DETAILED ACTION
Note: The present application is being examined under the pre-AIA first to invent provisions.
Election/Restrictions
Applicant’s election without traverse of Claims 23-28, 31, and 33 in the reply filed on June 4, 2026 is acknowledged. Claims 18-22, 29, 30, and 32 are withdrawn from consideration.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under U.S.C. 120, 121, or 365 is acknowledged. The prior-filed applications (15/06714 filed on March 10, 2016; 13/785384 filed March 5, 2013; and 61/606542 filed on March 5, 2012) are acknowledged.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on May 7, 2024 has been considered by the examiner.
Claim Rejections - 35 USC § 112B
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.
Claims 23-28, 31, and 33 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 23, and all dependent claims thereof, recites:
“the monitoring” in line 1, which lacks antecedent basis.
“the improvement” in line 5, which lacks antecedent basis.
“the relationship” in line 8, which lacks antecedent basis.
“the prediction horizons” in lines 15-16, which lacks antecedent basis.
“the glucose levels” in lines 16-17, which lacks antecedent basis.
“the confidence levels” in line 18, which lacks antecedent basis.
Claim 31 recites “the glucose-insulin relationship” in line 2, which lacks antecedent basis.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 23-28, 31, and 33 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. A streamlined analysis of claim 23 follows.
Regarding claim 23, the claim recites a system for determining a failure in the monitoring or insulin treatment of a patient.
Thus, the claim is directed to a machine/apparatus, which is one of the statutory categories of invention.
The claim is then analyzed to determine whether it is directed to any judicial exception. The following limitations set forth a judicial exception:
“…select a model in innovation from among a plurality of models that describe the relationship between glucose data measured by the continuous glucose monitoring system and insulin injected by the insulin pump…failure detection module incorporating a discrete-time reiterative filter that is at least partially derived based on the model, wherein the discrete-time reiterative filter is configured to calculate one or more a predicted glucose levels based on the insulin data, the glucose data, wherein the discrete-time reiterative filter is further configured to calculate confidence intervals associated with one or more of the predicted glucose levels so that the confidence intervals are based on model accuracy and the confidence intervals are able to increase as the prediction horizons increase, the processor further programmed to generate an alert based on the glucose levels being different from the predicted glucose levels by an amount that depends on a statistical comparison therebetween, taking into account the confidence levels.”
These limitations describe a mathematical calculation. Furthermore, the limitations also describe a mental process as the skilled artisan is capable of performing the recited limitations and making a mental assessment thereafter. Examiner also notes that nothing from the claims suggest that the limitations cannot be practically performed by a human, or using simple pen/paper.
Next, the claim as a whole is analyzed to determine whether any element, or combination of elements, integrates the identified judicial exception into a practical application.
For this part of the 101 analysis, the following additional limitations are considered:
“…a continuous glucose monitoring system that is configured to generate glucose data indicative of the patient's glucose level and an insulin pump that is configured to inject insulin into the patient and that is configured to generate insulin data regarding insulin that has been injected into the patient…”
These additional limitations do not integrate the judicial exception into a practical application. Rather, the additional limitations are each recited at a high level of generality such that it amounts to insignificant extra-solution activity, i.e., mere data gathering steps necessary to perform the identified judicial exception fail to integrate the claims into a practical application. See MPEP 2106.05(g).
The additional limitations also do not add significantly more to the identified judicial exception because they pertain to widely-understood, routine, and conventional techniques in CGM technology for obtaining known types of data (glucose data indicative of the patient's glucose level and insulin data).
Dependent claims 24-28, 31, and 33 also fail to add something more to the abstract independent claims as they merely further limit the abstract idea, recite limitations that do not integrate the claims into a practical application for substantially similar reasons as set forth above, and/or do not recite significantly more than the identified abstract idea for substantially similar reasons as set forth above.
Therefore, claims 23-28, 31, and 33 are not patent eligible under 35 USC 101.
Claim Rejections - 35 USC § 103
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).
Claims 23-28, 31, and 33 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Knobbe et al. (US PG Pub. No. 2003/0195404 A1) (hereinafter “Knobbe”) in view of Bequette (Continuous Glucose Monitoring: Real-Time Algorithms for Calibration, Filtering, and Alarms, Journal of Diabetes Science and Technology, Volume 4, Issue 2, March 2010, pgs. 404-418).
