches the invention substantially as claimed above, it is not explicitly taught to modify the medicament dosage protocol when an event driven signal indicative of an infusion cannula change is detected. In other words, while Kamath learns how to best administer insulin based on blood glucose readings, it is not explicitly taught to further consider site changes for insulin dosages via insulin pump. Rosinko teaches insulin effectiveness can vary based on time of insertion and determines a dosage based on a detected site change and a time from when the change occurred. Specifically, inflammatory response occurs during a site change which requires additional insulin to be delivered which tapers off over time. In addition, insulin potency decreases over time which then requires additional insulin to be administered [0025-0027, 0030-0031].
It would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Rosinko into the Kamath system because it would provide a way to account for insulin effectiveness over time based on a time at an insertion site. It is interpreted that in the Kamath-Rosinko combination that the machine learning/neural network would further be used to consider the injection site duration information since it provides further consideration how the controller should administer insulin to the patient.
Referring to claim 82, it was argued above that it would have been obvious to include a neural network in Kamath. The architecture of neural networks includes for example, nodes within a hidden layer whose weights are adjusted as the model learns. The updating of the node weights during a learning process are interpreted as modifying historical settings since the weights in the machine learning/neural network model are representative of the historical settings over time. Since Rosinko further introduces the need to adjust medicament based on changing of the cannula insertion site, the Kamath-Rosinko combination would obviously update the node weights in further accordance with the site changes since those node weights are ultimately responsible for medicament dosage control.
Referring to claim 94, Kamath teaches increasing/decreasing a dosage over time based on the learning process [0220]. This implies that the prior (i.e., historical) dose is saved and modified accordingly. While this relates to a basal dose (i.e., a slow continuous long-term insulin), Kamath further suggests the same adjustments can also occur with bolus doses as well [0240].
Referring to claim 95-96, Kamath further teaches that if a glucose level is out of range, an adjustment to the dosage is made until the dose is determined to be correct [0240]. This implies that if a previous dosage is too little, then that dosage is increased. If the new dosage is still too little, new dosage is increased. This would occur multiple times until the dose is satisfactory. This also suggests that only the latest dose is used as it is the relevant dosage to be modified. In other words, if a dosage is increased four times before determining it is correct, only the third dosage is factored when determining the increase since the first and second dosages would be irrelevant. In the Kamath-Rosinko combination, this would include testing the insulin dosage in response to changing sites as the combination would attempt to learn how to better administer insulin in response to relocating to another site.
Referring to claim 97, this is rejected on the same basis as set forth hereinabove. Claim 97 incorporates a portion of the limitations in claim 73 and 94 which are rejected above.
Referring to claims 98-99 and 108, these are rejected on the same basis as claims 95-96 and 82 respectively.
Claim(s) 78-81, 93, 104-107 and 119 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kamath and Rosinko as applied to claims 73, 82, 94-99 and 108 above, and further in view of Dassau et al [Dassau] PGPUB 2022/0257857.
Referring to claims 78-81 and 93, while the Kamath-Rosinko combination teaches the invention substantially as claimed above, it is not explicitly taught to perform calculations using a Kalman filter or model predictive control nor controlling via PID or fuzzy logic controllers. Dassau teaches an artificial pancreas (i.e., a system like that described in the Kamath-Rosinko combination) who teaches that calculations and control are performed using Kalman filter and model predictive control and controlled via PID and fuzzy logic controllers [0043, 0047, 0059]. It would have been obvious to one of ordinary skill in the art before the effective filing date to include the teachings of Dassau into the Kamath-Rosinko combination because doing so would provide what is commonly used to facilitate operation in an artificial pancreas.
Referring to claims 104-107 and 119, these are rejected on the same basis as claims 78-81 and 93 respectively.
Conclusion
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/MARK A CONNOLLY/Primary Examiner, Art Unit 2115 12/31/25