DETAILED ACTION
Note: The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Applicant’s arguments filed in the reply on February 6, 2026 were received and fully considered. Claims 1, 3, 7, 14, 16, and 19 were amended. Please see corresponding rejection headings and response to arguments section below for more detail.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant’s submission filed on February 6, 2026 has been entered.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 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 the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 2, 3, 5, 7-10, 14-16, 18, 19, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Nicholson et al. (US PG Pub. No. 2019/0298230 A1) (hereinafter “Nicholson”), El-Khatib et al. (WO 2022/235714 A1) (hereinafter “El-Khatib”), and Mensinger et al. (US PG Pub. No. 2017/0143251 A1) (hereinafter “Mensinger”).
Nicholson and El-Khatib were applied in the previous office action.
With respect to claims 1, 14, 19, Nicholson teaches a computer-implemented method (abstract; par.0017) comprising: receiving, by one or more processors and originating from a continuous glucose monitor, a plurality of continuous sensor measurements for a user over an evaluation time period (par.0026 “obtain at least one physiological data point associated with a user using at least one health-tracking device… glucose meter”; software + processor 110 in Fig. 1), generating, by the one or more processors, an input data object based on the plurality of continuous sensor measurements (par.0035 “identify whether one or more physiological data points in the trend are abnormally high or low… Responsive to this identification, an embodiment may determine whether this abnormality is an expected result based upon the activity and/or additional context data… user was determined to be performing cardio circuits… sleeping… activity levels… walking in an airport…”; see also par.0036 “an embodiment may provide additional confirmation input to a system… capture raw images of food intake”); identifying, by the one or more processors and using a detection model and a sensitivity threshold that is adjusted based on a variability metric of the input data object to increase or decrease a number of change points from the input data object, a change point for the input data object, wherein the change point corresponds to a sensor measurement of the plurality of continuous sensor measurements (par.0030 “may adjust a threshold range for a physiological metric based on the determined activity type”; par.0032 “threshold ranges for each physiological metric may be further be adjusted based upon user-specific health considerations (e.g., age, weight, specific health condition, etc.”; par.0035 “may also utilize additional context data to adjust the threshold range(s) for one or more physiological metrics based upon the expected activity levels that correspond with the context”; par.0036 “an embodiment may implement a new “normal” threshold range, or adjust an existing threshold range, associated with a user's blood glucose levels to correspond with the anticipated spike in a user's blood glucose”); determining, by the one or more processors, a data spike from the plurality of continuous sensor measurements based on the change point, wherein the data spike corresponds to a sub-time period of the evaluation time period (par.0035 “identify whether one or more physiological data points in the trend are abnormally high or low with respect to the other points”; par.0036 “blood glucose levels to correspond with the anticipated spike in a user's blood glucose”); determining, by the one or more processors, a sub-time period classification based on the data spike (par.0035 “physiological micro-event data may refer to a physiological data point trend over a predetermined amount of time (e.g., the past 5 seconds, 30 seconds, 1 minute, etc.”); determining, by the one or more processors, a predictive classification for the input data object based on the sub-time period classification (par.0035 “Responsive to this identification, an embodiment may determine whether this abnormality is an expected result based upon the activity and/or additional context data…may recognize that periodic bursts in heart rate, even above a threshold range for the cardio circuit activity, should be expected based upon this activity and may therefore not provide a notification…determines that a user is sleeping when these abnormal bursts occur, an embodiment may recognize that there is not a natural reason for these bursts to occur and may therefore provide a notification”).
However, Nicholson does not explicitly teach wherein the evaluation time period begins after an initiation of an insulin treatment of the user… wherein the variability metric is determined based on at least one of a data range, a variance, or a standard deviation of the plurality of continuous sensor measurements and a threshold corresponding to the at least one of the data range, the variance, or the standard deviation… and modifying, by the one or more processors, the insulin treatment for the user based on the predictive classification.