With respect to claim 23, Knobbe teaches In a system for determining a failure in the monitoring or insulin treatment of a patient that includes a continuous glucose monitoring system that is configured to generate glucose data indicative of the patient's glucose level and an insulin pump that is configured to inject insulin into the patient and that is configured to generate insulin data regarding insulin that has been injected into the patient (par.0047 “Kalman estimator can also use additional information to more closely predict time propagation of glucose levels, such as exercise, food intake, insulin administration, or other factors which influence glucose levels or the function of the sensor”; par.0060 “ability to estimate sensor scale factor and/or detect sensor failure may be improved by modeling glucose propagation changes due to insulin injections or ingestion of sugar”), the improvement comprising: a processor (par.0020 “implemented using respective microprocessor”), in communication with the continuous glucose monitoring system (par.0010 “glucose monitoring device which can be worn by a patient for continuous monitoring”) and the insulin pump (par.0098-99 “controllable pump”), that is programmed to select a model in innovation from among a plurality of models that describe the relationship between glucose data measured by the continuous glucose monitoring system and insulin injected by the insulin pump (par.0011+ “dynamic models”; par.0041-42 “models should reflect the latest and most complete information available”), the processor further programmed with a failure detection module incorporating a discrete-time reiterative filter that is at least partially derived based on the model (par.0042 “linearized Kalman filter”), wherein the discrete-time reiterative filter is configured to calculate one or more a predicted glucose levels based on the insulin data, the glucose data (par.0047 “Kalman estimator can also use additional information to more closely predict time propagation of glucose levels… insulin administration”; see also par.0059),
However, Knobbe does not expressly teach wherein the discrete-time reiterative filter is further configured to calculate confidence intervals associated with one or more of the predicted glucose levels so that the confidence intervals are based on model accuracy and the confidence intervals are able to increase as the prediction horizons increase, the processor further programmed to generate an alert based on the glucose levels being different from the predicted glucose levels by an amount that depends on a statistical comparison therebetween, taking into account the confidence levels.
Bequette teaches wherein the discrete-time reiterative filter is further configured to calculate confidence intervals associated with one or more of the predicted glucose levels so that the confidence intervals are based on model accuracy and the confidence intervals are able to increase as the prediction horizons increase, the processor further programmed to generate an alert based on the glucose levels being different from the predicted glucose levels by an amount that depends on a statistical comparison therebetween, taking into account the confidence levels (pgs. 407-408 “Glucose Estimation and Prediction for Hypoglycemic and/or Hyperglycemic Alarms…Linear Regression and Linear-in-Time Projections… confidence intervals can also be used in the future prediction… considered several different prediction horizons… Optimal Estimation and Prediction Theory… Kalman filter-based approach… explored the effect of sample time… and prediction horizon on the sensitivity and specificity of the predictions”; see also pg.411 “Artificial Neural Networks… used an ANN, based on CGM and additional patient diary information… insulin infusion… to predict glucose values”).
Therefore, it would have been prima facie obvious to a person having ordinary skill in the art (“PHOSITA”) at the time of invention to modify Knobbe to incorporate Bequette’s real-time prediction and alarm technique in order to provide improved glucose (and rate of change) estimation and prediction, as evidence by Bequette (see pg. 414, Conclusions). Furthermore, PHOSITA would have had predictable success combining Knobbe and Bequette since both teachings relate to the same narrow field of endeavor, i.e. utilizing Kalman filters to improve accuracy of CGMs, in real-time.
With respect to claim 24, Knobbe teaches wherein the insulin pump comprises a continuous subcutaneous insulin infusion pump (par.0097).
With respect to claim 25, Knobbe teaches wherein the discrete-time reiterative filter of the failure prediction module is a discrete-time Kalman filter predictor (par.0047).
With respect to claim 26, Knobbe teaches wherein the alert comprises an audible alarm (par.0086).
With respect to claim 27, Knobbe does not explicitly teach wherein the alert comprises a visual notification. However, further modifying Knobbe to utilize a widely known visual alert (in place of Knobbe’s audible alert, see par.86) would only involve routine skill in the art as a simple substitution.
With respect to claim 28, Knobbe does not explicitly teach the alert comprises a vibration. However, further modifying Knobbe to utilize a widely known vibration alert (in place of Knobbe’s audible alert, see par.86) would only involve routine skill in the art as a simple substitution.
With respect to claim 31, Knobbe teaches wherein the model includes a mathematical description of the glucose-insulin relationship expected in the patient (par.0011, 0060).
With respect to claim 33, Bequette teaches wherein the model is selected from the group consisting of a modified version of a numerical algorithm for subspace state space system identification (N4SID), a numerical algorithm for subspace state identification designed to handle closed-loop systems including a glucose-insulin model, black-box input-output models, an autoregressive with exogenous inputs (ARX) model, an autoregressive-moving average with exogenous inputs (ARMAX) model, Box-Jenkins nonparametric models based on stable splines, or neural networks, and combinations thereof (pg.411 “Artificial Neural Networks”). Therefore, it would have been prima facie obvious to a person having ordinary skill in the art (“PHOSITA”) at the time of invention to incorporate a model selected from the group recited (e.g. ANN) in order to obtain a glucose prediction with desired accuracy levels, as evidence by Bequette.
Conclusion
No claim is allowed.
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/PUYA AGAHI/Primary Examiner, Art Unit 3791
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