El-Khatib teaches the evaluation time period begins after an initiation of an insulin treatment of the user; and modifying, by the one or more processors, the insulin treatment for the user based on the predictive classification (abstract; par.0280 “predictive model may be used to estimate a physiological effect of the therapy in order to adjust the therapy delivery according to an intended physiological effect”; par.0757 “modification to the insulin dose may be accounted for… as it adapts the control algorithm based on the therapy provided to the subject and measured physiological parameters… may determine or predict the glucose level of the subject based on the modification to the insulin dose and may modify therapy over time accordingly…”)
Mensinger teaches wherein the variability metric is determined based on at least one of a data range, a variance, or a standard deviation of the plurality of continuous sensor measurements and a threshold corresponding to the at least one of the data range, the variance, or the standard deviation (par.0101-0108 “determines one or more statistical values… include, for example, one or more indications of glucose variability, such as… standard deviation of glucose levels… a target glucose range, a hyperglycemic range, a hypoglycemic range, and/or any other custom defined range… and any other related statistic… determines one or more performance indicators that indicate differences and/or similarities between the statistical values… may include comparison metrics… calculated statistical values may include, for example, one or more of various indications of glucose variability”).
Therefore, it would have been prima facie obvious to person having ordinary skill in the art (“PHOSITA”) when the invention was filed to modify Nicholson to modify insulin treatment based on predictive classification (sensed interaction, event, activity level, artifact/noise, etc.) in order to adjust the therapy delivery according to an intended physiological effect, as evidence by El-Khatib (par.0280). Additionally, PHOSITA would have had predictable success when the invention was filed to modify Nicholson to incorporate determining the variability metric in the manner recited for the purpose of indicating performance indicators between continuous sensor data from two or more analysis time periods, as evidence by Mensinger (par.0099-0108).
With respect to claims 2 and 15, Nicholson teaches wherein the plurality of continuous sensor measurements comprises a plurality of time-stamped continuous glucose monitoring sensor measurements (par.0026 “the physiological data point may correspond to a data point at a particular point in time”).
With respect to claims 3 and 16, Nicholson teaches wherein determining the data spike comprises: identifying a spiking measurement corresponding to the change point based on a threshold measurement change, wherein the spiking measurement corresponds to a sensor measurement within a measurement time period subsequent to the change point; and determining the data spike based on the spiking measurement (par.0035-36).
With respect to claims 5 and 18. Nicholson teaches wherein the sensitivity threshold is based on the input data object (par.0035-36).
With respect to claim 7, Nicholson suggests wherein determining the sub-time period classification based on the data spike comprises: determining that the data spike is a first data spike for the sub-time period; and in response to determining that the data spike if the first data spike: identifying a pre-spike measurement for the data spike, wherein the pre-spike measurement is another sensor measurement of the plurality of continuous sensor measurements that corresponds to a pre-spike measurement time period preceding a start time of the data spike; and determining the fasting phenotype classification for the sub-time period based on a comparison between the pre-spike measurement and a threshold pre-spike measurement (par.0035-36).
With respect to claim 8, Nicholson suggests wherein the threshold pre-spike measurement corresponds to a previous evaluation time period for the input data object (par.0035-36).
With respect to claim 9, Nicholson suggests wherein determining one or more sub-time period classification based on the data spike comprises: identifying a start change point for the data spike, wherein the start change point corresponds to a first sensor measurement; identifying an endpoint for the data spike, wherein the endpoint corresponds to a post-spike sensor measurement that is subsequent to the start change point and is within a threshold margin of the first sensor measurement; determining a spike duration for the data spike based on the start change point and the endpoint; determining an evaluation ratio based on the spike duration, wherein the evaluation ratio is indicative of a first measurement area corresponding to the data spike relative to a second measurement area corresponding to one or more portions of the sub-time period that are outside the data spike; and determining the post-prandial phenotype classification for the sub-time period based on a comparison between the evaluation ratio and a threshold ratio (par.0032, 35-36).
With respect to claim 10, Nicholson suggests wherein the threshold ratio corresponds to a previous evaluation time period for the input data object (par.0032, 35-36).
With respect to claim 21, El-Khatib teaches wherein the insulin treatment comprises at least one of the following treatments: Metformin, basal insulin, thiazolidinediones, Meglitinides, Sulfonylureas, DPP4-inhibitors, bolus insulin, short-acting GLP-1 agonist, or alpha-glucoside inhibitors (par.0027 “relates to administering of a basal insulin dose”). Therefore, it would have been prima facie obvious to PHOSITA when the invention was filed to modify Nicholson to modify insulin treatment (e.g. basal insulin) based on predictive classification (sensed interaction, event, activity level, artifact/noise, etc.) in order to adjust the therapy delivery according to an intended physiological effect, as evidence by El-Khatib (par.0280).
Claims 4 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Nicholson, El-Khatib, and Mensinger, as applied above to claims 1 and 14, in view of Malecha (US PG Pub. No. 2009/0105572 A1).
Malecha was applied in the previous office action.
With respect to claims 4 and 17, Nicholson, El-Khatib, and Mensinger teach a computer-implemented method as established above.
However, Nicholson, El-Khatib, and Mensinger do not teach the limitations further recited in claims 4 and 17.
Regarding claims 4 and 17, Malecha teaches wherein the detection model comprises a Bayesian ensemble model, and wherein identifying the change point comprises: generating, using the Bayesian ensemble model, a change point probability for the sensor measurement; and identifying the change point based on a comparison between the change point probability and the sensitivity threshold (par.0050).
Therefore, it would have been prima facie obvious to PHOSITA when the invention was filed to modify Nicholson, El-Khatib, and Mensinger to incorporate Bayesian ensemble model and plurality change point probabilities in the manner recited in order to output a set of glucose concentration probabilities for the predetermined future time, as evidence by Malecha (par.0050).
Claims 6, 11, 12, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Nicholson, El-Khatib, and Mensinger, as applied above to claims 1, 6, 11, and 19, in view of Nakatsugawa et al. (US PG Pub. No. 2022/0061711 A1) (hereinafter “Nakatsugawa”).
Nakatsugawa was applied in the previous office action.
With respect to claims 6, 11, 12, and 20, Nicholson, El-Khatib, and Mensinger teach a computer-implemented method as established above.
However, Nicholson, El-Khatib, and Mensinger do not teach the limitations further recited in claims 6, 11, 12, and 20.
Regarding claims 6 and 20, Nakatsugawa teaches wherein the sub-time period classification comprises at least one of a fasting phenotype classification or a post-prandial phenotype classification (par.0051, 0079+).
Regarding claim 11, Nakatsugawa suggests wherein the evaluation time period comprises a plurality of sub-time periods, and wherein determining the predictive classification comprises: determining a plurality of sub-time period classifications for the evaluation time period, wherein the plurality of sub-time period classifications comprise at least one of the fasting phenotype classification or the post-prandial phenotype classification for each of the plurality of sub-time periods; and determining the predictive classification based on a comparison between the plurality of sub-time period classifications and one or more classification thresholds (par. 0051, 0079+).
Regarding claim 12, Nakatsugawa suggests wherein the fasting phenotype classification is associated with a first threshold, and the post-prandial phenotype classification is associated with a second threshold (par. 0051, 0079+).
Therefore, it would have been prima facie obvious to PHOSITA when the invention was filed to modify Nicholson, El-Khatib, and Mensinger to incorporate at least one of a fasting phenotype classification or a post-prandial phenotype classification, in the manner recited, for the purpose of predicting a timing at which a postprandial hyperglycemic spike, as evidence by Nakatsugawa (par.0051, 0079+).
Response to Arguments
Applicant’s arguments filed with respect to the prior art rejections raised in the previous office action have been considered, but are moot in view of the current combination of references that were necessitated by amendment. Please see prior art section above for more detail, updated citations (new secondary reference, Mensinger), and updated obviousness rationale.
Conclusion
No claim is allowed.
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/PUYA AGAHI/Primary Examiner, Art Unit 3791
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